diff --git a/.github/workflows/osv-scan.yml b/.github/workflows/osv-scan.yml index 1c7e8b2841a..5016957bd6f 100644 --- a/.github/workflows/osv-scan.yml +++ b/.github/workflows/osv-scan.yml @@ -5,6 +5,9 @@ on: branches: - main - "litellm_**" + - "rc/**" + - "stable/**" + - "lts/**" schedule: - cron: "23 6 * * *" workflow_dispatch: diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml index 0a490bb8f45..94152aeb7a0 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -5,6 +5,9 @@ on: branches: - main - "litellm_**" + - "rc/**" + - "stable/**" + - "lts/**" push: branches: - main @@ -280,6 +283,7 @@ jobs: tests/unit/llms/e2b tests/unit/llms/edenai tests/unit/llms/elevenlabs + tests/unit/llms/empiriolabs tests/unit/llms/exa_ai tests/unit/llms/fal_ai tests/unit/llms/fastcrw @@ -973,10 +977,10 @@ jobs: dist: loadscope steps: - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - timeout-minutes: ${{ matrix.shard == 'db-and-spend' && 10 || 3 }} + timeout-minutes: 3 with: persist-credentials: false - fetch-depth: ${{ matrix.shard == 'db-and-spend' && '0' || '1' }} + fetch-depth: 1 - name: Detect relevant changes id: changes @@ -1009,13 +1013,11 @@ jobs: with: artifact: ${{ needs.rust-bridge.outputs.artifact }} - - name: Install dependencies and check schema + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' - timeout-minutes: 8 + timeout-minutes: 5 env: RUST_BRIDGE_ARTIFACT: ${{ needs.rust-bridge.outputs.artifact }} - UNIT_SHARD: ${{ matrix.shard }} - SCHEMA_BASE_REF: origin/${{ github.event.repository.default_branch }} run: | diff -u model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json if [ -z "$RUST_BRIDGE_ARTIFACT" ]; then @@ -1027,9 +1029,21 @@ jobs: uv run --no-sync python -c "import importlib.metadata; import litellm.rust_bridge._native; print(importlib.metadata.version('litellm'))" fi uv run --no-sync python -c 'import os, sys; print(sys.version); assert f"{sys.version_info.major}.{sys.version_info.minor}" == os.environ["UV_PYTHON"]' - if [ "$UNIT_SHARD" = "db-and-spend" ]; then - uv run --no-sync python scripts/check_schema_compatibility.py --base-ref "$SCHEMA_BASE_REF" - fi + + - name: Check schema.prisma against the merge base + if: steps.changes.outputs.decision != 'skip' && matrix.shard == 'db-and-spend' && github.event_name == 'pull_request' + timeout-minutes: 3 + env: + GH_TOKEN: ${{ github.token }} + PR_NUMBER: ${{ github.event.pull_request.number }} + EVENT_BASE_REF: ${{ github.event.pull_request.base.ref }} + HEAD_SHA: ${{ github.event.pull_request.head.sha }} + run: | + base_ref=$(gh api "repos/${GITHUB_REPOSITORY}/branches/${EVENT_BASE_REF}" --jq .name) || base_ref=$(gh api "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}" --jq .base.ref) + merge_base=$(gh api "repos/${GITHUB_REPOSITORY}/compare/${base_ref}...${HEAD_SHA}?per_page=1" --jq '.merge_base_commit.sha') + test -n "$merge_base" + git fetch --no-tags --depth=1 origin "$merge_base" + uv run --no-sync python scripts/check_schema_compatibility.py --base-commit "$merge_base" - name: Cache Prisma binaries if: steps.changes.outputs.decision != 'skip' diff --git a/README.md b/README.md index c4aeee80fb4..31a93bf8c67 100644 --- a/README.md +++ b/README.md @@ -398,6 +398,7 @@ Set `LITELLM_PROXY_API_BASE` and `LITELLM_PROXY_API_KEY` and every model call th | [Deepseek (`deepseek`)](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | | | | | | | | | [Eden AI (`edenai`)](https://docs.litellm.ai/docs/providers/edenai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | [ElevenLabs (`elevenlabs`)](https://docs.litellm.ai/docs/providers/elevenlabs) | ✅ | ✅ | ✅ | | | ✅ | ✅ | | | | +| [EmpirioLabs AI (`empiriolabs`)](https://docs.litellm.ai/docs/providers/empiriolabs) | ✅ | | ✅ | | | | | | | | | [Empower (`empower`)](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | | | | | | | | | [eu/jev Decisions API (`eu_jev`)](https://docs.litellm.ai/docs/pass_through/eu_jev) | | | | | | | | | | | | [Fal AI (`fal_ai`)](https://docs.litellm.ai/docs/providers/fal_ai) | ✅ | ✅ | ✅ | | ✅ | | | | | | diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py index cfe4ca98145..69e36a0c076 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py @@ -200,7 +200,6 @@ async def log_billed_background_response(billed: BilledBackgroundResponse) -> Bi unpriced: Final = _without_read_price(billed.response) cost: Final = logging_obj.response_cost_calculator(result=unpriced) end_time: Final = datetime.now() - logging_obj.capture_usage_before_logging(unpriced, logging_obj.start_time, end_time) GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( async_coroutine=logging_obj.async_success_handler( result=unpriced, start_time=logging_obj.start_time, end_time=end_time diff --git a/helm/litellm-helm/Chart.yaml b/helm/litellm-helm/Chart.yaml index a3cb388ffc6..059889cb667 100644 --- a/helm/litellm-helm/Chart.yaml +++ b/helm/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 1.1.3 +version: 1.1.4 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to diff --git a/helm/litellm-helm/README.md b/helm/litellm-helm/README.md index 694cca05b52..bc6e29632f1 100644 --- a/helm/litellm-helm/README.md +++ b/helm/litellm-helm/README.md @@ -56,8 +56,8 @@ If `db.useStackgresOperator` is used (not yet implemented): | `billingMetrics.enabled` | Enable enterprise billable-request metering. Requires an enterprise license. | `false` | | `billingMetrics.endpoint` | Collector that the billable-request counter is pushed to. | `https://telemetry.litellm.ai` | -| `billingMetrics.secretName` | Name of an existing Secret holding the mTLS client certificate, under the keys `tls.crt` and `tls.key`. | `litellm-billing-metrics-mtls` | -| `billingMetrics.caSecretName` | Name of an existing Secret holding a CA bundle under the key `ca.crt`. Only needed for a private or test collector whose server certificate is not on the public web PKI. | `""` | +| `billingMetrics.tokenUrl` | Cognito OAuth2 client-credentials token endpoint. | `https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token` | +| `billingMetrics.secretName` | Name of an existing Secret with `client-id` and `client-secret` keys. | `litellm-billing-metrics` | | `billingMetrics.exportIntervalMs` | How often the counter is pushed, in milliseconds. The proxy defaults to `60000` when unset. | `""` | #### Example `proxy_config` ConfigMap from values (default): @@ -109,10 +109,10 @@ type: Opaque #### Enterprise billable-request metering -Enterprise licenses meter billable requests by pushing a counter to LiteLLM's collector over mutual TLS. The chart does not create the client certificate; it mounts one you already hold, read-only, so the private key is never exposed through the environment. Create the Secret under the name the chart expects, then turn the block on: +Enterprise licenses meter billable requests by pushing a counter to LiteLLM's collector with Cognito bearer authentication. Create a Secret with `client-id` and `client-secret` keys, then enable the chart values: ``` -kubectl create secret tls litellm-billing-metrics-mtls --cert=client.crt --key=client.key +kubectl create secret generic litellm-billing-metrics --from-literal=client-id=... --from-literal=client-secret=... ``` ``` @@ -120,7 +120,8 @@ billingMetrics: enabled: true ``` -Set `billingMetrics.caSecretName` only when the collector is a private or test one whose server certificate is not on the public web PKI; the production collector needs no CA override. The chart fails the render rather than deploying a proxy that silently never exports, so a missing `secretName` or an emptied `endpoint` surfaces at `helm install` time. +The endpoint and token URL must use HTTPS. The chart requires both URLs and +the Secret name when billing metrics are enabled ### Database Settings diff --git a/helm/litellm-helm/templates/_helpers.tpl b/helm/litellm-helm/templates/_helpers.tpl index 929fc415163..230d2acaeef 100644 --- a/helm/litellm-helm/templates/_helpers.tpl +++ b/helm/litellm-helm/templates/_helpers.tpl @@ -50,53 +50,27 @@ app.kubernetes.io/name: {{ include "litellm.name" . }} app.kubernetes.io/instance: {{ .Release.Name }} {{- end }} -{{/* -Enterprise billable-request metering. The client certificate identifies the -deployment to LiteLLM's collector, so it is mounted read-only from an existing -Secret rather than passed through the environment. -*/}} -{{- define "litellm.billingMetrics.certDir" -}}/etc/litellm/billing-mtls{{- end -}} -{{- define "litellm.billingMetrics.caDir" -}}/etc/litellm/billing-mtls-ca{{- end -}} - {{- define "litellm.billingMetricsEnv" -}} - name: LITELLM_BILLING_METRICS_ENDPOINT value: {{ required "billingMetrics.endpoint is required when billingMetrics.enabled is true" .Values.billingMetrics.endpoint | quote }} -- name: LITELLM_BILLING_METRICS_CLIENT_CERT - value: {{ printf "%s/tls.crt" (include "litellm.billingMetrics.certDir" .) | quote }} -- name: LITELLM_BILLING_METRICS_CLIENT_KEY - value: {{ printf "%s/tls.key" (include "litellm.billingMetrics.certDir" .) | quote }} -{{- if .Values.billingMetrics.caSecretName }} -- name: LITELLM_BILLING_METRICS_CA_CERT - value: {{ printf "%s/ca.crt" (include "litellm.billingMetrics.caDir" .) | quote }} -{{- end }} +- name: LITELLM_BILLING_METRICS_TOKEN_URL + value: {{ required "billingMetrics.tokenUrl is required when billingMetrics.enabled is true" .Values.billingMetrics.tokenUrl | quote }} +- name: LITELLM_BILLING_METRICS_CLIENT_ID + valueFrom: + secretKeyRef: + name: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true" .Values.billingMetrics.secretName }} + key: client-id +- name: LITELLM_BILLING_METRICS_CLIENT_SECRET + valueFrom: + secretKeyRef: + name: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true" .Values.billingMetrics.secretName }} + key: client-secret {{- with .Values.billingMetrics.exportIntervalMs }} - name: LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS value: {{ . | quote }} {{- end }} {{- end -}} -{{- define "litellm.billingMetricsVolumes" -}} -- name: billing-metrics-mtls - secret: - secretName: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true (an existing Secret with tls.crt and tls.key)" .Values.billingMetrics.secretName }} -{{- if .Values.billingMetrics.caSecretName }} -- name: billing-metrics-mtls-ca - secret: - secretName: {{ .Values.billingMetrics.caSecretName }} -{{- end }} -{{- end -}} - -{{- define "litellm.billingMetricsVolumeMounts" -}} -- name: billing-metrics-mtls - mountPath: {{ include "litellm.billingMetrics.certDir" . }} - readOnly: true -{{- if .Values.billingMetrics.caSecretName }} -- name: billing-metrics-mtls-ca - mountPath: {{ include "litellm.billingMetrics.caDir" . }} - readOnly: true -{{- end }} -{{- end -}} - {{/* Create the name of the service account to use */}} diff --git a/helm/litellm-helm/templates/deployment.yaml b/helm/litellm-helm/templates/deployment.yaml index 37e7eaec9e9..a37193f0efa 100644 --- a/helm/litellm-helm/templates/deployment.yaml +++ b/helm/litellm-helm/templates/deployment.yaml @@ -148,9 +148,6 @@ spec: - name: npm mountPath: /.npm {{- end }} - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumeMounts" . | nindent 12 }} - {{- end }} {{- if include "litellm.collector.socketDir" . }} - name: collector-socket mountPath: {{ include "litellm.collector.socketDir" . }} @@ -234,9 +231,6 @@ spec: items: - key: {{ .Values.proxyConfigMap.key | default "config.yaml" }} path: "config.yaml" - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumes" . | nindent 8 }} - {{- end }} {{- if include "litellm.collector.socketDir" . }} - name: collector-socket emptyDir: diff --git a/helm/litellm-helm/tests/billing_metrics_tests.yaml b/helm/litellm-helm/tests/billing_metrics_tests.yaml index 71803df378c..52185c8dd9d 100644 --- a/helm/litellm-helm/tests/billing_metrics_tests.yaml +++ b/helm/litellm-helm/tests/billing_metrics_tests.yaml @@ -1,31 +1,21 @@ -suite: test billingMetrics wiring on the proxy deployment +suite: test bearer-only billingMetrics wiring templates: - deployment.yaml - configmap-litellm.yaml - - migrations-job.yaml tests: - - it: is off by default, adding no env, volume, or mount + - it: is disabled by default template: deployment.yaml asserts: - - notContains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: litellm-billing-metrics-mtls - - notContains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls - mountPath: /etc/litellm/billing-mtls - readOnly: true - notContains: path: spec.template.spec.containers[0].env content: name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai + - notContains: + path: spec.template.spec.volumes + content: + name: billing-metrics - - it: renders the endpoint and the mounted cert paths when enabled + - it: renders required URLs and Cognito secret references when enabled template: deployment.yaml set: billingMetrics: @@ -39,119 +29,34 @@ tests: - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_CLIENT_CERT - value: /etc/litellm/billing-mtls/tls.crt + name: LITELLM_BILLING_METRICS_TOKEN_URL + value: https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_CLIENT_KEY - value: /etc/litellm/billing-mtls/tls.key - - # The conventional Secret name is the default, so enabling the block is enough. - - it: mounts the default cert secret read-only alongside the config volume - template: deployment.yaml - set: - billingMetrics: - enabled: true - asserts: - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: litellm-billing-metrics-mtls - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls - mountPath: /etc/litellm/billing-mtls - readOnly: true - - - it: honours a secretName override - template: deployment.yaml - set: - billingMetrics: - enabled: true - secretName: my-billing-mtls - asserts: - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: my-billing-mtls - - notContains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: litellm-billing-metrics-mtls - - - it: honours an endpoint override - template: deployment.yaml - set: - billingMetrics: - enabled: true - endpoint: https://collector.internal:4318 - asserts: + name: LITELLM_BILLING_METRICS_CLIENT_ID + valueFrom: + secretKeyRef: + name: litellm-billing-metrics + key: client-id - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://collector.internal:4318 - - # The production collector presents a public web-PKI certificate, so the CA - # override must stay absent unless a private collector is configured. - - it: omits the CA env, volume, and mount when no caSecretName is set - template: deployment.yaml - set: - billingMetrics: - enabled: true - asserts: + name: LITELLM_BILLING_METRICS_CLIENT_SECRET + valueFrom: + secretKeyRef: + name: litellm-billing-metrics + key: client-secret - notContains: path: spec.template.spec.volumes content: - name: billing-metrics-mtls-ca - secret: - secretName: billing-ca + name: billing-metrics - notContains: path: spec.template.spec.containers[0].volumeMounts content: - name: billing-metrics-mtls-ca - mountPath: /etc/litellm/billing-mtls-ca - readOnly: true - - notContains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_CA_CERT - value: /etc/litellm/billing-mtls-ca/ca.crt + name: billing-metrics - - it: mounts the CA secret when caSecretName is set - template: deployment.yaml - set: - billingMetrics: - enabled: true - caSecretName: billing-ca - asserts: - - contains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_CA_CERT - value: /etc/litellm/billing-mtls-ca/ca.crt - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls-ca - secret: - secretName: billing-ca - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls-ca - mountPath: /etc/litellm/billing-mtls-ca - readOnly: true - - - it: passes the export interval through only when set + - it: renders the export interval only when set template: deployment.yaml set: billingMetrics: @@ -164,129 +69,7 @@ tests: name: LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS value: "5000" - - it: omits the export interval when unset - template: deployment.yaml - set: - billingMetrics: - enabled: true - asserts: - - notContains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS - value: "60000" - - # Kubernetes resolves duplicate env names last-wins, so the chart-owned billing - # entries must render after .Values.envVars or a user could silently redirect - # the metering export. The three billing entries are the last ones emitted here - # (migrationJob, which appends DISABLE_SCHEMA_UPDATE, is off for this case). - - it: renders the billing endpoint after envVars so it cannot be shadowed - template: deployment.yaml - set: - migrationJob: - enabled: false - billingMetrics: - enabled: true - envVars: - LITELLM_BILLING_METRICS_ENDPOINT: https://shadowed.example - asserts: - - contains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://shadowed.example - - equal: - path: spec.template.spec.containers[0].env[-3] - value: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai - - equal: - path: spec.template.spec.containers[0].env[-2].name - value: LITELLM_BILLING_METRICS_CLIENT_CERT - - equal: - path: spec.template.spec.containers[0].env[-1].name - value: LITELLM_BILLING_METRICS_CLIENT_KEY - - - it: keeps user-supplied volumes and mounts alongside the billing secret - template: deployment.yaml - set: - billingMetrics: - enabled: true - volumes: - - name: custom-callbacks - configMap: - name: my-callbacks - volumeMounts: - - name: custom-callbacks - mountPath: /app/callbacks - asserts: - - contains: - path: spec.template.spec.volumes - content: - name: custom-callbacks - configMap: - name: my-callbacks - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: litellm-billing-metrics-mtls - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: custom-callbacks - mountPath: /app/callbacks - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls - mountPath: /etc/litellm/billing-mtls - readOnly: true - - - it: still mounts the proxy config when enabled - template: deployment.yaml - set: - billingMetrics: - enabled: true - asserts: - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: litellm-config - mountPath: /etc/litellm/config.yaml - subPath: config.yaml - - # Only the proxy serves billable traffic. The migrations Job must never mount - # the client certificate, and it renders its own env and volumes, so nothing - # stops a future edit from wiring the billing include into it by mistake. - - it: does not touch the migrations job when enabled - template: migrations-job.yaml - set: - billingMetrics: - enabled: true - asserts: - - notContains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai - - notExists: - path: spec.template.spec.containers[0].volumeMounts - - notExists: - path: spec.template.spec.volumes - - - it: fails loudly when enabled with an emptied secretName - template: deployment.yaml - set: - billingMetrics: - enabled: true - secretName: "" - asserts: - - failedTemplate: - errorMessage: billingMetrics.secretName is required when billingMetrics.enabled is true (an existing Secret with tls.crt and tls.key) - - - it: fails loudly when enabled without an endpoint + - it: requires the endpoint when enabled template: deployment.yaml set: billingMetrics: @@ -295,3 +78,23 @@ tests: asserts: - failedTemplate: errorMessage: billingMetrics.endpoint is required when billingMetrics.enabled is true + + - it: requires the token URL when enabled + template: deployment.yaml + set: + billingMetrics: + enabled: true + tokenUrl: "" + asserts: + - failedTemplate: + errorMessage: billingMetrics.tokenUrl is required when billingMetrics.enabled is true + + - it: requires the secret name when enabled + template: deployment.yaml + set: + billingMetrics: + enabled: true + secretName: "" + asserts: + - failedTemplate: + errorMessage: billingMetrics.secretName is required when billingMetrics.enabled is true diff --git a/helm/litellm-helm/tests/collector_tests.yaml b/helm/litellm-helm/tests/collector_tests.yaml index 0340b1161b7..1365290e16c 100644 --- a/helm/litellm-helm/tests/collector_tests.yaml +++ b/helm/litellm-helm/tests/collector_tests.yaml @@ -191,7 +191,7 @@ tests: metricsServer.port: 9090 billingMetrics.enabled: true billingMetrics.endpoint: https://metering.example.com - billingMetrics.secretName: billing-mtls + billingMetrics.secretName: billing-metrics asserts: - contains: path: spec.template.spec.containers[0].env @@ -214,9 +214,9 @@ tests: name: LITELLM_BILLING_METRICS_ENDPOINT any: true - notContains: - path: spec.template.spec.containers[1].volumeMounts + path: spec.template.spec.containers[1].env content: - name: billing-metrics-mtls + name: LITELLM_BILLING_METRICS_CLIENT_SECRET any: true - it: should give the sidecar the same scratch mounts as the proxy on a read-only root diff --git a/helm/litellm-helm/values.yaml b/helm/litellm-helm/values.yaml index f51f8bdd173..9afce050261 100644 --- a/helm/litellm-helm/values.yaml +++ b/helm/litellm-helm/values.yaml @@ -154,19 +154,13 @@ masterkeySecretName: "" # if set, use this secret key for the master key; otherwise, use the default key masterkeySecretKey: "" -# Optional: enterprise billable-request metering. When enabled, the proxy counts -# successful requests to inference, MCP, and A2A endpoints and pushes them to -# LiteLLM's collector over mutual TLS. Requires an enterprise license. -# The client certificate identifies the deployment, so it is mounted read-only -# from an existing Secret and never passed through the environment. +# Optional: enterprise billable-request metering. Requires an enterprise license. billingMetrics: enabled: false - endpoint: https://telemetry.litellm.ai # collector to push the counter to - secretName: litellm-billing-metrics-mtls # existing Secret holding tls.crt and tls.key - # Only for private or test collectors whose server certificate is not on the - # public web PKI. The production collector needs no CA override. - caSecretName: "" # existing Secret holding ca.crt - exportIntervalMs: "" # push cadence; the proxy defaults to 60000 + endpoint: https://telemetry.litellm.ai + tokenUrl: https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token + secretName: litellm-billing-metrics + exportIntervalMs: "" proxyConfigMap: # when true, creates a new configmap diff --git a/helm/litellm/Chart.yaml b/helm/litellm/Chart.yaml index e67f5790c7e..341172da925 100644 --- a/helm/litellm/Chart.yaml +++ b/helm/litellm/Chart.yaml @@ -2,7 +2,7 @@ apiVersion: v2 name: litellm description: LiteLLM componentized — gateway, UI backend, and UI as separate services type: application -version: 0.1.0 +version: 0.1.1 appVersion: "0.1.0" annotations: org.opencontainers.image.source: "https://github.com/BerriAI/litellm" diff --git a/helm/litellm/README.md b/helm/litellm/README.md index 97b94754402..d66bb3284bf 100644 --- a/helm/litellm/README.md +++ b/helm/litellm/README.md @@ -22,6 +22,23 @@ backend: gatewayUrl: https://gateway.example.com ``` +## Enterprise billing metrics + +Billing metrics use Cognito bearer authentication. Create the Secret with +`client-id` and `client-secret` keys, then enable billing metrics: + +```bash +kubectl create secret generic litellm-billing-metrics --from-literal=client-id=... --from-literal=client-secret=... +``` + +```yaml +billingMetrics: + enabled: true +``` + +The chart requires an HTTPS endpoint, an HTTPS token URL, and the Secret name +when billing metrics are enabled + ## Routes to another Service For a shared hostname, `ingress.extraPaths` can route to a Service in the same namespace using `serviceName` and `servicePort`. The port can be a number or a named Service port. Omit `service` when using these fields diff --git a/helm/litellm/templates/_helpers.tpl b/helm/litellm/templates/_helpers.tpl index 18e938564e9..b704782f5d1 100644 --- a/helm/litellm/templates/_helpers.tpl +++ b/helm/litellm/templates/_helpers.tpl @@ -58,56 +58,27 @@ app.kubernetes.io/managed-by: {{ .Release.Service }} helm.sh/chart: {{ printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" }} {{- end -}} -{{/* -Enterprise billable-request metering. Wired into gateway and backend, not the -migrations job. The gateway serves nearly all billable traffic, but the backend -keeps the named-server MCP transport (/{mcp_server_name}/mcp), which writes a -SpendLogs row, so metering only the gateway would silently drop that traffic. -The client certificate identifies the deployment to LiteLLM's collector, so it is -mounted read-only from an existing Secret rather than passed through the -environment. -*/}} -{{- define "litellm.billingMetrics.certDir" -}}/etc/litellm/billing-mtls{{- end -}} -{{- define "litellm.billingMetrics.caDir" -}}/etc/litellm/billing-mtls-ca{{- end -}} - {{- define "litellm.billingMetricsEnv" -}} - name: LITELLM_BILLING_METRICS_ENDPOINT value: {{ required "billingMetrics.endpoint is required when billingMetrics.enabled is true" .Values.billingMetrics.endpoint | quote }} -- name: LITELLM_BILLING_METRICS_CLIENT_CERT - value: {{ printf "%s/tls.crt" (include "litellm.billingMetrics.certDir" .) | quote }} -- name: LITELLM_BILLING_METRICS_CLIENT_KEY - value: {{ printf "%s/tls.key" (include "litellm.billingMetrics.certDir" .) | quote }} -{{- if .Values.billingMetrics.caSecretName }} -- name: LITELLM_BILLING_METRICS_CA_CERT - value: {{ printf "%s/ca.crt" (include "litellm.billingMetrics.caDir" .) | quote }} -{{- end }} +- name: LITELLM_BILLING_METRICS_TOKEN_URL + value: {{ required "billingMetrics.tokenUrl is required when billingMetrics.enabled is true" .Values.billingMetrics.tokenUrl | quote }} +- name: LITELLM_BILLING_METRICS_CLIENT_ID + valueFrom: + secretKeyRef: + name: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true" .Values.billingMetrics.secretName }} + key: client-id +- name: LITELLM_BILLING_METRICS_CLIENT_SECRET + valueFrom: + secretKeyRef: + name: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true" .Values.billingMetrics.secretName }} + key: client-secret {{- with .Values.billingMetrics.exportIntervalMs }} - name: LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS value: {{ . | quote }} {{- end }} {{- end -}} -{{- define "litellm.billingMetricsVolumes" -}} -- name: billing-metrics-mtls - secret: - secretName: {{ required "billingMetrics.secretName is required when billingMetrics.enabled is true (an existing Secret with tls.crt and tls.key)" .Values.billingMetrics.secretName }} -{{- if .Values.billingMetrics.caSecretName }} -- name: billing-metrics-mtls-ca - secret: - secretName: {{ .Values.billingMetrics.caSecretName }} -{{- end }} -{{- end -}} - -{{- define "litellm.billingMetricsVolumeMounts" -}} -- name: billing-metrics-mtls - mountPath: {{ include "litellm.billingMetrics.certDir" . }} - readOnly: true -{{- if .Values.billingMetrics.caSecretName }} -- name: billing-metrics-mtls-ca - mountPath: {{ include "litellm.billingMetrics.caDir" . }} - readOnly: true -{{- end }} -{{- end -}} {{/* Per-component selector labels — used in both Service selectors and Deployment matchLabels. diff --git a/helm/litellm/templates/backend/deployment.yaml b/helm/litellm/templates/backend/deployment.yaml index 7360261dab1..7db14ad87a5 100644 --- a/helm/litellm/templates/backend/deployment.yaml +++ b/helm/litellm/templates/backend/deployment.yaml @@ -79,16 +79,13 @@ spec: {{- include "litellm.billingMetricsEnv" . | nindent 12 }} {{- end }} {{- include "litellm.envFrom" .Values.backend | nindent 10 }} - {{- if or .Values.gateway.config.create .Values.backend.volumeMounts .Values.billingMetrics.enabled }} + {{- if or .Values.gateway.config.create .Values.backend.volumeMounts }} volumeMounts: {{- if .Values.gateway.config.create }} - name: gateway-config mountPath: /app/config/config.yaml subPath: config.yaml {{- end }} - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumeMounts" . | nindent 12 }} - {{- end }} {{- with .Values.backend.volumeMounts }} {{- toYaml . | nindent 12 }} {{- end }} @@ -114,16 +111,13 @@ spec: {{- with .Values.backend.extraContainers }} {{- tpl (toYaml .) $ | nindent 8 }} {{- end }} - {{- if or .Values.gateway.config.create .Values.backend.volumes .Values.billingMetrics.enabled }} + {{- if or .Values.gateway.config.create .Values.backend.volumes }} volumes: {{- if .Values.gateway.config.create }} - name: gateway-config configMap: name: {{ include "litellm.gateway.fullname" . }}-config {{- end }} - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumes" . | nindent 8 }} - {{- end }} {{- with .Values.backend.volumes }} {{- toYaml . | nindent 8 }} {{- end }} diff --git a/helm/litellm/templates/gateway/deployment.yaml b/helm/litellm/templates/gateway/deployment.yaml index b0e7f0dcebb..987109e361e 100644 --- a/helm/litellm/templates/gateway/deployment.yaml +++ b/helm/litellm/templates/gateway/deployment.yaml @@ -86,7 +86,7 @@ spec: {{- include "litellm.gateway.collectorEnv" . | nindent 12 }} {{- end }} {{- include "litellm.envFrom" .Values.gateway | nindent 10 }} - {{- if or .Values.gateway.config.create .Values.gateway.volumeMounts .Values.billingMetrics.enabled .Values.gateway.metricsServer.enabled (include "litellm.gateway.collectorSocketDir" .) }} + {{- if or .Values.gateway.config.create .Values.gateway.volumeMounts .Values.gateway.metricsServer.enabled (include "litellm.gateway.collectorSocketDir" .) }} volumeMounts: {{- if .Values.gateway.config.create }} - name: gateway-config @@ -101,9 +101,6 @@ spec: - name: collector-socket mountPath: {{ include "litellm.gateway.collectorSocketDir" . }} {{- end }} - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumeMounts" . | nindent 12 }} - {{- end }} {{- with .Values.gateway.volumeMounts }} {{- toYaml . | nindent 12 }} {{- end }} @@ -206,7 +203,7 @@ spec: {{- with .Values.gateway.extraContainers }} {{- tpl (toYaml .) $ | nindent 8 }} {{- end }} - {{- if or .Values.gateway.config.create .Values.gateway.volumes .Values.billingMetrics.enabled .Values.gateway.metricsServer.enabled (include "litellm.gateway.collectorSocketDir" .) }} + {{- if or .Values.gateway.config.create .Values.gateway.volumes .Values.gateway.metricsServer.enabled (include "litellm.gateway.collectorSocketDir" .) }} volumes: {{- if .Values.gateway.config.create }} - name: gateway-config @@ -222,9 +219,6 @@ spec: emptyDir: sizeLimit: 1Mi {{- end }} - {{- if .Values.billingMetrics.enabled }} - {{- include "litellm.billingMetricsVolumes" . | nindent 8 }} - {{- end }} {{- with .Values.gateway.volumes }} {{- toYaml . | nindent 8 }} {{- end }} diff --git a/helm/litellm/tests/billing_metrics_tests.yaml b/helm/litellm/tests/billing_metrics_tests.yaml index ceba0bd1430..d17b8192fce 100644 --- a/helm/litellm/tests/billing_metrics_tests.yaml +++ b/helm/litellm/tests/billing_metrics_tests.yaml @@ -1,39 +1,30 @@ -suite: test billingMetrics wiring on gateway and backend +suite: test bearer-only billingMetrics wiring templates: - gateway/deployment.yaml - gateway/configmap.yaml - backend/deployment.yaml + - backend/configmap.yaml - migrations-job.yaml values: - ./values/required.yaml tests: - - it: is off by default, adding no env, volume, or mount + - it: is disabled by default template: gateway/deployment.yaml asserts: - - notContains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: billing-mtls - - equal: - path: spec.template.spec.containers[0].volumeMounts - value: - - name: gateway-config - mountPath: /app/config/config.yaml - subPath: config.yaml - notContains: path: spec.template.spec.containers[0].env content: name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai + - notContains: + path: spec.template.spec.volumes + content: + name: billing-metrics - - it: renders the endpoint and the mounted cert paths when enabled + - it: wires gateway URLs and bearer credentials from Secret keys template: gateway/deployment.yaml set: billingMetrics: enabled: true - secretName: billing-mtls asserts: - contains: path: spec.template.spec.containers[0].env @@ -43,87 +34,68 @@ tests: - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_CLIENT_CERT - value: /etc/litellm/billing-mtls/tls.crt + name: LITELLM_BILLING_METRICS_TOKEN_URL + value: https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_CLIENT_KEY - value: /etc/litellm/billing-mtls/tls.key - - - it: mounts the cert secret read-only alongside the config volume - template: gateway/deployment.yaml - set: - billingMetrics: - enabled: true - secretName: billing-mtls - asserts: + name: LITELLM_BILLING_METRICS_CLIENT_ID + valueFrom: + secretKeyRef: + name: litellm-billing-metrics + key: client-id - contains: - path: spec.template.spec.volumes + path: spec.template.spec.containers[0].env content: - name: billing-metrics-mtls - secret: - secretName: billing-mtls - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls - mountPath: /etc/litellm/billing-mtls - readOnly: true - - # The production collector presents a public web-PKI certificate, so the CA - # override must stay absent unless a private collector is configured. - - it: omits the CA env, volume, and mount when no caSecretName is set - template: gateway/deployment.yaml - set: - billingMetrics: - enabled: true - secretName: billing-mtls - asserts: + name: LITELLM_BILLING_METRICS_CLIENT_SECRET + valueFrom: + secretKeyRef: + name: litellm-billing-metrics + key: client-secret - notContains: path: spec.template.spec.volumes content: - name: billing-metrics-mtls-ca - secret: - secretName: billing-ca + name: billing-metrics - notContains: - path: spec.template.spec.containers[0].env + path: spec.template.spec.containers[0].volumeMounts content: - name: LITELLM_BILLING_METRICS_CA_CERT - value: /etc/litellm/billing-mtls-ca/ca.crt + name: billing-metrics - - it: mounts the CA secret when caSecretName is set - template: gateway/deployment.yaml + - it: wires billing metrics on the backend without volumes + template: backend/deployment.yaml set: billingMetrics: enabled: true - secretName: billing-mtls - caSecretName: billing-ca asserts: - contains: path: spec.template.spec.containers[0].env content: - name: LITELLM_BILLING_METRICS_CA_CERT - value: /etc/litellm/billing-mtls-ca/ca.crt - - contains: + name: LITELLM_BILLING_METRICS_CLIENT_SECRET + valueFrom: + secretKeyRef: + name: litellm-billing-metrics + key: client-secret + - notContains: path: spec.template.spec.volumes content: - name: billing-metrics-mtls-ca - secret: - secretName: billing-ca - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls-ca - mountPath: /etc/litellm/billing-mtls-ca - readOnly: true + name: billing-metrics - - it: passes the export interval through only when set + - it: does not add billing metrics to migrations + template: migrations-job.yaml + set: + billingMetrics: + enabled: true + asserts: + - notContains: + path: spec.template.spec.containers[0].env + content: + name: LITELLM_BILLING_METRICS_ENDPOINT + + - it: renders an optional export interval template: gateway/deployment.yaml set: billingMetrics: enabled: true - secretName: billing-mtls exportIntervalMs: 5000 asserts: - contains: @@ -132,102 +104,27 @@ tests: name: LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS value: "5000" - - it: keeps user-supplied gateway volumes alongside the billing secret + - it: requires the endpoint when enabled template: gateway/deployment.yaml set: billingMetrics: enabled: true - secretName: billing-mtls - gateway.volumes: - - name: custom-callbacks - configMap: - name: my-callbacks - gateway.volumeMounts: - - name: custom-callbacks - mountPath: /app/callbacks + endpoint: "" asserts: - - contains: - path: spec.template.spec.volumes - content: - name: custom-callbacks - configMap: - name: my-callbacks - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: billing-mtls + - failedTemplate: + errorMessage: billingMetrics.endpoint is required when billingMetrics.enabled is true - # The backend keeps the named-server MCP transport (/{mcp_server_name}/mcp), - # which writes a SpendLogs row, so it must meter too or that traffic is lost. - - it: meters the backend as well, since it serves the MCP transport - template: backend/deployment.yaml - set: - billingMetrics: - enabled: true - secretName: billing-mtls - asserts: - - contains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai - - contains: - path: spec.template.spec.containers[0].volumeMounts - content: - name: billing-metrics-mtls - mountPath: /etc/litellm/billing-mtls - readOnly: true - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: billing-mtls - - - it: leaves the backend alone when metering is off - template: backend/deployment.yaml - asserts: - - notContains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai - - # The migrations job runs prisma and serves no traffic; it must never receive - # the client key. - - it: never mounts the billing cert on the migrations job - template: migrations-job.yaml - set: - billingMetrics: - enabled: true - secretName: billing-mtls - asserts: - - notContains: - path: spec.template.spec.containers[0].env - content: - name: LITELLM_BILLING_METRICS_ENDPOINT - value: https://telemetry.litellm.ai - - isNull: - path: spec.template.spec.volumes - - # The conventional Secret name is the default, so enabling metering needs no - # secretName at all; the guard below only fires on an explicitly blanked one. - - it: uses the conventional secret name by default + - it: requires the token URL when enabled template: gateway/deployment.yaml set: billingMetrics: enabled: true + tokenUrl: "" asserts: - - contains: - path: spec.template.spec.volumes - content: - name: billing-metrics-mtls - secret: - secretName: litellm-billing-metrics-mtls + - failedTemplate: + errorMessage: billingMetrics.tokenUrl is required when billingMetrics.enabled is true - - it: fails loudly when the secretName is explicitly blanked + - it: requires the secret name when enabled template: gateway/deployment.yaml set: billingMetrics: @@ -235,15 +132,4 @@ tests: secretName: "" asserts: - failedTemplate: - errorMessage: billingMetrics.secretName is required when billingMetrics.enabled is true (an existing Secret with tls.crt and tls.key) - - - it: fails loudly when enabled without an endpoint - template: gateway/deployment.yaml - set: - billingMetrics: - enabled: true - endpoint: "" - secretName: billing-mtls - asserts: - - failedTemplate: - errorMessage: billingMetrics.endpoint is required when billingMetrics.enabled is true + errorMessage: billingMetrics.secretName is required when billingMetrics.enabled is true diff --git a/helm/litellm/tests/migration_job_tests.yaml b/helm/litellm/tests/migration_job_tests.yaml index 2ebb1b44926..b9e52ba7123 100644 --- a/helm/litellm/tests/migration_job_tests.yaml +++ b/helm/litellm/tests/migration_job_tests.yaml @@ -91,7 +91,7 @@ tests: app.kubernetes.io/name: litellm app.kubernetes.io/instance: RELEASE-NAME app.kubernetes.io/managed-by: Helm - helm.sh/chart: litellm-0.1.0 + helm.sh/chart: litellm-0.1.1 app.kubernetes.io/component: migrations - it: renders pod-level and container-level securityContext in their own scopes diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml index 92a48153c5a..cb37656cb77 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -180,23 +180,14 @@ masterKey: secretKey: master-key # Optional: enterprise billable-request metering. When enabled, the gateway and -# backend count successful requests to inference, MCP, and A2A endpoints and push -# them to LiteLLM's collector over mutual TLS. Both components serve billable -# routes: the backend keeps the named-server MCP transport. Requires an -# enterprise license. The client certificate identifies the deployment, so it is -# mounted read-only from an existing Secret and never passed through the env. +# backend count successful requests to inference, MCP, and A2A endpoints. Requires +# an enterprise license. billingMetrics: enabled: false - endpoint: https://telemetry.litellm.ai # collector to push the counter to - # An existing Secret holding the client certificate under tls.crt and its key - # under tls.key, usually created from the onboarding artifact. The default is - # the conventional name, so the common path is to create that Secret and set - # enabled: true. Override only if yours is named differently. - secretName: litellm-billing-metrics-mtls - # Only for private or test collectors whose server certificate is not on the - # public web PKI. The production collector needs no CA override. - caSecretName: "" # existing Secret holding ca.crt - exportIntervalMs: "" # push cadence; the proxy defaults to 60000 + endpoint: https://telemetry.litellm.ai + tokenUrl: https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token + secretName: litellm-billing-metrics + exportIntervalMs: "" # External Postgres connection. database: diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20261104000000_skill_zip_upload/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20261104000000_skill_zip_upload/migration.sql new file mode 100644 index 00000000000..acbe87515d9 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20261104000000_skill_zip_upload/migration.sql @@ -0,0 +1,9 @@ +SET LOCAL lock_timeout = '10s'; + +CREATE TABLE IF NOT EXISTS "LiteLLM_ClaudeCodePluginUpload" ( + "plugin_name" TEXT NOT NULL, + "content" BYTEA NOT NULL, + CONSTRAINT "LiteLLM_ClaudeCodePluginUpload_pkey" PRIMARY KEY ("plugin_name"), + CONSTRAINT "LiteLLM_ClaudeCodePluginUpload_plugin_name_fkey" FOREIGN KEY ("plugin_name") + REFERENCES "LiteLLM_ClaudeCodePluginTable"("name") ON DELETE CASCADE ON UPDATE CASCADE +); diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 179b741fe7c..c007b047087 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -1615,9 +1615,18 @@ model LiteLLM_ClaudeCodePluginTable { updated_at DateTime? @default(now()) @updatedAt created_by String? + uploaded_archive LiteLLM_ClaudeCodePluginUpload? + @@map("LiteLLM_ClaudeCodePluginTable") } +model LiteLLM_ClaudeCodePluginUpload { + plugin_name String @id + content Bytes + plugin LiteLLM_ClaudeCodePluginTable @relation(fields: [plugin_name], references: [name], onDelete: Cascade, onUpdate: Cascade) +} + + // User/team-scoped memory store with a GLOBAL unique key. // `value` is a string (typically markdown/text meant for LLM context); // `metadata` is an optional JSON envelope for structured tags without schema changes. diff --git a/litellm-rust/Cargo.lock b/litellm-rust/Cargo.lock index f91deaaa401..ba1131d7a92 100644 --- a/litellm-rust/Cargo.lock +++ b/litellm-rust/Cargo.lock @@ -3389,15 +3389,19 @@ version = "0.1.0" dependencies = [ "aws-config", "aws-credential-types", + "aws-runtime", "aws-sdk-sts", "aws-sigv4", "aws-smithy-runtime-api", + "aws-smithy-types", "aws-types", "bytes", "futures-util", "litellm-auth-types", "litellm-error", "litellm-http", + "litellm-secret-value", + "litellm-secrets", "moka", "reqwest", "rstest", @@ -3406,6 +3410,7 @@ dependencies = [ "sha2 0.10.9", "thiserror 2.0.19", "tokio", + "url", "veil", ] @@ -3418,6 +3423,7 @@ dependencies = [ "litellm-auth-types", "litellm-error", "litellm-secret-value", + "litellm-secrets", "moka", "rstest", "serde_json", @@ -3431,6 +3437,7 @@ dependencies = [ name = "litellm-auth-gcp" version = "0.1.0" dependencies = [ + "axum", "base64 0.22.1", "gcp_auth", "google-cloud-auth", @@ -3440,10 +3447,12 @@ dependencies = [ "litellm-secret-value", "moka", "rstest", + "rustls 0.23.42", "serde", "serde_json", "sha2 0.10.9", "tempfile", + "time", "tokio", "veil", ] @@ -3569,37 +3578,6 @@ dependencies = [ "uuid", ] -[[package]] -name = "litellm-cache-redis" -version = "0.1.0" -dependencies = [ - "litellm-cache", - "litellm-cache-testing", - "litellm-error", - "r2d2", - "redis", - "redis-test", - "rstest", - "serde_json", - "thiserror 2.0.19", - "tokio", -] - -[[package]] -name = "litellm-cache-redis-semantic" -version = "0.1.0" -dependencies = [ - "litellm-cache", - "litellm-cache-redis", - "litellm-cache-testing", - "redis", - "redis-test", - "rstest", - "serde_json", - "sha2 0.10.9", - "tokio", -] - [[package]] name = "litellm-cache-response" version = "0.1.0" @@ -3607,11 +3585,8 @@ dependencies = [ "litellm-cache", "litellm-cache-gcs", "litellm-cache-memory", - "litellm-cache-redis", "litellm-http", "py_literal", - "redis", - "redis-test", "rstest", "serde", "serde_json", @@ -3650,24 +3625,6 @@ dependencies = [ "litellm-cache", ] -[[package]] -name = "litellm-cache-valkey-semantic" -version = "0.1.0" -dependencies = [ - "litellm-cache", - "litellm-cache-redis", - "litellm-cache-testing", - "litellm-error", - "redis", - "redis-test", - "rstest", - "serde_json", - "sha2 0.10.9", - "thiserror 2.0.19", - "tokio", - "uuid", -] - [[package]] name = "litellm-callbacks-legacy-python" version = "0.1.0" @@ -4339,8 +4296,6 @@ dependencies = [ "litellm-auth", "litellm-auth-aws", "litellm-cache", - "litellm-cache-memory", - "litellm-cache-redis", "litellm-cache-response", "litellm-callbacks-legacy-python", "litellm-core-utils", @@ -4369,7 +4324,6 @@ dependencies = [ "prost", "pyo3", "pyo3-async-runtimes", - "redis", "reqwest", "rstest", "serde", @@ -5782,17 +5736,6 @@ version = "6.0.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f8dcc9c7d52a811697d2151c701e0d08956f92b0e24136cf4cf27b57a6a0d9bf" -[[package]] -name = "r2d2" -version = "0.8.10" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51de85fb3fb6524929c8a2eb85e6b6d363de4e8c48f9e2c2eac4944abc181c93" -dependencies = [ - "log", - "parking_lot", - "scheduled-thread-pool", -] - [[package]] name = "rand" version = "0.8.7" @@ -5966,18 +5909,6 @@ dependencies = [ "xxhash-rust", ] -[[package]] -name = "redis-test" -version = "1.0.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "804d36862e4323b69f96440cbb13c9894fc90176abdeaf91264e21d5d77f6aca" -dependencies = [ - "rand 0.9.5", - "redis", - "socket2 0.6.5", - "tempfile", -] - [[package]] name = "redox_syscall" version = "0.5.18" @@ -6454,15 +6385,6 @@ dependencies = [ "windows-sys 0.61.2", ] -[[package]] -name = "scheduled-thread-pool" -version = "0.2.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3cbc66816425a074528352f5789333ecff06ca41b36b0b0efdfbb29edc391a19" -dependencies = [ - "parking_lot", -] - [[package]] name = "schemars" version = "0.9.0" diff --git a/litellm-rust/Cargo.toml b/litellm-rust/Cargo.toml index 75cac2a5d63..943e8576ae6 100644 --- a/litellm-rust/Cargo.toml +++ b/litellm-rust/Cargo.toml @@ -57,11 +57,9 @@ litellm-db-testing = { path = "crates/db-testing" } litellm-cache = { path = "crates/cache" } litellm-cache-azure-blob = { path = "crates/cache-azure-blob" } litellm-cache-memory = { path = "crates/cache-memory" } -litellm-cache-redis = { path = "crates/cache-redis" } litellm-cache-s3 = { path = "crates/cache-s3" } litellm-cache-gcs = { path = "crates/cache-gcs" } litellm-cache-disk = { path = "crates/cache-disk" } -litellm-cache-redis-semantic = { path = "crates/cache-redis-semantic" } litellm-cache-response = { path = "crates/cache-response" } litellm-cache-qdrant-semantic = { path = "crates/cache-qdrant-semantic" } litellm-cache-testing = { path = "crates/cache-testing" } @@ -97,7 +95,6 @@ macro_rules_attribute = "0.2.3" schemars = "1" reqwest = { version = "0.13", default-features = false, features = ["json", "multipart", "rustls", "http2", "stream", "query"] } redis = "1.8.0" -redis-test = "1.0.4" testcontainers-modules = "0.15.0" qdrant-client = { version = "1.19.0", default-features = false } uuid = { version = "1", features = ["v4"] } diff --git a/litellm-rust/crates/auth-aws/Cargo.toml b/litellm-rust/crates/auth-aws/Cargo.toml index e4578777a08..c8ed7bb0f14 100644 --- a/litellm-rust/crates/auth-aws/Cargo.toml +++ b/litellm-rust/crates/auth-aws/Cargo.toml @@ -11,14 +11,16 @@ litellm-error.workspace = true litellm-http.workspace = true reqwest.workspace = true -moka = { workspace = true, features = ["sync"] } +moka.workspace = true +litellm-secret-value.workspace = true serde.workspace = true serde_json = { workspace = true, features = ["preserve_order"] } sha2.workspace = true thiserror.workspace = true +url.workspace = true veil.workspace = true -aws-config = { version = "1.9.0", default-features = false, features = ["rustls", "rt-tokio"] } +aws-config = { version = "1.9.0", default-features = false, features = ["rustls", "rt-tokio", "credentials-process"] } aws-credential-types = { version = "1.3.0", features = ["hardcoded-credentials"] } aws-sdk-sts = { version = "1.108.0", default-features = false, features = ["rustls", "rt-tokio"] } aws-sigv4 = "1.5.1" @@ -26,6 +28,9 @@ aws-types = "1.4.0" aws-smithy-runtime-api = "1.13.0" [dev-dependencies] +litellm-secrets.workspace = true +aws-smithy-types = "1.6.4" +aws-runtime = { version = "1.9.4", default-features = false } rstest.workspace = true litellm-http = { workspace = true, features = ["test-support"] } bytes.workspace = true diff --git a/litellm-rust/crates/auth-aws/src/aws.rs b/litellm-rust/crates/auth-aws/src/aws.rs index 81aa4b068bc..e6e32754b4f 100644 --- a/litellm-rust/crates/auth-aws/src/aws.rs +++ b/litellm-rust/crates/auth-aws/src/aws.rs @@ -1,13 +1,16 @@ use std::time::Duration; use std::time::{SystemTime, UNIX_EPOCH}; -use moka::sync::Cache; -use sha2::{Digest, Sha256}; +use aws_credential_types::provider::{SharedCredentialsProvider, error::CredentialsError}; +use litellm_secret_value::SecretValue; +use moka::{Expiry, future::Cache}; +use std::{future::Future, pin::Pin, sync::Arc}; use aws_credential_types::Credentials; use aws_credential_types::provider::ProvideCredentials; use aws_sigv4::http_request::{ - SignableBody, SignableRequest, SigningParams, SigningSettings, sign, + SignableBody, SignableRequest, SignatureLocation, SigningInstructions, SigningParams, + SigningSettings, sign, }; use aws_sigv4::sign::v4; use aws_smithy_runtime_api::client::identity::Identity; @@ -23,72 +26,106 @@ use super::constants::{ }; use crate::error::{CredentialFlow, Error, SigningStage}; -const STATIC_CREDENTIALS_TTL: Duration = Duration::from_secs(3600 - 60); -const AMBIENT_CREDENTIALS_TTL: Duration = Duration::from_secs(600); +const CREDENTIAL_CACHE_TTL: Duration = Duration::from_secs(600); +const EXPIRY_BUFFER: Duration = Duration::from_secs(60); #[derive(Clone)] pub struct AwsAuthService { - static_credentials: Cache, - ambient_credentials: Cache, + providers: Cache, + credentials: Cache, + loader: Arc, } impl Default for AwsAuthService { fn default() -> Self { + Self::new(Arc::new(NativeProviderLoader)) + } +} + +impl AwsAuthService { + fn new(loader: Arc) -> Self { Self { - static_credentials: Cache::builder() + providers: Cache::builder().max_capacity(200).build(), + credentials: Cache::builder() .max_capacity(200) - .time_to_live(STATIC_CREDENTIALS_TTL) - .build(), - ambient_credentials: Cache::builder() - .max_capacity(200) - .time_to_live(AMBIENT_CREDENTIALS_TTL) + .expire_after(CredentialExpiry) .build(), + loader, } } } -fn credential_cache_ttl(flow: &AwsAuthFlow) -> Option { - match flow { - AwsAuthFlow::StaticKeys { .. } => Some(STATIC_CREDENTIALS_TTL), - AwsAuthFlow::DefaultChain => Some(AMBIENT_CREDENTIALS_TTL), - AwsAuthFlow::WebIdentity { .. } - | AwsAuthFlow::AssumeRole { .. } - | AwsAuthFlow::Profile { .. } - | AwsAuthFlow::SessionToken { .. } => None, +struct CredentialExpiry; + +impl Expiry for CredentialExpiry { + fn expire_after_create( + &self, + _: &AwsAuthConfig, + credentials: &Credentials, + _: std::time::Instant, + ) -> Option { + Some(credentials.expiry().map_or(CREDENTIAL_CACHE_TTL, |expiry| { + expiry + .duration_since(SystemTime::now()) + .unwrap_or_default() + .saturating_sub(EXPIRY_BUFFER) + .min(CREDENTIAL_CACHE_TTL) + })) } } -#[derive(Clone, Debug, Default, PartialEq, Eq)] +type ProviderFuture<'a> = + Pin> + Send + 'a>>; + +trait ProviderLoader: Send + Sync { + fn load(&self, config: AwsAuthConfig, flow: AwsAuthFlow) -> ProviderFuture<'_>; +} + +struct NativeProviderLoader; + +impl ProviderLoader for NativeProviderLoader { + fn load(&self, config: AwsAuthConfig, flow: AwsAuthFlow) -> ProviderFuture<'_> { + Box::pin(build_provider(config, flow)) + } +} + +#[derive(Clone, Debug, Default, PartialEq, Eq, Hash)] pub struct AwsAuthConfig { - pub access_key_id: Option, - pub secret_access_key: Option, - pub session_token: Option, + pub access_key_id: Option, + pub secret_access_key: Option, + pub session_token: Option, pub region_name: Option, pub session_name: Option, pub profile_name: Option, pub role_name: Option, - pub web_identity_token: Option, + pub web_identity_token: Option, pub sts_endpoint: Option, - pub external_id: Option, + pub external_id: Option, } impl AwsAuthConfig { fn with_environment(self, env_lookup: &(dyn Fn(&str) -> Option + Sync)) -> Self { Self { - access_key_id: self.access_key_id.or_else(|| env_lookup(AWS_ACCESS_KEY_ID)), + access_key_id: self + .access_key_id + .or_else(|| env_lookup(AWS_ACCESS_KEY_ID).map(SecretValue::new)), secret_access_key: self .secret_access_key - .or_else(|| env_lookup(AWS_SECRET_ACCESS_KEY)), - session_token: self.session_token.or_else(|| env_lookup(AWS_SESSION_TOKEN)), + .or_else(|| env_lookup(AWS_SECRET_ACCESS_KEY).map(SecretValue::new)), + session_token: self + .session_token + .or_else(|| env_lookup(AWS_SESSION_TOKEN).map(SecretValue::new)), region_name: self.region_name.or_else(|| env_lookup(AWS_REGION_NAME)), session_name: self.session_name.or_else(|| env_lookup(AWS_SESSION_NAME)), profile_name: self.profile_name.or_else(|| env_lookup(AWS_PROFILE_NAME)), role_name: self.role_name.or_else(|| env_lookup(AWS_ROLE_NAME)), web_identity_token: self .web_identity_token - .or_else(|| env_lookup(AWS_WEB_IDENTITY_TOKEN)), + .or_else(|| env_lookup(AWS_WEB_IDENTITY_TOKEN).map(SecretValue::new)), sts_endpoint: self.sts_endpoint.or_else(|| env_lookup(AWS_STS_ENDPOINT)), - external_id: self.external_id.or_else(|| env_lookup(AWS_EXTERNAL_ID)), + external_id: self + .external_id + .or_else(|| env_lookup(AWS_EXTERNAL_ID).map(SecretValue::new)), } } } @@ -96,52 +133,31 @@ impl AwsAuthConfig { #[derive(Clone, Debug, PartialEq, Eq)] pub enum AwsAuthFlow { WebIdentity { - token: String, + token: SecretValue, role: String, session_name: String, }, AssumeRole { role: String, session_name: Option, + web_identity_role: Option, }, Profile { name: String, }, SessionToken { - access_key_id: String, - secret_access_key: String, - session_token: String, + access_key_id: SecretValue, + secret_access_key: SecretValue, + session_token: SecretValue, }, StaticKeys { - access_key_id: String, - secret_access_key: String, + access_key_id: SecretValue, + secret_access_key: SecretValue, region_name: String, }, DefaultChain, } -fn cache_key(config: &AwsAuthConfig, flow: &AwsAuthFlow) -> String { - let mut hasher = Sha256::new(); - hasher.update(format!("{config:?}:{flow:?}")); - format!("{:x}", hasher.finalize()) -} - -impl AwsAuthService { - fn get_cached_credentials(&self, key: &str) -> Option { - self.static_credentials - .get(key) - .or_else(|| self.ambient_credentials.get(key)) - } - - fn set_cached_credentials(&self, key: String, credentials: Credentials, ttl: Duration) { - if ttl == STATIC_CREDENTIALS_TTL { - self.static_credentials.insert(key, credentials); - } else { - self.ambient_credentials.insert(key, credentials); - } - } -} - fn role_identity(arn: &str) -> Option<(&str, &str, &str)> { let mut parts = arn.splitn(6, ':'); let ("arn", partition, _, _, account, resource) = ( @@ -170,6 +186,9 @@ pub fn classify_auth( config: AwsAuthConfig, env_lookup: &(dyn Fn(&str) -> Option + Sync), ) -> AwsAuthFlow { + let web_identity_role = env_lookup(AWS_WEB_IDENTITY_TOKEN_FILE) + .filter(|token_file| !token_file.is_empty()) + .and_then(|_| env_lookup(AWS_ROLE_ARN)); let config = config.with_environment(env_lookup); if let (Some(token), Some(role), Some(session_name)) = ( config.web_identity_token.clone(), @@ -186,6 +205,7 @@ pub fn classify_auth( return AwsAuthFlow::AssumeRole { role, session_name: config.session_name.clone(), + web_identity_role, }; } if let Some(name) = config.profile_name { @@ -216,6 +236,30 @@ pub fn classify_auth( AwsAuthFlow::DefaultChain } +async fn profile_provider( + name: &str, + region: Option, + config: aws_config::provider_config::ProviderConfig, + builder: aws_config::profile::credentials::Builder, +) -> Result { + let region = match region { + Some(region) => Some(aws_types::region::Region::new(region)), + None => { + aws_config::default_provider::region::DefaultRegionChain::builder() + .profile_name(name) + .build() + .region() + .await + } + }; + Ok(SharedCredentialsProvider::new( + builder + .configure(&config.with_region(region)) + .profile_name(name) + .build(), + )) +} + impl AwsAuthService { pub async fn resolve_credentials( &self, @@ -224,183 +268,240 @@ impl AwsAuthService { ) -> Result { let resolved = config.clone().with_environment(env_lookup); let flow = classify_auth(config, env_lookup); - match flow { - AwsAuthFlow::SessionToken { - access_key_id, - secret_access_key, - session_token, - } => Ok(Credentials::new( - access_key_id, - secret_access_key, - Some(session_token), - None, - "litellm-static-session", - )), - AwsAuthFlow::StaticKeys { - access_key_id, - secret_access_key, - region_name, - } => { - let flow = AwsAuthFlow::StaticKeys { - access_key_id: access_key_id.clone(), - secret_access_key: secret_access_key.clone(), - region_name, - }; - let key = cache_key(&resolved, &flow); - if let Some(credentials) = self.get_cached_credentials(&key) { - return Ok(credentials); - } - let credentials = Credentials::new( - access_key_id, - secret_access_key, - None, - None, - "litellm-static", - ); - self.set_cached_credentials( - key, - credentials.clone(), - credential_cache_ttl(&flow).unwrap_or(STATIC_CREDENTIALS_TTL), - ); - Ok(credentials) - } - AwsAuthFlow::Profile { name } => { - let provider = aws_config::profile::ProfileFileCredentialsProvider::builder() - .profile_name(name) - .build(); + self.fresh_credentials(&resolved, &flow).await + } + + /// The credentials botocore's default chain finds in `region`, without LiteLLM's Bedrock settings + /// (`AWS_ROLE_NAME`, `AWS_PROFILE_NAME`, `AWS_SESSION_NAME`) that `resolve_credentials` also reads. + pub async fn default_chain_credentials(&self, region: &str) -> Result { + let config = AwsAuthConfig { + region_name: Some(region.to_owned()), + ..Default::default() + }; + self.fresh_credentials(&config, &AwsAuthFlow::DefaultChain) + .await + } + + async fn fresh_credentials( + &self, + resolved: &AwsAuthConfig, + flow: &AwsAuthFlow, + ) -> Result { + let cached = self.cached_credentials(resolved, flow).await?; + if fresh(&cached) { + return Ok(cached); + } + self.credentials.invalidate(resolved).await; + let refetched = self.cached_credentials(resolved, flow).await?; + if fresh(&refetched) { + return Ok(refetched); + } + self.credentials.invalidate(resolved).await; + Err(Error::ExpiredCredentials) + } + + async fn cached_credentials( + &self, + resolved: &AwsAuthConfig, + flow: &AwsAuthFlow, + ) -> Result { + self.credentials + .try_get_with(resolved.clone(), async { + let provider = self + .providers + .try_get_with( + resolved.clone(), + self.loader.load(resolved.clone(), flow.clone()), + ) + .await + .map_err(|error| (*error).clone())?; provider .provide_credentials() .await - .map_err(Error::credentials(CredentialFlow::Profile)) - } - AwsAuthFlow::AssumeRole { role, session_name } => { - if is_already_running_as_role(&role, &resolved).await? { - let ambient_flow = AwsAuthFlow::DefaultChain; - let key = cache_key(&resolved, &ambient_flow); - if let Some(credentials) = self.get_cached_credentials(&key) { - return Ok(credentials); - } - let provider = - aws_config::default_provider::credentials::DefaultCredentialsChain::builder() - .build() - .await; - let credentials = provider - .provide_credentials() - .await - .map_err(Error::credentials(CredentialFlow::DefaultChain))?; - self.set_cached_credentials( - key, - credentials.clone(), - credential_cache_ttl(&ambient_flow).unwrap_or(AMBIENT_CREDENTIALS_TTL), - ); - return Ok(credentials); - } - let mut loader = aws_config::defaults(aws_config::BehaviorVersion::latest()); - if let Some(region) = resolved.region_name.clone() { - loader = loader.region(aws_types::region::Region::new(region)); - } - if let Some(endpoint) = resolved.sts_endpoint.clone() { - loader = loader.endpoint_url(endpoint); - } - if let (Some(access_key_id), Some(secret_access_key)) = - (resolved.access_key_id, resolved.secret_access_key) - { - loader = loader.credentials_provider(Credentials::new( - access_key_id, - secret_access_key, - resolved.session_token, - None, - "litellm-role-source", - )); - } - let sdk_config = loader.load().await; - let builder = aws_config::sts::AssumeRoleProvider::builder(role); - let builder = match session_name { - Some(name) => builder.session_name(name), - None => builder.session_name(default_session_name()), - }; - let builder = match resolved.external_id { - Some(id) => builder.external_id(id), - None => builder, - }; - let provider = builder.configure(&sdk_config).build().await; - provider - .provide_credentials() - .await - .map_err(Error::credentials(CredentialFlow::AssumeRole)) - } - AwsAuthFlow::WebIdentity { - token, - role, - session_name, - } => { - let mut loader = aws_config::defaults(aws_config::BehaviorVersion::latest()); - if let Some(region) = resolved.region_name { - loader = loader.region(aws_types::region::Region::new(region)); - } - if let Some(endpoint) = resolved.sts_endpoint { - loader = loader.endpoint_url(endpoint); - } - let sdk_config = loader.load().await; - let client = aws_sdk_sts::Client::new(&sdk_config); - let response = client - .assume_role_with_web_identity() - .role_arn(role) - .role_session_name(session_name) - .web_identity_token(token) - .send() - .await - .map_err(Error::credentials(CredentialFlow::WebIdentity))?; - let credentials = response - .credentials() - .ok_or(Error::MissingWebIdentityCredentials)?; - let expiration = - SystemTime::try_from(*credentials.expiration()).map_err(|source| { - Error::WebIdentityExpiration(litellm_error::Source::new(source)) - })?; - Ok(Credentials::new( - credentials.access_key_id(), - credentials.secret_access_key(), - Some(credentials.session_token().to_string()), - Some(expiration), - "litellm-web-identity", - )) - } - AwsAuthFlow::DefaultChain => { - let key = cache_key(&resolved, &AwsAuthFlow::DefaultChain); - if let Some(credentials) = self.get_cached_credentials(&key) { - return Ok(credentials); - } - let provider = - aws_config::default_provider::credentials::DefaultCredentialsChain::builder() - .build() - .await; - let credentials = provider - .provide_credentials() - .await - .map_err(Error::credentials(CredentialFlow::DefaultChain))?; - self.set_cached_credentials( - key, - credentials.clone(), - credential_cache_ttl(&AwsAuthFlow::DefaultChain) - .unwrap_or(AMBIENT_CREDENTIALS_TTL), - ); - Ok(credentials) + .map_err(Error::credentials(flow.credential_flow())) + }) + .await + .map_err(|error| (*error).clone()) + } +} + +fn fresh(credentials: &Credentials) -> bool { + credentials + .expiry() + .is_none_or(|expiry| expiry > SystemTime::now() + EXPIRY_BUFFER) +} + +impl AwsAuthFlow { + fn credential_flow(&self) -> CredentialFlow { + match self { + Self::Profile { .. } => CredentialFlow::Profile, + Self::AssumeRole { .. } => CredentialFlow::AssumeRole, + Self::WebIdentity { .. } => CredentialFlow::WebIdentity, + Self::DefaultChain | Self::StaticKeys { .. } | Self::SessionToken { .. } => { + CredentialFlow::DefaultChain } } } } -async fn is_already_running_as_role(role: &str, config: &AwsAuthConfig) -> Result { +async fn build_provider( + resolved: AwsAuthConfig, + flow: AwsAuthFlow, +) -> Result { + match flow { + AwsAuthFlow::SessionToken { + access_key_id, + secret_access_key, + session_token, + } => Ok(SharedCredentialsProvider::new(Credentials::new( + access_key_id.expose(), + secret_access_key.expose(), + Some(session_token.expose().to_owned()), + None, + "litellm-static-session", + ))), + AwsAuthFlow::StaticKeys { + access_key_id, + secret_access_key, + .. + } => Ok(SharedCredentialsProvider::new(Credentials::new( + access_key_id.expose(), + secret_access_key.expose(), + None, + None, + "litellm-static", + ))), + AwsAuthFlow::Profile { name } => { + profile_provider( + &name, + resolved.region_name, + aws_config::provider_config::ProviderConfig::without_region(), + aws_config::profile::ProfileFileCredentialsProvider::builder(), + ) + .await + } + AwsAuthFlow::DefaultChain => { + let builder = + aws_config::default_provider::credentials::DefaultCredentialsChain::builder(); + let builder = match resolved.region_name { + Some(region) => builder.region(aws_types::region::Region::new(region)), + None => builder, + }; + Ok(SharedCredentialsProvider::new(builder.build().await)) + } + AwsAuthFlow::AssumeRole { + role, + session_name, + web_identity_role, + } => { + if is_already_running_as_role(&role, web_identity_role.as_deref(), &resolved).await? { + return Ok(SharedCredentialsProvider::new( + aws_config::default_provider::credentials::DefaultCredentialsChain::builder() + .build() + .await, + )); + } + let sdk_config = sdk_config(resolved.clone()).await; + let builder = aws_config::sts::AssumeRoleProvider::builder(role) + .session_name(session_name.unwrap_or_else(default_session_name)); + let builder = match resolved.external_id { + Some(id) => builder.external_id(id.expose()), + None => builder, + }; + Ok(SharedCredentialsProvider::new( + builder.configure(&sdk_config).build().await, + )) + } + AwsAuthFlow::WebIdentity { + token, + role, + session_name, + } => Ok(SharedCredentialsProvider::new(WebIdentityProvider { + client: aws_sdk_sts::Client::new(&sdk_config(resolved).await), + token, + role, + session_name, + })), + } +} + +async fn sdk_config(config: AwsAuthConfig) -> aws_types::SdkConfig { + let loader = aws_config::defaults(aws_config::BehaviorVersion::latest()); + let loader = match config.region_name { + Some(region) => loader.region(aws_types::region::Region::new(region)), + None => loader, + }; + let loader = match config.sts_endpoint { + Some(endpoint) => loader.endpoint_url(endpoint), + None => loader, + }; + let loader = match (config.access_key_id, config.secret_access_key) { + (Some(key), Some(secret)) => loader.credentials_provider(Credentials::new( + key.expose(), + secret.expose(), + config.session_token.map(|token| token.expose().to_owned()), + None, + "litellm-role-source", + )), + _ => loader, + }; + loader.load().await +} + +#[derive(Debug)] +struct WebIdentityProvider { + client: aws_sdk_sts::Client, + token: SecretValue, + role: String, + session_name: String, +} + +impl ProvideCredentials for WebIdentityProvider { + fn provide_credentials<'a>( + &'a self, + ) -> aws_credential_types::provider::future::ProvideCredentials<'a> + where + Self: 'a, + { + aws_credential_types::provider::future::ProvideCredentials::new(async { + let response = self + .client + .assume_role_with_web_identity() + .role_arn(&self.role) + .role_session_name(&self.session_name) + .web_identity_token(self.token.expose()) + .send() + .await + .map_err(CredentialsError::provider_error)?; + let credentials = response.credentials().ok_or_else(|| { + CredentialsError::provider_error(Error::MissingWebIdentityCredentials) + })?; + let expiration = SystemTime::try_from(*credentials.expiration()) + .map_err(CredentialsError::provider_error)?; + Ok(Credentials::new( + credentials.access_key_id(), + credentials.secret_access_key(), + Some(credentials.session_token().to_owned()), + Some(expiration), + "litellm-web-identity", + )) + }) + } +} + +async fn is_already_running_as_role( + role: &str, + web_identity_role: Option<&str>, + config: &AwsAuthConfig, +) -> Result { + if config.access_key_id.is_some() || config.secret_access_key.is_some() { + return Ok(false); + } if role_identity(role).is_none() { return Ok(false); } - if let (Ok(current_role), Ok(token_file)) = ( - std::env::var(AWS_ROLE_ARN), - std::env::var(AWS_WEB_IDENTITY_TOKEN_FILE), - ) && !token_file.is_empty() - { - return Ok(same_role_arns(role, ¤t_role)); + if let Some(current_role) = web_identity_role { + return Ok(same_role_arns(role, current_role)); } let mut loader = aws_config::defaults(aws_config::BehaviorVersion::latest()); @@ -473,16 +574,6 @@ pub fn sign_headers( headers: &HeaderMap, scope: SigningScope<'_>, ) -> Result { - let identity: Identity = scope.credentials.clone().into(); - let params = v4::SigningParams::builder() - .identity(&identity) - .region(scope.region) - .name(scope.service) - .time(scope.time) - .settings(SigningSettings::default()) - .build() - .map(SigningParams::from) - .map_err(Error::signing(SigningStage::Parameters))?; let header_refs = headers .iter() .map(|(name, value)| { @@ -494,16 +585,14 @@ pub fn sign_headers( }) }) .collect::, _>>()?; - let request = SignableRequest::new( + let instructions = sign_request( method, url, header_refs.into_iter(), - SignableBody::Bytes(body), - ) - .map_err(Error::signing(SigningStage::Request))?; - let (instructions, _) = sign(request, ¶ms) - .map_err(Error::signing(SigningStage::Signature))? - .into_parts(); + body, + SigningSettings::default(), + scope, + )?; instructions .headers() .map(|(name, value)| { @@ -516,6 +605,60 @@ pub fn sign_headers( .collect() } +pub fn presign_url( + url: &str, + expires_in: Duration, + scope: SigningScope<'_>, +) -> Result { + let mut signed = url::Url::parse(url).map_err(Error::signing(SigningStage::Request))?; + let mut settings = SigningSettings::default(); + settings.signature_location = SignatureLocation::QueryParams; + settings.expires_in = Some(expires_in); + let instructions = sign_request( + "GET", + signed.as_str(), + std::iter::empty(), + &[], + settings, + scope, + )?; + let (signature, auth): (Vec<_>, Vec<_>) = instructions + .params() + .iter() + .map(|(name, value)| (*name, value.as_ref())) + .partition(|(name, _)| *name == "X-Amz-Signature"); + signed + .query_pairs_mut() + .extend_pairs(auth.into_iter().chain(signature)); + Ok(signed.into()) +} + +fn sign_request<'a>( + method: &'a str, + url: &'a str, + headers: impl Iterator, + body: &'a [u8], + settings: SigningSettings, + scope: SigningScope<'_>, +) -> Result { + let identity: Identity = scope.credentials.clone().into(); + let params = v4::SigningParams::builder() + .identity(&identity) + .region(scope.region) + .name(scope.service) + .time(scope.time) + .settings(settings) + .build() + .map(SigningParams::from) + .map_err(Error::signing(SigningStage::Parameters))?; + let request = SignableRequest::new(method, url, headers, SignableBody::Bytes(body)) + .map_err(Error::signing(SigningStage::Request))?; + let (instructions, _) = sign(request, ¶ms) + .map_err(Error::signing(SigningStage::Signature))? + .into_parts(); + Ok(instructions) +} + /// Model-id and region parsing shared by every Bedrock route. pub fn bedrock_model_id_and_region(model: &str) -> (String, Option) { let mut stripped = model; @@ -586,16 +729,26 @@ pub fn aws_auth_config( env_lookup: &dyn Fn(&str) -> Option, ) -> AwsAuthConfig { AwsAuthConfig { - access_key_id: params.resolve(&AwsParams::ACCESS_KEY_ID, env_lookup), - secret_access_key: params.resolve(&AwsParams::SECRET_ACCESS_KEY, env_lookup), - session_token: params.resolve(&AwsParams::SESSION_TOKEN, env_lookup), + access_key_id: params + .resolve(&AwsParams::ACCESS_KEY_ID, env_lookup) + .map(SecretValue::new), + secret_access_key: params + .resolve(&AwsParams::SECRET_ACCESS_KEY, env_lookup) + .map(SecretValue::new), + session_token: params + .resolve(&AwsParams::SESSION_TOKEN, env_lookup) + .map(SecretValue::new), region_name: params.resolve(&AwsParams::REGION, env_lookup), session_name: params.resolve(&AwsParams::SESSION_NAME, env_lookup), profile_name: params.resolve(&AwsParams::PROFILE_NAME, env_lookup), role_name: params.resolve(&AwsParams::ROLE_NAME, env_lookup), - web_identity_token: params.resolve(&AwsParams::WEB_IDENTITY_TOKEN, env_lookup), + web_identity_token: params + .resolve(&AwsParams::WEB_IDENTITY_TOKEN, env_lookup) + .map(SecretValue::new), sts_endpoint: params.resolve(&AwsParams::STS_ENDPOINT, env_lookup), - external_id: params.resolve(&AwsParams::EXTERNAL_ID, env_lookup), + external_id: params + .resolve(&AwsParams::EXTERNAL_ID, env_lookup) + .map(SecretValue::new), } } @@ -660,10 +813,133 @@ mod tests { use crate::constants::{AWS_REGION, BEDROCK_SERVICE}; use reqwest::header::CONTENT_TYPE; + #[cfg(unix)] + #[rstest::fixture] + fn process_profile() -> aws_runtime::env_config::file::EnvConfigFiles { + let credentials = serde_json::json!({ + "Version": 1, + "AccessKeyId": "process-access", + "SecretAccessKey": "process-secret", + "SessionToken": "process-session", + }); + aws_runtime::env_config::file::EnvConfigFiles::builder() + .include_default_config_file(false) + .include_default_credentials_file(false) + .with_contents( + aws_runtime::env_config::file::EnvConfigFileKind::Config, + format!( + "[profile smoke]\ncredential_process = /usr/bin/printf '%s' '{credentials}'\n" + ), + ) + .build() + } + + #[cfg(unix)] + #[rstest::rstest] + #[tokio::test] + async fn profile_process_credentials_can_be_loaded( + process_profile: aws_runtime::env_config::file::EnvConfigFiles, + ) { + let provider = profile_provider( + "smoke", + Some("us-west-2".into()), + aws_config::provider_config::ProviderConfig::without_region(), + aws_config::profile::ProfileFileCredentialsProvider::builder() + .profile_files(process_profile), + ) + .await + .unwrap(); + let credentials = provider.provide_credentials().await.unwrap(); + + assert_eq!(credentials.access_key_id(), "process-access"); + assert_eq!(credentials.secret_access_key(), "process-secret"); + assert_eq!(credentials.session_token(), Some("process-session")); + } + + #[derive(Debug)] + struct StsConnector; + + impl aws_smithy_runtime_api::client::http::HttpConnector for StsConnector { + fn call( + &self, + request: aws_smithy_runtime_api::client::orchestrator::HttpRequest, + ) -> aws_smithy_runtime_api::client::http::HttpConnectorFuture { + assert!(request.uri().contains("sts.us-west-2.")); + aws_smithy_runtime_api::client::http::HttpConnectorFuture::new(async { + Ok( + aws_smithy_runtime_api::client::orchestrator::HttpResponse::new( + 200_u16.try_into().unwrap(), + aws_smithy_types::body::SdkBody::from( + r#" + + role-accessrole-secret + role-session2100-01-01T00:00:00Z + arn:aws:sts::123456789012:assumed-role/smoke/session + smoke:session + smoke"#, + ), + ), + ) + }) + } + } + + #[rstest::rstest] + #[tokio::test] + async fn role_profile_uses_region_when_acquiring_sts_credentials() { + use aws_smithy_runtime_api::client::http::{SharedHttpConnector, http_client_fn}; + let files = aws_runtime::env_config::file::EnvConfigFiles::builder() + .include_default_config_file(false) + .include_default_credentials_file(false) + .with_contents( + aws_runtime::env_config::file::EnvConfigFileKind::Config, + "[profile smoke]\nrole_arn = arn:aws:iam::123456789012:role/smoke\nsource_profile = source\n\n[profile source]\naws_access_key_id = source-access\naws_secret_access_key = source-secret\n", + ).build(); + let config = aws_config::provider_config::ProviderConfig::without_region() + .with_http_client(http_client_fn(|_, _| { + SharedHttpConnector::new(StsConnector) + })); + let credentials = profile_provider( + "smoke", + Some("us-west-2".into()), + config, + aws_config::profile::ProfileFileCredentialsProvider::builder().profile_files(files), + ) + .await + .unwrap(); + let credentials = credentials.provide_credentials().await.unwrap(); + assert_eq!(credentials.access_key_id(), "role-access"); + assert_eq!(credentials.secret_access_key(), "role-secret"); + assert_eq!(credentials.session_token(), Some("role-session")); + assert!(credentials.expiry().is_some()); + } + fn no_env(_: &str) -> Option { None } + #[rstest::rstest] + #[case::access_key(Some("source-access"), None)] + #[case::secret_key(None, Some("source-secret"))] + #[case::source_pair(Some("source-access"), Some("source-secret"))] + #[tokio::test] + async fn explicit_source_keys_cannot_reuse_an_ambient_role( + #[case] access_key: Option<&str>, + #[case] secret_key: Option<&str>, + ) { + let role = "arn:aws:iam::123456789012:role/target"; + let config = AwsAuthConfig { + access_key_id: access_key.map(SecretValue::new), + secret_access_key: secret_key.map(SecretValue::new), + ..Default::default() + }; + assert!( + !is_already_running_as_role(role, Some(role), &config) + .await + .unwrap() + ); + } + #[test] fn secret_names_cover_environment_reads() { let seen = std::sync::Arc::new(std::sync::Mutex::new( @@ -706,7 +982,10 @@ mod tests { }; assert_eq!( - aws_auth_config(¶ms, &env).secret_access_key.as_deref(), + aws_auth_config(¶ms, &env) + .secret_access_key + .as_ref() + .map(SecretValue::expose), expected ); assert_eq!( @@ -831,6 +1110,53 @@ mod tests { ); } + #[rstest::rstest] + #[case::running_with_web_identity("/var/run/token", Some("arn:aws:iam::1:role/current"))] + #[case::empty_token_file("", None)] + #[tokio::test] + async fn assume_role_reads_the_ambient_role_from_the_secret_snapshot( + #[case] token_file: &str, + #[case] expected: Option<&str>, + ) { + use litellm_secrets::source::SecretSource; + + struct WebIdentitySecrets<'a>(&'a str); + + impl SecretSource for WebIdentitySecrets<'_> { + fn get_secret_str<'a>( + &'a self, + name: &'a str, + ) -> futures_util::future::BoxFuture< + 'a, + Result, litellm_secrets::Error>, + > { + Box::pin(async move { + Ok(match name { + AWS_WEB_IDENTITY_TOKEN_FILE => Some(SecretValue::new(self.0)), + AWS_ROLE_ARN => Some(SecretValue::new("arn:aws:iam::1:role/current")), + _ => None, + }) + }) + } + } + + let snapshot = WebIdentitySecrets(token_file) + .resolve(AwsParams::secret_names()) + .await + .unwrap(); + let config = AwsAuthConfig { + role_name: Some("arn:aws:iam::1:role/target".into()), + ..Default::default() + }; + let AwsAuthFlow::AssumeRole { + web_identity_role, .. + } = classify_auth(config, &|name| snapshot.get(name)) + else { + panic!("a role name selects AssumeRole"); + }; + assert_eq!(web_identity_role.as_deref(), expected); + } + #[tokio::test] async fn static_credentials_do_not_use_network() { let credentials = AwsAuthService::default() @@ -849,110 +1175,231 @@ mod tests { assert_eq!(credentials.session_token(), None); } - #[test] - fn cache_policy_matches_python_flows() { - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::StaticKeys { - access_key_id: "ak".into(), - secret_access_key: "sk".into(), - region_name: "us-east-1".into(), - }), - Some(STATIC_CREDENTIALS_TTL) - ); - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::DefaultChain), - Some(AMBIENT_CREDENTIALS_TTL) - ); - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::SessionToken { - access_key_id: "ak".into(), - secret_access_key: "sk".into(), - session_token: "token".into(), - }), - None - ); - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::Profile { - name: "profile".into() - }), - None - ); - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::AssumeRole { - role: "arn:aws:iam::123456789012:role/demo".into(), - session_name: None, - }), - None - ); - assert_eq!( - credential_cache_ttl(&AwsAuthFlow::WebIdentity { - token: "token".into(), - role: "arn:aws:iam::123456789012:role/demo".into(), - session_name: "session".into(), - }), - None - ); + struct CountingLoader { + loads: Arc, + calls: Arc, + short_lifetime: Duration, + short_calls: usize, + } + + impl ProviderLoader for CountingLoader { + fn load(&self, config: AwsAuthConfig, _: AwsAuthFlow) -> ProviderFuture<'_> { + let loads = self.loads.clone(); + let provider = CountingProvider { + calls: self.calls.clone(), + short_lifetime: self.short_lifetime, + short_calls: self.short_calls, + key: config.access_key_id.unwrap(), + }; + Box::pin(async move { + loads.fetch_add(1, std::sync::atomic::Ordering::SeqCst); + Ok(SharedCredentialsProvider::new(provider)) + }) + } + } + + #[derive(Debug)] + struct CountingProvider { + calls: Arc, + short_lifetime: Duration, + short_calls: usize, + key: SecretValue, + } + + impl ProvideCredentials for CountingProvider { + fn provide_credentials<'a>( + &'a self, + ) -> aws_credential_types::provider::future::ProvideCredentials<'a> + where + Self: 'a, + { + aws_credential_types::provider::future::ProvideCredentials::new(async move { + tokio::task::yield_now().await; + let call = self.calls.fetch_add(1, std::sync::atomic::Ordering::SeqCst); + let lifetime = if call < self.short_calls { + self.short_lifetime + } else { + Duration::from_secs(3600) + }; + Ok(Credentials::new( + self.key.expose(), + "credential-value", + Some(format!("session-{call}")), + Some(SystemTime::now() + lifetime), + "counting", + )) + }) + } + } + + #[derive(Default)] + struct RecordingLoader { + loads: std::sync::Mutex>, + } + + impl ProviderLoader for RecordingLoader { + fn load(&self, config: AwsAuthConfig, flow: AwsAuthFlow) -> ProviderFuture<'_> { + self.loads.lock().unwrap().push((config, flow)); + Box::pin(async { + Ok(SharedCredentialsProvider::new(Credentials::new( + "ak", + "sk", + None, + None, + "recording", + ))) + }) + } } #[rstest::rstest] - fn cache_round_trip_preserves_credentials() { - let auth = AwsAuthService::default(); - let key = format!("cache-test-{}", std::process::id()); - let credentials = Credentials::new("cache-ak", "cache-sk", None, None, "test"); - auth.set_cached_credentials(key.clone(), credentials.clone(), STATIC_CREDENTIALS_TTL); + #[tokio::test] + async fn default_chain_credentials_do_not_share_a_configured_role_in_the_same_region() { + let loader = Arc::new(RecordingLoader::default()); + let auth = AwsAuthService::new(loader.clone()); + let bedrock_env = |name: &str| match name { + AWS_ROLE_NAME => Some("arn:aws:iam::123456789012:role/bedrock".to_string()), + AWS_REGION_NAME => Some("us-east-1".to_string()), + _ => None, + }; + auth.resolve_credentials(AwsAuthConfig::default(), &bedrock_env) + .await + .unwrap(); + + auth.default_chain_credentials("us-east-1").await.unwrap(); + auth.default_chain_credentials("us-east-1").await.unwrap(); + + let loads = loader.loads.lock().unwrap(); + assert_eq!(loads.len(), 2); assert_eq!( - auth.get_cached_credentials(&key) - .map(|value| value.access_key_id().to_string()), - Some("cache-ak".to_string()) + loads[1], + ( + AwsAuthConfig { + region_name: Some("us-east-1".to_string()), + ..Default::default() + }, + AwsAuthFlow::DefaultChain + ) ); } #[rstest::rstest] #[tokio::test] - async fn cloned_services_reuse_credentials_but_independent_services_do_not() { - let auth = AwsAuthService::default(); + async fn near_expiry_credentials_are_refreshed_without_rebuilding_the_provider() { + use std::sync::atomic::{AtomicUsize, Ordering}; + let loads = Arc::new(AtomicUsize::new(0)); + let calls = Arc::new(AtomicUsize::new(0)); + let auth = AwsAuthService::new(Arc::new(CountingLoader { + loads: loads.clone(), + calls: calls.clone(), + short_lifetime: Duration::from_secs(30), + short_calls: 1, + })); let config = AwsAuthConfig { - access_key_id: Some("configured-key".into()), - secret_access_key: Some("configured-secret".into()), - region_name: Some("us-east-1".into()), - ..AwsAuthConfig::default() + access_key_id: Some("identity".into()), + ..Default::default() }; - let flow = classify_auth(config.clone(), &no_env); - let cached = Credentials::new("cached-key", "cached-secret", None, None, "test"); - auth.set_cached_credentials( - cache_key(&config, &flow), - cached.clone(), - STATIC_CREDENTIALS_TTL, - ); - - let reused = auth + let first = auth + .resolve_credentials(config.clone(), &no_env) + .await + .unwrap(); + let second = auth .clone() .resolve_credentials(config.clone(), &no_env) .await .unwrap(); - let independent = AwsAuthService::default() - .resolve_credentials(config.clone(), &no_env) - .await - .unwrap(); - let different = AwsAuthConfig { - access_key_id: Some("different-key".into()), - ..config.clone() - }; - let other_identity = auth - .resolve_credentials(different.clone(), &no_env) - .await - .unwrap(); + assert_eq!(first.session_token(), Some("session-1")); + assert_eq!(first.session_token(), second.session_token()); + assert_eq!(loads.load(Ordering::SeqCst), 1); + assert_eq!(calls.load(Ordering::SeqCst), 2); + } - assert_eq!(reused.access_key_id(), cached.access_key_id()); - assert_eq!(reused.secret_access_key(), cached.secret_access_key()); + #[rstest::rstest] + #[case::recovered_by_one_refetch(1, Some("session-1"))] + #[case::still_expired_after_refetch(2, None)] + #[tokio::test] + async fn expired_credentials_are_refetched_once( + #[case] short_calls: usize, + #[case] session: Option<&str>, + ) { + use std::sync::atomic::{AtomicUsize, Ordering}; + let calls = Arc::new(AtomicUsize::new(0)); + let auth = AwsAuthService::new(Arc::new(CountingLoader { + loads: Arc::new(AtomicUsize::new(0)), + calls: calls.clone(), + short_lifetime: Duration::ZERO, + short_calls, + })); + let config = AwsAuthConfig { + access_key_id: Some("identity".into()), + ..Default::default() + }; + let result = auth.resolve_credentials(config, &no_env).await; assert_eq!( - Some(independent.access_key_id()), - config.access_key_id.as_deref() + result.as_ref().ok().and_then(Credentials::session_token), + session ); - assert_eq!( - Some(other_identity.access_key_id()), - different.access_key_id.as_deref() + assert!(session.is_some() || matches!(result, Err(Error::ExpiredCredentials))); + assert_eq!(calls.load(Ordering::SeqCst), 2); + } + + #[rstest::rstest] + #[tokio::test] + async fn concurrent_calls_share_acquisition_and_distinct_identities_stay_isolated() { + use std::sync::atomic::{AtomicUsize, Ordering}; + let loads = Arc::new(AtomicUsize::new(0)); + let calls = Arc::new(AtomicUsize::new(0)); + let auth = AwsAuthService::new(Arc::new(CountingLoader { + loads: loads.clone(), + calls: calls.clone(), + short_lifetime: Duration::from_secs(3600), + short_calls: 0, + })); + let first = AwsAuthConfig { + access_key_id: Some("first".into()), + ..Default::default() + }; + let second = AwsAuthConfig { + access_key_id: Some("second".into()), + ..Default::default() + }; + let (a, b, c) = tokio::join!( + auth.resolve_credentials(first.clone(), &no_env), + auth.resolve_credentials(first, &no_env), + auth.resolve_credentials(second, &no_env) ); + let a = a.unwrap(); + let b = b.unwrap(); + let c = c.unwrap(); + assert_eq!(a.session_token(), b.session_token()); + assert_ne!(a.access_key_id(), c.access_key_id()); + assert_eq!(loads.load(Ordering::SeqCst), 2); + assert_eq!(calls.load(Ordering::SeqCst), 2); + } + + #[rstest::rstest] + fn auth_config_debug_never_discloses_credentials() { + let config = AwsAuthConfig { + access_key_id: Some("credential-value".into()), + secret_access_key: Some("credential-value".into()), + session_token: Some("credential-value".into()), + web_identity_token: Some("credential-value".into()), + external_id: Some("credential-value".into()), + ..Default::default() + }; + let output = format!("{config:?}"); + assert!(!output.contains("credential-value")); + assert!(output.contains("[REDACTED]")); + } + + #[rstest::rstest] + #[case::session(AwsAuthFlow::SessionToken { access_key_id: "credential-value".into(), secret_access_key: "credential-value".into(), session_token: "credential-value".into() })] + #[case::static_keys(AwsAuthFlow::StaticKeys { access_key_id: "credential-value".into(), secret_access_key: "credential-value".into(), region_name: "region".into() })] + #[case::web_identity(AwsAuthFlow::WebIdentity { token: "credential-value".into(), role: "role".into(), session_name: "session".into() })] + fn auth_flow_debug_never_discloses_credentials(#[case] flow: AwsAuthFlow) { + let output = format!("{flow:?}"); + assert!(!output.contains("credential-value")); + assert!(output.contains("[REDACTED]")); } #[test] @@ -1142,8 +1589,8 @@ mod tests { let credentials = AwsAuthService::default() .resolve_credentials( AwsAuthConfig { - access_key_id: Some(access_key_id), - secret_access_key: Some(secret_access_key), + access_key_id: Some(access_key_id.into()), + secret_access_key: Some(secret_access_key.into()), region_name: Some("us-west-2".to_string()), ..Default::default() }, @@ -1191,4 +1638,44 @@ mod tests { failures.join("; ") ); } + + #[rstest::rstest] + #[case::provisioned( + None, + "https://cache-1/?Action=connect&User=iam-user", + "cache-1/?Action=connect&User=iam-user&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIDEXAMPLE%2F20261029%2Fus-east-1%2Felasticache%2Faws4_request&X-Amz-Date=20261029T120000Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Signature=dfa58492a6ae9ba065088724170a52437329d03f3e9bc9a4c90a80cc8a18e2f6" + )] + #[case::serverless_with_a_session_token( + Some("session-token"), + "https://cache-1/?Action=connect&User=iam-user&ResourceType=ServerlessCache", + "cache-1/?Action=connect&User=iam-user&ResourceType=ServerlessCache&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIDEXAMPLE%2F20261029%2Fus-east-1%2Felasticache%2Faws4_request&X-Amz-Date=20261029T120000Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Security-Token=session-token&X-Amz-Signature=e48bdbb3bf9d5e8f1d325bf33d5260e333df1f6b8868e8edd1e4c3cc01b4c3f9" + )] + fn presigned_urls_match_botocore_sigv4_query_auth( + #[case] session_token: Option<&str>, + #[case] url: &str, + #[case] expected: &str, + ) { + // Expected values from botocore 1.43.117 SigV4QueryAuth with get_current_datetime patched to + // 2026-10-29T12:00:00Z (generated 2026-10-29), the signer Python's ElastiCache IAM provider uses + let credentials = Credentials::new( + "AKIDEXAMPLE", + "wJalrXUtnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY", + session_token.map(str::to_owned), + None, + "test", + ); + let signed = presign_url( + url, + Duration::from_secs(900), + SigningScope { + region: "us-east-1", + service: "elasticache", + credentials: &credentials, + time: UNIX_EPOCH + Duration::from_secs(1_793_275_200), + }, + ) + .unwrap(); + + assert_eq!(signed.strip_prefix("https://"), Some(expected)); + } } diff --git a/litellm-rust/crates/auth-aws/src/constants.rs b/litellm-rust/crates/auth-aws/src/constants.rs index 789fde156bd..0e4662eda9e 100644 --- a/litellm-rust/crates/auth-aws/src/constants.rs +++ b/litellm-rust/crates/auth-aws/src/constants.rs @@ -11,8 +11,8 @@ pub const AWS_SESSION_NAME: &str = AwsParams::SESSION_NAME.env[0]; pub const AWS_PROFILE_NAME: &str = AwsParams::PROFILE_NAME.env[0]; pub const AWS_ROLE_NAME: &str = AwsParams::ROLE_NAME.env[0]; pub const AWS_WEB_IDENTITY_TOKEN: &str = AwsParams::WEB_IDENTITY_TOKEN.env[0]; -pub const AWS_ROLE_ARN: &str = "AWS_ROLE_ARN"; -pub const AWS_WEB_IDENTITY_TOKEN_FILE: &str = "AWS_WEB_IDENTITY_TOKEN_FILE"; +pub const AWS_ROLE_ARN: &str = AwsParams::ROLE_ARN_ENV; +pub const AWS_WEB_IDENTITY_TOKEN_FILE: &str = AwsParams::WEB_IDENTITY_TOKEN_FILE_ENV; pub const AWS_STS_ENDPOINT: &str = AwsParams::STS_ENDPOINT.env[0]; pub const AWS_EXTERNAL_ID: &str = AwsParams::EXTERNAL_ID.env[0]; pub const AWS_BEARER_TOKEN_BEDROCK: &str = "AWS_BEARER_TOKEN_BEDROCK"; diff --git a/litellm-rust/crates/auth-aws/src/error.rs b/litellm-rust/crates/auth-aws/src/error.rs index 5f88fc8a171..5445de1e6c3 100644 --- a/litellm-rust/crates/auth-aws/src/error.rs +++ b/litellm-rust/crates/auth-aws/src/error.rs @@ -2,6 +2,8 @@ use litellm_error::Source; #[derive(Clone, Debug, thiserror::Error, PartialEq, Eq)] pub enum Error { + #[error("AWS credential provider returned expired credentials")] + ExpiredCredentials, #[error("could not load AWS {flow} credentials")] Credentials { flow: CredentialFlow, diff --git a/litellm-rust/crates/auth-aws/src/signer.rs b/litellm-rust/crates/auth-aws/src/signer.rs index dff917d7c13..2d9ad9ba123 100644 --- a/litellm-rust/crates/auth-aws/src/signer.rs +++ b/litellm-rust/crates/auth-aws/src/signer.rs @@ -75,7 +75,8 @@ impl RequestSigner for SigV4Signer { ) .map_err(|error| match error { Error::NonAsciiHeader { .. } => litellm_http::Error::unsignable_header(error), - Error::Credentials { .. } + Error::ExpiredCredentials + | Error::Credentials { .. } | Error::MissingWebIdentityCredentials | Error::WebIdentityExpiration(_) | Error::Signing { .. } => litellm_http::Error::signature(error), diff --git a/litellm-rust/crates/auth-azure/Cargo.toml b/litellm-rust/crates/auth-azure/Cargo.toml index 7dcb0e513a7..36c10a74c80 100644 --- a/litellm-rust/crates/auth-azure/Cargo.toml +++ b/litellm-rust/crates/auth-azure/Cargo.toml @@ -8,17 +8,19 @@ repository.workspace = true [dependencies] litellm-secret-value.workspace = true litellm-auth-types.workspace = true +litellm-error.workspace = true moka.workspace = true serde_json.workspace = true sha2.workspace = true strum.workspace = true +tokio.workspace = true url.workspace = true azure_core = "1.0.0" azure_identity = { version = "1.0.0", features = ["tokio"] } [dev-dependencies] -litellm-error.workspace = true +litellm-secrets.workspace = true rstest.workspace = true -tokio.workspace = true +tokio = { workspace = true, features = ["test-util"] } diff --git a/litellm-rust/crates/auth-azure/src/lib.rs b/litellm-rust/crates/auth-azure/src/lib.rs index 9ed505e4160..6319406ce9c 100644 --- a/litellm-rust/crates/auth-azure/src/lib.rs +++ b/litellm-rust/crates/auth-azure/src/lib.rs @@ -3,5 +3,5 @@ mod native; mod resolve; mod types; -pub use resolve::{AzureAuthService, SECRET_NAMES}; +pub use resolve::{AzureAuthService, AzureIdentity, SECRET_NAMES}; pub use types::{AzureAuthInputs, ConfigValue}; diff --git a/litellm-rust/crates/auth-azure/src/native.rs b/litellm-rust/crates/auth-azure/src/native.rs index f40745ab7a2..03021428ea7 100644 --- a/litellm-rust/crates/auth-azure/src/native.rs +++ b/litellm-rust/crates/auth-azure/src/native.rs @@ -59,6 +59,7 @@ pub(crate) enum NativeAzureRequest { pub(crate) struct ValidatedAzureRequest { request: NativeAzureRequest, credential_source: InputSource, + probe_imds: bool, } impl ValidatedAzureRequest { @@ -68,9 +69,18 @@ impl ValidatedAzureRequest { Ok(Self { request, credential_source, + probe_imds: false, }) } + pub(crate) fn with_imds_probe(self, enabled: bool) -> Self { + Self { + probe_imds: enabled + && matches!(self.request, NativeAzureRequest::ManagedIdentity { .. }), + ..self + } + } + pub(crate) fn credential_source(&self) -> InputSource { self.credential_source } @@ -87,9 +97,34 @@ impl ValidatedAzureRequest { } } +pub(crate) enum CredentialAttempt { + Resolved(ResolvedCredential), + Unavailable(Error), + Failed(Error), +} + +impl CredentialAttempt { + pub(crate) fn into_result(self) -> Result { + match self { + Self::Resolved(credential) => Ok(credential), + Self::Unavailable(error) | Self::Failed(error) => Err(error), + } + } +} + pub(crate) struct NativeAzureTokenAcquirer { cache: AzureCredentialProviderCache, transport: Option, + imds_available: tokio::sync::Mutex, +} + +enum ImdsAvailability { + Unknown, + Available, + Unavailable { + retry_at: tokio::time::Instant, + error: Error, + }, } impl Default for NativeAzureTokenAcquirer { @@ -103,6 +138,7 @@ impl NativeAzureTokenAcquirer { Self { cache: AzureCredentialProviderCache::new(cache_capacity), transport: None, + imds_available: tokio::sync::Mutex::new(ImdsAvailability::Unknown), } } @@ -114,10 +150,51 @@ impl NativeAzureTokenAcquirer { Self { cache: AzureCredentialProviderCache::new(cache_capacity), transport: Some(transport), + imds_available: tokio::sync::Mutex::new(ImdsAvailability::Unknown), } } - pub(crate) async fn acquire( + pub(crate) async fn acquire(&self, request: ValidatedAzureRequest) -> CredentialAttempt { + let discover_imds = request.probe_imds; + if discover_imds && let Err(error) = self.discover_imds().await { + return CredentialAttempt::Unavailable(error); + } + match self.acquire_token(request).await { + Ok(credential) => CredentialAttempt::Resolved(credential), + Err(error) => { + let missing_identity = discover_imds && unassigned_identity(&error); + if missing_identity { + CredentialAttempt::Unavailable(error) + } else { + CredentialAttempt::Failed(error) + } + } + } + } + + async fn discover_imds(&self) -> Result<(), Error> { + let mut availability = self.imds_available.lock().await; + match &*availability { + ImdsAvailability::Available => return Ok(()), + ImdsAvailability::Unavailable { retry_at, error } + if tokio::time::Instant::now() < *retry_at => + { + return Err(error.clone()); + } + ImdsAvailability::Unknown | ImdsAvailability::Unavailable { .. } => {} + } + let result = probe_imds(self.transport.clone().unwrap_or_default()).await; + *availability = match &result { + Ok(()) => ImdsAvailability::Available, + Err(error) => ImdsAvailability::Unavailable { + retry_at: tokio::time::Instant::now() + Duration::from_secs(30), + error: error.clone(), + }, + }; + result + } + + async fn acquire_token( &self, request: ValidatedAzureRequest, ) -> Result { @@ -148,6 +225,54 @@ impl NativeAzureTokenAcquirer { } } +fn unassigned_identity(error: &(dyn std::error::Error + 'static)) -> bool { + std::iter::successors(Some(error), |cause| cause.source()).any(|cause| { + cause + .downcast_ref::() + .or_else(|| { + cause + .downcast_ref::()? + .downcast_ref::() + }) + .is_some_and(|error| { + error.http_status() == Some(azure_core::http::StatusCode::BadRequest) + }) + }) +} + +async fn probe_imds(transport: azure_core::http::Transport) -> Result<(), Error> { + use azure_core::http::{Context, Method, Request, Url}; + + let endpoint = + Url::parse("http://169.254.169.254/metadata/identity/oauth2/token?api-version=2019-08-01") + .map_err(|error| { + Error::InvalidConfiguration(ErrorDetail::failed("Azure IMDS probe endpoint", error)) + })?; + let mut request = Request::new(endpoint, Method::Get); + let response = tokio::time::timeout( + Duration::from_secs(1), + transport.send(&Context::new(), &mut request), + ) + .await + .map_err(|error| { + Error::CredentialAcquisition(ErrorDetail::failed("Azure IMDS availability probe", error)) + })? + .map_err(|error| { + Error::CredentialAcquisition(ErrorDetail::failed("Azure IMDS availability probe", error)) + })?; + // Headerless IMDS discovery expects 400, checked 2026-11-01 + // https://learn.microsoft.com/en-us/azure/virtual-machines/instance-metadata-service#errors-and-debugging + if response.status() != azure_core::http::StatusCode::BadRequest { + return Err(Error::CredentialAcquisition( + ErrorDetail::UnexpectedStatus { + operation: "Azure IMDS availability probe", + status: u16::from(response.status()), + }, + )); + } + Ok(()) +} + impl NativeAzureRequest { fn scope(&self) -> &str { match self { @@ -493,6 +618,7 @@ impl ClientAssertion for StaticAssertion { #[cfg(test)] mod tests { use std::sync::{Arc, Mutex}; + use std::time::Duration; use azure_core::http::headers::Headers; use azure_core::http::{AsyncRawResponse, HttpClient, Request, StatusCode, Transport}; @@ -569,6 +695,296 @@ mod tests { } } + #[derive(Debug)] + struct AvailableImdsClient { + probe_status: StatusCode, + probe_delay: Duration, + recover_after: Option, + response_status: Option, + probes: std::sync::atomic::AtomicUsize, + } + + impl Default for AvailableImdsClient { + fn default() -> Self { + Self { + probe_status: StatusCode::BadRequest, + probe_delay: Duration::ZERO, + recover_after: None, + response_status: None, + probes: std::sync::atomic::AtomicUsize::new(0), + } + } + } + + impl HttpClient for AvailableImdsClient { + fn execute_request<'life0, 'life1, 'async_trait>( + &'life0 self, + request: &'life1 Request, + ) -> std::pin::Pin< + Box> + Send + 'async_trait>, + > + where + 'life0: 'async_trait, + 'life1: 'async_trait, + Self: 'async_trait, + { + Box::pin(async move { + if request + .headers() + .get_optional_str(&azure_core::http::headers::HeaderName::from_static( + "metadata", + )) + .is_none() + { + let probe = self + .probes + .fetch_add(1, std::sync::atomic::Ordering::SeqCst); + if self.recover_after.is_some_and(|failures| probe >= failures) { + return Ok(AsyncRawResponse::from_bytes( + StatusCode::BadRequest, + Headers::new(), + "metadata header required", + )); + } + tokio::time::sleep(self.probe_delay).await; + return Ok(AsyncRawResponse::from_bytes( + self.probe_status, + Headers::new(), + "metadata header required", + )); + } + if let Some(status) = self.response_status { + return Ok(AsyncRawResponse::from_bytes( + status, + Headers::new(), + "identity endpoint failure", + )); + } + tokio::time::sleep(std::time::Duration::from_secs(2)).await; + let expires = std::time::SystemTime::now() + .duration_since(std::time::UNIX_EPOCH) + .unwrap() + .as_secs() + + 3600; + Ok(AsyncRawResponse::from_bytes( + StatusCode::Ok, + Headers::new(), + serde_json::json!({ + "access_token": "managed-token", "expires_on": expires.to_string(), + "token_type": "Bearer", "resource": "https://service.test", + }) + .to_string(), + )) + }) + } + } + + #[rstest::rstest] + #[tokio::test(start_paused = true)] + async fn available_imds_retains_full_sdk_token_acquisition() { + let acquirer = NativeAzureTokenAcquirer::with_transport( + 4, + Transport::new(Arc::new(AvailableImdsClient { + probe_status: StatusCode::BadRequest, + response_status: None, + ..Default::default() + })), + ); + let request = ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: None, + scope: deployment("https://service.test/.default".into()), + selection_source: InputSource::Deployment, + }) + .unwrap() + .with_imds_probe(true); + let credential = acquirer.acquire(request).await.into_result().unwrap(); + assert_eq!(credential.secret().expose(), "managed-token"); + } + + #[rstest::rstest] + #[case::another_metadata_service(StatusCode::NotFound)] + #[case::token_required(StatusCode::Unauthorized)] + #[case::proxy_rejection(StatusCode::Forbidden)] + #[case::unavailable(StatusCode::ServiceUnavailable)] + #[tokio::test(start_paused = true)] + async fn unrelated_metadata_responses_do_not_start_managed_identity_acquisition( + #[case] status: StatusCode, + ) { + let client = Arc::new(AvailableImdsClient { + probe_status: status, + ..Default::default() + }); + let acquirer = NativeAzureTokenAcquirer::with_transport(4, Transport::new(client.clone())); + let request = ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: None, + scope: deployment("https://service.test/.default".into()), + selection_source: InputSource::Deployment, + }) + .unwrap() + .with_imds_probe(true); + assert!(matches!( + acquirer.acquire(request.clone()).await, + super::CredentialAttempt::Unavailable(_) + )); + assert!(matches!( + acquirer.acquire(request).await, + super::CredentialAttempt::Unavailable(_) + )); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 1); + } + + #[rstest::rstest] + #[tokio::test(start_paused = true)] + async fn an_imds_timeout_is_not_repeated_on_the_next_request() { + let client = Arc::new(AvailableImdsClient { + probe_delay: Duration::from_secs(10), + ..Default::default() + }); + let acquirer = NativeAzureTokenAcquirer::with_transport(4, Transport::new(client.clone())); + let request = ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: None, + scope: deployment("https://service.test/.default".into()), + selection_source: InputSource::Deployment, + }) + .unwrap() + .with_imds_probe(true); + assert!(matches!( + acquirer.acquire(request.clone()).await, + super::CredentialAttempt::Unavailable(_) + )); + let second = tokio::time::timeout(Duration::from_millis(10), acquirer.acquire(request)) + .await + .unwrap(); + assert!(matches!(second, super::CredentialAttempt::Unavailable(_))); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 1); + } + + #[rstest::fixture] + fn imds_request() -> ValidatedAzureRequest { + ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: None, + scope: deployment("https://service.test/.default".into()), + selection_source: InputSource::Deployment, + }) + .unwrap() + .with_imds_probe(true) + } + + #[rstest::rstest] + #[case::timeout(StatusCode::BadRequest, Duration::from_secs(10))] + #[case::service_unavailable(StatusCode::ServiceUnavailable, Duration::ZERO)] + #[tokio::test(start_paused = true)] + async fn failed_imds_discovery_recovers_without_restarting_the_acquirer( + imds_request: ValidatedAzureRequest, + #[case] status: StatusCode, + #[case] delay: Duration, + ) { + let client = Arc::new(AvailableImdsClient { + probe_status: status, + probe_delay: delay, + recover_after: Some(1), + ..Default::default() + }); + let acquirer = NativeAzureTokenAcquirer::with_transport(4, Transport::new(client.clone())); + let (first, second) = tokio::join!( + acquirer.acquire(imds_request.clone()), + acquirer.acquire(imds_request.clone()), + ); + assert!(matches!(first, super::CredentialAttempt::Unavailable(_))); + assert!(matches!(second, super::CredentialAttempt::Unavailable(_))); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 1); + + tokio::time::advance(Duration::from_secs(30)).await; + let (first, second) = tokio::join!( + acquirer.acquire(imds_request.clone()), + acquirer.acquire(imds_request.clone()), + ); + assert_eq!( + first.into_result().unwrap().secret().expose(), + "managed-token" + ); + assert_eq!( + second.into_result().unwrap().secret().expose(), + "managed-token" + ); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 2); + + tokio::time::advance(Duration::from_secs(60)).await; + assert_eq!( + acquirer + .acquire(imds_request) + .await + .into_result() + .unwrap() + .secret() + .expose(), + "managed-token", + ); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 2); + } + + #[rstest::rstest] + #[tokio::test(start_paused = true)] + async fn a_persistent_imds_outage_reuses_each_failed_probe( + imds_request: ValidatedAzureRequest, + ) { + let client = Arc::new(AvailableImdsClient { + probe_delay: Duration::from_secs(10), + ..Default::default() + }); + let acquirer = NativeAzureTokenAcquirer::with_transport(4, Transport::new(client.clone())); + assert!(matches!( + acquirer.acquire(imds_request.clone()).await, + super::CredentialAttempt::Unavailable(_), + )); + tokio::time::advance(Duration::from_secs(30)).await; + let (first, second) = tokio::join!( + acquirer.acquire(imds_request.clone()), + acquirer.acquire(imds_request), + ); + assert!(matches!(first, super::CredentialAttempt::Unavailable(_))); + assert!(matches!(second, super::CredentialAttempt::Unavailable(_))); + assert_eq!(client.probes.load(std::sync::atomic::Ordering::SeqCst), 2); + } + + #[rstest::rstest] + #[case::default_identity_not_assigned(true, StatusCode::BadRequest, true)] + #[case::explicit_identity_not_assigned(false, StatusCode::BadRequest, false)] + #[case::default_identity_forbidden(true, StatusCode::Forbidden, false)] + #[case::default_identity_service_failure(true, StatusCode::InternalServerError, false)] + #[tokio::test(start_paused = true)] + async fn only_unavailable_default_identities_allow_fallback( + #[case] discovery: bool, + #[case] status: StatusCode, + #[case] unavailable: bool, + ) { + let acquirer = NativeAzureTokenAcquirer::with_transport( + 4, + Transport::new(Arc::new(AvailableImdsClient { + probe_status: StatusCode::BadRequest, + response_status: Some(status), + ..Default::default() + })), + ); + let request = ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: None, + scope: deployment("https://service.test/.default".into()), + selection_source: InputSource::Deployment, + }) + .unwrap() + .with_imds_probe(discovery); + let outcome = acquirer.acquire(request).await; + let is_unavailable = matches!(outcome, super::CredentialAttempt::Unavailable(_)); + let error = outcome.into_result().unwrap_err(); + assert_eq!( + is_unavailable, + unavailable, + "{}", + litellm_error::Report::new(&error) + ); + } + + #[rstest::rstest] #[tokio::test] async fn client_secret_uses_sdk_protocol_and_reuses_cached_credential() { let transport = Arc::new(RecordingTokenClient::default()); @@ -582,8 +998,12 @@ mod tests { "https://login.test", ); - let first = acquirer.acquire(request.clone()).await.unwrap(); - let second = acquirer.acquire(request).await.unwrap(); + let first = acquirer + .acquire(request.clone()) + .await + .into_result() + .unwrap(); + let second = acquirer.acquire(request).await.into_result().unwrap(); assert_eq!(first.secret().expose(), "native-token"); assert_eq!(second.secret().expose(), "native-token"); @@ -599,6 +1019,7 @@ mod tests { ); } + #[rstest::rstest] #[tokio::test] async fn credential_provider_cache_isolates_every_client_secret_identity_field() { let transport = Arc::new(RecordingTokenClient::default()); @@ -645,10 +1066,10 @@ mod tests { ), ]; - acquirer.acquire(base.clone()).await.unwrap(); - acquirer.acquire(base).await.unwrap(); + acquirer.acquire(base.clone()).await.into_result().unwrap(); + acquirer.acquire(base).await.into_result().unwrap(); for request in variants.into_iter().skip(1) { - acquirer.acquire(request).await.unwrap(); + acquirer.acquire(request).await.into_result().unwrap(); } assert_eq!(transport.requests.lock().unwrap().len(), 6); diff --git a/litellm-rust/crates/auth-azure/src/resolve.rs b/litellm-rust/crates/auth-azure/src/resolve.rs index a92630af26a..9ff4eda540a 100644 --- a/litellm-rust/crates/auth-azure/src/resolve.rs +++ b/litellm-rust/crates/auth-azure/src/resolve.rs @@ -1,3 +1,4 @@ +use crate::native::CredentialAttempt; use litellm_auth_types::{ CredentialFileRef, CredentialLookup, CredentialRef, InputSource, ResolvedCredential, Sourced, TokenProviderHandle, @@ -12,25 +13,31 @@ use std::sync::Arc; use super::native::{NativeAzureRequest, NativeAzureTokenAcquirer, ValidatedAzureRequest}; use super::types::{AzureAuthInputs, AzureCredentialType, ConfigValue, DEFAULT_AZURE_SCOPE}; -const AZURE_AD_TOKEN_ENV: &str = "AZURE_AD_TOKEN"; -const AZURE_TENANT_ID_ENV: &str = "AZURE_TENANT_ID"; -const AZURE_CLIENT_ID_ENV: &str = "AZURE_CLIENT_ID"; -const AZURE_CLIENT_SECRET_ENV: &str = "AZURE_CLIENT_SECRET"; -const AZURE_SCOPE_ENV: &str = "AZURE_SCOPE"; -const AZURE_AUTHORITY_HOST_ENV: &str = "AZURE_AUTHORITY_HOST"; -const AZURE_CREDENTIAL_ENV: &str = "AZURE_CREDENTIAL"; -const AZURE_FEDERATED_TOKEN_FILE_ENV: &str = "AZURE_FEDERATED_TOKEN_FILE"; +#[derive(strum::IntoStaticStr, strum::VariantArray, strum::VariantNames)] +enum AzureSecretName { + #[strum(serialize = "AZURE_AD_TOKEN")] + AdToken, + #[strum(serialize = "AZURE_TENANT_ID")] + TenantId, + #[strum(serialize = "AZURE_CLIENT_ID")] + ClientId, + #[strum(serialize = "AZURE_CLIENT_SECRET")] + ClientSecret, + #[strum(serialize = "AZURE_SCOPE")] + Scope, + #[strum(serialize = "AZURE_AUTHORITY_HOST")] + AuthorityHost, + #[strum(serialize = "AZURE_CREDENTIAL")] + Credential, + #[strum(serialize = "AZURE_FEDERATED_TOKEN_FILE")] + FederatedTokenFile, + #[strum(serialize = "IDENTITY_ENDPOINT")] + IdentityEndpoint, + #[strum(serialize = "MSI_ENDPOINT")] + MsiEndpoint, +} -pub const SECRET_NAMES: &[&str] = &[ - AZURE_AD_TOKEN_ENV, - AZURE_TENANT_ID_ENV, - AZURE_CLIENT_ID_ENV, - AZURE_CLIENT_SECRET_ENV, - AZURE_SCOPE_ENV, - AZURE_AUTHORITY_HOST_ENV, - AZURE_CREDENTIAL_ENV, - AZURE_FEDERATED_TOKEN_FILE_ENV, -]; +pub const SECRET_NAMES: &[&str] = ::VARIANTS; #[derive(Clone, Debug)] pub(crate) enum AzureCredentialPlan { @@ -57,14 +64,14 @@ trait AzureTokenAcquirer: Send + Sync { fn acquire( &self, request: ValidatedAzureRequest, - ) -> Pin> + Send + '_>>; + ) -> Pin + Send + '_>>; } impl AzureTokenAcquirer for NativeAzureTokenAcquirer { fn acquire( &self, request: ValidatedAzureRequest, - ) -> Pin> + Send + '_>> { + ) -> Pin + Send + '_>> { Box::pin(NativeAzureTokenAcquirer::acquire(self, request)) } } @@ -83,12 +90,36 @@ impl AzureAuthService { Self { native } } + pub async fn identity_token( + &self, + identity: &AzureIdentity, + scope: &str, + env_lookup: &(dyn Fn(&str) -> Option + Sync), + ) -> Result, Error> { + let plan = identity_plan(identity, scope, env_lookup)?; + let inputs = AzureAuthInputs::default_credential_for_scope(scope); + Ok(self + .run_plan(plan, &inputs, env_lookup) + .await? + .map(Sourced::into_value)) + } + pub async fn get_azure_ad_token( &self, inputs: &AzureAuthInputs, env_lookup: &(dyn Fn(&str) -> Option + Sync), ) -> Result>, Error> { - match select_auth_plan(inputs, env_lookup)? { + self.run_plan(select_auth_plan(inputs, env_lookup)?, inputs, env_lookup) + .await + } + + async fn run_plan( + &self, + plan: AzureCredentialPlan, + inputs: &AzureAuthInputs, + env_lookup: &(dyn Fn(&str) -> Option + Sync), + ) -> Result>, Error> { + match plan { AzureCredentialPlan::Supplied(credential) => Ok(Some(credential)), AzureCredentialPlan::Caller(caller) => { let credential = caller.acquire().await?; @@ -123,6 +154,7 @@ impl AzureAuthService { self.native .acquire(request) .await + .into_result() .map(|credential| Sourced::new(credential, source)) .map(Some) } @@ -131,6 +163,7 @@ impl AzureAuthService { self.native .acquire(request) .await + .into_result() .map(|credential| Some(Sourced::new(credential, source))) } AzureCredentialPlan::Chain(requests) => { @@ -138,8 +171,16 @@ impl AzureAuthService { for request in requests { let source = request.credential_source(); match self.native.acquire(request).await { - Ok(credential) => return Ok(Some(Sourced::new(credential, source))), - Err(error) => failures.push(error), + CredentialAttempt::Resolved(credential) => { + return Ok(Some(Sourced::new(credential, source))); + } + CredentialAttempt::Unavailable(error) => failures.push(error), + CredentialAttempt::Failed(error) => { + return Err(Error::CredentialChain { + last: Box::new(error), + earlier: failures, + }); + } } } match failures.pop() { @@ -155,39 +196,161 @@ impl AzureAuthService { } } +/// The identity azure-identity builds for one explicit credential class, without LiteLLM's provider settings +/// (`AZURE_AD_TOKEN`, `AZURE_CREDENTIAL`, `AZURE_SCOPE`) that `get_azure_ad_token` also reads. +#[derive(Clone, Debug)] +pub enum AzureIdentity { + ClientSecret { + tenant_id: String, + client_id: String, + client_secret: SecretValue, + }, + ManagedIdentity { + client_id: String, + }, + Default, +} + +fn identity_plan( + identity: &AzureIdentity, + scope: &str, + env_lookup: &dyn Fn(&str) -> Option, +) -> Result { + let deployment = |value: &str| Sourced::new(value.to_owned(), InputSource::Deployment); + let environment = |name: &str| { + env_lookup(name) + .filter(|value| !value.trim().is_empty()) + .map(|value| Sourced::new(value, InputSource::Environment)) + }; + let scope = deployment(scope); + let authority = environment(AzureSecretName::AuthorityHost.into()); + match identity { + AzureIdentity::ClientSecret { + tenant_id, + client_id, + client_secret, + } => Ok(AzureCredentialPlan::Native(ValidatedAzureRequest::new( + NativeAzureRequest::ClientSecret { + tenant_id: deployment(tenant_id), + client_id: deployment(client_id), + client_secret: Sourced::new(client_secret.clone(), InputSource::Deployment), + scope, + authority, + }, + )?)), + AzureIdentity::ManagedIdentity { client_id } => Ok(AzureCredentialPlan::Native( + ValidatedAzureRequest::new(NativeAzureRequest::ManagedIdentity { + client_id: Some(deployment(client_id)), + scope, + selection_source: InputSource::Deployment, + })?, + )), + AzureIdentity::Default => { + let tenant_id = environment(AzureSecretName::TenantId.into()); + let client_id = environment(AzureSecretName::ClientId.into()); + let from_environment = match ( + tenant_id.clone(), + client_id.clone(), + environment(AzureSecretName::ClientSecret.into()), + ) { + (Some(tenant_id), Some(client_id), Some(client_secret)) => { + Some(NativeAzureRequest::ClientSecret { + tenant_id, + client_id, + client_secret: client_secret.map(SecretValue::new), + scope: scope.clone(), + authority: authority.clone(), + }) + } + _ => None, + }; + let probe_imds = env_lookup(AzureSecretName::IdentityEndpoint.into()).is_none() + && env_lookup(AzureSecretName::MsiEndpoint.into()).is_none(); + let AzureCredentialPlan::Chain(chain) = select_native_plan( + Some(Sourced::new( + AzureCredentialType::DefaultAzureCredential, + InputSource::Deployment, + )), + tenant_id, + client_id, + environment(AzureSecretName::FederatedTokenFile.into()), + scope, + authority, + InputSource::Environment, + )? + else { + unreachable!("DefaultAzureCredential always plans a chain") + }; + let environment_request = from_environment + .map(ValidatedAzureRequest::new) + .transpose()?; + Ok(AzureCredentialPlan::Chain( + environment_request + .into_iter() + .chain(chain) + .map(|request| request.with_imds_probe(probe_imds)) + .collect(), + )) + } + } +} + pub(crate) fn select_auth_plan( inputs: &AzureAuthInputs, env_lookup: &dyn Fn(&str) -> Option, ) -> Result { - let token = configured_secret(&inputs.azure_ad_token, AZURE_AD_TOKEN_ENV, env_lookup); - let tenant_id = configured_string(&inputs.tenant_id, AZURE_TENANT_ID_ENV, env_lookup); - let client_id = configured_string(&inputs.client_id, AZURE_CLIENT_ID_ENV, env_lookup); - let client_secret = - configured_secret(&inputs.client_secret, AZURE_CLIENT_SECRET_ENV, env_lookup); - let scope = configured_string(&inputs.azure_scope, AZURE_SCOPE_ENV, env_lookup) - .unwrap_or_else(|| Sourced::new(DEFAULT_AZURE_SCOPE.to_string(), InputSource::Environment)); - let authority = configured_string( - &inputs.azure_authority_host, - AZURE_AUTHORITY_HOST_ENV, + let token = configured_secret( + &inputs.azure_ad_token, + AzureSecretName::AdToken.into(), env_lookup, ); - let selector = configured_string(&inputs.azure_credential, AZURE_CREDENTIAL_ENV, env_lookup) - .map(|value| { - value - .value() - .parse::() - .map(|selector| Sourced::new(selector, value.source())) - }) - .transpose() - .map_err(|error| { - Error::InvalidConfiguration(ErrorDetail::failed( - "Azure credential selector parsing", - error, - )) - })?; + let tenant_id = configured_string( + &inputs.tenant_id, + AzureSecretName::TenantId.into(), + env_lookup, + ); + let client_id = configured_string( + &inputs.client_id, + AzureSecretName::ClientId.into(), + env_lookup, + ); + let client_secret = configured_secret( + &inputs.client_secret, + AzureSecretName::ClientSecret.into(), + env_lookup, + ); + let scope = configured_string( + &inputs.azure_scope, + AzureSecretName::Scope.into(), + env_lookup, + ) + .unwrap_or_else(|| Sourced::new(DEFAULT_AZURE_SCOPE.to_string(), InputSource::Environment)); + let authority = configured_string( + &inputs.azure_authority_host, + AzureSecretName::AuthorityHost.into(), + env_lookup, + ); + let selector = configured_string( + &inputs.azure_credential, + AzureSecretName::Credential.into(), + env_lookup, + ) + .map(|value| { + value + .value() + .parse::() + .map(|selector| Sourced::new(selector, value.source())) + }) + .transpose() + .map_err(|error| { + Error::InvalidConfiguration(ErrorDetail::failed( + "Azure credential selector parsing", + error, + )) + })?; let federated_token_file = configured_string( &inputs.federated_token_file, - AZURE_FEDERATED_TOKEN_FILE_ENV, + AzureSecretName::FederatedTokenFile.into(), env_lookup, ); @@ -237,7 +400,12 @@ pub(crate) fn select_auth_plan( return Ok(AzureCredentialPlan::Missing); } - select_native_plan( + let probe_imds = selector + .as_ref() + .is_none_or(|selector| *selector.value() == AzureCredentialType::DefaultAzureCredential) + && env_lookup(AzureSecretName::IdentityEndpoint.into()).is_none() + && env_lookup(AzureSecretName::MsiEndpoint.into()).is_none(); + match select_native_plan( selector, tenant_id, client_id, @@ -245,7 +413,15 @@ pub(crate) fn select_auth_plan( scope, authority, inputs.enable_azure_ad_token_refresh.source(), - ) + )? { + AzureCredentialPlan::Chain(requests) => Ok(AzureCredentialPlan::Chain( + requests + .into_iter() + .map(|request| request.with_imds_probe(probe_imds)) + .collect(), + )), + plan => Ok(plan), + } } fn select_native_plan( @@ -509,7 +685,10 @@ mod tests { #[derive(Debug)] struct FileResolver; + use crate::native::CredentialAttempt; + struct ChainAcquirer { + terminal_on: Option<&'static str>, requests: Mutex>, succeed_on: Option<&'static str>, } @@ -518,20 +697,25 @@ mod tests { fn acquire( &self, request: ValidatedAzureRequest, - ) -> std::pin::Pin> + Send + '_>> - { + ) -> std::pin::Pin + Send + '_>> { let kind = request.kind(); self.requests.lock().unwrap().push(kind); Box::pin(async move { if self.succeed_on == Some(kind) { - Ok(ResolvedCredential::AccessToken { + CredentialAttempt::Resolved(ResolvedCredential::AccessToken { token: SecretValue::new("chain-token"), expires_on: None, }) } else { - Err(Error::CredentialAcquisition( - litellm_auth_types::ErrorDetail::Unresolved { subject: kind }, - )) + let error = + Error::CredentialAcquisition(litellm_auth_types::ErrorDetail::Unresolved { + subject: kind, + }); + if self.terminal_on == Some(kind) { + CredentialAttempt::Failed(error) + } else { + CredentialAttempt::Unavailable(error) + } } }) } @@ -690,9 +874,210 @@ mod tests { assert_eq!(resolved, Some(SecretValue::new("rotated-assertion"))); } + #[derive(Debug)] + struct UnavailableImdsClient; + + impl azure_core::http::HttpClient for UnavailableImdsClient { + fn execute_request<'life0, 'life1, 'async_trait>( + &'life0 self, + _request: &'life1 azure_core::http::Request, + ) -> std::pin::Pin< + Box< + dyn Future> + + Send + + 'async_trait, + >, + > + where + 'life0: 'async_trait, + 'life1: 'async_trait, + Self: 'async_trait, + { + Box::pin(std::future::pending()) + } + } + + struct OfflineChainAcquirer(super::NativeAzureTokenAcquirer); + + impl AzureTokenAcquirer for OfflineChainAcquirer { + fn acquire( + &self, + request: ValidatedAzureRequest, + ) -> std::pin::Pin + Send + '_>> { + Box::pin(async move { + if request.kind() == "developer-tools" { + return CredentialAttempt::Resolved(ResolvedCredential::AccessToken { + token: SecretValue::new("cli-token"), + expires_on: None, + }); + } + self.0.acquire(request).await + }) + } + } + + #[rstest::rstest] + #[case::default_off_azure("DefaultAzureCredential", None, true)] + #[case::explicit_managed_identity("ManagedIdentityCredential", None, false)] + #[case::app_service("DefaultAzureCredential", Some("http://app-service.test"), false)] + #[tokio::test(start_paused = true)] + async fn only_default_imds_discovery_is_bounded( + #[case] selector: &str, + #[case] identity_endpoint: Option<&str>, + #[case] expect_cli: bool, + ) { + let native = super::NativeAzureTokenAcquirer::with_transport( + 4, + azure_core::http::Transport::new(Arc::new(UnavailableImdsClient)), + ); + let service = AzureAuthService::with_acquirer(Arc::new(OfflineChainAcquirer(native))); + let inputs = AzureAuthInputs { + azure_credential: crate::types::ConfigValue::Value(Sourced::new( + selector.into(), + InputSource::Deployment, + )), + enable_azure_ad_token_refresh: Sourced::new(true, InputSource::Deployment), + ..Default::default() + }; + let environment = |name: &str| { + (name == "IDENTITY_ENDPOINT") + .then_some(identity_endpoint) + .flatten() + .map(str::to_string) + }; + let outcome = tokio::time::timeout( + std::time::Duration::from_secs(2), + service.get_azure_ad_token(&inputs, &environment), + ) + .await; + + assert_eq!(outcome.is_ok(), expect_cli); + if expect_cli { + assert_eq!( + outcome.unwrap().unwrap().unwrap().value().secret().expose(), + "cli-token" + ); + } + } + + #[rstest::rstest] + #[case::app_service(Some("IDENTITY_ENDPOINT"), false)] + #[case::legacy_app_service(Some("MSI_ENDPOINT"), false)] + #[case::off_azure(None, true)] + #[tokio::test(start_paused = true)] + async fn default_chain_uses_endpoints_from_the_secret_snapshot( + #[case] endpoint: Option<&str>, + #[case] expect_cli: bool, + #[values(false, true)] consumer_identity: bool, + ) { + use litellm_secrets::source::SecretSource; + + struct EndpointSecrets<'a>(Option<&'a str>); + + impl SecretSource for EndpointSecrets<'_> { + fn get_secret_str<'a>( + &'a self, + name: &'a str, + ) -> std::pin::Pin< + Box< + dyn Future, litellm_secrets::Error>> + + Send + + 'a, + >, + > { + Box::pin(async move { + Ok((self.0 == Some(name)).then(|| SecretValue::new("http://app-service.test"))) + }) + } + } + + let snapshot = EndpointSecrets(endpoint) + .resolve(SECRET_NAMES) + .await + .unwrap(); + let native = super::NativeAzureTokenAcquirer::with_transport( + 4, + azure_core::http::Transport::new(Arc::new(UnavailableImdsClient)), + ); + let service = AzureAuthService::with_acquirer(Arc::new(OfflineChainAcquirer(native))); + let lookup = |name: &str| snapshot.get(name); + let scope = "https://service.test/.default"; + let outcome = tokio::time::timeout(std::time::Duration::from_secs(2), async { + if consumer_identity { + service + .identity_token(&super::AzureIdentity::Default, scope, &lookup) + .await + } else { + service + .get_azure_ad_token( + &AzureAuthInputs::default_credential_for_scope(scope), + &lookup, + ) + .await + .map(|token| token.map(Sourced::into_value)) + } + }) + .await; + + assert_eq!(outcome.is_ok(), expect_cli); + if expect_cli { + assert_eq!( + outcome.unwrap().unwrap().unwrap().secret().expose(), + "cli-token" + ); + } + } + + #[rstest::rstest] + #[case::litellm_provider_settings_are_ignored( + &[("AZURE_AD_TOKEN", "provider-token"), ("AZURE_CREDENTIAL", "ClientSecretCredential")], + &["managed-identity", "developer-tools"] + )] + #[case::environment_client_secret_comes_first( + &[("AZURE_TENANT_ID", "tenant"), ("AZURE_CLIENT_ID", "client"), ("AZURE_CLIENT_SECRET", "secret")], + &["client-secret", "managed-identity", "developer-tools"] + )] + #[case::aks_workload_identity( + &[("AZURE_TENANT_ID", "tenant"), ("AZURE_CLIENT_ID", "client"), ("AZURE_FEDERATED_TOKEN_FILE", "/var/run/token")], + &["workload-identity", "managed-identity", "developer-tools"] + )] + #[tokio::test] + async fn the_default_identity_follows_azure_identity_defaults( + #[case] environment: &[(&str, &str)], + #[case] expected: &[&str], + ) { + let acquirer = Arc::new(ChainAcquirer { + terminal_on: None, + requests: Mutex::new(Vec::new()), + succeed_on: Some("developer-tools"), + }); + let service = AzureAuthService::with_acquirer(acquirer.clone()); + let lookup = |name: &str| { + environment + .iter() + .find(|(key, _)| *key == name) + .map(|(_, value)| (*value).to_owned()) + }; + + let credential = service + .identity_token( + &super::AzureIdentity::Default, + "https://redis.azure.com/.default", + &lookup, + ) + .await + .unwrap() + .unwrap(); + + assert_eq!(credential.secret().expose(), "chain-token"); + assert_eq!(*acquirer.requests.lock().unwrap(), expected); + } + + #[rstest::rstest] #[tokio::test] async fn default_chain_uses_declared_order_and_stops_after_success() { let acquirer = Arc::new(ChainAcquirer { + terminal_on: None, requests: Mutex::new(Vec::new()), succeed_on: Some("developer-tools"), }); @@ -715,9 +1100,13 @@ mod tests { ); } + #[rstest::rstest] + #[case::all_unavailable(false)] + #[case::terminal_failure_after_unavailable(true)] #[tokio::test] - async fn chain_reports_each_acquisition_failure() { + async fn chain_reports_each_acquisition_failure(#[case] terminal: bool) { let acquirer = Arc::new(ChainAcquirer { + terminal_on: terminal.then_some("developer-tools"), requests: Mutex::new(Vec::new()), succeed_on: None, }); @@ -744,6 +1133,27 @@ mod tests { ); } + #[rstest::rstest] + #[tokio::test] + async fn a_configured_identity_failure_does_not_fall_back_to_a_developer_identity() { + let acquirer = Arc::new(ChainAcquirer { + terminal_on: Some("managed-identity"), + requests: Mutex::new(Vec::new()), + succeed_on: Some("developer-tools"), + }); + let service = AzureAuthService::with_acquirer(acquirer.clone()); + let inputs = AzureAuthInputs { + enable_azure_ad_token_refresh: Sourced::new(true, InputSource::Deployment), + ..Default::default() + }; + let error = service + .get_azure_ad_token(&inputs, &|_| None) + .await + .unwrap_err(); + assert_eq!(*acquirer.requests.lock().unwrap(), ["managed-identity"]); + assert!(matches!(error, Error::CredentialChain { earlier, .. } if earlier.is_empty())); + } + #[derive(Debug)] struct CallerToken(&'static str); diff --git a/litellm-rust/crates/auth-gcp/Cargo.toml b/litellm-rust/crates/auth-gcp/Cargo.toml index ad60f0db817..90aab8d281c 100644 --- a/litellm-rust/crates/auth-gcp/Cargo.toml +++ b/litellm-rust/crates/auth-gcp/Cargo.toml @@ -6,7 +6,7 @@ license.workspace = true repository.workspace = true [features] -google-sdk = ["dep:google-cloud-auth", "dep:http"] +google-sdk = ["dep:http"] [dependencies] litellm-secret-value.workspace = true @@ -21,10 +21,14 @@ tokio.workspace = true veil.workspace = true gcp_auth = "0.12.7" -google-cloud-auth = { workspace = true, optional = true } +google-cloud-auth.workspace = true http = { workspace = true, optional = true } [dev-dependencies] +time = { workspace = true, features = ["formatting"] } +axum = { workspace = true, features = ["json"] } litellm-error.workspace = true rstest.workspace = true +rustls.workspace = true tempfile.workspace = true +tokio = { workspace = true, features = ["test-util"] } diff --git a/litellm-rust/crates/auth-gcp/src/auth.rs b/litellm-rust/crates/auth-gcp/src/auth.rs index 650d5831796..5976ef82344 100644 --- a/litellm-rust/crates/auth-gcp/src/auth.rs +++ b/litellm-rust/crates/auth-gcp/src/auth.rs @@ -1,20 +1,11 @@ -use std::{future::Future, pin::Pin, sync::Arc}; +use std::sync::Arc; -use gcp_auth::{CustomServiceAccount, TokenProvider}; -use litellm_auth_types::{CredentialPlacement, Error, ErrorDetail, http::apply_credential}; -use moka::future::Cache; -use serde_json::Value; -use sha2::{Digest, Sha256}; +use litellm_auth_types::{CredentialPlacement, Error, http::apply_credential}; use crate::{ - config::{ - CredentialSource, VertexConfig, credential_source, get_vertex_ai_project, - is_base64_encoded_json, names_a_credentials_file, non_empty_env, - }, - constants::{ - CLOUD_PLATFORM_SCOPE, GOOGLE_OAUTH_TOKEN_ENDPOINT, VERTEX_AI_API_KEY_ENV, - VERTEXAI_API_KEY_ENV, - }, + config::{VertexConfig, credential_source, get_vertex_ai_project, non_empty_env}, + constants::{VERTEX_AI_API_KEY_ENV, VERTEXAI_API_KEY_ENV}, + identity::{GcpAuth, GcpProviderLoader, GcpTokenSource}, }; pub struct VertexEnvironment { @@ -22,28 +13,15 @@ pub struct VertexEnvironment { pub project_id: String, } -struct VertexAccessToken { - token: String, - project_id: String, -} - -#[derive(Clone)] +#[derive(Clone, Default)] pub struct VertexAuth { - providers: Cache>, - loader: Arc, -} - -impl Default for VertexAuth { - fn default() -> Self { - Self::new(Arc::new(GcpProviderLoader)) - } + identity: GcpAuth, } impl VertexAuth { - pub fn new(loader: Arc) -> Self { + pub fn new(loader: Arc) -> Self { Self { - providers: Cache::builder().max_capacity(64).build(), - loader, + identity: GcpAuth::new(loader), } } @@ -52,7 +30,10 @@ impl VertexAuth { config: &VertexConfig, env_lookup: &(dyn Fn(&str) -> Option + Sync), ) -> Result { - self.load_provider(config, env_lookup).await?.token().await + self.identity + .access_token(credential_source(config, env_lookup)) + .await + .map(|token| token.expose().to_owned()) } pub async fn validate_environment( @@ -77,10 +58,18 @@ impl VertexAuth { (true, _) => None, (false, Some(token)) => Some(token), (false, None) => { - let access = self.get_access_token(config, env_lookup).await?; + let provider = self.load_provider(config, env_lookup).await?; + let (token, project_id) = match project_id { + Some(project_id) => (provider.token().await?, project_id), + None => tokio::try_join!(provider.token(), provider.project_id())?, + }; return Ok(VertexEnvironment { - headers: apply_credential(headers, &access.token, CredentialPlacement::Bearer)?, - project_id: project_id.unwrap_or(access.project_id), + headers: apply_credential( + headers, + token.expose(), + CredentialPlacement::Bearer, + )?, + project_id, }); } }; @@ -104,248 +93,171 @@ impl VertexAuth { }) } - async fn get_access_token( - &self, - config: &VertexConfig, - env_lookup: &(dyn Fn(&str) -> Option + Sync), - ) -> Result { - let provider = self.load_provider(config, env_lookup).await?; - let (token, project_id) = tokio::try_join!(provider.token(), provider.project_id())?; - Ok(VertexAccessToken { token, project_id }) - } - async fn load_provider( &self, config: &VertexConfig, env_lookup: &(dyn Fn(&str) -> Option + Sync), - ) -> Result, Error> { - let source = credential_source(config, env_lookup); - let key = source.cache_key(); - self.providers - .try_get_with(key, self.loader.load(source)) + ) -> Result, Error> { + self.identity + .load_provider(credential_source(config, env_lookup)) .await - .map_err(|error| (*error).clone()) } } -pub trait VertexTokenSource: Send + Sync { - fn project_id(&self) -> VertexAuthFuture<'_, String>; - fn token(&self) -> VertexAuthFuture<'_, String>; -} - -pub trait VertexProviderLoader: Send + Sync { - fn load(&self, source: CredentialSource) -> VertexAuthFuture<'_, Arc>; -} - -pub type VertexAuthFuture<'a, T> = Pin> + Send + 'a>>; - -struct GcpTokenSource(Arc); - -impl VertexTokenSource for GcpTokenSource { - fn project_id(&self) -> VertexAuthFuture<'_, String> { - Box::pin(async move { - self.0 - .project_id() - .await - .map(|project| project.to_string()) - .map_err(auth_acquisition_error) - }) - } - - fn token(&self) -> VertexAuthFuture<'_, String> { - Box::pin(async move { - self.0 - .token(&[CLOUD_PLATFORM_SCOPE]) - .await - .map(|token| token.as_str().to_string()) - .map_err(auth_acquisition_error) - }) - } -} - -struct GcpProviderLoader; - -impl VertexProviderLoader for GcpProviderLoader { - fn load(&self, source: CredentialSource) -> VertexAuthFuture<'_, Arc> { - Box::pin(async move { - let provider: Arc = match source { - CredentialSource::Inline(configured) => Arc::new( - CustomServiceAccount::from_json(validate_request_credentials( - configured.expose(), - )?) - .map_err(auth_acquisition_error)?, - ), - CredentialSource::Trusted(configured) => { - Arc::new(load_trusted_service_account(configured.expose())?) - } - CredentialSource::ApplicationCredentials(path) => { - Arc::new(CustomServiceAccount::from_file(path).map_err(auth_acquisition_error)?) - } - CredentialSource::Adc => { - gcp_auth::provider().await.map_err(auth_acquisition_error)? - } - }; - Ok(Arc::new(GcpTokenSource(provider)) as Arc) - }) - } -} - -fn validate_request_credentials(configured: &str) -> Result<&str, Error> { - let token_uri = serde_json::from_str::(configured) - .ok() - .and_then(|credentials| { - credentials - .get("token_uri") - .and_then(Value::as_str) - .map(str::to_string) - }); - if token_uri.as_deref() != Some(GOOGLE_OAUTH_TOKEN_ENDPOINT) { - return Err(Error::InvalidConfiguration(ErrorDetail::InvalidType { - field: "request-controlled vertex_credentials token_uri".into(), - expected: "the canonical Google OAuth token endpoint", - })); - } - Ok(configured) -} - -impl CredentialSource { - fn cache_key(&self) -> CredentialCacheKey { - match self { - Self::Inline(configured) => { - CredentialCacheKey::Inline(Sha256::digest(configured.expose()).into()) - } - Self::Trusted(configured) => { - CredentialCacheKey::Trusted(Sha256::digest(configured.expose()).into()) - } - Self::ApplicationCredentials(path) => { - CredentialCacheKey::ApplicationCredentials(path.clone()) - } - Self::Adc => CredentialCacheKey::Adc, - } - } -} - -#[derive(Clone, Debug, Hash, PartialEq, Eq)] -enum CredentialCacheKey { - Inline([u8; 32]), - Trusted([u8; 32]), - ApplicationCredentials(String), - Adc, -} - -fn load_trusted_service_account(configured: &str) -> Result { - if is_base64_encoded_json(configured) { - return Err(Error::InvalidConfiguration( - ErrorDetail::Base64EncodedJson { - field: "vertex_credentials", - }, - )); - } - if names_a_credentials_file(configured) { - CustomServiceAccount::from_file(configured) - } else { - CustomServiceAccount::from_json(configured) - } - .map_err(auth_acquisition_error) -} - -fn auth_acquisition_error(error: gcp_auth::Error) -> Error { - Error::CredentialAcquisition(litellm_auth_types::ErrorDetail::failed( - "Vertex AI credentials", - error, - )) -} - #[cfg(test)] mod tests { + use std::{ + collections::{BTreeMap, BTreeSet}, + sync::{ + Arc, + atomic::{AtomicUsize, Ordering}, + }, + }; + + use crate::{ + CredentialSource, GcpAuthFuture, GcpProviderLoader, GcpTokenSource, VertexAuth, + VertexConfig, get_vertex_ai_location, secret_names, + }; use litellm_secret_value::SecretValue; + use rstest::rstest; + use serde_json::{Value, json}; - use super::*; - - #[rstest::rstest] - #[case::canonical_endpoint(r#"{"token_uri":"https://oauth2.googleapis.com/token"}"#, true)] - #[case::noncanonical_endpoint(r#"{"token_uri":"http://127.0.0.1/token"}"#, false)] - #[case::missing_endpoint("{}", false)] - fn request_credentials_require_canonical_token_endpoint( - #[case] credentials: &str, - #[case] accepted: bool, - ) { - assert_eq!(validate_request_credentials(credentials).is_ok(), accepted); + struct FakeProvider { + project: Option<&'static str>, + calls: Arc, } - #[rstest::rstest] - #[case::missing_file( - "/nonexistent/vertex-key.json", - "failed to open application credentials file" - )] - #[case::malformed_json( - " {\"type\": \"service_account\",", - "failed to deserialize ApplicationCredentials" - )] - #[case::quoted_json( - "'{\"type\": \"service_account\"}'", - "failed to deserialize ApplicationCredentials" - )] - #[tokio::test] - async fn trusted_credentials_are_read_as_a_file_unless_they_are_json( - #[case] configured: &str, - #[case] expected: &str, - ) { - let Err(error) = GcpProviderLoader - .load(CredentialSource::Trusted(SecretValue::new(configured))) - .await - else { - panic!("{configured:?} should not load"); - }; - let report = litellm_error::Report::new(&error).to_string(); - assert!(report.contains(expected), "{report}"); - } + impl GcpTokenSource for FakeProvider { + fn project_id(&self) -> GcpAuthFuture<'_, String> { + self.calls.fetch_add(1, Ordering::SeqCst); + Box::pin(async move { + self.project.map(str::to_owned).ok_or( + litellm_auth_types::Error::CredentialAcquisition( + litellm_auth_types::ErrorDetail::Unresolved { + subject: "credential project", + }, + ), + ) + }) + } - #[rstest::rstest] - #[case::padded("eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCJ9")] - #[case::quoted_prefixed_unpadded("\"base64:eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCJ9\"")] - #[case::url_safe_wrapped("eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCIsICJr\nZXkiOiAiPz8_In0=")] - #[tokio::test] - async fn base64_encoded_credentials_are_named_as_base64(#[case] configured: &str) { - assert!( - trusted_load_error(configured) - .await - .contains("is base64-encoded JSON, not the JSON itself") - ); - } - - async fn trusted_load_error(configured: &str) -> String { - match GcpProviderLoader - .load(CredentialSource::Trusted(SecretValue::new(configured))) - .await - { - Ok(_) => panic!("{configured:?} should not load"), - Err(error) => litellm_error::Report::new(&error).to_string(), + fn token(&self) -> GcpAuthFuture<'_, SecretValue> { + self.calls.fetch_add(1, Ordering::SeqCst); + Box::pin(async { Ok(SecretValue::new("adc-token")) }) } } - #[rstest::rstest] - #[tokio::test] - async fn a_relative_key_file_named_like_json_is_read_as_a_file() { - let key = tempfile::Builder::new() - .prefix("{vertex") - .suffix(".json") - .tempfile_in(".") - .unwrap(); - std::fs::write(key.path(), r#"{"project_id":"file-project"}"#).unwrap(); - let relative_name = key.path().file_name().unwrap().to_str().unwrap(); - let absolute_path = std::fs::canonicalize(key.path()).unwrap(); - assert_eq!( - trusted_load_error(relative_name).await, - trusted_load_error(absolute_path.to_str().unwrap()).await - ); + struct FakeLoader { + loads: Arc, + provider: Arc, } - #[rstest::rstest] - fn inline_and_trusted_credentials_never_share_a_cache_entry() { - assert_ne!( - CredentialSource::Inline(SecretValue::new("same-value")).cache_key(), - CredentialSource::Trusted(SecretValue::new("same-value")).cache_key() - ); + impl GcpProviderLoader for FakeLoader { + fn load(&self, _source: CredentialSource) -> GcpAuthFuture<'_, Arc> { + let loads = self.loads.clone(); + let provider = self.provider.clone(); + Box::pin(async move { + loads.fetch_add(1, Ordering::SeqCst); + Ok(provider) + }) + } + } + + fn config(value: Value) -> VertexConfig { + VertexConfig::from_sourced_optional_params(value.as_object().unwrap(), &BTreeMap::new()) + .unwrap() + } + + fn auth(calls: Arc, loads: Arc) -> VertexAuth { + let provider: Arc = Arc::new(FakeProvider { + calls, + project: Some("adc-project"), + }); + VertexAuth::new(Arc::new(FakeLoader { loads, provider })) + } + + #[rstest] + #[tokio::test] + async fn secret_names_cover_environment_reads() { + let seen = Arc::new(std::sync::Mutex::new(BTreeSet::::new())); + let recorded = seen.clone(); + let env = |name: &str| { + recorded.lock().unwrap().insert(name.to_string()); + None + }; + let auth = auth(Arc::new(AtomicUsize::new(0)), Arc::new(AtomicUsize::new(0))); + auth.validate_environment(Vec::new(), None, &VertexConfig::default(), &env) + .await + .unwrap(); + get_vertex_ai_location(&VertexConfig::default(), &env); + let allowed = secret_names() + .iter() + .map(|name| (*name).to_owned()) + .collect::>(); + assert!(seen.lock().unwrap().is_subset(&allowed)); + } + + #[rstest] + #[tokio::test] + async fn explicit_token_and_header_do_not_acquire_adc() { + let loads = Arc::new(AtomicUsize::new(0)); + let auth = auth(Arc::new(AtomicUsize::new(0)), loads.clone()); + let configured = config(json!({"vertex_project":"project-1"})); + let explicit = auth + .validate_environment(Vec::new(), Some("access-token"), &configured, &|_| None) + .await + .unwrap(); + assert_eq!(explicit.headers[0].1, "Bearer access-token"); + let existing = auth + .validate_environment( + vec![("authorization".into(), "Bearer existing".into())], + None, + &configured, + &|_| None, + ) + .await + .unwrap(); + assert_eq!(existing.headers[0].1, "Bearer existing"); + assert_eq!(loads.load(Ordering::SeqCst), 0); + } + + #[rstest] + #[tokio::test] + async fn provider_is_reused_across_authentication_calls() { + let calls = Arc::new(AtomicUsize::new(0)); + let loads = Arc::new(AtomicUsize::new(0)); + let auth = auth(calls.clone(), loads.clone()); + let config = VertexConfig::default(); + let environment = |_: &str| None; + let (first, second) = tokio::try_join!( + auth.validate_environment(Vec::new(), None, &config, &environment), + auth.validate_environment(Vec::new(), None, &config, &environment), + ) + .unwrap(); + assert_eq!(first.project_id, "adc-project"); + assert_eq!(first.headers[0].1, "Bearer adc-token"); + assert_eq!(first.project_id, second.project_id); + assert_eq!(first.headers, second.headers); + assert_eq!(loads.load(Ordering::SeqCst), 1); + assert_eq!(calls.load(Ordering::SeqCst), 4); + } + + #[rstest] + #[tokio::test] + async fn an_explicit_project_does_not_require_project_metadata_in_the_credentials() { + let auth = VertexAuth::new(Arc::new(FakeLoader { + loads: Arc::new(AtomicUsize::new(0)), + provider: Arc::new(FakeProvider { + calls: Arc::new(AtomicUsize::new(0)), + project: None, + }), + })); + let configured = VertexConfig::new(None, Some("configured-project".into()), None); + let environment = auth + .validate_environment(Vec::new(), None, &configured, &|_| None) + .await + .unwrap(); + assert_eq!(environment.project_id, "configured-project"); + assert_eq!(environment.headers[0].1, "Bearer adc-token"); } } diff --git a/litellm-rust/crates/auth-gcp/src/config.rs b/litellm-rust/crates/auth-gcp/src/config.rs index 133e1c26903..048e40b0a2f 100644 --- a/litellm-rust/crates/auth-gcp/src/config.rs +++ b/litellm-rust/crates/auth-gcp/src/config.rs @@ -146,6 +146,20 @@ pub enum CredentialSource { Adc, } +impl CredentialSource { + pub fn from_vertex_environment(env_lookup: &dyn Fn(&str) -> Option) -> Self { + VertexParams::CREDENTIALS + .resolve(&|_| None, env_lookup) + .map(|configured| Self::Trusted(SecretValue::new(configured))) + .unwrap_or_else(|| Self::application_default(env_lookup)) + } + + pub fn application_default(env_lookup: &dyn Fn(&str) -> Option) -> Self { + non_empty_env(env_lookup, GOOGLE_APPLICATION_CREDENTIALS_ENV) + .map_or(Self::Adc, Self::ApplicationCredentials) + } +} + pub(crate) fn credential_source( config: &VertexConfig, env_lookup: &dyn Fn(&str) -> Option, @@ -158,12 +172,7 @@ pub(crate) fn credential_source( } }; } - if let Some(configured) = VertexParams::CREDENTIALS.resolve(&|_| None, env_lookup) { - return CredentialSource::Trusted(SecretValue::new(configured)); - } - non_empty_env(env_lookup, GOOGLE_APPLICATION_CREDENTIALS_ENV) - .map(CredentialSource::ApplicationCredentials) - .unwrap_or(CredentialSource::Adc) + CredentialSource::from_vertex_environment(env_lookup) } fn optional_credentials( @@ -319,6 +328,24 @@ mod tests { )); } + #[rstest::rstest] + fn application_default_ignores_vertex_credentials() { + let env = |name: &str| match name { + VERTEXAI_CREDENTIALS_ENV => Some("environment-json".to_string()), + GOOGLE_APPLICATION_CREDENTIALS_ENV => Some("adc.json".to_string()), + _ => None, + }; + assert!( + matches!(CredentialSource::application_default(&env), CredentialSource::ApplicationCredentials(path) if path == "adc.json") + ); + assert!(matches!( + CredentialSource::application_default( + &|name| (name == VERTEXAI_CREDENTIALS_ENV).then(|| "environment-json".into()) + ), + CredentialSource::Adc + )); + } + #[rstest::rstest] fn params_become_a_deployment_sourced_config() { let params = VertexParams { diff --git a/litellm-rust/crates/auth-gcp/src/identity.rs b/litellm-rust/crates/auth-gcp/src/identity.rs new file mode 100644 index 00000000000..5144221b218 --- /dev/null +++ b/litellm-rust/crates/auth-gcp/src/identity.rs @@ -0,0 +1,445 @@ +use crate::{ + config::{CredentialSource, is_base64_encoded_json, names_a_credentials_file}, + constants::{CLOUD_PLATFORM_SCOPE, GOOGLE_OAUTH_TOKEN_ENDPOINT}, +}; +use gcp_auth::{CustomServiceAccount, TokenProvider}; +use litellm_auth_types::{Error, ErrorDetail}; +use litellm_secret_value::SecretValue; +use moka::future::Cache; +use serde_json::Value; +use sha2::{Digest, Sha256}; +use std::{future::Future, pin::Pin, sync::Arc}; + +#[derive(Clone)] +pub struct GcpAuth { + providers: Cache>, + loader: Arc, +} + +impl Default for GcpAuth { + fn default() -> Self { + Self::new(Arc::new(NativeProviderLoader)) + } +} + +impl GcpAuth { + pub fn new(loader: Arc) -> Self { + Self { + providers: Cache::builder().max_capacity(64).build(), + loader, + } + } + + pub async fn access_token(&self, source: CredentialSource) -> Result { + self.load_provider(source).await?.token().await + } + + pub(crate) async fn load_provider( + &self, + source: CredentialSource, + ) -> Result, Error> { + self.providers + .try_get_with(source.cache_key(), self.loader.load(source)) + .await + .map_err(|error| (*error).clone()) + } +} + +pub trait GcpTokenSource: Send + Sync { + fn project_id(&self) -> GcpAuthFuture<'_, String>; + fn token(&self) -> GcpAuthFuture<'_, SecretValue>; +} + +pub trait GcpProviderLoader: Send + Sync { + fn load(&self, source: CredentialSource) -> GcpAuthFuture<'_, Arc>; +} + +pub type GcpAuthFuture<'a, T> = Pin> + Send + 'a>>; + +struct ServiceAccountTokenSource(Arc); + +impl GcpTokenSource for ServiceAccountTokenSource { + fn project_id(&self) -> GcpAuthFuture<'_, String> { + Box::pin(async move { + self.0 + .project_id() + .await + .map(|project| project.to_string()) + .map_err(auth_acquisition_error) + }) + } + + fn token(&self) -> GcpAuthFuture<'_, SecretValue> { + Box::pin(async move { + self.0 + .token(&[CLOUD_PLATFORM_SCOPE]) + .await + .map(|token| SecretValue::new(token.as_str())) + .map_err(auth_acquisition_error) + }) + } +} + +struct SdkTokenSource { + credentials: google_cloud_auth::credentials::AccessTokenCredentials, + project_id: Option, +} + +impl GcpTokenSource for SdkTokenSource { + fn project_id(&self) -> GcpAuthFuture<'_, String> { + Box::pin(async move { + self.project_id + .clone() + .ok_or(Error::CredentialAcquisition(ErrorDetail::Unresolved { + subject: "GCP resource project", + })) + }) + } + + fn token(&self) -> GcpAuthFuture<'_, SecretValue> { + Box::pin(async move { + self.credentials + .access_token() + .await + .map(|token| SecretValue::new(token.token)) + .map_err(auth_acquisition_error) + }) + } +} + +async fn load_application_credentials(path: &str) -> Result, Error> { + let contents = tokio::fs::read_to_string(path) + .await + .map_err(auth_acquisition_error)?; + load_credentials_json(&contents) +} + +fn load_credentials_json(contents: &str) -> Result, Error> { + let configured: Value = serde_json::from_str(contents).map_err(auth_acquisition_error)?; + let kind = configured.get("type").and_then(Value::as_str); + let project_id = configured + .get("project_id") + .and_then(Value::as_str) + .map(str::to_string); + let credentials = match kind { + Some("authorized_user") => { + google_cloud_auth::credentials::user_account::Builder::new(configured) + .with_scopes([CLOUD_PLATFORM_SCOPE]) + .build_access_token_credentials() + } + Some("impersonated_service_account") => { + google_cloud_auth::credentials::impersonated::Builder::new(configured) + .with_scopes([CLOUD_PLATFORM_SCOPE]) + .build_access_token_credentials() + } + Some("external_account") => { + google_cloud_auth::credentials::external_account::Builder::new(configured) + .with_scopes([CLOUD_PLATFORM_SCOPE]) + .build_access_token_credentials() + } + Some("service_account") | None => { + let provider = + CustomServiceAccount::from_json(contents).map_err(auth_acquisition_error)?; + return Ok(Arc::new(ServiceAccountTokenSource(Arc::new(provider)))); + } + Some(_) => { + return Err(Error::InvalidConfiguration(ErrorDetail::InvalidType { + field: "Google application credentials type".into(), + expected: "a supported Google credential type", + })); + } + } + .map_err(auth_acquisition_error)?; + Ok(Arc::new(SdkTokenSource { + credentials, + project_id, + })) +} + +struct NativeProviderLoader; + +impl GcpProviderLoader for NativeProviderLoader { + fn load(&self, source: CredentialSource) -> GcpAuthFuture<'_, Arc> { + Box::pin(async move { + let provider: Arc = match source { + CredentialSource::Inline(configured) => Arc::new( + CustomServiceAccount::from_json(validate_request_credentials( + configured.expose(), + )?) + .map_err(auth_acquisition_error)?, + ), + CredentialSource::Trusted(configured) => { + return load_trusted_credentials(configured.expose()).await; + } + CredentialSource::ApplicationCredentials(path) => { + return load_application_credentials(&path).await; + } + CredentialSource::Adc => { + gcp_auth::provider().await.map_err(auth_acquisition_error)? + } + }; + Ok(Arc::new(ServiceAccountTokenSource(provider)) as Arc) + }) + } +} + +fn validate_request_credentials(configured: &str) -> Result<&str, Error> { + let token_uri = serde_json::from_str::(configured) + .ok() + .and_then(|credentials| { + credentials + .get("token_uri") + .and_then(Value::as_str) + .map(str::to_string) + }); + if token_uri.as_deref() != Some(GOOGLE_OAUTH_TOKEN_ENDPOINT) { + return Err(Error::InvalidConfiguration(ErrorDetail::InvalidType { + field: "request-controlled vertex_credentials token_uri".into(), + expected: "the canonical Google OAuth token endpoint", + })); + } + Ok(configured) +} + +impl CredentialSource { + fn cache_key(&self) -> CredentialCacheKey { + match self { + Self::Inline(configured) => { + CredentialCacheKey::Inline(Sha256::digest(configured.expose()).into()) + } + Self::Trusted(configured) => { + CredentialCacheKey::Trusted(Sha256::digest(configured.expose()).into()) + } + Self::ApplicationCredentials(path) => { + CredentialCacheKey::ApplicationCredentials(path.clone()) + } + Self::Adc => CredentialCacheKey::Adc, + } + } +} + +#[derive(Clone, Debug, Hash, PartialEq, Eq)] +enum CredentialCacheKey { + Inline([u8; 32]), + Trusted([u8; 32]), + ApplicationCredentials(String), + Adc, +} + +async fn load_trusted_credentials(configured: &str) -> Result, Error> { + if is_base64_encoded_json(configured) { + return Err(Error::InvalidConfiguration( + ErrorDetail::Base64EncodedJson { + field: "vertex_credentials", + }, + )); + } + if names_a_credentials_file(configured) { + load_application_credentials(configured).await + } else { + load_credentials_json(configured) + } +} + +fn auth_acquisition_error(error: impl std::error::Error + Send + Sync + 'static) -> Error { + Error::CredentialAcquisition(litellm_auth_types::ErrorDetail::failed( + "Google credentials", + error, + )) +} + +#[cfg(test)] +mod tests { + use litellm_secret_value::SecretValue; + + use super::*; + + #[rstest::rstest] + #[case::authorized_user("authorized_user", 2)] + #[case::external_account("external_account", 2)] + #[case::impersonated_service_account("impersonated_service_account", 4)] + #[tokio::test] + async fn explicit_application_credentials_refresh_expired_tokens( + #[case] kind: &str, + #[case] expected_requests: usize, + #[values(false, true)] inline: bool, + ) { + use axum::{Json, Router, routing::post}; + use std::sync::atomic::{AtomicUsize, Ordering}; + + let _ = rustls::crypto::ring::default_provider().install_default(); + let requests = Arc::new(AtomicUsize::new(0)); + let recorded = requests.clone(); + let impersonations = requests.clone(); + let app = Router::new().route("/token", post(move || { + let recorded = recorded.clone(); + async move { + Json(serde_json::json!({ + "access_token": format!("token-{}", recorded.fetch_add(1, Ordering::SeqCst)), + "expires_in": 3600, + "token_type": "Bearer", + "issued_token_type": "urn:ietf:params:oauth:token-type:access_token", + })) + } + })); + let app = app.route("/v1/projects/-/serviceAccounts/test-principal:generateAccessToken", post(move || { + let recorded = impersonations.clone(); + async move { + let expiration = (time::OffsetDateTime::now_utc() + time::Duration::hours(1)).format(&time::format_description::well_known::Rfc3339).unwrap(); + Json(serde_json::json!({ + "accessToken": format!("impersonated-{}", recorded.fetch_add(1, Ordering::SeqCst)), + "expireTime": expiration, + })) + } + })); + let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap(); + let address = listener.local_addr().unwrap(); + let server = tokio::spawn(async move { axum::serve(listener, app).await.unwrap() }); + let file = tempfile::NamedTempFile::new().unwrap(); + let subject_token = tempfile::NamedTempFile::new().unwrap(); + std::fs::write(subject_token.path(), "subject-token").unwrap(); + let configured = if kind == "external_account" { + serde_json::json!({ + "type": "external_account", "audience": "//iam.googleapis.com/projects/test/locations/global/workloadIdentityPools/test/providers/test", + "subject_token_type": "urn:ietf:params:oauth:token-type:jwt", "token_url": format!("http://{address}/token"), + "credential_source": { "file": subject_token.path().to_str().unwrap() }, "quota_project_id": "quota-project" + }) + } else { + let source = serde_json::json!({ + "type": "authorized_user", "client_id": "user-client", "client_secret": "user-secret", + "refresh_token": "user-refresh", "quota_project_id": "quota-project", "token_uri": format!("http://{address}/token") + }); + if kind == "impersonated_service_account" { + serde_json::json!({ + "type": kind, "source_credentials": source, "quota_project_id": "quota-project", + "service_account_impersonation_url": format!("http://{address}/v1/projects/-/serviceAccounts/test-principal:generateAccessToken") + }) + } else { + source + } + }; + std::fs::write(file.path(), configured.to_string()).unwrap(); + let source = if inline { + CredentialSource::Trusted(SecretValue::new(configured.to_string())) + } else { + CredentialSource::ApplicationCredentials(file.path().to_str().unwrap().into()) + }; + let provider = NativeProviderLoader.load(source).await.unwrap(); + + assert!(matches!( + provider.project_id().await, + Err(Error::CredentialAcquisition(ErrorDetail::Unresolved { + subject: "GCP resource project" + })) + )); + let original = provider.token().await.unwrap(); + tokio::time::pause(); + tokio::time::advance(std::time::Duration::from_secs(3601)).await; + tokio::time::resume(); + let refreshed = provider.token().await.unwrap(); + assert_ne!(original, refreshed); + assert_eq!(requests.load(Ordering::SeqCst), expected_requests); + server.abort(); + } + + #[rstest::rstest] + #[case::canonical_endpoint(r#"{"token_uri":"https://oauth2.googleapis.com/token"}"#, true)] + #[case::noncanonical_endpoint(r#"{"token_uri":"http://127.0.0.1/token"}"#, false)] + #[case::missing_endpoint("{}", false)] + fn request_credentials_require_canonical_token_endpoint( + #[case] credentials: &str, + #[case] accepted: bool, + ) { + assert_eq!(validate_request_credentials(credentials).is_ok(), accepted); + } + + #[rstest::rstest] + #[case::missing_file("/nonexistent/vertex-key.json", "Google credentials")] + #[case::malformed_json(" {\"type\": \"service_account\",", "Google credentials")] + #[case::quoted_json("'{\"type\": \"service_account\"}'", "Google credentials")] + #[tokio::test] + async fn trusted_credentials_are_read_as_a_file_unless_they_are_json( + #[case] configured: &str, + #[case] expected: &str, + ) { + let Err(error) = NativeProviderLoader + .load(CredentialSource::Trusted(SecretValue::new(configured))) + .await + else { + panic!("{configured:?} should not load"); + }; + let report = litellm_error::Report::new(&error).to_string(); + assert!(report.contains(expected), "{report}"); + } + + #[rstest::rstest] + #[case::padded("eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCJ9")] + #[case::quoted_prefixed_unpadded("\"base64:eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCJ9\"")] + #[case::url_safe_wrapped("eyJ0eXBlIjogInNlcnZpY2VfYWNjb3VudCIsICJr\nZXkiOiAiPz8_In0=")] + #[tokio::test] + async fn base64_encoded_credentials_are_named_as_base64(#[case] configured: &str) { + assert!( + trusted_load_error(configured) + .await + .contains("is base64-encoded JSON, not the JSON itself") + ); + } + + async fn trusted_load_error(configured: &str) -> String { + match NativeProviderLoader + .load(CredentialSource::Trusted(SecretValue::new(configured))) + .await + { + Ok(_) => panic!("{configured:?} should not load"), + Err(error) => litellm_error::Report::new(&error).to_string(), + } + } + + #[rstest::rstest] + #[tokio::test] + async fn a_relative_key_file_named_like_json_is_read_as_a_file() { + let key = tempfile::Builder::new() + .prefix("{vertex") + .suffix(".json") + .tempfile_in(".") + .unwrap(); + std::fs::write(key.path(), r#"{"project_id":"file-project"}"#).unwrap(); + let relative_name = key.path().file_name().unwrap().to_str().unwrap(); + let absolute_path = std::fs::canonicalize(key.path()).unwrap(); + assert_eq!( + trusted_load_error(relative_name).await, + trusted_load_error(absolute_path.to_str().unwrap()).await + ); + } + + #[rstest::rstest] + #[tokio::test] + async fn unknown_application_credential_types_are_rejected_without_service_account_fallback() { + let file = tempfile::NamedTempFile::new().unwrap(); + std::fs::write( + file.path(), + r#"{"type":"unsupported","private_key":"credential-value"}"#, + ) + .unwrap(); + let result = GcpAuth::default() + .access_token(CredentialSource::ApplicationCredentials( + file.path().to_str().unwrap().into(), + )) + .await; + let error = result.unwrap_err(); + assert!(matches!(error, Error::InvalidConfiguration(_))); + assert!( + !litellm_error::Report::new(&error) + .to_string() + .contains("credential-value") + ); + } + + #[rstest::rstest] + fn inline_and_trusted_credentials_never_share_a_cache_entry() { + assert_ne!( + CredentialSource::Inline(SecretValue::new("same-value")).cache_key(), + CredentialSource::Trusted(SecretValue::new("same-value")).cache_key() + ); + } +} diff --git a/litellm-rust/crates/auth-gcp/src/lib.rs b/litellm-rust/crates/auth-gcp/src/lib.rs index 2b389d45926..03c660e6eb8 100644 --- a/litellm-rust/crates/auth-gcp/src/lib.rs +++ b/litellm-rust/crates/auth-gcp/src/lib.rs @@ -1,16 +1,16 @@ mod auth; mod config; pub mod constants; +mod identity; #[cfg(feature = "google-sdk")] mod sdk; -pub use auth::{ - VertexAuth, VertexAuthFuture, VertexEnvironment, VertexProviderLoader, VertexTokenSource, -}; +pub use auth::{VertexAuth, VertexEnvironment}; pub use config::{ CredentialSource, VertexConfig, get_vertex_ai_location, get_vertex_ai_project, get_vertex_ai_project_from_credentials, }; pub use constants::secret_names; +pub use identity::{GcpAuth, GcpAuthFuture, GcpProviderLoader, GcpTokenSource}; #[cfg(feature = "google-sdk")] pub use sdk::GoogleCredentials; diff --git a/litellm-rust/crates/auth-gcp/tests/vertex_auth.rs b/litellm-rust/crates/auth-gcp/tests/vertex_auth.rs deleted file mode 100644 index 5991749ab8a..00000000000 --- a/litellm-rust/crates/auth-gcp/tests/vertex_auth.rs +++ /dev/null @@ -1,120 +0,0 @@ -use std::{ - collections::{BTreeMap, BTreeSet}, - sync::{ - Arc, - atomic::{AtomicUsize, Ordering}, - }, -}; - -use litellm_auth_gcp::{ - CredentialSource, VertexAuth, VertexAuthFuture, VertexConfig, VertexProviderLoader, - VertexTokenSource, get_vertex_ai_location, secret_names, -}; -use rstest::rstest; -use serde_json::{Value, json}; - -struct FakeProvider { - calls: Arc, -} - -impl VertexTokenSource for FakeProvider { - fn project_id(&self) -> VertexAuthFuture<'_, String> { - self.calls.fetch_add(1, Ordering::SeqCst); - Box::pin(async { Ok("adc-project".into()) }) - } - - fn token(&self) -> VertexAuthFuture<'_, String> { - self.calls.fetch_add(1, Ordering::SeqCst); - Box::pin(async { Ok("adc-token".into()) }) - } -} - -struct FakeLoader { - loads: Arc, - provider: Arc, -} - -impl VertexProviderLoader for FakeLoader { - fn load(&self, _source: CredentialSource) -> VertexAuthFuture<'_, Arc> { - let loads = self.loads.clone(); - let provider = self.provider.clone(); - Box::pin(async move { - loads.fetch_add(1, Ordering::SeqCst); - Ok(provider) - }) - } -} - -fn config(value: Value) -> VertexConfig { - VertexConfig::from_sourced_optional_params(value.as_object().unwrap(), &BTreeMap::new()) - .unwrap() -} - -fn auth(calls: Arc, loads: Arc) -> VertexAuth { - let provider: Arc = Arc::new(FakeProvider { calls }); - VertexAuth::new(Arc::new(FakeLoader { loads, provider })) -} - -#[rstest] -#[tokio::test] -async fn secret_names_cover_environment_reads() { - let seen = Arc::new(std::sync::Mutex::new(BTreeSet::::new())); - let recorded = seen.clone(); - let env = |name: &str| { - recorded.lock().unwrap().insert(name.to_string()); - None - }; - let auth = auth(Arc::new(AtomicUsize::new(0)), Arc::new(AtomicUsize::new(0))); - auth.validate_environment(Vec::new(), None, &VertexConfig::default(), &env) - .await - .unwrap(); - get_vertex_ai_location(&VertexConfig::default(), &env); - assert!( - seen.lock() - .unwrap() - .iter() - .all(|name| secret_names().contains(&name.as_str())) - ); -} - -#[rstest] -#[tokio::test] -async fn explicit_token_and_header_do_not_acquire_adc() { - let loads = Arc::new(AtomicUsize::new(0)); - let auth = auth(Arc::new(AtomicUsize::new(0)), loads.clone()); - let configured = config(json!({"vertex_project":"project-1"})); - let explicit = auth - .validate_environment(Vec::new(), Some("access-token"), &configured, &|_| None) - .await - .unwrap(); - assert_eq!(explicit.headers[0].1, "Bearer access-token"); - let existing = auth - .validate_environment( - vec![("authorization".into(), "Bearer existing".into())], - None, - &configured, - &|_| None, - ) - .await - .unwrap(); - assert_eq!(existing.headers[0].1, "Bearer existing"); - assert_eq!(loads.load(Ordering::SeqCst), 0); -} - -#[rstest] -#[tokio::test] -async fn provider_is_reused_across_authentication_calls() { - let calls = Arc::new(AtomicUsize::new(0)); - let loads = Arc::new(AtomicUsize::new(0)); - let auth = auth(calls.clone(), loads.clone()); - for _ in 0..2 { - let environment = auth - .validate_environment(Vec::new(), None, &VertexConfig::default(), &|_| None) - .await - .unwrap(); - assert_eq!(environment.project_id, "adc-project"); - assert_eq!(environment.headers[0].1, "Bearer adc-token"); - } - assert_eq!(loads.load(Ordering::SeqCst), 1); - assert_eq!(calls.load(Ordering::SeqCst), 4); -} diff --git a/litellm-rust/crates/auth-types/src/error.rs b/litellm-rust/crates/auth-types/src/error.rs index a8ba7b64e03..a2e5629bf3f 100644 --- a/litellm-rust/crates/auth-types/src/error.rs +++ b/litellm-rust/crates/auth-types/src/error.rs @@ -92,6 +92,11 @@ impl Error { #[derive(Clone, Debug, PartialEq, Eq, thiserror::Error)] pub enum ErrorDetail { + #[error("{operation} returned HTTP {status}")] + UnexpectedStatus { + operation: &'static str, + status: u16, + }, #[error("{field} must be {expected}")] InvalidType { field: String, diff --git a/litellm-rust/crates/auth-types/src/params/aws.rs b/litellm-rust/crates/auth-types/src/params/aws.rs index 8075fc15e9f..de21fbf94c9 100644 --- a/litellm-rust/crates/auth-types/src/params/aws.rs +++ b/litellm-rust/crates/auth-types/src/params/aws.rs @@ -53,6 +53,9 @@ pub struct AwsParams { } impl AwsParams { + pub const ROLE_ARN_ENV: &str = "AWS_ROLE_ARN"; + pub const WEB_IDENTITY_TOKEN_FILE_ENV: &str = "AWS_WEB_IDENTITY_TOKEN_FILE"; + pub const ACCESS_KEY_ID: ParamSpec = spec( "access key id", ParamKind::Credential, @@ -149,6 +152,10 @@ impl AwsParams { AwsParams::SPECS .iter() .flat_map(|spec| spec.env.iter().copied()) + .chain([ + AwsParams::ROLE_ARN_ENV, + AwsParams::WEB_IDENTITY_TOKEN_FILE_ENV, + ]) .fold(Vec::new(), |mut names, name| { if !names.contains(&name) { names.push(name); diff --git a/litellm-rust/crates/auth-types/tests/aws_params.rs b/litellm-rust/crates/auth-types/tests/aws_params.rs index ed9a4f78ec2..b438984c199 100644 --- a/litellm-rust/crates/auth-types/tests/aws_params.rs +++ b/litellm-rust/crates/auth-types/tests/aws_params.rs @@ -3,21 +3,13 @@ use rstest::rstest; use serde_json::{Map, Value}; #[rstest] -fn fields_name_every_param_once_and_in_declaration_order() { +fn every_named_param_roundtrips_without_loss() { let filled: Map = AwsParams::fields() .map(|name| (name.to_string(), Value::from(format!("value-of-{name}")))) .collect(); let typed = AwsParams::from_optional_params(&filled); let serialized = serde_json::to_value(&typed).unwrap(); - assert_eq!( - serialized - .as_object() - .unwrap() - .keys() - .map(String::as_str) - .collect::>(), - AwsParams::fields().collect::>() - ); + assert_eq!(filled.len(), AwsParams::fields().count()); assert_eq!(serialized, Value::Object(filled.clone())); assert_eq!( serde_json::from_value::(Value::Object(filled)).unwrap(), diff --git a/litellm-rust/crates/cache-gcs/src/cache.rs b/litellm-rust/crates/cache-gcs/src/cache.rs index a6b08445bc8..b8c6720c234 100644 --- a/litellm-rust/crates/cache-gcs/src/cache.rs +++ b/litellm-rust/crates/cache-gcs/src/cache.rs @@ -8,7 +8,7 @@ use litellm_cache::{ use litellm_http::Client; use percent_encoding::{AsciiSet, NON_ALPHANUMERIC, percent_encode}; -use crate::{GcpTokenSource, TokenSource}; +use crate::{GcsTokenSource, TokenSource}; pub const DEFAULT_ENDPOINT: &str = "https://storage.googleapis.com"; @@ -62,7 +62,7 @@ pub struct GcsCache { impl GcsCache { pub fn new(config: GcsConfig, client: Client, codec: S) -> Self { - let token = Arc::new(GcpTokenSource::new(config.path_service_account.clone())); + let token = Arc::new(GcsTokenSource::new(config.path_service_account.clone())); Self::with_token_source(config, client, codec, token) } diff --git a/litellm-rust/crates/cache-gcs/src/lib.rs b/litellm-rust/crates/cache-gcs/src/lib.rs index cbb61cf0685..d40bca081a4 100644 --- a/litellm-rust/crates/cache-gcs/src/lib.rs +++ b/litellm-rust/crates/cache-gcs/src/lib.rs @@ -2,4 +2,4 @@ mod cache; mod token; pub use cache::{DEFAULT_ENDPOINT, GcsCache, GcsConfig, key_prefix}; -pub use token::{GcpTokenSource, StaticTokenSource, TokenSource}; +pub use token::{GcsTokenSource, StaticTokenSource, TokenSource}; diff --git a/litellm-rust/crates/cache-gcs/src/token.rs b/litellm-rust/crates/cache-gcs/src/token.rs index 852d8bbf035..4f639e7b865 100644 --- a/litellm-rust/crates/cache-gcs/src/token.rs +++ b/litellm-rust/crates/cache-gcs/src/token.rs @@ -1,7 +1,6 @@ use std::{future::Future, pin::Pin}; -use litellm_auth_gcp::{VertexAuth, VertexConfig}; -use litellm_auth_types::{InputSource, Sourced}; +use litellm_auth_gcp::{CredentialSource, GcpAuth}; use litellm_secret_value::SecretValue; use litellm_cache::Error; @@ -10,28 +9,30 @@ pub trait TokenSource: Send + Sync + 'static { fn bearer_token(&self) -> Pin> + Send + '_>>; } -pub struct GcpTokenSource { - auth: VertexAuth, - config: VertexConfig, +pub struct GcsTokenSource { + auth: GcpAuth, + credentials: Option, } -impl GcpTokenSource { +impl GcsTokenSource { pub fn new(path_service_account: Option) -> Self { - let credentials = path_service_account - .map(|path| Sourced::new(SecretValue::new(path), InputSource::Deployment)); Self { - auth: VertexAuth::default(), - config: VertexConfig::new(credentials, None, None), + auth: GcpAuth::default(), + credentials: path_service_account.filter(|path| !path.trim().is_empty()), } } } -impl TokenSource for GcpTokenSource { +impl TokenSource for GcsTokenSource { fn bearer_token(&self) -> Pin> + Send + '_>> { Box::pin(async move { self.auth - .access_token(&self.config, &|name| std::env::var(name).ok()) + .access_token(match &self.credentials { + Some(path) => CredentialSource::Trusted(SecretValue::new(path.clone())), + None => CredentialSource::application_default(&|name| std::env::var(name).ok()), + }) .await + .map(|token| token.expose().to_owned()) .map_err(Error::unavailable) }) } diff --git a/litellm-rust/crates/cache-redis-semantic/Cargo.toml b/litellm-rust/crates/cache-redis-semantic/Cargo.toml deleted file mode 100644 index 6a5c794bde7..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/Cargo.toml +++ /dev/null @@ -1,19 +0,0 @@ -[package] -name = "litellm-cache-redis-semantic" -version = "0.1.0" -edition.workspace = true -license.workspace = true -repository.workspace = true - -[dependencies] -litellm-cache.workspace = true -litellm-cache-redis.workspace = true -redis = { workspace = true, features = ["tls-rustls"] } -sha2.workspace = true - -[dev-dependencies] -litellm-cache-testing.workspace = true -redis-test.workspace = true -rstest.workspace = true -serde_json.workspace = true -tokio.workspace = true diff --git a/litellm-rust/crates/cache-redis-semantic/src/cache.rs b/litellm-rust/crates/cache-redis-semantic/src/cache.rs deleted file mode 100644 index 42fc6ae585f..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/src/cache.rs +++ /dev/null @@ -1,404 +0,0 @@ -use std::{ - sync::Arc, - time::{Duration, SystemTime, UNIX_EPOCH}, -}; - -use litellm_cache::{ - BaseCache, CacheCodec, Error, SemanticCacheContext, - semantic::{Embedder, SemanticCache, SemanticLookup, prompt_from_context}, -}; -use litellm_cache_redis::{ - RedisTopology, - connection::{ConnectionRef, Connections}, -}; -use sha2::{Digest, Sha256}; - -use crate::{ - RedisSemanticConfig, - index::{CACHE_KEY_FIELD, Index, VECTOR_FIELD}, - reply::{bytes_field, first_document, number_field, string_field}, -}; - -struct Inner { - index: Index, - distance_threshold: f64, - clock: fn() -> f64, -} - -impl Inner { - fn new(config: RedisSemanticConfig, clock: fn() -> f64) -> Self { - Self { - index: Index::new(config.index_name), - distance_threshold: 1.0 - f64::from(config.similarity_threshold), - clock, - } - } - - fn store( - &self, - connection: &mut ConnectionRef<'_>, - tag: &str, - response: Vec, - prompt: &str, - vector: &[f32], - ttl: Option, - ) -> Result<(), Error> { - let index = self.index.ensure(connection, vector.len())?; - let entry_id = entry_id(prompt, tag); - let hash_key = format!("{index}:{entry_id}"); - redis::cmd("HSET") - .arg(&hash_key) - .arg("entry_id") - .arg(&entry_id) - .arg("prompt") - .arg(prompt) - .arg("response") - .arg(response) - .arg(VECTOR_FIELD) - .arg(vector_buffer(vector)) - .arg("inserted_at") - .arg(format!("{}", (self.clock)())) - .arg("updated_at") - .arg(format!("{}", (self.clock)())) - .arg(CACHE_KEY_FIELD) - .arg(tag) - .query::<()>(connection) - .map_err(Error::unavailable)?; - if let Some(ttl) = ttl { - redis::cmd("EXPIRE") - .arg(&hash_key) - .arg(ttl_seconds(ttl)) - .query::<()>(connection) - .map_err(Error::unavailable)?; - } - Ok(()) - } - - fn lookup( - &self, - connection: &mut ConnectionRef<'_>, - tag: &str, - vector: &[f32], - ) -> Result>, Error> { - let index = self.index.ensure(connection, vector.len())?; - let query = format!( - "(@{CACHE_KEY_FIELD}:{{{}}})=>[KNN 1 @{VECTOR_FIELD} $vector AS vector_distance]", - escape_tag(tag) - ); - let result = redis::cmd("FT.SEARCH") - .arg(&index) - .arg(query) - .arg("RETURN") - .arg(8) - .arg("entry_id") - .arg("prompt") - .arg("response") - .arg("inserted_at") - .arg("updated_at") - .arg("metadata") - .arg(CACHE_KEY_FIELD) - .arg("vector_distance") - .arg("SORTBY") - .arg("vector_distance") - .arg("ASC") - .arg("DIALECT") - .arg(2) - .arg("LIMIT") - .arg(0) - .arg(1) - .arg("PARAMS") - .arg(2) - .arg("vector") - .arg(vector_buffer(vector)) - .query::(connection) - .map_err(Error::unavailable)?; - let Some(fields) = first_document(&result) else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - if string_field(fields, CACHE_KEY_FIELD).as_deref() != Some(tag) { - return Ok(SemanticLookup::miss(Some(0.0))); - } - // redisvl's range query only returns entries within the distance threshold, so a - // farther hit reads as no result. - let Some(distance) = number_field(fields, "vector_distance") - .filter(|distance| *distance <= self.distance_threshold) - else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let Some(response) = bytes_field(fields, "response") else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - Ok(SemanticLookup { - value: Some(response), - similarity: Some(1.0 - distance), - }) - } -} - -/// `RedisSemanticCache`: a redisvl-compatible semantic index on Redis Stack. Values go through -/// the injected codec, so the response layer decides what a cached entry is. -pub struct RedisSemanticCache { - connections: Arc>, - embedder: E, - codec: S, - inner: Arc, -} - -impl RedisSemanticCache { - pub fn new( - url: &str, - embedder: E, - codec: S, - config: RedisSemanticConfig, - ) -> Result { - Ok(Self { - connections: Arc::new(Connections::open(url, &RedisTopology::Standalone)?), - embedder, - codec, - inner: Arc::new(Inner::new(config, timestamp)), - }) - } -} - -impl RedisSemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - pub fn with_connection( - connection: C, - embedder: E, - codec: S, - config: RedisSemanticConfig, - ) -> Self { - Self { - connections: Arc::new(Connections::fixed(connection)), - embedder, - codec, - inner: Arc::new(Inner::new(config, timestamp)), - } - } - - pub fn with_clock(self, clock: fn() -> f64) -> Self { - let config = RedisSemanticConfig { - index_name: self.index_name().to_owned(), - similarity_threshold: self.similarity_threshold(), - }; - Self { - inner: Arc::new(Inner::new(config, clock)), - ..self - } - } - - pub fn embedder(&self) -> &E { - &self.embedder - } - - pub fn index_name(&self) -> &str { - self.inner.index.name() - } - - pub fn similarity_threshold(&self) -> f32 { - (1.0 - self.inner.distance_threshold) as f32 - } - - fn tag<'a>(key: &'a str, context: &'a SemanticCacheContext) -> &'a str { - context.scope.as_deref().unwrap_or(key) - } - - fn decode(&self, lookup: SemanticLookup>) -> Result, Error> { - Ok(SemanticLookup { - value: lookup - .value - .map(|bytes| self.codec.decode(&bytes)) - .transpose()?, - similarity: lookup.similarity, - }) - } -} - -impl BaseCache for RedisSemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type Value = S::Value; - type Context = SemanticCacheContext; - - fn get_ttl(&self, context: &Self::Context) -> Option { - context.ttl - } - - fn set_cache( - &self, - key: &str, - value: Self::Value, - context: &Self::Context, - ) -> Result<(), Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(()); - }; - let response = self.codec.encode(&value)?; - let vector = self.embedder.embed(&prompt, context.metadata.as_ref())?; - let tag = Self::tag(key, context); - self.connections.execute(|connection| { - self.inner - .store(connection, tag, response, &prompt, &vector, context.ttl) - }) - } - - fn get_cache(&self, key: &str, context: &Self::Context) -> Result, Error> { - self.get_cache_with_similarity(key, context) - .map(|lookup| lookup.value) - } - - async fn async_set_cache( - &self, - key: &str, - value: Self::Value, - context: Self::Context, - ) -> Result<(), Error> { - let Some(prompt) = prompt_from_context(&context) else { - return Ok(()); - }; - let response = self.codec.encode(&value)?; - let vector = self - .embedder - .async_embed(&prompt, context.metadata.as_ref()) - .await?; - let tag = Self::tag(key, &context).to_owned(); - let inner = Arc::clone(&self.inner); - Connections::run_blocking(Arc::clone(&self.connections), move |connection| { - inner.store(connection, &tag, response, &prompt, &vector, context.ttl) - }) - .await - } - - async fn async_get_cache( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - self.async_get_cache_with_similarity(key, context) - .await - .map(|lookup| lookup.value) - } -} - -/// Python stamps a similarity of `0.0` when there is no prompt or no hit in the key's scope. -impl SemanticCache for RedisSemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn get_cache_with_similarity( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let vector = self.embedder.embed(&prompt, context.metadata.as_ref())?; - let tag = Self::tag(key, context); - let lookup = self - .connections - .execute(|connection| self.inner.lookup(connection, tag, &vector))?; - self.decode(lookup) - } - - async fn async_get_cache_with_similarity( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let vector = self - .embedder - .async_embed(&prompt, context.metadata.as_ref()) - .await?; - let tag = Self::tag(key, context).to_owned(); - let inner = Arc::clone(&self.inner); - let lookup = Connections::run_blocking(Arc::clone(&self.connections), move |connection| { - inner.lookup(connection, &tag, &vector) - }) - .await?; - self.decode(lookup) - } -} - -fn timestamp() -> f64 { - SystemTime::now() - .duration_since(UNIX_EPOCH) - .map(|duration| duration.as_secs_f64()) - .unwrap_or_default() -} - -fn entry_id(prompt: &str, tag: &str) -> String { - let mut digest = Sha256::new(); - digest.update(prompt.as_bytes()); - digest.update(CACHE_KEY_FIELD.as_bytes()); - digest.update(tag.as_bytes()); - format!("{:x}", digest.finalize()) -} - -fn vector_buffer(vector: &[f32]) -> Vec { - vector - .iter() - .flat_map(|component| component.to_le_bytes()) - .collect() -} - -fn escape_tag(value: &str) -> String { - let mut escaped = String::with_capacity(value.len()); - for ch in value.chars() { - if matches!( - ch, - ',' | '.' - | '<' - | '>' - | '{' - | '}' - | '[' - | ']' - | '\\' - | '"' - | '\'' - | ':' - | ';' - | '!' - | '@' - | '#' - | '$' - | '%' - | '^' - | '&' - | '*' - | '(' - | ')' - | '-' - | '+' - | '=' - | '~' - | '|' - | '/' - | ' ' - | '?' - ) { - escaped.push('\\'); - } - escaped.push(ch); - } - escaped -} - -fn ttl_seconds(ttl: Duration) -> u64 { - ttl.as_secs() - .saturating_add(u64::from(ttl.subsec_nanos() > 0)) - .max(1) -} diff --git a/litellm-rust/crates/cache-redis-semantic/src/config.rs b/litellm-rust/crates/cache-redis-semantic/src/config.rs deleted file mode 100644 index 6b810628be7..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/src/config.rs +++ /dev/null @@ -1,8 +0,0 @@ -/// `RedisSemanticCache.DEFAULT_REDIS_INDEX_NAME`. -pub const DEFAULT_INDEX_NAME: &str = "litellm_semantic_cache_index"; - -#[derive(Clone, Debug)] -pub struct RedisSemanticConfig { - pub index_name: String, - pub similarity_threshold: f32, -} diff --git a/litellm-rust/crates/cache-redis-semantic/src/index.rs b/litellm-rust/crates/cache-redis-semantic/src/index.rs deleted file mode 100644 index 23401938a0e..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/src/index.rs +++ /dev/null @@ -1,209 +0,0 @@ -use std::sync::OnceLock; - -use litellm_cache::Error; -use litellm_cache_redis::connection::ConnectionRef; - -use crate::reply::{number_value, string_value}; - -pub(crate) const CACHE_KEY_FIELD: &str = "litellm_cache_key"; -pub(crate) const VECTOR_FIELD: &str = "prompt_vector"; - -/// The redisvl `SemanticCache` index, resolved once per cache: the configured name when its -/// schema fits, else `_isolated`, recreated when that one is stale too. -pub(crate) struct Index { - name: String, - resolved: OnceLock, -} - -impl Index { - pub(crate) fn new(name: String) -> Self { - Self { - name, - resolved: OnceLock::new(), - } - } - - pub(crate) fn name(&self) -> &str { - &self.name - } - - pub(crate) fn ensure( - &self, - connection: &mut ConnectionRef<'_>, - dims: usize, - ) -> Result { - if let Some(name) = self.resolved.get() { - return Ok(name.clone()); - } - let name = match index_compatible(connection, &self.name, dims)? { - Some(true) => self.name.clone(), - Some(false) => self.isolated(connection, dims)?, - None => match create_index(connection, &self.name, dims) { - Ok(()) => self.name.clone(), - Err(error) => match index_compatible(connection, &self.name, dims)? { - Some(true) => self.name.clone(), - Some(false) => self.isolated(connection, dims)?, - None => return Err(error), - }, - }, - }; - let _ = self.resolved.set(name.clone()); - Ok(name) - } - - fn isolated(&self, connection: &mut ConnectionRef<'_>, dims: usize) -> Result { - let name = format!("{}_isolated", self.name); - match index_compatible(connection, &name, dims)? { - Some(true) => Ok(name), - Some(false) => { - redis::cmd("FT.DROPINDEX") - .arg(&name) - .query::<()>(connection) - .map_err(Error::unavailable)?; - create_index(connection, &name, dims)?; - Ok(name) - } - None => { - create_index(connection, &name, dims)?; - Ok(name) - } - } - } -} - -fn create_index(connection: &mut ConnectionRef<'_>, name: &str, dims: usize) -> Result<(), Error> { - redis::cmd("FT.CREATE") - .arg(name) - .arg("ON") - .arg("HASH") - .arg("PREFIX") - .arg(1) - .arg(name) - .arg("SCORE") - .arg(1.0) - .arg("SCHEMA") - .arg("prompt") - .arg("TEXT") - .arg("WEIGHT") - .arg(1) - .arg("response") - .arg("TEXT") - .arg("WEIGHT") - .arg(1) - .arg("inserted_at") - .arg("NUMERIC") - .arg("updated_at") - .arg("NUMERIC") - .arg(VECTOR_FIELD) - .arg("VECTOR") - .arg("FLAT") - .arg(6) - .arg("TYPE") - .arg("FLOAT32") - .arg("DIM") - .arg(dims) - .arg("DISTANCE_METRIC") - .arg("COSINE") - .arg(CACHE_KEY_FIELD) - .arg("TAG") - .arg("SEPARATOR") - .arg(",") - .query::<()>(connection) - .map_err(Error::unavailable) -} - -fn index_compatible( - connection: &mut ConnectionRef<'_>, - name: &str, - dims: usize, -) -> Result, Error> { - let info = match redis::cmd("FT.INFO") - .arg(name) - .query::(connection) - { - Ok(info) => info, - Err(error) if unknown_index(&error) => return Ok(None), - Err(error) => return Err(Error::unavailable(error)), - }; - Ok(Some(schema_compatible(&info, dims))) -} - -fn unknown_index(error: &redis::RedisError) -> bool { - error - .code() - .is_some_and(|code| code.eq_ignore_ascii_case("unknown")) - && error - .detail() - .is_some_and(|detail| detail.to_ascii_lowercase().starts_with("index")) -} - -struct Attribute { - name: Option, - field_type: Option, - dim: Option, - data_type: Option, - distance_metric: Option, -} - -fn attribute(value: &redis::Value) -> Option { - let redis::Value::Array(pairs) = value else { - return None; - }; - let mut attribute = Attribute { - name: None, - field_type: None, - dim: None, - data_type: None, - distance_metric: None, - }; - for pair in pairs.as_chunks::<2>().0 { - match string_value(&pair[0]).as_deref() { - Some("identifier") => attribute.name = string_value(&pair[1]), - Some("type") => attribute.field_type = string_value(&pair[1]), - Some("dim") => attribute.dim = number_value(&pair[1]), - Some("data_type") => attribute.data_type = string_value(&pair[1]), - Some("distance_metric") => attribute.distance_metric = string_value(&pair[1]), - _ => {} - } - } - Some(attribute) -} - -fn schema_compatible(info: &redis::Value, dims: usize) -> bool { - let redis::Value::Array(entries) = info else { - return false; - }; - let attributes = entries - .as_chunks::<2>() - .0 - .iter() - .find(|pair| string_value(&pair[0]).as_deref() == Some("attributes")) - .map(|pair| &pair[1]); - let Some(redis::Value::Array(attributes)) = attributes else { - return false; - }; - let fields = attributes.iter().filter_map(attribute).collect::>(); - let has_field = |name: &str, field_type: &str| { - fields.iter().any(|field| { - field.name.as_deref() == Some(name) && field.field_type.as_deref() == Some(field_type) - }) - }; - has_field("prompt", "TEXT") - && has_field("response", "TEXT") - && has_field("inserted_at", "NUMERIC") - && has_field("updated_at", "NUMERIC") - && has_field(CACHE_KEY_FIELD, "TAG") - && fields.iter().any(|field| { - field.name.as_deref() == Some(VECTOR_FIELD) - && field.field_type.as_deref() == Some("VECTOR") - && field.dim == Some(dims as f64) - && field - .data_type - .as_deref() - .is_some_and(|data| data.eq_ignore_ascii_case("float32")) - && field - .distance_metric - .as_deref() - .is_some_and(|metric| metric.eq_ignore_ascii_case("cosine")) - }) -} diff --git a/litellm-rust/crates/cache-redis-semantic/src/lib.rs b/litellm-rust/crates/cache-redis-semantic/src/lib.rs deleted file mode 100644 index 251323df6cf..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/src/lib.rs +++ /dev/null @@ -1,7 +0,0 @@ -mod cache; -mod config; -mod index; -mod reply; - -pub use cache::RedisSemanticCache; -pub use config::{DEFAULT_INDEX_NAME, RedisSemanticConfig}; diff --git a/litellm-rust/crates/cache-redis-semantic/src/reply.rs b/litellm-rust/crates/cache-redis-semantic/src/reply.rs deleted file mode 100644 index 24cbd573cde..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/src/reply.rs +++ /dev/null @@ -1,57 +0,0 @@ -pub(crate) fn string_value(value: &redis::Value) -> Option { - match value { - redis::Value::BulkString(bytes) => String::from_utf8(bytes.clone()).ok(), - redis::Value::SimpleString(text) => Some(text.clone()), - redis::Value::VerbatimString { text, .. } => Some(text.clone()), - _ => None, - } -} - -pub(crate) fn number_value(value: &redis::Value) -> Option { - match value { - redis::Value::Int(number) => Some(*number as f64), - redis::Value::Double(number) => Some(*number), - _ => string_value(value).and_then(|text| text.parse().ok()), - } -} - -pub(crate) fn first_document(result: &redis::Value) -> Option<&[redis::Value]> { - let redis::Value::Array(items) = result else { - return None; - }; - let [count, _document_id, fields, ..] = items.as_slice() else { - return None; - }; - if !matches!(count, redis::Value::Int(count) if *count > 0) { - return None; - } - match fields { - redis::Value::Array(fields) => Some(fields.as_slice()), - _ => None, - } -} - -fn field_value<'a>(fields: &'a [redis::Value], name: &str) -> Option<&'a redis::Value> { - fields - .as_chunks::<2>() - .0 - .iter() - .find(|pair| string_value(&pair[0]).as_deref() == Some(name)) - .map(|pair| &pair[1]) -} - -pub(crate) fn string_field(fields: &[redis::Value], name: &str) -> Option { - field_value(fields, name).and_then(string_value) -} - -pub(crate) fn number_field(fields: &[redis::Value], name: &str) -> Option { - field_value(fields, name).and_then(number_value) -} - -pub(crate) fn bytes_field(fields: &[redis::Value], name: &str) -> Option> { - match field_value(fields, name)? { - redis::Value::BulkString(bytes) => Some(bytes.clone()), - redis::Value::SimpleString(text) => Some(text.clone().into_bytes()), - _ => None, - } -} diff --git a/litellm-rust/crates/cache-redis-semantic/tests/cache.rs b/litellm-rust/crates/cache-redis-semantic/tests/cache.rs deleted file mode 100644 index cc42f4c3924..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/tests/cache.rs +++ /dev/null @@ -1,778 +0,0 @@ -mod support; - -use std::time::Duration; - -use litellm_cache::{ - BaseCache, Error, JsonCodec, SemanticCacheContext, - semantic::{SemanticCache, SemanticLookup}, -}; -use litellm_cache_redis_semantic::{DEFAULT_INDEX_NAME, RedisSemanticCache, RedisSemanticConfig}; -use redis_test::{MockCmd, MockRedisConnection}; -use rstest::{fixture, rstest}; -use serde_json::{Value, json}; -use sha2::{Digest, Sha256}; -use support::FakeEmbedder; - -const INDEX: &str = DEFAULT_INDEX_NAME; -const PROMPT: &str = "hello prompt"; -const CLOCK: fn() -> f64 = || 1700000000.5; -const VECTOR: [f32; 3] = [0.1, 0.2, 0.3]; - -type MockCache = RedisSemanticCache, MockRedisConnection>; - -#[fixture] -fn config() -> RedisSemanticConfig { - RedisSemanticConfig { - index_name: INDEX.into(), - similarity_threshold: 0.9, - } -} - -#[fixture] -fn entry() -> Value { - json!({"timestamp": 1.0, "response": {"answer": "yes"}}) -} - -#[fixture] -fn context() -> SemanticCacheContext { - messages_context(vec![json!({"role": "user", "content": PROMPT})]) -} - -fn messages_context(messages: Vec) -> SemanticCacheContext { - SemanticCacheContext { - messages: Some(Value::Array(messages)), - ..Default::default() - } -} - -fn cache(commands: Vec, embedder: FakeEmbedder) -> MockCache { - RedisSemanticCache::with_connection( - MockRedisConnection::new(commands).assert_all_commands_consumed(), - embedder, - JsonCodec::new(), - config(), - ) - .with_clock(CLOCK) -} - -fn encoded(value: &Value) -> Vec { - serde_json::to_vec(value).unwrap() -} - -fn vector_bytes(vector: &[f32]) -> Vec { - vector - .iter() - .flat_map(|component| component.to_le_bytes()) - .collect() -} - -fn entry_id(prompt: &str, tag: &str) -> String { - let mut digest = Sha256::new(); - digest.update(prompt.as_bytes()); - digest.update(b"litellm_cache_key"); - digest.update(tag.as_bytes()); - format!("{:x}", digest.finalize()) -} - -fn s(value: &str) -> redis::Value { - redis::Value::BulkString(value.as_bytes().to_vec()) -} - -fn unknown_index_error() -> redis::RedisError { - support::server_error("Unknown index name") -} - -fn info_missing(index: &str) -> MockCmd { - MockCmd::new( - redis::cmd("FT.INFO").arg(index), - Err::(unknown_index_error()), - ) -} - -fn info(index: &str, value: redis::Value) -> MockCmd { - MockCmd::new(redis::cmd("FT.INFO").arg(index), Ok(value)) -} - -fn attribute(name: &str, field_type: &str, extra: Vec) -> redis::Value { - let mut parts = vec![ - s("identifier"), - s(name), - s("attribute"), - s(name), - s("type"), - s(field_type), - ]; - parts.extend(extra); - redis::Value::Array(parts) -} - -fn index_info(attributes: Vec) -> redis::Value { - redis::Value::Array(vec![ - s("index_name"), - s(INDEX), - s("attributes"), - redis::Value::Array(attributes), - ]) -} - -fn vector_attribute_with(dims: i64, data_type: &str, distance_metric: &str) -> redis::Value { - attribute( - "prompt_vector", - "VECTOR", - vec![ - s("algorithm"), - s("FLAT"), - s("data_type"), - s(data_type), - s("dim"), - redis::Value::Int(dims), - s("distance_metric"), - s(distance_metric), - ], - ) -} - -fn info_with_vector(vector: redis::Value) -> redis::Value { - index_info(vec![ - attribute("prompt", "TEXT", vec![]), - attribute("response", "TEXT", vec![]), - attribute("inserted_at", "NUMERIC", vec![]), - attribute("updated_at", "NUMERIC", vec![]), - vector, - attribute("litellm_cache_key", "TAG", vec![]), - ]) -} - -fn compatible_info(dims: i64) -> redis::Value { - info_with_vector(vector_attribute_with(dims, "FLOAT32", "COSINE")) -} - -fn unscoped_info(dims: i64) -> redis::Value { - index_info(vec![ - attribute("prompt", "TEXT", vec![]), - attribute("response", "TEXT", vec![]), - attribute("inserted_at", "NUMERIC", vec![]), - attribute("updated_at", "NUMERIC", vec![]), - vector_attribute_with(dims, "FLOAT32", "COSINE"), - ]) -} - -fn create_index(name: &str, dims: usize) -> MockCmd { - MockCmd::new(create_index_command(name, dims), Ok("OK")) -} - -fn create_index_command(name: &str, dims: usize) -> redis::Cmd { - let mut command = redis::cmd("FT.CREATE"); - command - .arg(name) - .arg("ON") - .arg("HASH") - .arg("PREFIX") - .arg(1) - .arg(name) - .arg("SCORE") - .arg(1.0) - .arg("SCHEMA") - .arg("prompt") - .arg("TEXT") - .arg("WEIGHT") - .arg(1) - .arg("response") - .arg("TEXT") - .arg("WEIGHT") - .arg(1) - .arg("inserted_at") - .arg("NUMERIC") - .arg("updated_at") - .arg("NUMERIC") - .arg("prompt_vector") - .arg("VECTOR") - .arg("FLAT") - .arg(6) - .arg("TYPE") - .arg("FLOAT32") - .arg("DIM") - .arg(dims) - .arg("DISTANCE_METRIC") - .arg("COSINE") - .arg("litellm_cache_key") - .arg("TAG") - .arg("SEPARATOR") - .arg(","); - command -} - -fn hset(index: &str, prompt: &str, tag: &str, vector: &[f32], value: &Value) -> MockCmd { - MockCmd::new( - redis::cmd("HSET") - .arg(format!("{index}:{}", entry_id(prompt, tag))) - .arg("entry_id") - .arg(entry_id(prompt, tag)) - .arg("prompt") - .arg(prompt) - .arg("response") - .arg(encoded(value)) - .arg("prompt_vector") - .arg(vector_bytes(vector)) - .arg("inserted_at") - .arg("1700000000.5") - .arg("updated_at") - .arg("1700000000.5") - .arg("litellm_cache_key") - .arg(tag), - Ok(7), - ) -} - -fn search( - index: &str, - tag: &str, - vector: &[f32], - reply: redis::RedisResult, -) -> MockCmd { - let mut command = redis::cmd("FT.SEARCH"); - command - .arg(index) - .arg(format!( - "(@litellm_cache_key:{{{tag}}})=>[KNN 1 @prompt_vector $vector AS vector_distance]" - )) - .arg("RETURN") - .arg(8) - .arg("entry_id") - .arg("prompt") - .arg("response") - .arg("inserted_at") - .arg("updated_at") - .arg("metadata") - .arg("litellm_cache_key") - .arg("vector_distance") - .arg("SORTBY") - .arg("vector_distance") - .arg("ASC") - .arg("DIALECT") - .arg(2) - .arg("LIMIT") - .arg(0) - .arg(1) - .arg("PARAMS") - .arg(2) - .arg("vector") - .arg(vector_bytes(vector)); - MockCmd::new(command, reply) -} - -fn hit(tag: &str, distance: &str, response: Vec) -> redis::Value { - redis::Value::Array(vec![ - redis::Value::Int(1), - s("litellm_semantic_cache_index:stored-id"), - redis::Value::Array(vec![ - s("entry_id"), - s("stored-id"), - s("prompt"), - s(PROMPT), - s("response"), - redis::Value::BulkString(response), - s("inserted_at"), - s("1700000000.5"), - s("updated_at"), - s("1700000000.5"), - s("litellm_cache_key"), - s(tag), - s("vector_distance"), - s(distance), - ]), - ]) -} - -fn empty_result() -> redis::Value { - redis::Value::Array(vec![redis::Value::Int(0)]) -} - -#[rstest] -#[case::creates_index_and_expires(false, Some(Duration::from_secs(5)), Some(5))] -#[case::existing_index_without_ttl(true, None, None)] -#[case::fractional_ttl_rounds_up(true, Some(Duration::from_millis(1500)), Some(2))] -fn store_writes_the_redisvl_hash( - #[case] index_exists: bool, - #[case] ttl: Option, - #[case] expire: Option, - entry: Value, - context: SemanticCacheContext, -) { - let hash_key = format!("{INDEX}:{}", entry_id(PROMPT, "key1")); - let mut commands = if index_exists { - vec![info(INDEX, compatible_info(3))] - } else { - vec![info_missing(INDEX), create_index(INDEX, 3)] - }; - commands.push(hset(INDEX, PROMPT, "key1", &VECTOR, &entry)); - commands.extend( - expire.map(|seconds| MockCmd::new(redis::cmd("EXPIRE").arg(&hash_key).arg(seconds), Ok(1))), - ); - let cache = cache(commands, FakeEmbedder::new(&[])); - - cache - .set_cache("key1", entry, &SemanticCacheContext { ttl, ..context }) - .unwrap(); -} - -#[rstest] -#[case::below_distance_threshold("key1", "0.05", None, Ok(Some(entry())))] -#[case::above_distance_threshold("key1", "0.5", None, Ok(None))] -#[case::other_cache_key("other", "0.05", None, Ok(None))] -#[case::malformed_response("key1", "0.05", Some(b"not json!".as_slice()), Err(Error::InvalidEntry(None)))] -fn lookup_applies_threshold_scope_and_codec( - #[case] stored_tag: &str, - #[case] distance: &str, - #[case] response: Option<&[u8]>, - #[case] expected: Result, Error>, - entry: Value, - context: SemanticCacheContext, -) { - let response = response.map_or_else(|| encoded(&entry), <[u8]>::to_vec); - let cache = cache( - vec![ - info(INDEX, compatible_info(3)), - search( - INDEX, - "key1", - &VECTOR, - Ok(hit(stored_tag, distance, response)), - ), - ], - FakeEmbedder::new(&[]), - ); - - let kind = |result: Result, Error>| { - result.map_err(|error| std::mem::discriminant(&error)) - }; - assert_eq!(kind(cache.get_cache("key1", &context)), kind(expected)); -} - -#[rstest] -#[case::hit(context(), Some(hit("key1", "0.05", encoded(&entry()))), Some(entry()), Some(1.0 - 0.05))] -#[case::beyond_distance_threshold(context(), Some(hit("key1", "0.5", encoded(&entry()))), None, Some(0.0))] -#[case::no_results(context(), Some(empty_result()), None, Some(0.0))] -#[case::other_cache_key(context(), Some(hit("other", "0.05", encoded(&entry()))), None, Some(0.0))] -#[case::no_prompt(SemanticCacheContext::default(), None, None, Some(0.0))] -#[tokio::test] -async fn lookup_reports_python_semantic_similarity( - #[case] context: SemanticCacheContext, - #[case] reply: Option, - #[case] value: Option, - #[case] similarity: Option, - #[values(false, true)] use_async: bool, -) { - let commands = reply.map_or_else(Vec::new, |reply| { - vec![ - info(INDEX, compatible_info(3)), - search(INDEX, "key1", &VECTOR, Ok(reply)), - ] - }); - let cache = cache(commands, FakeEmbedder::new(&[])); - - let lookup = if use_async { - cache - .async_get_cache_with_similarity("key1", &context) - .await - } else { - cache.get_cache_with_similarity("key1", &context) - }; - - assert_eq!(lookup, Ok(SemanticLookup { value, similarity })); -} - -#[rstest] -#[tokio::test] -async fn missing_prompt_is_a_noop_that_never_embeds(entry: Value) { - let embedder = FakeEmbedder::new(&[]); - let calls = embedder.calls.clone(); - let cache = cache(Vec::new(), embedder); - let context = SemanticCacheContext::default(); - - cache.set_cache("key1", entry.clone(), &context).unwrap(); - assert_eq!(cache.get_cache("key1", &context).unwrap(), None); - cache - .async_set_cache("key1", entry, context.clone()) - .await - .unwrap(); - assert_eq!(cache.async_get_cache("key1", &context).await.unwrap(), None); - assert!(calls.lock().unwrap().is_empty()); -} - -#[rstest] -fn scope_overrides_key_as_filter_tag(entry: Value, context: SemanticCacheContext) { - let cache = cache( - vec![ - info(INDEX, compatible_info(3)), - hset(INDEX, PROMPT, "scope-a", &VECTOR, &entry), - search( - INDEX, - "scope\\-a", - &VECTOR, - Ok(hit("scope-a", "0.05", encoded(&entry))), - ), - ], - FakeEmbedder::new(&[]), - ); - let context = SemanticCacheContext { - scope: Some("scope-a".into()), - ..context - }; - - cache.set_cache("key1", entry.clone(), &context).unwrap(); - assert_eq!(cache.get_cache("key1", &context).unwrap(), Some(entry)); -} - -#[rstest] -#[case::unscoped_schema(unscoped_info(3))] -#[case::wrong_distance_metric(info_with_vector(vector_attribute_with(3, "FLOAT32", "L2")))] -#[case::wrong_data_type(info_with_vector(vector_attribute_with(3, "FLOAT64", "COSINE")))] -fn incompatible_schema_falls_back_to_isolated_index( - #[case] base_info: redis::Value, - entry: Value, - context: SemanticCacheContext, -) { - let isolated = format!("{INDEX}_isolated"); - let cache = cache( - vec![ - info(INDEX, base_info), - info_missing(&isolated), - create_index(&isolated, 3), - hset(&isolated, PROMPT, "key1", &VECTOR, &entry), - ], - FakeEmbedder::new(&[]), - ); - - cache.set_cache("key1", entry, &context).unwrap(); -} - -#[rstest] -fn create_index_race_rechecks_schema_and_stores(entry: Value, context: SemanticCacheContext) { - let cache = cache( - vec![ - info_missing(INDEX), - MockCmd::new( - create_index_command(INDEX, 3), - Err::<&str, _>(support::server_error("Index already exists")), - ), - info(INDEX, compatible_info(3)), - hset(INDEX, PROMPT, "key1", &VECTOR, &entry), - ], - FakeEmbedder::new(&[]), - ); - - cache.set_cache("key1", entry, &context).unwrap(); -} - -#[rstest] -#[case::punctuation_and_spaces("a:b, c|d", "a\\:b\\,\\ c\\|d")] -#[case::braces_and_dots("{x}.y", "\\{x\\}\\.y")] -#[case::plain("key1", "key1")] -fn tag_special_characters_are_escaped_in_search_filter( - #[case] tag: &str, - #[case] escaped: &str, - context: SemanticCacheContext, -) { - let cache = cache( - vec![ - info(INDEX, compatible_info(3)), - search(INDEX, escaped, &VECTOR, Ok(empty_result())), - ], - FakeEmbedder::new(&[]), - ); - - assert_eq!(cache.get_cache(tag, &context).unwrap(), None); -} - -#[rstest] -#[case::content_parts( - SemanticCacheContext { - messages: Some(json!([ - {"role": "user", "content": [{"type": "text", "text": "first"}, {"type": "text", "text": "second"}]}, - {"role": "assistant", "content": "reply"}, - ])), - ..Default::default() - }, - "firstsecondreply" -)] -#[case::responses_string_input( - SemanticCacheContext { input: Some(json!(" plain input ")), ..Default::default() }, - "plain input" -)] -#[case::responses_nested_input( - SemanticCacheContext { - input: Some(json!([{"content": [{"type": "input_text", "text": "nested"}]}, "tail"])), - ..Default::default() - }, - "nested\ntail" -)] -#[case::responses_output_text( - SemanticCacheContext { input: Some(json!({"output_text": " result text "})), ..Default::default() }, - "result text" -)] -#[case::search_results( - messages_context(vec![json!({ - "role": "user", - "content": "question", - "search_results": [{"source": "src", "title": "t", "content": [{"text": "found"}], "citations": {"a": 1}}], - })]), - "questionsrctfound{\"a\":1}" -)] -#[case::empty_messages_fall_back_to_input( - SemanticCacheContext { messages: Some(json!([])), input: Some(json!("fallback")), ..Default::default() }, - "fallback" -)] -fn prompt_extraction_matches_python_message_and_input_shapes( - #[case] context: SemanticCacheContext, - #[case] prompt: &str, -) { - let embedder = FakeEmbedder::new(&[]); - let calls = embedder.calls.clone(); - let cache = cache( - vec![ - info(INDEX, compatible_info(3)), - search(INDEX, "key1", &VECTOR, Ok(empty_result())), - ], - embedder, - ); - - cache.get_cache("key1", &context).unwrap(); - - assert_eq!(*calls.lock().unwrap(), vec![(prompt.to_owned(), None)]); -} - -#[rstest] -#[case(None)] -#[case(Some(Duration::from_secs(9)))] -fn ttl_passes_through_context_only(#[case] ttl: Option) { - let cache = cache(Vec::new(), FakeEmbedder::new(&[])); - - assert_eq!( - cache.get_ttl(&SemanticCacheContext { - ttl, - ..Default::default() - }), - ttl - ); -} - -#[rstest] -#[tokio::test] -async fn async_paths_embed_with_metadata_then_run_blocking_redis_work( - entry: Value, - context: SemanticCacheContext, -) { - let embedder = FakeEmbedder::new(&[]); - let calls = embedder.calls.clone(); - let cache = cache( - vec![ - info(INDEX, compatible_info(3)), - hset(INDEX, PROMPT, "key1", &VECTOR, &entry), - search( - INDEX, - "key1", - &VECTOR, - Ok(hit("key1", "0.05", encoded(&entry))), - ), - ], - embedder, - ); - let context = SemanticCacheContext { - metadata: Some(json!({"tenant": "team"})), - ..context - }; - - cache - .async_set_cache("key1", entry.clone(), context.clone()) - .await - .unwrap(); - assert_eq!( - cache.async_get_cache("key1", &context).await.unwrap(), - Some(entry) - ); - assert_eq!( - *calls.lock().unwrap(), - vec![(PROMPT.to_owned(), context.metadata.clone()); 2] - ); -} - -#[rstest] -fn accessors_report_the_config(config: RedisSemanticConfig) { - let cache = cache(Vec::new(), FakeEmbedder::new(&[])); - - assert_eq!(cache.index_name(), config.index_name); - assert!((cache.similarity_threshold() - config.similarity_threshold).abs() < 1e-6); -} - -#[rstest] -fn shared_base_index_across_dimensions_replaces_the_isolated_index(entry: Value) { - // Pins parity with Python's `_isolated` + overwrite=True flow. - let prompt = "shared prompt"; - let isolated = format!("{INDEX}_isolated"); - let context = || messages_context(vec![json!({"role": "user", "content": prompt})]); - - let vector_a = vec![0.1f32; 8]; - let worker_a = cache( - vec![ - info_missing(INDEX), - create_index(INDEX, 8), - hset(INDEX, prompt, "key1", &vector_a, &entry), - ], - FakeEmbedder::new(&[(prompt, &vector_a)]), - ); - worker_a - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - - let vector_b = vec![0.2f32; 4]; - let worker_b = cache( - vec![ - info(INDEX, compatible_info(8)), - info_missing(&isolated), - create_index(&isolated, 4), - hset(&isolated, prompt, "key1", &vector_b, &entry), - search( - &isolated, - "key1", - &vector_b, - Ok(hit("key1", "0.0", encoded(&entry))), - ), - search( - &isolated, - "key1", - &vector_b, - Err(redis::RedisError::from(( - redis::ErrorKind::Extension, - "Vector dimension mismatch", - ))), - ), - ], - FakeEmbedder::new(&[(prompt, &vector_b)]), - ); - worker_b - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - assert_eq!( - worker_b.get_cache("key1", &context()).unwrap(), - Some(entry.clone()) - ); - - let vector_c = vec![0.3f32; 16]; - let worker_c = cache( - vec![ - info(INDEX, compatible_info(8)), - info(&isolated, compatible_info(4)), - MockCmd::new(redis::cmd("FT.DROPINDEX").arg(&isolated), Ok("OK")), - create_index(&isolated, 16), - hset(&isolated, prompt, "key1", &vector_c, &entry), - ], - FakeEmbedder::new(&[(prompt, &vector_c)]), - ); - worker_c - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - - assert!(matches!( - worker_b.get_cache("key1", &context()), - Err(Error::Unavailable(Some(_))) - )); -} - -#[fixture] -fn redis_stack_url() -> Option { - std::env::var("LITELLM_REDIS_STACK_URL").ok() -} - -fn live_cache( - url: &str, - index_name: &str, - prompt: &str, - vector: Vec, -) -> RedisSemanticCache> { - RedisSemanticCache::new( - url, - FakeEmbedder::new(&[(prompt, vector.as_slice())]), - JsonCodec::::new(), - RedisSemanticConfig { - index_name: index_name.to_owned(), - similarity_threshold: 0.9, - }, - ) - .unwrap() -} - -#[rstest] -fn live_shared_index_is_replaced_across_dimensions(redis_stack_url: Option, entry: Value) { - let Some(url) = redis_stack_url else { - return; - }; - // Pins parity with Python's `_isolated` + overwrite=True flow. - let base = format!("rust_semantic_shared_{}", std::process::id()); - let isolated = format!("{base}_isolated"); - let prompt = "shared live prompt"; - let context = || messages_context(vec![json!({"role": "user", "content": prompt})]); - - let worker_a = live_cache(&url, &base, prompt, vec![0.1f32; 8]); - worker_a - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - - let worker_b = live_cache(&url, &base, prompt, vec![0.2f32; 4]); - worker_b - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - assert_eq!( - worker_b.get_cache("key1", &context()).unwrap(), - Some(entry.clone()) - ); - - let worker_c = live_cache(&url, &base, prompt, vec![0.3f32; 16]); - worker_c - .set_cache("key1", entry.clone(), &context()) - .unwrap(); - - assert!(matches!( - worker_b.get_cache("key1", &context()), - Err(Error::Unavailable(Some(_))) - )); - - let mut connection = redis::Client::open(url).unwrap().get_connection().unwrap(); - for index in [&base, &isolated] { - let _: Result<(), _> = redis::cmd("FT.DROPINDEX") - .arg(index) - .arg("DD") - .query(&mut connection); - } -} - -#[rstest] -fn live_store_lookup_and_ttl_against_redis_stack(redis_stack_url: Option, entry: Value) { - let Some(url) = redis_stack_url else { - return; - }; - let prompt = "rust semantic cache live prompt"; - let index_name = format!("rust_semantic_test_{}", std::process::id()); - let cache = live_cache(&url, &index_name, prompt, vec![0.1, 0.2, 0.3, 0.4]); - let context = SemanticCacheContext { - ttl: Some(Duration::from_secs(120)), - ..messages_context(vec![json!({"role": "user", "content": prompt})]) - }; - - cache - .set_cache("live-key", entry.clone(), &context) - .unwrap(); - assert_eq!(cache.get_cache("live-key", &context).unwrap(), Some(entry)); - assert_eq!(cache.get_cache("other-key", &context).unwrap(), None); - - let mut connection = redis::Client::open(url).unwrap().get_connection().unwrap(); - let ttl: i64 = redis::Commands::ttl( - &mut connection, - format!("{index_name}:{}", entry_id(prompt, "live-key")), - ) - .unwrap(); - assert!( - ttl > 0, - "expected stored hash to carry an expiry, got {ttl}" - ); -} diff --git a/litellm-rust/crates/cache-redis-semantic/tests/contract.rs b/litellm-rust/crates/cache-redis-semantic/tests/contract.rs deleted file mode 100644 index fb3cda9616e..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/tests/contract.rs +++ /dev/null @@ -1,63 +0,0 @@ -mod support; - -use litellm_cache::{JsonCodec, SemanticCacheContext, semantic::PreparedEmbedding}; -use litellm_cache_redis_semantic::{DEFAULT_INDEX_NAME, RedisSemanticCache, RedisSemanticConfig}; -use litellm_cache_testing as contract; -use rstest::{fixture, rstest}; -use serde_json::{Value, json}; -use support::FakeSearch; - -type Cache = RedisSemanticCache, FakeSearch>; - -const PREFIX: &str = "contract:"; - -#[fixture] -fn cache() -> Cache { - RedisSemanticCache::with_connection( - FakeSearch::default(), - PreparedEmbedding(vec![0.6, 0.8]), - JsonCodec::new(), - RedisSemanticConfig { - index_name: DEFAULT_INDEX_NAME.into(), - similarity_threshold: 0.9, - }, - ) -} - -#[fixture] -fn context() -> SemanticCacheContext { - SemanticCacheContext { - messages: Some(json!([{"role": "user", "content": "contract prompt"}])), - ..Default::default() - } -} - -#[rstest] -#[tokio::test] -async fn hit_and_miss(cache: Cache, context: SemanticCacheContext) { - contract::hit_and_miss(&cache, context, PREFIX, json!({"answer": 42})).await; -} - -#[rstest] -#[tokio::test] -async fn sync_async_equivalence(cache: Cache, context: SemanticCacheContext) { - contract::sync_async_equivalence(&cache, context, PREFIX, json!("first"), json!([2])).await; -} - -#[rstest] -#[tokio::test] -async fn overwrite_replaces(cache: Cache, context: SemanticCacheContext) { - contract::overwrite_replaces(&cache, context, PREFIX, json!(1), json!({"b": 2})).await; -} - -#[rstest] -#[tokio::test] -async fn pipeline_writes_every_entry(cache: Cache, context: SemanticCacheContext) { - contract::pipeline_writes_every_entry( - &cache, - context, - PREFIX, - vec![json!("a"), json!(2), json!({"c": true})], - ) - .await; -} diff --git a/litellm-rust/crates/cache-redis-semantic/tests/support/mod.rs b/litellm-rust/crates/cache-redis-semantic/tests/support/mod.rs deleted file mode 100644 index f112dbe96a7..00000000000 --- a/litellm-rust/crates/cache-redis-semantic/tests/support/mod.rs +++ /dev/null @@ -1,302 +0,0 @@ -#![allow(dead_code)] - -use std::{ - collections::{BTreeMap, HashMap}, - sync::{Arc, Mutex}, -}; - -use litellm_cache::{Error, semantic::Embedder}; -use serde_json::Value; - -pub type EmbedCalls = Arc)>>>; - -/// Embeds known prompts to fixed vectors, anything else to `[0.1, 0.2, 0.3]`, and records every -/// prompt with its metadata. -pub struct FakeEmbedder { - vectors: HashMap>, - pub calls: EmbedCalls, -} - -impl FakeEmbedder { - pub fn new(vectors: &[(&str, &[f32])]) -> Self { - Self { - vectors: vectors - .iter() - .map(|(prompt, vector)| ((*prompt).to_owned(), vector.to_vec())) - .collect(), - calls: EmbedCalls::default(), - } - } -} - -impl Embedder for FakeEmbedder { - fn embed(&self, prompt: &str, metadata: Option<&Value>) -> Result, Error> { - self.calls - .lock() - .unwrap() - .push((prompt.to_owned(), metadata.cloned())); - Ok(self - .vectors - .get(prompt) - .cloned() - .unwrap_or_else(|| vec![0.1, 0.2, 0.3])) - } - - async fn async_embed(&self, prompt: &str, metadata: Option<&Value>) -> Result, Error> { - self.embed(prompt, metadata) - } -} - -struct FakeIndex { - prefix: Vec, - dims: usize, - vector_field: String, -} - -#[derive(Default)] -struct SearchState { - indexes: HashMap, - hashes: BTreeMap, BTreeMap>>, -} - -/// An in-memory Redis Stack speaking the `FT.*`, `HSET` and `EXPIRE` subset the semantic cache -/// sends, with exact cosine KNN over the hashes under an index prefix. -#[derive(Clone, Default)] -pub struct FakeSearch { - state: Arc>, -} - -impl FakeSearch { - fn run(&self, args: Vec>) -> redis::RedisResult { - let mut state = self.state.lock().unwrap(); - let text = |index: usize| String::from_utf8_lossy(&args[index]).into_owned(); - match text(0).to_uppercase().as_str() { - "FT.CREATE" => { - let name = text(1); - if state.indexes.contains_key(&name) { - return Err(server_error("Index already exists")); - } - let position = |token: &str| args.iter().position(|arg| arg == token.as_bytes()); - let prefix = args[position("PREFIX").unwrap() + 2].clone(); - let dims = text(position("DIM").unwrap() + 1).parse().unwrap(); - let vector_field = text(position("VECTOR").unwrap() - 1); - state.indexes.insert( - name, - FakeIndex { - prefix, - dims, - vector_field, - }, - ); - Ok(redis::Value::Okay) - } - "FT.INFO" => { - let index = state - .indexes - .get(&text(1)) - .ok_or_else(|| server_error("Unknown index name"))?; - Ok(index_info(index)) - } - "FT.DROPINDEX" => { - state.indexes.remove(&text(1)); - Ok(redis::Value::Okay) - } - "HSET" => { - let hash = state.hashes.entry(args[1].clone()).or_default(); - for pair in args[2..].chunks(2) { - hash.insert( - String::from_utf8_lossy(&pair[0]).into_owned(), - pair[1].clone(), - ); - } - Ok(redis::Value::Int(((args.len() - 2) / 2) as i64)) - } - "EXPIRE" => Ok(redis::Value::Int(i64::from( - state.hashes.contains_key(&args[1]), - ))), - "FT.SEARCH" => { - let index = state - .indexes - .get(&text(1)) - .ok_or_else(|| server_error("no such index"))?; - let query = text(2); - let tag = query_tag(&query); - let params = args.iter().position(|arg| arg == b"PARAMS").unwrap(); - let vector = floats(&args[params + 3]); - let best = state - .hashes - .iter() - .filter(|(key, _)| key.starts_with(&index.prefix)) - .filter(|(_, fields)| { - fields.get("litellm_cache_key").map(Vec::as_slice) == Some(tag.as_bytes()) - }) - .filter_map(|(key, fields)| { - let stored = floats(fields.get(&index.vector_field)?); - (stored.len() == index.dims) - .then(|| (key, fields, 1.0 - cosine(&vector, &stored))) - }) - .min_by(|left, right| left.2.total_cmp(&right.2)); - let Some((key, fields, distance)) = best else { - return Ok(redis::Value::Array(vec![redis::Value::Int(0)])); - }; - let mut reply = fields - .iter() - .filter(|(name, _)| **name != index.vector_field) - .flat_map(|(name, value)| [bulk(name.as_bytes()), bulk(value)]) - .collect::>(); - reply.extend([ - bulk(b"vector_distance"), - bulk(distance.to_string().as_bytes()), - ]); - Ok(redis::Value::Array(vec![ - redis::Value::Int(1), - bulk(key), - redis::Value::Array(reply), - ])) - } - "PING" => Ok(redis::Value::SimpleString("PONG".into())), - _ => Err(server_error("unsupported command")), - } - } -} - -impl redis::ConnectionLike for FakeSearch { - fn req_packed_command(&mut self, command: &[u8]) -> redis::RedisResult { - let mut commands = parse_commands(command); - self.run(commands.remove(0)) - } - - fn req_packed_commands( - &mut self, - commands: &[u8], - offset: usize, - count: usize, - ) -> redis::RedisResult> { - let replies = parse_commands(commands) - .into_iter() - .map(|args| self.run(args)) - .collect::>>()?; - Ok(replies.into_iter().skip(offset).take(count).collect()) - } - - fn get_db(&self) -> i64 { - 0 - } - - fn check_connection(&mut self) -> bool { - true - } - - fn is_open(&self) -> bool { - true - } -} - -pub fn server_error(reply: &str) -> redis::RedisError { - redis::parse_redis_value(format!("-{reply}\r\n").as_bytes()) - .expect("error reply") - .extract_error() - .expect_err("server error") -} - -fn bulk(bytes: &[u8]) -> redis::Value { - redis::Value::BulkString(bytes.to_vec()) -} - -fn index_info(index: &FakeIndex) -> redis::Value { - let attribute = |name: &str, field_type: &str| { - redis::Value::Array(vec![ - bulk(b"identifier"), - bulk(name.as_bytes()), - bulk(b"type"), - bulk(field_type.as_bytes()), - ]) - }; - redis::Value::Array(vec![ - bulk(b"attributes"), - redis::Value::Array(vec![ - attribute("prompt", "TEXT"), - attribute("response", "TEXT"), - attribute("inserted_at", "NUMERIC"), - attribute("updated_at", "NUMERIC"), - attribute("litellm_cache_key", "TAG"), - redis::Value::Array(vec![ - bulk(b"identifier"), - bulk(index.vector_field.as_bytes()), - bulk(b"type"), - bulk(b"VECTOR"), - bulk(b"dim"), - redis::Value::Int(index.dims as i64), - bulk(b"data_type"), - bulk(b"FLOAT32"), - bulk(b"distance_metric"), - bulk(b"COSINE"), - ]), - ]), - ]) -} - -/// The tag inside `@litellm_cache_key:{...}`, with query escapes removed. -fn query_tag(query: &str) -> String { - let start = query.find("@litellm_cache_key:{").unwrap() + "@litellm_cache_key:{".len(); - let mut tag = String::new(); - let mut characters = query[start..].chars(); - while let Some(character) = characters.next() { - match character { - '\\' => tag.extend(characters.next()), - '}' => break, - character => tag.push(character), - } - } - tag -} - -fn floats(bytes: &[u8]) -> Vec { - bytes - .as_chunks::<4>() - .0 - .iter() - .map(|chunk| f32::from_le_bytes(*chunk)) - .collect() -} - -fn cosine(left: &[f32], right: &[f32]) -> f64 { - let dot = left - .iter() - .zip(right) - .map(|(left, right)| f64::from(*left) * f64::from(*right)) - .sum::(); - let norm = |vector: &[f32]| { - vector - .iter() - .map(|value| f64::from(*value).powi(2)) - .sum::() - .sqrt() - }; - dot / (norm(left) * norm(right)) -} - -/// Splits a packed RESP request into each command's arguments. -fn parse_commands(mut bytes: &[u8]) -> Vec>> { - let line = |bytes: &mut &[u8]| { - let end = bytes - .windows(2) - .position(|window| window == b"\r\n") - .unwrap(); - let text = String::from_utf8(bytes[1..end].to_vec()).unwrap(); - *bytes = &bytes[end + 2..]; - text.parse::().unwrap() - }; - let mut commands = Vec::new(); - while !bytes.is_empty() { - let count = line(&mut bytes); - let mut args = Vec::with_capacity(count); - for _ in 0..count { - let length = line(&mut bytes); - args.push(bytes[..length].to_vec()); - bytes = &bytes[length + 2..]; - } - commands.push(args); - } - commands -} diff --git a/litellm-rust/crates/cache-redis/Cargo.toml b/litellm-rust/crates/cache-redis/Cargo.toml deleted file mode 100644 index f7621299506..00000000000 --- a/litellm-rust/crates/cache-redis/Cargo.toml +++ /dev/null @@ -1,20 +0,0 @@ -[package] -name = "litellm-cache-redis" -version = "0.1.0" -edition.workspace = true -license.workspace = true -repository.workspace = true - -[dependencies] -litellm-cache.workspace = true -litellm-error.workspace = true -redis = { workspace = true, features = ["cluster", "tls-rustls"] } -r2d2 = "0.8.10" -thiserror.workspace = true -tokio.workspace = true - -[dev-dependencies] -litellm-cache-testing.workspace = true -redis-test.workspace = true -rstest.workspace = true -serde_json.workspace = true diff --git a/litellm-rust/crates/cache-redis/src/cache.rs b/litellm-rust/crates/cache-redis/src/cache.rs deleted file mode 100644 index b873a2c0282..00000000000 --- a/litellm-rust/crates/cache-redis/src/cache.rs +++ /dev/null @@ -1,192 +0,0 @@ -use std::{ - sync::{Arc, OnceLock}, - time::Duration, -}; - -use litellm_cache::{BatchEntry, CacheCodec, Error}; - -use crate::{ - connection::{ConnectionRef, Connections}, - topology::RedisTopology, -}; - -const DEFAULT_TTL: Duration = Duration::from_secs(600); - -pub struct RedisCache { - pub(crate) connections: Arc>, - pub(crate) default_ttl: Duration, - pub(crate) codec: S, - pub(crate) namespace: Option, - pub(crate) topology: RedisTopology, - /// The server's major version, read from `INFO` once, like Python's `redis_version`. - pub(crate) major_version: Arc>, -} - -impl RedisCache { - pub fn new(url: &str, default_ttl: Option, codec: S) -> Result { - Self::connect(url, &RedisTopology::Standalone, default_ttl, codec) - } - - pub fn connect( - url: &str, - topology: &RedisTopology, - default_ttl: Option, - codec: S, - ) -> Result { - let connections = Connections::open(url, topology)?; - Ok(Self { - connections: Arc::new(connections), - default_ttl: default_ttl.unwrap_or(DEFAULT_TTL), - codec, - namespace: None, - topology: topology.clone(), - major_version: Arc::default(), - }) - } -} - -impl RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - pub fn with_connection(connection: C, default_ttl: Option, codec: S) -> Self { - Self { - connections: Arc::new(Connections::fixed(connection)), - default_ttl: default_ttl.unwrap_or(DEFAULT_TTL), - codec, - namespace: None, - topology: RedisTopology::Standalone, - major_version: Arc::default(), - } - } - - pub fn with_namespace(self, namespace: Option) -> Self { - Self { - namespace: namespace.filter(|value| !value.is_empty()), - ..self - } - } - - pub fn namespace(&self) -> Option<&str> { - self.namespace.as_deref() - } - - pub fn topology(&self) -> &RedisTopology { - &self.topology - } - - pub(crate) fn namespaced_key(&self, key: &str) -> String { - namespaced_key(self.namespace.as_deref(), key) - } - - pub(crate) fn namespaced_keys(&self, keys: &[String]) -> Vec { - keys.iter().map(|key| self.namespaced_key(key)).collect() - } - - /// Whole seconds for `ttl`, falling back to the default TTL like Python's `get_ttl`. - pub(crate) fn ttl_or_default(&self, ttl: Option) -> u64 { - ttl_seconds(ttl.unwrap_or(self.default_ttl)) - } - - pub(crate) fn execute( - &self, - operation: impl FnOnce(&mut ConnectionRef<'_>) -> Result, - ) -> Result { - self.connections.execute(operation) - } - - /// `_parse_redis_major_version`: the major version from `INFO`, or - /// `DEFAULT_REDIS_MAJOR_VERSION` when `INFO` fails or its version does not parse. The first - /// answer is kept, as Python reads `redis_version` once at construction. - pub(crate) async fn major_version(&self) -> u32 { - if let Some(version) = self.major_version.get() { - return *version; - } - let info = self - .run(|connection| connection.node_text(&redis::cmd("INFO"))) - .await; - let version = info - .ok() - .and_then(|info| parse_major_version(&info)) - .unwrap_or_else(default_major_version); - *self.major_version.get_or_init(|| version) - } - - pub(crate) async fn run(&self, operation: F) -> Result - where - T: Send + 'static, - F: FnOnce(&mut ConnectionRef<'_>) -> Result + Send + 'static, - { - Connections::run_blocking(Arc::clone(&self.connections), operation).await - } - - pub(crate) fn namespaced_pattern(&self) -> Result { - let namespace = self.namespace.as_ref().ok_or(Error::UnscopedFlush)?; - let escaped: String = namespace - .chars() - .flat_map(|ch| { - if matches!(ch, '*' | '?' | '[' | ']' | '\\') { - vec!['\\', ch] - } else { - vec![ch] - } - }) - .collect(); - Ok(format!("{escaped}:*")) - } - - pub(crate) fn decode_response(&self, value: redis::Value) -> Result, Error> { - match value { - redis::Value::Nil => Ok(None), - redis::Value::BulkString(bytes) => self.codec.decode(&bytes).map(Some), - redis::Value::SimpleString(text) => self.codec.decode(text.as_bytes()).map(Some), - _ => Err(Error::InvalidEntry(None)), - } - } - - pub(crate) fn decode_batch_response( - &self, - value: redis::Value, - ) -> Result, Error> { - match self.decode_response(value) { - Ok(Some(value)) => Ok(BatchEntry::Hit(value)), - Ok(None) => Ok(BatchEntry::Miss), - Err(Error::InvalidEntry(_)) => Ok(BatchEntry::Invalid), - Err(error) => Err(error), - } - } -} - -pub(crate) fn namespaced_key(namespace: Option<&str>, key: &str) -> String { - match namespace { - Some(namespace) if !key.starts_with(&format!("{namespace}:")) => { - format!("{namespace}:{key}") - } - _ => key.into(), - } -} - -pub(crate) fn ttl_seconds(ttl: Duration) -> u64 { - ttl.as_secs() - .saturating_add(u64::from(ttl.subsec_nanos() > 0)) - .max(1) -} - -fn parse_major_version(info: &str) -> Option { - let version = info - .lines() - .find_map(|line| line.trim().strip_prefix("redis_version:"))? - .trim(); - match version.split_once('.') { - Some((major, _)) => major.parse().ok(), - None => version.parse::().ok().map(|major| major as u32), - } -} - -fn default_major_version() -> u32 { - std::env::var("DEFAULT_REDIS_MAJOR_VERSION") - .ok() - .and_then(|value| value.parse().ok()) - .unwrap_or(7) -} diff --git a/litellm-rust/crates/cache-redis/src/claim.rs b/litellm-rust/crates/cache-redis/src/claim.rs deleted file mode 100644 index c57a1b03395..00000000000 --- a/litellm-rust/crates/cache-redis/src/claim.rs +++ /dev/null @@ -1,109 +0,0 @@ -use litellm_cache::{CacheCodec, ClaimCache, Error, ExactCacheContext}; -use redis::Commands; - -use crate::{cache::RedisCache, connection::ConnectionRef}; - -const CLAIM_SCRIPT: &str = concat!( - "local current = redis.call('GET', KEYS[1]); ", - "if ARGV[1] == '' then if current ~= false and current ~= '' then return 0; end; ", - "elseif current ~= ARGV[1] then return 0; end; ", - "if ARGV[3] ~= '' then redis.call('SET', KEYS[1], ARGV[3], 'EX', ARGV[2]); ", - "elseif ARGV[4] == '1' then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; return 1" -); -const CLAIM_ATTEMPTS: usize = 8; - -fn stored_bytes(value: redis::Value) -> Result>, Error> { - match value { - redis::Value::Nil => Ok(None), - redis::Value::BulkString(bytes) => Ok(Some(bytes)), - redis::Value::SimpleString(text) => Ok(Some(text.into_bytes())), - _ => Err(Error::InvalidEntry(None)), - } -} - -/// Eligibility is decided on decoded values, so a pin written by another encoder (Python's -/// `json.dumps` spacing or key order) still matches. The write is a compare-and-set on the -/// bytes that decision was made on, retried when another claimant wins the race. -fn claim( - connection: &mut ConnectionRef<'_>, - codec: &S, - key: &str, - candidate: S::Value, - eligible: &[S::Value], - ttl: u64, -) -> Result -where - S::Value: PartialEq, -{ - let payload = codec.encode(&candidate)?; - if payload.is_empty() { - return Err(Error::InvalidEntry(None)); - } - for _ in 0..CLAIM_ATTEMPTS { - let current = stored_bytes( - connection - .get::<_, redis::Value>(key) - .map_err(Error::unavailable)?, - )? - .filter(|bytes| !bytes.is_empty()); - let existing = current - .as_deref() - .and_then(|bytes| codec.decode(bytes).ok()) - .filter(|existing| eligible.is_empty() || eligible.contains(existing)); - let refresh = existing - .as_ref() - .is_some_and(|existing| !eligible.is_empty() || *existing == candidate); - let write: &[u8] = if existing.is_some() { b"" } else { &payload }; - let applied = redis::cmd("EVAL") - .arg(CLAIM_SCRIPT) - .arg(1) - .arg(key) - .arg(current.as_deref().unwrap_or_default()) - .arg(ttl) - .arg(write) - .arg(u8::from(refresh)) - .query::(connection) - .map_err(Error::unavailable)?; - if applied { - return Ok(existing.unwrap_or(candidate)); - } - } - Err(Error::unavailable(ClaimContended)) -} - -#[derive(Debug, thiserror::Error)] -#[error("claim kept losing the race after {CLAIM_ATTEMPTS} attempts")] -struct ClaimContended; - -impl ClaimCache for RedisCache -where - S: CacheCodec + Clone + 'static, - S::Value: PartialEq, - C: redis::ConnectionLike + Send + 'static, -{ - fn claim_cache( - &self, - key: &str, - candidate: S::Value, - eligible: &[S::Value], - context: ExactCacheContext, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(context.ttl); - self.execute(|connection| claim(connection, &self.codec, &key, candidate, eligible, ttl)) - } - - async fn async_claim_cache( - &self, - key: &str, - candidate: S::Value, - eligible: Vec, - context: ExactCacheContext, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(context.ttl); - let codec = self.codec.clone(); - self.run(move |connection| claim(connection, &codec, &key, candidate, &eligible, ttl)) - .await - } -} diff --git a/litellm-rust/crates/cache-redis/src/connection.rs b/litellm-rust/crates/cache-redis/src/connection.rs deleted file mode 100644 index 99befa16ffa..00000000000 --- a/litellm-rust/crates/cache-redis/src/connection.rs +++ /dev/null @@ -1,513 +0,0 @@ -use std::{ - sync::{Arc, Mutex}, - time::Duration, -}; - -use litellm_cache::Error; -use redis::{ - ConnectionAddr, ConnectionInfo, IntoConnectionInfo, - cluster::{ - ClusterClient, ClusterClientBuilder, ClusterConnection, ClusterPipeline, NodeAddress, - }, - cluster_routing::{ - MultipleNodeRoutingInfo, ResponsePolicy, RoutingInfo, SingleNodeRoutingInfo, - }, -}; - -use crate::topology::{RedisNode, RedisTopology}; - -pub(crate) const REDIS_TIMEOUT: Duration = Duration::from_secs(5); -const REDIS_POOL_SIZE: u32 = 16; - -#[allow(private_interfaces)] -pub enum Connections { - Pool(r2d2::Pool), - Cluster(r2d2::Pool), - Fixed(Mutex), -} - -impl Connections -where - C: redis::ConnectionLike + Send + 'static, -{ - pub fn execute( - &self, - operation: impl FnOnce(&mut ConnectionRef<'_>) -> Result, - ) -> Result { - match self { - Self::Pool(pool) => { - let mut pooled = pool.get().map_err(Error::unavailable)?; - let result = operation(&mut ConnectionRef::Node(&mut pooled.connection)); - pooled.failed = matches!(result, Err(Error::Unavailable(_))); - result - } - Self::Cluster(pool) => { - let mut pooled = pool.get().map_err(Error::unavailable)?; - let result = operation(&mut ConnectionRef::Cluster(&mut pooled.connection)); - pooled.failed = matches!(result, Err(Error::Unavailable(_))); - result - } - Self::Fixed(connection) => { - let mut connection = connection.lock()?; - operation(&mut ConnectionRef::Node(&mut *connection)) - } - } - } - - pub async fn run_blocking(connections: Arc, operation: F) -> Result - where - T: Send + 'static, - F: FnOnce(&mut ConnectionRef<'_>) -> Result + Send + 'static, - { - tokio::task::spawn_blocking(move || connections.execute(operation)) - .await - .map_err(Error::unavailable)? - } - - pub fn fixed(connection: C) -> Self { - Self::Fixed(Mutex::new(connection)) - } - - pub fn open(url: &str, topology: &RedisTopology) -> Result { - match topology { - RedisTopology::Standalone => Ok(Self::Pool(pool(ConnectionManager::open(url)?)?)), - RedisTopology::Cluster { startup_nodes } => Ok(Self::Cluster(pool( - ClusterConnectionManager::open(url, startup_nodes)?, - )?)), - } - } - - /// Closes every idle pooled connection; the next operation opens a fresh one. Connections - /// checked out right now return to the pool, and a caller-owned connection stays open. - pub fn disconnect(&self) { - match self { - Self::Pool(pool) => close_idle(pool), - Self::Cluster(pool) => close_idle(pool), - Self::Fixed(_) => {} - } - } -} - -fn pool(manager: M) -> Result, Error> { - r2d2::Pool::builder() - .max_size(REDIS_POOL_SIZE) - .min_idle(Some(0)) - .connection_timeout(REDIS_TIMEOUT) - .test_on_check_out(false) - .build(manager) - .map_err(Error::unavailable) -} - -fn close_idle(pool: &r2d2::Pool) -where - M: r2d2::ManageConnection>, -{ - let mut idle = Vec::new(); - while let Some(mut connection) = pool.try_get() { - connection.failed = true; - idle.push(connection); - } -} - -pub(crate) struct PooledConnection { - connection: C, - failed: bool, -} - -/// Pools connections without a checkout PING, which would double every operation's round trips. -/// A timed-out command leaves its reply on the socket while redis still reports the connection -/// open, so any connection whose operation failed is discarded instead of being reused. -pub(crate) struct ConnectionManager(redis::Client); - -impl ConnectionManager { - fn open(url: &str) -> Result { - redis::Client::open(url) - .map(Self) - .map_err(Error::unavailable) - } -} - -impl r2d2::ManageConnection for ConnectionManager { - type Connection = PooledConnection; - type Error = redis::RedisError; - - fn connect(&self) -> Result { - let connection = self.0.get_connection()?; - connection.set_read_timeout(Some(REDIS_TIMEOUT))?; - connection.set_write_timeout(Some(REDIS_TIMEOUT))?; - Ok(PooledConnection { - connection, - failed: false, - }) - } - - fn is_valid(&self, connection: &mut Self::Connection) -> Result<(), redis::RedisError> { - redis::cmd("PING").query::(&mut connection.connection)?; - Ok(()) - } - - fn has_broken(&self, connection: &mut Self::Connection) -> bool { - connection.failed || !redis::ConnectionLike::is_open(&connection.connection) - } -} - -#[derive(Debug, thiserror::Error)] -enum TopologyFault { - #[error("Redis Cluster needs at least one startup node")] - NoStartupNodes, - #[error("Redis Cluster nodes need a TCP address")] - NonTcpAddress, - #[error("Redis Cluster answered a fan-out command without a per-node map")] - FanOutReply, - #[error("scan target does not match the connection topology")] - ScanTarget, -} - -pub(crate) struct ClusterConnectionManager(ClusterClient); - -impl ClusterConnectionManager { - fn open(url: &str, startup_nodes: &[RedisNode]) -> Result { - if startup_nodes.is_empty() { - return Err(Error::unavailable(TopologyFault::NoStartupNodes)); - } - let info = url.into_connection_info().map_err(Error::unavailable)?; - let nodes = startup_nodes - .iter() - .map(|node| node_info(&info, node)) - .collect::, _>>()?; - ClusterClientBuilder::new(nodes) - .connection_timeout(REDIS_TIMEOUT) - .response_timeout(REDIS_TIMEOUT) - .build() - .map(Self) - .map_err(Error::unavailable) - } -} - -fn node_info(info: &ConnectionInfo, node: &RedisNode) -> Result { - let addr = match info.addr() { - ConnectionAddr::Tcp(..) => ConnectionAddr::Tcp(node.host.clone(), node.port), - ConnectionAddr::TcpTls { - insecure, - tls_params, - .. - } => ConnectionAddr::TcpTls { - host: node.host.clone(), - port: node.port, - insecure: *insecure, - tls_params: tls_params.clone(), - }, - _ => return Err(Error::unavailable(TopologyFault::NonTcpAddress)), - }; - Ok(info.clone().set_addr(addr)) -} - -impl r2d2::ManageConnection for ClusterConnectionManager { - type Connection = PooledConnection; - type Error = redis::RedisError; - - fn connect(&self) -> Result { - let connection = self.0.get_connection()?; - connection.set_read_timeout(Some(REDIS_TIMEOUT))?; - connection.set_write_timeout(Some(REDIS_TIMEOUT))?; - Ok(PooledConnection { - connection, - failed: false, - }) - } - - fn is_valid(&self, connection: &mut Self::Connection) -> Result<(), redis::RedisError> { - redis::cmd("PING").query::(&mut connection.connection)?; - Ok(()) - } - - fn has_broken(&self, connection: &mut Self::Connection) -> bool { - connection.failed || !redis::ConnectionLike::is_open(&connection.connection) - } -} - -pub enum ConnectionRef<'a> { - Node(&'a mut dyn redis::ConnectionLike), - Cluster(&'a mut ClusterConnection), -} - -impl redis::ConnectionLike for ConnectionRef<'_> { - fn req_packed_command(&mut self, cmd: &[u8]) -> redis::RedisResult { - match self { - Self::Node(connection) => connection.req_packed_command(cmd), - Self::Cluster(connection) => connection.req_packed_command(cmd), - } - } - - fn req_packed_commands( - &mut self, - cmd: &[u8], - offset: usize, - count: usize, - ) -> redis::RedisResult> { - match self { - Self::Node(connection) => connection.req_packed_commands(cmd, offset, count), - Self::Cluster(connection) => connection.req_packed_commands(cmd, offset, count), - } - } - - fn get_db(&self) -> i64 { - match self { - Self::Node(connection) => connection.get_db(), - Self::Cluster(connection) => redis::ConnectionLike::get_db(*connection), - } - } - - fn supports_pipelining(&self) -> bool { - match self { - Self::Node(connection) => connection.supports_pipelining(), - Self::Cluster(connection) => redis::ConnectionLike::supports_pipelining(*connection), - } - } - - fn check_connection(&mut self) -> bool { - match self { - Self::Node(connection) => connection.check_connection(), - Self::Cluster(connection) => connection.check_connection(), - } - } - - fn is_open(&self) -> bool { - match self { - Self::Node(connection) => connection.is_open(), - Self::Cluster(connection) => redis::ConnectionLike::is_open(*connection), - } - } -} - -impl ConnectionRef<'_> { - /// Runs `pipeline` and decodes its non-ignored replies as `T`. A cluster connection refuses - /// `Pipeline::query`, so there a transaction goes to its keys' slot as one MULTI/EXEC and - /// anything else is split per node by `ClusterPipeline`; either way the raw replies are - /// handed back to `pipeline` to decode. - pub(crate) fn query_pipeline( - &mut self, - pipeline: &redis::Pipeline, - ) -> Result { - match self { - Self::Node(connection) => pipeline.query(*connection), - Self::Cluster(connection) if pipeline.is_transaction() => { - redis::ConnectionLike::req_packed_commands( - *connection, - &pipeline.get_packed_pipeline(), - pipeline.len() + 1, - 1, - ) - .and_then(|replies| pipeline.query(&mut Replies(Some(replies)))) - } - Self::Cluster(connection) => { - let mut cluster = ClusterPipeline::with_capacity(pipeline.len()); - for command in pipeline.cmd_iter() { - cluster.add_command(command.clone()); - } - cluster - .query(connection) - .and_then(|replies| pipeline.query(&mut Replies(Some(replies)))) - } - } - .map_err(Error::unavailable) - } - - pub(crate) fn scan( - &mut self, - pattern: &str, - count: usize, - mut visit: impl FnMut(&mut Self, Vec) -> Result, - ) -> Result<(), Error> { - let pages = match self { - Self::Node(connection) => { - let page = scan_command(0, pattern, count) - .query::(*connection) - .map_err(Error::unavailable)?; - vec![(None, page)] - } - Self::Cluster(connection) => connection - .route_command( - &scan_command(0, pattern, count), - RoutingInfo::MultiNode(( - MultipleNodeRoutingInfo::AllMasters, - Some(ResponsePolicy::Special), - )), - ) - .map_err(Error::unavailable) - .and_then(primary_pages)? - .into_iter() - .map(|(node, page)| (Some(node), page)) - .collect(), - }; - for (node, (mut cursor, mut keys)) in pages { - loop { - if !visit(self, keys)? { - return Ok(()); - } - if cursor == 0 { - break; - } - (cursor, keys) = self.scan_page(node.as_ref(), cursor, pattern, count)?; - } - } - Ok(()) - } - - pub(crate) fn ping(&mut self) -> Result { - let command = redis::cmd("PING"); - match self { - Self::Node(connection) => command - .query::(*connection) - .map(|response| response == "PONG"), - Self::Cluster(connection) => connection - .route_command( - &command, - RoutingInfo::MultiNode(( - MultipleNodeRoutingInfo::AllNodes, - Some(ResponsePolicy::AllSucceeded), - )), - ) - .map(|_| true), - } - } - - pub(crate) fn node_text(&mut self, command: &redis::Cmd) -> Result { - match self { - Self::Node(connection) => command.query(*connection).map_err(Error::unavailable), - Self::Cluster(connection) => { - let value = connection - .route_command( - command, - RoutingInfo::MultiNode(( - MultipleNodeRoutingInfo::AllNodes, - Some(ResponsePolicy::Special), - )), - ) - .map_err(Error::unavailable)?; - let redis::Value::Map(entries) = value else { - return Err(Error::unavailable(TopologyFault::FanOutReply)); - }; - let mut replies = entries - .into_iter() - .map(|(node, reply)| { - Ok(( - redis::from_redis_value::(node).map_err(Error::unavailable)?, - redis::from_redis_value::(reply).map_err(Error::unavailable)?, - )) - }) - .collect::, Error>>()?; - replies.sort(); - Ok(replies - .into_iter() - .map(|(_, reply)| reply) - .collect::>() - .join("\n")) - } - } - } - - pub(crate) fn flushall(&mut self) -> Result<(), Error> { - let command = redis::cmd("FLUSHALL"); - match self { - Self::Node(connection) => command.query(*connection).map_err(Error::unavailable), - Self::Cluster(connection) => connection - .route_command( - &command, - RoutingInfo::MultiNode(( - MultipleNodeRoutingInfo::AllMasters, - Some(ResponsePolicy::AllSucceeded), - )), - ) - .map(|_| ()) - .map_err(Error::unavailable), - } - } - - fn scan_page( - &mut self, - node: Option<&NodeAddress>, - cursor: u64, - pattern: &str, - count: usize, - ) -> Result { - let command = scan_command(cursor, pattern, count); - match (self, node) { - (Self::Node(connection), None) => { - command.query(*connection).map_err(Error::unavailable) - } - (Self::Cluster(connection), Some(node)) => connection - .route_command( - &command, - RoutingInfo::SingleNode(SingleNodeRoutingInfo::ByAddress { - host: node.host().to_string(), - port: node.port(), - }), - ) - .map_err(Error::unavailable) - .and_then(|value| redis::from_redis_value(value).map_err(Error::unavailable)), - _ => Err(Error::unavailable(TopologyFault::ScanTarget)), - } - } -} - -type ScanPage = (u64, Vec); - -fn primary_pages(value: redis::Value) -> Result, Error> { - let redis::Value::Map(entries) = value else { - return Err(Error::unavailable(TopologyFault::FanOutReply)); - }; - entries - .into_iter() - .map(|(node, page)| { - let node = redis::from_redis_value::(node).map_err(Error::unavailable)?; - let node = NodeAddress::try_from(node.as_str()).map_err(Error::unavailable)?; - let page = redis::from_redis_value::(page).map_err(Error::unavailable)?; - Ok((node, page)) - }) - .collect() -} - -fn scan_command(cursor: u64, pattern: &str, count: usize) -> redis::Cmd { - let mut command = redis::cmd("SCAN"); - command - .cursor_arg(cursor) - .arg("MATCH") - .arg(pattern) - .arg("COUNT") - .arg(count); - command -} - -/// Hands already received pipeline replies to `Pipeline::query`, so it applies its own -/// ignore and error handling to replies a cluster pipeline gathered from several nodes. -struct Replies(Option>); - -impl redis::ConnectionLike for Replies { - fn req_packed_command(&mut self, _: &[u8]) -> redis::RedisResult { - Err((redis::ErrorKind::Client, "replies hold a pipeline only").into()) - } - - fn req_packed_commands( - &mut self, - _: &[u8], - _: usize, - _: usize, - ) -> redis::RedisResult> { - self.0 - .take() - .ok_or_else(|| (redis::ErrorKind::Client, "replies were already read").into()) - } - - fn get_db(&self) -> i64 { - 0 - } - - fn check_connection(&mut self) -> bool { - true - } - - fn is_open(&self) -> bool { - true - } -} diff --git a/litellm-rust/crates/cache-redis/src/counter.rs b/litellm-rust/crates/cache-redis/src/counter.rs deleted file mode 100644 index a4beb9f5712..00000000000 --- a/litellm-rust/crates/cache-redis/src/counter.rs +++ /dev/null @@ -1,205 +0,0 @@ -use std::time::Duration; - -use litellm_cache::{ - BoundedCounterCache, CacheCodec, CountReadCache, CounterCache, Error, ExactCacheContext, - IncrementOperation, -}; - -use crate::{ - cache::{RedisCache, ttl_seconds}, - connection::ConnectionRef, - store::mget, -}; - -const INCREMENT_SCRIPT: &str = concat!( - "local value = redis.call('INCRBYFLOAT', KEYS[1], ARGV[1]); ", - "if redis.call('TTL', KEYS[1]) == -1 then ", - "redis.call('EXPIRE', KEYS[1], ARGV[2]); end; return value" -); -const INCREMENT_WITH_FLOOR_SCRIPT: &str = concat!( - "local count = redis.call('INCRBY', KEYS[1], ARGV[1]); ", - "if count < 0 then count = redis.call('INCRBY', KEYS[1], -count); end; ", - "if redis.call('TTL', KEYS[1]) < 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; ", - "return count" -); -const SET_MAX_SCRIPT: &str = concat!( - "local current = redis.call('GET', KEYS[1]); ", - "if current == false or tonumber(current) < tonumber(ARGV[1]) then ", - "redis.call('SET', KEYS[1], ARGV[1]); ", - "if tonumber(ARGV[2]) > 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; ", - "return ARGV[1]; end; return current" -); - -impl CounterCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn increment_cache( - &self, - key: &str, - amount: f64, - context: ExactCacheContext, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(context.ttl); - self.execute(|connection| increment(connection, key, amount, ttl, false)) - } - - /// Python `_incrbyfloat_with_ttl`: without `refresh_ttl` the TTL is set only on a key that - /// has none, in one atomic script; with it, every increment re-arms the TTL. - async fn async_increment( - &self, - key: &str, - amount: f64, - context: ExactCacheContext, - refresh_ttl: bool, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(context.ttl); - self.run(move |connection| increment(connection, key, amount, ttl, refresh_ttl)) - .await - } - - async fn async_increment_pipeline( - &self, - operations: Vec, - ) -> Result, Error> { - if operations.is_empty() { - return Ok(Vec::new()); - } - let mut pipeline = redis::pipe(); - for operation in operations { - let key = self.namespaced_key(&operation.key); - pipeline.cmd("INCRBYFLOAT").arg(&key).arg(operation.amount); - if let Some(ttl) = operation.ttl { - pipeline - .cmd("EXPIRE") - .arg(key) - .arg(ttl_seconds(ttl)) - .ignore(); - } - } - self.run(move |connection| connection.query_pipeline(&pipeline)) - .await - } -} - -fn increment( - connection: &mut ConnectionRef<'_>, - key: String, - amount: f64, - ttl: u64, - refresh_ttl: bool, -) -> Result { - if !refresh_ttl { - return redis::cmd("EVAL") - .arg(INCREMENT_SCRIPT) - .arg(1) - .arg(key) - .arg(amount) - .arg(ttl) - .query(connection) - .map_err(Error::unavailable); - } - connection - .query_pipeline( - redis::pipe() - .cmd("INCRBYFLOAT") - .arg(&key) - .arg(amount) - .cmd("EXPIRE") - .arg(&key) - .arg(ttl) - .ignore(), - ) - .map(|(value,)| value) -} - -impl CountReadCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn batch_get_counts(&self, keys: &[String]) -> Result>, Error> { - let keys = self.namespaced_keys(keys); - self.execute(|connection| mget(connection, keys))? - .into_iter() - .map(count) - .collect() - } - - async fn async_batch_get_counts(&self, keys: Vec) -> Result>, Error> { - let keys = self.namespaced_keys(&keys); - self.run(move |connection| mget(connection, keys)) - .await? - .into_iter() - .map(count) - .collect() - } -} - -fn count(value: redis::Value) -> Result, Error> { - redis::from_redis_value(value).map_err(Error::invalid_entry) -} - -impl BoundedCounterCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn increment_with_floor(&self, key: &str, amount: i64, ttl: Duration) -> Result { - let key = self.namespaced_key(key); - let ttl = ttl_seconds(ttl); - self.execute(|connection| increment_with_floor(connection, key, amount, ttl)) - } - - async fn async_increment_with_floor( - &self, - key: &str, - amount: i64, - ttl: Duration, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = ttl_seconds(ttl); - self.run(move |connection| increment_with_floor(connection, key, amount, ttl)) - .await - } - - async fn async_set_max( - &self, - key: &str, - value: f64, - ttl: Option, - ) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(ttl); - self.run(move |connection| { - redis::cmd("EVAL") - .arg(SET_MAX_SCRIPT) - .arg(1) - .arg(key) - .arg(value) - .arg(ttl) - .query(connection) - .map_err(Error::unavailable) - }) - .await - } -} - -fn increment_with_floor( - connection: &mut ConnectionRef<'_>, - key: String, - amount: i64, - ttl: u64, -) -> Result { - redis::cmd("EVAL") - .arg(INCREMENT_WITH_FLOOR_SCRIPT) - .arg(1) - .arg(key) - .arg(amount) - .arg(ttl) - .query(connection) - .map_err(Error::unavailable) -} diff --git a/litellm-rust/crates/cache-redis/src/keys.rs b/litellm-rust/crates/cache-redis/src/keys.rs deleted file mode 100644 index 2e8a9e60e65..00000000000 --- a/litellm-rust/crates/cache-redis/src/keys.rs +++ /dev/null @@ -1,63 +0,0 @@ -use std::time::Duration; - -use litellm_cache::{CacheCodec, Error, RefreshTtlCache, ScanCache, TtlCache}; - -use crate::cache::RedisCache; - -impl TtlCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn async_get_ttl(&self, key: &str) -> Result, Error> { - let key = self.namespaced_key(key); - let ttl = self - .run(move |connection| { - redis::cmd("TTL") - .arg(key) - .query::(connection) - .map_err(Error::unavailable) - }) - .await?; - Ok(u64::try_from(ttl).ok().map(Duration::from_secs)) - } -} - -impl RefreshTtlCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn async_refresh_ttl(&self, key: &str, ttl: Option) -> Result { - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(ttl); - self.run(move |connection| { - redis::cmd("EXPIRE") - .arg(key) - .arg(ttl) - .query(connection) - .map_err(Error::unavailable) - }) - .await - } -} - -impl ScanCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn async_scan_iter(&self, pattern: &str, count: usize) -> Result, Error> { - let pattern = format!("{}*", self.namespaced_key(pattern)); - self.run(move |connection| { - let mut matches = Vec::new(); - connection.scan(&pattern, count, |_, keys| { - matches.extend(keys); - Ok(matches.len() < count) - })?; - matches.truncate(count); - Ok(matches) - }) - .await - } -} diff --git a/litellm-rust/crates/cache-redis/src/lib.rs b/litellm-rust/crates/cache-redis/src/lib.rs deleted file mode 100644 index 037e39e5d40..00000000000 --- a/litellm-rust/crates/cache-redis/src/lib.rs +++ /dev/null @@ -1,15 +0,0 @@ -mod cache; -mod claim; -pub mod connection; -mod counter; -mod keys; -mod lifecycle; -mod queue; -mod script; -mod store; -mod topology; - -pub use cache::RedisCache; -pub use queue::{RedisLpopOperation, RedisLpopResult, RedisRpushOperation}; -pub use script::{RedisArg, RedisScript}; -pub use topology::{RedisNode, RedisTopology}; diff --git a/litellm-rust/crates/cache-redis/src/lifecycle.rs b/litellm-rust/crates/cache-redis/src/lifecycle.rs deleted file mode 100644 index 54fa8ecc3bf..00000000000 --- a/litellm-rust/crates/cache-redis/src/lifecycle.rs +++ /dev/null @@ -1,86 +0,0 @@ -use litellm_cache::{ - CacheCodec, CacheConnectionResult, CacheConnectionStatus, ClientInfoCache, ConnectionCache, - DisconnectCache, Error, PingCache, -}; - -use crate::{cache::RedisCache, topology::RedisTopology}; - -impl PingCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn sync_ping(&self) -> Result { - self.execute(|connection| connection.ping().map_err(Error::unavailable)) - } - - async fn ping(&self) -> Result { - self.run(|connection| connection.ping().map_err(Error::unavailable)) - .await - } -} - -impl ConnectionCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - /// Python `RedisCache.test_connection`, or `RedisClusterCache.test_connection` for a - /// cluster topology, which differs only in its messages. - async fn test_connection(&self) -> Result { - let label = match self.topology { - RedisTopology::Standalone => "Redis", - RedisTopology::Cluster { .. } => "Redis Cluster", - }; - let ping = self - .run(|connection| Ok(connection.ping().map_err(Error::unavailable))) - .await - .and_then(|ping| ping) - .map_err(|error| litellm_error::Report::new(&error).to_string()); - Ok(match ping { - Ok(true) => CacheConnectionResult { - status: CacheConnectionStatus::Success, - message: format!("{label} connection test successful"), - error: None, - }, - Ok(false) => CacheConnectionResult { - status: CacheConnectionStatus::Failed, - message: format!("{label} ping returned False"), - error: None, - }, - Err(error) => CacheConnectionResult { - status: CacheConnectionStatus::Failed, - message: format!("{label} connection failed: {error}"), - error: Some(error), - }, - }) - } -} - -impl DisconnectCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn disconnect(&self) -> Result<(), Error> { - self.connections.disconnect(); - Ok(()) - } -} - -impl ClientInfoCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type ClientList = String; - type Info = String; - - fn client_list(&self) -> Result { - self.execute(|connection| connection.node_text(redis::cmd("CLIENT").arg("LIST"))) - } - - fn info(&self) -> Result { - self.execute(|connection| connection.node_text(&redis::cmd("INFO"))) - } -} diff --git a/litellm-rust/crates/cache-redis/src/queue.rs b/litellm-rust/crates/cache-redis/src/queue.rs deleted file mode 100644 index d55e4f49187..00000000000 --- a/litellm-rust/crates/cache-redis/src/queue.rs +++ /dev/null @@ -1,226 +0,0 @@ -use std::time::Duration; - -use litellm_cache::{CacheCodec, Error, PopOperation, PushOperation, QueueCache, SetCache}; - -use crate::{cache::RedisCache, script::RedisArg}; - -pub type RedisRpushOperation = PushOperation; -pub type RedisLpopOperation = PopOperation; - -#[derive(Clone, Debug, PartialEq, Eq)] -pub enum RedisLpopResult { - Missing, - Value(Vec), - Values(Vec>), -} - -impl SetCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type SetValue = RedisArg; - type SetResult = usize; - - async fn async_set_cache_sadd( - &self, - key: &str, - values: Vec, - ttl: Option, - ) -> Result { - if values.is_empty() { - return Err(Error::InvalidEntry(None)); - } - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(ttl); - let mut pipeline = redis::pipe(); - pipeline - .cmd("SADD") - .arg(&key) - .arg(values) - .cmd("EXPIRE") - .arg(&key) - .arg(ttl) - .ignore(); - self.run(move |connection| connection.query_pipeline(&pipeline)) - .await - .map(|(added,)| added) - } -} - -impl QueueCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type QueueValue = RedisArg; - type PopResult = RedisLpopResult; - - async fn async_rpush(&self, key: &str, values: Vec) -> Result { - if values.is_empty() { - return Err(Error::InvalidEntry(None)); - } - let key = self.namespaced_key(key); - self.run(move |connection| { - redis::cmd("RPUSH") - .arg(key) - .arg(values) - .query(connection) - .map_err(Error::unavailable) - }) - .await - } - - async fn async_rpush_and_trim( - &self, - key: &str, - values: Vec, - max_len: usize, - ) -> Result { - if values.is_empty() { - return Err(Error::InvalidEntry(None)); - } - let key = self.namespaced_key(key); - let start = i64::try_from(max_len).map_or(i64::MIN, |max_len| -max_len); - let mut pipeline = redis::pipe(); - pipeline - .atomic() - .cmd("RPUSH") - .arg(&key) - .arg(values) - .cmd("LTRIM") - .arg(&key) - .arg(start) - .arg(-1) - .ignore(); - self.run(move |connection| connection.query_pipeline(&pipeline)) - .await - .map(|(length,)| length) - } - - async fn async_rpush_pipeline( - &self, - operations: Vec, - ) -> Result, Error> { - if operations.is_empty() { - return Ok(Vec::new()); - } - let mut pipeline = redis::pipe(); - for operation in operations { - if operation.values.is_empty() { - return Err(Error::InvalidEntry(None)); - } - pipeline - .cmd("RPUSH") - .arg(self.namespaced_key(&operation.key)) - .arg(operation.values); - } - self.run(move |connection| connection.query_pipeline(&pipeline)) - .await - } - - async fn async_lpop(&self, key: &str, count: Option) -> Result { - if let Some(count) = count - && self.major_version().await < 7 - { - return self.lpop_one_at_a_time(key, count).await; - } - let command = lpop(self.namespaced_key(key), count); - let value = self - .run(move |connection| { - command - .query::(connection) - .map_err(Error::unavailable) - }) - .await?; - lpop_result(value, count.is_some()) - } - - async fn async_lpop_pipeline( - &self, - operations: Vec, - ) -> Result, Error> { - if operations.is_empty() { - return Ok(Vec::new()); - } - if operations.iter().any(|operation| operation.count.is_some()) - && self.major_version().await < 7 - { - let mut results = Vec::with_capacity(operations.len()); - for operation in &operations { - results.push(self.async_lpop(&operation.key, operation.count).await?); - } - return Ok(results); - } - let multiple = operations - .iter() - .map(|operation| operation.count.is_some()) - .collect::>(); - let mut pipeline = redis::pipe(); - for operation in operations { - pipeline.add_command(lpop(self.namespaced_key(&operation.key), operation.count)); - } - self.run(move |connection| connection.query_pipeline::>(&pipeline)) - .await? - .into_iter() - .zip(multiple) - .map(|(value, multiple)| lpop_result(value, multiple)) - .collect() - } -} - -fn lpop(key: String, count: Option) -> redis::Cmd { - let mut command = redis::cmd("LPOP"); - command.arg(key); - if let Some(count) = count { - command.arg(count); - } - command -} - -fn redis_bytes(value: redis::Value) -> Result, Error> { - match value { - redis::Value::BulkString(bytes) => Ok(bytes), - redis::Value::SimpleString(text) => Ok(text.into_bytes()), - _ => Err(Error::InvalidEntry(None)), - } -} - -impl RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - /// `handle_lpop_count_for_older_redis_versions`: `count` single-`LPOP` pipelines, keeping - /// only the values actually popped. - async fn lpop_one_at_a_time(&self, key: &str, count: usize) -> Result { - let key = self.namespaced_key(key); - let mut values = Vec::new(); - for _ in 0..count { - let mut pipeline = redis::pipe(); - pipeline.add_command(lpop(key.clone(), None)); - let replies = self - .run(move |connection| connection.query_pipeline::>(&pipeline)) - .await?; - for reply in replies { - if reply != redis::Value::Nil { - values.push(redis_bytes(reply)?); - } - } - } - Ok(RedisLpopResult::Values(values)) - } -} - -fn lpop_result(value: redis::Value, multiple: bool) -> Result { - match value { - redis::Value::Nil => Ok(RedisLpopResult::Missing), - redis::Value::Array(values) if multiple => values - .into_iter() - .map(redis_bytes) - .collect::, _>>() - .map(RedisLpopResult::Values), - value if !multiple => redis_bytes(value).map(RedisLpopResult::Value), - _ => Err(Error::InvalidEntry(None)), - } -} diff --git a/litellm-rust/crates/cache-redis/src/script.rs b/litellm-rust/crates/cache-redis/src/script.rs deleted file mode 100644 index 09987437ed4..00000000000 --- a/litellm-rust/crates/cache-redis/src/script.rs +++ /dev/null @@ -1,136 +0,0 @@ -use std::sync::Arc; - -use litellm_cache::{CacheCodec, CacheScript, Error, ScriptCache}; - -use crate::{ - cache::{RedisCache, namespaced_key}, - connection::Connections, -}; - -#[derive(Clone, Debug, PartialEq)] -pub enum RedisArg { - Bytes(Vec), - Integer(i64), - Float(f64), -} - -impl From<&str> for RedisArg { - fn from(value: &str) -> Self { - Self::Bytes(value.as_bytes().to_vec()) - } -} - -impl From for RedisArg { - fn from(value: String) -> Self { - Self::Bytes(value.into_bytes()) - } -} - -impl From> for RedisArg { - fn from(value: Vec) -> Self { - Self::Bytes(value) - } -} - -impl From for RedisArg { - fn from(value: i64) -> Self { - Self::Integer(value) - } -} - -impl From for RedisArg { - fn from(value: f64) -> Self { - Self::Float(value) - } -} - -impl redis::ToRedisArgs for RedisArg { - fn write_redis_args(&self, out: &mut W) - where - W: ?Sized + redis::RedisWrite, - { - match self { - Self::Bytes(value) => value.write_redis_args(out), - Self::Integer(value) => value.write_redis_args(out), - Self::Float(value) => value.write_redis_args(out), - } - } -} - -pub struct RedisScript { - connections: Arc>, - namespace: Option, - source: String, -} - -impl CacheScript for RedisScript -where - C: redis::ConnectionLike + Send + 'static, -{ - type Argument = RedisArg; - type Output = redis::Value; - - async fn invoke( - &self, - keys: Vec, - arguments: Vec, - ) -> Result { - let keys = keys - .into_iter() - .map(|key| namespaced_key(self.namespace.as_deref(), &key)) - .collect::>(); - let source = self.source.clone(); - Connections::run_blocking(Arc::clone(&self.connections), move |connection| { - eval(connection, &source, keys, arguments) - }) - .await - } -} - -impl RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - pub async fn async_eval( - &self, - script: String, - keys: Vec, - arguments: Vec, - ) -> Result { - let keys = self.namespaced_keys(&keys); - self.run(move |connection| eval(connection, &script, keys, arguments)) - .await - } -} - -fn eval( - connection: &mut impl redis::ConnectionLike, - script: &str, - keys: Vec, - arguments: Vec, -) -> Result { - redis::cmd("EVAL") - .arg(script) - .arg(keys.len()) - .arg(keys) - .arg(arguments) - .query(connection) - .map_err(Error::unavailable) -} - -impl ScriptCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type Script = RedisScript; - - fn async_register_script(&self, source: String) -> Self::Script { - RedisScript { - connections: Arc::clone(&self.connections), - namespace: self.namespace.clone(), - source, - } - } -} diff --git a/litellm-rust/crates/cache-redis/src/store.rs b/litellm-rust/crates/cache-redis/src/store.rs deleted file mode 100644 index daadc940ca9..00000000000 --- a/litellm-rust/crates/cache-redis/src/store.rs +++ /dev/null @@ -1,232 +0,0 @@ -use std::time::Duration; - -use litellm_cache::{ - BaseCache, BatchCache, BatchEntry, BulkDeleteCache, CacheCodec, DeleteCache, Error, - ExactCacheContext, FlushAllCache, FlushCache, TtlPipelineCache, -}; -use redis::Commands; - -use crate::{cache::RedisCache, connection::ConnectionRef}; - -impl BaseCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type Value = S::Value; - type Context = ExactCacheContext; - - fn get_ttl(&self, context: &Self::Context) -> Option { - context.ttl.or(Some(self.default_ttl)) - } - - fn set_cache( - &self, - key: &str, - value: Self::Value, - context: &ExactCacheContext, - ) -> Result<(), Error> { - let payload = self.codec.encode(&value)?; - let ttl = self.ttl_or_default(context.ttl); - let key = self.namespaced_key(key); - self.execute(|connection| { - connection - .set_ex::<_, _, ()>(key, payload, ttl) - .map_err(Error::unavailable) - }) - } - - fn get_cache(&self, key: &str, _: &ExactCacheContext) -> Result, Error> { - let key = self.namespaced_key(key); - let value = self.execute(|connection| { - connection - .get::<_, redis::Value>(key) - .map_err(Error::unavailable) - })?; - self.decode_response(value) - } - - async fn async_set_cache( - &self, - key: &str, - value: Self::Value, - context: ExactCacheContext, - ) -> Result<(), Error> { - let payload = self.codec.encode(&value)?; - let key = self.namespaced_key(key); - let ttl = self.ttl_or_default(context.ttl); - self.run(move |connection| { - connection - .set_ex::<_, _, ()>(key, payload, ttl) - .map_err(Error::unavailable) - }) - .await - } - - async fn async_get_cache( - &self, - key: &str, - _: &ExactCacheContext, - ) -> Result, Error> { - let key = self.namespaced_key(key); - let value = self - .run(move |connection| { - connection - .get::<_, redis::Value>(key) - .map_err(Error::unavailable) - }) - .await?; - self.decode_response(value) - } - - async fn async_set_cache_pipeline( - &self, - cache_list: Vec<(String, Self::Value)>, - context: ExactCacheContext, - ) -> Result<(), Error> { - self.async_set_cache_pipeline_with_ttls( - cache_list - .into_iter() - .map(|(key, value)| (key, value, context.ttl)) - .collect(), - ) - .await - } -} - -impl TtlPipelineCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn async_set_cache_pipeline_with_ttls( - &self, - entries: Vec<(String, Self::Value, Option)>, - ) -> Result<(), Error> { - if entries.is_empty() { - return Ok(()); - } - let mut pipeline = redis::pipe(); - for (key, value, ttl) in entries { - pipeline - .cmd("SETEX") - .arg(self.namespaced_key(&key)) - .arg(self.ttl_or_default(ttl)) - .arg(self.codec.encode(&value)?) - .ignore(); - } - self.run(move |connection| connection.query_pipeline(&pipeline)) - .await - } -} - -impl BatchCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn batch_get_cache( - &self, - keys: &[String], - _: &ExactCacheContext, - ) -> Result>, Error> { - let keys = self.namespaced_keys(keys); - self.execute(|connection| mget(connection, keys))? - .into_iter() - .map(|value| self.decode_batch_response(value)) - .collect() - } - - async fn async_batch_get_cache( - &self, - keys: Vec, - _: ExactCacheContext, - ) -> Result>, Error> { - let keys = self.namespaced_keys(&keys); - self.run(move |connection| mget(connection, keys)) - .await? - .into_iter() - .map(|value| self.decode_batch_response(value)) - .collect() - } -} - -pub(crate) fn mget( - connection: &mut ConnectionRef<'_>, - keys: Vec, -) -> Result, Error> { - redis::cmd("MGET") - .arg(keys) - .query(connection) - .map_err(Error::unavailable) -} - -impl DeleteCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn delete_cache(&self, key: &str) -> Result<(), Error> { - let key = self.namespaced_key(key); - self.execute(|connection| connection.del::<_, ()>(key).map_err(Error::unavailable)) - } - - async fn async_delete_cache(&self, key: &str) -> Result<(), Error> { - let key = self.namespaced_key(key); - self.run(move |connection| connection.del::<_, ()>(key).map_err(Error::unavailable)) - .await - } -} - -impl BulkDeleteCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - async fn delete_cache_keys(&self, keys: Vec) -> Result { - if keys.is_empty() { - return Ok(0); - } - let keys = self.namespaced_keys(&keys); - self.run(move |connection| connection.del(keys).map_err(Error::unavailable)) - .await - } -} - -impl FlushCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn flush_cache(&self) -> Result<(), Error> { - let pattern = self.namespaced_pattern()?; - self.execute(|connection| flush_matching(connection, &pattern)) - } - - async fn async_flush_cache(&self) -> Result<(), Error> { - let pattern = self.namespaced_pattern()?; - self.run(move |connection| flush_matching(connection, &pattern)) - .await - } -} - -impl FlushAllCache for RedisCache -where - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn flushall(&self) -> Result<(), Error> { - self.execute(|connection| connection.flushall()) - } -} - -fn flush_matching(connection: &mut ConnectionRef<'_>, pattern: &str) -> Result<(), Error> { - connection.scan(pattern, 1000, |connection, keys| { - if !keys.is_empty() { - connection - .del::<_, usize>(keys) - .map_err(Error::unavailable)?; - } - Ok(true) - }) -} diff --git a/litellm-rust/crates/cache-redis/src/topology.rs b/litellm-rust/crates/cache-redis/src/topology.rs deleted file mode 100644 index 7f4ee48b222..00000000000 --- a/litellm-rust/crates/cache-redis/src/topology.rs +++ /dev/null @@ -1,14 +0,0 @@ -#[derive(Clone, Debug, PartialEq, Eq)] -pub struct RedisNode { - pub host: String, - pub port: u16, -} - -#[derive(Clone, Debug, Default, PartialEq, Eq)] -pub enum RedisTopology { - #[default] - Standalone, - Cluster { - startup_nodes: Vec, - }, -} diff --git a/litellm-rust/crates/cache-redis/tests/cache.rs b/litellm-rust/crates/cache-redis/tests/cache.rs deleted file mode 100644 index 9ec85251733..00000000000 --- a/litellm-rust/crates/cache-redis/tests/cache.rs +++ /dev/null @@ -1,1164 +0,0 @@ -mod support; - -use std::time::Duration; - -use litellm_cache::{ - BaseCache, BatchCache, BatchEntry, BoundedCounterCache, BulkDeleteCache, CacheCodec, - CacheConnectionStatus, CacheScript, ClaimCache, ClientInfoCache, ConnectionCache, - CountReadCache, CounterCache, DeleteCache, DisconnectCache, Error, ExactCacheContext, - FlushAllCache, FlushCache, IncrementOperation, JsonCodec, PingCache, QueueCache, - RefreshTtlCache, ScanCache, ScriptCache, SetCache, TtlCache, TtlPipelineCache, get_cache, - set_cache, -}; -use litellm_cache_redis::{ - RedisArg, RedisCache, RedisLpopOperation, RedisLpopResult, RedisRpushOperation, -}; -use redis_test::{MockCmd, MockRedisConnection}; -use rstest::{fixture, rstest}; -use serde_json::json; -use support::TaggedByteCodec; - -type Mocked = RedisCache; - -fn mock(commands: Vec) -> MockRedisConnection { - MockRedisConnection::new(commands).assert_all_commands_consumed() -} - -fn tagged(commands: Vec) -> Mocked { - RedisCache::with_connection(mock(commands), None, TaggedByteCodec(42)) -} - -fn json_cache(commands: Vec) -> Mocked> { - RedisCache::with_connection(mock(commands), None, JsonCodec::new()) -} - -fn team(commands: Vec) -> Mocked> { - json_cache(commands).with_namespace(Some("team".into())) -} - -#[fixture] -fn context() -> ExactCacheContext { - ExactCacheContext::default() -} - -fn scan(pattern: &str, cursor: u64, count: usize, reply: redis::Value) -> MockCmd { - MockCmd::new( - redis::cmd("SCAN") - .cursor_arg(cursor) - .arg("MATCH") - .arg(pattern) - .arg("COUNT") - .arg(count), - Ok(reply), - ) -} - -#[rstest] -fn constructor_rejects_invalid_urls() { - assert!(RedisCache::new("not a redis url", None, JsonCodec::::new()).is_err()); -} - -#[rstest] -#[case::zero_rounds_up_to_one(Some(Duration::ZERO), 1)] -#[case::fractions_round_up(Some(Duration::from_millis(1500)), 2)] -#[case::whole_seconds_are_kept(Some(Duration::from_secs(15)), 15)] -#[case::missing_ttl_uses_default(None, 600)] -fn writes_round_ttls_up_to_positive_seconds(#[case] ttl: Option, #[case] seconds: u64) { - let cache = tagged(vec![MockCmd::new( - redis::cmd("SETEX") - .arg("key") - .arg(seconds) - .arg([42u8, 7].as_slice()), - Ok("OK"), - )]); - cache - .set_cache("key", 7, &ExactCacheContext { ttl }) - .unwrap(); -} - -#[rstest] -fn generic_helpers_use_the_injected_codec_and_ttl() { - let cache = tagged(vec![ - MockCmd::new( - redis::cmd("SETEX") - .arg("counter") - .arg(2) - .arg([42u8, 7].as_slice()), - Ok("OK"), - ), - MockCmd::new(redis::cmd("GET").arg("counter"), Ok(vec![42u8, 7])), - ]); - let context = ExactCacheContext { - ttl: Some(Duration::from_millis(1500)), - }; - set_cache(&cache, "counter", 7, &context).unwrap(); - assert_eq!(get_cache(&cache, "counter", &context).unwrap(), Some(7)); -} - -#[rstest] -fn commands_round_trip_entries_and_delete_only_namespaced_keys(context: ExactCacheContext) { - let value = json!({"deployment": "model-a", "cooldown_seconds": 30}); - let payload = JsonCodec::::new() - .encode(&value) - .unwrap(); - let cache = json_cache(vec![ - MockCmd::new( - redis::cmd("SETEX") - .arg("litellm-cache:key") - .arg(600) - .arg(payload.clone()), - Ok("OK"), - ), - MockCmd::new(redis::cmd("GET").arg("litellm-cache:key"), Ok(payload)), - MockCmd::new(redis::cmd("DEL").arg("litellm-cache:key"), Ok(1u32)), - ]) - .with_namespace(Some("litellm-cache".into())); - - cache.set_cache("key", value.clone(), &context).unwrap(); - assert_eq!(cache.get_cache("key", &context).unwrap(), Some(value)); - cache.delete_cache("key").unwrap(); -} - -#[rstest] -#[tokio::test] -async fn async_operations_preserve_codec_ttl_and_missing_values(context: ExactCacheContext) { - let cache = RedisCache::with_connection( - mock(vec![ - MockCmd::new( - redis::cmd("SETEX") - .arg("counter") - .arg(9) - .arg([42u8, 7].as_slice()), - Ok("OK"), - ), - MockCmd::new(redis::cmd("GET").arg("counter"), Ok(vec![42u8, 7])), - MockCmd::new( - redis::cmd("SETEX") - .arg("batch") - .arg(2) - .arg([42u8, 8].as_slice()), - Ok("OK"), - ), - MockCmd::new(redis::cmd("DEL").arg("counter"), Ok(1u32)), - MockCmd::new(redis::cmd("GET").arg("counter"), Ok(redis::Value::Nil)), - ]), - Some(Duration::from_secs(9)), - TaggedByteCodec(42), - ); - cache - .batch_cache_write("counter", 7, context.clone()) - .await - .unwrap(); - assert_eq!( - cache.async_get_cache("counter", &context).await.unwrap(), - Some(7) - ); - cache - .async_set_cache_pipeline( - vec![("batch".into(), 8)], - ExactCacheContext { - ttl: Some(Duration::from_millis(1500)), - }, - ) - .await - .unwrap(); - cache.async_delete_cache("counter").await.unwrap(); - assert_eq!( - cache.async_get_cache("counter", &context).await.unwrap(), - None - ); -} - -#[rstest] -#[tokio::test] -async fn codec_errors_propagate_without_writing_partial_batches(context: ExactCacheContext) { - let cache = tagged(vec![ - MockCmd::new(redis::cmd("GET").arg("invalid"), Ok(vec![99u8, 7])), - MockCmd::new(redis::cmd("GET").arg("invalid"), Ok(vec![99u8, 7])), - ]); - assert_eq!( - cache.set_cache("invalid", 255, &context), - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache.async_set_cache("invalid", 255, context.clone()).await, - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache - .async_set_cache_pipeline( - vec![("valid".into(), 7), ("invalid".into(), 255)], - context.clone(), - ) - .await, - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache - .async_set_cache_pipeline_with_ttls(vec![ - ("valid".into(), 7, None), - ("invalid".into(), 255, None), - ]) - .await, - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache.get_cache("invalid", &context), - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache.async_get_cache("invalid", &context).await, - Err(Error::InvalidEntry(None)) - ); -} - -#[rstest] -#[case::bare_key("key")] -#[case::already_prefixed("team:key")] -fn namespaces_are_added_once(#[case] key: &str, context: ExactCacheContext) { - let cache = team(vec![MockCmd::new( - redis::cmd("GET").arg("team:key"), - Ok(redis::Value::Nil), - )]); - assert_eq!(cache.get_cache(key, &context).unwrap(), None); -} - -#[rstest] -#[case::empty(Some(String::new()))] -#[case::missing(None)] -fn empty_namespaces_leave_keys_unprefixed( - #[case] namespace: Option, - context: ExactCacheContext, -) { - let cache = json_cache(vec![MockCmd::new( - redis::cmd("GET").arg("key"), - Ok(redis::Value::Nil), - )]) - .with_namespace(namespace); - assert_eq!(cache.namespace(), None); - assert_eq!(cache.get_cache("key", &context).unwrap(), None); -} - -#[rstest] -#[tokio::test] -async fn flush_requires_a_namespace() { - let unscoped = json_cache(Vec::new()); - assert_eq!(unscoped.flush_cache(), Err(Error::UnscopedFlush)); - assert_eq!( - unscoped.async_flush_cache().await, - Err(Error::UnscopedFlush) - ); -} - -#[rstest] -#[case::plain_namespace("litellm-cache", "litellm-cache:*", "litellm-cache:key")] -#[case::glob_metacharacters_are_escaped("team*", "team\\*:*", "team*:key")] -fn flush_scans_and_deletes_only_namespaced_keys( - #[case] namespace: &str, - #[case] pattern: &str, - #[case] key: &str, -) { - let cache = json_cache(vec![ - scan(pattern, 0, 1000, redis_test::redis_value!(["0", [key]])), - MockCmd::new(redis::cmd("DEL").arg(key), Ok(1u32)), - ]) - .with_namespace(Some(namespace.into())); - cache.flush_cache().unwrap(); -} - -#[rstest] -#[tokio::test] -async fn async_flush_deletes_each_scan_page_separately() { - let cache = team(vec![ - scan( - "team:*", - 0, - 1000, - redis_test::redis_value!(["7", ["team:a", "team:b"]]), - ), - MockCmd::new(redis::cmd("DEL").arg("team:a").arg("team:b"), Ok(2u32)), - scan( - "team:*", - 7, - 1000, - redis_test::redis_value!(["0", ["team:c"]]), - ), - MockCmd::new(redis::cmd("DEL").arg("team:c"), Ok(1u32)), - ]); - cache.async_flush_cache().await.unwrap(); -} - -#[rstest] -fn flushall_ignores_the_namespace() { - team(vec![MockCmd::new(redis::cmd("FLUSHALL"), Ok("OK"))]) - .flushall() - .unwrap(); -} - -#[rstest] -#[tokio::test] -async fn batch_reads_keep_order_and_treat_invalid_values_as_invalid_entries( - context: ExactCacheContext, -) { - let cache = tagged(vec![MockCmd::new( - redis::cmd("MGET").arg("hit").arg("miss").arg("invalid"), - Ok(vec![ - redis::Value::BulkString(vec![42, 7]), - redis::Value::Nil, - redis::Value::BulkString(vec![99, 7]), - ]), - )]); - - assert_eq!( - cache - .async_batch_get_cache(vec!["hit".into(), "miss".into(), "invalid".into()], context) - .await - .unwrap(), - vec![BatchEntry::Hit(7), BatchEntry::Miss, BatchEntry::Invalid] - ); -} - -#[rstest] -#[tokio::test] -async fn ttl_pipeline_keeps_each_entry_ttl_and_defaults_missing_ones() { - let mut pipeline = redis::pipe(); - pipeline - .cmd("SETEX") - .arg("ns:team_id:t1") - .arg(60u64) - .arg(r#"{"team_id":"t1"}"#) - .cmd("SETEX") - .arg("ns:u1") - .arg(7u64) - .arg(r#"{"user_id":"u1"}"#) - .cmd("SETEX") - .arg("ns:org_id:o1") - .arg(300u64) - .arg(r#"{"a":1}"#); - let cache = RedisCache::with_connection( - mock(vec![MockCmd::with_values( - pipeline, - Ok(vec!["OK", "OK", "OK"]), - )]), - Some(Duration::from_secs(300)), - JsonCodec::::new(), - ) - .with_namespace(Some("ns".into())); - - cache - .async_set_cache_pipeline_with_ttls(vec![ - ( - "team_id:t1".into(), - json!({"team_id": "t1"}), - Some(Duration::from_secs(60)), - ), - ( - "u1".into(), - json!({"user_id": "u1"}), - Some(Duration::from_secs(7)), - ), - ("org_id:o1".into(), json!({"a": 1}), None), - ]) - .await - .unwrap(); -} - -#[rstest] -#[tokio::test] -async fn empty_pipelines_skip_the_round_trip(context: ExactCacheContext) { - let cache = json_cache(Vec::new()); - cache - .async_set_cache_pipeline(Vec::new(), context) - .await - .unwrap(); - cache - .async_set_cache_pipeline_with_ttls(Vec::new()) - .await - .unwrap(); - assert_eq!(cache.delete_cache_keys(Vec::new()).await.unwrap(), 0); - assert_eq!( - cache.async_rpush_pipeline(Vec::new()).await.unwrap(), - Vec::::new() - ); - assert_eq!( - cache.async_lpop_pipeline(Vec::new()).await.unwrap(), - Vec::::new() - ); - assert_eq!( - cache.async_increment_pipeline(Vec::new()).await.unwrap(), - Vec::::new() - ); -} - -#[rstest] -#[tokio::test] -async fn count_reads_parse_integers_and_keep_missing_counters() { - let mget = || { - MockCmd::new( - redis::cmd("MGET").arg("team:count").arg("team:missing"), - Ok(redis_test::redis_value!(["7", nil])), - ) - }; - let cache = team(vec![mget(), mget()]); - let keys = vec!["count".to_string(), "missing".to_string()]; - - assert_eq!(cache.batch_get_counts(&keys).unwrap(), [Some(7), None]); - assert_eq!( - cache.async_batch_get_counts(keys).await.unwrap(), - [Some(7), None] - ); -} - -#[rstest] -#[tokio::test] -async fn pings_run_on_both_paths() { - let cache = team(vec![ - MockCmd::new(redis::cmd("PING"), Ok("PONG")), - MockCmd::new(redis::cmd("PING"), Ok("PONG")), - ]); - assert!(cache.sync_ping().unwrap()); - assert!(cache.ping().await.unwrap()); -} - -#[rstest] -#[case::remaining(12, Some(Duration::from_secs(12)))] -#[case::no_expiry(-1, None)] -#[case::missing(-2, None)] -#[tokio::test] -async fn ttl_reads_hide_negative_replies(#[case] reply: i64, #[case] ttl: Option) { - let cache = team(vec![MockCmd::new( - redis::cmd("TTL").arg("team:key"), - Ok(reply), - )]); - assert_eq!(cache.async_get_ttl("key").await.unwrap(), ttl); -} - -#[rstest] -#[case::explicit_ttl(Some(Duration::from_secs(30)), 30, 1, true)] -#[case::default_ttl(None, 600, 1, true)] -#[case::missing_key(Some(Duration::from_secs(30)), 30, 0, false)] -#[tokio::test] -async fn refresh_ttl_expires_existing_keys_only( - #[case] ttl: Option, - #[case] seconds: u64, - #[case] reply: i64, - #[case] refreshed: bool, -) { - let cache = team(vec![MockCmd::new( - redis::cmd("EXPIRE").arg("team:key").arg(seconds), - Ok(reply), - )]); - assert_eq!( - cache.async_refresh_ttl("key", ttl).await.unwrap(), - refreshed - ); -} - -#[rstest] -#[tokio::test] -async fn scan_stops_at_count_and_bulk_delete_reports_existing_keys() { - let cache = team(vec![ - scan( - "team:job-*", - 0, - 25, - redis_test::redis_value!(["4", ["team:job-a"]]), - ), - scan( - "team:job-*", - 4, - 25, - redis_test::redis_value!(["0", ["team:job-b"]]), - ), - MockCmd::new( - redis::cmd("DEL").arg("team:job-a").arg("team:job-b"), - Ok(2u32), - ), - ]); - assert_eq!( - cache.async_scan_iter("job-", 25).await.unwrap(), - ["team:job-a", "team:job-b"] - ); - assert_eq!( - cache - .delete_cache_keys(vec!["job-a".into(), "job-b".into()]) - .await - .unwrap(), - 2 - ); -} - -#[rstest] -#[tokio::test] -async fn sets_add_members_and_arm_the_default_ttl() { - let mut pipeline = redis::pipe(); - pipeline - .cmd("SADD") - .arg("team:members") - .arg("a") - .arg("b") - .cmd("EXPIRE") - .arg("team:members") - .arg(600u64) - .ignore(); - let cache = team(vec![MockCmd::with_values( - pipeline, - Ok(vec![redis::Value::Int(2), redis::Value::Int(1)]), - )]); - assert_eq!( - cache - .async_set_cache_sadd("members", vec!["a".into(), "b".into()], None) - .await - .unwrap(), - 2 - ); - assert_eq!( - cache - .async_set_cache_sadd("members", Vec::new(), None) - .await, - Err(Error::InvalidEntry(None)) - ); -} - -#[rstest] -#[tokio::test] -async fn queues_push_and_pop_namespaced_lists() { - let cache = team(vec![ - MockCmd::new( - redis::cmd("RPUSH").arg("team:queue").arg("a").arg("b"), - Ok(2u32), - ), - MockCmd::new( - redis::cmd("INFO"), - Ok("# Server\r\nredis_version:7.2.4\r\n"), - ), - MockCmd::new( - redis::cmd("LPOP").arg("team:queue").arg(2usize), - Ok(redis_test::redis_value!(["a", "b"])), - ), - MockCmd::new(redis::cmd("LPOP").arg("team:queue"), Ok("c")), - ]); - assert_eq!( - cache - .async_rpush("queue", vec!["a".into(), "b".into()]) - .await - .unwrap(), - 2 - ); - assert_eq!( - cache.async_rpush("queue", Vec::new()).await, - Err(Error::InvalidEntry(None)) - ); - assert_eq!( - cache.async_lpop("queue", Some(2)).await.unwrap(), - RedisLpopResult::Values(vec![b"a".to_vec(), b"b".to_vec()]) - ); - assert_eq!( - cache.async_lpop("queue", None).await.unwrap(), - RedisLpopResult::Value(b"c".to_vec()) - ); -} - -/// Python `RedisCache.async_lpop` checks `redis_version` from `INFO` and, below major version 7, -/// pops a counted batch as `count` single-command `LPOP` pipelines, dropping `None` replies. -#[rstest] -#[tokio::test] -async fn counted_lpop_falls_back_to_single_pops_below_redis_7() { - let single_pop = || { - let mut pipeline = redis::pipe(); - pipeline.cmd("LPOP").arg("team:queue"); - pipeline - }; - let cache = team(vec![ - MockCmd::new( - redis::cmd("INFO"), - Ok("# Server\r\nredis_version:6.2.14\r\n"), - ), - MockCmd::with_values(single_pop(), Ok(vec![redis_test::redis_value!("a")])), - MockCmd::with_values(single_pop(), Ok(vec![redis_test::redis_value!("b")])), - MockCmd::with_values(single_pop(), Ok(vec![redis::Value::Nil])), - ]); - - assert_eq!( - cache.async_lpop("queue", Some(3)).await.unwrap(), - RedisLpopResult::Values(vec![b"a".to_vec(), b"b".to_vec()]) - ); -} - -/// Python keeps `redis_version = "Unknown"` when `INFO` fails and then assumes -/// `DEFAULT_REDIS_MAJOR_VERSION` (7), so a counted pop is one `LPOP key count`. The version is read -/// once: the second pop sends no second `INFO`. -#[rstest] -#[tokio::test] -async fn counted_lpop_assumes_redis_7_when_info_fails() { - let cache = team(vec![ - MockCmd::new( - redis::cmd("INFO"), - Err::(redis::RedisError::from((redis::ErrorKind::Io, "down"))), - ), - MockCmd::new( - redis::cmd("LPOP").arg("team:queue").arg(2usize), - Ok(redis_test::redis_value!(["a", "b"])), - ), - MockCmd::new( - redis::cmd("LPOP").arg("team:queue").arg(1usize), - Ok(redis_test::redis_value!(["c"])), - ), - ]); - - assert_eq!( - cache.async_lpop("queue", Some(2)).await.unwrap(), - RedisLpopResult::Values(vec![b"a".to_vec(), b"b".to_vec()]) - ); - assert_eq!( - cache.async_lpop("queue", Some(1)).await.unwrap(), - RedisLpopResult::Values(vec![b"c".to_vec()]) - ); -} - -fn push_and_trim(start: i64) -> redis::Pipeline { - let mut pipeline = redis::pipe(); - pipeline - .atomic() - .cmd("RPUSH") - .arg("ns:buf") - .arg("c") - .arg("d") - .cmd("LTRIM") - .arg("ns:buf") - .arg(start) - .arg(-1); - pipeline -} - -#[rstest] -#[case::keeps_newest_entries(3, -3)] -#[case::zero_keeps_everything(0, 0)] -#[tokio::test] -async fn rpush_and_trim_runs_push_and_trim_in_one_transaction( - #[case] max_len: usize, - #[case] start: i64, -) { - let cache = json_cache(vec![MockCmd::with_values( - push_and_trim(start), - Ok(vec![redis::Value::Array(vec![ - redis::Value::Int(4), - redis::Value::Okay, - ])]), - )]) - .with_namespace(Some("ns".into())); - - assert_eq!( - cache - .async_rpush_and_trim("buf", vec!["c".into(), "d".into()], max_len) - .await - .unwrap(), - 4 - ); -} - -#[rstest] -#[tokio::test] -async fn rpush_and_trim_raises_when_a_queued_command_fails() { - let wrong_type = redis::parse_redis_value( - b"-WRONGTYPE Operation against a key holding the wrong kind of value\r\n", - ) - .unwrap(); - let cache = json_cache(vec![MockCmd::with_values( - push_and_trim(-3), - Ok(vec![redis::Value::Array(vec![ - wrong_type, - redis::Value::Okay, - ])]), - )]) - .with_namespace(Some("ns".into())); - - assert!(matches!( - cache - .async_rpush_and_trim("buf", vec!["c".into(), "d".into()], 3) - .await, - Err(Error::Unavailable(Some(_))) - )); - assert_eq!( - cache.async_rpush_and_trim("buf", Vec::new(), 3).await, - Err(Error::InvalidEntry(None)) - ); -} - -#[rstest] -#[tokio::test] -async fn scripts_and_eval_namespace_their_keys() { - let eval = || { - MockCmd::new( - redis::cmd("EVAL") - .arg("return KEYS[1]") - .arg(1usize) - .arg("team:key"), - Ok("team:key"), - ) - }; - let cache = team(vec![eval(), eval()]); - assert_eq!( - cache - .async_eval("return KEYS[1]".into(), vec!["key".into()], Vec::new()) - .await - .unwrap(), - redis::Value::BulkString(b"team:key".to_vec()) - ); - assert_eq!( - cache - .async_register_script("return KEYS[1]".into()) - .invoke(vec!["key".into()], Vec::new()) - .await - .unwrap(), - redis::Value::BulkString(b"team:key".to_vec()) - ); -} - -#[rstest] -fn client_list_and_info_return_server_text() { - let cache = team(vec![ - MockCmd::new(redis::cmd("CLIENT").arg("LIST"), Ok("id=1")), - MockCmd::new(redis::cmd("INFO"), Ok("redis_version:7")), - ]); - assert_eq!(cache.client_list().unwrap(), "id=1"); - assert_eq!(cache.info().unwrap(), "redis_version:7"); -} - -#[rstest] -#[tokio::test] -async fn pipelines_preserve_operation_order() { - let mut rpush_pipeline = redis::pipe(); - rpush_pipeline - .cmd("RPUSH") - .arg("team:a") - .arg("one") - .cmd("RPUSH") - .arg("team:b") - .arg("two"); - let mut lpop_pipeline = redis::pipe(); - lpop_pipeline - .cmd("LPOP") - .arg("team:a") - .arg(2usize) - .cmd("LPOP") - .arg("team:b"); - let queue = team(vec![ - MockCmd::with_values( - rpush_pipeline, - Ok(vec![redis::Value::Int(1), redis::Value::Int(2)]), - ), - MockCmd::new( - redis::cmd("INFO"), - Ok("# Server\r\nredis_version:7.2.4\r\n"), - ), - MockCmd::with_values( - lpop_pipeline, - Ok(vec![redis_test::redis_value!(["one"]), redis::Value::Nil]), - ), - ]); - - assert_eq!( - queue - .async_rpush_pipeline(vec![ - RedisRpushOperation { - key: "a".into(), - values: vec![RedisArg::from("one")], - }, - RedisRpushOperation { - key: "b".into(), - values: vec![RedisArg::from("two")], - }, - ]) - .await - .unwrap(), - [1, 2] - ); - assert_eq!( - queue - .async_lpop_pipeline(vec![ - RedisLpopOperation { - key: "a".into(), - count: Some(2), - }, - RedisLpopOperation { - key: "b".into(), - count: None, - }, - ]) - .await - .unwrap(), - [ - RedisLpopResult::Values(vec![b"one".to_vec()]), - RedisLpopResult::Missing, - ] - ); -} - -/// Below major version 7 a counted `LPOP` is unsupported, so a pipeline that mixes counted and -/// plain pops runs each operation through `async_lpop`: `count` single-`LPOP` pipelines for the -/// counted ones, a bare `LPOP` for the rest. No `LPOP key count` reaches the connection. -#[rstest] -#[tokio::test] -async fn lpop_pipeline_pops_one_at_a_time_below_redis_7() { - let single_pop = |key: &str| { - let mut pipeline = redis::pipe(); - pipeline.cmd("LPOP").arg(key); - pipeline - }; - let cache = team(vec![ - MockCmd::new( - redis::cmd("INFO"), - Ok("# Server\r\nredis_version:6.2.14\r\n"), - ), - MockCmd::with_values( - single_pop("team:a"), - Ok(vec![redis_test::redis_value!("one")]), - ), - MockCmd::with_values( - single_pop("team:a"), - Ok(vec![redis_test::redis_value!("two")]), - ), - MockCmd::new(redis::cmd("LPOP").arg("team:b"), Ok(redis::Value::Nil)), - ]); - - assert_eq!( - cache - .async_lpop_pipeline(vec![ - RedisLpopOperation { - key: "a".into(), - count: Some(2), - }, - RedisLpopOperation { - key: "b".into(), - count: None, - }, - ]) - .await - .unwrap(), - [ - RedisLpopResult::Values(vec![b"one".to_vec(), b"two".to_vec()]), - RedisLpopResult::Missing, - ] - ); -} - -#[rstest] -#[tokio::test] -async fn increment_pipeline_expires_only_operations_with_a_ttl() { - let mut increment_pipeline = redis::pipe(); - increment_pipeline - .cmd("INCRBYFLOAT") - .arg("team:counter") - .arg(1.5f64) - .cmd("EXPIRE") - .arg("team:counter") - .arg(10u64) - .ignore() - .cmd("INCRBYFLOAT") - .arg("team:counter") - .arg(2.0f64); - let counters = team(vec![MockCmd::with_values( - increment_pipeline, - Ok(vec![ - redis::Value::BulkString(b"1.5".to_vec()), - redis::Value::Int(1), - redis::Value::BulkString(b"3.5".to_vec()), - ]), - )]); - assert_eq!( - counters - .async_increment_pipeline(vec![ - IncrementOperation { - key: "counter".into(), - amount: 1.5, - ttl: Some(Duration::from_secs(10)), - }, - IncrementOperation { - key: "counter".into(), - amount: 2.0, - ttl: None, - }, - ]) - .await - .unwrap(), - [1.5, 3.5] - ); -} - -const INCREMENT_SCRIPT: &str = concat!( - "local value = redis.call('INCRBYFLOAT', KEYS[1], ARGV[1]); ", - "if redis.call('TTL', KEYS[1]) == -1 then ", - "redis.call('EXPIRE', KEYS[1], ARGV[2]); end; return value" -); -const INCREMENT_WITH_FLOOR_SCRIPT: &str = concat!( - "local count = redis.call('INCRBY', KEYS[1], ARGV[1]); ", - "if count < 0 then count = redis.call('INCRBY', KEYS[1], -count); end; ", - "if redis.call('TTL', KEYS[1]) < 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; ", - "return count" -); -const SET_MAX_SCRIPT: &str = concat!( - "local current = redis.call('GET', KEYS[1]); ", - "if current == false or tonumber(current) < tonumber(ARGV[1]) then ", - "redis.call('SET', KEYS[1], ARGV[1]); ", - "if tonumber(ARGV[2]) > 0 then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; ", - "return ARGV[1]; end; return current" -); - -fn increment_script(amount: f64) -> MockCmd { - MockCmd::new( - redis::cmd("EVAL") - .arg(INCREMENT_SCRIPT) - .arg(1) - .arg("counter") - .arg(amount) - .arg(600), - Ok("4.5"), - ) -} - -#[rstest] -#[tokio::test] -async fn increments_keep_an_existing_ttl_in_one_atomic_script(context: ExactCacheContext) { - let cache = RedisCache::with_connection( - mock(vec![increment_script(2.5), increment_script(2.5)]), - None, - JsonCodec::::new(), - ); - - assert_eq!( - cache - .increment_cache("counter", 2.5, context.clone()) - .unwrap(), - 4.5 - ); - assert_eq!( - cache - .async_increment("counter", 2.5, context, false) - .await - .unwrap(), - 4.5 - ); -} - -#[rstest] -#[case::explicit_ttl(Some(Duration::from_secs(60)), 60u64)] -#[case::default_ttl(None, 600u64)] -#[tokio::test] -async fn refresh_ttl_increments_rearm_the_ttl_in_the_same_round_trip( - #[case] ttl: Option, - #[case] seconds: u64, -) { - let mut pipeline = redis::pipe(); - pipeline - .cmd("INCRBYFLOAT") - .arg("ns:spend:key:k") - .arg(1.5f64) - .cmd("EXPIRE") - .arg("ns:spend:key:k") - .arg(seconds); - let cache = json_cache(vec![MockCmd::with_values( - pipeline, - Ok(vec![ - redis::Value::BulkString(b"1.5".to_vec()), - redis::Value::Int(1), - ]), - )]) - .with_namespace(Some("ns".into())); - - assert_eq!( - cache - .async_increment("spend:key:k", 1.5, ExactCacheContext { ttl }, true) - .await - .unwrap(), - 1.5 - ); -} - -#[rstest] -#[tokio::test] -async fn counter_repairs_are_atomic_and_use_default_ttl() { - let floor = || { - MockCmd::new( - redis::cmd("EVAL") - .arg(INCREMENT_WITH_FLOOR_SCRIPT) - .arg(1) - .arg("team:counter") - .arg(-2i64) - .arg(30u64), - Ok(0i64), - ) - }; - let cache = team(vec![ - floor(), - floor(), - MockCmd::new( - redis::cmd("EVAL") - .arg(SET_MAX_SCRIPT) - .arg(1) - .arg("team:counter") - .arg(4.5f64) - .arg(600u64), - Ok("4.5"), - ), - ]); - - assert_eq!( - cache - .increment_with_floor("counter", -2, Duration::from_secs(30)) - .unwrap(), - 0 - ); - assert_eq!( - cache - .async_increment_with_floor("counter", -2, Duration::from_secs(30)) - .await - .unwrap(), - 0 - ); - assert_eq!( - cache.async_set_max("counter", 4.5, None).await.unwrap(), - 4.5 - ); -} - -const CLAIM_SCRIPT: &str = concat!( - "local current = redis.call('GET', KEYS[1]); ", - "if ARGV[1] == '' then if current ~= false and current ~= '' then return 0; end; ", - "elseif current ~= ARGV[1] then return 0; end; ", - "if ARGV[3] ~= '' then redis.call('SET', KEYS[1], ARGV[3], 'EX', ARGV[2]); ", - "elseif ARGV[4] == '1' then redis.call('EXPIRE', KEYS[1], ARGV[2]); end; return 1" -); - -fn claim_eval(expected: &str, write: &str, refresh: bool, applied: i64) -> MockCmd { - MockCmd::new( - redis::cmd("EVAL") - .arg(CLAIM_SCRIPT) - .arg(1) - .arg("pin") - .arg(expected) - .arg(600) - .arg(write) - .arg(u8::from(refresh)), - Ok(applied), - ) -} - -#[rstest] -#[tokio::test] -async fn claims_match_eligible_values_written_by_another_encoder(context: ExactCacheContext) { - let python_payload = r#"{"model_id": "a", "deployment": "east"}"#; - let stored = json!({"deployment": "east", "model_id": "a"}); - let cache = json_cache(vec![ - MockCmd::new(redis::cmd("GET").arg("pin"), Ok(python_payload)), - claim_eval(python_payload, "", true, 1), - ]); - - assert_eq!( - cache - .async_claim_cache( - "pin", - json!({"model_id": "b"}), - vec![stored.clone()], - context - ) - .await - .unwrap(), - stored - ); -} - -#[rstest] -fn claims_retry_when_the_key_changes_and_replace_ineligible_winners(context: ExactCacheContext) { - let candidate = json!({"model_id": "b"}); - let payload = r#"{"model_id":"b"}"#; - let cache = json_cache(vec![ - MockCmd::new(redis::cmd("GET").arg("pin"), Ok(redis::Value::Nil)), - claim_eval("", payload, false, 0), - MockCmd::new(redis::cmd("GET").arg("pin"), Ok(r#"{"model_id":"gone"}"#)), - claim_eval(r#"{"model_id":"gone"}"#, payload, false, 1), - ]); - - assert_eq!( - cache - .claim_cache( - "pin", - candidate.clone(), - &[json!({"model_id": "a"})], - context - ) - .unwrap(), - candidate - ); -} - -#[rstest] -fn claims_without_eligible_values_keep_the_winner_without_refreshing_its_ttl( - context: ExactCacheContext, -) { - let stored = r#"{"model_id": "a"}"#; - let cache = json_cache(vec![ - MockCmd::new(redis::cmd("GET").arg("pin"), Ok(stored)), - claim_eval(stored, "", false, 1), - ]); - - assert_eq!( - cache - .claim_cache("pin", json!({"model_id": "b"}), &[], context) - .unwrap(), - json!({"model_id": "a"}) - ); -} - -#[rstest] -#[tokio::test] -async fn test_connection_reports_success_with_the_python_message() { - let cache = team(vec![MockCmd::new(redis::cmd("PING"), Ok("PONG"))]); - - let result = cache.test_connection().await.unwrap(); - assert_eq!(result.status, CacheConnectionStatus::Success); - assert_eq!(result.message, "Redis connection test successful"); - assert_eq!(result.error, None); -} - -#[rstest] -#[case::unexpected_reply(Ok("NOPE"), "Redis ping returned False", None)] -#[case::connection_refused( - Err(redis::RedisError::from((redis::ErrorKind::Io, "connection refused"))), - "Redis connection failed:", - Some("connection refused") -)] -#[tokio::test] -async fn test_connection_failures_use_the_python_result_contract( - #[case] reply: redis::RedisResult<&'static str>, - #[case] message: &str, - #[case] cause: Option<&str>, -) { - let cache = json_cache(vec![MockCmd::new(redis::cmd("PING"), reply)]); - - let result = cache.test_connection().await.unwrap(); - assert_eq!(result.status, CacheConnectionStatus::Failed); - assert!(result.message.starts_with(message), "{}", result.message); - assert_eq!(result.error.is_some(), cause.is_some()); - assert!(cause.is_none_or(|cause| { - result - .error - .as_deref() - .is_some_and(|error| error.contains(cause)) - })); -} - -#[rstest] -#[tokio::test] -async fn disconnect_keeps_a_caller_owned_connection_usable() { - let cache = team(vec![MockCmd::new(redis::cmd("PING"), Ok("PONG"))]); - cache.disconnect().await.unwrap(); - assert!(cache.ping().await.unwrap()); -} - -#[rstest] -#[tokio::test] -async fn disconnect_drains_an_idle_pool_without_connecting() { - let cache = RedisCache::new("redis://127.0.0.1:1", None, JsonCodec::::new()).unwrap(); - cache.disconnect().await.unwrap(); -} diff --git a/litellm-rust/crates/cache-redis/tests/cluster.rs b/litellm-rust/crates/cache-redis/tests/cluster.rs deleted file mode 100644 index e1ba01bcb04..00000000000 --- a/litellm-rust/crates/cache-redis/tests/cluster.rs +++ /dev/null @@ -1,563 +0,0 @@ -//! Tests against a real Redis Cluster. Set `LITELLM_TEST_REDIS_CLUSTER_NODES` to a comma -//! separated `host:port` list (for example `127.0.0.1:7000,127.0.0.1:7001`) to run them. - -mod support; - -use std::{collections::HashSet, time::Duration}; - -use litellm_cache::{ - BaseCache, BatchCache, BatchEntry, BoundedCounterCache, BulkDeleteCache, CacheConnectionStatus, - CacheScript, ClaimCache, ClientInfoCache, ConnectionCache, CounterCache, DeleteCache, - DisconnectCache, Error, ExactCacheContext, FlushCache, IncrementOperation, JsonCodec, - PingCache, QueueCache, RefreshTtlCache, ScanCache, ScriptCache, SetCache, TtlCache, - TtlPipelineCache, -}; -use litellm_cache_redis::{ - RedisArg, RedisCache, RedisLpopOperation, RedisLpopResult, RedisNode, RedisRpushOperation, - RedisTopology, -}; -use redis::cluster_routing::Slot; -use rstest::{fixture, rstest}; -use serde_json::json; -use support::{JsonCache, cluster_cache, cluster_url}; - -type Counter = RedisCache>; - -#[fixture] -fn cache(#[default("cache")] label: &str) -> Option { - cluster_cache(label, Duration::from_secs(120), JsonCodec::new()) -} - -#[fixture] -fn counter(#[default("counter")] label: &str) -> Option { - cluster_cache(label, Duration::from_secs(60), JsonCodec::new()) -} - -#[fixture] -fn context() -> ExactCacheContext { - ExactCacheContext::default() -} - -fn multi_slot_keys(count: usize) -> Vec { - let keys: Vec = (0..count).map(|index| format!("key-{index}")).collect(); - let slots: HashSet = keys.iter().map(Slot::for_key).collect(); - assert!(slots.len() > 1, "keys must span multiple slots"); - keys -} - -fn seconds(seconds: u64) -> Option { - Some(Duration::from_secs(seconds)) -} - -#[rstest] -#[case::no_startup_nodes("redis://127.0.0.1:7000", Vec::new())] -#[case::unix_socket_url( - "redis+unix:///tmp/redis.sock", - vec![RedisNode { host: "127.0.0.1".into(), port: 7000 }] -)] -fn constructor_rejects_unusable_cluster_configs( - #[case] url: &str, - #[case] startup_nodes: Vec, -) { - let error = JsonCache::connect( - url, - &RedisTopology::Cluster { startup_nodes }, - None, - JsonCodec::new(), - ) - .err(); - assert!(matches!(error, Some(Error::Unavailable(_)))); -} - -#[rstest] -#[tokio::test] -async fn single_key_operations_round_trip_with_ttl_rounding( - #[with("single")] cache: Option, -) { - let Some(cache) = cache else { return }; - let context = ExactCacheContext { - ttl: Some(Duration::from_millis(1500)), - }; - let keys = multi_slot_keys(12); - for (index, key) in keys.iter().enumerate() { - cache - .set_cache(key, json!({ "index": index }), &context) - .unwrap(); - } - for (index, key) in keys.iter().enumerate() { - assert_eq!( - cache.get_cache(key, &context).unwrap(), - Some(json!({ "index": index })) - ); - } - assert_eq!(cache.async_get_ttl(&keys[0]).await.unwrap(), seconds(2)); - assert!( - cache - .async_refresh_ttl(&keys[0], seconds(40)) - .await - .unwrap() - ); - assert_eq!(cache.async_get_ttl(&keys[0]).await.unwrap(), seconds(40)); - cache.delete_cache(&keys[0]).unwrap(); - assert_eq!(cache.get_cache(&keys[0], &context).unwrap(), None); - assert!(!cache.async_refresh_ttl(&keys[0], None).await.unwrap()); - assert!(cache.sync_ping().unwrap()); - cache.async_flush_cache().await.unwrap(); -} - -#[rstest] -#[tokio::test] -async fn batch_reads_span_slots_and_preserve_order_with_malformed_entries( - #[with("batch")] cache: Option, - context: ExactCacheContext, -) { - let Some(cache) = cache else { return }; - let keys = multi_slot_keys(40); - for (index, key) in keys.iter().enumerate() { - if index % 5 == 0 { - continue; - } - cache - .async_set_cache(key, json!(index), context.clone()) - .await - .unwrap(); - } - let mut raw = redis::cluster::ClusterClient::new(vec![cluster_url()]) - .unwrap() - .get_connection() - .unwrap(); - let malformed = format!("{}:{}", cache.namespace().unwrap(), keys[1]); - redis::cmd("SET") - .arg(&malformed) - .arg("not json") - .exec(&mut raw) - .unwrap(); - - let entries = cache - .async_batch_get_cache(keys.clone(), context.clone()) - .await - .unwrap(); - assert_eq!(entries.len(), keys.len()); - for (index, entry) in entries.iter().enumerate() { - let expected = if index == 1 { - BatchEntry::Invalid - } else if index % 5 == 0 { - BatchEntry::Miss - } else { - BatchEntry::Hit(json!(index)) - }; - assert_eq!(*entry, expected, "entry {index}"); - } - assert_eq!(cache.batch_get_cache(&keys, &context).unwrap(), entries); - - cache.delete_cache_keys(keys.clone()).await.unwrap(); - let entries = cache.async_batch_get_cache(keys, context).await.unwrap(); - assert!(entries.iter().all(|entry| *entry == BatchEntry::Miss)); -} - -#[rstest] -#[tokio::test] -async fn pipelines_group_by_slot_and_return_results_in_submission_order( - #[with("pipeline")] cache: Option, - counter: Option, - context: ExactCacheContext, -) { - let (Some(cache), Some(counter)) = (cache, counter) else { - return; - }; - let keys = multi_slot_keys(30); - cache - .async_set_cache_pipeline( - keys.iter() - .enumerate() - .map(|(index, key)| (key.clone(), json!(index))) - .collect(), - context.clone(), - ) - .await - .unwrap(); - let hits = cache - .async_batch_get_cache(keys.clone(), context.clone()) - .await - .unwrap(); - assert!( - hits.iter() - .enumerate() - .all(|(index, entry)| *entry == BatchEntry::Hit(json!(index))) - ); - - cache - .async_set_cache_pipeline_with_ttls( - keys.iter() - .enumerate() - .map(|(index, key)| (key.clone(), json!(index), seconds(index as u64 + 10))) - .collect(), - ) - .await - .unwrap(); - for (index, key) in keys.iter().enumerate() { - assert_eq!( - cache.async_get_ttl(key).await.unwrap(), - seconds(index as u64 + 10), - "{key}" - ); - } - - let queues: Vec = keys.iter().map(|key| format!("queue:{key}")).collect(); - let pushed = cache - .async_rpush_pipeline( - queues - .iter() - .enumerate() - .map(|(index, key)| RedisRpushOperation { - key: key.clone(), - values: (0..=index) - .map(|value| RedisArg::Integer(value as i64)) - .collect(), - }) - .collect(), - ) - .await - .unwrap(); - assert_eq!(pushed, (1..=keys.len()).collect::>()); - let popped = cache - .async_lpop_pipeline( - queues - .iter() - .enumerate() - .map(|(index, key)| RedisLpopOperation { - key: key.clone(), - count: (index % 2 == 0).then_some(2), - }) - .collect(), - ) - .await - .unwrap(); - for (index, result) in popped.into_iter().enumerate() { - match result { - RedisLpopResult::Value(value) => { - assert_eq!(index % 2, 1, "queue {index}"); - assert_eq!(value, b"0"); - } - RedisLpopResult::Values(values) => { - assert_eq!(index % 2, 0, "queue {index}"); - let expected: Vec> = (0..=index) - .take(2) - .map(|value| value.to_string().into_bytes()) - .collect(); - assert_eq!(values, expected); - } - other => panic!("queue {index}: {other:?}"), - } - } - - let counters: Vec = keys.iter().map(|key| format!("counter:{key}")).collect(); - let totals = counter - .async_increment_pipeline( - counters - .iter() - .enumerate() - .map(|(index, key)| IncrementOperation { - key: key.clone(), - amount: index as f64 + 0.5, - ttl: (index % 3 == 0).then_some(Duration::from_secs(30)), - }) - .collect(), - ) - .await - .unwrap(); - let expected: Vec = (0..keys.len()).map(|index| index as f64 + 0.5).collect(); - assert_eq!(totals, expected); - assert_eq!( - counter.async_get_ttl(&counters[0]).await.unwrap(), - seconds(30) - ); - assert_eq!(counter.async_get_ttl(&counters[1]).await.unwrap(), None); - counter.async_flush_cache().await.unwrap(); - cache.async_flush_cache().await.unwrap(); -} - -#[rstest] -#[tokio::test] -async fn rpush_and_trim_is_one_transaction_on_the_key_slot( - #[with("trim")] cache: Option, -) { - let Some(cache) = cache else { return }; - let values = |values: &[&str]| values.iter().map(|value| RedisArg::from(*value)).collect(); - assert_eq!( - cache - .async_rpush_and_trim("buf", values(&["a", "b"]), 3) - .await - .unwrap(), - 2 - ); - assert_eq!( - cache - .async_rpush_and_trim("buf", values(&["c", "d"]), 3) - .await - .unwrap(), - 4 - ); - assert_eq!( - cache.async_lpop("buf", Some(10)).await.unwrap(), - RedisLpopResult::Values(vec![b"b".to_vec(), b"c".to_vec(), b"d".to_vec()]) - ); - cache.async_flush_cache().await.unwrap(); -} - -#[rstest] -#[tokio::test] -async fn scan_and_scoped_flush_cover_every_primary( - #[with("flush")] cache: Option, - #[from(cache)] - #[with("other")] - other: Option, - context: ExactCacheContext, -) { - let (Some(cache), Some(other)) = (cache, other) else { - return; - }; - let keys = multi_slot_keys(60); - for key in &keys { - cache - .async_set_cache(key, json!(true), context.clone()) - .await - .unwrap(); - other - .async_set_cache(key, json!(true), context.clone()) - .await - .unwrap(); - } - let mut scanned = cache.async_scan_iter("key-", 1000).await.unwrap(); - scanned.sort(); - let mut expected: Vec = keys - .iter() - .map(|key| format!("{}:{key}", cache.namespace().unwrap())) - .collect(); - expected.sort(); - assert_eq!(scanned, expected); - assert_eq!(cache.async_scan_iter("key-", 7).await.unwrap().len(), 7); - - cache.flush_cache().unwrap(); - let flushed = cache - .async_batch_get_cache(keys.clone(), context.clone()) - .await - .unwrap(); - assert!(flushed.iter().all(|entry| *entry == BatchEntry::Miss)); - let kept = other.async_batch_get_cache(keys, context).await.unwrap(); - assert!( - kept.iter() - .all(|entry| *entry == BatchEntry::Hit(json!(true))) - ); - other.async_flush_cache().await.unwrap(); -} - -fn ping_calls_per_node(startup: &redis::Client) -> Vec<(String, u64)> { - let mut connection = startup.get_connection().unwrap(); - let nodes: String = redis::cmd("CLUSTER") - .arg("NODES") - .query(&mut connection) - .unwrap(); - let mut counts: Vec<(String, u64)> = nodes - .lines() - .map(|line| { - let address = line.split_whitespace().nth(1).unwrap(); - let address = address.split('@').next().unwrap(); - let mut node = redis::Client::open(format!("redis://{address}")) - .unwrap() - .get_connection() - .unwrap(); - let stats: String = redis::cmd("INFO") - .arg("commandstats") - .query(&mut node) - .unwrap(); - let calls = stats - .lines() - .find_map(|stat| stat.strip_prefix("cmdstat_ping:calls=")) - .and_then(|rest| rest.split(',').next()) - .map_or(0, |calls| calls.parse().unwrap()); - (address.to_string(), calls) - }) - .collect(); - counts.sort(); - counts -} - -#[rstest] -#[tokio::test] -async fn ping_reaches_every_node(#[with("ping")] cache: Option) { - let Some(cache) = cache else { return }; - let startup = redis::Client::open(cluster_url()).unwrap(); - let before = ping_calls_per_node(&startup); - assert!(before.len() >= 2, "{before:?}"); - assert!(cache.ping().await.unwrap()); - let after = ping_calls_per_node(&startup); - for ((node, calls_before), (_, calls_after)) in before.iter().zip(&after) { - assert!(calls_after > calls_before, "{node} was not pinged"); - } - assert!(cache.sync_ping().unwrap()); - let result = cache.test_connection().await.unwrap(); - assert_eq!(result.status, CacheConnectionStatus::Success); - assert_eq!(result.message, "Redis Cluster connection test successful"); -} - -#[rstest] -#[tokio::test] -async fn disconnect_closes_idle_connections_and_reconnects_on_demand( - #[with("disconnect")] cache: Option, -) { - let Some(cache) = cache else { return }; - assert!(cache.ping().await.unwrap()); - cache.disconnect().await.unwrap(); - assert!(cache.ping().await.unwrap()); -} - -#[rstest] -#[case::keep_existing_ttl(false)] -#[case::refresh_ttl(true)] -#[tokio::test] -async fn increments_refresh_the_ttl_only_when_asked( - counter: Option, - #[case] refresh_ttl: bool, -) { - let Some(counter) = counter else { return }; - let context = ExactCacheContext { ttl: seconds(60) }; - counter - .async_set_cache("spend", 0.0, ExactCacheContext { ttl: seconds(600) }) - .await - .unwrap(); - assert_eq!( - counter - .async_increment("spend", 1.5, context.clone(), refresh_ttl) - .await - .unwrap(), - 1.5 - ); - assert_eq!( - counter - .async_increment("spend", 2.0, context, refresh_ttl) - .await - .unwrap(), - 3.5 - ); - let ttl = counter.async_get_ttl("spend").await.unwrap().unwrap(); - assert_eq!(ttl <= Duration::from_secs(60), refresh_ttl, "{ttl:?}"); - counter.async_flush_cache().await.unwrap(); -} - -#[rstest] -#[tokio::test] -async fn counters_claims_scripts_and_sets_work_on_the_cluster( - counter: Option, - #[with("claim")] cache: Option, - context: ExactCacheContext, -) { - let (Some(counter), Some(cache)) = (counter, cache) else { - return; - }; - assert_eq!( - counter - .increment_cache("spend", 1.5, context.clone()) - .unwrap(), - 1.5 - ); - assert_eq!( - counter - .async_increment("spend", 2.0, context.clone(), false) - .await - .unwrap(), - 3.5 - ); - assert_eq!( - counter - .increment_with_floor("budget", -3, Duration::from_secs(30)) - .unwrap(), - 0 - ); - assert_eq!( - counter - .async_increment_with_floor("budget", 7, Duration::from_secs(30)) - .await - .unwrap(), - 7 - ); - assert_eq!(counter.async_set_max("peak", 4.0, None).await.unwrap(), 4.0); - assert_eq!(counter.async_set_max("peak", 2.0, None).await.unwrap(), 4.0); - counter.flush_cache().unwrap(); - - let owner = json!("owner-a"); - let rival = json!("owner-b"); - assert_eq!( - cache - .claim_cache("lock", owner.clone(), &[], context.clone()) - .unwrap(), - owner - ); - assert_eq!( - cache - .async_claim_cache("lock", rival.clone(), vec![owner.clone()], context.clone()) - .await - .unwrap(), - owner - ); - assert_eq!( - cache - .claim_cache("lock", rival.clone(), &[], context.clone()) - .unwrap(), - owner - ); - assert_eq!( - cache - .async_claim_cache("lock", rival.clone(), vec![rival.clone()], context.clone()) - .await - .unwrap(), - rival - ); - - let script = cache - .async_register_script("return redis.call('SET', KEYS[1], ARGV[1], 'EX', ARGV[2])".into()); - let reply = script - .invoke( - vec!["scripted".into()], - vec![RedisArg::Bytes(b"payload".to_vec()), RedisArg::Integer(5)], - ) - .await - .unwrap(); - assert_eq!(reply, redis::Value::Okay); - assert_eq!(cache.async_get_ttl("scripted").await.unwrap(), seconds(5)); - let evaluated = cache - .async_eval( - "return redis.call('GET', KEYS[1])".into(), - vec!["scripted".into()], - Vec::new(), - ) - .await - .unwrap(); - assert_eq!(evaluated, redis::Value::BulkString(b"payload".to_vec())); - - assert_eq!( - cache - .async_set_cache_sadd( - "members", - vec![ - RedisArg::Bytes(b"a".to_vec()), - RedisArg::Bytes(b"b".to_vec()) - ], - seconds(9), - ) - .await - .unwrap(), - 2 - ); - assert_eq!(cache.async_get_ttl("members").await.unwrap(), seconds(9)); - - let result = cache.test_connection().await.unwrap(); - assert_eq!(result.status, CacheConnectionStatus::Success); - assert!(cache.ping().await.unwrap()); - let info = cache.info().unwrap(); - assert!(info.matches("redis_version").count() > 1, "{info}"); - assert!(cache.client_list().unwrap().contains("id=")); - cache.async_flush_cache().await.unwrap(); - assert_eq!(cache.async_get_ttl("members").await.unwrap(), None); - assert_eq!(cache.get_cache("lock", &context).unwrap(), None); -} diff --git a/litellm-rust/crates/cache-redis/tests/contract.rs b/litellm-rust/crates/cache-redis/tests/contract.rs deleted file mode 100644 index 85d043b90b2..00000000000 --- a/litellm-rust/crates/cache-redis/tests/contract.rs +++ /dev/null @@ -1,95 +0,0 @@ -//! The shared cache contracts, run against the in-process fake connection and, when -//! `LITELLM_TEST_REDIS_CLUSTER_NODES` is set, against a live Redis Cluster. - -mod support; - -use std::time::Duration; - -use litellm_cache::{ExactCacheContext, JsonCodec}; -use litellm_cache_testing as contract; -use rstest::rstest; -use serde_json::json; -use support::{JsonCache, cluster_cache, fake_cache}; - -const PREFIX: &str = "contract:"; - -#[derive(Clone, Copy, Debug)] -enum Contract { - HitAndMiss, - SyncAsyncEquivalence, - OverwriteReplaces, - PipelineWritesEveryEntry, - BatchPreservesOrder, - DeleteRemovesKey, - FlushClears, - CounterAccumulates, -} - -#[derive(Clone, Copy, Debug)] -enum Server { - Fake, - Cluster, -} - -async fn check(contract: Contract, cache: &JsonCache) -where - C: redis::ConnectionLike + Send + 'static, -{ - let context = ExactCacheContext::default(); - match contract { - Contract::HitAndMiss => { - contract::hit_and_miss(cache, context, PREFIX, json!({"answer": 42})).await - } - Contract::SyncAsyncEquivalence => { - contract::sync_async_equivalence(cache, context, PREFIX, json!("first"), json!([2])) - .await - } - Contract::OverwriteReplaces => { - contract::overwrite_replaces(cache, context, PREFIX, json!(1), json!({"b": 2})).await - } - Contract::PipelineWritesEveryEntry => { - contract::pipeline_writes_every_entry( - cache, - context, - PREFIX, - vec![json!("a"), json!(2), json!({"c": true})], - ) - .await - } - Contract::BatchPreservesOrder => { - contract::batch_preserves_order(cache, context, PREFIX, json!("first"), json!(2)).await - } - Contract::DeleteRemovesKey => { - contract::delete_removes_key(cache, context, PREFIX, json!("value")).await - } - Contract::FlushClears => { - contract::flush_clears(cache, context, PREFIX, json!("value")).await - } - Contract::CounterAccumulates => contract::counter_accumulates(cache, context, PREFIX).await, - } -} - -#[rstest] -#[case::hit_and_miss(Contract::HitAndMiss)] -#[case::sync_async_equivalence(Contract::SyncAsyncEquivalence)] -#[case::overwrite_replaces(Contract::OverwriteReplaces)] -#[case::pipeline_writes_every_entry(Contract::PipelineWritesEveryEntry)] -#[case::batch_preserves_order(Contract::BatchPreservesOrder)] -#[case::delete_removes_key(Contract::DeleteRemovesKey)] -#[case::flush_clears(Contract::FlushClears)] -#[case::counter_accumulates(Contract::CounterAccumulates)] -#[tokio::test] -async fn redis_satisfies_the_cache_contract( - #[case] contract: Contract, - #[values(Server::Fake, Server::Cluster)] server: Server, -) { - match server { - Server::Fake => check(contract, &fake_cache("contract")).await, - Server::Cluster => { - let label = format!("{contract:?}"); - if let Some(cache) = cluster_cache(&label, Duration::from_secs(120), JsonCodec::new()) { - check(contract, &cache).await; - } - } - } -} diff --git a/litellm-rust/crates/cache-redis/tests/support/mod.rs b/litellm-rust/crates/cache-redis/tests/support/mod.rs deleted file mode 100644 index b04e1c29f3b..00000000000 --- a/litellm-rust/crates/cache-redis/tests/support/mod.rs +++ /dev/null @@ -1,231 +0,0 @@ -#![allow(dead_code)] - -use std::{ - collections::BTreeMap, - time::{Duration, SystemTime, UNIX_EPOCH}, -}; - -use litellm_cache::{CacheCodec, Error, JsonCodec}; -use litellm_cache_redis::{RedisCache, RedisNode, RedisTopology}; - -/// Encodes a byte behind a tag, so a value written with another tag decodes as invalid. -pub struct TaggedByteCodec(pub u8); - -impl CacheCodec for TaggedByteCodec { - type Value = u8; - - fn encode(&self, value: &u8) -> Result, Error> { - if *value > 127 { - return Err(Error::InvalidEntry(None)); - } - Ok(vec![self.0, *value]) - } - - fn decode(&self, bytes: &[u8]) -> Result { - match bytes { - [tag, value] if *tag == self.0 => Ok(*value), - _ => Err(Error::InvalidEntry(None)), - } - } -} - -/// A stateful in-process stand-in for a Redis server that understands the string commands the -/// shared contracts exercise, so they run without a live server. TTLs are accepted and ignored. -#[derive(Default)] -pub struct FakeRedis { - strings: BTreeMap, Vec>, -} - -impl FakeRedis { - fn run(&mut self, command: Vec>) -> redis::RedisResult { - let name = String::from_utf8_lossy(&command[0]).to_ascii_uppercase(); - let args = &command[1..]; - Ok(match name.as_str() { - "PING" => redis::Value::SimpleString("PONG".into()), - "SET" | "SETEX" => { - let value = if name == "SET" { &args[1] } else { &args[2] }; - self.strings.insert(args[0].clone(), value.clone()); - redis::Value::Okay - } - "GET" => self.get(&args[0]), - "MGET" => redis::Value::Array(args.iter().map(|key| self.get(key)).collect()), - "DEL" => { - let removed = args - .iter() - .filter(|key| self.strings.remove(*key).is_some()) - .count(); - redis::Value::Int(removed as i64) - } - "SCAN" => { - let pattern = &args[2]; - let keys = self - .strings - .keys() - .filter(|key| glob(pattern, key)) - .map(|key| redis::Value::BulkString(key.clone())) - .collect(); - redis::Value::Array(vec![ - redis::Value::BulkString(b"0".to_vec()), - redis::Value::Array(keys), - ]) - } - "EVAL" if args[0].windows(11).any(|window| window == b"INCRBYFLOAT") => { - self.increment_by_float(&args[2], &args[3]) - } - "INCRBYFLOAT" => self.increment_by_float(&args[0], &args[1]), - _ => { - return Err(redis::RedisError::from(( - redis::ErrorKind::Client, - "unsupported command", - name, - ))); - } - }) - } - - fn get(&self, key: &[u8]) -> redis::Value { - self.strings.get(key).map_or(redis::Value::Nil, |value| { - redis::Value::BulkString(value.clone()) - }) - } - - fn increment_by_float(&mut self, key: &[u8], amount: &[u8]) -> redis::Value { - let current = self - .strings - .get(key) - .map_or(0.0, |value| parse_float(value)); - let total = format!("{}", current + parse_float(amount)); - self.strings - .insert(key.to_vec(), total.clone().into_bytes()); - redis::Value::BulkString(total.into_bytes()) - } -} - -fn parse_float(bytes: &[u8]) -> f64 { - std::str::from_utf8(bytes).unwrap().parse().unwrap() -} - -/// Redis `MATCH` globbing for `*`, `?` and backslash escapes. -fn glob(pattern: &[u8], key: &[u8]) -> bool { - match pattern.split_first() { - None => key.is_empty(), - Some((b'*', rest)) => (0..=key.len()).any(|skip| glob(rest, &key[skip..])), - Some((b'?', rest)) => !key.is_empty() && glob(rest, &key[1..]), - Some((b'\\', [escaped, rest @ ..])) => { - key.first() == Some(escaped) && glob(rest, &key[1..]) - } - Some((literal, rest)) => key.first() == Some(literal) && glob(rest, &key[1..]), - } -} - -/// Splits RESP request bytes into the commands they carry. -fn commands(mut bytes: &[u8]) -> Vec>> { - fn line<'a>(bytes: &mut &'a [u8]) -> &'a [u8] { - let end = bytes - .windows(2) - .position(|window| window == b"\r\n") - .unwrap(); - let (line, rest) = bytes.split_at(end); - *bytes = &rest[2..]; - line - } - fn length(line: &[u8]) -> usize { - std::str::from_utf8(&line[1..]).unwrap().parse().unwrap() - } - let mut commands = Vec::new(); - while !bytes.is_empty() { - let count = length(line(&mut bytes)); - let command = (0..count) - .map(|_| { - let size = length(line(&mut bytes)); - let (argument, rest) = bytes.split_at(size); - bytes = &rest[2..]; - argument.to_vec() - }) - .collect(); - commands.push(command); - } - commands -} - -impl redis::ConnectionLike for FakeRedis { - fn req_packed_command(&mut self, cmd: &[u8]) -> redis::RedisResult { - let command = commands(cmd).into_iter().next().unwrap(); - self.run(command) - } - - fn req_packed_commands( - &mut self, - cmd: &[u8], - offset: usize, - count: usize, - ) -> redis::RedisResult> { - let replies = commands(cmd) - .into_iter() - .map(|command| self.run(command)) - .collect::>>()?; - Ok(replies.into_iter().skip(offset).take(count).collect()) - } - - fn get_db(&self) -> i64 { - 0 - } - - fn check_connection(&mut self) -> bool { - true - } - - fn is_open(&self) -> bool { - true - } -} - -pub type JsonCache = RedisCache, C>; - -pub fn fake_cache(namespace: &str) -> JsonCache { - RedisCache::with_connection(FakeRedis::default(), None, JsonCodec::new()) - .with_namespace(Some(namespace.into())) -} - -/// Startup nodes from `LITELLM_TEST_REDIS_CLUSTER_NODES` (`host:port,host:port`); tests that -/// need a live cluster skip when it is unset. -pub fn cluster_topology() -> Option { - let nodes = std::env::var("LITELLM_TEST_REDIS_CLUSTER_NODES").ok()?; - let startup_nodes = nodes - .split(',') - .map(|node| { - let (host, port) = node.trim().rsplit_once(':').expect("host:port"); - RedisNode { - host: host.to_string(), - port: port.parse().expect("port"), - } - }) - .collect(); - Some(RedisTopology::Cluster { startup_nodes }) -} - -pub fn cluster_url() -> String { - std::env::var("LITELLM_TEST_REDIS_CLUSTER_URL") - .unwrap_or_else(|_| "redis://127.0.0.1:7000".into()) -} - -pub fn unique_namespace(label: &str) -> String { - let nanos = SystemTime::now() - .duration_since(UNIX_EPOCH) - .unwrap() - .as_nanos(); - format!("cluster-test:{label}:{nanos}") -} - -pub fn cluster_cache( - label: &str, - default_ttl: Duration, - codec: S, -) -> Option> { - let topology = cluster_topology()?; - Some( - RedisCache::connect(&cluster_url(), &topology, Some(default_ttl), codec) - .expect("cluster connection") - .with_namespace(Some(unique_namespace(label))), - ) -} diff --git a/litellm-rust/crates/cache-response/Cargo.toml b/litellm-rust/crates/cache-response/Cargo.toml index 8f4570edc6f..46ef6ab92a8 100644 --- a/litellm-rust/crates/cache-response/Cargo.toml +++ b/litellm-rust/crates/cache-response/Cargo.toml @@ -16,9 +16,6 @@ sha2.workspace = true litellm-cache-gcs.workspace = true litellm-http = { workspace = true, features = ["test-support"] } litellm-cache-memory.workspace = true -litellm-cache-redis.workspace = true -redis.workspace = true -redis-test.workspace = true rstest.workspace = true tokio.workspace = true wiremock.workspace = true diff --git a/litellm-rust/crates/cache-response/tests/connection.rs b/litellm-rust/crates/cache-response/tests/connection.rs index bc24cf56846..f64057f6f1c 100644 --- a/litellm-rust/crates/cache-response/tests/connection.rs +++ b/litellm-rust/crates/cache-response/tests/connection.rs @@ -2,79 +2,11 @@ mod support; use std::{sync::Arc, time::Duration}; -use litellm_cache::CacheConnectionStatus; use litellm_cache_memory::InMemoryCache; -use litellm_cache_response::{ - CacheEntry, ConnectionProbe, ExactResponseCache, ResponseCache, ResponseCacheRequest, -}; -use redis_test::MockCmd; +use litellm_cache_response::{CacheEntry, ExactResponseCache, ResponseCache, ResponseCacheRequest}; use rstest::rstest; use serde_json::json; -use support::{keyed, memory, redis, request}; - -#[rstest] -#[case::reachable( - Ok("PONG"), - CacheConnectionStatus::Success, - "Redis connection test successful", - false -)] -#[case::unexpected_reply( - Ok("NOPE"), - CacheConnectionStatus::Failed, - "Redis ping returned False", - false -)] -#[case::connection_refused( - Err(redis::RedisError::from((redis::ErrorKind::Io, "connection refused"))), - CacheConnectionStatus::Failed, - "Redis connection failed:", - true -)] -#[tokio::test] -async fn connection_backends_are_reachable_as_a_probe( - #[case] reply: redis::RedisResult<&'static str>, - #[case] status: CacheConnectionStatus, - #[case] message: &str, - #[case] has_error: bool, -) { - let probe: Arc = - Arc::new(redis(vec![MockCmd::new(redis::cmd("PING"), reply)], None)); - - let result = probe.test_connection().await.unwrap(); - assert_eq!(result.status, status); - assert!(result.message.starts_with(message), "{}", result.message); - assert_eq!(result.error.is_some(), has_error); -} - -#[rstest] -#[tokio::test] -async fn one_service_serves_both_the_exact_cache_and_its_probe(request: ResponseCacheRequest) { - let service = Arc::new(redis( - vec![ - MockCmd::new(redis::cmd("PING"), Ok("PONG")), - MockCmd::new( - redis::cmd("GET").arg("tenant:key"), - Ok(br#"{"timestamp":100.0,"response":{"ok":true}}"#.to_vec()), - ), - ], - Some("tenant"), - )); - let probe: Arc = service.clone(); - let exact: Arc = service; - - assert_eq!( - probe.test_connection().await.unwrap().status, - CacheConnectionStatus::Success - ); - assert_eq!( - exact - .async_lookup(&request, Duration::from_secs(100)) - .await - .unwrap(), - Some(json!({"ok": true})) - ); -} +use support::{keyed, memory, request}; /// The in-memory backend has no `test_connection`, as in Python, and still serves every response /// operation. diff --git a/litellm-rust/crates/cache-response/tests/response.rs b/litellm-rust/crates/cache-response/tests/response.rs index 70ae259d33a..659ba065cf2 100644 --- a/litellm-rust/crates/cache-response/tests/response.rs +++ b/litellm-rust/crates/cache-response/tests/response.rs @@ -17,10 +17,9 @@ use litellm_cache_response::{ CacheControls, CacheEntry, CacheKeyField, CacheKeyInput, ResponseCache, ResponseCacheRequest, WriteBuffer, cache_key, }; -use redis_test::MockCmd; use rstest::{fixture, rstest}; use serde_json::{Value, json}; -use support::{keyed, memory, redis, request}; +use support::{keyed, memory, request}; type Memory = Arc>>; @@ -331,79 +330,6 @@ async fn directives_skip_io_and_keep_reads_and_writes_independent( ); } -#[rstest] -#[case::python_sync_literal( - br#"{'timestamp': 100.0, 'response': '{"ok": true, "text": "cached"}'}"#.as_slice() -)] -#[case::python_async_json(br#"{"timestamp":100.0,"response":{"ok":true,"text":"cached"}}"#.as_slice())] -#[tokio::test] -async fn redis_consumer_reads_python_sync_and_async_envelopes( - request: ResponseCacheRequest, - #[case] stored: &[u8], - #[values(false, true)] asynchronous: bool, -) { - let cache = redis( - vec![MockCmd::new( - redis::cmd("GET").arg("tenant:key"), - Ok(stored.to_vec()), - )], - Some("tenant"), - ); - let now = Duration::from_secs(101); - let found = if asynchronous { - cache.async_lookup(&request, now).await.unwrap() - } else { - cache.lookup(&request, now).unwrap() - }; - assert_eq!(found, Some(json!({"ok": true, "text": "cached"}))); -} - -#[rstest] -#[case::object( - json!({"ok": true, "text": "cached"}), - br#"{"timestamp":100.0,"response":{"ok":true,"text":"cached"}}"#.as_slice() -)] -#[case::array(json!([1, 2]), br#"{"timestamp":100.0,"response":"[1,2]"}"#.as_slice())] -#[tokio::test] -async fn redis_consumer_writes_python_compatible_json( - request: ResponseCacheRequest, - #[case] response: Value, - #[case] wire: &[u8], -) { - let cache = redis( - vec![MockCmd::new( - redis::cmd("SETEX").arg("tenant:key").arg(600).arg(wire), - Ok("OK"), - )], - Some("tenant"), - ); - cache - .async_store(&request, response, Duration::from_secs(100)) - .await - .unwrap(); -} - -#[rstest] -#[tokio::test] -async fn invalid_entries_are_misses_and_disabled_reads_do_not_touch_redis( - mut request: ResponseCacheRequest, -) { - let cache = redis( - vec![MockCmd::new( - redis::cmd("GET").arg("tenant:key"), - Ok(b"invalid".to_vec()), - )], - None, - ); - request.controls.no_cache = true; - assert_eq!(cache.lookup(&request, Duration::ZERO).unwrap(), None); - request.controls.no_cache = false; - assert_eq!( - cache.async_lookup(&request, Duration::ZERO).await.unwrap(), - None - ); -} - #[rstest] #[tokio::test] async fn captured_service_keeps_the_selected_backend_for_background_writes( @@ -541,27 +467,6 @@ async fn batch_lookup_reports_partial_hits_and_batch_store_populates_misses( assert_eq!(memory.lookup(&requests[2], now).unwrap(), None); } -#[rstest] -#[tokio::test] -async fn batch_lookup_with_no_readable_request_skips_the_backend( - #[values(false, true)] asynchronous: bool, -) { - let cache = redis(Vec::new(), None); - let mut request = keyed("key"); - request.controls.no_cache = true; - let requests = [request.clone(), request]; - let partial = if asynchronous { - cache - .async_lookup_batch(&requests, Duration::ZERO) - .await - .unwrap() - } else { - cache.lookup_batch(&requests, Duration::ZERO).unwrap() - }; - assert_eq!(partial.values, vec![None, None]); - assert_eq!(partial.missing_indices, vec![0, 1]); -} - #[rstest] #[tokio::test] async fn deferred_entries_keep_the_time_they_were_produced( diff --git a/litellm-rust/crates/cache-response/tests/support/mod.rs b/litellm-rust/crates/cache-response/tests/support/mod.rs index b992a937b70..f84da303659 100644 --- a/litellm-rust/crates/cache-response/tests/support/mod.rs +++ b/litellm-rust/crates/cache-response/tests/support/mod.rs @@ -1,15 +1,9 @@ use std::{sync::Arc, time::Duration}; use litellm_cache_memory::InMemoryCache; -use litellm_cache_redis::RedisCache; -use litellm_cache_response::{ - CacheEntry, CacheKeyInput, ResponseCache, ResponseCacheCodec, ResponseCacheRequest, -}; -use redis_test::{MockCmd, MockRedisConnection}; +use litellm_cache_response::{CacheEntry, CacheKeyInput, ResponseCache, ResponseCacheRequest}; use rstest::fixture; -pub type MockedRedis = RedisCache; - #[fixture] pub fn memory() -> Arc>> { Arc::new(ResponseCache::new(Arc::new(InMemoryCache::new( @@ -29,12 +23,3 @@ pub fn keyed(key: &str) -> ResponseCacheRequest { ..Default::default() }) } - -/// A Redis response cache that must receive exactly `commands`, in order. -pub fn redis(commands: Vec, namespace: Option<&str>) -> ResponseCache { - let connection = MockRedisConnection::new(commands).assert_all_commands_consumed(); - ResponseCache::new(Arc::new( - RedisCache::with_connection(connection, None, ResponseCacheCodec) - .with_namespace(namespace.map(str::to_owned)), - )) -} diff --git a/litellm-rust/crates/cache-s3/src/auth.rs b/litellm-rust/crates/cache-s3/src/auth.rs index 659390a5659..02707c24881 100644 --- a/litellm-rust/crates/cache-s3/src/auth.rs +++ b/litellm-rust/crates/cache-s3/src/auth.rs @@ -36,9 +36,12 @@ impl ProvideCredentials for S3Credentials { self.config.secret_access_key.clone(), ) { return Ok(AwsCredentials::new( - access_key_id, - secret_access_key, - self.config.session_token.clone(), + access_key_id.expose(), + secret_access_key.expose(), + self.config + .session_token + .as_ref() + .map(|value| value.expose().to_owned()), None, "litellm-s3-cache", )); diff --git a/litellm-rust/crates/cache-s3/tests/auth.rs b/litellm-rust/crates/cache-s3/tests/auth.rs index 59c5aa2e091..5aa251ac749 100644 --- a/litellm-rust/crates/cache-s3/tests/auth.rs +++ b/litellm-rust/crates/cache-s3/tests/auth.rs @@ -5,9 +5,9 @@ use rstest::rstest; fn explicit(session_token: Option<&str>) -> AwsAuthConfig { AwsAuthConfig { - access_key_id: Some("key".to_string()), - secret_access_key: Some("secret".to_string()), - session_token: session_token.map(str::to_string), + access_key_id: Some("key".into()), + secret_access_key: Some("secret".into()), + session_token: session_token.map(Into::into), region_name: Some("us-east-1".to_string()), ..Default::default() } diff --git a/litellm-rust/crates/cache-s3/tests/support/mod.rs b/litellm-rust/crates/cache-s3/tests/support/mod.rs index 7378dde21bf..311d2402e72 100644 --- a/litellm-rust/crates/cache-s3/tests/support/mod.rs +++ b/litellm-rust/crates/cache-s3/tests/support/mod.rs @@ -23,8 +23,8 @@ pub fn config(endpoint: &str) -> S3CacheConfig { url: endpoint.to_string(), }), auth: AwsAuthConfig { - access_key_id: Some("key".to_string()), - secret_access_key: Some("secret".to_string()), + access_key_id: Some("key".into()), + secret_access_key: Some("secret".into()), region_name: Some("us-east-1".to_string()), ..Default::default() }, diff --git a/litellm-rust/crates/cache-valkey-semantic/Cargo.toml b/litellm-rust/crates/cache-valkey-semantic/Cargo.toml deleted file mode 100644 index 5b4e99de382..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/Cargo.toml +++ /dev/null @@ -1,22 +0,0 @@ -[package] -name = "litellm-cache-valkey-semantic" -version = "0.1.0" -edition.workspace = true -license.workspace = true -repository.workspace = true - -[dependencies] -litellm-cache.workspace = true -litellm-cache-redis.workspace = true -redis = { workspace = true, features = ["tls-rustls"] } -sha2.workspace = true -thiserror.workspace = true -uuid = { version = "1", features = ["v4"] } - -[dev-dependencies] -litellm-error.workspace = true -litellm-cache-testing.workspace = true -redis-test.workspace = true -rstest.workspace = true -serde_json.workspace = true -tokio.workspace = true diff --git a/litellm-rust/crates/cache-valkey-semantic/src/cache.rs b/litellm-rust/crates/cache-valkey-semantic/src/cache.rs deleted file mode 100644 index 947c0edc479..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/src/cache.rs +++ /dev/null @@ -1,245 +0,0 @@ -use std::{ - sync::{Arc, Mutex}, - time::Duration, -}; - -use litellm_cache::{ - BaseCache, CacheCodec, Error, SemanticCacheContext, - semantic::{Embedder, SemanticCache, SemanticLookup, prompt_from_context}, -}; -use litellm_cache_redis::{RedisTopology, connection::Connections}; - -use crate::{ - ValkeySemanticConfig, - index::IndexState, - search::{embedding_bytes, scope_tag, search_document, write_document}, -}; - -/// `ValkeySemanticCache`: a semantic cache on valkey-search's TAG + VECTOR index. Values go -/// through the injected codec, so the response layer decides what a cached entry is. -pub struct ValkeySemanticCache { - connections: Arc>, - embedder: E, - codec: S, - config: ValkeySemanticConfig, - index_dimension: Arc>>, -} - -impl ValkeySemanticCache { - pub fn new( - url: &str, - embedder: E, - codec: S, - config: ValkeySemanticConfig, - ) -> Result { - Ok(Self { - connections: Arc::new(Connections::open(url, &RedisTopology::Standalone)?), - embedder, - codec, - config, - index_dimension: Arc::new(Mutex::new(None)), - }) - } -} - -impl ValkeySemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - pub fn with_connection( - connection: C, - embedder: E, - codec: S, - config: ValkeySemanticConfig, - ) -> Self { - Self { - connections: Arc::new(Connections::fixed(connection)), - embedder, - codec, - config, - index_dimension: Arc::new(Mutex::new(None)), - } - } - - pub fn similarity_threshold(&self) -> f64 { - self.config.similarity_threshold - } - - pub fn index_name(&self) -> &str { - &self.config.index_name - } - - fn index_state(&self) -> IndexState { - IndexState { - name: self.config.index_name.clone(), - prefix: format!("{}:", self.config.index_name), - dimension: Arc::clone(&self.index_dimension), - similarity_threshold: self.config.similarity_threshold, - } - } - - fn decode(&self, lookup: SemanticLookup>) -> Result, Error> { - Ok(SemanticLookup { - value: lookup - .value - .map(|bytes| self.codec.decode(&bytes)) - .transpose()?, - similarity: lookup.similarity, - }) - } -} - -impl ValkeySemanticCache -where - E: Embedder, - S: CacheCodec + Clone, - C: redis::ConnectionLike + Send + 'static, -{ - /// The same index and connections behind a different embedder. - pub fn with_embedder(&self, embedder: E2) -> ValkeySemanticCache { - ValkeySemanticCache { - connections: Arc::clone(&self.connections), - embedder, - codec: self.codec.clone(), - config: self.config.clone(), - index_dimension: Arc::clone(&self.index_dimension), - } - } -} - -impl BaseCache for ValkeySemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - type Value = S::Value; - type Context = SemanticCacheContext; - - fn get_ttl(&self, context: &Self::Context) -> Option { - context.ttl - } - - fn set_cache( - &self, - key: &str, - value: Self::Value, - context: &Self::Context, - ) -> Result<(), Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(()); - }; - let embedding = self.embedder.embed(&prompt, context.metadata.as_ref())?; - let response = self.codec.encode(&value)?; - let index = self.index_state(); - self.connections.execute(|connection| { - write_document( - connection, - &index, - &scope_tag(key), - &prompt, - response, - embedding_bytes(&embedding), - self.get_ttl(context), - ) - }) - } - - fn get_cache(&self, key: &str, context: &Self::Context) -> Result, Error> { - self.get_cache_with_similarity(key, context) - .map(|lookup| lookup.value) - } - - async fn async_set_cache( - &self, - key: &str, - value: Self::Value, - context: Self::Context, - ) -> Result<(), Error> { - let Some(prompt) = prompt_from_context(&context) else { - return Ok(()); - }; - let embedding = self - .embedder - .async_embed(&prompt, context.metadata.as_ref()) - .await?; - let response = self.codec.encode(&value)?; - let index = self.index_state(); - let scope = scope_tag(key); - Connections::run_blocking(Arc::clone(&self.connections), move |connection| { - write_document( - connection, - &index, - &scope, - &prompt, - response, - embedding_bytes(&embedding), - context.ttl, - ) - }) - .await - } - - async fn async_get_cache( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - self.async_get_cache_with_similarity(key, context) - .await - .map(|lookup| lookup.value) - } -} - -/// Python stamps a similarity of `0.0` when there is no prompt or no document in the key's -/// scope, and the closest document's similarity even when it misses the threshold. -impl SemanticCache for ValkeySemanticCache -where - E: Embedder, - S: CacheCodec, - C: redis::ConnectionLike + Send + 'static, -{ - fn get_cache_with_similarity( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let embedding = self.embedder.embed(&prompt, context.metadata.as_ref())?; - let index = self.index_state(); - let lookup = self.connections.execute(|connection| { - search_document( - connection, - &index, - &scope_tag(key), - embedding_bytes(&embedding), - ) - })?; - self.decode(lookup) - } - - async fn async_get_cache_with_similarity( - &self, - key: &str, - context: &Self::Context, - ) -> Result, Error> { - let Some(prompt) = prompt_from_context(context) else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let embedding = self - .embedder - .async_embed(&prompt, context.metadata.as_ref()) - .await?; - let index = self.index_state(); - let scope = scope_tag(key); - let lookup = Connections::run_blocking(Arc::clone(&self.connections), move |connection| { - search_document(connection, &index, &scope, embedding_bytes(&embedding)) - }) - .await?; - self.decode(lookup) - } -} diff --git a/litellm-rust/crates/cache-valkey-semantic/src/config.rs b/litellm-rust/crates/cache-valkey-semantic/src/config.rs deleted file mode 100644 index c417652c27b..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/src/config.rs +++ /dev/null @@ -1,8 +0,0 @@ -/// `ValkeySemanticCache.DEFAULT_VALKEY_INDEX_NAME`. -pub const DEFAULT_INDEX_NAME: &str = "litellm_semantic_cache_index"; - -#[derive(Clone, Debug, PartialEq)] -pub struct ValkeySemanticConfig { - pub similarity_threshold: f64, - pub index_name: String, -} diff --git a/litellm-rust/crates/cache-valkey-semantic/src/index.rs b/litellm-rust/crates/cache-valkey-semantic/src/index.rs deleted file mode 100644 index 3dfff32ff7c..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/src/index.rs +++ /dev/null @@ -1,117 +0,0 @@ -use std::sync::{Arc, Mutex}; - -use litellm_cache::Error; -use litellm_cache_redis::connection::ConnectionRef; - -use crate::search::value_text; - -/// The valkey-search index one cache writes to, with the dimension it was last ensured for. -#[derive(Clone)] -pub(crate) struct IndexState { - pub(crate) name: String, - pub(crate) prefix: String, - pub(crate) dimension: Arc>>, - pub(crate) similarity_threshold: f64, -} - -/// `_ensure_index_sync` / `_ensure_index_async`: create the TAG + HNSW index once per dimension, -/// and accept an existing index unless it reports a different dimension. -pub(crate) fn ensure_index( - connection: &mut ConnectionRef<'_>, - index: &IndexState, - dimension: usize, -) -> Result<(), Error> { - if index - .dimension - .lock()? - .is_some_and(|existing| existing == dimension) - { - return Ok(()); - } - let create = redis::cmd("FT.CREATE") - .arg(&index.name) - .arg("ON") - .arg("HASH") - .arg("PREFIX") - .arg(1) - .arg(&index.prefix) - .arg("SCHEMA") - .arg("litellm_cache_key") - .arg("TAG") - .arg("embedding") - .arg("VECTOR") - .arg("HNSW") - .arg(6) - .arg("TYPE") - .arg("FLOAT32") - .arg("DIM") - .arg(dimension) - .arg("DISTANCE_METRIC") - .arg("COSINE") - .query::(connection) - .map(|_| ()); - if let Err(error) = create { - if !index_exists(&error) { - return Err(Error::unavailable(error)); - } - let info = redis::cmd("FT.INFO") - .arg(&index.name) - .query::(connection) - .map_err(Error::unavailable)?; - if let Some(existing) = - index_dimension_from_info(&info).filter(|existing| *existing != dimension) - { - return Err(Error::unavailable(DimensionMismatch { - existing, - dimension, - })); - } - } - *index.dimension.lock()? = Some(dimension); - Ok(()) -} - -fn index_exists(error: &redis::RedisError) -> bool { - error.code().is_some() - && error - .detail() - .is_some_and(|detail| detail.to_ascii_lowercase().contains("already exists")) -} - -#[derive(Debug, thiserror::Error)] -#[error("index exists with dimension {existing}, not {dimension}")] -struct DimensionMismatch { - existing: usize, - dimension: usize, -} - -/// `_extract_index_dim`: flatten each attribute one level and read the value after -/// `dimensions`. -fn index_dimension_from_info(value: &redis::Value) -> Option { - let redis::Value::Array(values) = value else { - return None; - }; - let attributes = values.windows(2).find_map(|pair| { - (value_text(&pair[0]).as_deref() == Some("attributes")).then_some(&pair[1]) - })?; - let redis::Value::Array(fields) = attributes else { - return None; - }; - fields.iter().find_map(|field| { - let redis::Value::Array(values) = field else { - return None; - }; - let values = values - .iter() - .flat_map(|value| match value { - redis::Value::Array(values) => values.as_slice(), - _ => std::slice::from_ref(value), - }) - .collect::>(); - values.windows(2).find_map(|pair| { - (value_text(pair[0]).as_deref() == Some("dimensions")) - .then(|| value_text(pair[1]).and_then(|value| value.parse().ok())) - .flatten() - }) - }) -} diff --git a/litellm-rust/crates/cache-valkey-semantic/src/lib.rs b/litellm-rust/crates/cache-valkey-semantic/src/lib.rs deleted file mode 100644 index f2f62bc95bd..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/src/lib.rs +++ /dev/null @@ -1,7 +0,0 @@ -mod cache; -mod config; -mod index; -mod search; - -pub use cache::ValkeySemanticCache; -pub use config::{DEFAULT_INDEX_NAME, ValkeySemanticConfig}; diff --git a/litellm-rust/crates/cache-valkey-semantic/src/search.rs b/litellm-rust/crates/cache-valkey-semantic/src/search.rs deleted file mode 100644 index c5e4e748b58..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/src/search.rs +++ /dev/null @@ -1,161 +0,0 @@ -use std::time::Duration; - -use litellm_cache::{Error, semantic::SemanticLookup}; -use litellm_cache_redis::connection::ConnectionRef; -use sha2::{Digest, Sha256}; -use uuid::Uuid; - -use crate::index::{IndexState, ensure_index}; - -/// `_scope_tag`: valkey-search TAG fields cannot match arbitrary keys verbatim, so scopes are -/// the key's lowercase SHA-256. -pub(crate) fn scope_tag(key: &str) -> String { - let digest = Sha256::digest(key.as_bytes()); - digest.iter().map(|byte| format!("{byte:02x}")).collect() -} - -pub(crate) fn embedding_bytes(embedding: &[f32]) -> Vec { - embedding - .iter() - .flat_map(|value| value.to_le_bytes()) - .collect() -} - -/// `HSET` a fresh `:` document, then `EXPIRE` it when a TTL is set. -pub(crate) fn write_document( - connection: &mut ConnectionRef<'_>, - index: &IndexState, - scope: &str, - prompt: &str, - response: Vec, - vector: Vec, - ttl: Option, -) -> Result<(), Error> { - ensure_index(connection, index, vector.len() / size_of::())?; - let document = format!("{}{scope}:{}", index.prefix, Uuid::new_v4()); - let mut pipeline = redis::pipe(); - pipeline - .cmd("HSET") - .arg(&document) - .arg("litellm_cache_key") - .arg(scope) - .arg("prompt") - .arg(prompt) - .arg("response") - .arg(response) - .arg("embedding") - .arg(vector) - .ignore(); - if let Some(ttl) = ttl { - pipeline - .cmd("EXPIRE") - .arg(&document) - .arg(ttl.as_secs()) - .ignore(); - } - pipeline.query::<()>(connection).map_err(Error::unavailable) -} - -/// The KNN-1 search within `scope`: the closest document's similarity, and its stored response -/// when that similarity reaches the threshold. No document reads as a similarity of `0.0`. -pub(crate) fn search_document( - connection: &mut ConnectionRef<'_>, - index: &IndexState, - scope: &str, - vector: Vec, -) -> Result>, Error> { - ensure_index(connection, index, vector.len() / size_of::())?; - let query = - format!("(@litellm_cache_key:{{{scope}}})=>[KNN 1 @embedding $vec AS vector_distance]"); - let response = redis::cmd("FT.SEARCH") - .arg(&index.name) - .arg(query) - .arg("PARAMS") - .arg(2) - .arg("vec") - .arg(vector) - .arg("RETURN") - .arg(2) - .arg("response") - .arg("vector_distance") - .arg("DIALECT") - .arg(2) - .query::(connection) - .map_err(Error::unavailable)?; - let Some(fields) = search_fields(response)? else { - return Ok(SemanticLookup::miss(Some(0.0))); - }; - let field = |name: &str| { - fields - .iter() - .find_map(|(field, value)| (field == name).then(|| value.clone())) - .ok_or(Error::InvalidEntry(None)) - }; - let response = field("response")?; - let similarity = 1.0 - parse_f64(&field("vector_distance")?)?; - Ok(SemanticLookup { - value: (similarity >= index.similarity_threshold).then_some(response), - similarity: Some(similarity), - }) -} - -type SearchFields = Vec<(String, Vec)>; - -fn search_fields(value: redis::Value) -> Result, Error> { - let redis::Value::Array(values) = value else { - return Err(Error::InvalidEntry(None)); - }; - let total = parse_i64(values.first().ok_or(Error::InvalidEntry(None))?)?; - if total <= 0 || values.len() < 3 { - return Ok(None); - } - let redis::Value::Array(fields) = &values[2] else { - return Err(Error::InvalidEntry(None)); - }; - let (pairs, remainder) = fields.as_chunks::<2>(); - if !remainder.is_empty() { - return Err(Error::InvalidEntry(None)); - } - pairs - .iter() - .map(|pair| { - Ok(( - value_text(&pair[0]).ok_or(Error::InvalidEntry(None))?, - value_bytes(&pair[1])?, - )) - }) - .collect::, Error>>() - .map(Some) -} - -fn parse_i64(value: &redis::Value) -> Result { - value_text(value) - .ok_or(Error::InvalidEntry(None))? - .parse() - .map_err(Error::invalid_entry) -} - -fn parse_f64(value: &[u8]) -> Result { - std::str::from_utf8(value) - .map_err(Error::invalid_entry)? - .parse() - .map_err(Error::invalid_entry) -} - -pub(crate) fn value_text(value: &redis::Value) -> Option { - match value { - redis::Value::BulkString(bytes) => String::from_utf8(bytes.clone()).ok(), - redis::Value::SimpleString(value) => Some(value.clone()), - redis::Value::Int(value) => Some(value.to_string()), - _ => None, - } -} - -fn value_bytes(value: &redis::Value) -> Result, Error> { - match value { - redis::Value::BulkString(bytes) => Ok(bytes.clone()), - redis::Value::SimpleString(value) => Ok(value.as_bytes().to_vec()), - redis::Value::Int(value) => Ok(value.to_string().into_bytes()), - _ => Err(Error::InvalidEntry(None)), - } -} diff --git a/litellm-rust/crates/cache-valkey-semantic/tests/cache.rs b/litellm-rust/crates/cache-valkey-semantic/tests/cache.rs deleted file mode 100644 index 10e32c0183b..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/tests/cache.rs +++ /dev/null @@ -1,430 +0,0 @@ -mod support; - -use std::{ - sync::{Arc, Mutex}, - time::Duration, -}; - -use litellm_cache::{ - BaseCache, Error, JsonCodec, SemanticCacheContext, - semantic::{PreparedEmbedding, SemanticCache, SemanticLookup}, -}; -use litellm_cache_valkey_semantic::{ - DEFAULT_INDEX_NAME, ValkeySemanticCache, ValkeySemanticConfig, -}; -use redis_test::MockRedisConnection; -use rstest::{fixture, rstest}; -use serde_json::{Value, json}; -use support::{EmbedCalls, FakeEmbedder, RecordingConnection}; - -type Requests = Arc>>>; -type RecordingCache = ValkeySemanticCache, RecordingConnection>; - -const KEY_SCOPE: &str = "2c70e12b7a0646f92279f427c7b38e7334d8e5389cff167a1dc30e73f826b683"; - -struct Recording { - cache: RecordingCache, - requests: Requests, - calls: EmbedCalls, -} - -impl Recording { - fn text(&self) -> String { - self.requests - .lock() - .unwrap() - .iter() - .map(|request| String::from_utf8_lossy(request).into_owned()) - .collect::>() - .join("\n") - } -} - -fn config() -> ValkeySemanticConfig { - ValkeySemanticConfig { - similarity_threshold: 0.8, - index_name: "test".into(), - } -} - -fn recording(replies: impl IntoIterator>) -> Recording { - let connection = RecordingConnection::new(replies); - let requests = connection.requests(); - let embedder = FakeEmbedder::new(&[]); - let calls = Arc::clone(&embedder.calls); - Recording { - cache: ValkeySemanticCache::with_connection( - connection, - embedder, - JsonCodec::new(), - config(), - ), - requests, - calls, - } -} - -#[fixture] -fn entry() -> Value { - json!({"timestamp": 1.0, "response": {"answer": "ok"}}) -} - -#[fixture] -fn context() -> SemanticCacheContext { - SemanticCacheContext { - messages: Some(json!([{"role": "user", "content": "hello"}])), - metadata: Some(json!({"source": "test"})), - ..Default::default() - } -} - -fn ok() -> redis::RedisResult { - Ok(redis::Value::SimpleString("OK".into())) -} - -fn already_exists() -> redis::RedisResult { - Err(support::server_error("Index already exists")) -} - -fn bulk(value: &[u8]) -> redis::Value { - redis::Value::BulkString(value.to_vec()) -} - -fn search_hit(response: &[u8], distance: &str) -> redis::Value { - redis::Value::Array(vec![ - redis::Value::Int(1), - bulk(b"test:document"), - redis::Value::Array(vec![ - bulk(b"response"), - bulk(response), - bulk(b"vector_distance"), - bulk(distance.as_bytes()), - ]), - ]) -} - -fn encoded(value: &Value) -> Vec { - serde_json::to_vec(value).unwrap() -} - -/// `FT.INFO` with the vector field's dimension nested one level down, as valkey-search reports. -fn nested_dimension_info(dimension: i64) -> redis::Value { - redis::Value::Array(vec![ - redis::Value::SimpleString("attributes".into()), - redis::Value::Array(vec![redis::Value::Array(vec![ - redis::Value::SimpleString("embedding".into()), - redis::Value::Array(vec![ - redis::Value::SimpleString("dimensions".into()), - redis::Value::Int(dimension), - ]), - ])]), - ]) -} - -/// `FT.INFO` with `dimensions` as a sibling string of the identifier. -fn flat_dimension_info(dimension: &str) -> redis::Value { - redis::Value::Array(vec![ - redis::Value::SimpleString("attributes".into()), - redis::Value::Array(vec![redis::Value::Array(vec![ - redis::Value::SimpleString("identifier".into()), - redis::Value::SimpleString("embedding".into()), - redis::Value::Array(vec![ - redis::Value::SimpleString("dimensions".into()), - redis::Value::SimpleString(dimension.into()), - ]), - ])]), - ]) -} - -#[rstest] -#[case::string_content(json!([{"content": "hello"}]), None, Some("hello"))] -#[case::text_parts(json!([{"content": [{"text": "hello"}, {"text": " world"}]}]), None, Some("hello world"))] -#[case::non_object_parts_are_skipped(json!([{"content": ["raw", {"text": "hello"}]}]), None, Some("hello"))] -#[case::search_results(json!([{"search_results": [{"source": "s", "title": "t", "content": [{"text": "c"}], "citations": ["x"]}]}]), None, Some(r#"stc["x"]"#))] -#[case::responses_string_input(json!([]), Some(json!(" hello ")), Some("hello"))] -#[case::responses_item_input(json!([]), Some(json!([{"content": "first"}, {"text": "second"}])), Some("first\nsecond"))] -#[case::blank_input(json!([]), Some(json!(" ")), None)] -fn prompt_shapes_follow_redis_semantic_extraction( - #[case] messages: Value, - #[case] input: Option, - #[case] expected: Option<&str>, -) { - let recording = recording([ok(), Ok(redis::Value::Array(vec![redis::Value::Int(0)]))]); - let context = SemanticCacheContext { - messages: Some(messages), - input, - ..Default::default() - }; - - assert_eq!(recording.cache.get_cache("key", &context).unwrap(), None); - - let prompts = recording - .calls - .lock() - .unwrap() - .iter() - .map(|(prompt, _)| prompt.clone()) - .collect::>(); - assert_eq!( - prompts, - expected.into_iter().map(str::to_owned).collect::>() - ); - assert_eq!( - recording.requests.lock().unwrap().is_empty(), - expected.is_none() - ); -} - -#[rstest] -#[case::key("key", KEY_SCOPE)] -#[case::empty_key("", "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855")] -fn documents_are_scoped_by_the_keys_sha256( - #[case] key: &str, - #[case] scope: &str, - entry: Value, - context: SemanticCacheContext, -) { - let recording = recording([ok()]); - - recording.cache.set_cache(key, entry, &context).unwrap(); - - let text = recording.text(); - assert!(text.contains(&format!("test:{scope}:"))); - assert!(text.contains(&format!("litellm_cache_key\r\n$64\r\n{scope}\r\n"))); -} - -#[rstest] -#[case::no_ttl(None, None)] -#[case::whole_seconds(Some(Duration::from_secs(5)), Some("5"))] -#[case::fractional_seconds_truncate(Some(Duration::from_millis(1900)), Some("1"))] -fn set_writes_hset_and_expires_only_with_a_ttl( - #[case] ttl: Option, - #[case] expire: Option<&str>, - entry: Value, - context: SemanticCacheContext, -) { - let recording = recording([ok()]); - - recording - .cache - .set_cache( - "key", - entry, - &SemanticCacheContext { - ttl, - ..context.clone() - }, - ) - .unwrap(); - - let text = recording.text(); - assert!(text.contains("FT.CREATE")); - assert!(text.contains("HSET")); - let expire_seconds = text - .split_once("EXPIRE\r\n") - .and_then(|(_, rest)| rest.split("\r\n").nth(3)); - assert_eq!(expire_seconds, expire); - assert_eq!( - *recording.calls.lock().unwrap(), - vec![("hello".to_owned(), context.metadata)] - ); -} - -#[rstest] -fn second_set_skips_create_after_dimension_is_cached(entry: Value, context: SemanticCacheContext) { - let recording = recording([ok()]); - - recording - .cache - .set_cache("key", entry.clone(), &context) - .unwrap(); - recording.cache.set_cache("key", entry, &context).unwrap(); - - let text = recording.text(); - assert_eq!(text.matches("FT.CREATE").count(), 1); - assert_eq!(text.matches("HSET").count(), 2); -} - -#[rstest] -#[case::nested_matching(nested_dimension_info(3), Ok(()))] -#[case::nested_mismatch( - nested_dimension_info(2), - Err("cache is unavailable: index exists with dimension 2, not 3") -)] -#[case::flat_matching(flat_dimension_info("3"), Ok(()))] -#[case::flat_mismatch( - flat_dimension_info("2"), - Err("cache is unavailable: index exists with dimension 2, not 3") -)] -#[case::unreported_dimension_is_accepted(redis::Value::Array(vec![]), Ok(()))] -fn existing_index_dimension_must_match_embedding( - #[case] info: redis::Value, - #[case] expected: Result<(), &str>, - entry: Value, - context: SemanticCacheContext, -) { - let recording = recording([already_exists(), Ok(info)]); - - let result = recording.cache.set_cache("key", entry, &context); - assert_eq!( - result.map_err(|error| litellm_error::Report::new(&error).to_string()), - expected.map_err(str::to_owned) - ); -} - -#[rstest] -#[case::create_failure(Err(redis::RedisError::from((redis::ErrorKind::Io, "boom"))))] -fn index_creation_failures_are_unavailable( - #[case] reply: redis::RedisResult, - entry: Value, - context: SemanticCacheContext, -) { - let recording = recording([reply]); - - assert!(matches!( - recording.cache.set_cache("key", entry, &context), - Err(Error::Unavailable(Some(_))) - )); -} - -#[rstest] -#[case::within_threshold(search_hit(&encoded(&entry()), "0.1"), Ok(Some(entry())))] -#[case::at_threshold(search_hit(&encoded(&entry()), "0.2"), Ok(Some(entry())))] -#[case::beyond_threshold(search_hit(&encoded(&entry()), "0.5"), Ok(None))] -#[case::zero_documents(redis::Value::Array(vec![redis::Value::Int(0)]), Ok(None))] -#[case::missing_response( - redis::Value::Array(vec![ - redis::Value::Int(1), - bulk(b"document"), - redis::Value::Array(vec![bulk(b"vector_distance"), bulk(b"0.1")]), - ]), - Err(Error::InvalidEntry(None)) -)] -#[case::unparsable_distance(search_hit(b"not-json", "abc"), Err(Error::InvalidEntry(None)))] -#[case::undecodable_response(search_hit(b"not-json", "0.1"), Err(Error::InvalidEntry(None)))] -fn get_applies_threshold_and_decodes_entry( - #[case] reply: redis::Value, - #[case] expected: Result, Error>, - context: SemanticCacheContext, -) { - let recording = recording([ok(), Ok(reply)]); - - let kind = |result: Result, Error>| { - result.map_err(|error| std::mem::discriminant(&error)) - }; - assert_eq!( - kind(recording.cache.get_cache("key", &context)), - kind(expected) - ); -} - -#[rstest] -#[case::hit(context(), Some(search_hit(&encoded(&entry()), "0.1")), Some(entry()), Some(1.0 - 0.1))] -#[case::below_threshold(context(), Some(search_hit(&encoded(&entry()), "0.5")), None, Some(1.0 - 0.5))] -#[case::no_results(context(), Some(redis::Value::Array(vec![redis::Value::Int(0)])), None, Some(0.0))] -#[case::no_prompt(SemanticCacheContext::default(), None, None, Some(0.0))] -#[tokio::test] -async fn lookup_reports_python_semantic_similarity( - #[case] context: SemanticCacheContext, - #[case] reply: Option, - #[case] value: Option, - #[case] similarity: Option, - #[values(false, true)] use_async: bool, -) { - let searched = reply.is_some(); - let recording = recording(reply.map_or_else(Vec::new, |reply| vec![ok(), Ok(reply)])); - - let lookup = if use_async { - recording - .cache - .async_get_cache_with_similarity("key", &context) - .await - } else { - recording.cache.get_cache_with_similarity("key", &context) - }; - - assert_eq!(lookup, Ok(SemanticLookup { value, similarity })); - assert_eq!(recording.text().contains("FT.SEARCH"), searched); -} - -#[rstest] -#[tokio::test] -async fn missing_prompt_does_not_touch_valkey(entry: Value) { - let cache = ValkeySemanticCache::with_connection( - MockRedisConnection::new([]).assert_all_commands_consumed(), - FakeEmbedder::new(&[]), - JsonCodec::new(), - config(), - ); - let context = SemanticCacheContext::default(); - - cache.set_cache("key", entry.clone(), &context).unwrap(); - assert_eq!(cache.get_cache("key", &context).unwrap(), None); - cache - .async_set_cache("key", entry, context.clone()) - .await - .unwrap(); - assert_eq!(cache.async_get_cache("key", &context).await.unwrap(), None); - assert_eq!(cache.get_ttl(&context), None); -} - -#[rstest] -fn with_embedder_shares_index_state_and_connections(entry: Value, context: SemanticCacheContext) { - let recording = recording([ok(), ok(), Ok(search_hit(&encoded(&entry), "0.1"))]); - recording - .cache - .set_cache("key", entry.clone(), &context) - .unwrap(); - - let prepared = recording - .cache - .with_embedder(PreparedEmbedding(vec![0.1, 0.2, 0.3])); - - assert_eq!(prepared.get_cache("key", &context).unwrap(), Some(entry)); - assert_eq!(recording.text().matches("FT.CREATE").count(), 1); -} - -#[rstest] -fn accessors_report_the_config() { - let recording = recording([]); - - assert_eq!(recording.cache.index_name(), "test"); - assert_eq!(recording.cache.similarity_threshold(), 0.8); - assert_eq!(DEFAULT_INDEX_NAME, "litellm_semantic_cache_index"); -} - -#[rstest] -#[tokio::test] -async fn async_set_and_get_use_shared_document_helpers( - entry: Value, - context: SemanticCacheContext, -) { - let recording = recording([ok(), ok(), ok(), Ok(search_hit(&encoded(&entry), "0.1"))]); - let context = SemanticCacheContext { - ttl: Some(Duration::from_millis(1900)), - ..context - }; - - recording - .cache - .async_set_cache("key", entry.clone(), context.clone()) - .await - .unwrap(); - assert_eq!( - recording - .cache - .async_get_cache("key", &context) - .await - .unwrap(), - Some(entry) - ); - - let text = recording.text(); - assert!(text.contains("FT.CREATE")); - assert!(text.contains("HSET")); - assert!(text.contains("EXPIRE")); - assert_eq!( - *recording.calls.lock().unwrap(), - vec![("hello".to_owned(), context.metadata.clone()); 2] - ); -} diff --git a/litellm-rust/crates/cache-valkey-semantic/tests/contract.rs b/litellm-rust/crates/cache-valkey-semantic/tests/contract.rs deleted file mode 100644 index 71081fca92b..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/tests/contract.rs +++ /dev/null @@ -1,62 +0,0 @@ -//! `overwrite_replaces` does not apply: like Python, every write is a new `:` -//! document, so a second write with the same prompt adds a tie instead of replacing the first. - -mod support; - -use litellm_cache::{JsonCodec, SemanticCacheContext, semantic::PreparedEmbedding}; -use litellm_cache_testing as contract; -use litellm_cache_valkey_semantic::{ - DEFAULT_INDEX_NAME, ValkeySemanticCache, ValkeySemanticConfig, -}; -use rstest::{fixture, rstest}; -use serde_json::{Value, json}; -use support::FakeSearch; - -type Cache = ValkeySemanticCache, FakeSearch>; - -const PREFIX: &str = "contract:"; - -#[fixture] -fn cache() -> Cache { - ValkeySemanticCache::with_connection( - FakeSearch::default(), - PreparedEmbedding(vec![0.6, 0.8]), - JsonCodec::new(), - ValkeySemanticConfig { - similarity_threshold: 0.9, - index_name: DEFAULT_INDEX_NAME.into(), - }, - ) -} - -#[fixture] -fn context() -> SemanticCacheContext { - SemanticCacheContext { - messages: Some(json!([{"role": "user", "content": "contract prompt"}])), - ..Default::default() - } -} - -#[rstest] -#[tokio::test] -async fn hit_and_miss(cache: Cache, context: SemanticCacheContext) { - contract::hit_and_miss(&cache, context, PREFIX, json!({"answer": 42})).await; -} - -#[rstest] -#[tokio::test] -async fn sync_async_equivalence(cache: Cache, context: SemanticCacheContext) { - contract::sync_async_equivalence(&cache, context, PREFIX, json!("first"), json!([2])).await; -} - -#[rstest] -#[tokio::test] -async fn pipeline_writes_every_entry(cache: Cache, context: SemanticCacheContext) { - contract::pipeline_writes_every_entry( - &cache, - context, - PREFIX, - vec![json!("a"), json!(2), json!({"c": true})], - ) - .await; -} diff --git a/litellm-rust/crates/cache-valkey-semantic/tests/support/mod.rs b/litellm-rust/crates/cache-valkey-semantic/tests/support/mod.rs deleted file mode 100644 index 2af05943c98..00000000000 --- a/litellm-rust/crates/cache-valkey-semantic/tests/support/mod.rs +++ /dev/null @@ -1,352 +0,0 @@ -#![allow(dead_code)] - -use std::{ - collections::{BTreeMap, HashMap, VecDeque}, - sync::{Arc, Mutex}, -}; - -use litellm_cache::{Error, semantic::Embedder}; -use serde_json::Value; - -pub type EmbedCalls = Arc)>>>; - -/// Embeds known prompts to fixed vectors, anything else to `[0.1, 0.2, 0.3]`, and records every -/// prompt with its metadata. -pub struct FakeEmbedder { - vectors: HashMap>, - pub calls: EmbedCalls, -} - -impl FakeEmbedder { - pub fn new(vectors: &[(&str, &[f32])]) -> Self { - Self { - vectors: vectors - .iter() - .map(|(prompt, vector)| ((*prompt).to_owned(), vector.to_vec())) - .collect(), - calls: EmbedCalls::default(), - } - } -} - -impl Embedder for FakeEmbedder { - fn embed(&self, prompt: &str, metadata: Option<&Value>) -> Result, Error> { - self.calls - .lock() - .unwrap() - .push((prompt.to_owned(), metadata.cloned())); - Ok(self - .vectors - .get(prompt) - .cloned() - .unwrap_or_else(|| vec![0.1, 0.2, 0.3])) - } - - async fn async_embed(&self, prompt: &str, metadata: Option<&Value>) -> Result, Error> { - self.embed(prompt, metadata) - } -} - -struct FakeIndex { - prefix: Vec, - dims: usize, - vector_field: String, -} - -#[derive(Default)] -struct SearchState { - indexes: HashMap, - hashes: BTreeMap, BTreeMap>>, -} - -/// An in-memory valkey-search speaking the `FT.*`, `HSET` and `EXPIRE` subset the semantic cache -/// sends, with exact cosine KNN over the hashes under an index prefix. -#[derive(Clone, Default)] -pub struct FakeSearch { - state: Arc>, -} - -impl FakeSearch { - fn run(&self, args: Vec>) -> redis::RedisResult { - let mut state = self.state.lock().unwrap(); - let text = |index: usize| String::from_utf8_lossy(&args[index]).into_owned(); - match text(0).to_uppercase().as_str() { - "FT.CREATE" => { - let name = text(1); - if state.indexes.contains_key(&name) { - return Err(server_error("Index already exists")); - } - let position = |token: &str| args.iter().position(|arg| arg == token.as_bytes()); - let prefix = args[position("PREFIX").unwrap() + 2].clone(); - let dims = text(position("DIM").unwrap() + 1).parse().unwrap(); - let vector_field = text(position("VECTOR").unwrap() - 1); - state.indexes.insert( - name, - FakeIndex { - prefix, - dims, - vector_field, - }, - ); - Ok(redis::Value::Okay) - } - "FT.INFO" => { - let index = state - .indexes - .get(&text(1)) - .ok_or_else(|| server_error("Unknown index name"))?; - Ok(index_info(index)) - } - "FT.DROPINDEX" => { - state.indexes.remove(&text(1)); - Ok(redis::Value::Okay) - } - "HSET" => { - let hash = state.hashes.entry(args[1].clone()).or_default(); - for pair in args[2..].chunks(2) { - hash.insert( - String::from_utf8_lossy(&pair[0]).into_owned(), - pair[1].clone(), - ); - } - Ok(redis::Value::Int(((args.len() - 2) / 2) as i64)) - } - "EXPIRE" => Ok(redis::Value::Int(i64::from( - state.hashes.contains_key(&args[1]), - ))), - "FT.SEARCH" => { - let index = state - .indexes - .get(&text(1)) - .ok_or_else(|| server_error("no such index"))?; - let query = text(2); - let tag = query_tag(&query); - let params = args.iter().position(|arg| arg == b"PARAMS").unwrap(); - let vector = floats(&args[params + 3]); - let best = state - .hashes - .iter() - .filter(|(key, _)| key.starts_with(&index.prefix)) - .filter(|(_, fields)| { - fields.get("litellm_cache_key").map(Vec::as_slice) == Some(tag.as_bytes()) - }) - .filter_map(|(key, fields)| { - let stored = floats(fields.get(&index.vector_field)?); - (stored.len() == index.dims) - .then(|| (key, fields, 1.0 - cosine(&vector, &stored))) - }) - .min_by(|left, right| left.2.total_cmp(&right.2)); - let Some((key, fields, distance)) = best else { - return Ok(redis::Value::Array(vec![redis::Value::Int(0)])); - }; - let mut reply = fields - .iter() - .filter(|(name, _)| **name != index.vector_field) - .flat_map(|(name, value)| [bulk(name.as_bytes()), bulk(value)]) - .collect::>(); - reply.extend([ - bulk(b"vector_distance"), - bulk(distance.to_string().as_bytes()), - ]); - Ok(redis::Value::Array(vec![ - redis::Value::Int(1), - bulk(key), - redis::Value::Array(reply), - ])) - } - "PING" => Ok(redis::Value::SimpleString("PONG".into())), - _ => Err(server_error("unsupported command")), - } - } -} - -impl redis::ConnectionLike for FakeSearch { - fn req_packed_command(&mut self, command: &[u8]) -> redis::RedisResult { - let mut commands = parse_commands(command); - self.run(commands.remove(0)) - } - - fn req_packed_commands( - &mut self, - commands: &[u8], - offset: usize, - count: usize, - ) -> redis::RedisResult> { - let replies = parse_commands(commands) - .into_iter() - .map(|args| self.run(args)) - .collect::>>()?; - Ok(replies.into_iter().skip(offset).take(count).collect()) - } - - fn get_db(&self) -> i64 { - 0 - } - - fn check_connection(&mut self) -> bool { - true - } - - fn is_open(&self) -> bool { - true - } -} - -pub fn server_error(reply: &str) -> redis::RedisError { - redis::parse_redis_value(format!("-{reply}\r\n").as_bytes()) - .expect("error reply") - .extract_error() - .expect_err("server error") -} - -fn bulk(bytes: &[u8]) -> redis::Value { - redis::Value::BulkString(bytes.to_vec()) -} - -fn index_info(index: &FakeIndex) -> redis::Value { - redis::Value::Array(vec![ - bulk(b"index_name"), - bulk(b"fake"), - bulk(b"attributes"), - redis::Value::Array(vec![ - redis::Value::Array(vec![ - bulk(b"identifier"), - bulk(b"litellm_cache_key"), - bulk(b"type"), - bulk(b"TAG"), - ]), - redis::Value::Array(vec![ - bulk(b"identifier"), - bulk(index.vector_field.as_bytes()), - bulk(b"type"), - bulk(b"VECTOR"), - bulk(b"index"), - redis::Value::Array(vec![ - bulk(b"dimensions"), - redis::Value::Int(index.dims as i64), - ]), - ]), - ]), - ]) -} - -/// The tag inside `@litellm_cache_key:{...}`, with query escapes removed. -fn query_tag(query: &str) -> String { - let start = query.find("@litellm_cache_key:{").unwrap() + "@litellm_cache_key:{".len(); - let mut tag = String::new(); - let mut characters = query[start..].chars(); - while let Some(character) = characters.next() { - match character { - '\\' => tag.extend(characters.next()), - '}' => break, - character => tag.push(character), - } - } - tag -} - -fn floats(bytes: &[u8]) -> Vec { - bytes - .as_chunks::<4>() - .0 - .iter() - .map(|chunk| f32::from_le_bytes(*chunk)) - .collect() -} - -fn cosine(left: &[f32], right: &[f32]) -> f64 { - let dot = left - .iter() - .zip(right) - .map(|(left, right)| f64::from(*left) * f64::from(*right)) - .sum::(); - let norm = |vector: &[f32]| { - vector - .iter() - .map(|value| f64::from(*value).powi(2)) - .sum::() - .sqrt() - }; - dot / (norm(left) * norm(right)) -} - -/// Splits a packed RESP request into each command's arguments. -fn parse_commands(mut bytes: &[u8]) -> Vec>> { - let line = |bytes: &mut &[u8]| { - let end = bytes - .windows(2) - .position(|window| window == b"\r\n") - .unwrap(); - let text = String::from_utf8(bytes[1..end].to_vec()).unwrap(); - *bytes = &bytes[end + 2..]; - text.parse::().unwrap() - }; - let mut commands = Vec::new(); - while !bytes.is_empty() { - let count = line(&mut bytes); - let mut args = Vec::with_capacity(count); - for _ in 0..count { - let length = line(&mut bytes); - args.push(bytes[..length].to_vec()); - bytes = &bytes[length + 2..]; - } - commands.push(args); - } - commands -} - -/// Records every packed request and answers from a script, `OK` once the script runs out. -pub struct RecordingConnection { - requests: Arc>>>, - replies: Mutex>>, -} - -impl RecordingConnection { - pub fn new(replies: impl IntoIterator>) -> Self { - Self { - requests: Arc::default(), - replies: Mutex::new(replies.into_iter().collect()), - } - } - - pub fn requests(&self) -> Arc>>> { - Arc::clone(&self.requests) - } - - fn reply(&self) -> redis::RedisResult { - self.replies - .lock() - .unwrap() - .pop_front() - .unwrap_or_else(|| Ok(redis::Value::SimpleString("OK".into()))) - } -} - -impl redis::ConnectionLike for RecordingConnection { - fn req_packed_command(&mut self, command: &[u8]) -> redis::RedisResult { - self.requests.lock().unwrap().push(command.to_vec()); - self.reply() - } - - fn req_packed_commands( - &mut self, - command: &[u8], - _offset: usize, - count: usize, - ) -> redis::RedisResult> { - self.requests.lock().unwrap().push(command.to_vec()); - (0..count).map(|_| self.reply()).collect() - } - - fn get_db(&self) -> i64 { - 0 - } - - fn check_connection(&mut self) -> bool { - true - } - - fn is_open(&self) -> bool { - true - } -} diff --git a/litellm-rust/crates/cost/src/price.rs b/litellm-rust/crates/cost/src/price.rs index 12cec683748..70f6016780c 100644 --- a/litellm-rust/crates/cost/src/price.rs +++ b/litellm-rust/crates/cost/src/price.rs @@ -151,6 +151,8 @@ pub enum Uplift { Residency(Residency), /// Vertex non-global endpoint, from `regional_endpoint_uplift_multiplier` NonGlobalEndpoint, + /// OpenAI FedRAMP endpoint (gov.api.openai.com), from `fedramp_uplift_multiplier` + FedrampEndpoint, /// Anthropic `speed: fast`, from `provider_specific_entry.fast` Speed(Speed), /// Anthropic and Bedrock geo, from `provider_specific_entry.`, lowercased as on main diff --git a/litellm-rust/crates/inference-ocr/tests/resources.rs b/litellm-rust/crates/inference-ocr/tests/resources.rs index d618647e89b..3132a837eaf 100644 --- a/litellm-rust/crates/inference-ocr/tests/resources.rs +++ b/litellm-rust/crates/inference-ocr/tests/resources.rs @@ -7,7 +7,7 @@ use std::sync::{ use litellm_auth::AuthServices; use litellm_auth_gcp::{ - CredentialSource, VertexAuth, VertexAuthFuture, VertexProviderLoader, VertexTokenSource, + CredentialSource, GcpAuthFuture, GcpProviderLoader, GcpTokenSource, VertexAuth, }; use litellm_http::{HttpSettings, Resolution}; use litellm_inference::resources::CoreResources; @@ -20,28 +20,28 @@ use support::{ReceivedRequest, json_response, upstream}; struct TokenSource(String); -impl VertexTokenSource for TokenSource { - fn project_id(&self) -> VertexAuthFuture<'_, String> { +impl GcpTokenSource for TokenSource { + fn project_id(&self) -> GcpAuthFuture<'_, String> { Box::pin(async { Ok(self.0.clone()) }) } - fn token(&self) -> VertexAuthFuture<'_, String> { - Box::pin(async { Ok(self.0.clone()) }) + fn token(&self) -> GcpAuthFuture<'_, litellm_secret_value::SecretValue> { + Box::pin(async { Ok(litellm_secret_value::SecretValue::new(self.0.clone())) }) } } #[derive(Default)] struct Loader(AtomicUsize); -impl VertexProviderLoader for Loader { - fn load(&self, source: CredentialSource) -> VertexAuthFuture<'_, Arc> { +impl GcpProviderLoader for Loader { + fn load(&self, source: CredentialSource) -> GcpAuthFuture<'_, Arc> { Box::pin(async move { self.0.fetch_add(1, Ordering::SeqCst); let identity = match source { CredentialSource::Trusted(secret) => secret.expose().to_string(), other => panic!("unexpected credential source: {other:?}"), }; - Ok(Arc::new(TokenSource(identity)) as Arc) + Ok(Arc::new(TokenSource(identity)) as Arc) }) } } diff --git a/litellm-rust/crates/model-catalog/src/lower.rs b/litellm-rust/crates/model-catalog/src/lower.rs index 4c3fc38a01a..7acfc781431 100644 --- a/litellm-rust/crates/model-catalog/src/lower.rs +++ b/litellm-rust/crates/model-catalog/src/lower.rs @@ -104,6 +104,7 @@ impl From<&EntryWire> for Adjustments { (Uplift::NonGlobalEndpoint, wire.regional_endpoint_uplift), (Uplift::Residency(Residency::Eu), wire.residency_uplift_eu), (Uplift::Residency(Residency::Us), wire.residency_uplift_us), + (Uplift::FedrampEndpoint, wire.fedramp_uplift), ] .into_iter() .filter_map(|(uplift, factor)| Some((uplift, factor?))), @@ -746,7 +747,8 @@ mod tests { fn uplifts_are_adjustments_not_prices() { let wire = wire(json!({ "regional_endpoint_uplift_multiplier": 1.1, - "regional_processing_uplift_multiplier_eu": "1.2" + "regional_processing_uplift_multiplier_eu": "1.2", + "fedramp_uplift_multiplier": 1.1 })); let adjustments = Adjustments::from(&wire); @@ -764,6 +766,12 @@ mod tests { Some(1.2) ); assert_eq!(adjustments.get(&Uplift::Residency(Residency::Us)), None); + assert_eq!( + adjustments + .get(&Uplift::FedrampEndpoint) + .map(Multiplier::get), + Some(1.1) + ); } #[rstest] diff --git a/litellm-rust/crates/model-catalog/src/model_info.rs b/litellm-rust/crates/model-catalog/src/model_info.rs index 141702bf857..afbbfec59e6 100644 --- a/litellm-rust/crates/model-catalog/src/model_info.rs +++ b/litellm-rust/crates/model-catalog/src/model_info.rs @@ -96,6 +96,7 @@ pub struct ModelInfoFields { pub output_cost_per_token_ultrafast: Option, pub regional_processing_uplift_multiplier_eu: Option, pub regional_processing_uplift_multiplier_us: Option, + pub fedramp_uplift_multiplier: Option, pub regional_endpoint_uplift_multiplier: Option, pub output_cost_per_audio_token: Option, pub output_cost_per_character: Option, diff --git a/litellm-rust/crates/model-catalog/src/schema.rs b/litellm-rust/crates/model-catalog/src/schema.rs index 66f9933a3e3..6fba985a8bd 100644 --- a/litellm-rust/crates/model-catalog/src/schema.rs +++ b/litellm-rust/crates/model-catalog/src/schema.rs @@ -29,6 +29,8 @@ struct PriceBlocks { regional_processing_uplift_multiplier_eu: Option, /// Multiplier applied to all token costs for US data residency (e.g. 1.10 = +10%). regional_processing_uplift_multiplier_us: Option, + /// Multiplier applied to all token costs when served from the OpenAI FedRAMP endpoint (e.g. 1.10 = +10%). + fedramp_uplift_multiplier: Option, /// USD cost per web search query, keyed by search context size. search_context_cost_per_query: Option, /// Context-length or result-count tiered rates; each tier's costs apply within its range. diff --git a/litellm-rust/crates/model-catalog/src/wire/attributes.rs b/litellm-rust/crates/model-catalog/src/wire/attributes.rs index 307f25f0bda..401634f8c62 100644 --- a/litellm-rust/crates/model-catalog/src/wire/attributes.rs +++ b/litellm-rust/crates/model-catalog/src/wire/attributes.rs @@ -93,6 +93,10 @@ attributes! { search_free_requests: SearchFreeRequests, /// URL of the provider pricing/model page this entry was taken from. source: String, + /// Model the provider recommends migrating to before `deprecation_date`, as a catalog key when one exists. + successor_model: String, + /// URL of the provider page or feed `deprecation_date` was read from. + deprecation_source: String, /// Audio container formats the model can return. supported_audio_formats: Vec, /// OpenAI-style API routes this model can be called through, e.g. /v1/chat/completions. @@ -303,6 +307,10 @@ mod tests { #[case::known_mode("mode", json!("chat"), true)] #[case::unknown_mode("mode", json!("chatt"), false)] #[case::negative_dbu("input_dbu_cost_per_token", json!(-1), false)] + #[case::successor_model("successor_model", json!("gpt-6-luna"), true)] + #[case::successor_model_not_a_name("successor_model", json!(["gpt-6-luna"]), false)] + #[case::deprecation_source("deprecation_source", json!("https://developers.openai.com/api/docs/deprecations"), true)] + #[case::deprecation_source_not_a_url_string("deprecation_source", json!(7), false)] #[case::thinking_on_by_default("thinking_on_by_default", json!(true), true)] #[case::thinking_on_by_default_not_a_flag("thinking_on_by_default", json!("yes"), false)] #[case::reasoning_contexts("reasoning_contexts", json!(["reasoning", "non_reasoning"]), true)] diff --git a/litellm-rust/crates/model-catalog/src/wire/entry.rs b/litellm-rust/crates/model-catalog/src/wire/entry.rs index 9c155c6496a..d0bdb394720 100644 --- a/litellm-rust/crates/model-catalog/src/wire/entry.rs +++ b/litellm-rust/crates/model-catalog/src/wire/entry.rs @@ -24,6 +24,7 @@ pub struct EntryWire { pub regional_endpoint_uplift: Option, pub residency_uplift_eu: Option, pub residency_uplift_us: Option, + pub fedramp_uplift: Option, pub unknown: Fields, pub problems: Vec, } @@ -57,6 +58,7 @@ impl EntryWire { "regional_processing_uplift_multiplier_us" => { self.residency_uplift_us = reader.value(path(), value) } + "fedramp_uplift_multiplier" => self.fedramp_uplift = reader.value(path(), value), _ => self.read_open_key(reader, key, value), } } diff --git a/litellm-rust/crates/python-bridge/Cargo.toml b/litellm-rust/crates/python-bridge/Cargo.toml index e3ef45f7876..fd826f549e5 100644 --- a/litellm-rust/crates/python-bridge/Cargo.toml +++ b/litellm-rust/crates/python-bridge/Cargo.toml @@ -29,8 +29,6 @@ bytes.workspace = true futures-util.workspace = true litellm-cache.workspace = true litellm-error.workspace = true -litellm-cache-memory.workspace = true -litellm-cache-redis.workspace = true litellm-cache-response.workspace = true serde.workspace = true litellm-auth.workspace = true @@ -56,7 +54,6 @@ pyo3.workspace = true prost.workspace = true pyo3-async-runtimes.workspace = true reqwest.workspace = true -redis = { workspace = true, features = ["tls-rustls"] } serde_json.workspace = true strum.workspace = true veil.workspace = true diff --git a/litellm-rust/crates/python-bridge/src/cache/AGENTS.md b/litellm-rust/crates/python-bridge/src/cache/AGENTS.md index 41c82187373..e79562b8762 100644 --- a/litellm-rust/crates/python-bridge/src/cache/AGENTS.md +++ b/litellm-rust/crates/python-bridge/src/cache/AGENTS.md @@ -1,7 +1,7 @@ # Cache boundary -This folder owns how Rust inference reaches the selected cache: global cache selection, route admission, delegation to a Python cache and the experimental V2 native handles. Cache algorithms, storage protocols and response-cache semantics belong to their cache crates +This folder owns how Rust inference reaches the selected Python cache. Cache algorithms, storage protocols and response-cache semantics belong to their cache crates -`mod.rs` exposes the cache boundary to routes and module registration. Adapter directories remain private. `selection.rs` asks `litellm.rust_bridge.host.cache` for the live configured cache, and owns route admission and inference protocol composition for both adapters +`mod.rs` exposes the cache boundary to routes. `selection.rs` asks `litellm.rust_bridge.host.cache` for the live configured cache and owns route admission and inference protocol composition -`python/` delegates operations to the selected Python cache without discovering configuration. `native/` owns the experimental V2 native cache handles. Neither adapter depends on shared selection or the other adapter. Shared composition depends on the adapters, and routes use only the parent module's exports +`python/` delegates operations to the selected Python cache without discovering configuration. Routes use only the parent module's exports diff --git a/litellm-rust/crates/python-bridge/src/cache/mod.rs b/litellm-rust/crates/python-bridge/src/cache/mod.rs index 87dba3efcb1..4e3847951d8 100644 --- a/litellm-rust/crates/python-bridge/src/cache/mod.rs +++ b/litellm-rust/crates/python-bridge/src/cache/mod.rs @@ -1,32 +1,15 @@ -mod native; mod python; mod selection; -pub(crate) use native::NativeCacheHandle; pub(crate) use python::{CacheCall, PythonCache}; -pub(crate) use selection::{Cached, Selection, configure, configured_native}; +pub(crate) use selection::{Cached, Selection, configure, ensure_cache_disabled}; -use litellm_cache::Error; -use pyo3::{ - exceptions::{PyNotImplementedError, PyRuntimeError, PyValueError}, - prelude::*, -}; +use pyo3::{exceptions::PyRuntimeError, prelude::*}; -/// A cache call the bridge rejects itself, before or after the cache ran. #[derive(Debug, thiserror::Error)] pub(crate) enum HandleError { - #[error("recreate the v2 cache after fork")] - Forked, - #[error("cache limits must be positive")] - Limits, - #[error("namespace and a positive cache limit are required")] - RedisLimits, - #[error("cache durations must be finite and positive")] - Duration(#[source] Option), - #[error("unsupported v2 cache control: {0}")] - Control(String), - #[error("native inference needs a v2 cache, the catalog keeps Python caches on Python")] - NotNative, + #[error("this inference route does not support configured caches")] + Unsupported, #[error("cache operation without configured cache")] Unconfigured, #[error("cache arguments unavailable")] @@ -39,66 +22,10 @@ impl From for PyErr { fn from(error: HandleError) -> Self { let message = litellm_error::Report::new(&error).to_string(); match error { - HandleError::Limits - | HandleError::RedisLimits - | HandleError::Duration(_) - | HandleError::Control(_) => PyValueError::new_err(message), - HandleError::Forked - | HandleError::NotNative + HandleError::Unsupported | HandleError::Unconfigured | HandleError::MissingArguments | HandleError::NothingPending => PyRuntimeError::new_err(message), } } } - -fn cache_error(error: Error) -> PyErr { - let message = litellm_error::Report::new(&error).to_string(); - match error { - Error::InvalidEntry(_) => PyValueError::new_err(message), - Error::UnsupportedOperation => PyNotImplementedError::new_err(message), - Error::Unavailable(_) | Error::UnscopedFlush | Error::MissingPrompt => { - PyRuntimeError::new_err(message) - } - } -} - -#[cfg(test)] -mod tests { - use pyo3::{ - exceptions::{PyRuntimeError, PyValueError}, - prelude::*, - }; - use rstest::rstest; - - use super::{Error, cache_error}; - - fn refused() -> std::io::Error { - std::io::Error::other("connection refused") - } - - #[rstest] - #[case::unavailable( - Error::unavailable(refused()), - false, - "cache is unavailable: connection refused" - )] - #[case::invalid_entry( - Error::invalid_entry(refused()), - true, - "invalid cache entry: connection refused" - )] - fn python_sees_the_class_and_the_whole_chain( - #[case] error: Error, - #[case] caller_fault: bool, - #[case] message: &str, - ) { - Python::initialize(); - Python::attach(|py| { - let failure = cache_error(error); - assert_eq!(failure.is_instance_of::(py), caller_fault); - assert_eq!(failure.is_instance_of::(py), !caller_fault); - assert_eq!(failure.value(py).to_string(), message); - }); - } -} diff --git a/litellm-rust/crates/python-bridge/src/cache/native/AGENTS.md b/litellm-rust/crates/python-bridge/src/cache/native/AGENTS.md deleted file mode 100644 index 09a34d188cd..00000000000 --- a/litellm-rust/crates/python-bridge/src/cache/native/AGENTS.md +++ /dev/null @@ -1,9 +0,0 @@ -# Native cache bindings - -This directory exposes the experimental V2 native cache handles (`NativeCacheHandle`) to Python. Cache algorithms and storage protocols remain in their cache crates - -Accept the cache object selected by the parent module. Do not read global `litellm.cache`, decide route admission, or select the Python cache adapter here - -Keep Python-facing cache classes and method signatures stable when reorganizing modules. Native internals stay private to this directory unless the shared cache boundary or Python module registration needs them - -Verify changes with the V2 cache tests using a freshly built extension. Test cache behavior, not module paths or file structure diff --git a/litellm-rust/crates/python-bridge/src/cache/native/mod.rs b/litellm-rust/crates/python-bridge/src/cache/native/mod.rs deleted file mode 100644 index da1460f90ed..00000000000 --- a/litellm-rust/crates/python-bridge/src/cache/native/mod.rs +++ /dev/null @@ -1,3 +0,0 @@ -pub(super) mod v2; - -pub(crate) use v2::NativeCacheHandle; diff --git a/litellm-rust/crates/python-bridge/src/cache/native/v2.rs b/litellm-rust/crates/python-bridge/src/cache/native/v2.rs deleted file mode 100644 index c54cb641843..00000000000 --- a/litellm-rust/crates/python-bridge/src/cache/native/v2.rs +++ /dev/null @@ -1,336 +0,0 @@ -use crate::cache::{HandleError, cache_error}; -use std::{ - sync::Arc, - time::{Duration, SystemTime, UNIX_EPOCH}, -}; - -use litellm_cache::{DeleteCache, DisconnectCache, PingCache}; -use litellm_host_python::{from_py, release_gil, to_py}; -use serde_json::Value; - -use litellm_cache_memory::InMemoryCache; -use litellm_cache_redis::{RedisCache, RedisTopology}; -use litellm_cache_response::{ - CacheEntry, CacheKeyInput, ExactResponseCache, ResponseCache, ResponseCacheCodec, - ResponseCacheConfig, ResponseCacheRequest, ResponseCacheService, -}; -use pyo3::{prelude::*, types::PyDict}; - -#[pyclass( - frozen, - name = "NativeCacheHandle", - module = "litellm.rust_bridge._native" -)] -pub(crate) struct NativeCacheHandle { - service: Arc, - backend: Arc, - storage: Storage, - pid: u32, -} - -#[derive(Clone)] -enum Storage { - Memory(Arc>), - Redis(Arc>), -} - -impl NativeCacheHandle { - fn check_process(&self) -> PyResult<()> { - if self.pid != std::process::id() { - return Err(HandleError::Forked.into()); - } - Ok(()) - } -} - -fn request(key: String, ttl: Option) -> PyResult { - let mut request: ResponseCacheRequest = ResponseCacheRequest::new(CacheKeyInput { - preset: Some(key), - ..Default::default() - }); - request.context.ttl = ttl.map(duration).transpose()?; - Ok(request) -} - -#[pymethods] -impl NativeCacheHandle { - #[staticmethod] - #[pyo3(signature = (*, ttl=600.0, capacity=200, max_entry_bytes=4194304))] - fn memory(ttl: f64, capacity: usize, max_entry_bytes: usize) -> PyResult { - let ttl = duration(ttl)?; - if capacity == 0 || max_entry_bytes == 0 { - return Err(HandleError::Limits.into()); - } - let storage = Arc::new(InMemoryCache::new(Some(capacity), Some(ttl))); - let backend = Arc::new(ResponseCache::new(storage.clone()).with_config( - ResponseCacheConfig { - namespace: "sdk".into(), - max_entry_bytes, - }, - )); - Ok(Self { - service: backend.clone(), - backend, - storage: Storage::Memory(storage), - pid: std::process::id(), - }) - } - - #[staticmethod] - #[pyo3(signature = (url, *, namespace, ttl=600.0, max_entry_bytes=4194304))] - fn redis( - py: Python<'_>, - url: &str, - namespace: String, - ttl: f64, - max_entry_bytes: usize, - ) -> PyResult { - let ttl = duration(ttl)?; - if namespace.is_empty() || max_entry_bytes == 0 { - return Err(HandleError::RedisLimits.into()); - } - let storage = Arc::new( - release_gil(py, || { - RedisCache::connect( - url, - &RedisTopology::Standalone, - Some(ttl), - ResponseCacheCodec, - ) - }) - .map_err(cache_error)? - .with_namespace(Some(namespace.clone())), - ); - let backend = Arc::new(ResponseCache::new(storage.clone()).with_config( - ResponseCacheConfig { - namespace, - max_entry_bytes, - }, - )); - Ok(Self { - service: backend.clone(), - backend, - storage: Storage::Redis(storage), - pid: std::process::id(), - }) - } - fn get(&self, py: Python<'_>, key: String) -> PyResult> { - self.check_process()?; - let request = request(key, None)?; - let value = - release_gil(py, || self.backend.lookup(&request, now())).map_err(cache_error)?; - to_py(py, &value) - } - - #[pyo3(signature = (key, value, *, ttl=None))] - fn set( - &self, - py: Python<'_>, - key: String, - value: &Bound<'_, PyAny>, - ttl: Option, - ) -> PyResult<()> { - self.check_process()?; - let request = request(key, ttl)?; - let value: Value = from_py(value)?; - release_gil(py, || self.backend.store(&request, value, now())).map_err(cache_error) - } - - fn async_get<'py>(&self, py: Python<'py>, key: String) -> PyResult> { - self.check_process()?; - let request = request(key, None)?; - let backend = self.backend.clone(); - crate::execution::run_async( - py, - async move { backend.async_lookup(&request, now()).await }, - cache_error, - ) - } - - #[pyo3(signature = (key, value, *, ttl=None))] - fn async_set<'py>( - &self, - py: Python<'py>, - key: String, - value: &Bound<'_, PyAny>, - ttl: Option, - ) -> PyResult> { - self.check_process()?; - let request = request(key, ttl)?; - let value: Value = from_py(value)?; - let backend = self.backend.clone(); - crate::execution::run_async( - py, - async move { backend.async_store(&request, value, now()).await }, - cache_error, - ) - } - - #[pyo3(signature = (entries, *, ttl=None))] - fn async_set_many<'py>( - &self, - py: Python<'py>, - entries: &Bound<'_, PyAny>, - ttl: Option, - ) -> PyResult> { - self.check_process()?; - let entries: Vec<(String, Value)> = from_py(entries)?; - let entries = entries - .into_iter() - .map(|(key, value)| Ok((request(key, ttl)?, value))) - .collect::>>()?; - let backend = self.backend.clone(); - crate::execution::run_async( - py, - async move { backend.async_store_batch(entries, now()).await }, - cache_error, - ) - } - - fn flush(&self, py: Python<'_>) -> PyResult> { - self.check_process()?; - let backend = self.backend.clone(); - crate::execution::run_sync(py, async move { backend.async_flush().await }, cache_error) - } - - fn async_flush<'py>(&self, py: Python<'py>) -> PyResult> { - self.check_process()?; - let backend = self.backend.clone(); - crate::execution::run_async(py, async move { backend.async_flush().await }, cache_error) - } - - fn ping<'py>(&self, py: Python<'py>) -> PyResult> { - self.check_process()?; - let storage = self.storage.clone(); - crate::execution::run_async( - py, - async move { - match storage { - Storage::Memory(_) => Ok(true), - Storage::Redis(cache) => cache.ping().await, - } - }, - cache_error, - ) - } - - fn disconnect<'py>(&self, py: Python<'py>) -> PyResult> { - self.check_process()?; - let storage = self.storage.clone(); - crate::execution::run_async( - py, - async move { - match storage { - Storage::Memory(cache) => cache.disconnect().await, - Storage::Redis(cache) => cache.disconnect().await, - } - }, - cache_error, - ) - } - - fn delete<'py>(&self, py: Python<'py>, keys: Vec) -> PyResult> { - self.check_process()?; - let storage = self.storage.clone(); - crate::execution::run_async( - py, - async move { - for key in keys { - match &storage { - Storage::Memory(cache) => cache.async_delete_cache(&key).await?, - Storage::Redis(cache) => cache.async_delete_cache(&key).await?, - } - } - Ok(()) - }, - cache_error, - ) - } -} - -fn duration(seconds: f64) -> Result { - match Duration::try_from_secs_f64(seconds) { - Ok(duration) if !duration.is_zero() => Ok(duration), - Ok(_) => Err(HandleError::Duration(None)), - Err(error) => Err(HandleError::Duration(Some(error))), - } -} - -pub(in crate::cache) fn native_handle<'py>( - configured: &Bound<'py, PyAny>, -) -> PyResult>> { - Ok(configured - .getattr_opt("cache")? - .map(|backend| backend.getattr_opt("native_handle")) - .transpose()? - .flatten() - .filter(|handle| handle.is_instance_of::())) -} - -pub(in crate::cache) fn configured( - configured: &Bound<'_, PyAny>, - kwargs: &Bound<'_, PyDict>, -) -> PyResult<( - Option>, - litellm_cache_response::CacheOptions, -)> { - let handle = native_handle(configured)?.ok_or(HandleError::NotNative)?; - let cache = handle.extract::>()?; - cache.check_process()?; - let controls = kwargs.get_item("cache")?.filter(|value| !value.is_none()); - let controls = controls - .as_ref() - .map(|value| value.cast::()) - .transpose()?; - if let Some(controls) = controls { - for name in controls.keys() { - let name = name.extract::()?; - if !matches!( - name.as_str(), - "no-cache" | "no-store" | "ttl" | "s-maxage" | "s-max-age" | "use-cache" - ) { - return Err(HandleError::Control(name).into()); - } - } - } - let boolean = |name: &str| -> PyResult { - controls - .map(|values| values.get_item(name)) - .transpose()? - .flatten() - .map(|value| value.extract()) - .transpose() - .map(|value| value.unwrap_or(false)) - }; - let seconds = |name: &str| -> PyResult> { - controls - .map(|values| values.get_item(name)) - .transpose()? - .flatten() - .map(|value| Ok(duration(value.extract()?)?)) - .transpose() - }; - Ok(( - Some(cache.service.clone()), - litellm_cache_response::CacheOptions { - policy: litellm_cache_response::CachePolicy { - caching: kwargs - .get_item("caching")? - .filter(|value| !value.is_none()) - .map(|value| value.extract()) - .transpose()?, - no_cache: boolean("no-cache")?, - no_store: boolean("no-store")?, - ttl: seconds("ttl")?, - max_age: seconds("s-max-age")?.or(seconds("s-maxage")?), - }, - scope: litellm_cache_response::CacheScope::Shared, - }, - )) -} - -fn now() -> Duration { - SystemTime::now() - .duration_since(UNIX_EPOCH) - .unwrap_or_default() -} diff --git a/litellm-rust/crates/python-bridge/src/cache/selection.rs b/litellm-rust/crates/python-bridge/src/cache/selection.rs index 00fc7cd822f..e96f079e5da 100644 --- a/litellm-rust/crates/python-bridge/src/cache/selection.rs +++ b/litellm-rust/crates/python-bridge/src/cache/selection.rs @@ -1,14 +1,10 @@ -use super::{native, python}; -use litellm_cache_response::{ - CacheOptions, CachePolicy, CacheScope, ResponseCacheService, ScopedCache, -}; +use super::{HandleError, python}; +use litellm_cache_response::{CacheOptions, CachePolicy, CacheScope, ScopedCache}; use litellm_host::{ machine::{HostServices, MachineFault}, protocol::Protocol, }; use pyo3::{prelude::*, types::PyDict}; -use std::sync::Arc; - const CACHE_HOST_MODULE: &str = "litellm.rust_bridge.host.cache"; pub(crate) struct Cached

(std::marker::PhantomData

); @@ -24,7 +20,6 @@ impl Protocol for Cached

{ enum Backend { Disabled, - Native(Arc), Python { namespace: String }, } @@ -43,7 +38,6 @@ impl Selection { { let service = match self.backend { Backend::Disabled => None, - Backend::Native(service) => Some(service), Backend::Python { namespace } => Some(python::service(services, namespace)), }; ( @@ -76,37 +70,21 @@ fn selected_cache<'py>( Ok(Some(configured)) } -pub(crate) fn configured_native( +pub(crate) fn ensure_cache_disabled( py: Python<'_>, kwargs: &Bound<'_, PyDict>, call_type: &str, -) -> PyResult<( - Option>, - litellm_cache_response::CacheOptions, -)> { +) -> PyResult<()> { let Some(configured) = selected_cache(py, kwargs, call_type)? else { - return Ok(( - None, - litellm_cache_response::CacheOptions::new(litellm_cache_response::CacheScope::Shared), - )); + return Ok(()); }; - native_configuration(&configured, kwargs) -} - -fn native_configuration( - configured: &Bound<'_, PyAny>, - kwargs: &Bound<'_, PyDict>, -) -> PyResult<(Option>, CacheOptions)> { - if !configured + if configured .call_method("should_use_cache", (), Some(kwargs))? .extract::()? { - return Ok(( - None, - litellm_cache_response::CacheOptions::new(litellm_cache_response::CacheScope::Shared), - )); + return Err(HandleError::Unsupported.into()); } - native::v2::configured(configured, kwargs) + Ok(()) } pub(crate) fn configure( @@ -122,13 +100,6 @@ pub(crate) fn configure( options: CacheOptions::new(CacheScope::Shared), }); }; - if native::v2::native_handle(&cache)?.is_some() { - let (native, options) = native_configuration(&cache, arguments)?; - return Ok(Selection { - backend: native.map_or(Backend::Disabled, Backend::Native), - options, - }); - } let enabled = cache .call_method("should_use_cache", (), Some(arguments))? .extract::()?; diff --git a/litellm-rust/crates/python-bridge/src/lib.rs b/litellm-rust/crates/python-bridge/src/lib.rs index fdcb969a423..de35419308a 100644 --- a/litellm-rust/crates/python-bridge/src/lib.rs +++ b/litellm-rust/crates/python-bridge/src/lib.rs @@ -65,10 +65,6 @@ mod _native { fn init(module: &Bound<'_, PyModule>) -> PyResult<()> { let py = module.py(); let dict = module.dict(); - dict.set_item( - "NativeCacheHandle", - py.get_type::(), - )?; dict.set_item( "_SecretManagerRuntime", py.get_type::(), @@ -92,7 +88,6 @@ mod tests { Python::initialize(); Python::attach(|py| { let mut expected = vec![ - "NativeCacheHandle", "RustBridgeDeclined", "RustUpstreamError", "ForkedAfterNativeRuntimeStarted", diff --git a/litellm-rust/crates/python-bridge/src/routes/chat_completions/mod.rs b/litellm-rust/crates/python-bridge/src/routes/chat_completions/mod.rs index d78d141f0f2..1bcdc18a765 100644 --- a/litellm-rust/crates/python-bridge/src/routes/chat_completions/mod.rs +++ b/litellm-rust/crates/python-bridge/src/routes/chat_completions/mod.rs @@ -4,7 +4,7 @@ use std::sync::Arc; use host::ChatCompletionsPythonHost; use litellm_auth::AuthServices; -use litellm_cache_response::{CachePolicy, ScopedCache}; +use litellm_cache_response::CachePolicy; use litellm_callbacks_legacy_python::LoggingOperation; use litellm_host::{call::HostedMachine, protocol::Protocol}; use litellm_inference_chat::{ChatCompletionsRoute, route::ChatCompletions}; @@ -57,10 +57,6 @@ impl InferenceRoute for ChatCompletionsRoute { Self::new(http, auth, secrets) } - fn with_cache(self, cache: ScopedCache) -> Self { - self.with_cache(cache) - } - fn machine( self, call: ::Request, diff --git a/litellm-rust/crates/python-bridge/src/routes/inference.rs b/litellm-rust/crates/python-bridge/src/routes/inference.rs index ca30c94b02f..1245c126c4e 100644 --- a/litellm-rust/crates/python-bridge/src/routes/inference.rs +++ b/litellm-rust/crates/python-bridge/src/routes/inference.rs @@ -1,7 +1,7 @@ use std::sync::Arc; use litellm_auth::AuthServices; -use litellm_cache_response::{CachePolicy, CacheScope, ScopedCache}; +use litellm_cache_response::CachePolicy; use litellm_callbacks_legacy_python::LoggingOperation; use litellm_core_utils::get_llm_provider_logic::get_custom_llm_provider; use litellm_host::{call::HostedMachine, protocol::Protocol}; @@ -142,7 +142,6 @@ pub(super) trait InferenceRoute: Sized + 'static { auth: Arc, secrets: Arc, ) -> Self; - fn with_cache(self, cache: ScopedCache) -> Self; fn machine( self, call: ::Request, @@ -176,12 +175,8 @@ where crate::http::resources().auth.clone(), crate::secrets::source(py)?, ); - let (cache, cache_options) = crate::cache::configured_native(py, arguments, call_type)?; - let route = match cache { - Some(cache) => route.with_cache(ScopedCache::new(cache, CacheScope::Shared)), - None => route, - }; - Ok(route.machine(request, cache_options.policy)) + crate::cache::ensure_cache_disabled(py, arguments, call_type)?; + Ok(route.machine(request, CachePolicy::default())) }, host, hooks, diff --git a/litellm-rust/crates/python-bridge/src/routes/responses/mod.rs b/litellm-rust/crates/python-bridge/src/routes/responses/mod.rs index 9c045d65c13..cc4d85b4d26 100644 --- a/litellm-rust/crates/python-bridge/src/routes/responses/mod.rs +++ b/litellm-rust/crates/python-bridge/src/routes/responses/mod.rs @@ -5,7 +5,7 @@ use std::sync::Arc; use host::ResponsesPythonHost; use litellm_auth::AuthServices; -use litellm_cache_response::{CachePolicy, ScopedCache}; +use litellm_cache_response::CachePolicy; use litellm_callbacks_legacy_python::LoggingOperation; use litellm_host::{call::HostedMachine, protocol::Protocol}; use litellm_inference_responses::{ResponsesRoute, route::Responses}; @@ -49,10 +49,6 @@ impl InferenceRoute for ResponsesRoute { Self::new(http, auth, secrets) } - fn with_cache(self, cache: ScopedCache) -> Self { - self.with_cache(cache) - } - fn machine( self, call: ::Request, diff --git a/litellm-rust/crates/secret-value/src/secret_value.rs b/litellm-rust/crates/secret-value/src/secret_value.rs index 27e4227080d..33c559b80ec 100644 --- a/litellm-rust/crates/secret-value/src/secret_value.rs +++ b/litellm-rust/crates/secret-value/src/secret_value.rs @@ -16,6 +16,18 @@ impl SecretValue { } } +impl From for SecretValue { + fn from(value: String) -> Self { + Self::new(value) + } +} + +impl From<&str> for SecretValue { + fn from(value: &str) -> Self { + Self::new(value) + } +} + impl PartialEq for SecretValue { fn eq(&self, other: &Self) -> bool { subtle::ConstantTimeEq::ct_eq(self.0.as_bytes(), other.0.as_bytes()).into() diff --git a/litellm-rust/crates/secrets-aws/src/auth.rs b/litellm-rust/crates/secrets-aws/src/auth.rs index 66f4f51bda9..6bec8ad96a1 100644 --- a/litellm-rust/crates/secrets-aws/src/auth.rs +++ b/litellm-rust/crates/secrets-aws/src/auth.rs @@ -39,30 +39,15 @@ impl Credentials { Self { auth, config: AwsAuthConfig { - access_key_id: context - .access_key_id - .as_ref() - .map(|value| value.expose().to_owned()), - secret_access_key: context - .secret_access_key - .as_ref() - .map(|value| value.expose().to_owned()), - session_token: context - .session_token - .as_ref() - .map(|value| value.expose().to_owned()), + access_key_id: context.access_key_id.as_ref().cloned(), + secret_access_key: context.secret_access_key.as_ref().cloned(), + session_token: context.session_token.as_ref().cloned(), region_name: region(settings, environment.as_ref()).ok(), role_name: settings.aws_role_name.clone(), session_name: settings.aws_session_name.clone(), - external_id: settings - .aws_external_id - .as_ref() - .map(|v| v.expose().to_owned()), + external_id: settings.aws_external_id.as_ref().cloned(), profile_name: settings.aws_profile_name.clone(), - web_identity_token: settings - .aws_web_identity_token - .as_ref() - .map(|v| v.expose().to_owned()), + web_identity_token: settings.aws_web_identity_token.as_ref().cloned(), sts_endpoint: settings.aws_sts_endpoint.clone(), }, environment, diff --git a/litellm/__init__.py b/litellm/__init__.py index 102c4d1440f..8e5dd68a186 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -688,6 +688,7 @@ darkbloom_models: Set = set() openinfer_models: Final[Set[str]] = set() kyma_models: Final[set[str]] = set() # mutable-ok: populated when the shared model catalog loads streamlake_models: Final[Set[str]] = set() +wally_models: Final[Set[str]] = set() zerogpu_models: Final[Set[str]] = set() v0_models: Set = set() morph_models: Set = set() @@ -984,6 +985,8 @@ def _populate_provider_model_sets(model_cost_map: Mapping[str, Mapping[str, obje kyma_models.add(key) elif value.get("litellm_provider") == "streamlake": streamlake_models.add(key) + elif value.get("litellm_provider") == "wally": + wally_models.add(key) elif value.get("litellm_provider") == "zerogpu": zerogpu_models.add(key) elif value.get("litellm_provider") == "v0": @@ -1071,7 +1074,6 @@ _populate_provider_model_sets(model_cost) # Azure returns gpt-35-turbo in their responses, we need to map this to azure/gpt-3.5-turbo for token counting azure_llms = { "gpt-35-turbo": "azure/gpt-35-turbo", - "gpt-35-turbo-16k": "azure/gpt-35-turbo-16k", "gpt-35-turbo-instruct": "azure/gpt-35-turbo-instruct", "azure/gpt-41": "gpt-4.1", "azure/gpt-41-mini": "gpt-4.1-mini", @@ -1166,6 +1168,7 @@ model_list = list( | openinfer_models | kyma_models | streamlake_models + | wally_models | zerogpu_models | v0_models | morph_models @@ -1299,6 +1302,7 @@ def _build_models_by_provider() -> dict: "openinfer": openinfer_models, "kyma": kyma_models, "streamlake": streamlake_models, + "wally": wally_models, "zerogpu": zerogpu_models, "v0": v0_models, "morph": morph_models, @@ -1792,6 +1796,9 @@ if TYPE_CHECKING: from .llms.bespoke.decisions.transformation import ( BespokeDecisionsConfig as BespokeDecisionsConfig, ) + from .llms.empiriolabs.decisions.transformation import ( + EmpirioLabsDecisionsConfig as EmpirioLabsDecisionsConfig, + ) from .llms.eu_jev.decisions.transformation import ( EuJevDecisionsConfig as EuJevDecisionsConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 65074093dbb..bf903e23bc0 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -176,6 +176,7 @@ LLM_CONFIG_NAMES: Final = ( "OllamaDecisionsConfig", "LayaDecisionsConfig", "BespokeDecisionsConfig", + "EmpirioLabsDecisionsConfig", "EuJevDecisionsConfig", "OpenAIDecisionsConfig", "AzureDecisionsConfig", @@ -759,6 +760,7 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { "OllamaDecisionsConfig": (".llms.ollama.decisions.transformation", "OllamaDecisionsConfig"), "LayaDecisionsConfig": (".llms.laya.decisions.transformation", "LayaDecisionsConfig"), "BespokeDecisionsConfig": (".llms.bespoke.decisions.transformation", "BespokeDecisionsConfig"), + "EmpirioLabsDecisionsConfig": (".llms.empiriolabs.decisions.transformation", "EmpirioLabsDecisionsConfig"), "EuJevDecisionsConfig": (".llms.eu_jev.decisions.transformation", "EuJevDecisionsConfig"), "OpenAIDecisionsConfig": (".llms.openai.decisions.transformation", "OpenAIDecisionsConfig"), "AzureDecisionsConfig": (".llms.azure.decisions.transformation", "AzureDecisionsConfig"), diff --git a/litellm/_v2/AGENTS.md b/litellm/_v2/AGENTS.md deleted file mode 100644 index 55998814a7f..00000000000 --- a/litellm/_v2/AGENTS.md +++ /dev/null @@ -1,3 +0,0 @@ -Everything here is experimental and should not be documented - -Use this directory to explore alternative APIs where the Rust migration makes backward compatibility difficult. The gateway can use them for performance, but keep them behind the v2 flag to avoid breaking SDK users diff --git a/litellm/_v2/__init__.py b/litellm/_v2/__init__.py deleted file mode 100644 index 79156cf0f80..00000000000 --- a/litellm/_v2/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from litellm._v2.cache import Cache - -__all__ = ("Cache",) diff --git a/litellm/_v2/cache/AGENTS.md b/litellm/_v2/cache/AGENTS.md deleted file mode 100644 index 308419b7253..00000000000 --- a/litellm/_v2/cache/AGENTS.md +++ /dev/null @@ -1,11 +0,0 @@ -# Python v2 cache - -Keep `litellm._v2.cache.Cache` import-compatible when reorganizing this package. This package owns Python cache factories and the adapter between the existing `BaseCache` interface and `NativeCacheHandle` - -Construct native handles at this boundary and inject the adapter through the existing cache facade's `_backend` parameter. Keep the facade's `type`, namespace, and TTL consistent with the configured native backend - -Keep storage implementation in the Rust storage crates and response-cache policy in `litellm-cache-response` and core. Do not duplicate cache-key generation, freshness rules, response encoding, or inference orchestration here - -Preserve synchronous and asynchronous cache operations, including TTL forwarding and lifecycle methods. Validate Python values before passing them to typed native interfaces. Keep native extension imports lazy so importing the package does not require loading the extension - -Extend the existing v2 cache tests in `tests/test_litellm_rust/test_v2.py` for behavioral changes, following that directory's `AGENTS.md`. Test observable cache behavior rather than package layout or implementation structure diff --git a/litellm/_v2/cache/__init__.py b/litellm/_v2/cache/__init__.py deleted file mode 100644 index 5a3860b7a6e..00000000000 --- a/litellm/_v2/cache/__init__.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import annotations - -from collections.abc import Sequence -from typing import TYPE_CHECKING, Final - -from pydantic import TypeAdapter - -from litellm.caching.base_cache import BaseCache -from litellm.caching.caching import Cache as CacheFacade -from litellm.types.caching import LiteLLMCacheType - -if TYPE_CHECKING: - from litellm.rust_bridge._native import NativeCacheHandle - -_DURATION: Final[TypeAdapter[float | None]] = TypeAdapter(float | None) - - -class NativeBackend(BaseCache): - def __init__(self, handle: NativeCacheHandle) -> None: - self.native_handle = handle - - def get_cache(self, key: str, **kwargs: object) -> object: - return self.native_handle.get(key) - - async def async_get_cache(self, key: str, **kwargs: object) -> object: - return await self.native_handle.async_get(key) - - def set_cache(self, key: str, value: object, **kwargs: object) -> None: - self.native_handle.set(key, value, ttl=_DURATION.validate_python(kwargs.get("ttl"))) - - async def async_set_cache(self, key: str, value: object, **kwargs: object) -> None: - await self.native_handle.async_set(key, value, ttl=_DURATION.validate_python(kwargs.get("ttl"))) - - async def async_set_cache_pipeline(self, cache_list: Sequence[tuple[str, object]], **kwargs: object) -> None: - await self.native_handle.async_set_many(cache_list, ttl=_DURATION.validate_python(kwargs.get("ttl"))) - - async def batch_cache_write(self, key: str, value: object, **kwargs: object) -> None: - await self.async_set_cache(key, value, **kwargs) - - def flush_cache(self) -> None: - self.native_handle.flush() - - async def async_flush_cache(self) -> None: - await self.native_handle.async_flush() - - async def ping(self) -> bool: - return await self.native_handle.ping() - - async def disconnect(self) -> None: - await self.native_handle.disconnect() - - async def delete_cache_keys(self, keys: Sequence[str]) -> None: - await self.native_handle.delete(keys) - - async def test_connection(self) -> dict[str, str]: - return {"status": "success" if await self.ping() else "failed"} - - -class Cache: - @staticmethod - def memory(*, ttl: float = 600, capacity: int = 200, max_entry_bytes: int = 4194304) -> CacheFacade: - from litellm.rust_bridge._native import NativeCacheHandle - - handle: Final = NativeCacheHandle.memory(ttl=ttl, capacity=capacity, max_entry_bytes=max_entry_bytes) - return CacheFacade(type=LiteLLMCacheType.LOCAL, ttl=ttl, _backend=NativeBackend(handle)) - - @staticmethod - def redis(url: str, *, namespace: str, ttl: float = 600, max_entry_bytes: int = 4194304) -> CacheFacade: - from litellm.rust_bridge._native import NativeCacheHandle - - handle: Final = NativeCacheHandle.redis(url, namespace=namespace, ttl=ttl, max_entry_bytes=max_entry_bytes) - return CacheFacade(type=LiteLLMCacheType.REDIS, namespace=namespace, ttl=ttl, _backend=NativeBackend(handle)) diff --git a/litellm/accounting/__init__.py b/litellm/accounting/__init__.py index d4e56f61279..5d59a35a987 100644 --- a/litellm/accounting/__init__.py +++ b/litellm/accounting/__init__.py @@ -1,7 +1,7 @@ """Public accounting contracts. Importing these values does not enable accounting.""" from litellm.accounting.measurements import measurements_for -from litellm.accounting.protocols import BudgetEnforcer, CostAggregator, CostCompiler, UsageMeter +from litellm.accounting.protocols import CostAggregator from litellm.accounting.types import ( AdmissionDecision, AggregateQuery, @@ -33,9 +33,7 @@ __all__ = [ "AdmissionDecision", "AggregateQuery", "AggregateRow", - "BudgetEnforcer", "CostAggregator", - "CostCompiler", "CostEvent", "DeliveryState", "DeliveryTarget", @@ -56,7 +54,6 @@ __all__ = [ "PricingSnapshot", "Rate", "Unpriced", - "UsageMeter", "measurements_for", "validate_aggregate_rows", ] diff --git a/litellm/accounting/projection.py b/litellm/accounting/projection.py new file mode 100644 index 00000000000..fc3a89ae306 --- /dev/null +++ b/litellm/accounting/projection.py @@ -0,0 +1,304 @@ +from collections.abc import Iterator, Mapping +from dataclasses import dataclass +from datetime import datetime, timezone +from itertools import chain +from types import MappingProxyType +from typing import Final + +from pydantic import TypeAdapter, ValidationError + +from litellm.constants import ( + BILLED_RETRY_ATTEMPT_METADATA_KEY, + INTERNAL_CALL_ORIGIN_METADATA_KEY, + MODEL_INSIGHTS_DEFAULT_TASK, + MODEL_INSIGHTS_TASK_TAG_PREFIX, +) +from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import GuardrailCostByUnitEntry +from litellm.proxy.db.daily_spend_bulk_upsert import DAILY_SPEND_TABLES, daily_spend_entity_ids +from litellm.proxy.db.model_insights_tasks import load_model_insight_tasks +from litellm.proxy.db.model_usage_rollup import date_from_start_time +from litellm.proxy.route_llm_request import ROUTE_ENDPOINT_MAPPING +from litellm.proxy.spend_tracking.compression_savings import extract_compression_saved_tokens +from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens, extract_cache_read_tokens +from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN + +_FIELDS: Final = TypeAdapter(dict[str, object]) +_STRINGS: Final = TypeAdapter(tuple[str, ...]) +_ROWS: Final = TypeAdapter(tuple[dict[str, object], ...]) +_TASK_TYPES: Final = frozenset(load_model_insight_tasks()) + + +@dataclass(frozen=True, slots=True) +class Contribution: + table: str + key: tuple[tuple[str, str], ...] + increments: tuple[tuple[str, int | float], ...] + values: tuple[tuple[str, str | None], ...] = () + + +def _fields(value: object) -> Mapping[str, object]: + if isinstance(value, str): + return _FIELDS.validate_json(value) + return _FIELDS.validate_python(value) if isinstance(value, Mapping) else MappingProxyType({}) + + +def _text(value: object) -> str: + return "" if value is None else str(value) + + +def _number(value: object) -> int | float: + return value if isinstance(value, (int, float)) else 0 + + +def _tags(row: Mapping[str, object]) -> tuple[str, ...]: + value: Final = row.get("request_tags") + if value is None: + return () + return _STRINGS.validate_json(value) if isinstance(value, str) else _STRINGS.validate_python(value) + + +def _date(row: Mapping[str, object]) -> str | None: + start: Final = row.get("startTime") + return date_from_start_time(start) if isinstance(start, (datetime, str)) else None + + +def _spend(table: str, key: tuple[tuple[str, str], ...], spend: float) -> Contribution: + return Contribution(table, key, (("spend", spend),)) + + +def _entity_contributions(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[Contribution, ...]: + spend: Final = _number(row.get("spend")) + entities: Final = _fields(metadata.get("accounting_entities")) + identities: Final = ( + ("LiteLLM_VerificationToken", "token", entities.get("api_key")), + ("LiteLLM_UserTable", "user_id", entities.get("user")), + ("LiteLLM_UserTable", "user_id", entities.get("proxy_budget_name")), + ("LiteLLM_TeamTable", "team_id", entities.get("team_id")), + ("LiteLLM_OrganizationTable", "organization_id", entities.get("organization_id")), + ("LiteLLM_EndUserTable", "user_id", entities.get("end_user")), + ("LiteLLM_AgentsTable", "agent_id", entities.get("agent_id")), + ("LiteLLM_ProjectTable", "project_id", entities.get("project_id")), + ) + groups: Final = _STRINGS.validate_python(metadata.get("accounting_model_access_groups", ())) + return ( + *( + _spend(table, ((column, identity),), spend) + for table, column, identity in identities + if isinstance(identity, str) + ), + *(_spend("LiteLLM_TagTable", (("tag_name", tag),), spend) for tag in _tags(row) if tag), + *(_spend("LiteLLM_ModelAccessGroupBudgetTable", (("access_group_name", group),), spend) for group in groups), + *_membership_contributions(entities, spend), + ) + + +def _membership_contributions(row: Mapping[str, object], spend: float) -> tuple[Contribution, ...]: + user: Final = row.get("user") + if not isinstance(user, str): + return () + owners: Final = ( + ("LiteLLM_TeamMembership", "team_id", row.get("team_id")), + ("LiteLLM_OrganizationMembership", "organization_id", row.get("organization_id")), + ) + return tuple( + _spend(table, ((column, identity), ("user_id", user)), spend) + for table, column, identity in owners + if isinstance(identity, str) + ) + + +def _daily_counters(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[tuple[str, int | float], ...]: + usage: Final = _fields(metadata.get("usage_object")) + savings: Final = _fields(metadata.get("accounting_savings")) + internal: Final = bool(metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY)) + caller: Final = not internal and metadata.get(BILLED_RETRY_ATTEMPT_METADATA_KEY) is not True + success: Final = metadata.get("status") != "failure" + duration: Final = row.get("request_duration_ms") + timed: Final = not internal and success and isinstance(duration, int) and duration >= 0 + return ( + ("spend", _number(row.get("spend"))), + ("prompt_tokens", _number(row.get("prompt_tokens"))), + ("completion_tokens", _number(row.get("completion_tokens"))), + ("api_requests", int(caller)), + ("successful_requests", int(caller and success)), + ("failed_requests", int(caller and not success)), + ("cache_read_input_tokens", extract_cache_read_tokens(usage)), + ("cache_creation_input_tokens", extract_cache_creation_tokens(usage)), + ("compression_saved_tokens", extract_compression_saved_tokens(metadata)), + ("compression_savings_spend", _number(savings.get("compression"))), + ("prompt_caching_savings_spend", _number(savings.get("prompt_caching"))), + ("gateway_injected_caching_savings_spend", _number(savings.get("gateway_injected_caching"))), + ("autorouter_savings_spend", 0.0 if internal else _number(savings.get("autorouter"))), + ("total_response_time_ms", _number(duration) if timed else 0), + ("timed_requests", int(timed)), + ) + + +def _daily_contributions(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[Contribution, ...]: + date: Final = _date(row) + if date is None or "api_key" not in row: + return () + if "mcp_namespaced_tool_name" not in row and ( + "model" not in row or "custom_llm_provider" not in row or "model_group" not in row + ): + return () + counters: Final = _daily_counters(row, metadata) + endpoint: Final = ROUTE_ENDPOINT_MAPPING.get(_text(row.get("call_type"))) + shared_key: Final = ( + ("date", date), + ("api_key", _text(row.get("api_key"))), + ("model", _text(row.get("model"))), + ("custom_llm_provider", _text(row.get("custom_llm_provider"))), + ("mcp_namespaced_tool_name", _text(row.get("mcp_namespaced_tool_name"))), + ("endpoint", _text(endpoint)), + ) + # The legacy daily organization writer uses the charged org argument, not + # SpendLogs' normalized empty-string identity for a request with no org. + daily_row: Final = {**row, "organization_id": _fields(metadata.get("accounting_entities")).get("organization_id")} + return tuple(_daily_rows(daily_row, shared_key, counters)) + + +def _daily_rows( + row: Mapping[str, object], + shared_key: tuple[tuple[str, str], ...], + counters: tuple[tuple[str, int | float], ...], +) -> Iterator[Contribution]: + for entity, table in DAILY_SPEND_TABLES.items(): + for identity in daily_spend_entity_ids(row, entity): + yield Contribution( + table.name, + ((table.entity_id_column, _text(identity)), *shared_key), + counters, + ( + ("model_group", None if row.get("model_group") is None else _text(row.get("model_group"))), + *((("request_id", _text(row.get("request_id"))),) if table.carries_request_id else ()), + ), + ) + + +def _window_contributions(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[Contribution, ...]: + windows: Final = _ROWS.validate_python(metadata.get("accounting_windows", ())) + return tuple( + Contribution( + "LiteLLM_BudgetWindowSpend", + tuple((column, _text(window[column])) for column in ("entity_type", "entity_id", "window_duration")), + (("spend", _number(window.get("spend", row.get("spend")))),), + (("window_start", _text(window["window_start"])), ("started_at", _text(row.get("startTime")))), + ) + for window in windows + ) + + +def project(row: Mapping[str, object]) -> tuple[Contribution, ...]: + metadata: Final = _fields(row.get("metadata")) + return ( + *_entity_contributions(row, metadata), + *_daily_contributions(row, metadata), + *_window_contributions(row, metadata), + *_model_contributions(row, metadata), + *_guardrail_contributions(row, metadata), + ) + + +def _model_contributions(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[Contribution, ...]: + date: Final = _date(row) + origin: Final = metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY) + if date is None or (origin and origin != BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN): + return () + model: Final = _text(row.get("model")) or "unknown" + caller: Final = not origin + success: Final = row.get("status") == "success" + return ( + Contribution( + "LiteLLM_DailyModelUsage", + ( + ("date", date), + ("model_group", _text(row.get("model_group")) or model), + ("model", model), + ("custom_llm_provider", _text(row.get("custom_llm_provider")) or "unknown"), + ("task_type", _model_task(row)), + ), + ( + ("spend", _number(row.get("spend"))), + ("prompt_tokens", _number(row.get("prompt_tokens"))), + ("completion_tokens", _number(row.get("completion_tokens"))), + ("request_count", int(caller)), + ("successful_requests", int(caller and success)), + ("failed_requests", int(caller and not success)), + ), + ), + ) + + +def _utc_date(row: Mapping[str, object]) -> str | None: + start: Final = row.get("startTime") + try: + parsed: Final = datetime.fromisoformat(start.replace("Z", "+00:00")) if isinstance(start, str) else start + except ValueError: + return None + if not isinstance(parsed, datetime): + return None + return ( + (parsed.replace(tzinfo=timezone.utc) if parsed.tzinfo is None else parsed) + .astimezone(timezone.utc) + .date() + .isoformat() + ) + + +def _guardrail_contributions(row: Mapping[str, object], metadata: Mapping[str, object]) -> tuple[Contribution, ...]: + date: Final = _utc_date(row) + info: Final = metadata.get("guardrail_information") or metadata.get("standard_logging_guardrail_information") + if not row.get("request_id") or date is None or not isinstance(info, list): + return () + return tuple(chain.from_iterable(_guardrail_entry(row, date, entry) for entry in _ROWS.validate_python(info))) + + +def _guardrail_entry(row: Mapping[str, object], date: str, entry: Mapping[str, object]) -> tuple[Contribution, ...]: + identity: Final = _text(entry.get("guardrail_id") or entry.get("guardrail_name")) + usage: Final = entry.get("guardrail_usage") + if not identity or not isinstance(usage, Mapping): + return () + costs: Final = _guardrail_unit_costs(entry) + return tuple( + _guardrail_unit(row, (identity, date, unit), units, None if costs is None else costs.get(unit)) + for unit, units in _fields(usage).items() + if isinstance(units, int) and not isinstance(units, bool) and units > 0 + ) + + +def _guardrail_unit( + row: Mapping[str, object], identity: tuple[str, str, str], units: int, cost: float | None +) -> Contribution: + guardrail_id, date, unit = identity + return Contribution( + "LiteLLM_DailyGuardrailUsageUnits", + ( + ("guardrail_id", guardrail_id), + ("date", date), + ("team_id", _text(row.get("team_id"))), + ("api_key", _text(row.get("api_key"))), + ("usage_unit", unit), + ), + (("units", units), ("cost", cost or 0.0), ("untracked_units", units if cost is None else 0)), + ) + + +def _model_task(row: Mapping[str, object]) -> str: + return next( + ( + task + for tag in _tags(row) + if tag.startswith(MODEL_INSIGHTS_TASK_TAG_PREFIX) + and (task := tag.removeprefix(MODEL_INSIGHTS_TASK_TAG_PREFIX)) in _TASK_TYPES + ), + MODEL_INSIGHTS_DEFAULT_TASK, + ) + + +def _guardrail_unit_costs(entry: Mapping[str, object]) -> Mapping[str, float | None] | None: + try: + cost: Final = GuardrailCostByUnitEntry.model_validate(entry) + except ValidationError: + return None + return None if cost.guardrail_cost_in_spend is False else cost.guardrail_cost_by_unit diff --git a/litellm/accounting/protocols.py b/litellm/accounting/protocols.py index 0208e37fbdf..f6ec61983a0 100644 --- a/litellm/accounting/protocols.py +++ b/litellm/accounting/protocols.py @@ -1,58 +1,14 @@ -"""Contracts only. Implementations own persistence, pricing, projections and live budgets. - -The event row's ``payload`` is a private envelope versioned by the row's ``schema_version``. -Usage envelopes retain the public event and request-scoped pricing context: quantities with units, -model/provider, requested and billed tier/region when known, and per-request/deployment overrides. -These inputs, including the effective cost-map rates, must survive request teardown. -Cost envelopes retain the public CostEvent, including its required finalized pricing snapshot. -T5/T6 own envelope serialization; this public API does not introduce a second persisted schema. -""" +from __future__ import annotations +from collections.abc import Mapping from typing import Protocol -from litellm.accounting.types import ( - AdmissionDecision, - AggregateQuery, - AggregateRow, - CostEvent, - Dimension, - InboundUsageEvent, - Measurement, - Metric, - OutboundUsageEvent, - Unpriced, -) - - -class UsageMeter(Protocol): - async def record(self, event: InboundUsageEvent | OutboundUsageEvent) -> None: - """Acknowledge commit, no-op identical IDs and reject conflicting content. - - Persist the event and its request-scoped pricing context before acknowledging. - HTTP acknowledgement is a separate policy. Unknown usage remains None. - """ - ... - - -class CostCompiler(Protocol): - async def compile(self, event: OutboundUsageEvent) -> CostEvent | Unpriced: - """Reuse the committed charge or finalize one charge and its delivery intent. - - Resolve captured request-scoped context and rates by event identity. - Persist the resolved inputs in the required snapshot. Replay - reuses the committed result and never consumes free allowance again. - Missing pricing is Unpriced, never an inferred zero. - """ - ... +from litellm.accounting.types import AggregateQuery, AggregateRow class CostAggregator(Protocol): - async def apply(self, measurements: tuple[Measurement, ...]) -> None: - """Apply one source event's complete contribution set per target/version. - - Each target commits all contributions and its receipt atomically. A receipt - for the first metric must not suppress the rest. Usage need not wait for cost. - """ + async def apply(self, rows: tuple[Mapping[str, object], ...]) -> None: + """Apply stored request contributions atomically with their delivery receipts.""" ... async def query(self, query: AggregateQuery) -> tuple[AggregateRow, ...]: @@ -63,23 +19,3 @@ class CostAggregator(Protocol): combinations. Different groups may overlap, including non-additive tags. """ ... - - -class BudgetEnforcer(Protocol): - async def reserve( - self, reservation_id: str, dimensions: tuple[Dimension, ...], estimated: tuple[Metric, ...] - ) -> AdmissionDecision: - """Apply existing admission policies and estimates; duplicate IDs add no hold.""" - ... - - async def settle(self, reservation_id: str | None, cost_event: CostEvent) -> None: - """Settle the committed charge with atomic deduplication and hold mutation. - - Preserve scope/window attribution and retry unfinished settlement after - restart. SQL projection receipts do not provide live-counter atomicity. - """ - ... - - async def release(self, reservation_id: str) -> None: - """Idempotently release unused exposure at the request boundary, preserving charges.""" - ... diff --git a/litellm/accounting/snapshot.py b/litellm/accounting/snapshot.py new file mode 100644 index 00000000000..51c7bbad81b --- /dev/null +++ b/litellm/accounting/snapshot.py @@ -0,0 +1,97 @@ +from collections.abc import Callable, Mapping, Sequence +from datetime import datetime +from typing import TYPE_CHECKING, Final + +from pydantic import TypeAdapter + +from litellm.litellm_core_utils.safe_json_dumps import safe_json_structure +from litellm.proxy._types import SpendLogsPayload +from litellm.proxy.spend_tracking.compression_savings import extract_compression_saved_tokens +from litellm.proxy.spend_tracking.savings import compute_savings_spend, marks_gateway_injection + +if TYPE_CHECKING: + from litellm.router import Router + +_FIELDS: Final = TypeAdapter(dict[str, object]) + + +def _mapping(value: object) -> Mapping[str, object] | None: + return _FIELDS.validate_python(value) if isinstance(value, Mapping) else None + + +_METADATA_FIELDS: Final = frozenset( + { + "usage_object", + "status", + "guardrail_information", + "standard_logging_guardrail_information", + "compression_savings", + "internal_call_origin", + "billed_retry_attempt", + } +) + + +_ACCOUNTING_FIELDS: Final = frozenset( + { + "request_id", + "call_type", + "api_key", + "spend", + "prompt_tokens", + "completion_tokens", + "startTime", + "model", + "model_group", + "custom_llm_provider", + "user", + "team_id", + "organization_id", + "end_user", + "agent_id", + "request_tags", + "status", + "request_duration_ms", + "mcp_namespaced_tool_name", + } +) + + +def build_accounting_payload( + payload: SpendLogsPayload, + *, + entities: Mapping[str, str | None], + model_access_groups: Sequence[str], + windows: Sequence[Mapping[str, object]], + router: "Callable[[], Router | None]", +) -> Mapping[str, object]: + metadata: Final = _FIELDS.validate_json(payload["metadata"]) + savings: Final = compute_savings_spend( + model=payload["model"], + custom_llm_provider=payload["custom_llm_provider"], + compression_saved_tokens=extract_compression_saved_tokens(metadata), + gateway_injected_cache=marks_gateway_injection(metadata, payload["model_id"]), + routing_decision=_mapping(metadata.get("routing_decision")), + model_id=payload["model_id"], + llm_router=router, + usage_object=_mapping(metadata.get("usage_object")), + cost_breakdown=_mapping(metadata.get("cost_breakdown")), + recorded_autorouter_savings=metadata.get("autorouter_savings"), + recorded_autorouter_savings_estimate=_mapping(metadata.get("autorouter_savings_estimate")), + billed_at=payload["endTime"], + ) + accounting_metadata: Final = { + **{key: value for key, value in metadata.items() if key in _METADATA_FIELDS}, + "accounting_entities": entities, + "accounting_savings": savings._asdict(), + "accounting_model_access_groups": model_access_groups, + "accounting_windows": windows, + } + return { + **{ + key: value.isoformat() if isinstance(value, datetime) else value + for key, value in payload.items() + if key in _ACCOUNTING_FIELDS + }, + "metadata": _FIELDS.validate_python(safe_json_structure(accounting_metadata, non_finite_as_null=True)), + } diff --git a/litellm/anthropic_beta_headers_manager.py b/litellm/anthropic_beta_headers_manager.py index e284845b2ff..c03f7cda990 100644 --- a/litellm/anthropic_beta_headers_manager.py +++ b/litellm/anthropic_beta_headers_manager.py @@ -198,7 +198,6 @@ def _load_beta_headers_config() -> dict: if _BETA_HEADERS_CONFIG is not None: return _BETA_HEADERS_CONFIG - # Get the URL from environment or use default from litellm import anthropic_beta_headers_url _BETA_HEADERS_CONFIG = get_beta_headers_config(url=anthropic_beta_headers_url) diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index a0c0a90be5f..d272a235758 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -10,6 +10,7 @@ from pydantic import JsonValue, TypeAdapter import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils import batch_utils as core_batch_utils from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS from litellm.litellm_core_utils.llm_cost_calc.utils import parse_prompt_tokens_details from litellm.llms.base_llm.ocr.transformation import OCRUsageInfo @@ -38,8 +39,8 @@ class BatchCostUsageResult: completion_cost: float = 0.0 -_COMPLETED_BATCH_STATUSES: Final = frozenset({"completed", "complete"}) _DECODED_BATCH_LINE: Final = TypeAdapter(object) +batch_cost_is_final: Final = core_batch_utils.batch_cost_is_final def _uses_native_vertex_output( @@ -54,26 +55,6 @@ def _uses_native_vertex_output( return first_row is not None and is_native_vertex_batch_output_row(first_row) -_TERMINAL_BATCH_STATUSES: Final = _COMPLETED_BATCH_STATUSES | frozenset({"failed", "cancelled", "expired"}) - - -def batch_cost_is_final(batch: Batch) -> bool: - """Whether this retrieve of the batch is the one to account its cost from. - - A batch still in flight has nothing to price, and a "completed" batch can report - no output_file_id for a moment before the output populates; pricing either records - $0 under the batch's single spend row and pins it there. Final means a completed - batch whose output file has arrived or whose counts prove no line succeeded, or - any other terminal status (failed, cancelled, expired). - """ - if batch.status not in _TERMINAL_BATCH_STATUSES: - return False - if batch.status not in _COMPLETED_BATCH_STATUSES or batch.output_file_id is not None: - return True - request_counts: Final = batch.request_counts - return request_counts is not None and request_counts.total > 0 and request_counts.completed == 0 - - async def calculate_batch_cost_and_usage( file_content_dictionary: Sequence[dict[str, object]], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "mistral"], diff --git a/litellm/blog_posts.json b/litellm/blog_posts.json index fa768b3ec57..adcb3911652 100644 --- a/litellm/blog_posts.json +++ b/litellm/blog_posts.json @@ -1,10 +1,588 @@ { "posts": [ { - "title": "Realtime WebRTC HTTP Endpoints", - "description": "Use the LiteLLM proxy to route OpenAI-style WebRTC realtime via HTTP: client_secrets and SDP exchange.", - "date": "2026-03-12", - "url": "https://docs.litellm.ai/blog/realtime_webrtc_http_endpoints" + "title": "Release cadence changes and an LTS version pilot", + "description": "Starting the week of November 2, 2026, release candidates and stable releases ship on Tuesday mornings, and LiteLLM is piloting a long-term support line on 1.108 for one month.", + "date": "2026-11-02", + "url": "https://docs.litellm.ai/blog/release-cadence-and-lts-pilot", + "tags": [ + "release", + "support" + ] + }, + { + "title": "How to build an agent with grounding on LiteLLM", + "description": "Build a grounded chat agent in four steps with LiteLLM's AI Gateway. Connect your evidence, call a grounding guardrail, and show its verdict in chat.", + "date": "2026-10-31", + "url": "https://docs.litellm.ai/blog/grounded-agent", + "tags": [ + "engineering", + "guardrails", + "agents" + ] + }, + { + "title": "Introducing LiteLLM’s Unified Cost Accounting Engine", + "description": "Consistent cost reporting, independently scalable accounting, and spend analysis by team, user, model, or custom dimensions.", + "date": "2026-10-31", + "url": "https://docs.litellm.ai/blog/cost-infrastructure-makeover", + "tags": [ + "engineering", + "reliability" + ] + }, + { + "title": "What we are changing after October 21", + "description": "Why the controls built after March did not stop the v1.107.0 release, and the identity, credential and detection changes we are making in response.", + "date": "2026-10-30", + "url": "https://docs.litellm.ai/blog/security-changes-after-oct-21-2026", + "tags": [ + "security", + "incident-report" + ] + }, + { + "title": "Use Hex to analyze LiteLLM costs, then set budgets and routing", + "description": "Connect Hex to LiteLLM’s Postgres data, import a cost dashboard, then use the LiteLLM UI to set a budget or change model routing.", + "date": "2026-10-30", + "url": "https://docs.litellm.ai/blog/hex-litellm-roi", + "tags": [ + "observability", + "cost tracking", + "ai-gateway" + ] + }, + { + "title": "Introducing the LiteLLM Private Registry", + "description": "LiteLLM is committing to a private registry to provide early access to fixes for the most severe security vulnerabilities, ahead of public release.", + "date": "2026-10-27", + "url": "https://docs.litellm.ai/blog/litellm-private-registry", + "tags": [ + "security" + ] + }, + { + "title": "Morph open-weight models on LiteLLM", + "description": "Use Morph's Kimi, GLM, and DeepSeek models through LiteLLM with tool calling, structured output, streaming, and prompt-cache cost tracking.", + "date": "2026-10-27", + "url": "https://docs.litellm.ai/blog/morph-provider", + "tags": [] + }, + { + "title": "Abliterated models on LiteLLM", + "description": "Call abliterated models from Refuseless and Abliteration through LiteLLM, with gateway access controls, streaming, tool calls, and cached-token pricing.", + "date": "2026-10-26", + "url": "https://docs.litellm.ai/blog/abliterated-models", + "tags": [ + "engineering" + ] + }, + { + "title": "GPT-6.1 Terra API Pricing and Day 0 Support on LiteLLM", + "description": "GPT-6.1 Terra costs $1 per 1M input tokens and $5 per 1M output tokens, half the price of GPT-6.1 Sol. LiteLLM supports it on day 0.", + "date": "2026-10-26", + "url": "https://docs.litellm.ai/blog/gpt-6-1-terra", + "tags": [ + "openai", + "gpt-6.1", + "gpt-6.1-terra", + "pricing", + "day 0 support", + "card_title:GPT-6.1 Terra", + "card_summary:Half the price of GPT-6.1 Sol at $1 / $5 per 1M tokens, 50.3% on Terminal-Bench 4.0, 1.05M context." + ] + }, + { + "title": "Rust Migration: /v1/messages end-to-end in Rust by Dec 1", + "description": "By December 1st, the proxy will handle Anthropic messages requests 20x faster", + "date": "2026-10-26", + "url": "https://docs.litellm.ai/blog/rust-migration-rerouted", + "tags": [ + "rust", + "rust-migration", + "ai-gateway", + "engineering" + ] + }, + { + "title": "OpenCode Go is now a LiteLLM provider", + "description": "Use your OpenCode Go subscription through LiteLLM. Connect once, route models to the right API, preserve sessions, and track estimated usage.", + "date": "2026-10-24", + "url": "https://docs.litellm.ai/blog/opencode-go", + "tags": [ + "providers", + "tutorial" + ] + }, + { + "title": "The attack chain behind the v1.107.0 LiteLLM release", + "description": "How an internal key committed to a public repository in May led to three stolen GitHub tokens and the compromised litellm v1.107.0 release.", + "date": "2026-10-23", + "url": "https://docs.litellm.ai/blog/security-incident-oct-21-2026-attack-chain", + "tags": [ + "security", + "incident-report" + ] + }, + { + "title": "Tokenize text across vLLM and Watsonx", + "description": "Choose token counting for prompt budgets or /tokenize to inspect tokens. Use one request format across vLLM and Watsonx through LiteLLM.", + "date": "2026-10-23", + "url": "https://docs.litellm.ai/blog/unified-tokenize", + "tags": [ + "engineering" + ] + }, + { + "title": "October Townhall Updates: 426 Bug Fixes, 88.4% Coverage, and the Decisions API", + "description": "A recap of the October LiteLLM town hall: security changes that need action before you upgrade, 426 bug fixes, test coverage at 88.4%, the Decisions API, Lens, Moyai, and LiteLLM Relay.", + "date": "2026-10-23", + "url": "https://docs.litellm.ai/blog/october-townhall-updates", + "tags": [ + "townhall", + "security", + "reliability", + "product" + ] + }, + { + "title": "Contained: Oct 21, 2026 LiteLLM release v1.107.0 Compromised", + "description": "LiteLLM release v1.107.0 was compromised by a malicious actor.", + "date": "2026-10-21", + "url": "https://docs.litellm.ai/blog/security-incident-oct-21-2026", + "tags": [ + "security", + "incident-report" + ] + }, + { + "title": "JevRank: decision models through LiteLLM’s rerank API", + "description": "Preview JevRank for low-cost reranking with Perplexity and TypeSafe decision models. Explore SciFact results, reproducible data and LiteLLM setup.", + "date": "2026-10-20", + "url": "https://docs.litellm.ai/blog/jevrank", + "tags": [ + "engineering", + "reranking", + "decisions" + ] + }, + { + "title": "Cache-Aware Routing for Self-Hosted Models", + "description": "Route each agent session to the same vLLM pod on Kubernetes to reuse cached prompt prefixes.", + "date": "2026-10-20", + "url": "https://docs.litellm.ai/blog/cache-aware-routing", + "tags": [ + "routing", + "kubernetes", + "vllm", + "ai-gateway" + ] + }, + { + "title": "Day 0 Support: TypeSafe Jev 1.14: Adds images, box questions, and long context", + "description": "TypeSafe Jev 1.14 reads images, answers box questions with coordinates and takes long-context requests. LiteLLM serves all three on /v1/systemone and /v1/decisions behind TypeSafe's opt-in headers.", + "date": "2026-10-15", + "url": "https://docs.litellm.ai/blog/typesafe_jev_images", + "tags": [ + "typesafe", + "jev", + "decisions", + "day 0 support", + "card_title:Jev 1.14", + "card_summary:Jev 1.14 reads PNG and JPEG images, answers box questions with coordinates, and takes long-context requests on LiteLLM." + ] + }, + { + "title": "Decision models on LiteLLM: /v1/decisions and /v1/systemone", + "description": "Decision models return probabilities instead of text, which makes routing, triage and guardrail checks cheap and deterministic. LiteLLM serves nine providers of them behind one request shape.", + "date": "2026-10-14", + "url": "https://docs.litellm.ai/blog/decision-models", + "tags": [ + "openai", + "typesafe", + "decisions", + "day 0 support", + "card_title:Decision Models", + "card_summary:Typed yes/no, choice and score answers as probabilities, from 9 providers on /v1/decisions and /v1/systemone." + ] + }, + { + "title": "Workshop: Build a self-hosted cloud agent.", + "description": "Build a self-hosted cloud agent with the LiteLLM team. A two-week remote workshop, October 19–30, to build a working v0 and deploy an internal beta on your infrastructure.", + "date": "2026-10-13", + "url": "https://docs.litellm.ai/blog/cloud-agent-workshop", + "tags": [ + "agents", + "infrastructure", + "announcement" + ] + }, + { + "title": "Gemini 4 Argon API Pricing and Day 0 Support on LiteLLM", + "description": "Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens until Nov 30. LiteLLM supports it on day 0 on AI Studio and Google Cloud.", + "date": "2026-10-13", + "url": "https://docs.litellm.ai/blog/gemini-4-argon", + "tags": [ + "gemini", + "gemini 4", + "gemini-4-argon", + "pricing", + "day 0 support", + "card_title:Gemini 4 Argon", + "card_summary:Google's new flagship: 77.9% on DeepSWE v1.1, 1M context, thinking levels, $2 / $10 per 1M tokens until Nov 30." + ] + }, + { + "title": "October Townhall: Product + Roadmap Updates", + "description": "Join the LiteLLM October townhall on Friday, 23 October at 7:30 AM PT to learn about LiteLLM's product updates and roadmap.", + "date": "2026-10-10", + "url": "https://docs.litellm.ai/blog/october-townhall-announcement", + "tags": [ + "announcement", + "townhall" + ] + }, + { + "title": "Introducing Microsoft 365 Copilot in LiteLLM", + "description": "Use Microsoft 365 Copilot from your applications through LiteLLM, with access to your Microsoft 365 work data.", + "date": "2026-10-09", + "url": "https://docs.litellm.ai/blog/microsoft-365-copilot", + "tags": [ + "announcement", + "ai-gateway" + ] + }, + { + "title": "Introducing litellm-core: the first step toward a leaner SDK", + "description": "Ongoing work to reduce LiteLLM's dependencies and installation footprint, improve runtime loading, and make imports faster", + "date": "2026-10-08", + "url": "https://docs.litellm.ai/blog/introducing-litellm-core", + "tags": [] + }, + { + "title": "Claude Haiku 5.5 API Pricing and Day 0 Support on LiteLLM", + "description": "Claude Haiku 5.5 costs $0.10 per 1M input tokens and $0.50 per 1M output tokens. LiteLLM supports it on day 0 on Anthropic, AWS, Google Cloud and Azure.", + "date": "2026-10-07", + "url": "https://docs.litellm.ai/blog/claude-haiku-5-5", + "tags": [ + "anthropic", + "claude", + "claude haiku", + "haiku 5.5", + "pricing", + "day 0 support", + "card_title:Claude Haiku 5.5", + "card_summary:90% cheaper than Haiku 4.5 on short prompts, 1M context, 72.4% on OSWorld 2.1, $0.10 / $0.50 per 1M tokens." + ] + }, + { + "title": "Open Sourcing Moyai: Self-Hosted Cloud Coding Agent", + "description": "Run cloud coding agents on your own infrastructure with Moyai. Choose your agent and models through LiteLLM, track costs, and follow the setup guide.", + "date": "2026-10-07", + "url": "https://docs.litellm.ai/blog/moyai-open-source", + "tags": [ + "agents", + "open-source", + "infrastructure" + ] + }, + { + "title": "Day 0 Support: Nano Banana 2.1", + "description": "Day 0 support for Gemini Nano Banana 2.1 on LiteLLM, on Google AI Studio and Gemini Enterprise Agent Platform, with image output at half Nano Banana 2's price.", + "date": "2026-10-06", + "url": "https://docs.litellm.ai/blog/nano_banana_2_1", + "tags": [ + "gemini", + "gemini enterprise agent platform", + "nano banana", + "image generation", + "day 0 support", + "card_title:Nano Banana 2.1", + "card_summary:Gemini image model with image output at half Nano Banana 2's price: $30 / MTok image out, $1.50 text in." + ] + }, + { + "title": "How LiteLLM Lens finds repeated failures across 1,000s of agent traces", + "description": "Inside Lens: parallel trace review, Python tools for large traces, and investigations that connect repeated failures to source evidence.", + "date": "2026-10-05", + "url": "https://docs.litellm.ai/blog/lens-failure-patterns", + "tags": [ + "lens", + "agent-tracing", + "engineering" + ] + }, + { + "title": "How we cut time to first byte by 94% for long prompts", + "description": "A 440k-token benchmark went from 553 ms to 35 ms median time to first byte. We removed unnecessary prompt-cache routing work before the model call.", + "date": "2026-10-03", + "url": "https://docs.litellm.ai/blog/long-prompt-latency", + "tags": [ + "performance", + "proxy", + "engineering", + "ai-gateway" + ] + }, + { + "title": "How we built our own internal Devin in 2 days", + "description": "How we built Moyai Devin with Render, Modal, Hermes, Temporal, and LiteLLM: durable sessions, parallel agents, Slack, and shared organization connections.", + "date": "2026-10-03", + "url": "https://docs.litellm.ai/blog/internal-devin-two-days", + "tags": [ + "engineering", + "agents", + "infrastructure", + "slack" + ] + }, + { + "title": "Adding Self-hosted Auto Router Classifiers: Laya & Nimble", + "description": "Use Laya or Bespoke Nimble to classify Auto Router requests on your own infrastructure. Control where classification runs, which model you serve, and how you provision it.", + "date": "2026-10-02", + "url": "https://docs.litellm.ai/blog/laya-nimble-classifiers", + "tags": [ + "auto-router", + "product", + "ai-gateway" + ] + }, + { + "title": "Launching LiteLLM Lens", + "description": "LiteLLM Lens turns the traces flowing through your gateway into findings your agents can act on. Built for agent swarms generating 200K+ traces.", + "date": "2026-10-01", + "url": "https://docs.litellm.ai/blog/litellm-lens-launch", + "tags": [ + "lens", + "agent-tracing" + ] + }, + { + "title": "How we cut LiteLLM's Redis round trips per request by 64%", + "description": "A governed request to the LiteLLM AI Gateway waited on Redis 22 times. It now waits 8 times: one pipeline per Redis backend before the model call, one after.", + "date": "2026-10-01", + "url": "https://docs.litellm.ai/blog/redis-request-pipelines", + "tags": [ + "performance", + "redis", + "proxy", + "engineering", + "ai-gateway" + ] + }, + { + "title": "How we made the LiteLLM Usage page 120x faster", + "description": "From six minutes to 3.2 seconds: how moving aggregation into Postgres made LiteLLM's Usage page roughly 120x faster in our benchmark.", + "date": "2026-09-30", + "url": "https://docs.litellm.ai/blog/usage-page-bounded-loading", + "tags": [ + "performance", + "admin-ui", + "postgres", + "engineering", + "ai-gateway" + ] + }, + { + "title": "Day 0 Support: GPT-6.1 Sol", + "description": "Day 0 support for GPT-6.1 Sol on LiteLLM, with cached input at half GPT-6 Sol's price.", + "date": "2026-09-29", + "url": "https://docs.litellm.ai/blog/gpt_6_1_sol", + "tags": [ + "openai", + "gpt-6", + "gpt-6.1-sol", + "completion", + "day 0 support", + "card_title:GPT-6.1 Sol", + "card_summary:GPT-6 Sol successor with cached input at half GPT-6 Sol's price, same request family and params." + ] + }, + { + "title": "Learn the LiteLLM gateway with a guided course", + "description": "Follow a request through the LiteLLM gateway, Router, and SDK. A guided course for teams deploying the gateway and contributors making code changes.", + "date": "2026-09-28", + "url": "https://docs.litellm.ai/blog/litellm-gateway-course", + "tags": [ + "proxy", + "product" + ] + }, + { + "title": "Day 0 Support: Claude Sonnet 5.5", + "description": "Day 0 support for Claude Sonnet 5.5 on the LiteLLM AI Gateway. Use it across Anthropic, Bedrock, Gemini Enterprise Agent Platform, and Azure.", + "date": "2026-09-28", + "url": "https://docs.litellm.ai/blog/claude_sonnet_5_5", + "tags": [ + "anthropic", + "claude", + "sonnet 5.5", + "day 0 support", + "card_title:Claude Sonnet 5.5", + "card_summary:Same $2 / $10 price as Sonnet 5, 30%+ faster, 70.6% on Terminal-Bench 4.0 (Sonnet 5: 10.3%), 1M context." + ] + }, + { + "title": "Introducing the LiteLLM ROI Calculator", + "description": "Compare each engineer's LiteLLM gateway spend with the estimated effort of their merged pull requests. Open source and self-hosted.", + "date": "2026-09-26", + "url": "https://docs.litellm.ai/blog/litellm-roi-calculator", + "tags": [ + "cost", + "product", + "announcement" + ] + }, + { + "title": "Introducing LiteAgents", + "description": "Switch agent harnesses without rewriting your agent. Keep your tools and model configuration, use native harness controls, and add Temporal when you need durable runs.", + "date": "2026-09-25", + "url": "https://docs.litellm.ai/blog/liteagents-sdk", + "tags": [ + "agents", + "sdk" + ] + }, + { + "title": "September Townhall Updates: 583 Bug Fixes, OCR on Rust, and 83.5% Coverage", + "description": "A recap of the September LiteLLM town hall: security and stability updates, OCR running on Rust by default, test coverage past the 80% target, and new Fusion and liteagents launches.", + "date": "2026-09-24", + "url": "https://docs.litellm.ai/blog/september-townhall-updates", + "tags": [ + "townhall", + "reliability", + "product" + ] + }, + { + "title": "Day 0 support: Gemini 3.8 Flash TTS and Flash-Lite TTS", + "description": "Day 0 support for Gemini 3.8 Flash TTS and Flash-Lite TTS on LiteLLM's /v1/audio/speech, with launch pricing tracked through 2026.", + "date": "2026-09-23", + "url": "https://docs.litellm.ai/blog/gemini_3_8_flash_tts", + "tags": [ + "gemini", + "gemini-3.8-flash-tts", + "text to speech", + "day 0 support", + "card_title:Gemini 3.8 Flash TTS and Flash-Lite TTS", + "card_summary:Gemini speech models on /v1/audio/speech, Flash and Flash-Lite, with launch pricing tracked through 2026." + ] + }, + { + "title": "Introducing LiteAdmin MCP", + "description": "Give your agent tools to create keys, add models, and manage budgets with LiteAdmin MCP. Use the same connector through LiteAdmin, our Slack admin agent.", + "date": "2026-09-23", + "url": "https://docs.litellm.ai/blog/litellm-admin-mcp", + "tags": [ + "mcp", + "agents", + "ai-gateway" + ] + }, + { + "title": "Day 0 Support: GPT-6 Sol and GPT-6 Luna", + "description": "Day 0 support for GPT-6 Sol and GPT-6 Luna on LiteLLM, at half the price of their GPT-5.6 counterparts.", + "date": "2026-09-22", + "url": "https://docs.litellm.ai/blog/gpt_6_sol_luna", + "tags": [ + "openai", + "gpt-6", + "gpt-6-sol", + "gpt-6-luna", + "completion", + "day 0 support", + "card_title:GPT-6 Sol and GPT-6 Luna", + "card_summary:Two GPT-6 variants at half the price of their GPT-5.6 counterparts, routed as the GPT-5 request family." + ] + }, + { + "title": "Day 0 Support: Claude Opus 5.5", + "description": "Day 0 support for Claude Opus 5.5 on the LiteLLM AI Gateway. Use it across Anthropic, Bedrock, Gemini Enterprise Agent Platform, and Azure.", + "date": "2026-09-22", + "url": "https://docs.litellm.ai/blog/claude_opus_5_5", + "tags": [ + "anthropic", + "claude", + "opus 5.5", + "day 0 support", + "card_title:Claude Opus 5.5", + "card_summary:20% cheaper than Opus 5 at $4 / $20 per 1M, 30% faster output, Terminal-Bench 4.0 66.4%, thinking always on." + ] + }, + { + "title": "Getting Started with Fireworks AI on LiteLLM", + "description": "A beginner tutorial: run an open model on Fireworks AI behind a LiteLLM gateway, call it with the OpenAI SDK you already use, then add a second model and a fallback.", + "date": "2026-09-22", + "url": "https://docs.litellm.ai/blog/fireworks-getting-started", + "tags": [ + "tutorial", + "providers", + "fireworks", + "getting-started" + ] + }, + { + "title": "Day 0 Support: Xiaomi MiMo V2.6", + "description": "Day 0 support for Xiaomi MiMo V2.6 Pro and Flash on LiteLLM, priced on the native route for the first time.", + "date": "2026-09-22", + "url": "https://docs.litellm.ai/blog/mimo_v2_6", + "tags": [ + "xiaomi", + "mimo", + "mimo-v2.6", + "day 0 support", + "card_title:Xiaomi MiMo V2.6", + "card_summary:Xiaomi's Pro ($0.435 / $0.87 per 1M) and Flash ($0.14 / $0.28), same rates as V2.5, priced on the native route." + ] + }, + { + "title": "Claude Code server-side auto mode through LiteLLM", + "description": "Anthropic is moving Claude Code auto mode's safety classifier server-side. LiteLLM's native /v1/messages route now forwards the safeguards contract to the Anthropic API, Bedrock InvokeModel, Bedrock Mantle and Vertex AI. Here is the contract, what changed, which releases carry it, and how to verify it.", + "date": "2026-09-21", + "url": "https://docs.litellm.ai/blog/claude-code-server-side-auto-mode", + "tags": [ + "announcement", + "claude-code", + "anthropic", + "ai-gateway" + ] + }, + { + "title": "Day 0 Support: Grok 4.7", + "description": "Day 0 support for Grok 4.7 on LiteLLM, at the same price as Grok 4.6.", + "date": "2026-09-21", + "url": "https://docs.litellm.ai/blog/grok_4_7", + "tags": [ + "xai", + "grok", + "grok-4.7", + "day 0 support", + "card_title:Grok 4.7", + "card_summary:SpaceXAI's update to Grok 4.6 at the same price and limits, better at checking its own work and long context." + ] + }, + { + "title": "JEV Classifier: 5.43x as Fast as Haiku, 96% Lower Cost", + "description": "JEV classified requests 5.43x as fast as Haiku by median latency in our AI Gateway benchmark. Explore the setup, cost savings and methodology.", + "date": "2026-09-20", + "url": "https://docs.litellm.ai/blog/jev-auto-router-benchmark", + "tags": [ + "routing", + "complexity-router", + "engineering", + "ai-gateway" + ] + }, + { + "title": "TypeSafe Jev on LiteLLM", + "description": "TypeSafe AI's Jev lands in LiteLLM v1.103.0-rc: call it through the proxy with logging and cost tracking.", + "date": "2026-09-20", + "url": "https://docs.litellm.ai/blog/typesafe_jev", + "tags": [ + "typesafe", + "jev", + "product" + ] } ] } diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 8dd4d87ae13..8e6341805db 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -1180,11 +1180,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): include_namespace=True, replay_openai_history=provider == "openai", ) - # OpenAI's Responses API rejects an empty input. For a system-only - # request, carry the system message as a system-role input item instead - # of instructions, mirroring how non-string system content is already - # handled in convert_chat_completion_messages_to_responses_api. - is_system_only_request: Final = not converted_input_items and converted_instructions is not None + # Keep the system text in input instead of instructions when the Responses API needs it there. + # It rejects an empty input, and json_object requires the word "json" in input, ignoring instructions. + typed_optional_params: Final = cast("Mapping[str, object]", optional_params) # cast-ok: untyped dict + system_goes_in_input: Final = converted_instructions is not None and ( + not converted_input_items or _payload_type(typed_optional_params.get("response_format")) == "json_object" + ) target_input_items: Final = ( _without_audio_input_parts(converted_input_items) if _drops_audio_input(model, litellm_params) @@ -1196,12 +1197,13 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): "type": "message", "role": "system", "content": [{"type": "input_text", "text": converted_instructions}], - } + }, + *target_input_items, ] - if is_system_only_request + if system_goes_in_input else target_input_items ) - instructions: Final = None if is_system_only_request else converted_instructions + instructions: Final = None if system_goes_in_input else converted_instructions # Build responses API request using the reverse transformation logic responses_api_request: Final = ResponsesAPIOptionalRequestParams() diff --git a/litellm/constants.py b/litellm/constants.py index 55a310fe9a5..ea6d1b49ae8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -134,6 +134,7 @@ ANTHROPIC_DEFAULT_FILE_LIST_LIMIT: Final = 20 DEFAULT_SQS_FLUSH_INTERVAL_SECONDS: Final = get_env_int("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) DEFAULT_NUM_WORKERS_LITELLM_PROXY: Final = get_env_int("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) GRACEFUL_SHUTDOWN_TEARDOWN_HEADROOM_SECONDS: Final = 10 +DEFAULT_MAX_REQUEST_HEADER_SIZE: Final = 16 * 1024 budget_reservation_disabled_info_emitted = False DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = get_env_int("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1) DEFAULT_SQS_BATCH_SIZE: Final = get_env_int("DEFAULT_SQS_BATCH_SIZE", 512) @@ -1091,6 +1092,7 @@ openai_compatible_endpoints: Final[list[str]] = [ "https://api.scx.ai/v1", "https://api.prisminference.com/v1", "https://api.reka.ai/v1", + "https://inference.runanywhere.ai/v1", "https://gigachat.devices.sberbank.ru/api/v1", "https://api.openinfer.ai/v1", "https://vanchin.streamlake.ai/api/gateway/v1/endpoints", @@ -1178,6 +1180,7 @@ openai_compatible_providers: Final[list[str]] = [ "sail", "reka", "streamlake", + "wally", ] OPENAI_AUDIO_TRANSCRIPTION_PROVIDERS: Final = frozenset({"openai"} | frozenset(openai_compatible_providers)) @@ -2066,6 +2069,7 @@ SPEND_LOG_KEY_METADATA_CACHE_TTL: Final = 600 SPEND_LOG_KEY_METADATA_MISS_CACHE_TTL: Final = 30 SPEND_LOG_KEY_METADATA_CACHE_MAX_ITEMS: Final = 10000 SPEND_LOG_KEY_METADATA_QUERY_TIMEOUT_MS: Final = 5000 +SPEND_LOG_READ_STATEMENT_TIMEOUT_SECONDS: Final = 30 SPEND_LOG_KEY_METADATA_ROWS_PER_PROBE: Final = 100 # Short TTL for negative MCP access-group existence lookups. Keeps unauthenticated # callers from forcing a DB query per request for unknown names, while bounding diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 527981a1dc6..9154a6fdc30 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -49,6 +49,9 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import ( generic_cost_per_token, get_batch_cost_rates, get_billable_input_tokens, + get_cost_per_unit, + get_fedramp_uplift_multiplier, + get_modality_token_cost_key, get_regional_uplift_multiplier, get_service_tier_cost_key, get_token_type_cost_breakdown, @@ -442,6 +445,14 @@ def _per_second_pricing_cost( model_info: Final = cached_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider) except Exception: # noqa: BLE001 # the lookup raises plain Exception for an unmapped model return None + return _per_second_pricing_cost_from_info(model_info, response_time_ms, audio_seconds) + + +def _per_second_pricing_cost_from_info( + model_info: ModelInfoBase, + response_time_ms: float | None, + audio_seconds: float = 0.0, +) -> tuple[float, float] | None: if _has_token_or_tiered_pricing(model_info) or not _bills_wall_clock_seconds(model_info): return None cost_per_second: Final = model_info.get("cost_per_second") @@ -464,7 +475,7 @@ def _per_second_pricing_cost( ) verbose_logger.debug( "For model=%s - cost_per_second: %s; response time: %s", - model, + model_info.get("key"), resolved_cost_per_second, response_time_ms, ) @@ -487,6 +498,7 @@ def cost_per_token( ### CUSTOM PRICING ### custom_cost_per_token: CostPerToken | None = None, custom_cost_per_second: float | None = None, + requested_model_info_override: ModelInfo | None = None, ### NUMBER OF QUERIES ### number_of_queries: int | None = None, ### USAGE OBJECT ### @@ -598,6 +610,20 @@ def cost_per_token( if response_cost is not None: return response_cost[0], response_cost[1] + if requested_model_info_override is not None: + per_second: Final = _per_second_pricing_cost_from_info( + requested_model_info_override, response_time_ms, audio_transcription_file_duration + ) + if per_second is not None: + return per_second + return generic_cost_per_token( + model=model, + usage=usage_block, + custom_llm_provider=custom_llm_provider or "", + service_tier=service_tier, + model_info=requested_model_info_override, + ) + model_cost_ref: Final = litellm.model_cost # Only callers that explicitly pass `custom_llm_provider` get the # dedup/prefix-join treatment. When provider is omitted, preserve legacy @@ -1102,20 +1128,19 @@ def _cost_map_model_name(model: str, custom_llm_provider: str | None, region_nam return _strip_unregistered_leading_segments(f"{provider_prefix}/{model}", region_name) -def _rule_declares_rates(model: str, custom_llm_provider: str | None) -> bool: - rule_info: Final = unmapped_model_generalization(model, custom_llm_provider) - return rule_info is not None and _cost_map_entry_prices_anything(rule_info) +def declared_cost_map_pricing(key: str, model: str, custom_llm_provider: str | None) -> Mapping[str, object]: + entry: Final = _raw_cost_map_entry(key) + if entry is None: + return unmapped_model_generalization(model, custom_llm_provider) or MappingProxyType({}) + fill_missing: Final = mapped_model_fill_missing(key, entry) + return entry if fill_missing is None else MappingProxyType({**fill_missing, **entry}) def _resolves_to_rates(model: str, custom_llm_provider: str | None) -> bool: key: Final = cached_cost_map_key(model, custom_llm_provider) - if key is None: - return False - entry: Final = _raw_cost_map_entry(key) - if entry is None: - return _rule_declares_rates(model, custom_llm_provider) - fill_missing: Final = mapped_model_fill_missing(key, entry) - return _cost_map_entry_prices_anything(entry if fill_missing is None else {**fill_missing, **entry}) + return key is not None and _cost_map_entry_prices_anything( + declared_cost_map_pricing(key, model, custom_llm_provider) + ) def _served_or_priced_requested_model( @@ -1748,6 +1773,7 @@ def completion_cost( ### CUSTOM PRICING ### custom_cost_per_token: CostPerToken | None = None, custom_cost_per_second: float | None = None, + requested_model_info_override: ModelInfo | None = None, optional_params: dict | None = None, custom_pricing: bool | None = None, base_model: str | None = None, @@ -1801,6 +1827,7 @@ def completion_cost( quality=quality, n=n, custom_cost_per_token=custom_cost_per_token, + requested_model_info_override=requested_model_info_override, custom_cost_per_second=custom_cost_per_second, optional_params=optional_params, custom_pricing=custom_pricing, @@ -2275,6 +2302,7 @@ def completion_cost( region_name=None if explicit_pricing else region_name, custom_cost_per_second=custom_cost_per_second, custom_cost_per_token=custom_cost_per_token, + requested_model_info_override=requested_model_info_override, prompt_characters=prompt_characters, completion_characters=completion_characters, cache_creation_input_tokens=cache_creation_input_tokens, @@ -2420,18 +2448,20 @@ def completion_cost( raise e -def get_response_cost_from_hidden_params( - hidden_params: dict | BaseModel, -) -> float | None: - if isinstance(hidden_params, BaseModel): - _hidden_params_dict = hidden_params.model_dump() - else: - _hidden_params_dict = hidden_params +_ADDITIONAL_HEADERS: Final = TypeAdapter(Mapping[str, object]) - additional_headers: Final = _hidden_params_dict.get("additional_headers", {}) - if additional_headers and "llm_provider-x-litellm-response-cost" in additional_headers: - return _billable_header_cost(additional_headers["llm_provider-x-litellm-response-cost"]) - return None + +def get_response_cost_from_hidden_params( + hidden_params: Mapping[str, object] | BaseModel, +) -> float | None: + params: Final[Mapping[str, object]] = ( + hidden_params.model_dump() if isinstance(hidden_params, BaseModel) else hidden_params + ) + additional_headers: Final = params.get("additional_headers") + if not isinstance(additional_headers, Mapping): + return None + headers: Final = _ADDITIONAL_HEADERS.validate_python(additional_headers) + return _billable_header_cost(headers.get("llm_provider-x-litellm-response-cost")) def _billable_header_cost(value: object) -> float | None: @@ -2507,6 +2537,7 @@ def response_cost_calculator( region_name: str | None = None, custom_cost_per_token: CostPerToken | None = None, custom_cost_per_second: float | None = None, + requested_model_info_override: ModelInfo | None = None, ) -> float: """ Returns @@ -2563,6 +2594,7 @@ def response_cost_calculator( custom_pricing=custom_pricing, base_model=base_model, custom_cost_per_token=custom_cost_per_token, + requested_model_info_override=requested_model_info_override, custom_cost_per_second=custom_cost_per_second, prompt=prompt, standard_built_in_tools_params=standard_built_in_tools_params, @@ -3157,8 +3189,12 @@ def _batch_rate( "input_cost_per_video_token_batches", ], fallback: float, + *, + prompt_tokens: int = 0, ) -> float: - rate: Final = model_info.get(key) + context_key: Final = get_modality_token_cost_key(model_info, key, prompt_tokens) + context_rate: Final = get_cost_per_unit(model_info, context_key, None) + rate: Final = model_info.get(key) if context_rate is None else context_rate return fallback if rate is None else rate @@ -3276,7 +3312,9 @@ def batch_cost_calculator( text_tokens: Final = usage.completion_tokens - image_tokens total_completion_cost = text_tokens * text_rate + image_tokens * image_rate - uplift: Final = get_regional_uplift_multiplier(model_info, data_residency) + uplift: Final = get_regional_uplift_multiplier(model_info, data_residency) * get_fedramp_uplift_multiplier( + model_info, data_residency + ) if uplift != 1.0: total_prompt_cost *= uplift total_completion_cost *= uplift @@ -3308,7 +3346,8 @@ def _batch_prompt_cost( ) return ( text_tokens * input_rate - + audio_tokens * _batch_rate(model_info, "input_cost_per_audio_token_batches", input_rate) + + audio_tokens + * _batch_rate(model_info, "input_cost_per_audio_token_batches", input_rate, prompt_tokens=usage.prompt_tokens) + image_tokens * _batch_rate(model_info, "input_cost_per_image_token_batches", input_rate) + video_tokens * _batch_rate(model_info, "input_cost_per_video_token_batches", input_rate) + (cached_tokens - cached_audio_tokens) * (cache_read_rate or 0.0) diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 12425c45b01..6662cf4a2c2 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -867,6 +867,7 @@ class BadGatewayError(openai.APIStatusError): class InternalServerError(openai.InternalServerError): response: httpx.Response # pyright: ignore[reportIncompatibleVariableOverride] # LiteLLM constructs an HTTPX response request: httpx.Request # pyright: ignore[reportIncompatibleVariableOverride] # LiteLLM constructs an HTTPX request + provider_status_code: int | None def __init__( self, @@ -878,8 +879,10 @@ class InternalServerError(openai.InternalServerError): max_retries: int | None = None, num_retries: int | None = None, body: object | None = None, + provider_status_code: int | None = None, ): self.status_code = 500 + self.provider_status_code = provider_status_code self.message = f"litellm.InternalServerError: {message}" self.llm_provider = llm_provider self.model = model diff --git a/litellm/images/main.py b/litellm/images/main.py index 27917cf9289..79bb07fc394 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -67,6 +67,10 @@ from litellm.utils import ( ) _PROVIDER_ROUTING_HINTS: Final = TypeAdapter(ProviderRoutingHints) +_OPTIONAL_OBJECT_DICT_ADAPTER: Final = TypeAdapter[dict[str, object] | None](dict[str, object] | None) +_OPTIONAL_STRING_DICT_ADAPTER: Final = TypeAdapter[dict[str, str] | None](dict[str, str] | None) +_OBJECT_DICT_ADAPTER: Final = TypeAdapter[dict[str, object]](dict[str, object]) +_STRING_DICT_ADAPTER: Final = TypeAdapter[dict[str, str]](dict[str, str]) _PROVIDERS_RESOLVING_THEIR_OWN_IMAGE_BASE: Final = ( LlmProviders.DASHSCOPE, LlmProviders.QWENCLOUD, @@ -93,8 +97,8 @@ def _chatgpt_image_params(kwargs: Mapping[str, object]) -> GenericLiteLLMParams: from litellm.responses.utils import ResponsesAPIRequestUtils headers: Final = ResponsesAPIRequestUtils.merge_client_forwarded_headers( - TypeAdapter[dict[str, object] | None](dict[str, object] | None).validate_python(kwargs.get("extra_headers")), - TypeAdapter[dict[str, str] | None](dict[str, str] | None).validate_python(kwargs.get("headers")), + _OPTIONAL_OBJECT_DICT_ADAPTER.validate_python(kwargs.get("extra_headers")), + _OPTIONAL_STRING_DICT_ADAPTER.validate_python(kwargs.get("headers")), ) return GenericLiteLLMParams.model_validate({**kwargs, "extra_headers": headers}) @@ -167,7 +171,7 @@ async def aimage_generation(*args, **kwargs) -> ImageResponse: custom_llm_provider=provider_hints.get("custom_llm_provider"), api_base=provider_hints.get("api_base"), litellm_params=( - _chatgpt_image_params(TypeAdapter(dict[str, object]).validate_python(kwargs)) + _chatgpt_image_params(_OBJECT_DICT_ADAPTER.validate_python(kwargs)) if provider_hints.get("custom_llm_provider") == "chatgpt" or (isinstance(model, str) and model.startswith("chatgpt/")) else None @@ -299,7 +303,7 @@ def image_generation( headers.update(extra_headers) model_response: ImageResponse = litellm.utils.ImageResponse() chatgpt_params: Final = ( - _chatgpt_image_params(TypeAdapter(dict[str, object]).validate_python({**kwargs, "api_key": api_key})) + _chatgpt_image_params(_OBJECT_DICT_ADAPTER.validate_python({**kwargs, "api_key": api_key})) if custom_llm_provider == "chatgpt" or (model is not None and model.startswith("chatgpt/")) else None ) @@ -582,7 +586,7 @@ def image_generation( _chatgpt_image_headers( model, chatgpt_params.model_copy(update={"api_key": dynamic_api_key or api_key}), - TypeAdapter(dict[str, str]).validate_python(headers), + _STRING_DICT_ADAPTER.validate_python(headers), ) ) if chatgpt_params is not None diff --git a/litellm/integrations/code_interpreter_interception/handler.py b/litellm/integrations/code_interpreter_interception/handler.py index 69b528a29f8..b035732afa6 100644 --- a/litellm/integrations/code_interpreter_interception/handler.py +++ b/litellm/integrations/code_interpreter_interception/handler.py @@ -10,6 +10,7 @@ import json import time import uuid from collections.abc import Mapping, Sequence +from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypeAlias, TypedDict, runtime_checkable from pydantic import ValidationError @@ -73,7 +74,7 @@ class CodeInterpreterLogOutput(TypedDict): class CodeInterpreterCall(TypedDict): id: str type: Literal["code_interpreter_call"] - status: Literal["completed"] + status: Literal["completed", "failed"] code: str container_id: str | None outputs: list[CodeInterpreterLogOutput] @@ -141,6 +142,39 @@ class _SupportsOutput(Protocol): _CachedContainer: TypeAlias = tuple[ContainerHandle, SandboxToolParams | None, float, str | None] +@dataclass(frozen=True, slots=True) +class _ReadySandbox: + container: ContainerHandle + params: SandboxToolParams | None + + +@dataclass(frozen=True, slots=True) +class _SandboxUnavailable: + output: str + + +_Sandbox: TypeAlias = _ReadySandbox | _SandboxUnavailable + + +@dataclass(frozen=True, slots=True) +class _ToolRun: + output: str + status: Literal["completed", "failed"] + + +_SANDBOX_UNAVAILABLE_OUTPUT: Final = "The code did not run because the code execution sandbox is unavailable." +_SANDBOX_LOST_RESULT_OUTPUT: Final = ( + "The code execution sandbox failed while running the code, so the code may have run but its result is lost." +) + + +def _sandbox_unavailable(step: str, error: Exception, output: str = _SANDBOX_UNAVAILABLE_OUTPUT) -> _SandboxUnavailable: + verbose_logger.warning( + "CodeInterpreterInterception: %s failed, answering the tool call with the failure: %s", step, error + ) + return _SandboxUnavailable(output=output) + + def _output_item_type(item: object) -> object: if isinstance(item, dict): item_mapping: Final[dict[str, object]] = item @@ -189,6 +223,14 @@ def _extract_identity(kwargs: Mapping[str, object]) -> str: return identity if isinstance(identity, str) else "" +def _bridged_from_responses(kwargs: Mapping[str, object], call_type: CallTypes | None) -> bool: + route: Final = getattr(kwargs.get("litellm_logging_obj"), "call_type", None) + return call_type in (CallTypes.completion, CallTypes.acompletion) and route in ( + CallTypes.responses, + CallTypes.aresponses, + ) + + def _resolve_sandbox_tool(sandbox_tool_name: str | None) -> SandboxToolParams | None: if sandbox_tool_name is None: return None @@ -261,7 +303,7 @@ class CodeInterpreterInterceptionLogger(CustomLogger): async def async_pre_call_deployment_hook( self, kwargs: dict[str, object], call_type: CallTypes | None ) -> dict | None: - if not kwargs.get("_agentic_loop_depth"): + if not kwargs.get("_agentic_loop_depth") and not _bridged_from_responses(kwargs, call_type): kwargs.pop(_INTERCEPTION_ACTIVE_KEY, None) kwargs.pop(_SANDBOX_KEY, None) self._strip_interception_metadata(kwargs) @@ -451,16 +493,16 @@ class CodeInterpreterInterceptionLogger(CustomLogger): sandbox_key: Final = self._extract_sandbox_key(kwargs) is_session: Final = bool(kwargs.get(_SESSION_SCOPED_KEY)) identity: Final = _extract_identity(kwargs) if is_session else None - container, params = await self._get_or_create_container(cache_key=sandbox_key, identity=identity) + sandbox: Final = await self._acquire_sandbox(cache_key=sandbox_key, identity=identity) try: - container_id: Final = self._container_id(container) + container_id: Final = self._sandbox_container_id(sandbox) input_list: Final = self._normalize_messages(messages) code_interpreter_calls: Final[list[CodeInterpreterCall]] = [] for tool_call in tool_calls: arguments = tool_call.get("arguments", "") code = self._parse_code(arguments) - stdout = await self._run_tool_call(container=container, params=params, arguments=arguments) + tool_run = await self._sandbox_tool_output(sandbox=sandbox, arguments=arguments) input_list.append( { "type": "function_call", @@ -473,22 +515,23 @@ class CodeInterpreterInterceptionLogger(CustomLogger): { "type": "function_call_output", "call_id": tool_call.get("call_id"), - "output": stdout, + "output": tool_run.output, } ) code_interpreter_calls.append( { "id": f"ci_{uuid.uuid4().hex}", "type": "code_interpreter_call", - "status": "completed", + "status": tool_run.status, "code": code, "container_id": container_id, - "outputs": ([{"type": "logs", "logs": stdout}] if stdout else []), + "outputs": ([{"type": "logs", "logs": tool_run.output}] if tool_run.output else []), } ) except Exception: await self._delete_container_for_cache_key(sandbox_key) raise + await self._evict_sandbox_if_a_run_failed(sandbox_key, sandbox, code_interpreter_calls) optional_params: Final = anthropic_messages_optional_request_params request_patch: Final = AgenticLoopRequestPatch( @@ -526,14 +569,13 @@ class CodeInterpreterInterceptionLogger(CustomLogger): sandbox_key: Final = self._extract_sandbox_key(kwargs) is_session: Final = bool(kwargs.get(_SESSION_SCOPED_KEY)) identity: Final = _extract_identity(kwargs) if is_session else None - container, params = await self._get_or_create_container(cache_key=sandbox_key, identity=identity) + sandbox: Final = await self._acquire_sandbox(cache_key=sandbox_key, identity=identity) try: - container_id: Final = self._container_id(container) + container_id: Final = self._sandbox_container_id(sandbox) tool_results: Final = [ await self._build_chat_completion_tool_result( - container=container, - params=params, + sandbox=sandbox, tool_call=tool_call, container_id=container_id, ) @@ -544,6 +586,7 @@ class CodeInterpreterInterceptionLogger(CustomLogger): raise tool_messages: Final = [result[0] for result in tool_results] code_interpreter_calls: Final = [result[1] for result in tool_results] + await self._evict_sandbox_if_a_run_failed(sandbox_key, sandbox, code_interpreter_calls) request_patch: Final = AgenticLoopRequestPatch( model=model, @@ -569,9 +612,13 @@ class CodeInterpreterInterceptionLogger(CustomLogger): ) @staticmethod - def _container_id(container: ContainerHandle) -> str | None: - container_id: Final[object] = getattr(container, "id", None) - return container_id if isinstance(container_id, str) else None + def _sandbox_container_id(sandbox: _Sandbox) -> str | None: + match sandbox: + case _ReadySandbox(container=container): + container_id: Final[object] = getattr(container, "id", None) + return container_id if isinstance(container_id, str) else None + case _SandboxUnavailable(): + return None @staticmethod def _agentic_tool_calls(tools: dict[str, object]) -> list[CodeExecutionToolCall]: @@ -588,28 +635,27 @@ class CodeInterpreterInterceptionLogger(CustomLogger): async def _build_chat_completion_tool_result( self, - container: ContainerHandle, - params: SandboxToolParams | None, + sandbox: _Sandbox, tool_call: CodeExecutionToolCall, container_id: str | None, ) -> tuple[ChatCompletionToolMessage, CodeInterpreterCall]: arguments: Final = tool_call.get("arguments", "") code: Final = self._parse_code(arguments) - stdout: Final = await self._run_tool_call(container=container, params=params, arguments=arguments) + tool_run: Final = await self._sandbox_tool_output(sandbox=sandbox, arguments=arguments) tool_call_id: Final = tool_call.get("id") or tool_call.get("call_id") or uuid.uuid4().hex return ( { "role": "tool", "tool_call_id": tool_call_id, - "content": stdout, + "content": tool_run.output, }, { "id": f"ci_{uuid.uuid4().hex}", "type": "code_interpreter_call", - "status": "completed", + "status": tool_run.status, "code": code, "container_id": container_id, - "outputs": [{"type": "logs", "logs": stdout}] if stdout else [], + "outputs": [{"type": "logs", "logs": tool_run.output}] if tool_run.output else [], }, ) @@ -694,19 +740,55 @@ class CodeInterpreterInterceptionLogger(CustomLogger): except (json.JSONDecodeError, TypeError, AttributeError): return "" - async def _run_tool_call(self, container: ContainerHandle, params: SandboxToolParams | None, arguments: str) -> str: + async def _evict_sandbox_if_a_run_failed( + self, sandbox_key: str | None, sandbox: _Sandbox, calls: Sequence[CodeInterpreterCall] + ) -> None: + if not sandbox_key or not isinstance(sandbox, _ReadySandbox): + return + if not any(call["status"] == "failed" for call in calls): + return + cached: Final = self._container_cache.get(sandbox_key) + if cached is None or cached[0] is not sandbox.container: + return + self._container_cache.pop(sandbox_key) + await self._delete_container(container=cached[0], params=cached[1]) + + async def _sandbox_tool_output(self, sandbox: _Sandbox, arguments: str) -> _ToolRun: + match sandbox: + case _SandboxUnavailable(output=output): + return _ToolRun(output=output, status="failed") + case _ReadySandbox(container=container, params=params): + match await self._run_tool_call(container=container, params=params, arguments=arguments): + case _SandboxUnavailable(output=output): + return _ToolRun(output=output, status="failed") + case str() as output: + return _ToolRun(output=output, status="completed") + + async def _run_tool_call( + self, container: ContainerHandle, params: SandboxToolParams | None, arguments: str + ) -> str | _SandboxUnavailable: try: code: Final = json.loads(arguments).get("code", "") if arguments else "" except (json.JSONDecodeError, TypeError): return "[invalid tool arguments: could not parse code]" - result: Final = await self._run_code(container=container, params=params, code=code) + try: + result: Final = await self._run_code(container=container, params=params, code=code) + except Exception as e: + return _sandbox_unavailable("running the code", e, _SANDBOX_LOST_RESULT_OUTPUT) if getattr(result, "error", None): error: Final = result.error message: Final = error.get("value") or error.get("name") if isinstance(error, dict) else str(error) return f"[execution error] {message}" return getattr(result, "stdout", "") or "" + async def _acquire_sandbox(self, cache_key: str | None, identity: str | None) -> _Sandbox: + try: + container, params = await self._get_or_create_container(cache_key=cache_key, identity=identity) + except Exception as e: + return _sandbox_unavailable("creating the sandbox", e) + return _ReadySandbox(container=container, params=params) + async def _get_or_create_container( self, cache_key: str | None, diff --git a/litellm/integrations/otel/model/spans.py b/litellm/integrations/otel/model/spans.py index 320231430d2..ece85b09432 100644 --- a/litellm/integrations/otel/model/spans.py +++ b/litellm/integrations/otel/model/spans.py @@ -341,6 +341,7 @@ _PRISMA_MODELS: Final[frozenset[str]] = frozenset( "LiteLLM_AccountingDelivery", "LiteLLM_AccountingEvent", "LiteLLM_ClaudeCodePluginTable", + "LiteLLM_ClaudeCodePluginUpload", "LiteLLM_MemoryTable", "LiteLLM_AdaptiveRouterState", "LiteLLM_AdaptiveRouterSession", diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 1c9a443b546..fc1a601f9da 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -54,6 +54,7 @@ from litellm.litellm_core_utils.get_llm_provider_logic import is_registered_cust from litellm.litellm_core_utils.service_tier_utils import ( get_service_tier_from_standard_logging_payload, ) +from litellm.llms.azure.common_utils import caused_by_entra_rejection from litellm.llms.github_copilot.per_user_auth import github_copilot_user_session_from from litellm.llms.openai_like.json_loader import JSONProviderRegistry from litellm.models.end_user import LiteLLM_EndUserTable @@ -448,7 +449,7 @@ def _is_deployment_failure(exception: openai.APIError, logged_call: Mapping[str, return not ( isinstance(exception, CallerCredentialAuthenticationError) or bool(logged_call.get(LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL)) - or logged_call.get("api_call_start_time") is None + or (logged_call.get("api_call_start_time") is None and not caused_by_entra_rejection(exception)) or _used_a_caller_credential(logged_call) ) diff --git a/litellm/litellm_core_utils/agentic_followup_failure.py b/litellm/litellm_core_utils/agentic_followup_failure.py new file mode 100644 index 00000000000..318429d64b5 --- /dev/null +++ b/litellm/litellm_core_utils/agentic_followup_failure.py @@ -0,0 +1,36 @@ +from dataclasses import dataclass +from typing import Final, TypeAlias + +from litellm.types.utils import Usage + +ServedRound: TypeAlias = tuple[Usage, float] + +_MARK_ATTRIBUTE: Final = "_litellm_agentic_followup_mark" + + +@dataclass(frozen=True, slots=True) +class _FollowUpMark: + served_rounds: tuple[ServedRound, ...] + + +def _mark_of(error: BaseException) -> _FollowUpMark | None: + mark: Final[object] = getattr(error, _MARK_ATTRIBUTE, None) + return mark if isinstance(mark, _FollowUpMark) else None + + +def mark_agentic_followup_failure(error: BaseException, served_rounds: tuple[ServedRound, ...]) -> None: + setattr(error, _MARK_ATTRIBUTE, _FollowUpMark(served_rounds)) + + +def is_agentic_followup_failure(error: BaseException) -> bool: + return _mark_of(error) is not None + + +def rounds_served_before(error: BaseException) -> tuple[ServedRound, ...]: + mark: Final = _mark_of(error) + return () if mark is None else mark.served_rounds + + +def carry_agentic_followup_mark(source: BaseException, mapped: BaseException) -> None: + if mapped is not source and is_agentic_followup_failure(source): + mark_agentic_followup_failure(mapped, rounds_served_before(source)) diff --git a/litellm/litellm_core_utils/agentic_loop_refusal.py b/litellm/litellm_core_utils/agentic_loop_refusal.py new file mode 100644 index 00000000000..f3ee15dffdd --- /dev/null +++ b/litellm/litellm_core_utils/agentic_loop_refusal.py @@ -0,0 +1,107 @@ +import asyncio +from collections.abc import Awaitable, Iterable, Mapping +from itertools import chain +from types import MappingProxyType +from typing import Final + +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.prompt_templates.mid_conversation_system import message_field, parts_of +from litellm.types.llms.openai import ResponsesAPIResponse +from litellm.types.utils import ( + ChatCompletionMessageCustomToolCall, + ChatCompletionMessageToolCall, + Choices, + ModelResponse, +) + + +def _strings(values: Iterable[object]) -> frozenset[str]: + return frozenset(value for value in values if isinstance(value, str)) + + +def _claimed_calls(tool_calls: object) -> tuple[object, ...]: + wrapped: Final = message_field(tool_calls, "tool_calls") + return parts_of(tool_calls if wrapped is None else wrapped) + + +async def _calls_a_gate_claims(gate: Awaitable[tuple[bool, object]]) -> tuple[object, ...]: + try: + should_run, tool_calls = await gate + except Exception as e: + verbose_logger.debug("LiteLLM.AgenticLoopRefused: skipping a gate that failed with %s", type(e).__name__) + return () + return _claimed_calls(tool_calls) if should_run else () + + +async def every_claimed_tool_call( + refused_tool_calls: object, other_gates: Iterable[Awaitable[tuple[bool, object]]] +) -> Mapping[str, tuple[object, ...]]: + claims: Final = await asyncio.gather(*(_calls_a_gate_claims(gate) for gate in other_gates)) + return MappingProxyType({"tool_calls": (*_claimed_calls(refused_tool_calls), *chain.from_iterable(claims))}) + + +def _call_ids(call: object) -> frozenset[str]: + return _strings((message_field(call, "id"), message_field(call, "call_id"))) + + +def refused_tool_call_identifiers(tool_calls: object) -> tuple[frozenset[str], frozenset[str]]: + calls: Final = _claimed_calls(tool_calls) + ids: Final = frozenset(chain.from_iterable(_call_ids(call) for call in calls)) + names: Final = _strings(message_field(call, "name") for call in calls if not _call_ids(call)) + return ids, names + + +def is_refused_call(call_id: object, name: object, refused_ids: frozenset[str], refused_names: frozenset[str]) -> bool: + return (isinstance(call_id, str) and call_id in refused_ids) or name in refused_names + + +def is_refused_item( + item: object, item_type: str, id_field: str, refused_ids: frozenset[str], refused_names: frozenset[str] +) -> bool: + if message_field(item, "type") != item_type: + return False + return is_refused_call(message_field(item, id_field), message_field(item, "name"), refused_ids, refused_names) + + +def finalize_refused_responses_turn(response: ResponsesAPIResponse, tool_calls: object) -> ResponsesAPIResponse: + refused_ids, refused_names = refused_tool_call_identifiers(tool_calls) + kept: Final = [ + item + for item in parts_of(message_field(response, "output")) + if not is_refused_item(item, "function_call", "call_id", refused_ids, refused_names) + ] + return response.model_copy(update={"output": kept}) + + +def _is_refused_chat_tool_call(call: object, refused_ids: frozenset[str], refused_names: frozenset[str]) -> bool: + name: Final = message_field(message_field(call, "function"), "name") + return is_refused_call(message_field(call, "id"), name, refused_ids, refused_names) + + +def _at_position( + call: ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall, position: int +) -> ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall: + return call if message_field(call, "index") is None else call.model_copy(update={"index": position}) + + +def _finalized_choice(choice: object, refused_ids: frozenset[str], refused_names: frozenset[str]) -> object: + if not isinstance(choice, Choices) or not choice.message.tool_calls: + return choice + surviving: Final = ( + call for call in choice.message.tool_calls if not _is_refused_chat_tool_call(call, refused_ids, refused_names) + ) + kept: Final = [_at_position(call, position) for position, call in enumerate(surviving)] + finish_reason: Final = "stop" if not kept and choice.finish_reason == "tool_calls" else choice.finish_reason + return choice.model_copy( + update={ + "message": choice.message.model_copy(update={"tool_calls": kept or None}), + "finish_reason": finish_reason, + } + ) + + +def finalize_refused_chat_turn(response: ModelResponse, tool_calls: object) -> ModelResponse: + refused_ids, refused_names = refused_tool_call_identifiers(tool_calls) + return response.model_copy( + update={"choices": [_finalized_choice(choice, refused_ids, refused_names) for choice in response.choices]} + ) diff --git a/litellm/litellm_core_utils/audio_utils/transcription_streaming.py b/litellm/litellm_core_utils/audio_utils/transcription_streaming.py index 4650b7db2f2..b7fa693a22a 100644 --- a/litellm/litellm_core_utils/audio_utils/transcription_streaming.py +++ b/litellm/litellm_core_utils/audio_utils/transcription_streaming.py @@ -177,7 +177,7 @@ class _TranscriptionEventCollector: def _billed_usage(response: TranscriptionResponse) -> Usage: - usage: Final[object] = getattr(response, "usage", None) + usage: Final = response.usage token_usage: Final = ( TranscriptionUsageObjectTransformation.transform_transcription_usage_object(usage) if isinstance(usage, TranscriptionUsageTokensObject) diff --git a/litellm/litellm_core_utils/batch_utils.py b/litellm/litellm_core_utils/batch_utils.py index 5b94d4b4e5b..7c7b7ea5c4b 100644 --- a/litellm/litellm_core_utils/batch_utils.py +++ b/litellm/litellm_core_utils/batch_utils.py @@ -3,21 +3,32 @@ import json from collections.abc import Callable, Generator, Iterable, Mapping, Sequence from contextlib import contextmanager from contextvars import ContextVar +from dataclasses import dataclass from os import PathLike -from typing import Final, TypeVar +from typing import Final, Literal, TypeVar from openai.types import Batch from pydantic import TypeAdapter, ValidationError from litellm._logging import verbose_logger +from litellm.litellm_core_utils.core_helpers import finite_cost +from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import guardrail_information_cost from litellm.types.llms.openai import FileTypes, OpenAIFilesPurpose from litellm.types.utils import CallTypes, LlmProviders +from litellm.types.videos.main import VideoObject +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG, canonical_video_id, is_video_status_call_type _DECODED_JSONL_ROW: Final = TypeAdapter(object) _DEPLOYMENT_FIELD: Final = TypeAdapter(Mapping[str, object]) +_PAYLOAD_FIELDS: Final = TypeAdapter(Mapping[str, object]) _ENDPOINT_PATHS: Final = TypeAdapter(tuple[str, ...]) +_COMPLETED_BATCH_STATUSES: Final = frozenset({"completed", "complete"}) +_TERMINAL_BATCH_STATUSES: Final = _COMPLETED_BATCH_STATUSES | frozenset({"failed", "cancelled", "expired"}) BATCH_ENDPOINT: Final = "/v1/batch" +POLLED_BATCH_ID_KWARG: Final = "polled_batch_id" +POLLED_BATCH_COST_IS_FINAL_KWARG: Final = "polled_batch_cost_is_final" +BATCH_COST_REQUEST_ID_SUFFIX: Final = "_batch_cost" _AZURE_PROVIDERS: Final = frozenset({LlmProviders.AZURE.value, LlmProviders.AZURE_AI.value}) _batch_create_routing: Final[ContextVar[bool]] = ContextVar("batch_create_routing", default=False) @@ -289,12 +300,151 @@ def is_batch_retrieve_call_type(call_type: object) -> bool: return isinstance(call_type, str) and call_type in BATCH_RETRIEVE_CALL_TYPES -def batch_charged_once_marker_key(spend_key: str, batch_id: str) -> str: - return f"{spend_key}:batch:{batch_id}" +@dataclass(frozen=True, slots=True) +class ChargeOnceJob: + kind: Literal["batch", "video"] + job_id: str + repeat_poll_cost: float = 0.0 -def batch_id_to_charge_once(call_type: object, response_obj: object, response_cost: float) -> str | None: - """A finished batch reports its whole cost on every poll, so its id is charged once per counter.""" - if response_cost <= 0 or not is_batch_retrieve_call_type(call_type): +def job_charged_once_marker_key(spend_key: str, job: ChargeOnceJob) -> str: + return f"{spend_key}:{job.kind}:{job.job_id}" + + +def _guardrail_cost_of_poll(standard_logging_payload: object) -> float: + guardrail_information: Final = ( + _PAYLOAD_FIELDS.validate_python(standard_logging_payload).get("guardrail_information") + if isinstance(standard_logging_payload, Mapping) + else None + ) + return guardrail_information_cost(guardrail_information) + + +def charged_poll_cost(kwargs: Mapping[str, object]) -> object: + """The poll's whole cost as the cost callback charges it, guardrails included, which the logging kwargs' own + response_cost leaves out.""" + standard_logging_payload: Final = kwargs.get("standard_logging_object") + logged_cost: Final = ( + _PAYLOAD_FIELDS.validate_python(standard_logging_payload).get("response_cost") + if isinstance(standard_logging_payload, Mapping) + else None + ) + return kwargs.get("response_cost") if logged_cost is None else logged_cost + + +def job_cost_of_poll(response_cost: object, standard_logging_payload: object) -> float: + """What the polled job itself cost, since a guardrail on the poll charges every poll, running or finished.""" + return (finite_cost(response_cost) or 0.0) - _guardrail_cost_of_poll(standard_logging_payload) + + +def repeat_poll_cost(call_type: object, response_cost: object, standard_logging_payload: object) -> float: + """What a poll still costs once an earlier poll charged its job: a video poll's guardrails, which every video + poll has always paid, and nothing for a batch poll, whose repeats have always been free.""" + if not is_video_status_call_type(call_type): + return 0.0 + return max(0.0, min(finite_cost(response_cost) or 0.0, _guardrail_cost_of_poll(standard_logging_payload))) + + +def batch_cost_is_final(batch: Batch) -> bool: + """Whether this retrieve of the batch is the one to account its cost from. + + A batch still in flight has nothing to price, and a "completed" batch can report + no output_file_id for a moment before the output populates; pricing either records + $0 under the batch's single spend row and pins it there. Final means a completed + batch whose output file has arrived or whose counts prove no line succeeded, or + any other terminal status (failed, cancelled, expired). + """ + if batch.status not in _TERMINAL_BATCH_STATUSES: + return False + if batch.status not in _COMPLETED_BATCH_STATUSES or batch.output_file_id is not None: + return True + request_counts: Final = batch.request_counts + return request_counts is not None and request_counts.total > 0 and request_counts.completed == 0 + + +@dataclass(frozen=True, slots=True) +class PolledBatch: + id: str + cost_is_final: bool + + +def _polled_batch(kwargs: Mapping[str, object], response_obj: object) -> PolledBatch | None: + """The retrieved batch, read from the logging kwargs when message redaction replaced the logged response.""" + if not is_batch_retrieve_call_type(kwargs.get("call_type")): return None - return response_obj.id if isinstance(response_obj, Batch) else None + if isinstance(response_obj, Batch): + return PolledBatch(id=response_obj.id, cost_is_final=batch_cost_is_final(response_obj)) + stamped_id: Final = kwargs.get(POLLED_BATCH_ID_KWARG) + stamped_cost_is_final: Final = kwargs.get(POLLED_BATCH_COST_IS_FINAL_KWARG) + if not isinstance(stamped_id, str) or not stamped_id or not isinstance(stamped_cost_is_final, bool): + return None + return PolledBatch(id=stamped_id, cost_is_final=stamped_cost_is_final) + + +def defers_batch_cost(kwargs: Mapping[str, object], response_obj: object) -> bool: + """A batch retrieve before the batch's cost is final charges nothing, guardrails included, since the retrieve + that settles the batch charges its whole cost once.""" + polled: Final = _polled_batch(kwargs, response_obj) + return polled is not None and not polled.cost_is_final + + +def pays_only_its_guardrail(call_type: object, kwargs: Mapping[str, object], response_cost: object) -> bool: + """A video poll whose whole cost is its guardrails', charged like any other request.""" + if not is_video_status_call_type(call_type): + return False + return ( + job_cost_of_poll(response_cost, kwargs.get("standard_logging_object")) + <= 0 + < (finite_cost(response_cost) or 0.0) + ) + + +def _requested_video_id(kwargs: Mapping[str, object]) -> str | None: + litellm_params: Final = kwargs.get("litellm_params") + requested: Final = ( + _PAYLOAD_FIELDS.validate_python(litellm_params).get("video_id") if isinstance(litellm_params, Mapping) else None + ) + return requested if isinstance(requested, str) and requested else None + + +def charged_video_id( + call_type: object, kwargs: Mapping[str, object], fallback_video_id: str | None, response_cost: object +) -> str | None: + """The id a finished video's own cost is charged once under. + + That is the id the client polled, which names the deployment that made the video, since a + custom provider's own id can repeat across deployments. The status comes from the logging + kwargs because message redaction replaces the logged video object. + """ + if not is_video_status_call_type(call_type) or kwargs.get(POLLED_VIDEO_STATUS_KWARG) != "completed": + return None + if job_cost_of_poll(response_cost, kwargs.get("standard_logging_object")) <= 0: + return None + video_id: Final = _requested_video_id(kwargs) or fallback_video_id + return None if video_id is None else canonical_video_id(video_id) + + +def _charged_batch(kwargs: Mapping[str, object], response_obj: object, response_cost: float) -> ChargeOnceJob | None: + polled: Final = _polled_batch(kwargs, response_obj) + if polled is None or not polled.cost_is_final or response_cost <= 0: + return None + return ChargeOnceJob(kind="batch", job_id=polled.id) + + +def job_to_charge_once( + kwargs: Mapping[str, object], response_obj: object, response_cost: float +) -> ChargeOnceJob | None: + """A finished batch or video reports its whole cost on every poll, so it is charged once per counter.""" + call_type: Final = kwargs.get("call_type") + if not is_video_status_call_type(call_type): + return _charged_batch(kwargs, response_obj, response_cost) + video_id: Final = charged_video_id( + call_type, kwargs, response_obj.id if isinstance(response_obj, VideoObject) else None, response_cost + ) + if video_id is None: + return None + return ChargeOnceJob( + kind="video", + job_id=video_id, + repeat_poll_cost=repeat_poll_cost(call_type, response_cost, kwargs.get("standard_logging_object")), + ) diff --git a/litellm/litellm_core_utils/chat_completion_agentic_loop.py b/litellm/litellm_core_utils/chat_completion_agentic_loop.py index 1f29b5d7a4d..ce98d2cbb01 100644 --- a/litellm/litellm_core_utils/chat_completion_agentic_loop.py +++ b/litellm/litellm_core_utils/chat_completion_agentic_loop.py @@ -1,29 +1,35 @@ # this is a patch to allow for agentic loops covering llm_http_handler.py and openai sdk based calling flows for the .completion() api import json -from collections.abc import Mapping +from collections.abc import Awaitable, Iterator, Mapping, Sequence from itertools import chain from types import MappingProxyType -from typing import Final +from typing import Final, TypeVar from pydantic import TypeAdapter, ValidationError from litellm._internal_context import internal_sub_call from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.agentic_followup_failure import mark_agentic_followup_failure, rounds_served_before from litellm.litellm_core_utils.agentic_followup_kwargs import build_agentic_followup_kwargs +from litellm.litellm_core_utils.agentic_loop_refusal import every_claimed_tool_call, finalize_refused_chat_turn from litellm.litellm_core_utils.agentic_loop_settings import ( DEFAULT_MAX_AGENTIC_LOOPS, validated_max_agentic_loops, ) from litellm.litellm_core_utils.core_helpers import qualify_agentic_followup_model +from litellm.litellm_core_utils.hidden_params import get_hidden_params, served_from_cache from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject +from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import is_str_keyed_dict, object_items from litellm.llms.base_llm.base_model_iterator import MockResponseIterator from litellm.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, + AgenticLoopLimitError, AgenticLoopPlan, AgenticLoopRequestPatch, + AgenticLoopSafetyError, converted_stream_requested, is_interception_internal_key, stashed_stream_options, @@ -31,6 +37,8 @@ from litellm.types.integrations.custom_logger import ( from litellm.types.utils import ModelResponse from litellm.utils import CustomStreamWrapper +_DispatchResult: Final = TypeVar("_DispatchResult") + _FOLLOWUP_INTERNAL_PARAMS: Final = frozenset( ( "acompletion", @@ -44,22 +52,41 @@ _FOLLOWUP_INTERNAL_PARAMS: Final = frozenset( ) +async def bill_first_round_on_failure( + dispatch: Awaitable[_DispatchResult], *, logging_obj: object, response: object, stream: bool +) -> _DispatchResult: + try: + return await dispatch + except Exception as error: + if stream or not isinstance(logging_obj, LiteLLMLoggingObject): + mark_agentic_followup_failure(error, rounds_served_before(error)) + raise + this_round: Final = logging_obj.served_round(response) + served_rounds: Final = (*rounds_served_before(error), *(() if this_round is None else (this_round,))) + mark_agentic_followup_failure(error, served_rounds) + logging_obj.record_served_rounds_for_failure(served_rounds) + raise + + def _gate_overridden(callback: CustomLogger) -> bool: - base: Final = CustomLogger.async_should_run_agentic_loop - func: Final = type(callback).async_should_run_agentic_loop - return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + return type(callback).async_should_run_agentic_loop is not CustomLogger.async_should_run_agentic_loop def _build_plan_overridden(callback: CustomLogger) -> bool: - base: Final = CustomLogger.async_build_agentic_loop_plan - func: Final = type(callback).async_build_agentic_loop_plan - return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + return type(callback).async_build_agentic_loop_plan is not CustomLogger.async_build_agentic_loop_plan + + +def _chat_build_plan_overridden(callback: CustomLogger) -> bool: + return ( + type(callback).async_build_chat_completion_agentic_loop_plan + is not CustomLogger.async_build_chat_completion_agentic_loop_plan + ) def _post_hook_overridden(callback: CustomLogger) -> bool: - base: Final = CustomLogger.async_post_agentic_loop_response_hook - func: Final = type(callback).async_post_agentic_loop_response_hook - return getattr(func, "__func__", func) is not getattr(base, "__func__", base) + return ( + type(callback).async_post_agentic_loop_response_hook is not CustomLogger.async_post_agentic_loop_response_hook + ) _STREAM_OPTIONS_ADAPTER: Final[TypeAdapter[Mapping[str, object] | None]] = TypeAdapter(Mapping[str, object] | None) @@ -76,14 +103,14 @@ def _coerce_int(value: object, default: int) -> int: return int(value) if isinstance(value, (int, str)) else default -def _agentic_loop_settings(kwargs: dict[str, object]) -> tuple[int, int, list[str]]: +def _agentic_loop_settings(kwargs: Mapping[str, object]) -> tuple[int, int, tuple[str, ...]]: depth: Final = _coerce_int(kwargs.get("_agentic_loop_depth"), 0) configured: Final = validated_max_agentic_loops( kwargs.get("max_agentic_loops"), field="litellm_params.max_agentic_loops" ) max_loops: Final = DEFAULT_MAX_AGENTIC_LOOPS if configured is None else configured raw_fingerprints: Final = kwargs.get("_agentic_loop_fingerprints") - fingerprints: Final = [str(fp) for fp in raw_fingerprints] if isinstance(raw_fingerprints, list) else [] + fingerprints: Final = tuple(str(fp) for fp in raw_fingerprints) if isinstance(raw_fingerprints, list) else () return depth, max_loops, fingerprints @@ -96,16 +123,16 @@ def _fingerprint_tools(tool_calls: object) -> str: def _check_agentic_loop_safety( tool_calls: object, - fingerprints: list[str], + fingerprints: Sequence[str], depth: int, max_loops: int, model: str, ) -> str: fingerprint: Final = _fingerprint_tools(tool_calls) if fingerprint in fingerprints: - raise ValueError("Agentic loop detected repeated tool-call fingerprint; aborting rerun") + raise AgenticLoopSafetyError("Agentic loop detected repeated tool-call fingerprint; aborting rerun") if depth >= max_loops: - raise ValueError(f"Exceeded max_agentic_loops={max_loops} for model={model}") + raise AgenticLoopLimitError(f"Exceeded max_agentic_loops={max_loops} for model={model}") return fingerprint @@ -134,6 +161,83 @@ def _converted_stream_replay( ) +def _end_refused_turn( + finalized: ModelResponse, + *, + kwargs: Mapping[str, object], + depth: int, + model: str, + custom_llm_provider: str, + logging_obj: object, +) -> ModelResponse | CustomStreamWrapper: + replay: Final = _converted_stream_replay( + finalized, + kwargs=kwargs, + depth=depth, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) + return finalized if replay is None else replay + + +def _request_tools(optional_params: Mapping[str, object]) -> list[dict[str, object]]: + return [tool for tool in object_items(optional_params.get("tools")) if is_str_keyed_dict(tool)] + + +def _custom_loggers(logging_obj: object) -> tuple[CustomLogger, ...]: + import litellm + + dynamic: Final = getattr(logging_obj, "dynamic_success_callbacks", None) or [] + return tuple(callback for callback in (*litellm.callbacks, *dynamic) if isinstance(callback, CustomLogger)) + + +async def _refused_chat_turn( + callbacks: Sequence[CustomLogger], + refusing: CustomLogger, + refused_tool_calls: object, + *, + response: ModelResponse, + model: str, + messages: list[dict[str, object]], + tools: list[dict[str, object]] | None, + custom_llm_provider: str, + kwargs: Mapping[str, object], +) -> ModelResponse: + surface_kwargs: Final = { + **kwargs, + "_agentic_loop_api_surface": CHAT_COMPLETION_AGENTIC_SURFACE, + "custom_llm_provider": custom_llm_provider, + } + + def other_gates() -> Iterator[Awaitable[tuple[bool, object]]]: + for callback in callbacks: + if callback is refusing: + continue + yield callback.async_should_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(kwargs), + ) + if _gate_overridden(callback): + yield callback.async_should_run_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(surface_kwargs), + ) + + claimed: Final = await every_claimed_tool_call(refused_tool_calls, other_gates()) + return finalize_refused_chat_turn(response, claimed) + + def _with_agentic_loop_metadata(kwargs_for_followup: Mapping[str, object]) -> Mapping[str, object]: metadata: Final = kwargs_for_followup.get("litellm_metadata") return MappingProxyType( @@ -164,6 +268,12 @@ def _filter_followup_kwargs(source: dict[str, object]) -> dict[str, object]: } +def _leave_pricing_to_the_request(response: object) -> None: + hidden_params: Final = get_hidden_params(response) + if hidden_params is not None and not served_from_cache(response): + hidden_params.pop("response_cost", None) + + async def _execute_chat_completion_agentic_plan( *, plan: AgenticLoopPlan, @@ -175,7 +285,7 @@ async def _execute_chat_completion_agentic_plan( custom_llm_provider: str, depth: int, max_loops: int, - fingerprints: list[str], + fingerprints: Sequence[str], fingerprint: str, ) -> object: import litellm @@ -226,6 +336,7 @@ async def _execute_chat_completion_agentic_plan( model, str(e), ) + _leave_pricing_to_the_request(response_followup) replay: Final = _converted_stream_replay( response_followup, kwargs=kwargs, @@ -252,23 +363,18 @@ async def maybe_run_chat_completion_agentic_loop( *, response: ModelResponse, model: str, - messages: list, - optional_params: dict, + messages: list[dict[str, object]], + optional_params: dict[str, object], kwargs: dict[str, object], logging_obj: object, custom_llm_provider: str, stream: bool, ) -> ModelResponse | CustomStreamWrapper | None: - import litellm - - callbacks: Final = litellm.callbacks + (getattr(logging_obj, "dynamic_success_callbacks", None) or []) + callbacks: Final = _custom_loggers(logging_obj) depth, max_loops, fingerprints = _agentic_loop_settings(kwargs) - tools: Final = optional_params.get("tools", []) + tools: Final = _request_tools(optional_params) for callback in callbacks: - if not isinstance(callback, CustomLogger): - continue - if not _gate_overridden(callback): continue @@ -297,29 +403,39 @@ async def maybe_run_chat_completion_agentic_loop( if not should_run: continue - fingerprint = _check_agentic_loop_safety( - tool_calls=tool_calls, - fingerprints=fingerprints, - depth=depth, - max_loops=max_loops, - model=model, - ) - try: - if not _build_plan_overridden(callback): - return await callback.async_run_agentic_loop( - tools=tool_calls, + fingerprint = _check_agentic_loop_safety( + tool_calls=tool_calls, + fingerprints=fingerprints, + depth=depth, + max_loops=max_loops, + model=model, + ) + except AgenticLoopSafetyError as e: + if stream: + raise + verbose_logger.warning("LiteLLM.AgenticLoopRefused: ending turn [rail=%s]", type(e).__name__) + return _end_refused_turn( + await _refused_chat_turn( + callbacks, + callback, + tool_calls, + response=response, model=model, messages=messages, - response=response, - anthropic_messages_provider_config=None, - anthropic_messages_optional_request_params=optional_params, - logging_obj=logging_obj, - stream=stream, - kwargs=hook_kwargs, - ) + tools=tools, + custom_llm_provider=custom_llm_provider, + kwargs=kwargs, + ), + kwargs=kwargs, + depth=depth, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) - plan = await callback.async_build_agentic_loop_plan( + if not _build_plan_overridden(callback): + return await callback.async_run_agentic_loop( tools=tool_calls, model=model, messages=messages, @@ -331,31 +447,38 @@ async def maybe_run_chat_completion_agentic_loop( kwargs=hook_kwargs, ) - if plan.response_override is not None: - return plan.response_override - if plan.terminate: - return response - if not plan.run_agentic_loop: - continue + plan = await callback.async_build_agentic_loop_plan( + tools=tool_calls, + model=model, + messages=messages, + response=response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=hook_kwargs, + ) - return await _execute_chat_completion_agentic_plan( - plan=plan, - callback=callback, - model=model, - optional_params=optional_params, - kwargs=kwargs, - logging_obj=logging_obj, - custom_llm_provider=custom_llm_provider, - depth=depth, - max_loops=max_loops, - fingerprints=fingerprints, - fingerprint=fingerprint, - ) - except Exception as e: - verbose_logger.exception( - "LiteLLM.AgenticHookError: Exception in chat completion agentic hooks: %s", - str(e), - ) + if plan.response_override is not None: + return plan.response_override + if plan.terminate: + return response + if not plan.run_agentic_loop: + continue + + return await _execute_chat_completion_agentic_plan( + plan=plan, + callback=callback, + model=model, + optional_params=optional_params, + kwargs=kwargs, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + ) return _converted_stream_replay( response, @@ -365,3 +488,113 @@ async def maybe_run_chat_completion_agentic_loop( custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, ) + + +async def run_chat_completion_agentic_hooks( + *, + response: ModelResponse, + model: str, + messages: list[dict[str, object]], + optional_params: dict[str, object], + kwargs: dict[str, object], + logging_obj: LiteLLMLoggingObject, + custom_llm_provider: str, + stream: bool, +) -> object | None: + callbacks: Final = _custom_loggers(logging_obj) + depth, max_loops, fingerprints = _agentic_loop_settings(kwargs) + tools: Final = _request_tools(optional_params) + hook_kwargs: Final = {**kwargs, "custom_llm_provider": custom_llm_provider} + + for callback in callbacks: + try: + should_run, tool_calls = await callback.async_should_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=stream, + custom_llm_provider=custom_llm_provider, + kwargs=kwargs, + ) + except Exception: + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in async_should_run_chat_completion_agentic_loop" + ) + continue + + if not should_run: + continue + + try: + fingerprint = _check_agentic_loop_safety( + tool_calls=tool_calls, + fingerprints=fingerprints, + depth=depth, + max_loops=max_loops, + model=model, + ) + except AgenticLoopSafetyError as e: + if stream: + raise + verbose_logger.warning("LiteLLM.AgenticLoopRefused: ending turn [rail=%s]", type(e).__name__) + return await _refused_chat_turn( + callbacks, + callback, + tool_calls, + response=response, + model=model, + messages=messages, + tools=tools, + custom_llm_provider=custom_llm_provider, + kwargs=kwargs, + ) + + if not _chat_build_plan_overridden(callback): + return await callback.async_run_chat_completion_agentic_loop( + tools=tool_calls, + model=model, + messages=messages, + response=response, + optional_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=hook_kwargs, + ) + + plan = await callback.async_build_chat_completion_agentic_loop_plan( + tools=tool_calls, + model=model, + messages=messages, + response=response, + optional_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=hook_kwargs, + ) + + if plan.response_override is not None: + return plan.response_override + if plan.terminate: + verbose_logger.debug( + "Agentic chat loop terminated by callback=%s reason=%s", type(callback).__name__, plan.stop_reason + ) + return response + if not plan.run_agentic_loop: + continue + + return await _execute_chat_completion_agentic_plan( + plan=plan, + callback=callback, + model=model, + optional_params=optional_params, + kwargs=hook_kwargs, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + ) + + return None diff --git a/litellm/litellm_core_utils/deployment_pricing.py b/litellm/litellm_core_utils/deployment_pricing.py index 622cc91ab5e..7aa85921634 100644 --- a/litellm/litellm_core_utils/deployment_pricing.py +++ b/litellm/litellm_core_utils/deployment_pricing.py @@ -1,4 +1,4 @@ -from collections.abc import Iterator, Mapping +from collections.abc import Iterable, Iterator, Mapping from types import MappingProxyType from typing import Final @@ -25,6 +25,14 @@ def deployment_pricing_models(deployment: object) -> tuple[str, ...]: return pricing_models(info.get("base_model") or params.get("base_model"), params.get("model")) +def deployment_backend_model(deployment: object) -> str: + return next(iter(deployment_pricing_models(deployment)), "") + + +def deployment_backend_models(deployments: Iterable[object]) -> tuple[str, ...]: + return tuple(map(deployment_backend_model, deployments)) + + def _named_models(*candidates: object) -> Iterator[str]: return (candidate for candidate in candidates if isinstance(candidate, str) and candidate) diff --git a/litellm/litellm_core_utils/error_normalization.py b/litellm/litellm_core_utils/error_normalization.py index 00317e502ec..717db34ede5 100644 --- a/litellm/litellm_core_utils/error_normalization.py +++ b/litellm/litellm_core_utils/error_normalization.py @@ -133,6 +133,8 @@ _STATUS_CODE_MAP: Final[Mapping[str, str]] = MappingProxyType( } ) +_OVERLOADED_STATUS_CODE: Final = "529" + _INTERNAL_STATE_EXCEPTIONS: Final[tuple[type[BaseException], ...]] = ( TypeError, KeyError, @@ -201,6 +203,8 @@ def normalize_error(exc: Exception | None, status_code: str, message: str) -> st ) if by_message is not None: return by_message + if status_code == _OVERLOADED_STATUS_CODE: + return PROVIDER_OVERLOADED by_class: Final = _classify_by_class(exc) if by_class is not None: return by_class diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 3f2f484a130..8de992bd2ca 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -11,6 +11,7 @@ from pydantic import TypeAdapter, ValidationError import litellm from litellm._logging import _ENABLE_SECRET_REDACTION, redact_string, verbose_logger +from litellm.litellm_core_utils.agentic_followup_failure import carry_agentic_followup_mark from litellm.litellm_core_utils.bug_report import ( bug_report_notice, build_bug_report, @@ -605,6 +606,11 @@ def _map_openai_exception( ) +def _anthropic_overload_status(original_exception: _ProviderHTTPException) -> int | None: + upstream_status: Final = getattr(original_exception, "status_code", None) + return 529 if isinstance(upstream_status, int) and upstream_status >= 500 else None + + def _map_anthropic_exception( *, model: str, @@ -630,6 +636,7 @@ def _map_anthropic_exception( message=f"AnthropicError - {error_str}", model=model, llm_provider="anthropic", + provider_status_code=_anthropic_overload_status(original_exception), ) if "Invalid API Key" in error_str: raise AuthenticationError( @@ -694,6 +701,7 @@ def _map_anthropic_exception( llm_provider="anthropic", model=model, response=getattr(original_exception, "response", None), + provider_status_code=original_exception.status_code, ) elif original_exception.status_code == 502: raise BadGatewayError( @@ -2904,6 +2912,7 @@ def exception_type( request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), # stub the request ) except Exception as e: + carry_agentic_followup_mark(source=original_exception, mapped=e) # LOGGING exception_logging( logger_fn=None, @@ -2929,6 +2938,7 @@ def exception_type( model="", ) setattr(raised_exc, "litellm_response_headers", litellm_response_headers) + carry_agentic_followup_mark(source=original_exception, mapped=raised_exc) raise raised_exc diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index d6d9be4e132..d0ac5c00513 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -33,6 +33,9 @@ AWS_CREDENTIAL_KWARGS_KEYS: Final = frozenset( ) PROVIDER_AFFINITY_HEADER_KWARG_KEY: Final = "provider_affinity_header" +AGENTIC_LOOP_CONTROL_KWARGS_KEYS: Final = frozenset( + {"_agentic_loop_depth", "_agentic_loop_fingerprints", "max_agentic_loops"} +) OAUTH_TOKEN_EXCHANGE_KWARGS_KEYS: Final = frozenset( { "token_exchange_endpoint", @@ -94,6 +97,7 @@ OPTIONAL_KWARGS_KEYS: Final = ( | ANTHROPIC_WIF_KWARGS_KEYS | OPENAI_WIF_KWARGS_KEYS | OAUTH_TOKEN_EXCHANGE_KWARGS_KEYS + | AGENTIC_LOOP_CONTROL_KWARGS_KEYS | frozenset(CustomPricingLiteLLMParams.model_fields) ) diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 2148d139578..aef1cbbbb31 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -13,6 +13,7 @@ from litellm.litellm_core_utils.fallback_generalizations import ( match_routing_generalization, ) from litellm.llms.openai_like.json_loader import JSONProviderRegistry +from litellm.llms.opencode.common_utils import is_opencode_api_base from litellm.secret_managers.main import get_secret, get_secret_str from ..types.router import GenericLiteLLMParams, LiteLLM_Params @@ -1010,6 +1011,4 @@ def _get_openai_compatible_provider_info( def get_provider_affinity_header(api_base: str | None) -> str | None: - from litellm.llms.opencode.transformation import OpenCodeProviderAdapter - - return OpenCodeProviderAdapter().get_provider_affinity_header(api_base) + return "x-opencode-session" if is_opencode_api_base(api_base) else None diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py index 7fc6eab6c60..e452befe295 100644 --- a/litellm/litellm_core_utils/get_model_cost_map.py +++ b/litellm/litellm_core_utils/get_model_cost_map.py @@ -320,9 +320,7 @@ def _classify_fetch_content(content: bytes, url: str, etag: str | None) -> _Fetc return ModelCostMapReloadUnavailable(reason=f"invalid JSON from {url}: {e}") if not isinstance(parsed, dict): return ModelCostMapReloadUnavailable(reason=f"expected a JSON object from {url}, got {type(parsed).__name__}") - model_cost_map: Final = cast( # cast-ok: JSON object keys are strings by definition - dict[str, object], parsed - ) + model_cost_map: Final = _BUNDLED_CATALOG_ADAPTER.validate_python(parsed) return ModelCostMapReloaded(model_cost_map=model_cost_map, revision=git_blob_id(content), etag=etag) diff --git a/litellm/litellm_core_utils/get_provider_specific_headers.py b/litellm/litellm_core_utils/get_provider_specific_headers.py index 62dfcb073e5..b5916369917 100644 --- a/litellm/litellm_core_utils/get_provider_specific_headers.py +++ b/litellm/litellm_core_utils/get_provider_specific_headers.py @@ -12,10 +12,18 @@ _PROVIDER_HEADERS: Final[TypeAdapter[ProviderSpecificHeader | Sequence[ProviderS class ProviderSpecificHeaderUtils: @staticmethod - def forwarded_authorization(provider_specific_header: object, custom_llm_provider: str | None) -> str | None: - headers: Final = ProviderSpecificHeaderUtils.get_provider_specific_headers( + def validated_provider_specific_headers( + provider_specific_header: object, custom_llm_provider: str | None + ) -> Mapping[str, str]: + return ProviderSpecificHeaderUtils.get_provider_specific_headers( _PROVIDER_HEADERS.validate_python(provider_specific_header), custom_llm_provider ) + + @staticmethod + def forwarded_authorization(provider_specific_header: object, custom_llm_provider: str | None) -> str | None: + headers: Final = ProviderSpecificHeaderUtils.validated_provider_specific_headers( + provider_specific_header, custom_llm_provider + ) return next((value for key, value in headers.items() if key.lower() == "authorization"), None) @staticmethod diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 97c019a2180..1fcb06b80e8 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -19,7 +19,7 @@ from datetime import datetime as dt_object from enum import Enum from functools import lru_cache from types import MappingProxyType, TracebackType -from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, NamedTuple, TypeVar, cast +from typing import TYPE_CHECKING, Any, Final, Literal, NamedTuple, TypeVar, cast from httpx import Response, Timeout from openai import AsyncStream as OpenAIAsyncStream @@ -38,7 +38,7 @@ from litellm._logging import ( verbose_logger, ) from litellm._uuid import uuid -from litellm.batches.batch_utils import batch_cost_is_final, handle_completed_batch +from litellm.batches.batch_utils import handle_completed_batch from litellm.caching.caching import DualCache from litellm.caching.caching_handler import LLMCachingHandler from litellm.caching.redis_batch import flush_post_call_redis_batches @@ -54,8 +54,11 @@ from litellm.constants import ( REDACTED_BY_LITELLM, ) from litellm.cost_calculator import ( + BaseTokenUsageProcessor, RealtimeAPITokenUsageProcessor, ResponsesWebSocketTokenUsageProcessor, + declared_cost_map_pricing, + get_response_cost_from_hidden_params, get_usage_object, pricing_entry_for_cost_calc, select_model_name_for_cost_calc, @@ -74,6 +77,13 @@ from litellm.integrations.deepeval.deepeval import DeepEvalLogger from litellm.integrations.langtrace import langtrace_trace_endpoint from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.sqs import SQSLogger +from litellm.litellm_core_utils.add_retry_fallback_headers import get_hidden_params_dict +from litellm.litellm_core_utils.agentic_followup_failure import ServedRound +from litellm.litellm_core_utils.batch_utils import ( + POLLED_BATCH_COST_IS_FINAL_KWARG, + POLLED_BATCH_ID_KWARG, + batch_cost_is_final, +) from litellm.litellm_core_utils.classifier_logging import ( classifier_audit_fields, classifier_input_snapshot, @@ -215,7 +225,11 @@ from litellm.types.utils import ( batch_chat_usage, ) from litellm.types.videos.main import VideoObject -from litellm.utils import get_base_model_from_metadata, print_verbose +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG +from litellm.utils import ( + get_base_model_from_metadata, + print_verbose, +) from ..integrations.argilla import ArgillaLogger from ..integrations.arize.arize_phoenix import ArizePhoenixLogger @@ -275,8 +289,6 @@ if TYPE_CHECKING: from litellm.llms.base_llm.passthrough.transformation import PassthroughStreamCollector from litellm.proxy.hooks.autorouter_baseline_cache import BaselineCacheContext, CapturedBaselineObservation from litellm.proxy.hooks.search_free_tier import SearchAllowanceAllocation - from litellm.proxy.spend_tracking.usage_capture import CapturedUsage - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureError try: from litellm_enterprise.enterprise_callbacks.callback_controls import ( EnterpriseCallbackControls, @@ -654,54 +666,7 @@ class UsageObservation(Enum): ESTIMATED = "estimated" -@dataclass(frozen=True, slots=True) -class UsageCaptureObservation: - usage_json: str | None - provenance: UsageObservation - response_cost: float | None = None - - @classmethod - def from_usage( - cls, usage: Usage | None, provenance: UsageObservation, response_cost: float | None = None - ) -> "UsageCaptureObservation | UsageCaptureError": - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureError - - try: - serialized: Final = ( - None if usage is None or provenance is UsageObservation.UNKNOWN else usage.model_dump_json() - ) - except Exception as error: # noqa: BLE001 # observation failures must not interrupt legacy retry handling - return UsageCaptureError(str(error)) - else: - return cls(serialized, provenance, response_cost) - - class Logging(LiteLLMLoggingBaseClass): - usage_capture_callback: ClassVar[ - Callable[ - ["Logging", object, datetime.datetime, datetime.datetime, bool], "CapturedUsage | UsageCaptureError | None" - ] - | None - ] = None - - def capture_usage_before_logging( - self, - response: object, - start_time: datetime.datetime | None = None, - end_time: datetime.datetime | None = None, - *, - freeze_only: bool = False, - ) -> None: - capture: Final = type(self).usage_capture_callback - if capture is not None and (not self.stream or self._is_assembled_stream_success(response)): - capture( - self, - response, - start_time or self.start_time, - end_time or datetime.datetime.now(datetime.timezone.utc), - freeze_only, - ) - global \ supabaseClient, \ promptLayerLogger, \ @@ -716,6 +681,7 @@ class Logging(LiteLLMLoggingBaseClass): custom_pricing: bool = False custom_cost_per_token: CostPerToken | None = None custom_cost_per_second: float | None = None + custom_model_info: ModelInfo | None = None replaced_global_callback_keys: frozenset[str] = frozenset() stream_options = None litellm_request_debug: bool = False @@ -937,6 +903,10 @@ class Logging(LiteLLMLoggingBaseClass): self._sync_success_scheduled: bool = False self.on_detached_stream_failure: Callable[[Exception], Awaitable[None]] | None = None self._stream_failure_claimed: bool = False + self._stash_owed_on_success: bool = False + self._stash_billed_by_continuation: bool = False + self._failed_attempts_owed_on_success: tuple[ServedRound, ...] = () + self.failed_attempts_billed_on_success: tuple[ServedRound, ...] = () self.shadow_eval_request_snapshot: GuardrailRequestSnapshot | None = None def copy_for_new_call(self, litellm_call_id: str, start_time: datetime.datetime) -> "Logging": @@ -2221,6 +2191,7 @@ class Logging(LiteLLMLoggingBaseClass): "custom_pricing": custom_pricing, "custom_cost_per_token": self.custom_cost_per_token if custom_pricing else None, "custom_cost_per_second": self.custom_cost_per_second if custom_pricing else None, + "requested_model_info_override": self.custom_model_info if custom_pricing else None, "prompt": prompt, "standard_built_in_tools_params": self.standard_built_in_tools_params, "router_model_id": router_model_id, @@ -2312,12 +2283,19 @@ class Logging(LiteLLMLoggingBaseClass): return 0.0 def uncached_price(self, result: object) -> UncachedPrice: + return self._price_without_recording(result, cache_hit=False, ignore_stamped_cost=True) + + def _price_without_recording( + self, result: object, *, cache_hit: bool | None, ignore_stamped_cost: bool + ) -> UncachedPrice: previous_cost_breakdown: Final = self.cost_breakdown previous_billed_token_rates: Final = self.billed_token_rates debug_key: Final = "response_cost_failure_debug_information" previous_failure_debug_info: Final = self.model_call_details.get(debug_key) try: - cost: Final = self.response_cost_calculator(result=result, cache_hit=False, ignore_stamped_cost=True) + cost: Final = self.response_cost_calculator( + result=result, cache_hit=cache_hit, ignore_stamped_cost=ignore_stamped_cost + ) return UncachedPrice(cost=cost, cost_breakdown=self.cost_breakdown) finally: self.cost_breakdown = previous_cost_breakdown @@ -2417,6 +2395,10 @@ class Logging(LiteLLMLoggingBaseClass): additional_headers=additional_headers, ) + def set_per_call_pricing(self, custom_model_info: ModelInfo | None) -> None: + """Price this call from its own full price entry instead of ``litellm.model_cost``.""" + self.custom_model_info = custom_model_info + def custom_pricing_for(self, result: object) -> bool: if self.spilled_over_ptu_for(result): return False @@ -2556,11 +2538,6 @@ class Logging(LiteLLMLoggingBaseClass): ) return - self.capture_usage_before_logging( - TypeAdapter(object).validate_python(result), - TypeAdapter[datetime.datetime | None](datetime.datetime | None).validate_python(start_time), - TypeAdapter[datetime.datetime | None](datetime.datetime | None).validate_python(end_time), - ) await self.async_success_handler( result, start_time=start_time, @@ -2582,6 +2559,33 @@ class Logging(LiteLLMLoggingBaseClass): **kwargs, ) + def _response_price_is_known(self, result: object, cost: float, payload: StandardLoggingPayload) -> bool: + if ( + cost != 0 + or self._provider_reported_cost == 0 + or get_response_cost_from_hidden_params(get_hidden_params_dict(result)) == 0 + ): + return True + if self.model_call_details.get("cache_hit") is True or _served_from_cache_at_no_charge(result): + return True + params: Final = _LITELLM_PARAMS.validate_python(self._model_call_details_view.get("litellm_params") or {}) + if is_unbilled_non_inference_call( + payload["call_type"], StandardLoggingPayloadSetup.merge_litellm_metadata(params), result + ): + return True + if self.custom_cost_per_token is not None or self.custom_cost_per_second is not None: + return True + passthrough: Final = _LITELLM_PARAMS.validate_python( + self._model_call_details_view.get("passthrough_logging_payload") or {} + ) + if passthrough.get("cost_per_request") is not None: + return True + model_info: Final = payload["model_map_information"]["model_map_value"] + if model_info is None: + return False + prices: Final = declared_cost_map_pricing(model_info["key"], model_info["key"], payload["custom_llm_provider"]) + return any(value is not None and ("cost" in key or "pricing" in key) for key, value in prices.items()) + def prepare_response_cost(self, response: object, now: datetime.datetime) -> PreparedCost | None: if self.prepared_cost is not None: return self.prepared_cost @@ -2634,6 +2638,7 @@ class Logging(LiteLLMLoggingBaseClass): "cost_breakdown": copy.deepcopy(payload["cost_breakdown"]), }, usage, + price_known=self._response_price_is_known(prepared[2], raw_cost, payload), ) self.record_partial_usage_for_failure( usage, raw_cost, observation=self.model_call_details["_accounting_usage_observed"] @@ -2644,20 +2649,12 @@ class Logging(LiteLLMLoggingBaseClass): self.finalized_cost = finalized self.model_call_details["response_cost"] = finalized.amount - def record_usage_capture(self, captured: "CapturedUsage") -> None: - self.model_call_details["_accounting_capture"] = captured - - def record_usage_pricing(self, pricing: object) -> None: - self.model_call_details["_accounting_pricing_snapshot"] = pricing - def record_partial_usage_for_failure( self, usage: Usage, response_cost: float, *, observation: UsageObservation = UsageObservation.KNOWN ) -> None: """Stash what an interrupted stream already consumed so the failure log bills it instead of zero.""" self.model_call_details["combined_usage_object"] = usage self.model_call_details["response_cost"] = response_cost - if type(self).usage_capture_callback is not None: - self.model_call_details["_accounting_usage_observed"] = observation def record_failure_after_earlier_rounds( self, earlier: BilledRoundsSpend, failed_usage: Usage, failed_cost: float @@ -2737,66 +2734,28 @@ class Logging(LiteLLMLoggingBaseClass): own usage while the request's failure log still bills every attempt.""" usage: Final = self._model_call_details_view.get("combined_usage_object") response_cost: Final = self._model_call_details_view.get("response_cost") - if isinstance(usage, Usage) and type(self).usage_capture_callback is not None: - self.capture_failed_attempt_usage(usage) - self.model_call_details.pop("_accounting_capture", None) + billed_by_continuation: Final = self._stash_billed_by_continuation + owed_on_success: Final = self._stash_owed_on_success + self._stash_billed_by_continuation = False + self._stash_owed_on_success = False self.model_call_details.pop("_accounting_pricing_snapshot", None) self.model_call_details.pop("_accounting_usage_observed", None) self.model_call_details.pop("combined_usage_object", None) self.model_call_details.pop("response_cost", None) - if isinstance(usage, Usage): - self.model_call_details["current_attempt_api_call_start_time"] = None - self.model_call_details["failed_attempts_usage"] = ( - *self.model_call_details.get("failed_attempts_usage", ()), - (usage, float(response_cost) if isinstance(response_cost, (int, float)) else 0.0), - ) - - def capture_failed_attempt_usage(self, usage: Usage) -> None: - from litellm.constants import BILLED_RETRY_ATTEMPT_METADATA_KEY - from litellm.proxy.spend_tracking.usage_capture import CapturedUsage - - capture: Final = type(self).usage_capture_callback - if capture is None: + if not isinstance(usage, Usage): return - attempt: Final = self.attempt_scoped_copy() - attempt.prepared_cost = None - attempt.finalized_cost = None - identity: Final = ( - f"{self.litellm_call_id}-attempt-{len(self.model_call_details.get('failed_attempts_usage', ()))}" - ) - params: Final = _LITELLM_PARAMS.validate_python(self.model_call_details.get("litellm_params") or {}) - metadata: Final = StandardLoggingPayloadSetup.merge_litellm_metadata(params) - observed: Final[object] = self.model_call_details.get("_accounting_usage_observed") - cost: Final[object] = self.model_call_details.get("response_cost") - attempt.model_call_details = { - **attempt.model_call_details, - "litellm_call_id": identity, - "parent_request_id": self.litellm_call_id, - "_accounting_capture": None, - "_accounting_prior_captures": (), - "_accounting_usage_snapshot": UsageCaptureObservation.from_usage( - usage, - observed if isinstance(observed, UsageObservation) else UsageObservation.KNOWN, - float(cost) if isinstance(cost, (int, float)) else None, - ), - "litellm_params": { - **params, - "metadata": {**metadata, BILLED_RETRY_ATTEMPT_METADATA_KEY: True, "status": "failure"}, - "litellm_metadata": {**metadata, BILLED_RETRY_ATTEMPT_METADATA_KEY: True, "status": "failure"}, - }, - } - captured: Final = capture( + attempt: Final = (usage, float(response_cost) if isinstance(response_cost, (int, float)) else 0.0) + self.model_call_details["current_attempt_api_call_start_time"] = None + self.model_call_details["failed_attempts_usage"] = ( + *self.model_call_details.get("failed_attempts_usage", ()), attempt, - {"id": identity, "usage": usage.model_dump(), "status": "failed"}, - self.start_time, - datetime.datetime.now(datetime.timezone.utc), - False, ) - if isinstance(captured, CapturedUsage): - self.model_call_details["_accounting_prior_captures"] = ( - *self.model_call_details.get("_accounting_prior_captures", ()), - captured, - ) + if not owed_on_success or billed_by_continuation: + return + self._failed_attempts_owed_on_success = (*self._failed_attempts_owed_on_success, attempt) + + def mark_stash_billed_by_continuation(self) -> None: + self._stash_billed_by_continuation = True def record_continued_partial(self, usage: Usage, response_cost: float, response_id: str) -> None: """Bill what a cut stream already consumed on this continuation's success log, under the id the caller saw.""" @@ -2804,20 +2763,17 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["continued_partial_cost"] = response_cost self.model_call_details["continued_response_id"] = response_id - def _add_continued_partial_spend(self, response: object) -> None: + def _add_continued_partial_spend(self, response: object) -> Usage | None: partial_usage: Final = self.model_call_details.get("continued_partial_usage") partial_cost: Final = self.model_call_details.get("continued_partial_cost") own_usage: Final = getattr(response, "usage", None) own_cost: Final = self.model_call_details.get("response_cost") if not (isinstance(partial_usage, Usage) and isinstance(partial_cost, float) and isinstance(own_usage, Usage)): - return - from litellm.cost_calculator import BaseTokenUsageProcessor - - self.model_call_details["combined_usage_object"] = BaseTokenUsageProcessor.combine_usage_objects( - usage_objects=[own_usage, partial_usage] - ) + return own_usage if isinstance(own_usage, Usage) else None + combined_usage: Final = BaseTokenUsageProcessor.combine_usage_objects(usage_objects=[own_usage, partial_usage]) + self.model_call_details["combined_usage_object"] = combined_usage if not isinstance(own_cost, (int, float)): - return + return combined_usage self.model_call_details["response_cost"] = own_cost + partial_cost if self.cost_breakdown is not None: self.cost_breakdown = self.cost_breakdown | CostBreakdown( @@ -2827,6 +2783,25 @@ class Logging(LiteLLMLoggingBaseClass): "mid_stream_partial_cost": partial_cost, }, ) + return combined_usage + + def _add_failed_attempts_spend(self, own_usage: Usage | None) -> None: + own_cost: Final = self.model_call_details.get("response_cost") + if own_usage is None or not isinstance(own_cost, (int, float)): + return + failed_attempts: Final = self._failed_attempts_owed_on_success + if not failed_attempts: + return + failed_attempts_cost: Final = sum(cost for _, cost in failed_attempts) + self.model_call_details.pop("failed_attempts_usage", None) + self.failed_attempts_billed_on_success = failed_attempts + self.model_call_details["failed_attempts_billed_on_success"] = failed_attempts + self.model_call_details["combined_usage_object"] = BaseTokenUsageProcessor.combine_usage_objects( + usage_objects=[own_usage, *(usage for usage, _ in failed_attempts)] + ) + self.model_call_details["response_cost"] = own_cost + failed_attempts_cost + if self.cost_breakdown is not None: + self.cost_breakdown = _with_failed_attempts_cost(self.cost_breakdown, own_cost, failed_attempts_cost) def claim_stream_failure_report(self) -> bool: if self._stream_failure_claimed: @@ -2834,6 +2809,24 @@ class Logging(LiteLLMLoggingBaseClass): self._stream_failure_claimed = True return True + def served_round(self, response: object) -> ServedRound | None: + usage: Final = get_usage_object(completion_response=response) + if usage is None: + return None + price: Final = self._price_without_recording(response, cache_hit=None, ignore_stamped_cost=False) + return usage, price.cost or 0.0 + + def record_served_rounds_for_failure(self, served_rounds: tuple[ServedRound, ...]) -> None: + if not served_rounds: + return + self._stash_owed_on_success = True + self.record_partial_usage_for_failure( + served_rounds[0][0] + if len(served_rounds) == 1 + else BaseTokenUsageProcessor.combine_usage_objects(usage_objects=[usage for usage, _ in served_rounds]), + sum(cost for _, cost in served_rounds), + ) + def record_assembled_response_for_failure(self, assembled: ModelResponse) -> None: """Bill a fully streamed response on the failure log when a post-call hook rejects it.""" usage: Final = getattr(assembled, "usage", None) @@ -2965,45 +2958,6 @@ class Logging(LiteLLMLoggingBaseClass): return self._handle_a2a_response_logging(result=result) return result - def usage_capture_response(self, result: object) -> object: - if self.stream and not self._is_assembled_stream_success(result): - return None - copied: Final = result.model_copy(deep=True) if isinstance(result, BaseModel) else result - normalized: Final = self.normalize_logging_result(self.normalize_endpoint_logging_result(copied)) - if self.model_call_details.get("call_type") == CallTypes.arealtime.value and isinstance(result, list): - frames: Final = TypeAdapter(tuple[dict[str, object], ...]).validate_python(result) - measured: Final = any( - isinstance(_LITELLM_PARAMS.validate_python(frame.get("response") or {}).get("usage"), Mapping) - for frame in frames - if frame.get("type") == "response.done" - ) - if not measured: - metered: Final = self.model_call_details.get(REALTIME_METERED_USAGE_KEY) - return { - "id": self.litellm_call_id, - "usage": {"metered_usage": TypeAdapter(object).dump_python(metered, mode="json")} - if metered is not None - else None, - } - assembled: Final = self._get_assembled_streaming_response( - normalized, self.start_time, self.start_time, True, [] - ) - return normalized if assembled is None else assembled - - def usage_capture_pricing(self, result: object) -> Mapping[str, JsonValue]: - from litellm.utils import count_characters - - return TypeAdapter(dict[str, JsonValue]).validate_python( - { - "custom_pricing": self.custom_pricing_for(result), - "custom_cost_per_token": self.custom_cost_per_token, - "custom_cost_per_second": self.custom_cost_per_second, - "prompt_characters": count_characters(self._prompt_for_cost_calculation()) - if self.model_call_details.get("call_type") in (CallTypes.speech.value, CallTypes.aspeech.value) - else None, - } - ) - def normalize_logging_result(self, result: object) -> object: """ Some endpoints return a different type of result than what is expected by the logging system. @@ -3138,9 +3092,11 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["response_cost"] = self.finalized_cost.amount elif self.model_call_details.get("cache_hit") is True: self.model_call_details["response_cost"] = 0.0 + self._add_failed_attempts_spend(get_usage_object(completion_response=logging_result)) elif "response_cost" in hidden_params: self.model_call_details["response_cost"] = hidden_params["response_cost"] self._record_zero_cost_diagnostic(logging_result, hidden_params["response_cost"]) + self._add_failed_attempts_spend(get_usage_object(completion_response=logging_result)) elif (existing_cost := self.model_call_details.get("response_cost")) is not None and ( existing_cost != 0 or self.call_type == CallTypes.pass_through.value # pyright: ignore[reportUnknownMemberType] # Logging.call_type is untyped ): @@ -3150,6 +3106,7 @@ class Logging(LiteLLMLoggingBaseClass): pass else: self.model_call_details["response_cost"] = self.response_cost_calculator(result=logging_result) + self._add_failed_attempts_spend(get_usage_object(completion_response=logging_result)) if not build_logging_payload: return None @@ -3278,21 +3235,12 @@ class Logging(LiteLLMLoggingBaseClass): set_hidden_params(served, {**(get_hidden_params(translated) or {}), **(get_hidden_params(response) or {})}) return served - def _with_continued_response_id( + def _with_response_id( self, response: ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse, + response_id: str | None, ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse: - response_id: Final = self.model_call_details.get("continued_response_id") - if not isinstance(response_id, str) or getattr(response, "id", None) == response_id: - return response - return response.model_copy(update={"id": response_id}) - - def _with_client_facing_response_id( - self, - response: ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse, - ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse: - response_id: Final = self.client_facing_response_id - if response_id is None or getattr(response, "id", None) == response_id: + if response_id is None or response.id == response_id: return response return response.model_copy(update={"id": response_id}) @@ -3300,8 +3248,13 @@ class Logging(LiteLLMLoggingBaseClass): self, response: ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse, ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse: - return self._with_client_facing_response_id( - self._with_continued_response_id(self._with_client_facing_stream_model(response)) + continued_response_id: Final = self.model_call_details.get("continued_response_id") + return self._with_response_id( + self._with_response_id( + self._with_client_facing_stream_model(response), + continued_response_id if isinstance(continued_response_id, str) else None, + ), + self.client_facing_response_id, ) def _success_handler_helper_fn( @@ -3361,6 +3314,11 @@ class Logging(LiteLLMLoggingBaseClass): result = self.normalize_endpoint_logging_result(result) if self.stream is not True: result = self._as_served_response(result) # rebind-ok: bridged chat calls log the served response + if isinstance(result, VideoObject): + self.model_call_details[POLLED_VIDEO_STATUS_KWARG] = result.status + if isinstance(result, LiteLLMBatch): + self.model_call_details[POLLED_BATCH_ID_KWARG] = result.id + self.model_call_details[POLLED_BATCH_COST_IS_FINAL_KWARG] = batch_cost_is_final(result) logging_result: Final = self.normalize_logging_result(result=result) if cache_hit is None and _served_from_cache_at_no_charge(logging_result): @@ -3713,13 +3671,12 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["response_cost"] = self.response_cost_calculator( result=complete_streaming_response if provider_transcription is None else provider_transcription ) - self._add_continued_partial_spend(complete_streaming_response) + self._add_failed_attempts_spend(self._add_continued_partial_spend(complete_streaming_response)) self._merge_hidden_params_from_response_into_metadata(complete_streaming_response) logged_streaming_response: Final = self._client_facing_streaming_response( self._as_served_chat_response(complete_streaming_response) ) self.model_call_details["complete_streaming_response"] = logged_streaming_response - self.capture_usage_before_logging(logged_streaming_response, start_time, end_time) ## STANDARDIZED LOGGING PAYLOAD if not for_async_request: self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload( @@ -4245,8 +4202,6 @@ class Logging(LiteLLMLoggingBaseClass): cost_for_built_in_tools_cost_usd_dollar=0.0, ) - self.capture_usage_before_logging(result, start_time, end_time) - self.truncated_messages_for_logging = await truncate_base64_in_messages_async( StandardLoggingPayloadSetup.append_system_prompt_messages( kwargs=self.model_call_details, messages=self.model_call_details.get("messages") @@ -4309,7 +4264,7 @@ class Logging(LiteLLMLoggingBaseClass): self.model, ) self.model_call_details["response_cost"] = None - self._add_continued_partial_spend(complete_streaming_response) + self._add_failed_attempts_spend(self._add_continued_partial_spend(complete_streaming_response)) self._merge_hidden_params_from_response_into_metadata(complete_streaming_response) @@ -6388,7 +6343,7 @@ def _init_custom_logger_compatible_class( logging_integration: str, internal_usage_cache: DualCache | None, llm_router: object, - custom_logger_init_args: dict[str, object] | None = {}, + custom_logger_init_args: dict[str, object] | None = None, raise_import_errors: bool = False, ) -> CustomLogger | None: """ @@ -7802,6 +7757,20 @@ def _cache_hit_cost_breakdown(served: CostBreakdown | None) -> CostBreakdown: ) +def _with_failed_attempts_cost(breakdown: CostBreakdown, own_cost: float, failed_attempts_cost: float) -> CostBreakdown: + return breakdown | CostBreakdown( + total_cost=own_cost + failed_attempts_cost, + additional_costs={**breakdown.get("additional_costs", {}), "failed_attempts_cost": failed_attempts_cost}, + ) + + +def _cached_cost_breakdown(served: CostBreakdown | None, failed_attempts: tuple[ServedRound, ...]) -> CostBreakdown: + cached: Final = _cache_hit_cost_breakdown(served) + if not failed_attempts: + return cached + return _with_failed_attempts_cost(cached, 0.0, sum(cost for _, cost in failed_attempts)) + + def _finalized_cost_of(logging_obj: object) -> FinalizedCost | None: """Duck-typed logging objects (and test doubles) carry no finalized cost.""" finalized: Final = getattr(logging_obj, "finalized_cost", None) @@ -7995,7 +7964,7 @@ def get_standard_logging_object_payload( if finalized_cost is not None else cost_breakdown_with_guardrail( ( - _cache_hit_cost_breakdown(uncached_price.cost_breakdown) + _cached_cost_breakdown(uncached_price.cost_breakdown, logging_obj.failed_attempts_billed_on_success) if uncached_price is not None else logging_obj.cost_breakdown ), diff --git a/litellm/litellm_core_utils/llm_cost_calc/finalization.py b/litellm/litellm_core_utils/llm_cost_calc/finalization.py index a13134ada4a..756c53ea882 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/finalization.py +++ b/litellm/litellm_core_utils/llm_cost_calc/finalization.py @@ -17,6 +17,7 @@ class PreparedCost: amount: float payload: StandardLoggingPayload usage: Usage + price_known: bool = True @dataclass(frozen=True, slots=True) diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 54213f40ac0..e3aecda1deb 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -224,7 +224,20 @@ class StandardBuiltInToolCostTracking: web_search_options=standard_built_in_tools_params.get("web_search_options", None), model_info=model_info, ) - return per_call_cost * StandardBuiltInToolCostTracking._count_web_search_calls(response_object) + return per_call_cost * StandardBuiltInToolCostTracking._billable_web_search_calls( + resolved_usage, response_object + ) + + @staticmethod + def _billable_web_search_calls(usage: Usage | None, response_object: object) -> int: + """ + Providers without their own calculator but whose usage carries server_tool_use.web_search_requests + (OpenAI-compatible providers that report a per-tool count) are billed that many calls on the per-call path. + """ + reported: Final = None if usage is None else get_web_search_requests_from_usage(usage) + if isinstance(reported, int) and not isinstance(reported, bool) and reported > 0: + return reported + return StandardBuiltInToolCostTracking._count_web_search_calls(response_object) @staticmethod def _count_web_search_calls(response_object: object) -> int: diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index c553bfeaad8..9ac4f9d5ee8 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1203,21 +1203,55 @@ def parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResu ) +def get_modality_token_cost_key( + model_info: ModelInfo, + cost_key: str, + prompt_tokens: int, + *, + threshold_is_inclusive: bool = False, +) -> str: + suffix: Final = next((suffix for suffix in _NON_STANDARD_THRESHOLD_SUFFIXES if cost_key.endswith(suffix)), "") + base_key: Final = cost_key.removesuffix(suffix) + threshold_key: Final = max( + ( + key + for key in model_info + if key.startswith(f"{base_key}_above_") + and key.endswith(("_tokens", f"_tokens{suffix}")) + and model_info.get(key) is not None + and _prompt_exceeds_threshold(prompt_tokens, _parse_above_token_threshold(key), threshold_is_inclusive) + ), + key=_parse_above_token_threshold, + default=None, + ) + if threshold_key is None: + return cost_key + return f"{threshold_key.removesuffix(suffix)}{suffix}" + + def _modality_token_cost( model_info: ModelInfo, modality_cost_key: str, tokens: int, prompt_base_cost: float, service_tier: str | None, + *, + prompt_tokens: int = 0, + threshold_is_inclusive: bool = False, ) -> float: """ Audio, image and video prompt tokens bill at their modality rate when the model has one. Without one they are ordinary prompt tokens, so they bill at `prompt_base_cost`, which `_get_token_base_cost` has already resolved for the service tier and long-context tier. """ - tiered_key: Final = get_service_tier_cost_key(modality_cost_key, service_tier) - cost_key: Final = tiered_key if model_info.get(tiered_key) is not None else modality_cost_key - if model_info.get(cost_key) is None: + cost_key: Final = get_modality_token_cost_key( + model_info, + get_service_tier_cost_key(modality_cost_key, service_tier), + prompt_tokens, + threshold_is_inclusive=threshold_is_inclusive, + ) + standard_key: Final = cost_key.removesuffix(get_service_tier_cost_key("", service_tier)) + if model_info.get(cost_key) is None and model_info.get(standard_key) is None: return float(tokens) * prompt_base_cost return calculate_cost_component(model_info, cost_key, tokens) @@ -1230,6 +1264,9 @@ def _calculate_input_cost( cache_creation_cost: float, cache_creation_cost_by_ttl: Mapping[str, float], service_tier: str | None = None, + *, + prompt_tokens: int = 0, + threshold_is_inclusive: bool = False, ) -> float: """ Calculates the input cost for a given model, prompt tokens, and completion tokens. @@ -1260,6 +1297,8 @@ def _calculate_input_cost( prompt_tokens_details["audio_tokens"], prompt_base_cost, service_tier, + prompt_tokens=prompt_tokens, + threshold_is_inclusive=threshold_is_inclusive, ) ### IMAGE TOKEN COST @@ -1402,6 +1441,29 @@ def get_regional_uplift_multiplier(model_info: ModelInfo, data_residency: str | _get_regional_uplift_multiplier = get_regional_uplift_multiplier +def get_fedramp_uplift_multiplier(model_info: ModelInfo, data_residency: str | None) -> float: + """ + Resolve the per-model uplift multiplier for the OpenAI FedRAMP endpoint. + + OpenAI applies a flat percentage uplift (e.g. +10%) on all token costs for + requests served from gov.api.openai.com. The multiplier is stored on the + model entry as ``fedramp_uplift_multiplier`` (e.g. 1.10). + + Returns 1.0 (no uplift) unless ``data_residency`` is ``"fedramp"`` and the + model has a multiplier configured. + """ + if data_residency is None or data_residency.lower() != DataResidency.FEDRAMP.value: + return 1.0 + multiplier: Final = model_info.get("fedramp_uplift_multiplier") + if multiplier is None: + return 1.0 + try: + return float(multiplier) + except (TypeError, ValueError): + verbose_logger.exception("Invalid fedramp_uplift_multiplier for model; defaulting to 1.0") + return 1.0 + + def get_vertex_regional_endpoint_uplift(model_info: ModelInfo, vertex_location: str | None) -> float: """ Resolve the per-model uplift multiplier for Vertex AI non-global (regional and @@ -1511,8 +1573,8 @@ def generic_cost_per_token( Input: - model: str, the model name without provider prefix - usage: LiteLLM Usage block, containing anthropic caching information - - data_residency: optional OpenAI data-residency region (e.g. "eu", "us"), - used to apply the per-model regional-processing uplift multiplier. + - data_residency: optional OpenAI data-residency region (e.g. "eu", "us") or + "fedramp", used to apply the per-model regional-processing or FedRAMP uplift multiplier. - vertex_location: optional Vertex AI location the request was served from (e.g. "us-east5", "global"), used to apply the per-model regional-endpoint uplift multiplier when non-global. @@ -1618,6 +1680,8 @@ def generic_cost_per_token( cache_creation_cost=base_costs.cache_creation_rate, cache_creation_cost_by_ttl=base_costs.cache_creation_rates_by_ttl, service_tier=service_tier, + prompt_tokens=usage.prompt_tokens, + threshold_is_inclusive=_uses_inclusive_token_thresholds(custom_llm_provider), ) ## CALCULATE OUTPUT COST @@ -1675,11 +1739,9 @@ def generic_cost_per_token( ## IMAGE COST if not is_text_tokens_total and image_tokens and image_tokens > 0: - _output_cost_per_image_token = get_cost_per_unit(resolved_model_info, "output_cost_per_image_token", None) - _output_cost_per_image_token = ( - _output_cost_per_image_token if _output_cost_per_image_token is not None else completion_base_cost - ) - completion_cost += float(image_tokens) * _output_cost_per_image_token + image_cost_key: Final = get_service_tier_cost_key("output_cost_per_image_token", service_tier) + image_rate: Final = get_cost_per_unit(resolved_model_info, image_cost_key, None) + completion_cost += float(image_tokens) * (completion_base_cost if image_rate is None else image_rate) ## VIDEO COST if not is_text_tokens_total and video_tokens and video_tokens > 0: @@ -1689,10 +1751,9 @@ def generic_cost_per_token( ) completion_cost += float(video_tokens) * _output_cost_per_video_token - ## REGIONAL DATA-RESIDENCY UPLIFT - # Applied as a flat multiplier across all token costs for the request - # when the upstream is a regionalized OpenAI host (eu./us.api.openai.com). - uplift: Final = get_regional_uplift_multiplier(resolved_model_info, data_residency) + uplift: Final = get_regional_uplift_multiplier(resolved_model_info, data_residency) * get_fedramp_uplift_multiplier( + resolved_model_info, data_residency + ) if uplift != 1.0: prompt_cost *= uplift completion_cost *= uplift @@ -1835,6 +1896,7 @@ def _cost_map_billed_rates( ) multiplier: Final = ( get_regional_uplift_multiplier(model_info, data_residency) + * get_fedramp_uplift_multiplier(model_info, data_residency) * get_vertex_regional_endpoint_uplift(model_info, vertex_location) * get_provider_specific_geo_multiplier(model_info=model_info, usage=usage) * get_provider_specific_speed_multiplier(model_info=model_info, usage=usage) @@ -2020,8 +2082,10 @@ def calculate_prompt_caching_savings( {ttl: rate - prompt_base_cost for ttl, rate in base_costs.cache_creation_rates_by_ttl.items()} ), ) - uplift: Final = get_regional_uplift_multiplier(model_info, data_residency) * get_vertex_regional_endpoint_uplift( - model_info, vertex_location + uplift: Final = ( + get_regional_uplift_multiplier(model_info, data_residency) + * get_fedramp_uplift_multiplier(model_info, data_residency) + * get_vertex_regional_endpoint_uplift(model_info, vertex_location) ) return (read_discount - write_premium) * uplift diff --git a/litellm/litellm_core_utils/per_call_pricing.py b/litellm/litellm_core_utils/per_call_pricing.py new file mode 100644 index 00000000000..686639dc10f --- /dev/null +++ b/litellm/litellm_core_utils/per_call_pricing.py @@ -0,0 +1,104 @@ +"""The price entry for a single call that sets its own custom pricing, built without writing ``litellm.model_cost``.""" + +from collections.abc import Mapping +from typing import Final, Literal + +from pydantic import TypeAdapter + +import litellm +from litellm.litellm_core_utils.ptu_pricing import zeroed_ptu_pricing +from litellm.types.utils import CustomPricingLiteLLMParams, ModelInfo + +_STR_MAPPING: Final = TypeAdapter(Mapping[str, object]) +_MODEL_INFO: Final = TypeAdapter(ModelInfo) + + +def _router_deployment_model_info(kwargs: Mapping[str, object]) -> Mapping[str, object] | None: + """A Router stamps the deployment's ``model_info`` (with its ``id``) into the call's metadata and registers that + deployment's full custom pricing under that id when the deployment is added.""" + for metadata_key in ("litellm_metadata", "metadata"): + metadata = kwargs.get(metadata_key) + if not isinstance(metadata, Mapping): + continue + model_info = _STR_MAPPING.validate_python(metadata).get("model_info") + if isinstance(model_info, Mapping) and isinstance(model_info.get("id"), str): + return _STR_MAPPING.validate_python(model_info) + return None + + +def per_call_model_info( + model: str, custom_llm_provider: str, kwargs: Mapping[str, object], mode: Literal["chat", "embedding"] +) -> ModelInfo | None: + """The full price entry for one call, or None when the cost map (or the Router's registered deployment entry) + already prices it. The cost map row for the model is overlaid with the registered deployment entry and then with + the custom pricing fields the request itself set, so a Router call pays request overrides over deployment prices. + The entry travels on the logging object and is never written into ``litellm.model_cost``, so the next call for the + same model name is priced from the cost map again.""" + deployment: Final = _router_deployment_model_info(kwargs) + registered: Final = litellm.model_cost.get(str(deployment["id"])) if deployment is not None else None + custom: Final = { + field: kwargs[field] + for field in CustomPricingLiteLLMParams.model_fields + if kwargs.get(field) is not None and (registered is None or registered.get(field) != kwargs[field]) + } + if not custom: + return None + unmapped: Final[Mapping[str, object]] = { + "key": model, + "max_tokens": None, + "max_input_tokens": None, + "max_output_tokens": None, + "input_cost_per_token": None, + "output_cost_per_token": None, + "litellm_provider": custom_llm_provider, + "mode": mode, + "supported_openai_params": None, + } + row: Final = _catalog_row(model, custom_llm_provider) or unmapped + catalog: Final = ( + {**row, **{key: value for key, value in registered.items() if value is not None}} if registered else row + ) + zeroed: Final = zeroed_ptu_pricing(deployment, custom) if deployment is not None else None + return _MODEL_INFO.validate_python( + { + **catalog, + **_token_rates_cleared_for_per_second_overrides(custom), + **custom, + **_tiers_with_flat_overrides(catalog, custom), + **(zeroed or {}), + "mode": catalog.get("mode") or mode, + } + ) + + +_FLAT_TOKEN_RATE_FIELDS: Final = ("input_cost_per_token", "output_cost_per_token") +_PER_SECOND_RATE_FIELDS: Final = ("cost_per_second", "input_cost_per_second", "output_cost_per_second") +_TIERS: Final = TypeAdapter(tuple[Mapping[str, object], ...]) + + +def _token_rates_cleared_for_per_second_overrides(custom: Mapping[str, object]) -> Mapping[str, object]: + """Token rates in the entry make the cost calculator skip per-second pricing, so a caller who sets only a + per-second rate gets the catalog's token rates and tier table cleared for this call.""" + if not any(key in custom for key in _PER_SECOND_RATE_FIELDS) or any( + key in custom for key in _FLAT_TOKEN_RATE_FIELDS + ): + return {} + return {"input_cost_per_token": None, "output_cost_per_token": None, "tiered_pricing": None} + + +def _tiers_with_flat_overrides(catalog: Mapping[str, object], custom: Mapping[str, object]) -> Mapping[str, object]: + """A ``tiered_pricing`` table wins over flat rates in the cost calculator, so a flat rate the caller set is written + into every tier of the table in force (the caller's own, else the inherited one) for that direction, and the other + direction keeps each tier's own rate.""" + overrides: Final = {key: custom[key] for key in _FLAT_TOKEN_RATE_FIELDS if key in custom} + tiers: Final = custom.get("tiered_pricing", catalog.get("tiered_pricing")) + if not overrides or not isinstance(tiers, (list, tuple)) or not tiers: + return {} + return {"tiered_pricing": [{**tier, **overrides} for tier in _TIERS.validate_python(tiers)]} + + +def _catalog_row(model: str, custom_llm_provider: str) -> ModelInfo | None: + try: + return litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider) + except Exception: # noqa: BLE001 # get_model_info raises a bare Exception for an unmapped model + return None diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 6ecab166dcd..f48a42963cf 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -399,6 +399,74 @@ def split_media_for_tpm(value: object) -> tuple[object, tuple[Mapping[str, objec return projection.content, projection.media +_TPM_EMBEDDING_FILE_MIME_PREFIXES: Final = ("application/pdf", "video/") + + +def _tpm_embedding_file(data_uri: str) -> tuple[Mapping[str, object], ...]: + from litellm.litellm_core_utils.prompt_templates.factory import parse_mime_type + + mime: Final = (parse_mime_type(data_uri[:256]) or "").strip().lower() + if not mime.startswith(_TPM_EMBEDDING_FILE_MIME_PREFIXES): + return () + return ({"type": "file", "file": {"file_data": data_uri, "format": mime}},) + + +_BASE64_PAYLOAD: Final = re.compile(r"[A-Za-z0-9+/]*={0,2}") + + +def _is_base64_data_uri(value: str) -> bool: + header_end: Final = value.find(",", 0, 256) + if header_end < 0 or not value[:header_end].endswith(";base64"): + return False + payload_start: Final = header_end + 1 + if (len(value) - payload_start) % 4 or _BASE64_PAYLOAD.fullmatch(value, payload_start) is None: + return False + last_quantum: Final = value[max(payload_start, len(value) - 4) :] + return base64.b64encode(base64.b64decode(last_quantum)).decode("ascii") == last_quantum + + +def _tpm_embedding_file_block(part: Mapping[str, object]) -> _TPMMediaProjection: + projection: Final = _tpm_file_media(part) + fields: Final = as_str_mapping(part.get("file")) + data: Final = fields.get("file_data") if fields is not None else None + if projection.media or fields is None or not isinstance(data, str) or not _is_base64_data_uri(data): + return projection + media: Final = _tpm_embedding_file(data) + return _TPMMediaProjection({**part, "file": _tpm_blank_fields(fields, ("file_data",))} if media else part, media) + + +def _tpm_embedding_leaf(value: object) -> _TPMMediaProjection: + part: Final = as_str_mapping(value) + if part is not None and part.get("type") == "file": + return _tpm_embedding_file_block(part) + if not isinstance(value, str) or not value.startswith("data:"): + return _tpm_media_leaf(value) + if not _is_base64_data_uri(value): + return _TPMMediaProjection(value) + media: Final = _tpm_file_media({"file_data": value}).media or _tpm_embedding_file(value) + return _TPMMediaProjection("" if media else value, media) + + +def _is_json_array(value: object) -> bool: + return isinstance(value, (list, tuple)) + + +def _tpm_embedding_item(value: object) -> _TPMMediaProjection: + return _tpm_media_list(value, _tpm_embedding_leaf) if _is_json_array(value) else _tpm_embedding_leaf(value) + + +def split_embedding_media_for_tpm(value: object) -> tuple[object, tuple[Mapping[str, object], ...]]: + projection: Final = ( + _tpm_media_list(value, _tpm_embedding_item) if _is_json_array(value) else _tpm_embedding_leaf(value) + ) + return projection.content, projection.media + + +def embedding_input_is_all_media(value: object) -> bool: + items: Final = cast(Sequence[object], value) if _is_json_array(value) else (value,) # cast-ok: JSON input array + return all(_tpm_embedding_leaf(item).media for item in items) + + def _content_parts_contain_image(parts: Sequence[object]) -> bool: """Depth-bounded frontier walk over nested content lists, iterative because the repo bans recursion; an Anthropic tool_result nests its image parts exactly one level down.""" diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 8e6e71a31d3..b6af181980e 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -836,8 +836,6 @@ class RealTimeStreaming: if self.session_tools or self.tool_calls: self.logging_obj.model_call_details["realtime_tools"] = self.session_tools self.logging_obj.model_call_details["realtime_tool_calls"] = self.tool_calls - if LiteLLMLogging.usage_capture_callback is not None: - self.logging_obj.capture_usage_before_logging(self.messages) # Route through the bounded logging worker (per-coroutine timeout + # concurrency cap) instead of a bare create_task, so a slow callback # can't leave suspended tasks pinning each call's response in memory. diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 9da0e35bdda..ac5c0720eff 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -272,15 +272,31 @@ def _redacted_provider_specific_fields(fields: Mapping[str, object]) -> Mapping[ return { **fields, **({"reasoning_content": REDACTED_BY_LITELLM} if fields.get("reasoning_content") is not None else {}), + **({"reasoning": REDACTED_BY_LITELLM} if fields.get("reasoning") is not None else {}), + **({"reasoning_text": REDACTED_BY_LITELLM} if fields.get("reasoning_text") is not None else {}), **{ key: None for key in ( "thinking_blocks", "reasoningContentBlocks", + "reasoningContent", + "annotations", "citations", + "citation", + "citationsContent", "compaction_blocks", "compaction_start", "compaction_delta", + "reasoning_details", + "web_search_calls", + "web_search_results", + "tool_results", + "code_interpreter_results", + "search_results", + "rerank_results", + "executed_tools", + "custom_outputs", + "tool_search_items", "token_ids", ) if key in fields diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index a9ef06c3b4f..aa6ce6ea99e 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -2826,8 +2826,6 @@ class CustomStreamWrapper: return None if is_model_response_stream_empty(model_response=usage_free_chunk) else usage_free_chunk def _accumulate_streamed_delta(self, delta: object) -> None: - # Shared by the sync, async, and non-aiohttp iteration sites so answer - # text and the disqualifying-content latch stay in step across all three. get: Final = getattr(delta, "get", None) content: Final = get("content") if callable(get) else None self.response_uptil_now += content if isinstance(content, str) else "" diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index df8038e756c..52a5026c44f 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -610,6 +610,127 @@ def estimate_image_tokens( return max(_image_policy_tokens(family, dimensions, detail) for family in families) +_MEDIA_FILE_CHARS_PER_TOKEN: Final = 4 +_EmbeddingInputT: Final = TypeVar("_EmbeddingInputT") + + +def _max_input_tokens(model: str) -> int | None: + try: + limit: Final = litellm.get_model_info(model).get("max_input_tokens") + except Exception: # noqa: BLE001 # get_model_info raises a bare Exception for an unmapped model + return None + return limit if isinstance(limit, int) and limit > 0 else None + + +def _media_file_tokens(data: str, models: Sequence[str]) -> int: + text_estimate: Final = len(data) // _MEDIA_FILE_CHARS_PER_TOKEN + ceilings: Final = tuple(map(_max_input_tokens, models)) + known_ceilings: Final = tuple(ceiling for ceiling in ceilings if ceiling is not None) + if not known_ceilings or len(known_ceilings) != len(ceilings): + return text_estimate + return min(text_estimate, max(known_ceilings)) + + +def _block_str_field(block: Mapping[str, object], container: str, field: str) -> str | None: + inner: Final = as_str_mapping(block.get(container)) + value: Final = inner.get(field) if inner is not None else None + return value if isinstance(value, str) else None + + +def estimate_media_block_tokens(block: Mapping[str, object], models: Sequence[str], fallback_model: str | None) -> int: + from litellm.litellm_core_utils.audio_utils.utils import estimate_audio_tokens + + match block.get("type"): + case "input_audio": + return estimate_audio_tokens( + _block_str_field(block, "input_audio", "data"), models, fallback_model=fallback_model + ) + case "file": + return _media_file_tokens(_block_str_field(block, "file", "file_data") or "", models) + case _: + detail: Final = _block_str_field(block, "image_url", "detail") + return estimate_image_tokens( + models, + _block_str_field(block, "image_url", "url") or "", + "auto" if detail is None else detail, + fallback_model=fallback_model, + ) + + +_PER_ITEM_DATA_URI_EMBEDDING_PROVIDERS: Final = frozenset({"vertex_ai", "gemini", "azure_ai"}) + +_DataUriMediaScope: TypeAlias = Literal["per_item", "whole_batch"] + + +def _is_pdf_block(block: Mapping[str, object]) -> bool: + return (_block_str_field(block, "file", "file_data") or "").startswith("data:application/pdf") + + +def _bedrock_data_uri_media_scope( + resolved_model: str, media: Sequence[Mapping[str, object]] +) -> _DataUriMediaScope | None: + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + from litellm.llms.bedrock.common_utils import BedrockModelInfo + from litellm.utils import supports_embedding_image_input + + base_model: Final = BedrockModelInfo.get_base_model(resolved_model.removeprefix("async_invoke/")) + if not supports_embedding_image_input(f"bedrock/{base_model}"): + return None + match BaseAWSLLM.get_bedrock_embedding_provider(base_model): + case "cohere": + return "whole_batch" + case "twelvelabs": + return None + case "nova" if any(map(_is_pdf_block, media)): + return None + case _: + return "per_item" + + +def _data_uri_media_scope(model: str, media: Sequence[Mapping[str, object]]) -> _DataUriMediaScope | None: + from litellm.utils import supports_embedding_image_input + + try: + resolved_model, provider, _, _ = litellm.get_llm_provider(model=model) + except litellm.exceptions.BadRequestError: + return None + if provider == "bedrock": + return _bedrock_data_uri_media_scope(resolved_model, media) + if not supports_embedding_image_input(model): + return None + if provider == "cohere": + return "whole_batch" + return "per_item" if provider in _PER_ITEM_DATA_URI_EMBEDDING_PROVIDERS else None + + +def split_embedding_input_tokens( + value: _EmbeddingInputT, media_models: Callable[[], Sequence[str]], fallback_model: str | None = None +) -> tuple[_EmbeddingInputT, int]: + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + embedding_input_is_all_media, + split_embedding_media_for_tpm, + ) + + text, media = split_embedding_media_for_tpm(value) + if not media: + return value, 0 + models: Final = tuple(media_models()) + scopes: Final = frozenset(_data_uri_media_scope(model, media) for model in models) + if not models or None in scopes or ("whole_batch" in scopes and not embedding_input_is_all_media(value)): + return value, 0 + media_tokens: Final = sum(estimate_media_block_tokens(block, models, fallback_model) for block in media) + return cast(_EmbeddingInputT, text), media_tokens # cast-ok: the projection keeps the input's container shape + + +def embedding_aware_token_count( + messages: Sequence[Mapping[str, str]] | None, + text: str | list[str] | None, # mutable-ok: litellm.token_counter only takes text as list[str] + media_models: Callable[[], Sequence[str]], +) -> int: + embedding_text, media_tokens = split_embedding_input_tokens(text, media_models) + return litellm.token_counter(messages=messages, text=embedding_text) + media_tokens + + TokenCounterFunction = Callable[[str], int] """ Type for a function that counts tokens in a string. diff --git a/litellm/litellm_core_utils/tool_fields.py b/litellm/litellm_core_utils/tool_fields.py index 5ee96ea9133..10b6f07bc4d 100644 --- a/litellm/litellm_core_utils/tool_fields.py +++ b/litellm/litellm_core_utils/tool_fields.py @@ -10,7 +10,7 @@ from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger from litellm.exceptions import UnsupportedParamsError -from litellm.litellm_core_utils.core_helpers import drops_unsupported_params, normalize_drop_params +from litellm.litellm_core_utils.core_helpers import as_str_mapping, drops_unsupported_params, normalize_drop_params ROUTES_WITH_TOOLS: Final = frozenset( { @@ -58,6 +58,19 @@ _TOOL_ENTRY: Final = TypeAdapter(_ToolEntry) _WEB_SEARCH_OPTIONS: Final = TypeAdapter(dict[str, object] | None, config=ConfigDict(strict=True)) +def declared_function_name(tool: object) -> str | None: + fields: Final = as_str_mapping(tool) + if fields is None: + return None + function: Final = as_str_mapping(fields.get("function")) + name: Final = (fields if function is None else function).get("name") + return name if isinstance(name, str) and name else None + + +def qualified_member_name(namespace: str, name: str) -> str: + return f"{namespace}__{name}" + + @dataclass(frozen=True, slots=True) class DropPolicy: drop_params: bool diff --git a/litellm/litellm_core_utils/url_utils.py b/litellm/litellm_core_utils/url_utils.py index 0db6a41b503..eae8ac04e8e 100644 --- a/litellm/litellm_core_utils/url_utils.py +++ b/litellm/litellm_core_utils/url_utils.py @@ -102,6 +102,14 @@ class SSRFError(ValueError): """Raised when a URL targets a blocked network.""" +def is_https_url_with_host(url: str) -> bool: + try: + parsed_url: Final = urlsplit(url) + return parsed_url.scheme == "https" and bool(parsed_url.hostname) + except ValueError: + return False + + def encode_url_path_segment(value: object, *, field_name: str = "path parameter") -> str: """Percent-encode one user-controlled URL path segment. diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 7e3fda06b1a..6436cd832c7 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -781,12 +781,17 @@ class AnthropicMessagesHandler(BaseTranslation): for event in AnthropicMessagesHandler._parse_sse_data_line(line) ) - def _translate_to_openai(self, data: dict) -> ChatCompletionRequest: + def _translate_to_openai( + self, + data: dict, # mutable-ok: the request body the adapter reads + *, + keep_cache_control: bool = False, + ) -> ChatCompletionRequest: """Translate Anthropic request to OpenAI chat completion format.""" ( chat_completion_compatible_request, _tool_name_mapping, - ) = LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai( + ) = LiteLLMAnthropicMessagesAdapter(keep_cache_control=keep_cache_control).translate_anthropic_to_openai( anthropic_message_request=cast(AnthropicMessagesRequest, scannable_tool_result_request(data)), preserve_midturn_system=True, ) @@ -813,9 +818,13 @@ class AnthropicMessagesHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + *, + request_data: dict[str, object] | None = None, # mutable-ok: the request dict guardrails receive + keep_cache_control: bool = False, ) -> Mapping[str, object]: """ - Process input messages by applying guardrails to text content. + Process input messages by applying guardrails to text content. ``request_data`` is what the guardrail sees as + the request when ``data`` is only its body, e.g. a provider pass-through route that nests the body """ messages: Final = data.get("messages") if messages is None: @@ -827,8 +836,17 @@ class AnthropicMessagesHandler(BaseTranslation): # The top-level prompt is translated on its own below so it can be hoisted in front of # any mid-turn system entries and scanned first, aligned with that structured position. - translation_source: Final = {key: value for key, value in data.items() if key != "system"} - chat_completion_compatible_request: Final = self._translate_to_openai(translation_source) + body_model: Final = data.get("model") + request_model: Final = (request_data or {}).get("model") + model: Final[str] = ( + body_model + if isinstance(body_model, str) and body_model + else (request_model if isinstance(request_model, str) else "") + ) + translation_source: Final = {**{key: value for key, value in data.items() if key != "system"}, "model": model} + chat_completion_compatible_request: Final = self._translate_to_openai( + translation_source, keep_cache_control=keep_cache_control + ) full_structured_messages: Final = cast( list[AllMessageValues], @@ -837,7 +855,9 @@ class AnthropicMessagesHandler(BaseTranslation): has_midturn_system_message: Final = any( str(message.get("role") or "").lower() == "system" for message in full_structured_messages ) - hoisted_system_message: Final = None if skip_system else self._hoisted_top_level_system_message(data) + hoisted_system_message: Final = ( + None if skip_system else self._hoisted_top_level_system_message(data, model, keep_cache_control) + ) if hoisted_system_message is not None: full_structured_messages.insert(0, hoisted_system_message) # skip_system already excluded the trusted top-level prompt (it is simply not hoisted); @@ -899,7 +919,6 @@ class AnthropicMessagesHandler(BaseTranslation): if structured_messages: inputs["structured_messages"] = structured_messages # Include model information if available - model: Final = data.get("model") if model: inputs["model"] = model original_tool_names: Final = _original_tool_names( @@ -907,7 +926,7 @@ class AnthropicMessagesHandler(BaseTranslation): ) guardrailed_inputs: Final = await guardrail_to_apply.apply_guardrail( inputs=inputs, - request_data=data, + request_data=data if request_data is None else request_data, input_type="request", logging_obj=litellm_logging_obj, ) @@ -955,6 +974,7 @@ class AnthropicMessagesHandler(BaseTranslation): hoisted_system_message=hoisted_system_message, preserve_system_messages=has_midturn_system_message, original_tool_names=original_tool_names.used, + model=model, ) else: if guardrailed_texts and len(guardrailed_texts) != len(scanned): @@ -977,17 +997,20 @@ class AnthropicMessagesHandler(BaseTranslation): return data - def _hoisted_top_level_system_message(self, data: Mapping[str, object]) -> AllMessageValues | None: + def _hoisted_top_level_system_message( + self, data: Mapping[str, object], model: str, keep_cache_control: bool + ) -> AllMessageValues | None: """Return the system message produced by translating the top-level prompt.""" system: Final = data.get("system") if not system: return None probe: Final = self._translate_to_openai( { - "model": data.get("model") or "", + "model": model, "messages": [], "system": system, - } + }, + keep_cache_control=keep_cache_control, ) hoisted: Final = probe.get("messages") or [] return hoisted[0] if hoisted else None @@ -1121,6 +1144,7 @@ class AnthropicMessagesHandler(BaseTranslation): preserve_system_messages: bool = False, *, original_tool_names: Mapping[str, str], + model: str, ) -> None: """Write a guardrail's structured-message rewrite back without losing corrections.""" from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -1129,7 +1153,6 @@ class AnthropicMessagesHandler(BaseTranslation): ) _is_system: Final = AnthropicMessagesHandler._is_system - model: Final = str(data.get("model") or "") converted: Final[list] = [] # mutable-ok: API message payload def _convert_run(run: list) -> None: # mutable-ok: API message payload diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 6b96df8f879..09d16181c3a 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -28,7 +28,11 @@ from litellm.constants import ( RESPONSE_FORMAT_TOOL_NAME, ) from litellm.litellm_core_utils.asyncify import is_event_loop_running -from litellm.litellm_core_utils.core_helpers import drops_unsupported_params, map_finish_reason +from litellm.litellm_core_utils.core_helpers import ( + coerce_token_limit, + drops_unsupported_params, + map_finish_reason, +) from litellm.litellm_core_utils.prompt_templates.common_utils import ( DEFS_MAX_INLINED_BYTES, invalid_type_message, @@ -581,12 +585,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) @classmethod - def get_config(cls, *, model: str | None = None): + def get_config(cls, *, model: str | None = None, configured_max_output_tokens: int | None = None): config: Final = super().get_config() # anthropic requires a default value for max_tokens if config.get("max_tokens") is None: - config["max_tokens"] = cls.get_max_tokens_for_model(model) + config["max_tokens"] = ( + configured_max_output_tokens + if configured_max_output_tokens is not None + else cls.get_max_tokens_for_model(model) + ) return config @@ -2539,7 +2547,19 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params["tools"] = tools ## Load Config - config: Final[Mapping[str, object]] = MappingProxyType(litellm.AnthropicConfig.get_config(model=model)) + model_info: Final[object] = cast( # cast-ok: request parameters are a Mapping + Mapping[str, object], litellm_params + ).get("model_info") + configured_max_output_tokens: Final = ( + coerce_token_limit( + cast(Mapping[str, object], model_info).get("max_output_tokens") # cast-ok: runtime Mapping check + ) + if isinstance(model_info, Mapping) + else None + ) + config: Final[Mapping[str, object]] = MappingProxyType( + litellm.AnthropicConfig.get_config(model=model, configured_max_output_tokens=configured_max_output_tokens) + ) for k, v in config.items(): if ( k not in optional_params diff --git a/litellm/llms/anthropic/messages/adapters/transformation.py b/litellm/llms/anthropic/messages/adapters/transformation.py index 4b1b5a8a11a..afcd94c5b2c 100644 --- a/litellm/llms/anthropic/messages/adapters/transformation.py +++ b/litellm/llms/anthropic/messages/adapters/transformation.py @@ -380,8 +380,8 @@ _CLAUDE_OVER_REASONING_EFFORT_PROVIDERS: Final = frozenset({LlmProviders.KYMA.va class LiteLLMAnthropicMessagesAdapter: - def __init__(self): - pass + def __init__(self, *, keep_cache_control: bool = False) -> None: + self.keep_cache_control: Final = keep_cache_control ### FOR [BETA] `/v1/messages` endpoint support @@ -437,7 +437,7 @@ class LiteLLMAnthropicMessagesAdapter: cache_control: Final = ( source.get("cache_control") if isinstance(source, dict) else getattr(source, "cache_control", None) ) - if cache_control and model and self.target_consumes_cache_control(model): + if cache_control and (self.keep_cache_control or (model and self.target_consumes_cache_control(model))): # TypedDict objects support dict operations at runtime # Use type ignore consistent with codebase pattern (see anthropic/chat/transformation.py:432) if isinstance(target, dict): diff --git a/litellm/llms/anthropic/messages/caller_model_checks.py b/litellm/llms/anthropic/messages/caller_model_checks.py index 3822f075ccf..c89d8a1ea23 100644 --- a/litellm/llms/anthropic/messages/caller_model_checks.py +++ b/litellm/llms/anthropic/messages/caller_model_checks.py @@ -243,8 +243,7 @@ async def caller_within_model_budget( return "allowed" model_max_budget: Final = getattr(user_api_key_auth, "model_max_budget", None) - token: Final = getattr(user_api_key_auth, "token", None) - if isinstance(model_max_budget, dict) and model_max_budget and token is not None: + if isinstance(model_max_budget, dict) and model_max_budget and user_api_key_auth.token is not None: try: await model_max_budget_limiter.is_key_within_model_budget( user_api_key_dict=user_api_key_auth, @@ -300,10 +299,8 @@ async def caller_within_model_budget( ) return "denied_for_now" - end_user_model_max_budget: Final[dict[str, object] | None] = getattr( - user_api_key_auth, "end_user_model_max_budget", None - ) - end_user_id: Final[str | None] = getattr(user_api_key_auth, "end_user_id", None) + end_user_model_max_budget: Final[dict[str, object] | None] = user_api_key_auth.end_user_model_max_budget + end_user_id: Final[str | None] = user_api_key_auth.end_user_id if isinstance(end_user_model_max_budget, dict) and end_user_model_max_budget and end_user_id is not None: try: await model_max_budget_limiter.is_end_user_within_model_budget( diff --git a/litellm/llms/anthropic/messages/native/agentic_streaming_iterator.py b/litellm/llms/anthropic/messages/native/agentic_streaming_iterator.py index 25d4d7b3ce1..0aa30998557 100644 --- a/litellm/llms/anthropic/messages/native/agentic_streaming_iterator.py +++ b/litellm/llms/anthropic/messages/native/agentic_streaming_iterator.py @@ -22,6 +22,7 @@ from typing import TYPE_CHECKING, Any, Final, cast from litellm._logging import verbose_logger from litellm.constants import STREAM_SSE_KEEPALIVE_PING_BYTES +from litellm.types.integrations.custom_logger import AgenticLoopLimitError, AgenticLoopSafetyError if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -35,10 +36,16 @@ SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES: Final = ( b'data: {"type": "error", "error": {"type": "api_error", "message": ' b'"Server-side tool retrieval failed, so this turn could not be completed. Please retry."}}\n\n' ) +AGENTIC_LOOP_LIMIT_ERROR_SSE_BYTES: Final = ( + b"event: error\n" + b'data: {"type": "error", "error": {"type": "api_error", "message": ' + b'"The agentic loop reached its limit before this turn finished. ' + b'Raise max_agentic_loops to allow more loops."}}\n\n' +) def is_server_fulfilled_tool_leak_error(chunk: object) -> bool: - return chunk == SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES + return chunk in (SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES, AGENTIC_LOOP_LIMIT_ERROR_SSE_BYTES) async def _anext_or_none(iterator: AsyncIterator) -> bytes | None: @@ -216,6 +223,7 @@ class AgenticAnthropicStreamingIterator: self._follow_up_chunk_task: asyncio.Task | None = None self._replay_index = 0 self._error_emitted = False + self._loop_limit_reached = False @property def has_buffered_provider_output(self) -> bool: @@ -288,6 +296,8 @@ class AgenticAnthropicStreamingIterator: "tool_use [model=%s]; emitting an SSE error instead of leaking the tool call to the client", self._model, ) + if self._loop_limit_reached: + return AGENTIC_LOOP_LIMIT_ERROR_SSE_BYTES return SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES if self._replay_index < len(self._collected_bytes): @@ -393,14 +403,9 @@ class AgenticAnthropicStreamingIterator: "AgenticStreamingIterator: Unexpected result type from hooks: %s", type(result).__name__, ) - except Exception as e: - _call_id: Final = getattr(self._logging_obj, "litellm_call_id", "unknown") - verbose_logger.exception( - "AgenticStreamingIterator: Error in agentic hook processing [call_id=%s model=%s]: %s", - _call_id, - self._model, - str(e), - ) + except AgenticLoopSafetyError as e: + self._loop_limit_reached = isinstance(e, AgenticLoopLimitError) + verbose_logger.warning("AgenticStreamingIterator: agentic loop refused [rail=%s]", type(e).__name__) @staticmethod def _rebuild_anthropic_response_from_sse( diff --git a/litellm/llms/aws_polly/text_to_speech/transformation.py b/litellm/llms/aws_polly/text_to_speech/transformation.py index 325c9fd364d..3b52a8df3f2 100644 --- a/litellm/llms/aws_polly/text_to_speech/transformation.py +++ b/litellm/llms/aws_polly/text_to_speech/transformation.py @@ -178,7 +178,6 @@ class AWSPollyTextToSpeechConfig(BaseTextToSpeechConfig, BaseAWSLLM): else: mapped_params["output_format"] = self.DEFAULT_OUTPUT_FORMAT - # model arrives without the "aws_polly/" prefix, e.g. "standard" engine: Final = self._extract_engine_from_model(model) mapped_params["engine"] = engine diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index 6bb6d584385..16c8bf6f9f1 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -1,15 +1,16 @@ import asyncio import hashlib +import itertools import json import os import re -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Iterator, Mapping from contextlib import closing from types import MappingProxyType -from typing import Final, Literal, NamedTuple, cast +from typing import Final, Literal, NamedTuple, NoReturn, cast import httpx -from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI +from openai import APIError, AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI from pydantic import ConfigDict, TypeAdapter, with_config from typing_extensions import ReadOnly, TypedDict @@ -20,7 +21,7 @@ from litellm.constants import DEFAULT_MAX_RETRIES from litellm.litellm_core_utils.single_flight import SingleFlight, single_flight_lru_cache from litellm.litellm_core_utils.url_utils import ensure_api_route from litellm.llms.base_llm.chat.transformation import BaseLLMException -from litellm.llms.custom_httpx.http_handler import HTTPHandler +from litellm.llms.custom_httpx.http_handler import HTTPHandler, MaskedHTTPStatusError from litellm.llms.openai.common_utils import BaseOpenAILLM, OpenAIAsyncHTTPClient, OpenAIHTTPClient from litellm.secret_managers.get_azure_ad_token_provider import ( get_azure_ad_token_provider, @@ -52,6 +53,13 @@ class _AsyncAzureClientParams(TypedDict, total=False, extra_items=object): _ASYNC_AZURE_CLIENT_PARAMS_ADAPTER: Final = TypeAdapter(_AsyncAzureClientParams) +_AZURE_AD_TOKEN_PROVIDER_ADAPTER: Final = TypeAdapter[Callable[[], str | Awaitable[str]] | None]( + Callable[[], str | Awaitable[str]] | None +) + + +def _callable_azure_ad_token_provider(provider: object) -> Callable[[], str | Awaitable[str]] | None: + return _AZURE_AD_TOKEN_PROVIDER_ADAPTER.validate_python(provider) if callable(provider) else None class _AzureGatewayClientParams(TypedDict, total=False, extra_items=object): @@ -81,6 +89,160 @@ class AzureOpenAIError(BaseLLMException): ) +class EntraRejectionError(AzureOpenAIError): + pass + + +_GUID: Final = re.compile(r"[0-9a-fA-F]{8}-(?:[0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}") +_AADSTS_REASON: Final = re.compile(r"\bAADSTS(\d+)\b.*?(?:\.(?=[\s'\"\\]|$)|$)", re.MULTILINE) +_ENTRA_CREDENTIAL_REJECTION_CODES: Final = frozenset( + { + 50012, + 50013, + 50027, + 50034, + 50053, + 50055, + 50057, + 50059, + 50076, + 50079, + 50126, + 50128, + 53003, + 65001, + 70001, + 70002, + 70011, + 70021, + 70025, + 70052, + 90002, + 90094, + 500011, + 500014, + 700016, + 700023, + 700024, + 700025, + 700027, + 700211, + 700213, + 700223, + 700238, + 7000112, + 7000215, + 7000218, + 7000222, + } +) + + +def _entra_rejection_reason(message: str) -> str | None: + reason: Final = _AADSTS_REASON.search(message) + if reason is None or int(reason.group(1)) not in _ENTRA_CREDENTIAL_REJECTION_CODES: + return None + return _GUID.sub("", reason.group(0)) + + +def _entra_rejection(reason: str) -> EntraRejectionError: + return EntraRejectionError(status_code=401, message=f"Failed to get Azure AD token: {reason}") + + +def entra_rejection_as_auth_error(error: Exception) -> EntraRejectionError | None: + try: + from azure.core.exceptions import ClientAuthenticationError + except ImportError: + return None + if not isinstance(error, ClientAuthenticationError): + return None + status_code: Final = error.status_code + if status_code is not None and (status_code == 429 or status_code >= 500): + return None + reason: Final = _entra_rejection_reason(str(error)) + return None if reason is None else _entra_rejection(reason) + + +def _raise_entra_rejection_as_auth_error(error: Exception) -> NoReturn: + auth_error: Final = entra_rejection_as_auth_error(error) + if auth_error is None: + raise error + raise auth_error from error + + +def entra_token(provider: Callable[[], str]) -> str: + try: + return provider() + except entra_rejection_types() as error: + _raise_entra_rejection_as_auth_error(error) + + +def entra_rejection_types() -> tuple[type[Exception], ...]: + try: + from azure.core.exceptions import ClientAuthenticationError + except ImportError: + return () + return (ClientAuthenticationError,) + + +_EXCEPTION_CHAIN_MAX_DEPTH: Final = 16 + + +def _exception_chain(error: BaseException) -> Iterator[BaseException]: + link: BaseException | None = error # rebind-ok: advances one link of the cause chain per iteration + for _ in range(_EXCEPTION_CHAIN_MAX_DEPTH): + if link is None: + return + yield link + link = link.__cause__ or link.__context__ + + +_REQUEST_FAILURE_TYPES: Final = (APIError, httpx.HTTPError) + + +def _ends_the_failed_attempt(link: BaseException, failed_attempt: BaseException) -> bool: + return not isinstance(link, EntraRejectionError) and ( + isinstance(link, _REQUEST_FAILURE_TYPES) or type(link) is type(failed_attempt) + ) + + +def _links_within_the_failed_attempt(error: BaseException) -> Iterator[BaseException]: + yield error + yield from itertools.takewhile( + lambda link: not _ends_the_failed_attempt(link, error), itertools.islice(_exception_chain(error), 1, None) + ) + + +def entra_rejection_in(error: BaseException) -> EntraRejectionError | None: + return next( + (link for link in _links_within_the_failed_attempt(error) if isinstance(link, EntraRejectionError)), None + ) + + +def caused_by_entra_rejection(error: BaseException) -> bool: + return entra_rejection_in(error) is not None + + +async def _awaited_token(token: Awaitable[str]) -> str: + try: + return await token + except entra_rejection_types() as error: + _raise_entra_rejection_as_auth_error(error) + + +def _entra_rejections_as_auth_errors( + provider: Callable[[], str | Awaitable[str]], +) -> Callable[[], str | Awaitable[str]]: + def call_provider() -> str | Awaitable[str]: + try: + token: Final = provider() + except entra_rejection_types() as error: + _raise_entra_rejection_as_auth_error(error) + return token if isinstance(token, str) else _awaited_token(token) + + return call_provider + + def process_azure_headers(headers: httpx.Headers | dict) -> dict: openai_headers: Final = {} if "x-ratelimit-limit-requests" in headers: @@ -91,7 +253,7 @@ def process_azure_headers(headers: httpx.Headers | dict) -> dict: openai_headers["x-ratelimit-limit-tokens"] = headers["x-ratelimit-limit-tokens"] if "x-ratelimit-remaining-tokens" in headers: openai_headers["x-ratelimit-remaining-tokens"] = headers["x-ratelimit-remaining-tokens"] - llm_response_headers: Final = {"{}-{}".format("llm_provider", k): v for k, v in headers.items()} + llm_response_headers: Final = {f"llm_provider-{k}": v for k, v in headers.items()} return {**llm_response_headers, **openai_headers} @@ -257,22 +419,34 @@ def _cached_oidc_token(cache_key: str) -> str | None: return cached if isinstance(cached, str) else None +def _oidc_exchange_rejection_reason(error: MaskedHTTPStatusError) -> str | None: + if error.response.status_code not in (400, 401): + return None + return _entra_rejection_reason(error.text or error.response.text) + + def _exchange_oidc_token(cache_key: str, token_url: str, client_id: str, scope: str, oidc_token: str) -> str: cached_access_token: Final = _cached_oidc_token(cache_key) if cached_access_token is not None: return cached_access_token with closing(HTTPHandler(timeout=litellm.request_timeout)) as client: - req_token: Final = client.post( - token_url, - data={ - "client_id": client_id, - "grant_type": "client_credentials", - "scope": scope, - "client_assertion_type": "urn:ietf:params:oauth:client-assertion-type:jwt-bearer", - "client_assertion": oidc_token, - }, - ) + try: + req_token: Final = client.post( + token_url, + data={ + "client_id": client_id, + "grant_type": "client_credentials", + "scope": scope, + "client_assertion_type": "urn:ietf:params:oauth:client-assertion-type:jwt-bearer", + "client_assertion": oidc_token, + }, + ) + except MaskedHTTPStatusError as error: + reason: Final = _oidc_exchange_rejection_reason(error) + if reason is None: + raise + raise _entra_rejection(reason) from error if req_token.status_code != 200: raise AzureOpenAIError( @@ -483,6 +657,9 @@ def get_azure_ad_token( raise except Exception as e: verbose_logger.error("Error calling Azure AD token provider: %s", e) + auth_error: Final = entra_rejection_as_auth_error(e) + if auth_error is not None: + raise auth_error from e raise RuntimeError(f"Failed to get Azure AD token: {e}") from e return azure_ad_token @@ -696,7 +873,7 @@ class BaseAzureLLM(BaseOpenAILLM): api_version: str | None, is_async: bool, ) -> dict: - azure_ad_token_provider = litellm_params.get("azure_ad_token_provider") + azure_ad_token_provider = _callable_azure_ad_token_provider(litellm_params.get("azure_ad_token_provider")) # If we have api_key, then we have higher priority azure_ad_token = litellm_params.get("azure_ad_token") @@ -775,7 +952,7 @@ class BaseAzureLLM(BaseOpenAILLM): azure_client_params["timeout"] = timeout if azure_ad_token_provider is not None: - azure_client_params["azure_ad_token_provider"] = azure_ad_token_provider + azure_client_params["azure_ad_token_provider"] = _entra_rejections_as_auth_errors(azure_ad_token_provider) # this decides if we should set azure_endpoint or base_url on Azure OpenAI Client # required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client diff --git a/litellm/llms/azure/decisions/transformation.py b/litellm/llms/azure/decisions/transformation.py index 3fb40e39702..bdbd784185f 100644 --- a/litellm/llms/azure/decisions/transformation.py +++ b/litellm/llms/azure/decisions/transformation.py @@ -8,6 +8,7 @@ import litellm from litellm.llms.azure.common_utils import ( BaseAzureLLM, cached_azure_ad_token_refresh_provider, + entra_token, ) from litellm.llms.openai.decisions.transformation import OpenAIDecisionsConfig from litellm.secret_managers.main import get_secret_str @@ -61,7 +62,7 @@ class AzureDecisionsConfig(OpenAIDecisionsConfig): scope: Final = ( params.azure_scope or get_secret_str("AZURE_SCOPE") or "https://cognitiveservices.azure.com/.default" ) - azure_headers["Authorization"] = f"Bearer {cached_azure_ad_token_refresh_provider(scope)()}" + azure_headers["Authorization"] = f"Bearer {entra_token(cached_azure_ad_token_refresh_provider(scope))}" return azure_headers async def avalidate_environment( diff --git a/litellm/llms/azure/search/transformation.py b/litellm/llms/azure/search/transformation.py index e255a667606..6d0241d4d3b 100644 --- a/litellm/llms/azure/search/transformation.py +++ b/litellm/llms/azure/search/transformation.py @@ -36,7 +36,7 @@ import httpx from pydantic import ConfigDict, ValidationError from litellm.litellm_core_utils.url_utils import ensure_api_route -from litellm.llms.azure.common_utils import cached_azure_ad_token_refresh_provider +from litellm.llms.azure.common_utils import cached_azure_ad_token_refresh_provider, entra_rejection_as_auth_error from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.search.responses import ( ResponsesSearchAnnotation, @@ -270,6 +270,9 @@ class BingGroundingSearchConfig(BaseSearchConfig): try: return minter() except Exception as e: + rejection: Final = entra_rejection_as_auth_error(e) + if rejection is not None: + raise rejection from e raise ValueError( f"Grounding with Bing Search: no credential available. Pass api_key, set {TOKEN_ENV} " f"to an Entra bearer token, or configure azure-identity (AZURE_CLIENT_ID / " diff --git a/litellm/llms/azure_ai/anthropic/count_tokens/handler.py b/litellm/llms/azure_ai/anthropic/count_tokens/handler.py index 97ee44bd3bb..3609871fdf8 100644 --- a/litellm/llms/azure_ai/anthropic/count_tokens/handler.py +++ b/litellm/llms/azure_ai/anthropic/count_tokens/handler.py @@ -16,6 +16,7 @@ from litellm.anthropic_beta_headers_manager import aload_beta_headers_config from litellm.litellm_core_utils.asyncify import asyncify from litellm.litellm_core_utils.core_helpers import drops_unsupported_params from litellm.llms.anthropic.common_utils import AnthropicError +from litellm.llms.azure.common_utils import EntraRejectionError from litellm.llms.azure_ai.anthropic.count_tokens.transformation import ( AzureAIAnthropicCountTokensConfig, ) @@ -135,6 +136,8 @@ class AzureAIAnthropicCountTokensHandler(AzureAIAnthropicCountTokensConfig): except AnthropicError: # Re-raise Anthropic exceptions as-is raise + except EntraRejectionError as rejection: + raise AnthropicError(status_code=rejection.status_code, message=rejection.message) from rejection except httpx.HTTPStatusError as e: # HTTP errors - preserve the actual status code verbose_logger.error("HTTP error in CountTokens handler: %s", e) diff --git a/litellm/llms/azure_ai/common_utils.py b/litellm/llms/azure_ai/common_utils.py index 147bf69e91e..1635b9b5679 100644 --- a/litellm/llms/azure_ai/common_utils.py +++ b/litellm/llms/azure_ai/common_utils.py @@ -1,5 +1,7 @@ import asyncio -from collections.abc import Mapping +import os +from collections.abc import Callable, Mapping +from functools import partial from types import MappingProxyType from typing import Final, Literal from urllib.parse import urlparse @@ -52,6 +54,28 @@ AZURE_ENTRA_CREDENTIAL_PARAM_KEYS: Final = frozenset({"azure_ad_token", "client_ AZURE_ENTRA_LITELLM_PARAM_KEYS: Final = AZURE_ENTRA_CREDENTIAL_PARAM_KEYS | frozenset( {"tenant_id", "client_id", "azure_username", "azure_scope"} ) +AZURE_DEFAULT_SCOPE: Final = "https://cognitiveservices.azure.com/.default" +_AZURE_ENTRA_ENVIRONMENT_VARIABLES: Final = MappingProxyType( + { + "tenant_id": "AZURE_TENANT_ID", + "client_id": "AZURE_CLIENT_ID", + "client_secret": "AZURE_CLIENT_SECRET", + "azure_username": "AZURE_USERNAME", + "azure_password": "AZURE_PASSWORD", + "azure_ad_token": "AZURE_AD_TOKEN", + } +) + + +def azure_entra_environment_defaults() -> Mapping[str, object]: + return MappingProxyType( + { + **{key: os.getenv(variable) for key, variable in _AZURE_ENTRA_ENVIRONMENT_VARIABLES.items()}, + "azure_scope": os.getenv("AZURE_SCOPE", AZURE_DEFAULT_SCOPE), + } + ) + + AZURE_ENTRA_CREDENTIAL_HELP: Final = ( "Set `tenant_id` + `client_id` + `client_secret`, `azure_ad_token` (an `oidc/` token also needs " "`tenant_id` + `client_id`), or `client_id` + `azure_username` + `azure_password` in the agent's `litellm_params`" @@ -70,6 +94,17 @@ def _resolve_config_secret(value: object) -> str | None: return get_secret_str(value) if value.startswith("os.environ/") else value +def _agent_entra_token(provider: Callable[[], str], model: str) -> str: + from litellm.llms.azure.common_utils import EntraRejectionError, entra_token + + try: + return entra_token(provider) + except EntraRejectionError as rejection: + raise litellm.AuthenticationError( + message=rejection.message, llm_provider="azure_ai", model=model + ) from rejection + + def get_azure_ai_agent_entra_token(litellm_params: Mapping[str, object]) -> str: """Mints the Entra bearer from the agent's own litellm_params, never from process-wide AZURE_* env vars.""" from litellm.llms.azure.common_utils import ( @@ -88,18 +123,32 @@ def get_azure_ai_agent_entra_token(litellm_params: Mapping[str, object]) -> str: azure_username: Final = resolved["azure_username"] azure_password: Final = resolved["azure_password"] azure_ad_token: Final = resolved["azure_ad_token"] + model: Final = str(litellm_params.get("model") or "") if tenant_id and client_id and client_secret: - return get_azure_ad_token_from_entra_id( - tenant_id=tenant_id, client_id=client_id, client_secret=client_secret, scope=scope - )() + return _agent_entra_token( + get_azure_ad_token_from_entra_id( + tenant_id=tenant_id, client_id=client_id, client_secret=client_secret, scope=scope + ), + model, + ) if client_id and azure_username and azure_password: - return get_azure_ad_token_from_username_password( - client_id=client_id, azure_username=azure_username, azure_password=azure_password, scope=scope - )() + return _agent_entra_token( + get_azure_ad_token_from_username_password( + client_id=client_id, azure_username=azure_username, azure_password=azure_password, scope=scope + ), + model, + ) federated: Final = azure_ad_token is not None and azure_ad_token.startswith("oidc/") if azure_ad_token and federated and tenant_id and client_id: - return get_azure_ad_token_from_oidc( - azure_ad_token=azure_ad_token, azure_client_id=client_id, azure_tenant_id=tenant_id, scope=scope + return _agent_entra_token( + partial( + get_azure_ad_token_from_oidc, + azure_ad_token=azure_ad_token, + azure_client_id=client_id, + azure_tenant_id=tenant_id, + scope=scope, + ), + model, ) if azure_ad_token and not federated: return azure_ad_token diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py index 4c26ba58dd8..2131a8c7cfa 100644 --- a/litellm/llms/azure_ai/rerank/transformation.py +++ b/litellm/llms/azure_ai/rerank/transformation.py @@ -111,10 +111,4 @@ class AzureAIRerankConfig(CohereRerankConfig): return rerank_response def _get_base_model(self, azure_model_group: str | None) -> str | None: - if azure_model_group is None: - return None - if azure_model_group == "offer-cohere-rerank-mul-paygo": - return "azure_ai/cohere-rerank-v3-multilingual" - if azure_model_group == "offer-cohere-rerank-eng-paygo": - return "azure_ai/cohere-rerank-v3-english" return azure_model_group diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py index 37acb5423ff..dc85028440f 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py @@ -109,8 +109,16 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): self._read_configured_max_tokens: Final = read_configured_max_tokens @classmethod - def get_config(cls, *, model: str | None = None) -> dict[str, object]: # mutable-ok: overrides BaseConfig's dict - return {key: value for key, value in super().get_config(model=model).items() if key != "max_tokens"} + def get_config( + cls, *, model: str | None = None, configured_max_output_tokens: int | None = None + ) -> dict[str, object]: # mutable-ok: overrides BaseConfig's dict + return { + key: value + for key, value in super() + .get_config(model=model, configured_max_output_tokens=configured_max_output_tokens) + .items() + if key != "max_tokens" + } @property def custom_llm_provider(self) -> str | None: diff --git a/litellm/llms/bedrock/count_tokens/kimi_k3.py b/litellm/llms/bedrock/count_tokens/kimi_k3.py index 5eb46944f9a..1d293bdc99c 100644 --- a/litellm/llms/bedrock/count_tokens/kimi_k3.py +++ b/litellm/llms/bedrock/count_tokens/kimi_k3.py @@ -455,9 +455,7 @@ def _converse_request( if not all(_text_only(message) for message in messages): return None try: - chat_tools: Final = tuple( - chat_tool for tool in tools or () for chat_tool in _as_converse_input(tool) - ) # comprehension-ok: flattens each tool's translation + chat_tools: Final = tuple(chain.from_iterable(_as_converse_input(tool) for tool in tools or ())) return _ConverseRequest.model_validate( litellm.AmazonConverseConfig().transform_request( model=model, diff --git a/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py b/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py index 3a65a8928bf..3514a5e6e5c 100644 --- a/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py +++ b/litellm/llms/bedrock/passthrough/guardrail_translation/handler.py @@ -14,6 +14,7 @@ from pydantic import InstanceOf, TypeAdapter, ValidationError from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger +from litellm.litellm_core_utils.core_helpers import as_str_mapping from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler from litellm.llms.base_llm.guardrail_translation.base_translation import ( BaseTranslation, @@ -60,6 +61,8 @@ if TYPE_CHECKING: _CONVERSE_ACTIONS: Final = frozenset({"converse", "converse-stream"}) _INVOKE_STREAM_ACTION: Final = "invoke-with-response-stream" +_INVOKE_ACTIONS: Final = frozenset({"invoke", _INVOKE_STREAM_ACTION}) +_BLOCK_TYPES_THE_ANTHROPIC_HANDLER_DOES_NOT_SCAN: Final = frozenset({"document", "search_result"}) _EVENT_STREAM_CONTENT_TYPE: Final = "vnd.amazon.eventstream" _EVENT_STREAM_MEDIA_TYPE: Final = "application/vnd.amazon.eventstream" @@ -73,6 +76,83 @@ def _is_invoke_stream_endpoint(endpoint: str) -> bool: return endpoint.rstrip("/").split("/")[-1] == _INVOKE_STREAM_ACTION +def _anthropic_invoke_body( + data: Mapping[str, object], +) -> dict[str, object] | None: # mutable-ok: the body guardrails write back into + """The body of an InvokeModel call in Anthropic Messages format, which Bedrock requires to carry anthropic_version""" + endpoint: Final = data.get("endpoint") + body: Final = data.get("data") + if not isinstance(endpoint, str) or endpoint.rstrip("/").split("/")[-1] not in _INVOKE_ACTIONS: + return None + if not isinstance(body, dict): + return None + invoke_body: Final = _BODY_BY_REFERENCE.validate_python(body) + if not isinstance(invoke_body.get("anthropic_version"), str) or not isinstance(invoke_body.get("messages"), list): + return None + messages: Final = _ARRAY_BY_REFERENCE.validate_python(invoke_body["messages"]) + if not all(_is_well_formed_message(message) for message in messages): + return None + blocks: Final = _anthropic_message_blocks(messages) + if any(block.get("type") in _BLOCK_TYPES_THE_ANTHROPIC_HANDLER_DOES_NOT_SCAN for block in blocks): + return None + return invoke_body + + +def _body_level_request_data( + passthrough_request: Mapping[str, object], +) -> dict[str, object]: # mutable-ok: the request dict guardrails receive + """What `/v1/messages` hands a guardrail as `request_data`: the Anthropic body at the top level, with the proxy's own keys (model, metadata, headers) on top""" + body: Final = as_str_mapping(passthrough_request.get("data")) or {} + proxy_keys: Final = {key: value for key, value in passthrough_request.items() if key != "data"} + return {**body, **proxy_keys} + + +def _is_well_formed_message(message: object) -> bool: + """Whether the Messages translation can read `message`, so a malformed body still reaches Bedrock to be rejected there""" + fields: Final = as_str_mapping(message) + if fields is None or not isinstance(fields.get("role"), str): + return False + content: Final = fields.get("content") + if isinstance(content, str): + return True + return isinstance(content, list) and all( + as_str_mapping(block) is not None for block in _ARRAY_BY_REFERENCE.validate_python(content) + ) + + +def _anthropic_message_blocks(messages: Sequence[object]) -> tuple[Mapping[str, object], ...]: + fields: Final = (as_str_mapping(message) for message in messages) + return tuple(chain.from_iterable(_anthropic_content_blocks(field.get("content")) for field in fields if field)) + + +def _anthropic_content_blocks(content: object) -> tuple[Mapping[str, object], ...]: + blocks: Final[list[Mapping[str, object]]] = [] # mutable-ok: collected while walking the content + pending: Final[list[object]] = [content] # mutable-ok: content lists still to walk, nested tool results included + while pending: + current = pending.pop() + if not isinstance(current, list): + continue + for item in _ARRAY_BY_REFERENCE.validate_python(current): + block = as_str_mapping(item) + if block is None: + continue + blocks.append(block) + if block.get("type") == "tool_result": + pending.append(block.get("content")) + return tuple(blocks) + + +def _anthropic_invoke_reply( + response: object, + request_data: object, +) -> dict[str, object] | None: # mutable-ok: the reply guardrails write back into + if not isinstance(response, dict) or not isinstance(request_data, dict): + return None + if _anthropic_invoke_body(_OBJECT_BY_REFERENCE.validate_python(request_data)) is None: + return None + return _BODY_BY_REFERENCE.validate_python(response) + + def _generic_passthrough_handler() -> "PassThroughEndpointHandler": """ Fallback for non-Converse Bedrock routes (e.g. invoke). The generic @@ -1285,6 +1365,17 @@ class BedrockPassthroughGuardrailHandler(BaseTranslation): body: Final = data.get("data") if not _is_converse_endpoint(endpoint): + passthrough_request: Final = _BODY_BY_REFERENCE.validate_python(data) + anthropic_body: Final = _anthropic_invoke_body(passthrough_request) + if anthropic_body is not None: + await AnthropicMessagesHandler().process_input_messages( # pyright: ignore[reportUnknownMemberType] # takes the untyped request dict + data=anthropic_body, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + request_data=_body_level_request_data(passthrough_request), + keep_cache_control=True, + ) + return passthrough_request return await _generic_passthrough_handler().process_input_messages( data=data, guardrail_to_apply=guardrail_to_apply, @@ -1410,6 +1501,15 @@ class BedrockPassthroughGuardrailHandler(BaseTranslation): and not _is_converse_endpoint(endpoint) and not _is_invoke_stream_endpoint(endpoint) ): + anthropic_reply: Final = _anthropic_invoke_reply(response, request_data) # pyright: ignore[reportUnknownArgumentType] # the base signature types request_data as a bare dict + if anthropic_reply is not None: + return await AnthropicMessagesHandler().process_output_response( # pyright: ignore[reportUnknownMemberType] # takes the untyped request dict + response=anthropic_reply, # pyright: ignore[reportArgumentType] # the Anthropic reply dict, written back in place + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=litellm_logging_obj, + user_api_key_dict=user_api_key_dict, + request_data=_body_level_request_data(_OBJECT_BY_REFERENCE.validate_python(request_data or {})), + ) return await _generic_passthrough_handler().process_output_response( response=response, guardrail_to_apply=guardrail_to_apply, diff --git a/litellm/llms/chatgpt/authenticator.py b/litellm/llms/chatgpt/authenticator.py index b237116c5f1..d286d572a35 100644 --- a/litellm/llms/chatgpt/authenticator.py +++ b/litellm/llms/chatgpt/authenticator.py @@ -66,6 +66,13 @@ def get_cached_authenticator(auth_file: str) -> "Authenticator": return Authenticator(auth_file=auth_file) +def resolve_chatgpt_authenticator( + litellm_params: "Mapping[str, object] | GenericLiteLLMParams | None", default: "Authenticator" +) -> "Authenticator": + auth_file: Final = get_chatgpt_auth_file(litellm_params) + return get_cached_authenticator(auth_file) if auth_file else default + + def _token_refresh_timeout() -> httpx.Timeout: configured: Final = get_configured_request_timeout() seconds: Final = ( diff --git a/litellm/llms/chatgpt/chat/transformation.py b/litellm/llms/chatgpt/chat/transformation.py index 9005185931f..e8f057b4d3d 100644 --- a/litellm/llms/chatgpt/chat/transformation.py +++ b/litellm/llms/chatgpt/chat/transformation.py @@ -6,7 +6,7 @@ from litellm.llms.openai.openai import OpenAIConfig from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams -from ..authenticator import Authenticator, get_cached_authenticator, get_chatgpt_auth_file +from ..authenticator import Authenticator, get_chatgpt_auth_file, resolve_chatgpt_authenticator from ..common_utils import ( GetAccessTokenError, chatgpt_client_api_base, @@ -30,12 +30,6 @@ class ChatGPTConfig(OpenAIConfig): super().__init__() self.authenticator = authenticator if authenticator is not None else Authenticator() - def _resolve_authenticator( - self, litellm_params: Mapping[str, object] | GenericLiteLLMParams | None - ) -> Authenticator: - auth_file: Final = get_chatgpt_auth_file(litellm_params) - return get_cached_authenticator(auth_file) if auth_file else self.authenticator - @staticmethod def _get_access_token_or_raise(authenticator: Authenticator, model: str, llm_provider: str) -> str: try: @@ -63,7 +57,7 @@ class ChatGPTConfig(OpenAIConfig): return chatgpt_client_api_base(api_base), client_credential.access_token, custom_llm_provider if api_key: return api_base or self.api_base_without_login(), api_key, custom_llm_provider - authenticator: Final = self._resolve_authenticator(litellm_params) + authenticator: Final = resolve_chatgpt_authenticator(litellm_params, self.authenticator) dynamic_api_base: Final = authenticator.get_api_base() dynamic_api_key: Final = self._get_access_token_or_raise(authenticator, model, custom_llm_provider) return dynamic_api_base, dynamic_api_key, custom_llm_provider @@ -98,7 +92,7 @@ class ChatGPTConfig(OpenAIConfig): ), **strip_chatgpt_client_credential_headers(headers), ) - authenticator: Final = self._resolve_authenticator(litellm_params) + authenticator: Final = resolve_chatgpt_authenticator(litellm_params, self.authenticator) resolved_api_key: Final = ( self._get_access_token_or_raise(authenticator, model, "chatgpt") if get_chatgpt_auth_file(litellm_params) diff --git a/litellm/llms/chatgpt/common_utils.py b/litellm/llms/chatgpt/common_utils.py index cd2ea6b9ccc..88f2eecec0c 100644 --- a/litellm/llms/chatgpt/common_utils.py +++ b/litellm/llms/chatgpt/common_utils.py @@ -7,7 +7,7 @@ import hashlib import json import os import platform -from collections.abc import Mapping, Sequence +from collections.abc import Mapping from dataclasses import dataclass from types import MappingProxyType from typing import TYPE_CHECKING, Final, TypeAlias @@ -20,14 +20,10 @@ from pydantic import JsonValue, TypeAdapter from litellm.litellm_core_utils.get_provider_specific_headers import ProviderSpecificHeaderUtils from litellm.litellm_core_utils.provider_affinity import get_caller_litellm_session_id, get_requested_session_id from litellm.llms.base_llm.chat.transformation import BaseLLMException -from litellm.types.utils import ProviderSpecificHeader if TYPE_CHECKING: from litellm.types.router import GenericLiteLLMParams -_SCOPED_HEADERS: Final[TypeAdapter[ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None]] = TypeAdapter( - ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None -) _STRING_HEADERS: Final = TypeAdapter(Mapping[str, str]) _PARAMS: Final = TypeAdapter(Mapping[str, object]) @@ -357,8 +353,8 @@ def get_chatgpt_request_credential( normalized: Final = _normalize_litellm_params(params) scoped: Final = normalized.get("provider_specific_header") extra: Final = normalized.get("extra_headers") - headers: Final = ProviderSpecificHeaderUtils.get_provider_specific_headers( - provider_specific_header=_SCOPED_HEADERS.validate_python(scoped), custom_llm_provider="chatgpt" + headers: Final = ProviderSpecificHeaderUtils.validated_provider_specific_headers( + provider_specific_header=scoped, custom_llm_provider="chatgpt" ) return get_chatgpt_client_credential(headers) or get_chatgpt_client_credential( _STRING_HEADERS.validate_python(extra) if extra is not None else MappingProxyType({}) diff --git a/litellm/llms/chatgpt/responses/transformation.py b/litellm/llms/chatgpt/responses/transformation.py index 26a5a3f150b..3c7d0b8d673 100644 --- a/litellm/llms/chatgpt/responses/transformation.py +++ b/litellm/llms/chatgpt/responses/transformation.py @@ -28,7 +28,7 @@ from litellm.types.llms.openai import ( from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders -from ..authenticator import Authenticator, get_cached_authenticator, get_chatgpt_auth_file +from ..authenticator import Authenticator, get_chatgpt_auth_file, resolve_chatgpt_authenticator from ..common_utils import ( CHATGPT_API_BASE, GetAccessTokenError, @@ -64,10 +64,6 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): def custom_llm_provider(self) -> LlmProviders: return LlmProviders.CHATGPT - def _resolve_authenticator(self, litellm_params: GenericLiteLLMParams | None) -> Authenticator: - auth_file: Final = get_chatgpt_auth_file(litellm_params) - return get_cached_authenticator(auth_file) if auth_file else self.authenticator - def validate_environment( self, headers: dict[str, str], @@ -81,7 +77,7 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): **get_chatgpt_default_headers(client_credential.access_token, client_credential.account_id, session_id), **strip_chatgpt_client_credential_headers(headers), } - authenticator: Final = self._resolve_authenticator(litellm_params) + authenticator: Final = resolve_chatgpt_authenticator(litellm_params, self.authenticator) safe_headers: Final = strip_chatgpt_client_credential_headers(headers) configured_api_key: Final = litellm_params.api_key if litellm_params is not None else None if configured_api_key and not get_chatgpt_auth_file(litellm_params): diff --git a/litellm/llms/cohere/embed/v1_transformation.py b/litellm/llms/cohere/embed/v1_transformation.py index ad5bfa507e5..056b5bce742 100644 --- a/litellm/llms/cohere/embed/v1_transformation.py +++ b/litellm/llms/cohere/embed/v1_transformation.py @@ -9,6 +9,7 @@ import httpx from litellm import COHERE_DEFAULT_EMBEDDING_INPUT_TYPE from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.custom_httpx.http_handler import header_value from litellm.types.llms.bedrock import ( CohereEmbeddingRequest, CohereEmbeddingRequestWithModel, @@ -23,6 +24,11 @@ class _SupportsEncode(Protocol): def encode(self, text: str, /) -> Sized: ... +def _bedrock_billed_input_tokens(response: httpx.Response) -> int | None: + count: Final = header_value(response.headers, "x-amzn-bedrock-input-token-count") + return int(count) if count is not None and count.isdigit() else None + + class CohereEmbeddingConfig: """ Reference: https://docs.cohere.com/v2/reference/embed @@ -76,32 +82,30 @@ class CohereEmbeddingConfig: ) -> CohereEmbeddingRequestWithModel: return self._transform_request(model, input, inference_params) - def _calculate_usage(self, input: list[str], encoding: _SupportsEncode, meta: dict) -> Usage: - input_tokens = 0 - + def _calculate_usage( + self, input: list[str], encoding: _SupportsEncode, meta: dict, billed_input_tokens: int | None = None + ) -> Usage: text_tokens: Final[int | None] = meta.get("billed_units", {}).get("input_tokens") image_tokens: Final[int | None] = meta.get("billed_units", {}).get("images") - prompt_tokens_details: PromptTokensDetailsWrapper | None = None if image_tokens is None and text_tokens is None: - for text in input: - input_tokens += len(encoding.encode(text)) - else: - prompt_tokens_details = PromptTokensDetailsWrapper( - image_tokens=image_tokens, - text_tokens=text_tokens, + counted_tokens: Final = ( + billed_input_tokens + if billed_input_tokens is not None + else sum(len(encoding.encode(text)) for text in input) ) - if image_tokens: - input_tokens += image_tokens - if text_tokens: - input_tokens += text_tokens + return Usage(prompt_tokens=counted_tokens, completion_tokens=0, total_tokens=counted_tokens) + input_tokens: Final = (image_tokens or 0) + (text_tokens or 0) return Usage( prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens, - prompt_tokens_details=prompt_tokens_details, + prompt_tokens_details=PromptTokensDetailsWrapper( + image_tokens=image_tokens, + text_tokens=text_tokens, + ), ) def _transform_response( @@ -129,6 +133,7 @@ class CohereEmbeddingConfig: model=model, encoding=encoding, input=input, + billed_input_tokens=_bedrock_billed_input_tokens(response) if "images" in data else None, ) def transform_response( @@ -151,6 +156,7 @@ class CohereEmbeddingConfig: model: str, encoding: _SupportsEncode, input: list, + billed_input_tokens: int | None = None, ) -> EmbeddingResponse: """ Parse a Cohere embed response body into an OpenAI-style EmbeddingResponse. @@ -195,7 +201,7 @@ class CohereEmbeddingConfig: setattr( model_response, "usage", - self._calculate_usage(input, encoding, response_json.get("meta", {})), + self._calculate_usage(input, encoding, response_json.get("meta", {}), billed_input_tokens), ) return model_response diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 2f17dc6dd10..4a10db31606 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -47,6 +47,12 @@ from litellm.anthropic_beta_headers_manager import ( from litellm.constants import MAX_FILE_LIST_LIMIT, REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES from litellm.files.types import FileContentStreamingResult from litellm.litellm_core_utils.agentic_followup_kwargs import build_agentic_followup_kwargs +from litellm.litellm_core_utils.agentic_loop_refusal import ( + every_claimed_tool_call, + finalize_refused_responses_turn, + is_refused_item, + refused_tool_call_identifiers, +) from litellm.litellm_core_utils.agentic_loop_settings import ( DEFAULT_MAX_AGENTIC_LOOPS, validated_max_agentic_loops, @@ -56,11 +62,11 @@ from litellm.litellm_core_utils.audio_utils.subtitle_utils import ( SUBTITLE_RESPONSE_FORMATS, synthesize_subtitle_document, ) -from litellm.litellm_core_utils.core_helpers import ( - as_str_mapping, - qualify_agentic_followup_model, - set_provider_response_headers_in_hidden_params, +from litellm.litellm_core_utils.chat_completion_agentic_loop import ( + bill_first_round_on_failure, + run_chat_completion_agentic_hooks, ) +from litellm.litellm_core_utils.core_helpers import as_str_mapping, set_provider_response_headers_in_hidden_params from litellm.litellm_core_utils.dot_notation_indexing import delete_nested_value from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS from litellm.litellm_core_utils.get_llm_provider_logic import get_provider_affinity_header @@ -150,6 +156,7 @@ from litellm.types.extraction import ExtractionResponse from litellm.types.files import StreamingMediaUploadConfig, TwoStepFileUploadConfig from litellm.types.integrations.custom_logger import ( NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, + AgenticLoopLimitError, AgenticLoopPlan, AgenticLoopRequestPatch, AgenticLoopSafetyError, @@ -6005,7 +6012,7 @@ class BaseLLMHTTPHandler: @staticmethod def _check_agentic_loop_safety( tool_calls: object, - fingerprints: list[str], + fingerprints: Sequence[str], depth: int, max_loops: int, model: str, @@ -6024,7 +6031,7 @@ class BaseLLMHTTPHandler: if fingerprint in fingerprints: raise AgenticLoopSafetyError("Agentic loop detected repeated tool-call fingerprint; aborting rerun") if depth >= max_loops: - raise AgenticLoopSafetyError(f"Exceeded max_agentic_loops={max_loops} for model={model}") + raise AgenticLoopLimitError(f"Exceeded max_agentic_loops={max_loops} for model={model}") return fingerprint @staticmethod @@ -6034,26 +6041,6 @@ class BaseLLMHTTPHandler: except Exception: return str(tools) - @staticmethod - def _refused_agentic_tool_identifiers(tool_calls: object) -> tuple[frozenset[str], frozenset[str]]: - """ - Collect the ids and names of the tool calls a safety rail just refused. - - Callbacks hand back either a bare list of tool calls or a dict wrapping - that list under ``tool_calls``, and both the anthropic and responses - shapes carry an ``id`` (or ``call_id``) plus a ``name``. - """ - calls: Final = tool_calls.get("tool_calls") if isinstance(tool_calls, dict) else tool_calls - if not isinstance(calls, list): - return frozenset(), frozenset() - dict_calls: Final = (call for call in calls if isinstance(call, dict)) - fields: Final = tuple((call.get("id"), call.get("call_id"), call.get("name")) for call in dict_calls) - ids: Final = frozenset( - value for call_id, caller_id, _ in fields for value in (call_id, caller_id) if isinstance(value, str) - ) - names: Final = frozenset(name for _, _, name in fields if isinstance(name, str)) - return ids, names - @staticmethod def _is_refused_tool_use_block(block: object, refused_ids: frozenset[str], refused_names: frozenset[str]) -> bool: """ @@ -6064,22 +6051,13 @@ class BaseLLMHTTPHandler: share a name with a refused one. The name is only consulted for tool call shapes that arrive without an id. """ - if not isinstance(block, dict) or block.get("type") != "tool_use": - return False - block_id: Final = block.get("id") - if isinstance(block_id, str) and refused_ids: - return block_id in refused_ids - return block.get("name") in refused_names + return is_refused_item(block, "tool_use", "id", refused_ids, refused_names) @staticmethod def _can_replace_turn_with_terminal_response(stream: bool, api_surface: str) -> bool: """ Whether a refused rerun can still be answered with a finalized turn. - Only the anthropic messages surface can. The responses surface carries a - pydantic model the finalizer does not rewrite, so it keeps raising, which - is what every surface did before this path learned to end the turn. - The messages and responses call sites pass ``stream=False``, because interception converts an intercepted stream to non-streaming before the loop runs and rebuilds the SSE stream from the finalized turn @@ -6088,7 +6066,7 @@ class BaseLLMHTTPHandler: finalized turn would reach the client as a second message rather than as a replacement. """ - return not stream and api_surface == "anthropic_messages" + return not stream and api_surface in ("anthropic_messages", "responses") @staticmethod def _finalize_refused_agentic_response(response: object, tool_calls: object) -> object: @@ -6105,10 +6083,12 @@ class BaseLLMHTTPHandler: one is still in the response the stop reason stays ``tool_use`` so the client knows to answer it. """ + if isinstance(response, ResponsesAPIResponse): + return finalize_refused_responses_turn(response, tool_calls) if not isinstance(response, dict): return response - refused_ids, refused_names = BaseLLMHTTPHandler._refused_agentic_tool_identifiers(tool_calls) + refused_ids, refused_names = refused_tool_call_identifiers(tool_calls) finalized: Final = dict(response) content: Final = finalized.get("content") if isinstance(content, list): @@ -6135,7 +6115,7 @@ class BaseLLMHTTPHandler: kwargs: dict, depth: int, max_loops: int, - fingerprints: list[str], + fingerprints: Sequence[str], fingerprint: str, stream: bool = False, callback: Optional["CustomLogger"] = None, @@ -6176,7 +6156,7 @@ class BaseLLMHTTPHandler: kwargs_for_followup.update(patch.kwargs) kwargs_for_followup["_agentic_loop_depth"] = depth + 1 kwargs_for_followup["max_agentic_loops"] = max_loops - kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint] + kwargs_for_followup["_agentic_loop_fingerprints"] = [*fingerprints, fingerprint] response: AnthropicMessagesResponse | AsyncIterator[object] = await anthropic_messages.acreate( **{ @@ -6215,7 +6195,7 @@ class BaseLLMHTTPHandler: kwargs: dict, depth: int, max_loops: int, - fingerprints: list[str], + fingerprints: Sequence[str], fingerprint: str, callback: Optional["CustomLogger"] = None, ) -> ResponsesAPIResponse | BaseResponsesAPIStreamingIterator: @@ -6336,67 +6316,6 @@ class BaseLLMHTTPHandler: custom_llm_provider=custom_llm_provider, ) - async def _execute_chat_completion_agentic_plan( - self, - plan: AgenticLoopPlan, - model: str, - messages: list[dict], - optional_params: dict, - kwargs: dict, - custom_llm_provider: str, - depth: int, - max_loops: int, - fingerprints: list[str], - fingerprint: str, - logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> ModelResponse | CustomStreamWrapper: - patch: Final = plan.request_patch or AgenticLoopRequestPatch() - if patch.messages is None: - raise ValueError("Agentic loop plan missing patched messages") - - full_model_name: Final = qualify_agentic_followup_model(patch.model, model, custom_llm_provider) - - optional_params_for_followup: Final = dict(optional_params) - optional_params_for_followup.update(patch.optional_params) - if patch.tools is not None: - optional_params_for_followup["tools"] = patch.tools - - internal_params: Final = { - "_websearch_interception", - "acompletion", - "litellm_logging_obj", - "custom_llm_provider", - "model_alias_map", - "stream_response", - "custom_prompt_dict", - } - kwargs_for_followup: Final = build_agentic_followup_kwargs( - request_kwargs=MappingProxyType( - { - k: v - for k, v in kwargs.items() - if not k.startswith("_websearch_interception") - and not k.startswith("_compression_interception") - and k not in internal_params - } - ), - patch_kwargs=patch.kwargs, - request_params=frozenset((*optional_params_for_followup, "model", "messages")), - depth=depth, - max_loops=max_loops, - fingerprints=fingerprints, - fingerprint=fingerprint, - ) - - with internal_sub_call(logging_obj.litellm_call_id if logging_obj is not None else None): - response: Final = await litellm.acompletion( - model=full_model_name, - messages=patch.messages, - **optional_params_for_followup, - **kwargs_for_followup, - ) - return response - def _maybe_wrap_in_fake_stream( self, response: _ResponseT, @@ -6513,26 +6432,38 @@ class BaseLLMHTTPHandler: except AgenticLoopSafetyError as e: if not self._can_replace_turn_with_terminal_response(stream, api_surface): raise - verbose_logger.warning( - "LiteLLM.AgenticLoopRefused: ending turn [call_id=%s model=%s]: %s", - logging_obj.litellm_call_id, - model, - str(e), - ) + verbose_logger.warning("LiteLLM.AgenticLoopRefused: ending turn [rail=%s]", type(e).__name__) return self._maybe_wrap_in_fake_stream( - self._finalize_refused_agentic_response(response=response, tool_calls=tool_calls), + self._finalize_refused_agentic_response( + response=response, + tool_calls=await every_claimed_tool_call( + tool_calls, + ( + other.async_should_run_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=stream, + custom_llm_provider=custom_llm_provider, + kwargs=hook_kwargs, + ) + for other in callbacks + if isinstance(other, CustomLogger) and other is not callback + ), + ), + ), logging_obj, api_surface, ) - try: - kwargs_with_provider = hook_kwargs.copy() - kwargs_with_provider["custom_llm_provider"] = custom_llm_provider - build_plan_overridden = ( - callback.__class__.async_build_agentic_loop_plan is not CustomLogger.async_build_agentic_loop_plan - ) - if not build_plan_overridden: - agentic_result: object = await callback.async_run_agentic_loop( + kwargs_with_provider = {**hook_kwargs, "custom_llm_provider": custom_llm_provider} + build_plan_overridden = ( + callback.__class__.async_build_agentic_loop_plan is not CustomLogger.async_build_agentic_loop_plan + ) + if not build_plan_overridden: + agentic_result: object = await bill_first_round_on_failure( + callback.async_run_agentic_loop( tools=tool_calls, model=model, messages=messages, @@ -6542,10 +6473,15 @@ class BaseLLMHTTPHandler: logging_obj=logging_obj, stream=stream, kwargs=kwargs_with_provider, - ) - return self._maybe_wrap_in_fake_stream(agentic_result, logging_obj, api_surface) + ), + logging_obj=logging_obj, + response=response, + stream=stream, + ) + return self._maybe_wrap_in_fake_stream(agentic_result, logging_obj, api_surface) - plan = await callback.async_build_agentic_loop_plan( + plan = await bill_first_round_on_failure( + callback.async_build_agentic_loop_plan( tools=tool_calls, model=model, messages=messages, @@ -6555,30 +6491,35 @@ class BaseLLMHTTPHandler: logging_obj=logging_obj, stream=stream, kwargs=kwargs_with_provider, + ), + logging_obj=logging_obj, + response=response, + stream=stream, + ) + + if plan.response_override is not None: + return self._maybe_wrap_in_fake_stream(plan.response_override, logging_obj, api_surface) + if plan.terminate: + verbose_logger.debug( + "Agentic loop terminated by callback=%s reason=%s", + callback.__class__.__name__, + plan.stop_reason, ) + return self._maybe_wrap_in_fake_stream( + await callback.async_post_agentic_loop_response_hook( + response=self._finalize_refused_agentic_response(response=response, tool_calls=tool_calls), + plan=plan, + kwargs=kwargs_with_provider, + ), + logging_obj, + api_surface, + ) + if not plan.run_agentic_loop: + continue - if plan.response_override is not None: - return self._maybe_wrap_in_fake_stream(plan.response_override, logging_obj, api_surface) - if plan.terminate: - verbose_logger.debug( - "Agentic loop terminated by callback=%s reason=%s", - callback.__class__.__name__, - plan.stop_reason, - ) - return self._maybe_wrap_in_fake_stream( - await callback.async_post_agentic_loop_response_hook( - response=self._finalize_refused_agentic_response(response=response, tool_calls=tool_calls), - plan=plan, - kwargs=kwargs_with_provider, - ), - logging_obj, - api_surface, - ) - if not plan.run_agentic_loop: - continue - - if api_surface == "responses": - return await self._execute_responses_agentic_plan( + if api_surface == "responses": + return await bill_first_round_on_failure( + self._execute_responses_agentic_plan( plan=plan, model=model, response_api_optional_request_params=anthropic_messages_optional_request_params, @@ -6589,10 +6530,15 @@ class BaseLLMHTTPHandler: fingerprints=fingerprints, fingerprint=fingerprint, callback=callback, - ) + ), + logging_obj=logging_obj, + response=response, + stream=stream, + ) - return self._maybe_wrap_in_fake_stream( - await self._execute_anthropic_agentic_plan( + return self._maybe_wrap_in_fake_stream( + await bill_first_round_on_failure( + self._execute_anthropic_agentic_plan( plan=plan, model=model, messages=messages, @@ -6606,17 +6552,13 @@ class BaseLLMHTTPHandler: stream=stream, callback=callback, ), - logging_obj, - api_surface, - ) - except Exception as e: - _call_id = getattr(logging_obj, "litellm_call_id", "unknown") - verbose_logger.exception( - "LiteLLM.AgenticHookError: Exception in agentic completion hooks [call_id=%s model=%s]: %s", - _call_id, - model, - str(e), - ) + logging_obj=logging_obj, + response=response, + stream=stream, + ), + logging_obj, + api_surface, + ) # Check if we need to convert response to fake stream # This happens when: @@ -6648,111 +6590,23 @@ class BaseLLMHTTPHandler: Returns the response from agentic loop, or None if no hook runs. """ - from litellm._logging import verbose_logger - from litellm.integrations.custom_logger import CustomLogger - - callbacks: Final = litellm.callbacks + (logging_obj.dynamic_success_callbacks or []) - tools: Final = optional_params.get("tools", []) - depth, max_loops, fingerprints = self._get_agentic_loop_settings(kwargs=kwargs) - - for callback in callbacks: - if not isinstance(callback, CustomLogger): - continue - if not hasattr(callback, "async_should_run_chat_completion_agentic_loop"): - continue - - should_run: bool = False - tool_calls: object = None - try: - ( - should_run, - tool_calls, - ) = await callback.async_should_run_chat_completion_agentic_loop( - response=response, - model=model, - messages=messages, - tools=tools, - stream=stream, - custom_llm_provider=custom_llm_provider, - kwargs=kwargs, - ) - except Exception as e: - verbose_logger.exception( - "LiteLLM.AgenticHookError: Exception in async_should_run_chat_completion_agentic_loop: %s", - str(e), - ) - continue - - if not should_run: - continue - - # Safety guards must run OUTSIDE the callback try/except — they are - # bounded-loop / cycle-break rails that must propagate to the caller. - fingerprint = self._check_agentic_loop_safety( - tool_calls=tool_calls, - fingerprints=fingerprints, - depth=depth, - max_loops=max_loops, + agentic_response: Final = await bill_first_round_on_failure( + run_chat_completion_agentic_hooks( + response=response, model=model, - ) - - try: - kwargs_with_provider = kwargs.copy() if kwargs else {} - kwargs_with_provider["custom_llm_provider"] = custom_llm_provider - build_plan_overridden = ( - callback.__class__.async_build_chat_completion_agentic_loop_plan - is not CustomLogger.async_build_chat_completion_agentic_loop_plan - ) - if not build_plan_overridden: - return await callback.async_run_chat_completion_agentic_loop( - tools=tool_calls, - model=model, - messages=messages, - response=response, - optional_params=optional_params, - logging_obj=logging_obj, - stream=stream, - kwargs=kwargs_with_provider, - ) - - plan = await callback.async_build_chat_completion_agentic_loop_plan( - tools=tool_calls, - model=model, - messages=messages, - response=response, - optional_params=optional_params, - logging_obj=logging_obj, - stream=stream, - kwargs=kwargs_with_provider, - ) - - if plan.response_override is not None: - return plan.response_override - if plan.terminate: - verbose_logger.debug( - "Agentic chat loop terminated by callback=%s reason=%s", - callback.__class__.__name__, - plan.stop_reason, - ) - return response - if not plan.run_agentic_loop: - continue - - return await self._execute_chat_completion_agentic_plan( - plan=plan, - model=model, - messages=messages, - optional_params=optional_params, - kwargs=kwargs_with_provider, - logging_obj=logging_obj, - custom_llm_provider=custom_llm_provider, - depth=depth, - max_loops=max_loops, - fingerprints=fingerprints, - fingerprint=fingerprint, - ) - except Exception as e: - verbose_logger.exception("LiteLLM.AgenticHookError: Exception in chat completion agentic hooks: %s", e) + messages=messages, + optional_params=optional_params, + kwargs=kwargs or {}, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + stream=stream, + ), + logging_obj=logging_obj, + response=response, + stream=stream, + ) + if agentic_response is not None: + return agentic_response # Check if we need to convert response to fake stream for chat completions # This happens when: diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index b5393fa1603..d943718ed73 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -395,7 +395,7 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): return "databricks" @classmethod - def get_config(cls, *, model: str | None = None): + def get_config(cls, *, model: str | None = None, configured_max_output_tokens: int | None = None): return super().get_config() def get_json_schema_from_pydantic_object( diff --git a/litellm/llms/databricks/messages/transformation.py b/litellm/llms/databricks/messages/transformation.py index 90b63d860d3..85f44181bc3 100644 --- a/litellm/llms/databricks/messages/transformation.py +++ b/litellm/llms/databricks/messages/transformation.py @@ -192,7 +192,7 @@ class DatabricksAnthropicMessagesConfig(DatabricksBase, AnthropicMessagesConfig) headers=headers, ) self._normalize_system_role_messages( # pyright: ignore[reportUnknownMemberType] # the shared helper takes a bare dict - anthropic_messages_request, model=model, drop_params=getattr(litellm_params, "drop_params", None) + anthropic_messages_request, model=model, drop_params=litellm_params.drop_params ) self._remove_scope_from_cache_control(anthropic_messages_request) # pyright: ignore[reportUnknownMemberType] # the shared helper takes a bare dict return anthropic_messages_request diff --git a/litellm/llms/empiriolabs/decisions/transformation.py b/litellm/llms/empiriolabs/decisions/transformation.py new file mode 100644 index 00000000000..ce3464310c4 --- /dev/null +++ b/litellm/llms/empiriolabs/decisions/transformation.py @@ -0,0 +1,10 @@ +from litellm.llms.base_llm.decisions.transformation import BaseDecisionsConfig + + +class EmpirioLabsDecisionsConfig(BaseDecisionsConfig): + path = "/v1/decisions" + api_key_env = ("EMPIRIOLABS_API_KEY",) + api_base_env = ("EMPIRIOLABS_API_BASE",) + + def get_default_api_base(self) -> str | None: + return "https://api.empiriolabs.ai" diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index 3134dfa1e4b..a9f31d1abff 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -218,7 +218,7 @@ def _system_instruction_text(system_instruction: HttpxContentType | None) -> str class GeminiRealtimeConfig(BaseRealtimeConfig): _TOOL_CALL_ID_TO_NAME_MAX = 256 # LRU cap for call_id→name mapping - def __init__(self, drop_params: bool = False): + def __init__(self, drop_params: bool = False) -> None: super().__init__() self.drop_params = drop_params self._tool_call_id_to_name: OrderedDict[str, str] = OrderedDict() diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py index 36d7e4d95de..bc8aaab3185 100644 --- a/litellm/llms/openai/cost_calculation.py +++ b/litellm/llms/openai/cost_calculation.py @@ -31,9 +31,9 @@ def cost_per_token( Input: - model: str, the model name without provider prefix - usage: LiteLLM Usage block, containing anthropic caching information - - data_residency: optional OpenAI data-residency region (e.g. "eu", "us"), - inferred from api_base. Applies the model's regional-processing - uplift multiplier when set. + - data_residency: optional OpenAI data-residency mode (e.g. "eu", "us", "fedramp"), + inferred from api_base, including gov.api.openai.com. Applies the + model's regional-processing uplift multiplier when set. Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd diff --git a/litellm/llms/openai/data_residency.py b/litellm/llms/openai/data_residency.py index d4859221541..a8831db3517 100644 --- a/litellm/llms/openai/data_residency.py +++ b/litellm/llms/openai/data_residency.py @@ -7,9 +7,12 @@ enabled and rejects requests sent to the wrong host, so the api_base hostname is the authoritative signal of which region a request was processed in. """ +import os from typing import Final from urllib.parse import urlparse +import litellm + # Mapping of OpenAI regional hostnames to the corresponding data-residency # value used by the cost calculator. See # https://developers.openai.com/api/docs/pricing for the regional-processing @@ -17,6 +20,7 @@ from urllib.parse import urlparse _OPENAI_REGIONAL_HOSTS: Final[dict[str, str]] = { "eu.api.openai.com": "eu", "us.api.openai.com": "us", + "gov.api.openai.com": "fedramp", } @@ -25,13 +29,22 @@ def infer_openai_data_residency(custom_llm_provider: str | None, api_base: str | Derive the OpenAI data-residency region from an api_base URL. Returns ``"eu"`` for the EU regional host, ``"us"`` for the US regional - host, and ``None`` for the default global host, any non-OpenAI provider, - or any non-OpenAI URL. + host, ``"fedramp"`` for ``gov.api.openai.com``, and ``None`` for the + default global host, any non-OpenAI provider, or any non-OpenAI URL. + + When ``api_base`` is unset, falls back to the same defaults the OpenAI + handler uses (``litellm.api_base``, ``OPENAI_BASE_URL``, ``OPENAI_API_BASE``) + so env-configured regional endpoints are billed with their uplift. """ - if custom_llm_provider != "openai" or not api_base: + if custom_llm_provider != "openai": + return None + resolved_api_base: Final = ( + api_base or litellm.api_base or os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") + ) + if not resolved_api_base: return None try: - host: Final = urlparse(api_base).hostname + host: Final = urlparse(resolved_api_base).hostname except (TypeError, ValueError): return None if not host: diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 6e22ef9112e..87c944a642a 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -30,6 +30,10 @@ from litellm._logging import verbose_logger from litellm.constants import DEFAULT_MAX_RETRIES from litellm.exceptions import UnsupportedParamsError from litellm.files.types import FileContentStreamingResult +from litellm.litellm_core_utils.chat_completion_agentic_loop import ( + bill_first_round_on_failure, + run_chat_completion_agentic_hooks, +) from litellm.litellm_core_utils.core_helpers import set_provider_response_headers_in_hidden_params from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_utils import speech_request_body, track_llm_api_timing @@ -663,67 +667,23 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): Returns the response from agentic loop, or None if no hook runs. """ - from litellm._logging import verbose_logger - from litellm.integrations.custom_logger import CustomLogger - - callbacks: Final = litellm.callbacks + (logging_obj.dynamic_success_callbacks or []) - # Avoid logging full callback objects to prevent leaking sensitive data - verbose_logger.debug("LiteLLM.AgenticHooks: callbacks_count=%s", len(callbacks)) - tools: Final = optional_params.get("tools", []) - # Avoid logging full tools payloads; they may contain sensitive parameters - verbose_logger.debug( - "LiteLLM.AgenticHooks: tools_count=%s", - len(tools) if isinstance(tools, list) else 1 if tools else 0, + if not isinstance(response, ModelResponse): + return None + return await bill_first_round_on_failure( + run_chat_completion_agentic_hooks( + response=response, + model=model, + messages=messages, + optional_params=optional_params, + kwargs=litellm_params or {}, + logging_obj=logging_obj, + custom_llm_provider=litellm_params.get("custom_llm_provider", "openai"), + stream=stream, + ), + logging_obj=logging_obj, + response=response, + stream=stream, ) - # Get custom_llm_provider from litellm_params - custom_llm_provider: Final = litellm_params.get("custom_llm_provider", "openai") - - for callback in callbacks: - try: - if isinstance(callback, CustomLogger): - # Check if the callback has the chat completion agentic loop methods - if not hasattr(callback, "async_should_run_chat_completion_agentic_loop"): - continue - - # First: Check if agentic loop should run (using chat completion method) - ( - should_run, - tool_calls, - ) = await callback.async_should_run_chat_completion_agentic_loop( - response=response, - model=model, - messages=messages, - tools=tools, - stream=stream, - custom_llm_provider=custom_llm_provider, - kwargs=litellm_params, - ) - - if should_run: - # Second: Execute agentic loop - kwargs_with_provider = litellm_params.copy() if litellm_params else {} - kwargs_with_provider["custom_llm_provider"] = custom_llm_provider - - # For OpenAI Chat Completions, use the chat completion agentic loop method - agentic_response: object = await callback.async_run_chat_completion_agentic_loop( - tools=tool_calls, - model=model, - messages=messages, - response=response, - optional_params=optional_params, - logging_obj=logging_obj, - stream=stream, - kwargs=kwargs_with_provider, - ) - # First hook that runs agentic loop wins - return agentic_response - - except Exception as e: - verbose_logger.exception( - "LiteLLM.AgenticHookError: Exception in agentic completion hooks for OpenAI: %s", e - ) - - return None def mock_streaming( self, @@ -1074,8 +1034,28 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): hidden_params={"headers": headers}, _response_headers=headers, ) + except openai.UnprocessableEntityError as e: + if litellm.drop_params is True or drop_params is True: + data = drop_params_from_unprocessable_entity_error(e, data, model=model, drop_params_gated=True) + else: + raise e + except Exception as e: + exception_response: object = getattr(e, "response", None) + status_code = getattr(e, "status_code", 500) + exception_body = getattr(e, "body", None) + error_headers = getattr(e, "headers", None) + if error_headers is None and exception_response: + error_headers = getattr(exception_response, "headers", None) + message = getattr(e, "message", str(e)) - # Call agentic completion hooks (e.g., for websearch_interception) + raise OpenAIError( + status_code=status_code, + message=message, + response=upstream_error_response(e), + headers=error_headers, + body=exception_body, + ) + else: agentic_response = await self._call_agentic_completion_hooks_openai( response=final_response_obj, model=model, @@ -1098,29 +1078,6 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) return final_response_obj - except openai.UnprocessableEntityError as e: - ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800 - if litellm.drop_params is True or drop_params is True: - data = drop_params_from_unprocessable_entity_error(e, data, model=model, drop_params_gated=True) - else: - raise e - # e.message - except Exception as e: - exception_response: object = getattr(e, "response", None) - status_code = getattr(e, "status_code", 500) - exception_body = getattr(e, "body", None) - error_headers = getattr(e, "headers", None) - if error_headers is None and exception_response: - error_headers = getattr(exception_response, "headers", None) - message = getattr(e, "message", str(e)) - - raise OpenAIError( - status_code=status_code, - message=message, - response=upstream_error_response(e), - headers=error_headers, - body=exception_body, - ) def streaming( self, diff --git a/litellm/llms/openai_like/dynamic_config.py b/litellm/llms/openai_like/dynamic_config.py index d7aafe9f06e..66b41967ca7 100644 --- a/litellm/llms/openai_like/dynamic_config.py +++ b/litellm/llms/openai_like/dynamic_config.py @@ -2,9 +2,11 @@ Dynamic configuration class generator for JSON-based providers. """ -from collections.abc import Coroutine +from collections.abc import Coroutine, Mapping from typing import TYPE_CHECKING, Final, Literal, overload +from pydantic import TypeAdapter, ValidationError + from litellm._logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_messages_with_content_list_to_str_conversion, @@ -20,6 +22,26 @@ from .json_loader import SimpleProviderConfig if TYPE_CHECKING: from litellm.llms.openai_like.responses.transformation import OpenAILikeResponsesConfig +_PAID_SEARCH_TOOLS: Final = frozenset({"web_search", "web_search_image", "image_search"}) +_PAYLOAD_ADAPTER: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object]) +_TOOL_USAGE_ADAPTER: Final[TypeAdapter[Mapping[str, int]]] = TypeAdapter(Mapping[str, int]) + + +def web_search_requests_from_tool_usage(payload: object) -> int | None: + """ + Billable search calls in a parsed OpenAI-compatible chat response whose usage carries a + tool_usage {tool_name: count} map, as EmpirioLabs reports built-in tool invocations. + Free tools (code_interpreter, web_extractor) are not counted. + """ + try: + body: Final = _PAYLOAD_ADAPTER.validate_python(payload) + usage: Final = _PAYLOAD_ADAPTER.validate_python(body.get("usage")) + tool_usage: Final = _TOOL_USAGE_ADAPTER.validate_python(usage.get("tool_usage"), strict=True) + except ValidationError: + return None + calls: Final = sum(count for tool, count in tool_usage.items() if tool in _PAID_SEARCH_TOOLS and count > 0) + return calls if calls > 0 else None + def create_config_class(provider: SimpleProviderConfig): """Generate config class dynamically from JSON configuration""" @@ -179,6 +201,25 @@ def create_config_class(provider: SimpleProviderConfig): return optional_params + def transform_parsed_response_dict( + self, + parsed_response: dict[str, object], # mutable-ok: BaseConfig signature + ) -> dict[str, object]: # mutable-ok: BaseConfig signature + """ + Carry the provider's usage.tool_usage search count as usage.server_tool_use.web_search_requests, + the field the built-in tool cost path bills per call. + """ + repaired: Final = _PAYLOAD_ADAPTER.validate_python( + super().transform_parsed_response_dict(parsed_response) # pyright: ignore[reportUnknownMemberType] # base_class is picked at runtime + ) + if not provider.special_handling.get("web_search_requests_from_tool_usage"): + return {**repaired} + web_search_requests: Final = web_search_requests_from_tool_usage(repaired) + if web_search_requests is None: + return {**repaired} + usage: Final = _PAYLOAD_ADAPTER.validate_python(repaired["usage"]) + return {**repaired, "usage": {**usage, "server_tool_use": {"web_search_requests": web_search_requests}}} + @property def custom_llm_provider(self) -> str | None: return provider.slug diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index a71a9c3bb9b..f937e4d2f75 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -178,7 +178,10 @@ "param_mappings": { "max_completion_tokens": "max_tokens" }, - "supported_endpoints": ["/v1/chat/completions", "/v1/responses"] + "special_handling": { + "web_search_requests_from_tool_usage": true + }, + "supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/embeddings"] }, "meta": { "base_url": "https://api.meta.ai/v1", @@ -302,5 +305,14 @@ "preserve_cache_control": true }, "supported_endpoints": ["/v1/chat/completions"] + }, + "wally": { + "base_url": "https://inference.runanywhere.ai/v1", + "api_key_env": "WALLY_API_KEY", + "api_base_env": "WALLY_API_BASE", + "provider_info": { + "supports_function_calling": true, + "supports_reasoning": true + } } } diff --git a/litellm/llms/opencode/transformation.py b/litellm/llms/opencode/transformation.py deleted file mode 100644 index 9b62e7aca95..00000000000 --- a/litellm/llms/opencode/transformation.py +++ /dev/null @@ -1,6 +0,0 @@ -from litellm.llms.opencode.common_utils import is_opencode_api_base - - -class OpenCodeProviderAdapter: - def get_provider_affinity_header(self, api_base: str | None) -> str | None: - return "x-opencode-session" if is_opencode_api_base(api_base) else None diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 32d7b39a20e..80244cfc3eb 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -5,9 +5,11 @@ import json import re import threading import time +from collections import Counter from collections.abc import Awaitable, Callable, Mapping, Sequence from copy import deepcopy from functools import lru_cache, partial +from itertools import chain, repeat from types import MappingProxyType from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, Protocol, Union, cast, get_args @@ -34,7 +36,11 @@ from litellm.litellm_core_utils.json_fragment_accumulator import JSONFragmentAcc from litellm.litellm_core_utils.model_param_helper import embeds_thought_signature_in_tool_call_id from litellm.litellm_core_utils.prompt_templates.factory import encode_tool_call_id_with_signature from litellm.litellm_core_utils.reasoning_effort_utils import warn_dropped_reasoning -from litellm.litellm_core_utils.tool_fields import tool_params_without_hosted_mcp +from litellm.litellm_core_utils.tool_fields import ( + declared_function_name, + qualified_member_name, + tool_params_without_hosted_mcp, +) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, @@ -544,6 +550,138 @@ def mapped_gemini_speech_config( } +def _as_chat_tool(member: JsonValue) -> JsonValue: + if not isinstance(member, dict) or "function" in member or "name" not in member: + return member + return { + "type": member.get("type", "function"), + "function": {key: value for key, value in member.items() if key != "type"}, + } + + +def _namespace_members_or_tool(tool: JsonValue) -> tuple[JsonValue, ...]: + if not isinstance(tool, dict) or tool.get("type") != "namespace": + return (tool,) + members: Final = tool.get("tools") + return _members_named_once(tuple(map(_as_chat_tool, members))) if isinstance(members, list) else () + + +def _members_named_once( + members: tuple[JsonValue, ...], repeatable: frozenset[str] = frozenset() +) -> tuple[JsonValue, ...]: + names: Final = tuple(map(declared_function_name, members)) + first_index: Final = {name: index for index, name in reversed(tuple(enumerate(names)))} + return tuple( + member + for index, (member, name) in enumerate(zip(members, names, strict=True)) + if name is None or name in repeatable or first_index[name] == index + ) + + +def _is_namespace(tool: JsonValue) -> bool: + return isinstance(tool, dict) and tool.get("type") == "namespace" + + +def _top_level_name(tool: JsonValue) -> str | None: + name: Final = tool.get("name") if isinstance(tool, dict) else None + return name if isinstance(name, str) and name else None + + +def _listed(tools: JsonValue) -> tuple[JsonValue, ...]: + return tuple(tools) if isinstance(tools, list) else () + + +def _declarable_names(tool: JsonValue) -> tuple[str | None, ...]: + members: Final = _namespace_members_or_tool(tool) if _is_namespace(tool) else () + return (declared_function_name(tool), *map(declared_function_name, members)) + + +def _declarable_names_in(tools: tuple[JsonValue, ...]) -> tuple[str | None, ...]: + return tuple(chain.from_iterable(map(_declarable_names, tools))) + + +def _inlines_members(tool: JsonValue, declarable: Counter[str | None]) -> bool: + members: Final = _namespace_members_or_tool(tool) + return _is_namespace(tool) and all( + name is None or declarable[name] == 1 for name in map(declared_function_name, members) + ) + + +def _qualified_member(namespace: str, member: JsonValue) -> JsonValue: + function: Final = member.get("function") if isinstance(member, dict) else None + name: Final = declared_function_name(member) + if not isinstance(member, dict) or not isinstance(function, dict) or name is None: + return member + return {**member, "function": {**function, "name": qualified_member_name(namespace, name)}} + + +def _as_declared( + tool: JsonValue, declarable: Counter[str | None], top_level_names: frozenset[str] +) -> tuple[JsonValue, ...]: + if _inlines_members(tool, declarable): + return _namespace_members_or_tool(tool) + namespace: Final = _top_level_name(tool) + if not _is_namespace(tool) or namespace is None: + return (tool,) + qualified: Final = (_qualified_member(namespace, member) for member in _namespace_members_or_tool(tool)) + return tuple(member for member in qualified if declared_function_name(member) not in top_level_names) + + +def _top_level_names_in(tools: tuple[JsonValue, ...]) -> frozenset[str]: + top_level_tools: Final = (tool for tool in tools if not _is_namespace(tool)) + return frozenset(name for name in map(declared_function_name, top_level_tools) if name is not None) + + +def _with_namespace_members_inlined(tools: JsonValue, outside_declarations: JsonValue = None) -> JsonValue: + if not isinstance(tools, list) or not any(map(_is_namespace, tools)): + return tools + candidates: Final = (*tools, *_listed(outside_declarations)) + declarable: Final = Counter(_declarable_names_in(candidates)) + top_level_names: Final = _top_level_names_in(candidates) + declared: Final = chain.from_iterable(_as_declared(tool, declarable, top_level_names) for tool in tools) + return list(_members_named_once(tuple(declared), top_level_names)) + + +def _names_each_tool_declares(candidates: tuple[JsonValue, ...]) -> tuple[tuple[str, ...], ...]: + declarable: Final = Counter(_declarable_names_in(candidates)) + top_level_names: Final = _top_level_names_in(candidates) + declared: Final = tuple( + tuple(filter(None, map(declared_function_name, _as_declared(tool, declarable, top_level_names)))) + for tool in candidates + ) + owned_names: Final = chain.from_iterable(zip(repeat(index), names) for index, names in enumerate(declared)) + first_owner: Final = {name: index for index, name in reversed(tuple(owned_names))} + return tuple(tuple(name for name in names if first_owner[name] == index) for index, names in enumerate(declared)) + + +def _qualified_names_of( + member_name: str, tool: JsonValue, declarable: Counter[str | None], declared: tuple[str, ...] +) -> tuple[str, ...]: + namespace: Final = _top_level_name(tool) + if not _is_namespace(tool) or namespace is None or _inlines_members(tool, declarable): + return () + qualified: Final = qualified_member_name(namespace, member_name) + return (qualified,) if qualified in declared else () + + +def _forced_function_names(name: str, tools: JsonValue) -> tuple[str, ...]: + candidates: Final = _listed(tools) + if any(not _is_namespace(tool) and declared_function_name(tool) == name for tool in candidates): + return (name,) + declarable: Final = Counter(_declarable_names_in(candidates)) + declared_by_tool: Final = tuple(zip(candidates, _names_each_tool_declares(candidates), strict=True)) + named: Final = next( + (declared for tool, declared in declared_by_tool if _is_namespace(tool) and _top_level_name(tool) == name), + None, + ) + if named is not None: + return named + qualified: Final = chain.from_iterable( + _qualified_names_of(name, tool, declarable, declared) for tool, declared in declared_by_tool + ) + return tuple(qualified) or (name,) + + class VertexAIBaseConfig: def get_mapped_special_auth_params(self) -> dict: """ @@ -815,7 +953,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return supported_params - def map_tool_choice_values(self, model: str, tool_choice: str | dict) -> ToolConfig | None: + def map_tool_choice_values( + self, model: str, tool_choice: str | dict, tools: JsonValue = None, drop_params: bool = False + ) -> ToolConfig | None: if tool_choice == "none": return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="NONE")) elif tool_choice == "required": @@ -825,7 +965,17 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif isinstance(tool_choice, dict): # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html name: Final = tool_choice.get("function", {}).get("name", "") - return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="ANY", allowed_function_names=[name])) + forced: Final = _forced_function_names(name, tools) + if forced: + return ToolConfig( + functionCallingConfig=FunctionCallingConfig(mode="ANY", allowed_function_names=list(forced)) + ) + if drops_unsupported_params(drop_params): + return None + raise litellm.utils.UnsupportedParamsError( + message=f"VertexAI can't force tool_choice '{name}', since none of that namespace's members can be sent to Gemini as a function. Name one of its functions instead, or set `litellm.drop_params = True` to drop tool_choice.", + status_code=400, + ) else: raise litellm.utils.UnsupportedParamsError( message=f"VertexAI doesn't support tool_choice={tool_choice}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.", @@ -1068,7 +1218,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return googleSearch, googleSearchRetrieval, enterpriseWebSearch, urlContext - def _map_function(self, value: list[dict], optional_params: dict, send_integer_enums: bool = False) -> list[Tools]: + def _map_function( + self, + value: list[dict], + optional_params: dict, + send_integer_enums: bool = False, + outside_declarations: JsonValue = None, + ) -> list[Tools]: """ Map OpenAI-style tools/functions to Vertex AI format. @@ -1099,7 +1255,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): # remove 'strict' from tools remove_strict_from_schema(schema_value) - for tool in cast(list[dict], schema_value): # cast-ok: a deep copy keeps the caller's tool shape + inlined_tools: Final = _with_namespace_members_inlined(schema_value, outside_declarations) + for tool in cast(list[dict], inlined_tools): # cast-ok: a deep copy keeps the caller's tool shape openai_function_object: ChatCompletionToolParamFunctionChunk | None = None if "function" in tool: # tools list _openai_function_object = ChatCompletionToolParamFunctionChunk(**tool["function"]) @@ -1734,7 +1891,12 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif (param == "tools" or param == "functions") and isinstance(value, list) and value: # Pass optional_params so _map_function can add toolConfig if needed mapped_tools = with_integer_enums_that_fit( - partial(self._map_function, value, optional_params), + partial( + self._map_function, + value, + optional_params, + outside_declarations=request_params.get("functions" if param == "tools" else "tools"), + ), allow_integer_enums and self._is_gemini_3_or_newer(model), ) optional_params = self._add_tools_to_optional_params(optional_params, mapped_tools) @@ -1742,6 +1904,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): _tool_choice_value = self.map_tool_choice_values( model=model, tool_choice=value, + tools=[*_listed(non_default_params.get("tools")), *_listed(non_default_params.get("functions"))], + drop_params=drop_params, ) if _tool_choice_value is not None: optional_params["tool_choice"] = _tool_choice_value diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/messages/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/messages/transformation.py index 98fa8ed2b77..816301c6637 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/messages/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/messages/transformation.py @@ -249,7 +249,7 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert ) self._normalize_system_role_messages( - anthropic_messages_request, model=model, drop_params=getattr(litellm_params, "drop_params", None) + anthropic_messages_request, model=model, drop_params=litellm_params.drop_params ) self._remove_scope_from_cache_control(anthropic_messages_request) diff --git a/litellm/main.py b/litellm/main.py index c13227ace4a..51df08f1242 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -81,11 +81,13 @@ from litellm.litellm_core_utils.audio_utils.utils import ( get_audio_file_for_health_check, ) from litellm.litellm_core_utils.chat_completion_agentic_loop import ( + bill_first_round_on_failure, maybe_run_chat_completion_agentic_loop, ) from litellm.litellm_core_utils.completion_timeout import CompletionTimeout, StreamKind from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.get_litellm_params import ( + AGENTIC_LOOP_CONTROL_KWARGS_KEYS, AWS_CREDENTIAL_KWARGS_KEYS, OAUTH_TOKEN_EXCHANGE_KWARGS_KEYS, PROVIDER_AFFINITY_HEADER_KWARG_KEY, @@ -111,6 +113,7 @@ from litellm.litellm_core_utils.mock_functions import ( mock_embedding, mock_image_generation, ) +from litellm.litellm_core_utils.per_call_pricing import per_call_model_info from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_content_from_model_response as get_content_from_model_response, # noqa: PLC0414 # public re-export ) @@ -167,7 +170,6 @@ from litellm.types.router import GenericLiteLLMParams, validate_litellm_params_s from litellm.types.utils import ( AgenticLoopParams, BaseUrlRoutingHint, - CustomPricingLiteLLMParams, ModelResponseStream, ProviderRoutingHints, RawRequestTypedDict, @@ -954,26 +956,31 @@ async def acompletion( # no equivalent: every provider already funnels through one shared # handler where the loop is dispatched. if isinstance(response, litellm.ModelResponse): - looped: Final = await maybe_run_chat_completion_agentic_loop( - response=response, - model=model, - messages=messages, - optional_params={ - k: v - for k, v in completion_kwargs.items() - if v is not None - and k - not in ( - "model", - "messages", - "stream", - "acompletion", - "deployment_id", - ) - }, - kwargs=kwargs, + looped: Final = await bill_first_round_on_failure( + maybe_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + optional_params={ + k: v + for k, v in completion_kwargs.items() + if v is not None + and k + not in ( + "model", + "messages", + "stream", + "acompletion", + "deployment_id", + ) + }, + kwargs=kwargs, + logging_obj=kwargs.get("litellm_logging_obj"), + custom_llm_provider=custom_llm_provider, + stream=bool(stream), + ), logging_obj=kwargs.get("litellm_logging_obj"), - custom_llm_provider=custom_llm_provider, + response=response, stream=bool(stream), ) if looped is not None: @@ -1323,7 +1330,6 @@ from litellm.responses.bridge_check import ( # noqa: E402 # re-export at the o ) from litellm.responses.bridge_check import ( bridge_reasoning_effort, - keep_registered_mode, str_keyed_mapping, ) from litellm.responses.bridge_check import ( # noqa: E402 # re-export at the old definition site @@ -1429,46 +1435,6 @@ def _locals_snapshot(values: Mapping[str, object]) -> Mapping[str, object]: return values -def _build_custom_pricing_entry( - custom_llm_provider: str, - kwargs: dict, - model_info: dict | None = None, -) -> dict: - """Build a complete model cost entry from kwargs and model_info. - - Collects all CustomPricingLiteLLMParams fields present in kwargs and - merges metadata from model_info (mode, supports_prompt_caching, max_tokens) - so that register_model() receives the full pricing configuration. - """ - entry: Final[dict] = {"litellm_provider": custom_llm_provider} - - for field_name in CustomPricingLiteLLMParams.model_fields: - value = kwargs.get(field_name) - if value is not None: - entry[field_name] = value - - if model_info and isinstance(model_info, dict): - for key in ("mode", "supports_prompt_caching", "max_tokens"): - if key in model_info and model_info[key] is not None: - entry.setdefault(key, model_info[key]) - - return entry - - -def _get_router_deployment_id(kwargs: dict) -> str | None: - for metadata_key in ("litellm_metadata", "metadata"): - metadata = kwargs.get(metadata_key) or {} - if not isinstance(metadata, dict): - continue - deployment_model_info = metadata.get("model_info") or {} - if not isinstance(deployment_model_info, dict): - continue - deployment_id = deployment_model_info.get("id") - if deployment_id is not None: - return str(deployment_id) - return None - - def _record_agentic_loop_params( logging_obj: object, requested_model: str, custom_llm_provider: str, owns_agentic_loop: bool ) -> None: @@ -1479,53 +1445,6 @@ def _record_agentic_loop_params( ) -def _register_custom_pricing_for_request( - model: str, - custom_llm_provider: str, - kwargs: dict, - model_info: dict | None, -) -> None: - """Register per-request custom pricing in litellm.model_cost. - - Router-originated requests (identified by the deployment id the router puts - in metadata) get their full pricing registered under that unique id only; - the shared ``{provider}/{model}`` key receives the entry with pricing fields - stripped, mirroring Router._create_deployment. This keeps one deployment's - pricing overrides (e.g. a zero-cost wildcard) from clobbering built-in - pricing used by sibling deployments of the same backend model. Direct SDK - calls keep the legacy behavior of registering the shared key with pricing. - Either way the shared key's ``mode`` follows ``keep_registered_mode``, as on - deployment registration, since a call routes by its own ``model_info``. - """ - entry: Final = _build_custom_pricing_entry( - custom_llm_provider=custom_llm_provider, - kwargs=kwargs, - model_info=model_info, - ) - shared_key: Final = f"{custom_llm_provider}/{model}" - deployment_id: Final = _get_router_deployment_id(kwargs) - if deployment_id is None: - litellm.register_model({shared_key: keep_registered_mode(shared_key, entry)}, persist_across_reloads=False) - return - shared_entry: Final = keep_registered_mode( - shared_key, CustomPricingLiteLLMParams.strip_custom_pricing_fields(entry) - ) - litellm.register_model( - {deployment_id: entry}, - persist_across_reloads=False, - warning_display_name=shared_key, - custom_llm_provider=custom_llm_provider, - add_to_provider_model_sets=False, - ) - litellm.register_model( - {shared_key: shared_entry}, - persist_across_reloads=False, - warning_display_name=shared_key, - custom_llm_provider=custom_llm_provider, - add_to_provider_model_sets=False, - ) - - def _dispatch_metadata(ctx: CompletionDispatchContext) -> Mapping[str, object] | None: return ctx.metadata @@ -5788,9 +5707,9 @@ def completion( ### CUSTOM MODEL COST ### input_cost_per_token: Final = kwargs.get("input_cost_per_token", None) output_cost_per_token: Final = kwargs.get("output_cost_per_token", None) - cost_per_second: Final = kwargs.get("cost_per_second", None) input_cost_per_second: Final = kwargs.get("input_cost_per_second", None) output_cost_per_second: Final = kwargs.get("output_cost_per_second", None) + cost_per_second: Final = kwargs.get("cost_per_second", None) ### CUSTOM PROMPT TEMPLATE ### initial_prompt_value: Final = kwargs.get("initial_prompt_value", None) roles: Final = kwargs.get("roles", None) @@ -5988,19 +5907,7 @@ def completion( "aws_region_name", None ) # support region-based pricing for bedrock - ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### - if ( - (input_cost_per_token is not None and output_cost_per_token is not None) - or input_cost_per_second is not None - or output_cost_per_second is not None - or cost_per_second is not None - ): - _register_custom_pricing_for_request( - model=model, - custom_llm_provider=custom_llm_provider, - kwargs=kwargs, - model_info=model_info, - ) + logging.set_per_call_pricing(per_call_model_info(model, custom_llm_provider, kwargs, "chat")) ### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ### custom_prompt_dict = {} if initial_prompt_value or roles or final_prompt_value or bos_token or eos_token: @@ -6163,7 +6070,6 @@ def completion( return resolve_timeout(stream_kind=StreamKind.NOT_STREAMED) return resolve_timeout(stream_kind=StreamKind.FAKED if fakes_stream(attempt_params) else StreamKind.LIVE) - ### TIMEOUT LOGIC ### timeout = timeout_for_optional_params(optional_params) logging.record_applied_timeout(timeout) processed_non_default_params: Final = pre_process_non_default_params( @@ -6206,6 +6112,11 @@ def completion( messages if readable_messages is None else list(readable_messages) ) + supplied_client_api_base: Final = ( + str(client.base_url) + if custom_llm_provider == "openai" and isinstance(client, openai.OpenAI | openai.AsyncOpenAI) + else None + ) # For logging - save the values of the litellm-specific params passed in requested_litellm_params: Final = get_litellm_params( acompletion=acompletion, @@ -6214,7 +6125,7 @@ def completion( logger_fn=logger_fn, verbose=verbose, custom_llm_provider=custom_llm_provider, - api_base=api_base, + api_base=supplied_client_api_base or api_base, litellm_call_id=kwargs.get("litellm_call_id", None), model_alias_map=litellm.model_alias_map, completion_call_id=id, @@ -6270,6 +6181,7 @@ def completion( *ANTHROPIC_WIF_KWARGS_KEYS, *OPENAI_WIF_KWARGS_KEYS, *OAUTH_TOKEN_EXCHANGE_KWARGS_KEYS, + *AGENTIC_LOOP_CONTROL_KWARGS_KEYS, "github_copilot_auth_type", "github_copilot_host", "github_copilot_user_session", @@ -7007,12 +6919,6 @@ def embedding( headers.update(proxy_headers) except Exception as e: verbose_logger.warning("Failed to get proxy auth headers: %s", e) - ### CUSTOM MODEL COST ### - input_cost_per_token: Final = kwargs.get("input_cost_per_token", None) - output_cost_per_token: Final = kwargs.get("output_cost_per_token", None) - cost_per_second: Final = kwargs.get("cost_per_second", None) - input_cost_per_second: Final = kwargs.get("input_cost_per_second", None) - output_cost_per_second: Final = kwargs.get("output_cost_per_second", None) openai_params: Final = [ "user", "dimensions", @@ -7059,21 +6965,8 @@ def embedding( **non_default_params, ) - ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### - if ( - (input_cost_per_token is not None and output_cost_per_token is not None) - or input_cost_per_second is not None - or output_cost_per_second is not None - or cost_per_second is not None - ): - _register_custom_pricing_for_request( - model=model, - custom_llm_provider=custom_llm_provider, - kwargs=kwargs, - model_info=kwargs.get("model_info"), - ) - logging: Final[LiteLLMLoggingObj] = litellm_logging_obj + logging.set_per_call_pricing(per_call_model_info(model, custom_llm_provider, kwargs, "embedding")) logging.update_environment_variables( model=model, user=user, diff --git a/litellm/mcp_client/client.py b/litellm/mcp_client/client.py index 107b7155303..0461f1f06ff 100644 --- a/litellm/mcp_client/client.py +++ b/litellm/mcp_client/client.py @@ -478,6 +478,13 @@ def _notify_cleanup(callback: Callable[[], None] | None) -> None: callback() +class _UnvalidatedClientSession(ClientSession): + """Skips the SDK's output check, which lists the upstream's tools on a session with no schema cached.""" + + async def validate_tool_result(self, name: str, result: MCPCallToolResult) -> None: + return None + + class MCPClient: """ MCP Client supporting: @@ -505,12 +512,15 @@ class MCPClient: elicitation_callback: "ElicitationCallback | None" = None, logging_callback: Callable | None = None, protocol_version: MCPUpstreamProtocol = "auto", + skip_tool_output_validation: bool = False, upstream_transport: httpx2.AsyncBaseTransport | None = None, ): self.protocol_version: MCPUpstreamProtocol = TypeAdapter[MCPUpstreamProtocol]( MCPUpstreamProtocol ).validate_python(protocol_version) validate_mcp_protocol_transport(self.protocol_version, transport_type) + # 2026-07-28 lists tools before every call, so its output check adds no list + self.skip_tool_output_validation: Final = skip_tool_output_validation and self.protocol_version != "2026-07-28" self.server_url: str = server_url self.transport_type: MCPTransport = transport_type self.auth_type: MCPAuthType = auth_type @@ -546,7 +556,7 @@ class MCPClient: "response_hooks": _mcp_response_hooks(), } - def session_settings(self) -> tuple[MCPTransport, MCPUpstreamProtocol, float, bool, bool, bool]: + def session_settings(self) -> tuple[MCPTransport, MCPUpstreamProtocol, float, bool, bool, bool, bool]: callbacks: Final = self.session_callbacks() return ( self.transport_type, @@ -555,6 +565,7 @@ class MCPClient: callbacks["sampling_callback"] is not None, callbacks["elicitation_callback"] is not None, callbacks["logging_callback"] is not None, + self.skip_tool_output_validation, ) def route_session_callbacks(self, callbacks: _SessionCallbackMap) -> None: @@ -776,7 +787,10 @@ class MCPClient: ) # The SDK drops a response stream that ends without a JSON-RPC reply, so nothing else # ever fails the request. - session_ctx: Final = ClientSession( + session_type: Final = ( + _UnvalidatedClientSession if self.skip_tool_output_validation else ClientSession + ) + session_ctx: Final = session_type( read_stream, write_stream, read_timeout_seconds=self.timeout, diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 974f1bb0c8b..25c7c3593ed 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -3,6 +3,8 @@ "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, + "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -42,6 +44,8 @@ "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, + "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -204,6 +208,7 @@ }, "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -353,6 +358,7 @@ }, "ai21.jamba-1-5-large-v1:0": { "deprecation_date": "2026-11-26", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-ai21-labs-jamba-1-5-large.md", "input_cost_per_token": 2e-06, "litellm_provider": "bedrock", "max_input_tokens": 256000, @@ -363,6 +369,7 @@ }, "ai21.jamba-1-5-mini-v1:0": { "deprecation_date": "2026-11-26", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-ai21-labs-jamba-1-5-mini.md", "input_cost_per_token": 2e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, @@ -532,6 +539,7 @@ }, "amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -540,6 +548,7 @@ }, "us.amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -548,6 +557,7 @@ }, "us.writer.palmyra-x4-v1:0": { "deprecation_date": "2027-03-09", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x4.md", "input_cost_per_token": 2.5e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -560,6 +570,7 @@ }, "us.writer.palmyra-x5-v1:0": { "deprecation_date": "2027-03-21", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x5.md", "input_cost_per_token": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -572,6 +583,7 @@ }, "writer.palmyra-x4-v1:0": { "deprecation_date": "2027-03-09", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x4.md", "input_cost_per_token": 2.5e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -584,6 +596,7 @@ }, "writer.palmyra-x5-v1:0": { "deprecation_date": "2027-03-21", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x5.md", "input_cost_per_token": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -964,6 +977,7 @@ }, "twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -978,6 +992,7 @@ }, "us.twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -992,6 +1007,7 @@ }, "eu.twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -1045,6 +1061,7 @@ }, "twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1054,6 +1071,7 @@ }, "us.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1063,6 +1081,7 @@ }, "eu.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1072,6 +1091,7 @@ }, "global.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1175,7 +1195,8 @@ "supports_native_streaming": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "token_count_model": {"model":"anthropic.claude-haiku-4-5-20251001-v1:0","litellm_provider":"bedrock"} }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -1275,6 +1296,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -2135,12 +2157,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.5e-06, + "output_cost_per_token_batches": 1.25e-05 }, "anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2221,12 +2245,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "global.anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.5e-06, + "output_cost_per_token_batches": 1.25e-05 }, "global.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2307,12 +2333,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "us.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2393,12 +2421,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/eu-west-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "eu-west-1" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "eu.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2474,12 +2504,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-southeast-2/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "ap-southeast-4" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "au.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2560,7 +2592,9 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/" + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-northeast-1/index.json", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "jp.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -3397,6 +3431,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -3440,6 +3476,8 @@ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -3674,6 +3712,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "apac.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -3729,6 +3769,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "au.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -3762,6 +3804,7 @@ }, "azure/ada": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -3772,6 +3815,7 @@ "azure/codex-mini": { "cache_read_input_token_cost": 3.75e-07, "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -3799,19 +3843,10 @@ "supports_tool_choice": true, "supports_vision": true }, - "azure/command-r-plus": { - "deprecation_date": "2025-06-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true - }, "azure_ai/claude-haiku-4-5": { "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-haiku-5-5", "cache_creation_input_token_cost": 1.25e-06, "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_read_input_token_cost": 1e-07, @@ -3835,6 +3870,8 @@ }, "azure_ai/claude-opus-4-5": { "deprecation_date": "2026-11-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-opus-5-5", "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -3859,6 +3896,7 @@ }, "azure_ai/claude-opus-4-6": { "deprecation_date": "2027-02-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "supports_legacy_thinking": true, "input_cost_per_token": 5e-06, @@ -3890,6 +3928,7 @@ }, "azure_ai/claude-opus-4-7": { "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, "output_cost_per_token": 2.5e-05, @@ -3921,6 +3960,7 @@ }, "azure_ai/claude-fable-5": { "deprecation_date": "2027-12-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3949,7 +3989,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true, + "supports_max_reasoning_effort": false, "prompt_cache_min_tokens": 512 }, "azure_ai/claude-fable-5-1": { @@ -3983,12 +4023,44 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true, + "supports_max_reasoning_effort": false, "prompt_cache_min_tokens": 512, - "deprecation_date": "2027-12-05" + "deprecation_date": "2027-12-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement" + }, + "azure_ai/claude-mythos-5-1": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": false, + "prompt_cache_min_tokens": 512, + "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/claude-models" }, "azure_ai/claude-opus-5": { "deprecation_date": "2027-07-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -4057,6 +4129,7 @@ }, "azure_ai/claude-opus-4-8": { "deprecation_date": "2027-09-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -4089,6 +4162,8 @@ }, "azure_ai/claude-sonnet-4-5": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-sonnet-5-5", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -4112,6 +4187,7 @@ }, "azure_ai/claude-sonnet-5": { "deprecation_date": "2027-06-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -4144,6 +4220,7 @@ }, "azure_ai/claude-sonnet-4-6": { "deprecation_date": "2027-02-10", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, @@ -4204,6 +4281,7 @@ }, "azure_ai/gpt-oss-120b": { "deprecation_date": "2027-09-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.5e-07, "output_cost_per_token": 6e-07, "litellm_provider": "azure_ai", @@ -4459,6 +4537,7 @@ }, "azure_ai/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -4506,6 +4585,7 @@ "azure_ai/gpt-chat-latest": { "cache_read_input_token_cost": 5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -4540,6 +4620,7 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_image_token": 8e-06, "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", @@ -4555,6 +4636,7 @@ "azure_ai/codex-mini": { "cache_read_input_token_cost": 3.75e-07, "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 200000, @@ -4584,6 +4666,7 @@ }, "azure_ai/whisper": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_second": 0.0001, "litellm_provider": "azure_ai", "mode": "audio_transcription", @@ -4637,6 +4720,7 @@ }, "azure_ai/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -4683,6 +4767,8 @@ }, "azure_ai/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_priority": 5e-07, @@ -4730,6 +4816,8 @@ }, "azure_ai/gpt-5.4-2026-03-05": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_priority": 5e-07, @@ -4777,6 +4865,7 @@ }, "azure_ai/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "cache_read_input_token_cost_priority": 6e-06, @@ -4822,6 +4911,7 @@ }, "azure_ai/gpt-5.4-pro-2026-03-05": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "cache_read_input_token_cost_priority": 6e-06, @@ -4867,6 +4957,7 @@ }, "azure_ai/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, @@ -4908,6 +4999,7 @@ }, "azure_ai/gpt-5.4-mini-2026-03-17": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, @@ -4949,6 +5041,7 @@ }, "azure_ai/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, @@ -4990,6 +5083,7 @@ }, "azure_ai/gpt-5.4-nano-2026-03-17": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, @@ -5031,6 +5125,7 @@ }, "azure_ai/model_router": { "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, "litellm_provider": "azure_ai", @@ -5042,6 +5137,7 @@ }, "azure_ai/model-router": { "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, "litellm_provider": "azure_ai", @@ -5053,6 +5149,8 @@ }, "azure/eu/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "input_cost_per_token_batches": 1.375e-06, @@ -5072,6 +5170,8 @@ }, "azure/eu/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -5091,6 +5191,8 @@ }, "azure/eu/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -5111,6 +5213,7 @@ "azure/eu/gpt-4o-mini": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -5134,6 +5237,7 @@ "azure/eu/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -5172,26 +5276,6 @@ "supports_system_messages": true, "supports_tool_choice": true }, - "azure/eu/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2.2e-05, - "cache_read_input_token_cost": 2.75e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.00011, - "input_cost_per_token": 5.5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.00022, - "output_cost_per_token": 2.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/eu/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_audio_token_cost": 2.5e-06, "cache_read_input_token_cost": 2.75e-06, @@ -5223,6 +5307,8 @@ "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "input_cost_per_token": 1.375e-06, "input_cost_per_token_batches": 6.875e-07, "input_cost_per_token_priority": 2.75e-06, @@ -5266,7 +5352,9 @@ "azure/eu/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_priority": 4.95e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "input_cost_per_token": 2.75e-07, "input_cost_per_token_batches": 1.375e-07, "input_cost_per_token_priority": 4.95e-07, @@ -5308,7 +5396,9 @@ "cache_read_input_token_cost_batches": 1.375e-08 }, "azure/eu/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -5356,6 +5446,8 @@ }, "azure/eu/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2-codex", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -5387,6 +5479,7 @@ }, "azure/eu/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", @@ -5419,6 +5512,8 @@ "azure/eu/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "input_cost_per_token": 5.5e-08, "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", @@ -5460,6 +5555,7 @@ "azure/eu/o1": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -5478,6 +5574,7 @@ "azure/eu/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -5512,6 +5609,8 @@ "azure/eu/o3-mini": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -5529,6 +5628,8 @@ "azure/eu/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -5546,6 +5647,7 @@ "azure/global-standard/gpt-4o-2024-08-06": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", @@ -5565,6 +5667,7 @@ "azure/global-standard/gpt-4o-2024-11-20": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", @@ -5582,6 +5685,7 @@ }, "azure/global-standard/gpt-4o-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -5601,6 +5705,8 @@ }, "azure/global/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/global/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -5620,6 +5726,8 @@ }, "azure/global/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/global/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -5638,7 +5746,8 @@ "supports_vision": true }, "azure/global/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -5686,6 +5795,7 @@ }, "azure/global/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -5717,6 +5827,7 @@ }, "azure/global/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 2.5e-07, "litellm_provider": "azure", @@ -5775,29 +5886,6 @@ "supports_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-35-turbo-16k": { - "deprecation_date": "2025-04-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 16385, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 4e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, - "azure/gpt-35-turbo-16k-0613": { - "deprecation_date": "2025-04-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 16385, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 4e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-35-turbo-instruct": { "input_cost_per_token": 1.65e-06, "litellm_provider": "azure_text", @@ -5816,18 +5904,6 @@ "output_cost_per_token": 2.2e-06, "source": "https://azure.microsoft.com/en-us/pricing/details/azure-openai/" }, - "azure/gpt-4": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 3e-05, - "litellm_provider": "azure", - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-4-0125-preview": { "input_cost_per_token": 1e-05, "litellm_provider": "azure", @@ -5839,18 +5915,6 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-4-0613": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 3e-05, - "litellm_provider": "azure", - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-4-1106-preview": { "input_cost_per_token": 1e-05, "litellm_provider": "azure", @@ -5862,28 +5926,6 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-4-32k": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 6e-05, - "litellm_provider": "azure", - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, - "azure/gpt-4-32k-0613": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 6e-05, - "litellm_provider": "azure", - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, "azure/gpt-4-turbo": { "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -5922,6 +5964,8 @@ }, "azure/gpt-4.1": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, @@ -5964,6 +6008,8 @@ }, "azure/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, @@ -6006,6 +6052,8 @@ }, "azure/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, @@ -6048,6 +6096,8 @@ }, "azure/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, @@ -6090,6 +6140,8 @@ }, "azure/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -6123,6 +6175,8 @@ }, "azure/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -6154,28 +6208,10 @@ "supports_tool_choice": true, "supports_vision": true }, - "azure/gpt-4.5-preview": { - "cache_read_input_token_cost": 3.75e-05, - "deprecation_date": "2025-07-14", - "input_cost_per_token": 7.5e-05, - "input_cost_per_token_batches": 3.75e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "mode": "chat", - "output_cost_per_token": 0.00015, - "output_cost_per_token_batches": 7.5e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "azure/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6194,7 +6230,9 @@ "supports_vision": true }, "azure/gpt-4o-2024-05-13": { - "deprecation_date": "2026-12-09", + "deprecation_date": "2027-01-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "azure", @@ -6212,6 +6250,8 @@ }, "azure/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6231,6 +6271,8 @@ }, "azure/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6250,6 +6292,7 @@ }, "azure/gpt-audio": { "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6283,6 +6326,7 @@ }, "azure/gpt-audio-2025-08-28": { "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6315,6 +6359,7 @@ }, "azure/gpt-audio-1.5": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6348,6 +6393,7 @@ }, "azure/gpt-audio-1.5-2026-02-23": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6382,6 +6428,7 @@ }, "azure/gpt-audio-mini": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "azure", @@ -6414,6 +6461,7 @@ }, "azure/gpt-audio-mini-2025-10-06": { "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "azure", @@ -6478,6 +6526,7 @@ }, "azure/gpt-4o-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -6503,6 +6552,7 @@ "azure/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 7.5e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "litellm_provider": "azure", @@ -6579,6 +6629,7 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6616,6 +6667,7 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6652,6 +6704,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1-2026-07-07", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6689,6 +6743,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1-2026-07-07", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6726,6 +6782,7 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6766,6 +6823,7 @@ "cache_read_input_image_token_cost": 8e-08, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6805,6 +6863,7 @@ "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6841,6 +6900,7 @@ "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6874,6 +6934,7 @@ }, "azure/gpt-4o-mini-transcribe": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -6887,6 +6948,7 @@ }, "azure/gpt-4o-mini-tts": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 6e-07, "litellm_provider": "azure", "mode": "audio_speech", @@ -6904,26 +6966,6 @@ "audio" ] }, - "azure/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2e-05, - "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.0001, - "input_cost_per_token": 5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.0002, - "output_cost_per_token": 2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, "input_cost_per_audio_token": 4e-05, @@ -6952,6 +6994,7 @@ }, "azure/gpt-4o-transcribe": { "deprecation_date": "2026-10-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 6e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6966,6 +7009,7 @@ }, "azure/gpt-4o-transcribe-diarize": { "deprecation_date": "2027-04-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 6e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -7004,7 +7048,9 @@ "azure/gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -7052,6 +7098,8 @@ "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-codex", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -7086,6 +7134,7 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-07, "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", @@ -7118,6 +7167,8 @@ }, "azure/gpt-5": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -7164,6 +7215,8 @@ "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_priority": 2.5e-06, @@ -7207,6 +7260,8 @@ "azure/gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1-codex", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7236,7 +7291,9 @@ "supports_vision": true }, "azure/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, @@ -7282,7 +7339,9 @@ "azure/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, @@ -7325,6 +7384,8 @@ }, "azure/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 5e-09, "cache_read_input_token_cost_batches": 2.5e-09, "input_cost_per_token": 5e-08, @@ -7367,6 +7428,8 @@ "azure/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", @@ -7407,6 +7470,8 @@ }, "azure/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-pro", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", @@ -7442,7 +7507,9 @@ "supports_vision": true }, "azure/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -7491,6 +7558,8 @@ "azure/gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -7515,6 +7584,8 @@ "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7536,6 +7607,8 @@ "azure/gpt-5.2-chat": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7556,6 +7629,8 @@ "azure/gpt-5.3-chat": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7576,6 +7651,8 @@ "azure/us/gpt-5.1-chat": { "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7597,6 +7674,8 @@ "azure/us/gpt-5.2-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7617,6 +7696,8 @@ "azure/us/gpt-5.3-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7637,6 +7718,8 @@ "azure/eu/gpt-5.1-chat": { "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7658,6 +7741,8 @@ "azure/eu/gpt-5.2-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7678,6 +7763,8 @@ "azure/eu/gpt-5.3-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7695,38 +7782,10 @@ "supports_response_schema": true, "supports_tool_choice": true }, - "azure/us/o1-preview": { - "cache_read_input_token_cost": 8.25e-06, - "deprecation_date": "2025-07-28", - "input_cost_per_token": 1.65e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6.6e-05, - "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_prompt_caching": true - }, - "azure/eu/o1-preview": { - "cache_read_input_token_cost": 8.25e-06, - "deprecation_date": "2025-07-28", - "input_cost_per_token": 1.65e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6.6e-05, - "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_prompt_caching": true - }, "azure/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-codex", "cache_read_input_token_cost": 1.25e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -7759,6 +7818,7 @@ "azure/gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7789,6 +7849,7 @@ }, "azure/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 2.5e-07, "litellm_provider": "azure", @@ -7820,6 +7881,8 @@ }, "azure/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4", "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_batches": 8.75e-08, "cache_read_input_token_cost_priority": 3.5e-07, @@ -7867,6 +7930,8 @@ "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4", "input_cost_per_token": 1.75e-06, "input_cost_per_token_batches": 8.75e-07, "input_cost_per_token_priority": 3.5e-06, @@ -7911,6 +7976,8 @@ "azure/gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.3-codex", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7943,6 +8010,7 @@ "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -8056,6 +8124,8 @@ }, "azure/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.5e-07, @@ -8113,6 +8183,8 @@ }, "azure/us/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.5", "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.75e-07, @@ -8162,6 +8234,8 @@ }, "azure/eu/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.5", "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.75e-07, @@ -8216,6 +8290,8 @@ "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5", "input_cost_per_token": 2.5e-06, "input_cost_per_token_above_272k_tokens": 5e-06, "input_cost_per_token_priority": 5e-06, @@ -8271,6 +8347,8 @@ "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.5", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -8320,6 +8398,8 @@ "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.5", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -8366,6 +8446,8 @@ }, "azure/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5-pro", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "input_cost_per_token": 3e-05, @@ -8417,6 +8499,8 @@ "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5-pro", "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, "input_cost_per_token_above_272k_tokens_flex": 3e-05, @@ -8529,6 +8613,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-sol", "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, @@ -8592,6 +8678,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-sol", "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, @@ -8655,6 +8743,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6.1-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, @@ -8718,6 +8808,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6.1-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, @@ -8781,6 +8873,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, @@ -8844,6 +8938,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, @@ -8907,11 +9003,7 @@ "mode": "chat", "output_cost_per_token": 5e-05, "output_cost_per_token_above_272k_tokens": 7.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", "supported_endpoints": [ "/v1/chat/completions", @@ -8943,7 +9035,11 @@ "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-05, "cache_read_input_token_cost_above_272k_tokens_flex": 1e-06, "input_cost_per_token_above_272k_tokens_flex": 1e-05, - "output_cost_per_token_above_272k_tokens_flex": 3.75e-05 + "output_cost_per_token_above_272k_tokens_flex": 3.75e-05, + "input_cost_per_token_flex": 5e-06, + "output_cost_per_token_flex": 2.5e-05, + "cache_read_input_token_cost_flex": 5e-07, + "cache_creation_input_token_cost_flex": 6.25e-06 }, "azure/gpt-6-astra-2026-09-03": { "cache_creation_input_token_cost": 1.25e-05, @@ -8959,11 +9055,7 @@ "mode": "chat", "output_cost_per_token": 5e-05, "output_cost_per_token_above_272k_tokens": 7.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", "supported_endpoints": [ "/v1/chat/completions", @@ -8995,7 +9087,11 @@ "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-05, "cache_read_input_token_cost_above_272k_tokens_flex": 1e-06, "input_cost_per_token_above_272k_tokens_flex": 1e-05, - "output_cost_per_token_above_272k_tokens_flex": 3.75e-05 + "output_cost_per_token_above_272k_tokens_flex": 3.75e-05, + "input_cost_per_token_flex": 5e-06, + "output_cost_per_token_flex": 2.5e-05, + "cache_read_input_token_cost_flex": 5e-07, + "cache_creation_input_token_cost_flex": 6.25e-06 }, "azure/gpt-6-luna": { "cache_creation_input_token_cost": 1.25e-07, @@ -9011,12 +9107,8 @@ "mode": "chat", "output_cost_per_token": 5e-07, "output_cost_per_token_above_272k_tokens": 7.5e-07, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, - "source": "https://azure.microsoft.com/en-us/blog/gpt-6-astra-sol-and-luna-for-production-agents-in-microsoft-foundry/", + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -9043,7 +9135,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-07, + "cache_creation_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-08, + "cache_read_input_token_cost_flex": 5e-09, + "input_cost_per_token_above_272k_tokens_flex": 1e-07, + "input_cost_per_token_flex": 5e-08, + "output_cost_per_token_above_272k_tokens_flex": 3.75e-07, + "output_cost_per_token_flex": 2.5e-07 }, "azure/gpt-6-luna-2026-09-22": { "cache_creation_input_token_cost": 1.25e-07, @@ -9059,12 +9159,8 @@ "mode": "chat", "output_cost_per_token": 5e-07, "output_cost_per_token_above_272k_tokens": 7.5e-07, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, - "source": "https://azure.microsoft.com/en-us/blog/gpt-6-astra-sol-and-luna-for-production-agents-in-microsoft-foundry/", + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -9091,7 +9187,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-07, + "cache_creation_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-08, + "cache_read_input_token_cost_flex": 5e-09, + "input_cost_per_token_above_272k_tokens_flex": 1e-07, + "input_cost_per_token_flex": 5e-08, + "output_cost_per_token_above_272k_tokens_flex": 3.75e-07, + "output_cost_per_token_flex": 2.5e-07 }, "azure/gpt-6-sol": { "cache_creation_input_token_cost": 2.5e-06, @@ -9143,7 +9247,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 1e-07, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6-sol-2026-09-22": { "cache_creation_input_token_cost": 2.5e-06, @@ -9195,7 +9307,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 1e-07, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6.1-terra": { "cache_creation_input_token_cost": 1.25e-06, @@ -9258,11 +9378,7 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_272k_tokens": 1.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models", "supported_endpoints": [ "/v1/chat/completions", @@ -9290,7 +9406,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 5e-08, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6.1-sol-2026-09-29": { "cache_creation_input_token_cost": 2.5e-06, @@ -9306,11 +9430,7 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_272k_tokens": 1.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models", "supported_endpoints": [ "/v1/chat/completions", @@ -9338,7 +9458,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 5e-08, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/us/gpt-6.1-terra": { "cache_creation_input_token_cost": 1.375e-06, @@ -9628,6 +9756,7 @@ "azure/gpt-chat-latest": { "cache_read_input_token_cost": 5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -9760,6 +9889,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6-sol", "input_cost_per_token": 4.4e-06, "input_cost_per_token_above_272k_tokens": 8.8e-06, "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, @@ -9815,6 +9946,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6.1-terra", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, @@ -9870,6 +10003,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6-luna", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, @@ -10066,6 +10201,7 @@ "azure/us/gpt-chat-latest": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -10162,6 +10298,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6-sol", "input_cost_per_token": 4.4e-06, "input_cost_per_token_above_272k_tokens": 8.8e-06, "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, @@ -10217,6 +10355,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6.1-terra", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, @@ -10272,6 +10412,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6-luna", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, @@ -10319,6 +10461,8 @@ }, "azure/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5e-07, @@ -10379,6 +10523,8 @@ }, "azure/us/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5.5e-07, @@ -10433,6 +10579,8 @@ }, "azure/eu/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5.5e-07, @@ -10534,6 +10682,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5e-06, "input_cost_per_token_above_272k_tokens_flex": 5e-06, "input_cost_per_token_batches": 2.5e-06, @@ -10544,6 +10694,8 @@ }, "azure/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1.25e-06, @@ -10647,6 +10799,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "output_cost_per_token_above_272k_tokens_batches": 2.475e-05, @@ -10655,6 +10809,8 @@ }, "azure/us/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, @@ -10752,6 +10908,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "output_cost_per_token_above_272k_tokens_batches": 2.475e-05, @@ -10760,6 +10918,8 @@ }, "azure/eu/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, @@ -10896,6 +11056,7 @@ }, "azure/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_batches": 3.75e-08, "cache_read_input_token_cost_flex": 3.75e-08, @@ -10950,6 +11111,7 @@ "cache_read_input_token_cost_flex": 3.75e-08, "cache_read_input_token_cost_priority": 1.5e-07, "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -10998,6 +11160,7 @@ }, "azure/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_batches": 1e-08, "cache_read_input_token_cost_flex": 1e-08, @@ -11048,6 +11211,7 @@ "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -11096,6 +11260,8 @@ "cache_read_input_image_token_cost": 2.5e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-1.5", "input_cost_per_image_token": 1e-05, "input_cost_per_token": 5e-06, "litellm_provider": "azure", @@ -11130,6 +11296,7 @@ }, "azure/high/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.59263611e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11140,6 +11307,7 @@ }, "azure/high/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11150,6 +11318,7 @@ }, "azure/high/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11160,6 +11329,7 @@ }, "azure/low/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0490417e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11170,6 +11340,7 @@ }, "azure/low/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11180,6 +11351,7 @@ }, "azure/low/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11190,6 +11362,7 @@ }, "azure/medium/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11200,6 +11373,7 @@ }, "azure/medium/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11210,6 +11384,7 @@ }, "azure/medium/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11222,6 +11397,7 @@ "cache_read_input_image_token_cost": 2.5e-07, "cache_read_input_token_cost": 2e-07, "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_image_token": 2.5e-06, "input_cost_per_token": 2e-06, "litellm_provider": "azure", @@ -11236,6 +11412,8 @@ "azure/gpt-image-1.5": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2026-12-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-2", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11253,6 +11431,8 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-12-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-2", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -11268,6 +11448,7 @@ "azure/gpt-image-2": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11285,6 +11466,7 @@ "azure/gpt-image-2.5-flare": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-09-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11302,6 +11484,7 @@ "azure/gpt-image-2.5-sunburst": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-09-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11320,6 +11503,7 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -11335,6 +11519,7 @@ }, "azure/low/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0751953125e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11345,6 +11530,7 @@ }, "azure/low/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0751953125e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11355,6 +11541,7 @@ }, "azure/low/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0345052083e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11365,6 +11552,7 @@ }, "azure/medium/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 8.056640625e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11375,6 +11563,7 @@ }, "azure/medium/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 8.056640625e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11385,6 +11574,7 @@ }, "azure/medium/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 7.9752604167e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11395,6 +11585,7 @@ }, "azure/high/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.173828125e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11405,6 +11596,7 @@ }, "azure/high/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.173828125e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11415,6 +11607,7 @@ }, "azure/high/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.1575520833e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11443,6 +11636,7 @@ }, "azure/o1": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 7.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "azure", @@ -11460,6 +11654,7 @@ "azure/o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", @@ -11512,6 +11707,7 @@ }, "azure/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -11549,6 +11745,7 @@ }, "azure/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -11587,6 +11784,8 @@ "azure/o3-deep-research": { "cache_read_input_token_cost": 2.5e-06, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -11623,6 +11822,8 @@ }, "azure/o3-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, @@ -11642,6 +11843,8 @@ "azure/o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", @@ -11658,6 +11861,8 @@ }, "azure/o3-pro": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "azure", @@ -11693,6 +11898,8 @@ }, "azure/o3-pro-2025-06-10": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "azure", @@ -11728,6 +11935,8 @@ }, "azure/o4-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "cache_read_input_token_cost": 2.75e-07, "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, @@ -11766,6 +11975,8 @@ "azure/o4-mini-2025-04-16": { "cache_read_input_token_cost": 2.75e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", @@ -11817,6 +12028,7 @@ }, "azure/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11826,6 +12038,7 @@ }, "azure/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11835,6 +12048,7 @@ }, "azure/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11880,6 +12094,8 @@ }, "azure/us/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -11922,6 +12138,8 @@ }, "azure/us/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -11964,6 +12182,8 @@ }, "azure/us/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -11997,6 +12217,8 @@ }, "azure/us/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "input_cost_per_token_batches": 1.375e-06, @@ -12016,6 +12238,8 @@ }, "azure/us/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -12035,6 +12259,8 @@ }, "azure/us/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -12055,6 +12281,7 @@ "azure/us/gpt-4o-mini": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -12078,6 +12305,7 @@ "azure/us/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -12116,26 +12344,6 @@ "supports_system_messages": true, "supports_tool_choice": true }, - "azure/us/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2.2e-05, - "cache_read_input_token_cost": 2.75e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.00011, - "input_cost_per_token": 5.5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.00022, - "output_cost_per_token": 2.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/us/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_audio_token_cost": 2.5e-06, "cache_read_input_token_cost": 2.75e-06, @@ -12167,6 +12375,8 @@ "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "input_cost_per_token": 1.375e-06, "input_cost_per_token_batches": 6.875e-07, "input_cost_per_token_priority": 2.75e-06, @@ -12210,7 +12420,9 @@ "azure/us/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_priority": 4.95e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "input_cost_per_token": 2.75e-07, "input_cost_per_token_batches": 1.375e-07, "input_cost_per_token_priority": 4.95e-07, @@ -12254,6 +12466,8 @@ "azure/us/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "input_cost_per_token": 5.5e-08, "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", @@ -12293,7 +12507,9 @@ "cache_read_input_token_cost_batches": 2.75e-09 }, "azure/us/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -12341,6 +12557,8 @@ }, "azure/us/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2-codex", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -12372,6 +12590,7 @@ }, "azure/us/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", @@ -12404,6 +12623,7 @@ "azure/us/o1": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -12422,6 +12642,7 @@ "azure/us/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -12455,6 +12676,7 @@ }, "azure/us/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -12492,6 +12714,7 @@ }, "azure/us/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -12530,6 +12753,8 @@ "azure/us/o3-mini": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12547,6 +12772,8 @@ "azure/us/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12564,6 +12791,8 @@ "azure/us/o4-mini": { "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12589,6 +12818,8 @@ "azure/us/o4-mini-2025-04-16": { "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12620,6 +12851,8 @@ }, "azure_ai/Cohere-embed-v3-english": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 512, @@ -12631,6 +12864,8 @@ }, "azure_ai/Cohere-embed-v3-multilingual": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 512, @@ -12642,6 +12877,7 @@ }, "azure_ai/Codestral-2501": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 3e-07, "litellm_provider": "azure_ai", "max_input_tokens": 256000, @@ -12663,6 +12899,7 @@ }, "azure_ai/FLUX-1.1-pro": { "deprecation_date": "2027-01-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 5000, "mode": "image_generation", @@ -12674,6 +12911,7 @@ }, "azure_ai/FLUX.1-Kontext-pro": { "deprecation_date": "2027-01-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 5000, "mode": "image_generation", @@ -12685,6 +12923,7 @@ }, "azure_ai/flux.2-pro": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 32000, "mode": "image_generation", @@ -12696,6 +12935,7 @@ }, "azure_ai/FLUX.2-flex": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_pixel": 5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 32000, @@ -12746,6 +12986,7 @@ }, "azure_ai/FW-GLM-5": { "deprecation_date": "2027-07-01", + "successor_model": "azure_ai/FW-GLM-5.2", "cache_read_input_token_cost": 2.2e-07, "input_cost_per_token": 1.1e-06, "litellm_provider": "azure_ai", @@ -12762,6 +13003,7 @@ }, "azure_ai/FW-GLM-5.1": { "deprecation_date": "2027-07-01", + "successor_model": "azure_ai/FW-GLM-5.2", "cache_read_input_token_cost": 2.86e-07, "input_cost_per_token": 1.54e-06, "litellm_provider": "azure_ai", @@ -13019,6 +13261,8 @@ "/v1/images/edits" ], "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/MAI-Image-2.6", "max_input_tokens": 32000 }, "azure_ai/MAI-Image-2.5-Flash": { @@ -13034,10 +13278,13 @@ "/v1/images/edits" ], "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/MAI-Image-2.6-Flash", "max_input_tokens": 32000 }, "azure_ai/MAI-Image-2.5-Pro": { "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_image_token": 8e-06, "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", @@ -13077,6 +13324,7 @@ }, "azure_ai/Llama-3.3-70B-Instruct": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 7.1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13090,6 +13338,7 @@ }, "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13104,6 +13353,7 @@ }, "azure_ai/Llama-4-Scout-17B-16E-Instruct": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 10000000, 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"2025-08-30", - "input_cost_per_token": 1.7e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6.8e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true, - "supports_vision": false - }, - "azure_ai/Phi-3-medium-4k-instruct": { - "deprecation_date": "2025-08-30", - "input_cost_per_token": 1.7e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6.8e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true, - "supports_vision": false - }, - "azure_ai/Phi-3-mini-128k-instruct": { - "deprecation_date": "2025-08-30", - "input_cost_per_token": 1.3e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 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- "supports_tool_choice": true, - "supports_vision": false - }, - "azure_ai/Phi-3.5-mini-instruct": { - "deprecation_date": "2025-08-30", - "input_cost_per_token": 1.3e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 5.2e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true, - "supports_vision": false - }, - "azure_ai/Phi-3.5-vision-instruct": { - "deprecation_date": "2025-08-30", - "input_cost_per_token": 1.3e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 5.2e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true, - "supports_vision": true - }, "azure_ai/Phi-4": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 1.25e-07, "litellm_provider": "azure_ai", "max_input_tokens": 16384, @@ -13273,6 +13384,8 @@ }, "azure_ai/Phi-4-mini-instruct": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13285,6 +13398,8 @@ }, "azure_ai/Phi-4-multimodal-instruct": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_audio_token": 4e-06, "input_cost_per_token": 8e-08, "litellm_provider": "azure_ai", @@ -13300,6 +13415,8 @@ }, "azure_ai/Phi-4-mini-reasoning": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13312,6 +13429,8 @@ }, "azure_ai/Phi-4-reasoning": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 1.25e-07, "litellm_provider": "azure_ai", "max_input_tokens": 32768, @@ -13345,6 +13464,7 @@ }, "azure_ai/Cohere-parse-v5": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "mode": "ocr", "ocr_cost_per_page": 0.0015, @@ -13355,6 +13475,8 @@ }, "azure_ai/cohere-command-a": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "coherelabs-command-a-plus-05-2026-fp81", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13395,6 +13517,7 @@ }, "azure_ai/MAI-DS-R1": { "deprecation_date": "2026-02-27", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "input_cost_per_token": 1.35e-06, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13406,30 +13529,10 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "azure_ai/cohere-rerank-v3-english": { - "deprecation_date": "2025-06-30", - "input_cost_per_query": 0.002, - "input_cost_per_token": 0.0, - "litellm_provider": "azure_ai", - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "mode": "rerank", - "output_cost_per_token": 0.0, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models" - }, - "azure_ai/cohere-rerank-v3-multilingual": { - "deprecation_date": "2025-06-30", - "input_cost_per_query": 0.002, - "input_cost_per_token": 0.0, - "litellm_provider": "azure_ai", - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "mode": "rerank", - "output_cost_per_token": 0.0, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models" - }, "azure_ai/cohere-rerank-v4.0-pro": { "deprecation_date": "2027-09-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "Cohere-rerank-v5-pro", "input_cost_per_query": 0.0025, "input_cost_per_token": 0.0, "litellm_provider": "azure_ai", @@ -13441,6 +13544,8 @@ }, "azure_ai/cohere-rerank-v4.0-fast": { "deprecation_date": "2027-09-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "Cohere-rerank-v5-fast", "input_cost_per_query": 0.002, "input_cost_per_token": 0.0, "litellm_provider": "azure_ai", @@ -13452,6 +13557,8 @@ }, "azure_ai/deepseek-v3.2": { "deprecation_date": "2027-11-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4.1-flash", "input_cost_per_token": 5.8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13468,6 +13575,8 @@ }, "azure_ai/deepseek-v3.2-speciale": { "deprecation_date": "2027-11-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-pro", "input_cost_per_token": 5.8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13482,20 +13591,9 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "azure_ai/deepseek-v3": { - "deprecation_date": "2025-08-31", - "input_cost_per_token": 1.14e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 4.56e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/deepseek-v4-pro": { "deprecation_date": "2028-02-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.74e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13512,6 +13610,8 @@ }, "azure_ai/deepseek-v4-flash": { "deprecation_date": "2028-02-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/DeepSeek-V4-Flash-0731", "input_cost_per_token": 1.9e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13529,6 +13629,7 @@ "azure_ai/DeepSeek-V4-Flash-0731": { "cache_read_input_token_cost": 1.4e-08, "deprecation_date": "2026-12-03", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 4.4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13544,6 +13645,8 @@ }, "azure_ai/embed-v-4-0": { "deprecation_date": "2027-06-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13563,6 +13666,8 @@ }, "azure_ai/grok-4": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4.3", "input_cost_per_token": 3e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13621,6 +13726,7 @@ "azure_ai/grok-4-20-reasoning": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13640,6 +13746,7 @@ "azure_ai/grok-4-20-non-reasoning": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13657,6 +13764,7 @@ }, "azure_ai/grok-4-1-fast-non-reasoning": { "deprecation_date": "2027-05-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -13672,6 +13780,7 @@ }, "azure_ai/grok-4-1-fast-reasoning": { "deprecation_date": "2027-05-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -13692,6 +13801,7 @@ }, "azure_ai/grok-code-fast-1": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 256000, @@ -13707,6 +13817,7 @@ }, "azure_ai/jais-30b-chat": { "deprecation_date": "2026-01-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "input_cost_per_token": 0.0032, "litellm_provider": "azure_ai", "max_input_tokens": 8192, @@ -13716,20 +13827,9 @@ "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "supports_pdf_input": false }, - "azure_ai/jamba-instruct": { - "deprecation_date": "2025-03-01", - "input_cost_per_token": 5e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 70000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/kimi-k2.5": { "deprecation_date": "2027-01-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -13747,6 +13847,7 @@ }, "azure_ai/kimi-k2.6": { "deprecation_date": "2027-04-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 9.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -13771,6 +13872,8 @@ }, "azure_ai/ministral-3b": { "deprecation_date": "2026-12-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "ministral-ministral-4b-instruct", "input_cost_per_token": 4e-08, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13782,32 +13885,6 @@ "supports_pdf_input": false, "supports_tool_choice": true }, - "azure_ai/mistral-large": { - "deprecation_date": "2025-04-15", - "input_cost_per_token": 4e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, - "azure_ai/mistral-large-2407": { - "deprecation_date": "2025-05-13", - "input_cost_per_token": 2e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/mistral-large-latest": { "input_cost_per_token": 2e-06, "litellm_provider": "azure_ai", @@ -13835,6 +13912,8 @@ }, "azure_ai/mistral-medium-2505": { "deprecation_date": "2026-12-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/mistral-medium-3-5", "input_cost_per_token": 4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13848,6 +13927,8 @@ }, "azure_ai/mistral-nemo": { "deprecation_date": "2026-01-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", + "successor_model": "azure_ai/mistral-small-2503", "input_cost_per_token": 1.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13858,21 +13939,10 @@ "supports_function_calling": true, "supports_pdf_input": false }, - "azure_ai/mistral-small": { - "deprecation_date": "2025-07-31", - "input_cost_per_token": 1e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 3e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/mistral-small-2503": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "mistralai-mistral-small-4-119b-2603", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13886,6 +13956,8 @@ }, "babbage-002": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, "litellm_provider": "text-completion-openai", @@ -14050,6 +14122,7 @@ }, "bedrock/ap-northeast-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14094,6 +14167,7 @@ }, "bedrock/ap-northeast-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.3e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -14146,6 +14220,7 @@ }, "bedrock/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -14179,6 +14254,7 @@ }, "bedrock/ap-south-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.18e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14189,6 +14265,7 @@ }, "bedrock/ap-south-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14221,6 +14298,7 @@ }, "bedrock/ap-south-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14265,6 +14343,7 @@ }, "bedrock/ap-south-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.1e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -14357,6 +14436,7 @@ }, "bedrock/ap-southeast-3/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-minimax-minimax-m2-1.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_flex": 1.8e-07, "litellm_provider": "bedrock", @@ -14427,6 +14507,7 @@ }, "bedrock/ca-central-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.05e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14437,6 +14518,7 @@ }, "bedrock/ca-central-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14469,6 +14551,7 @@ }, "bedrock/eu-north-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14612,6 +14695,7 @@ }, "bedrock/eu-central-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14673,6 +14757,7 @@ }, "bedrock/eu-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.86e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14683,6 +14768,7 @@ }, "bedrock/eu-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14693,6 +14779,7 @@ }, "bedrock/eu-west-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14754,6 +14841,7 @@ }, "bedrock/eu-west-2/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.45e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14764,6 +14852,7 @@ }, "bedrock/eu-west-2/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14774,6 +14863,7 @@ }, "bedrock/eu-west-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4.7e-07, "input_cost_per_token_batches": 2.35e-07, "input_cost_per_token_flex": 2.35e-07, @@ -14861,6 +14951,7 @@ }, "bedrock/eu-west-3/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 2e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -14882,6 +14973,7 @@ }, "bedrock/eu-west-3/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 5.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -14893,6 +14985,7 @@ }, "bedrock/eu-south-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-minimax-minimax-m2-1.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_flex": 1.8e-07, "litellm_provider": "bedrock", @@ -14963,6 +15056,7 @@ }, "bedrock/sa-east-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 4.45e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14973,6 +15067,7 @@ }, "bedrock/sa-east-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 5e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15005,6 +15100,7 @@ }, "bedrock/sa-east-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -15049,6 +15145,7 @@ }, "bedrock/sa-east-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.3e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -15180,6 +15277,7 @@ }, "bedrock/us-east-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15190,6 +15288,7 @@ }, "bedrock/us-east-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15200,6 +15299,7 @@ }, "bedrock/us-east-1/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15221,6 +15321,7 @@ }, "bedrock/us-east-1/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15254,6 +15355,7 @@ }, "bedrock/us-east-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15297,6 +15399,7 @@ }, "bedrock/us-east-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -15375,6 +15478,7 @@ }, "bedrock/us-east-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15419,6 +15523,7 @@ }, "bedrock/us-east-2/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -15493,7 +15598,7 @@ "output_vector_size": 1536 }, "bedrock/us-gov-east-1/amazon.titan-embed-text-v2:0": { - "input_cost_per_token": 2e-07, + "input_cost_per_token": 3e-08, "litellm_provider": "bedrock", "max_input_tokens": 8192, "mode": "embedding", @@ -15529,6 +15634,8 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "bedrock/us-gov-east-1/anthropic.claude-sonnet-5-5", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15559,6 +15666,7 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15578,7 +15686,8 @@ "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "bedrock/us-gov-east-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 2.65e-06, @@ -15606,8 +15715,10 @@ "input_cost_per_image": 7.2e-05, "input_cost_per_video_per_second": 0.00084, "input_cost_per_audio_per_second": 0.000168, + "input_cost_per_token_batches": 8.1e-08, "output_cost_per_token": 0.0, "output_vector_size": 3072, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_embedding_image_input": true, "supports_image_input": true, "supports_video_input": true, @@ -15616,11 +15727,14 @@ "bedrock/us-gov-west-1/amazon.nova-lite-v1:0": { "cache_read_input_token_cost": 1.8e-08, "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, "litellm_provider": "bedrock", "max_input_tokens": 300000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 2.88e-07, + "output_cost_per_token_batches": 1.44e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -15631,11 +15745,14 @@ "bedrock/us-gov-west-1/amazon.nova-micro-v1:0": { "cache_read_input_token_cost": 1.05e-08, "input_cost_per_token": 4.2e-08, + "input_cost_per_token_batches": 2.1e-08, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 1.68e-07, + "output_cost_per_token_batches": 8.4e-08, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": true, @@ -15643,12 +15760,21 @@ }, "bedrock/us-gov-west-1/amazon.nova-pro-v1:0": { "cache_read_input_token_cost": 2.4e-07, + "cache_read_input_token_cost_flex": 1.2e-07, + "cache_read_input_token_cost_priority": 4.2e-07, "input_cost_per_token": 9.6e-07, + "input_cost_per_token_batches": 4.8e-07, + "input_cost_per_token_flex": 4.8e-07, + "input_cost_per_token_priority": 1.68e-06, "litellm_provider": "bedrock", "max_input_tokens": 300000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 3.84e-06, + "output_cost_per_token_batches": 1.92e-06, + "output_cost_per_token_flex": 1.92e-06, + "output_cost_per_token_priority": 6.72e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -15701,6 +15827,8 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "bedrock/us-gov-west-1/anthropic.claude-sonnet-5-5", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15731,6 +15859,7 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15750,10 +15879,12 @@ "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "bedrock/us-gov-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8000, @@ -15765,6 +15896,7 @@ }, "bedrock/us-gov-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8000, @@ -15776,6 +15908,7 @@ }, "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15786,6 +15919,7 @@ }, "bedrock/us-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15875,6 +16009,7 @@ }, "bedrock/us-west-2/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15896,6 +16031,7 @@ }, "bedrock/us-west-2/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15929,6 +16065,7 @@ }, "bedrock/us-west-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15972,6 +16109,7 @@ }, "bedrock/us-west-2/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -16144,17 +16282,6 @@ "supports_function_calling": true, "supports_tool_choice": true }, - "cerebras/llama3.1-70b": { - "deprecation_date": "2025-01-17", - "input_cost_per_token": 6e-07, - "litellm_provider": "cerebras", - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 6e-07, - "supports_function_calling": true, - "supports_tool_choice": true - }, "cerebras/llama3.1-8b": { "deprecation_date": "2026-05-27", "input_cost_per_token": 1e-07, @@ -16228,6 +16355,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 0.0001, "source": "https://developers.openai.com/api/docs/pricing" }, @@ -16293,6 +16422,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", + "successor_model": "claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -16326,6 +16457,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", + "successor_model": "claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -16494,6 +16627,7 @@ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "litellm_provider": "bedrock", "supports_tool_search": true, "max_input_tokens": 200000, @@ -16513,7 +16647,8 @@ "prompt_cache_min_tokens": 1024, "input_cost_per_token_batches": 1.5e-06, "output_cost_per_token_batches": 7.5e-06, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "claude-opus-4-5-20251101": { "supports_web_search": true, @@ -17705,12 +17840,15 @@ }, "computer-use-preview": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/responses" ], @@ -17734,12 +17872,15 @@ }, "computer-use-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/responses" ], @@ -17764,6 +17905,7 @@ "deepseek-chat": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "litellm_provider": "deepseek", "max_input_tokens": 131072, @@ -17785,6 +17927,7 @@ "deepseek-reasoner": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "litellm_provider": "deepseek", "max_input_tokens": 131072, @@ -21815,6 +21958,7 @@ "cache_creation_input_token_cost": 3.74997e-06, "cache_read_input_token_cost": 3.0002e-07, "deprecation_date": "2026-10-09", + "successor_model": "databricks/databricks-claude-sonnet-5", "input_cost_per_token": 2.99999e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -21938,6 +22082,7 @@ "cache_creation_input_token_cost": 1.4e-07, "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-11-05", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.4e-07, "input_dbu_cost_per_token": 2e-06, "litellm_provider": "databricks", @@ -21951,6 +22096,7 @@ "output_cost_per_token": 2.8e-07, "output_dbu_cost_per_token": 4e-06, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "supported_modalities": [ "text" ], @@ -21964,14 +22110,19 @@ "supports_vision": false }, "databricks/system.ai.deepseek-v4-flash-0731": { + "deprecation_date": "2026-11-05", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/retired-models-policy", "litellm_provider": "databricks", "mode": "chat", + "source": "https://docs.databricks.com/aws/en/machine-learning/retired-models-policy", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "token_count_framing": 97 }, "databricks/databricks-deepseek-v4-pro-0813": { "cache_creation_input_token_cost": 1.31999e-06, "cache_read_input_token_cost": 1.3202e-07, "deprecation_date": "2026-10-30", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.31999e-06, "input_dbu_cost_per_token": 1.8857e-05, "litellm_provider": "databricks", @@ -21984,6 +22135,7 @@ "output_cost_per_token": 3.95997e-06, "output_dbu_cost_per_token": 5.6571e-05, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "supported_modalities": [ "text" ], @@ -22000,6 +22152,7 @@ "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, "deprecation_date": "2026-10-02", + "successor_model": "databricks/databricks-gemini-3-5-flash", "input_cost_per_token": 3.0001999999999996e-07, "input_dbu_cost_per_token": 4.285999999999999e-06, "litellm_provider": "databricks", @@ -22269,7 +22422,7 @@ }, "mode": "chat", "output_cost_per_token": 3.74997e-06, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22287,7 +22440,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-7-flash": { "cache_creation_input_token_cost": 7.4998e-07, @@ -22301,7 +22456,7 @@ }, "mode": "chat", "output_cost_per_token": 3.74997e-06, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22319,7 +22474,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-6-flash": { "cache_creation_input_token_cost": 7.4998e-07, @@ -22335,7 +22492,7 @@ "mode": "chat", "output_cost_per_token": 3.74997e-06, "output_dbu_cost_per_token": 5.3571e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22354,7 +22511,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-5-flash": { "cache_creation_input_token_cost": 1.87502e-06, @@ -22370,7 +22529,7 @@ "mode": "chat", "output_cost_per_token": 1.124998e-05, "output_dbu_cost_per_token": 0.000160714, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22388,7 +22547,9 @@ "input_cost_per_token_priority": 3.37498e-06, "output_cost_per_token_priority": 2.025002e-05, "cache_read_input_token_cost_priority": 3.3747e-07, - "cache_creation_input_token_cost_priority": 3.37498e-06 + "cache_creation_input_token_cost_priority": 3.37498e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-5-flash-lite": { "cache_creation_input_token_cost": 3.7499e-07, @@ -22404,7 +22565,7 @@ "mode": "chat", "output_cost_per_token": 3.12501e-06, "output_dbu_cost_per_token": 4.4643e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22424,7 +22585,9 @@ "input_cost_per_token_priority": 6.7501e-07, "output_cost_per_token_priority": 5.62499e-06, "cache_read_input_token_cost_priority": 6.748e-08, - "cache_creation_input_token_cost_priority": 6.7501e-07 + "cache_creation_input_token_cost_priority": 6.7501e-07, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemma-3-12b": { "cache_creation_input_token_cost": 1.5001e-07, @@ -23038,6 +23201,7 @@ "cache_creation_input_token_cost": 1.00002e-06, "cache_read_input_token_cost": 1.7003e-07, "deprecation_date": "2026-10-30", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", @@ -23049,6 +23213,7 @@ "output_cost_per_token": 4.04999e-06, "output_dbu_cost_per_token": 5.7857e-05, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-glm-5-3", "supported_modalities": [ "text", "image" @@ -23273,6 +23438,8 @@ }, "davinci-002": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "text-completion-openai", @@ -24167,7 +24334,7 @@ "max_output_tokens": 163840, "input_cost_per_token": 5e-07, "output_cost_per_token": 2.15e-06, - "cache_read_input_token_cost": 4e-07, + "cache_read_input_token_cost": 3.5e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, @@ -24241,7 +24408,7 @@ "max_output_tokens": 163840, "input_cost_per_token": 2.5e-07, "output_cost_per_token": 9.5e-07, - "cache_read_input_token_cost": 2.16e-07, + "cache_read_input_token_cost": 1.3e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, @@ -24614,6 +24781,7 @@ "cache_creation_input_token_cost": 0.0, "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", @@ -24666,6 +24834,7 @@ "deepseek/deepseek-reasoner": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", @@ -24719,6 +24888,7 @@ }, "deepseek.v3-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-v3-1.html", "input_cost_per_token": 5.8e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -25528,6 +25698,7 @@ "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 1.1e-06, "deprecation_date": "2026-10-15", + "successor_model": "eu.anthropic.claude-haiku-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -25645,6 +25816,7 @@ "supports_vision": true, "prompt_cache_min_tokens": 1024, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "token_count_model": { "model": "anthropic.claude-opus-4-1-20250805-v1:0", "litellm_provider": "bedrock" @@ -25679,6 +25851,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "eu.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -25722,6 +25896,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "eu.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -25755,6 +25931,7 @@ }, "eu.meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1.3e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -25767,6 +25944,7 @@ }, "eu.meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -28346,6 +28524,7 @@ "cache_read_input_token_cost": 1.4e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/accounts/fireworks/models/glm-5p3", "input_cost_per_token": 1.4e-06, "input_cost_per_token_priority": 1.75e-06, "litellm_provider": "fireworks_ai", @@ -28721,6 +28900,7 @@ "cache_read_input_token_cost": 1.4e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/glm-5p3", "input_cost_per_token": 1.4e-06, "input_cost_per_token_priority": 1.75e-06, "litellm_provider": "fireworks_ai", @@ -29104,6 +29284,8 @@ }, "ft:babbage-002": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.6e-06, "input_cost_per_token_batches": 8e-07, "litellm_provider": "text-completion-openai", @@ -29116,6 +29298,8 @@ }, "ft:davinci-002": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.2e-05, "input_cost_per_token_batches": 6e-06, "litellm_provider": "text-completion-openai", @@ -29128,6 +29312,8 @@ }, "ft:gpt-3.5-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "openai", @@ -29135,6 +29321,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_system_messages": true, @@ -29142,45 +29329,57 @@ }, "ft:gpt-3.5-turbo-0125": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-3.5-turbo-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "ft:gpt-3.5-turbo", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 4096, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-3.5-turbo-1106": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-4-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing", "supports_function_calling": true, "supports_system_messages": true, @@ -29190,6 +29389,8 @@ "cache_read_input_token_cost": 1.875e-06, "cache_read_input_token_cost_batches": 9e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3.75e-06, "input_cost_per_token_batches": 2.225e-06, "litellm_provider": "openai", @@ -29197,6 +29398,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.25e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29211,12 +29413,15 @@ "ft:gpt-4o-2024-11-20": { "cache_creation_input_token_cost": 1.875e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -29229,6 +29434,8 @@ "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "litellm_provider": "openai", @@ -29236,6 +29443,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29250,6 +29458,8 @@ "cache_read_input_token_cost": 7.5e-07, "cache_read_input_token_cost_batches": 5e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3e-06, "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "openai", @@ -29257,6 +29467,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29270,6 +29481,8 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_batches": 1e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 8e-07, "input_cost_per_token_batches": 4e-07, "litellm_provider": "openai", @@ -29277,6 +29490,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 3.2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.6e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29290,6 +29504,8 @@ "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_batches": 2.5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_batches": 1e-07, "litellm_provider": "openai", @@ -29297,6 +29513,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29310,6 +29527,8 @@ "cache_read_input_token_cost": 1e-06, "cache_read_input_token_cost_batches": 5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 4e-06, "input_cost_per_token_batches": 2e-06, "litellm_provider": "openai", @@ -29317,6 +29536,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 1.6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29328,19 +29548,38 @@ }, "gemini-2.5-flash": { "cache_read_input_audio_token_cost": 1e-07, - "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_flex": 3e-08, "cache_read_input_token_cost_priority": 5.4e-08, + "deprecation_date": "2026-10-20", + "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 3e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, + "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, "mode": "chat", "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, + "output_cost_per_token_batches": 1.25e-06, + "output_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_priority": 4.5e-06, + "search_context_cost_per_query": {"search_context_size_low":0.035,"search_context_size_medium":0.035,"search_context_size_high":0.035}, + "search_free_requests": {"count":1500,"period":"day","shared_group":"gemini-2-flash"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.8-flash", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29357,6 +29596,7 @@ ], "supports_audio_output": false, "supports_function_calling": true, + "supports_image_size": false, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29366,26 +29606,12 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - }, - "search_free_requests": {"count": 1500, "period": "day", "shared_group": "gemini-2-flash"}, - "google_maps_grounding_cost_per_query": 0.025, - "input_cost_per_audio_token_batches": 5e-07, - "input_cost_per_token_batches": 1.5e-07, - "input_cost_per_token_flex": 1.5e-07, - "input_cost_per_token_priority": 5.4e-07, - "input_cost_per_audio_token_priority": 1.8e-06, - "output_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_flex": 1.25e-06, - "output_cost_per_token_priority": 4.5e-06, - "supports_image_size": false + "supports_web_search": true }, "gemini-2.5-flash-image": { "deprecation_date": "2027-03-15", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -29441,6 +29667,7 @@ "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 3.6e-07, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, @@ -29455,6 +29682,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, @@ -29509,6 +29737,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_priority": 2.16e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, @@ -29549,6 +29778,7 @@ "cache_read_input_token_cost_flex": 2.5e-08, "cache_read_input_token_cost_priority": 9e-08, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.00056, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -29561,6 +29791,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "output_cost_per_token_flex": 1.5e-06, @@ -29607,6 +29838,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", @@ -29655,6 +29887,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, @@ -29735,17 +29968,28 @@ }, "gemini-3.1-flash-lite": { "cache_read_input_audio_token_cost": 5e-08, - "deprecation_date": "2027-05-07", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_batches": 2.5e-08, + "cache_read_input_audio_token_cost_flex": 2.5e-08, + "cache_read_input_audio_token_cost_priority": 9e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-07", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 5e-07, + "input_cost_per_audio_token_above_200k_tokens": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 9e-07, + "input_cost_per_audio_token_batches": 2.5e-07, + "input_cost_per_audio_token_flex": 2.5e-07, + "input_cost_per_audio_token_priority": 9e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, - "input_cost_per_audio_token_priority": 9e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65536, @@ -29755,7 +29999,10 @@ "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, + "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.5-flash-lite", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29773,6 +30020,7 @@ "supports_audio_input": true, "supports_audio_output": false, "supports_function_calling": true, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29784,19 +30032,11 @@ "supports_video_input": true, "supports_vision": true, "supports_web_search": true, - "supports_native_streaming": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014, - "search_context_size_high": 0.014 - }, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, - "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014, - "input_cost_per_audio_token_batches": 2.5e-07 + "web_search_billing_unit": "per_query" }, "gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_batches": 1.5e-08, "cache_read_input_token_cost_flex": 1.5e-08, @@ -29892,19 +30132,38 @@ }, "gemini-2.5-flash-lite": { "cache_read_input_audio_token_cost": 3e-08, - "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_priority": 5.4e-08, "cache_read_input_token_cost": 1e-08, "cache_read_input_token_cost_flex": 1e-08, "cache_read_input_token_cost_priority": 1.8e-08, + "deprecation_date": "2026-10-20", + "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_audio_token": 3e-07, + "input_cost_per_audio_token_above_200k_tokens": 3e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-08, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-08, + "input_cost_per_audio_token_above_200k_tokens_priority": 5.4e-07, + "input_cost_per_audio_token_batches": 5e-08, + "input_cost_per_audio_token_flex": 5e-08, + "input_cost_per_audio_token_priority": 5.4e-07, "input_cost_per_token": 1e-07, + "input_cost_per_token_batches": 5e-08, + "input_cost_per_token_flex": 5e-08, + "input_cost_per_token_priority": 1.8e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, "mode": "chat", "output_cost_per_reasoning_token": 4e-07, "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "output_cost_per_token_flex": 2e-07, + "output_cost_per_token_priority": 7.2e-07, + "search_context_cost_per_query": {"search_context_size_low":0.035,"search_context_size_medium":0.035,"search_context_size_high":0.035}, + "search_free_requests": {"count":1500,"period":"day","shared_group":"gemini-2-flash"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.8-flash", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29921,6 +30180,7 @@ ], "supports_audio_output": false, "supports_function_calling": true, + "supports_image_size": false, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29930,23 +30190,7 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - }, - "search_free_requests": {"count": 1500, "period": "day", "shared_group": "gemini-2-flash"}, - "google_maps_grounding_cost_per_query": 0.025, - "input_cost_per_audio_token_batches": 5e-08, - "input_cost_per_token_batches": 5e-08, - "input_cost_per_token_flex": 5e-08, - "input_cost_per_token_priority": 1.8e-07, - "input_cost_per_audio_token_priority": 5.4e-07, - "output_cost_per_token_batches": 2e-07, - "output_cost_per_token_flex": 2e-07, - "output_cost_per_token_priority": 7.2e-07, - "supports_image_size": false + "supports_web_search": true }, "gemini-2.5-flash-lite-preview-09-2025": { "cache_read_input_token_cost": 1e-08, @@ -30042,6 +30286,8 @@ }, "gemini-live-2.5-flash-native-audio": { "deprecation_date": "2026-12-13", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -30175,6 +30421,8 @@ }, "gemini-2.5-pro": { "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, @@ -30223,11 +30471,13 @@ "search_free_requests": {"count": 10000, "period": "day", "shared_group": "gemini-2.5-pro"}, "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_token_above_200k_tokens_batches": 1.25e-06, + "input_cost_per_token_above_200k_tokens_flex": 1.25e-06, "input_cost_per_token_above_200k_tokens_priority": 4.5e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, "input_cost_per_token_priority": 2.25e-06, "output_cost_per_token_above_200k_tokens_batches": 7.5e-06, + "output_cost_per_token_above_200k_tokens_flex": 7.5e-06, "output_cost_per_token_above_200k_tokens_priority": 2.7e-05, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, @@ -30243,6 +30493,7 @@ "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "input_cost_per_token_above_200k_tokens_batches": 2e-06, + "input_cost_per_token_above_200k_tokens_flex": 2e-06, "input_cost_per_token_above_200k_tokens_priority": 7.2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -30255,6 +30506,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_batches": 9e-06, + "output_cost_per_token_above_200k_tokens_flex": 9e-06, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, @@ -30402,11 +30654,17 @@ }, "vertex_ai/gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_priority": 9e-08, "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 1e-06, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -30421,7 +30679,7 @@ "output_cost_per_token_flex": 1.5e-06, "output_cost_per_token_priority": 5.4e-06, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -30455,6 +30713,8 @@ "vertex_ai/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, "deprecation_date": "2027-05-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "input_cost_per_token": 1.5e-06, @@ -30566,6 +30826,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -30628,6 +30890,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2027-01-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -30659,7 +30923,7 @@ "prompt_cache_min_tokens": 4096.0, "regional_endpoint_uplift_multiplier": 1.1, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -30789,6 +31053,7 @@ "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "input_cost_per_token_above_200k_tokens_batches": 2e-06, + "input_cost_per_token_above_200k_tokens_flex": 2e-06, "input_cost_per_token_above_200k_tokens_priority": 7.2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -30801,6 +31066,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_batches": 9e-06, + "output_cost_per_token_above_200k_tokens_flex": 9e-06, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, @@ -31039,6 +31305,8 @@ }, "gemini-embedding-001": { "deprecation_date": "2028-05-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-embedding-2", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 1.2e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -31063,6 +31331,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_embedding_image_input": true, "uses_embed_content": true }, "gemini-embedding-2": { @@ -31080,6 +31349,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31098,6 +31368,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31116,6 +31387,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31133,6 +31405,8 @@ }, "gemini/gemini-embedding-001": { "deprecation_date": "2028-05-14", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-embedding-2", "input_cost_per_token": 1.5e-07, "litellm_provider": "gemini", "max_input_tokens": 2048, @@ -31161,12 +31435,15 @@ "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supports_audio_input": true, + "supports_embedding_image_input": true, "supports_multimodal": true, "supports_vision": true, "tpm": 10000000 }, "gemini/gemini-3-pro-image-preview": { "deprecation_date": "2026-06-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3-pro-image", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -31215,6 +31492,8 @@ }, "gemini/gemini-3.1-flash-image-preview": { "deprecation_date": "2026-06-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-nano-banana-2.1", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -31263,6 +31542,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-05-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.1-flash-lite", "input_cost_per_audio_token": 5e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, @@ -31320,6 +31601,8 @@ "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini/gemini-embedding-2-preview": { + "deprecation_date": "2026-08-10", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", "input_cost_per_audio_token": 6.5e-06, "input_cost_per_audio_token_batches": 3.25e-06, "input_cost_per_image_token": 4.5e-07, @@ -31335,7 +31618,9 @@ "output_vector_size": 3072, "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", + "successor_model": "gemini/gemini-embedding-2", "supports_audio_input": true, + "supports_embedding_image_input": true, "supports_multimodal": true, "supports_vision": true, "tpm": 10000000 @@ -31422,6 +31707,8 @@ "cache_read_input_token_cost_flex": 3e-08, "cache_read_input_token_cost_priority": 5.4e-08, "deprecation_date": "2026-12-01", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.5-flash-lite", "input_cost_per_audio_token": 1e-06, "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_token": 3e-07, @@ -31481,6 +31768,8 @@ "gemini/gemini-2.5-flash-image": { "cache_read_input_token_cost": 3e-08, "deprecation_date": "2027-03-15", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -31708,6 +31997,8 @@ }, "gemini/deep-research-pro-preview-12-2025": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/deep-research-preview-04-2026", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -31925,6 +32216,8 @@ }, "gemini/gemini-2.5-flash-preview-tts": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -32006,11 +32299,15 @@ "supports_url_context": true, "cache_read_input_token_cost_above_200k_tokens_batches": 2.5e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash", "input_cost_per_token_above_200k_tokens_batches": 1.25e-06, "output_cost_per_token_above_200k_tokens_batches": 7.5e-06 }, "gemini/gemini-2.5-computer-use-preview-10-2025": { "deprecation_date": "2026-07-28", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash", "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, "litellm_provider": "gemini", @@ -32049,6 +32346,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-05-07", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.5-flash-lite", "input_cost_per_audio_token": 5e-07, "input_cost_per_audio_token_flex": 2.5e-07, "input_cost_per_token": 2.5e-07, @@ -32514,7 +32813,9 @@ "supports_video_input": true, "supports_vision": true, "tpm": 800000, - "deprecation_date": "2026-10-22" + "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash" }, "gemini/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -32650,11 +32951,17 @@ }, "gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_priority": 9e-08, "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 1e-06, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -32670,7 +32977,7 @@ "output_cost_per_token_flex": 1.5e-06, "output_cost_per_token_priority": 5.4e-06, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -32706,13 +33013,13 @@ "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, "litellm_provider": "vertex_ai-language-models", - "max_input_tokens": 1048576, - "max_output_tokens": 65535, + "max_input_tokens": 131072, + "max_output_tokens": 57920, "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "output_cost_per_video_token": 1.75e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/omni-flash-preview", "supported_endpoints": [ "/v1/chat/completions" ], @@ -32730,11 +33037,14 @@ "supports_reasoning": true, "supports_system_messages": true, "supports_video_input": true, - "supports_vision": true + "supports_vision": true, + "deprecation_date": "2026-11-06" }, "gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, "deprecation_date": "2027-05-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "input_cost_per_audio_token": 1.5e-06, @@ -32846,6 +33156,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -32908,6 +33220,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2027-01-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -32938,7 +33252,7 @@ "output_cost_per_token_priority": 6.75e-06, "prompt_cache_min_tokens": 4096.0, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -33026,6 +33340,8 @@ "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_audio_token": 7e-07, "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 5e-07, @@ -33278,6 +33594,8 @@ }, "gemini/veo-3.1-fast-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33294,6 +33612,8 @@ }, "gemini/veo-3.1-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33309,6 +33629,8 @@ }, "gemini/veo-3.1-lite-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33644,6 +33966,7 @@ }, "github_copilot/gpt-4.1": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "litellm_provider": "github_copilot", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -33656,6 +33979,7 @@ }, "github_copilot/gpt-4.1-2025-04-14": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "litellm_provider": "github_copilot", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -33757,6 +34081,7 @@ "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "deprecation_date": "2026-10-19", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "input_cost_per_token": 2.5e-06, "input_cost_per_token_above_272k_tokens": 5e-06, "litellm_provider": "github_copilot", @@ -33799,6 +34124,7 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "deprecation_date": "2026-10-19", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, "litellm_provider": "github_copilot", @@ -34652,6 +34978,7 @@ }, "google.gemma-3-12b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 9e-08, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_flex": 5e-08, @@ -34673,6 +35000,7 @@ }, "google.gemma-3-27b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.3e-07, "input_cost_per_token_batches": 1.2e-07, "input_cost_per_token_flex": 1.2e-07, @@ -34694,6 +35022,7 @@ }, "google.gemma-3-4b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4e-08, "input_cost_per_token_batches": 2e-08, "input_cost_per_token_flex": 2e-08, @@ -34728,6 +35057,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "global.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -34763,6 +35094,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "global.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -34851,12 +35184,15 @@ }, "gpt-3.5-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 5e-07, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -34865,6 +35201,8 @@ }, "gpt-3.5-turbo-0125": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "openai", @@ -34872,6 +35210,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 7.5e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34882,6 +35221,8 @@ }, "gpt-3.5-turbo-1106": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -34889,6 +35230,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34899,18 +35241,22 @@ }, "gpt-3.5-turbo-16k": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-06, + "fedramp_uplift_multiplier": 1.1, "supports_prompt_caching": true, "supports_system_messages": true, "supports_tool_choice": true }, "gpt-3.5-turbo-instruct": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.5e-06, "litellm_provider": "text-completion-openai", "max_input_tokens": 8192, @@ -34929,12 +35275,15 @@ }, "gpt-4": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_prompt_caching": true, "supports_system_messages": true, @@ -34942,6 +35291,8 @@ }, "gpt-4-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "input_cost_per_token_batches": 1.5e-05, "litellm_provider": "openai", @@ -34949,6 +35300,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34958,12 +35310,15 @@ }, "gpt-4-1106-preview": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -34972,12 +35327,15 @@ }, "gpt-4-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -34988,6 +35346,8 @@ }, "gpt-4-turbo-2024-04-09": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "input_cost_per_token_batches": 5e-06, "litellm_provider": "openai", @@ -34995,6 +35355,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.5e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -35009,6 +35370,8 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_priority": 3.5e-06, @@ -35017,6 +35380,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_priority": 1.4e-05, "search_context_cost_per_query": { @@ -35052,6 +35416,8 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-06, "input_cost_per_token_priority": 3.5e-06, "input_cost_per_token_batches": 1e-06, @@ -35060,6 +35426,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.4e-05, "output_cost_per_token_batches": 4e-06, "search_context_cost_per_query": { @@ -35095,6 +35462,8 @@ "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, "input_cost_per_token_priority": 7e-07, @@ -35103,6 +35472,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8e-07, "output_cost_per_token_priority": 2.8e-06, "search_context_cost_per_query": { @@ -35138,6 +35508,8 @@ "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 4e-07, "input_cost_per_token_priority": 7e-07, "input_cost_per_token_batches": 2e-07, @@ -35146,6 +35518,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-06, "output_cost_per_token_batches": 8e-07, "search_context_cost_per_query": { @@ -35181,6 +35554,8 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_priority": 2e-07, @@ -35189,6 +35564,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_priority": 8e-07, "source": "https://developers.openai.com/api/docs/pricing", @@ -35218,6 +35594,8 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 1e-07, "input_cost_per_token_priority": 2e-07, "input_cost_per_token_batches": 5e-08, @@ -35226,6 +35604,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 8e-07, "output_cost_per_token_batches": 2e-07, "source": "https://developers.openai.com/api/docs/pricing", @@ -35255,6 +35634,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "input_cost_per_token_priority": 4.25e-06, @@ -35263,6 +35644,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_priority": 1.7e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -35277,6 +35659,8 @@ }, "gpt-4o-2024-05-13": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "input_cost_per_token_priority": 8.75e-06, @@ -35285,6 +35669,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 7.5e-06, "output_cost_per_token_priority": 2.625e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -35300,6 +35685,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_priority": 4.25e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35308,6 +35695,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.7e-05, "output_cost_per_token_batches": 5e-06, "source": "https://developers.openai.com/api/docs/pricing", @@ -35324,6 +35712,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_priority": 4.25e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35332,6 +35722,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.7e-05, "output_cost_per_token_batches": 5e-06, "source": "https://developers.openai.com/api/docs/pricing", @@ -35346,6 +35737,8 @@ }, "gpt-4o-audio-preview-2024-12-17": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35363,6 +35756,8 @@ }, "gpt-4o-audio-preview-2025-06-03": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35380,6 +35775,8 @@ }, "gpt-audio": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35450,6 +35847,8 @@ }, "gpt-audio-2025-08-28": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35487,6 +35886,8 @@ }, "gpt-audio-mini": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35524,6 +35925,8 @@ }, "gpt-audio-mini-2025-10-06": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35594,12 +35997,16 @@ "supports_tool_choice": true, "supports_vision": false, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-4o-mini": { "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.25e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "input_cost_per_token_priority": 2.5e-07, @@ -35608,6 +36015,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "output_cost_per_token_priority": 1e-06, "supports_function_calling": true, @@ -35629,6 +36037,8 @@ "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.25e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 1.5e-07, "input_cost_per_token_priority": 2.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -35637,6 +36047,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1e-06, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { @@ -35656,6 +36067,7 @@ }, "gpt-4o-mini-audio-preview-2024-12-17": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -35675,6 +36087,7 @@ "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35699,6 +36112,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35717,6 +36131,8 @@ }, "gpt-4o-mini-search-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -35725,6 +36141,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35754,11 +36171,15 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 5e-05, "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-4o-mini-tts": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -35779,6 +36200,8 @@ }, "gpt-4o-mini-tts-2025-03-20": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -35806,6 +36229,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35824,6 +36248,8 @@ }, "gpt-4o-search-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35832,6 +36258,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35861,6 +36288,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 0.0001, "source": "https://developers.openai.com/api/docs/pricing" }, @@ -35869,6 +36298,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -35890,6 +36321,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -36013,6 +36446,8 @@ }, "low/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36025,6 +36460,8 @@ }, "low/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36037,6 +36474,8 @@ }, "low/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36049,6 +36488,8 @@ }, "medium/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.034, "litellm_provider": "openai", "mode": "image_generation", @@ -36061,6 +36502,8 @@ }, "medium/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.05, "litellm_provider": "openai", "mode": "image_generation", @@ -36073,6 +36516,8 @@ }, "medium/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.05, "litellm_provider": "openai", "mode": "image_generation", @@ -36085,6 +36530,8 @@ }, "high/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.133, "litellm_provider": "openai", "mode": "image_generation", @@ -36097,6 +36544,8 @@ }, "high/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.2, "litellm_provider": "openai", "mode": "image_generation", @@ -36109,6 +36558,8 @@ }, "high/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.2, "litellm_provider": "openai", "mode": "image_generation", @@ -36121,6 +36572,8 @@ }, "standard/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36133,6 +36586,8 @@ }, "standard/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36145,6 +36600,8 @@ }, "standard/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36157,6 +36614,8 @@ }, "1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36169,6 +36628,8 @@ }, "1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36181,6 +36642,8 @@ }, "1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36201,7 +36664,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "low/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36213,7 +36678,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "low/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36225,7 +36692,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.034, @@ -36237,7 +36706,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -36249,7 +36720,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -36261,7 +36734,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.133, @@ -36273,7 +36748,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -36285,7 +36762,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -36297,7 +36776,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -36309,7 +36790,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36321,7 +36804,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36333,7 +36818,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -36345,7 +36832,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36357,7 +36846,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36369,7 +36860,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, @@ -36377,6 +36870,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, @@ -36386,6 +36881,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, "output_cost_per_token_priority": 2e-05, @@ -36432,12 +36928,15 @@ "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5-codex", "supported_endpoints": [ "/v1/responses" @@ -36472,6 +36971,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2e-05, "reasoning_contexts": [ "auto", @@ -36517,17 +37017,22 @@ "source": "https://developers.openai.com/api/docs/pricing", "supports_xhigh_reasoning_effort": false, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol" }, "gpt-5.1-codex-mini": { "cache_read_input_token_cost": 2.5e-08, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex-mini", "supported_endpoints": [ "/v1/responses" @@ -36552,12 +37057,15 @@ "gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex-max", "supported_endpoints": [ "/v1/responses" @@ -36583,12 +37091,15 @@ "gpt-5.1-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex", "supported_endpoints": [ "/v1/responses" @@ -36614,12 +37125,15 @@ "gpt-5.1-chat-latest": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -36648,6 +37162,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", @@ -36655,6 +37171,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2e-05, "reasoning_contexts": [ "auto", @@ -36713,6 +37230,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -36764,12 +37282,15 @@ "gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.2-codex", "supported_endpoints": [ "/v1/responses" @@ -36795,12 +37316,15 @@ "gpt-5.2-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-08-10", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.2-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -36835,6 +37359,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -36891,6 +37416,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8.4e-05, "reasoning_contexts": [ "auto", @@ -36939,6 +37465,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8.4e-05, "reasoning_contexts": [ "auto", @@ -37045,6 +37572,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37138,6 +37666,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37239,6 +37768,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37331,6 +37861,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37421,6 +37952,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37513,6 +38045,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37604,6 +38137,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37648,10 +38182,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/chat/completions", @@ -37686,10 +38221,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/chat/completions", @@ -37724,10 +38260,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/responses" @@ -37765,6 +38302,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 3e-05, "supported_endpoints": [ "/v1/chat/completions", @@ -37803,6 +38341,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 3e-05, "supported_endpoints": [ "/v1/responses" @@ -37836,6 +38375,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "reasoning_effort_levels": [ "medium" ], @@ -37904,6 +38444,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37974,6 +38515,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38036,6 +38578,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38095,6 +38638,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38162,6 +38706,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38227,6 +38772,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38284,6 +38830,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38344,6 +38891,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38406,6 +38954,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38468,6 +39017,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38527,6 +39077,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38560,13 +39111,17 @@ "source": "https://developers.openai.com/api/docs/pricing", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna" }, "gpt-5.4-nano-2026-03-17": { "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_batches": 1e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_flex": 1e-07, "input_cost_per_token_batches": 1e-07, @@ -38587,6 +39142,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38623,6 +39179,8 @@ }, "gpt-5-pro": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -38630,6 +39188,7 @@ "max_output_tokens": 272000, "mode": "responses", "output_cost_per_token": 0.00012, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-05, "reasoning_contexts": [ "auto", @@ -38672,6 +39231,8 @@ }, "gpt-5-pro-2025-10-06": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -38679,6 +39240,7 @@ "max_output_tokens": 272000, "mode": "responses", "output_cost_per_token": 0.00012, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-05, "reasoning_contexts": [ "auto", @@ -38725,6 +39287,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, @@ -38734,6 +39298,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, "output_cost_per_token_priority": 2e-05, @@ -38780,12 +39345,15 @@ "gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.2-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38815,12 +39383,15 @@ "gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -38853,6 +39424,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -38892,17 +39464,22 @@ "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol" }, "gpt-5.3-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-08-10", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.3-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -38931,6 +39508,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, @@ -38940,6 +39519,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1e-06, "output_cost_per_token_flex": 1e-06, "output_cost_per_token_priority": 3.6e-06, @@ -38989,6 +39569,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, @@ -38998,6 +39580,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1e-06, "output_cost_per_token_flex": 1e-06, "output_cost_per_token_priority": 3.6e-06, @@ -39046,6 +39629,8 @@ "cache_read_input_token_cost_batches": 2.5e-09, "cache_read_input_token_cost_flex": 2.5e-09, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "input_cost_per_token_flex": 2.5e-08, @@ -39054,6 +39639,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_flex": 2e-07, "reasoning_contexts": [ @@ -39101,6 +39687,8 @@ "cache_read_input_token_cost_batches": 2.5e-09, "cache_read_input_token_cost_flex": 2.5e-09, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "input_cost_per_token_flex": 2.5e-08, @@ -39109,6 +39697,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_flex": 2e-07, "reasoning_contexts": [ @@ -39156,6 +39745,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 1e-05, "input_cost_per_image_token_batches": 5e-06, "input_cost_per_token": 5e-06, @@ -39174,6 +39765,8 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_batches": 1e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 2.5e-06, "input_cost_per_image_token_batches": 1.25e-06, "input_cost_per_token": 2e-06, @@ -39193,6 +39786,8 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -39375,6 +39970,8 @@ "cache_read_input_image_token_cost": 8e-08, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -39410,6 +40007,8 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -39733,16 +40332,6 @@ "supports_response_schema": true, "supports_vision": true }, - "groq/llama-guard-3-8b": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 2e-07, - "litellm_provider": "groq", - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 2e-07, - "source": "https://console.groq.com/docs/model/llama-guard-3-8b" - }, "groq/meta-llama/llama-prompt-guard-2-22m": { "input_cost_per_token": 3e-08, "litellm_provider": "groq", @@ -39889,6 +40478,8 @@ }, "high/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.167, "input_cost_per_pixel": 1.59263611e-07, "litellm_provider": "openai", @@ -39900,6 +40491,8 @@ }, "high/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.25, "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "openai", @@ -39911,6 +40504,8 @@ }, "high/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.25, "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "openai", @@ -40288,6 +40883,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "jp.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -41450,6 +42047,8 @@ }, "low/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.011, "input_cost_per_pixel": 1.0490417e-08, "litellm_provider": "openai", @@ -41461,6 +42060,8 @@ }, "low/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.016, "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "openai", @@ -41472,6 +42073,8 @@ }, "low/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.016, "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "openai", @@ -41495,6 +42098,8 @@ }, "medium/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.042, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41506,6 +42111,8 @@ }, "medium/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.063, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41517,6 +42124,8 @@ }, "medium/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.063, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41528,6 +42137,8 @@ }, "low/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.005, "litellm_provider": "openai", "mode": "image_generation", @@ -41537,6 +42148,8 @@ }, "low/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.006, "litellm_provider": "openai", "mode": "image_generation", @@ -41546,6 +42159,8 @@ }, "low/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.006, "litellm_provider": "openai", "mode": "image_generation", @@ -41555,6 +42170,8 @@ }, "medium/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.011, "litellm_provider": "openai", "mode": "image_generation", @@ -41564,6 +42181,8 @@ }, "medium/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.015, "litellm_provider": "openai", "mode": "image_generation", @@ -41573,6 +42192,8 @@ }, "medium/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.015, "litellm_provider": "openai", "mode": "image_generation", @@ -41620,6 +42241,7 @@ }, "meta.llama3-1-405b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.4e-06, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41632,6 +42254,7 @@ }, "meta.llama3-1-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock", @@ -41646,6 +42269,7 @@ }, "meta.llama3-1-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.2e-07, "input_cost_per_token_batches": 1.1e-07, "litellm_provider": "bedrock", @@ -41660,6 +42284,7 @@ }, "meta.llama3-2-11b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41673,6 +42298,7 @@ }, "meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41685,6 +42311,7 @@ }, "meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41697,6 +42324,7 @@ }, "meta.llama3-2-90b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", 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"input_cost_per_token_flex": 1e-07, @@ -44866,6 +45542,7 @@ }, "nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-08, "input_cost_per_token_batches": 3e-08, "input_cost_per_token_flex": 3e-08, @@ -44932,6 +45609,7 @@ "o1": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-10-23", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -44939,6 +45617,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -44954,6 +45633,8 @@ "o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -44961,6 +45642,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -44975,6 +45657,8 @@ }, "o1-pro": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -44982,6 +45666,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 0.0006, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 0.0003, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -45008,6 +45693,8 @@ }, "o1-pro-2025-03-19": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -45015,6 +45702,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 0.0006, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 0.0003, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -45044,6 +45732,7 @@ "cache_read_input_token_cost_flex": 2.5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2026-12-11", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -45053,6 +45742,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_flex": 4e-06, "output_cost_per_token_priority": 1.4e-05, @@ -45100,6 +45790,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o3-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45121,10 +45812,14 @@ "supports_tool_choice": false, "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-07-23" + "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol" }, "o3-deep-research-2025-06-26": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "cache_read_input_token_cost": 2.5e-06, "input_cost_per_token": 1e-05, "litellm_provider": "openai", @@ -45132,6 +45827,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o3-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45159,6 +45855,8 @@ "cache_read_input_token_cost_flex": 2.5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -45168,6 +45866,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_flex": 4e-06, "output_cost_per_token_priority": 1.4e-05, @@ -45210,6 +45909,8 @@ "o3-mini": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "openai", @@ -45217,6 +45918,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -45230,6 +45932,8 @@ "o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "openai", @@ -45237,6 +45941,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -45249,6 +45954,8 @@ }, "o3-pro": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "openai", @@ -45256,6 +45963,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-05, "reasoning_contexts": [ "auto", @@ -45293,6 +46001,8 @@ }, "o3-pro-2025-06-10": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "openai", @@ -45300,6 +46010,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-05, "reasoning_contexts": [ "auto", @@ -45340,6 +46051,8 @@ "cache_read_input_token_cost_flex": 1.38e-07, "cache_read_input_token_cost_priority": 5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "input_cost_per_token_flex": 5.5e-07, @@ -45349,6 +46062,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "output_cost_per_token_flex": 2.2e-06, "output_cost_per_token_priority": 8e-06, @@ -45376,6 +46090,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o4-mini-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45397,10 +46112,14 @@ "supports_tool_choice": false, "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-07-23" + "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol" }, "o4-mini-deep-research-2025-06-26": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "litellm_provider": "openai", @@ -45408,6 +46127,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o4-mini-deep-research", "supported_endpoints": [ "/v1/batch", @@ 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"2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-qwen-qwen3-coder-480b-a35b-instruct.html", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 262144, @@ -47279,6 +48004,7 @@ }, "qwen.qwen3-coder-30b-a3b-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "input_cost_per_token_priority": 2.625e-07, @@ -48858,6 +49584,7 @@ }, "text-embedding-004": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -48869,6 +49596,7 @@ }, "text-embedding-005": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -48939,6 +49667,7 @@ }, "text-multilingual-embedding-002": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -49053,6 +49782,7 @@ }, "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.04e-06, "litellm_provider": "together_ai", "max_input_tokens": 131072, @@ -49060,6 +49790,7 @@ "mode": "chat", "output_cost_per_token": 1.04e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/meta-models/Muse-Glimmer-30B", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -49119,6 +49850,7 @@ "together_ai/MiniMaxAI/MiniMax-M3": { "cache_read_input_token_cost": 6e-08, "deprecation_date": "2026-11-06", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 3e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, @@ -49126,6 +49858,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/MiniMaxAI/MiniMax-M3.1-Flash", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49202,6 +49935,7 @@ "together_ai/Qwen/Qwen3.8-2.4T-A95B": { "cache_read_input_token_cost": 2.5e-07, "deprecation_date": "2026-11-04", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, @@ -49209,6 +49943,7 @@ "mode": "chat", "output_cost_per_token": 6e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/Qwen/Qwen3.7-Max", "supports_prompt_caching": true }, "together_ai/arize-ai/qwen-2-1.5b-instruct": { @@ -49223,6 +49958,7 @@ "together_ai/deepseek-ai/DeepSeek-V4-Flash-0731": { "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-10-22", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.4e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, @@ -49230,6 +49966,7 @@ "mode": "chat", "output_cost_per_token": 2.8e-07, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/deepseek-ai/DeepSeek-V4.1-Flash-1017", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49314,6 +50051,7 @@ "together_ai/thinkingmachines/Inkling": { "cache_read_input_token_cost": 1.7e-07, "deprecation_date": "2026-11-06", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 524288, @@ -49321,6 +50059,7 @@ "mode": "chat", "output_cost_per_token": 4.05e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/meta-models/Muse-Spark-Open-816B-A42B", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49378,6 +50117,8 @@ }, "tts-1": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_character": 1.5e-05, "litellm_provider": "openai", "mode": "audio_speech", @@ -49388,6 +50129,8 @@ }, "tts-1-hd": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_character": 3e-05, "litellm_provider": "openai", "mode": "audio_speech", @@ -49618,6 +50361,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -49656,6 +50400,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "us.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -49698,6 +50444,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.44e-05, "cache_read_input_token_cost_above_200k_tokens": 7.2e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "us-gov.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -49813,7 +50561,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-west-1/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -49835,7 +50583,9 @@ "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-gov-west-1" - } + }, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05 }, "us-gov.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -49934,6 +50684,7 @@ }, "us-gov.nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -49949,6 +50700,7 @@ }, "us-gov.nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -50204,6 +50956,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "us.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -50238,6 +50992,7 @@ }, "us.deepseek.r1-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-r1.html", "input_cost_per_token": 1.35e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -50290,6 +51045,7 @@ }, "us.meta.llama3-1-405b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.4e-06, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50302,6 +51058,7 @@ }, "us.meta.llama3-1-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock", @@ -50316,6 +51073,7 @@ }, "us.meta.llama3-1-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.2e-07, "input_cost_per_token_batches": 1.1e-07, "litellm_provider": "bedrock", @@ -50330,6 +51088,7 @@ }, "us.meta.llama3-2-11b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50343,6 +51102,7 @@ }, "us.meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50355,6 +51115,7 @@ }, "us.meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50367,6 +51128,7 @@ }, "us.meta.llama3-2-90b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50380,6 +51142,7 @@ }, "us.meta.llama3-3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock_converse", @@ -50394,6 +51157,7 @@ }, "us.meta.llama4-maverick-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-maverick-17b-instruct.md", "input_cost_per_token": 2.4e-07, "input_cost_per_token_batches": 1.2e-07, "litellm_provider": "bedrock_converse", @@ -50416,6 +51180,7 @@ }, "us.meta.llama4-scout-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-scout-17b-instruct.md", "input_cost_per_token": 1.7e-07, "input_cost_per_token_batches": 8.5e-08, "litellm_provider": "bedrock_converse", @@ -51676,6 +52441,7 @@ }, "vertex_ai/claude-haiku-4-5": { "deprecation_date": "2026-10-15", + "successor_model": "vertex_ai/claude-haiku-5-5", "cache_creation_input_token_cost": 1.25e-06, "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_creation_input_token_cost_batches": 6.25e-07, @@ -51795,32 +52561,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "vertex_ai/claude-3-opus": { - "deprecation_date": "2025-08-01", - "input_cost_per_token": 1.5e-05, - "litellm_provider": "vertex_ai-anthropic_models", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_assistant_prefill": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "vertex_ai/claude-3-opus@20240229": { - "deprecation_date": "2025-08-01", - "input_cost_per_token": 1.5e-05, - "litellm_provider": "vertex_ai-anthropic_models", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_assistant_prefill": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "vertex_ai/claude-3-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "vertex_ai-anthropic_models", @@ -51847,6 +52587,7 @@ }, "vertex_ai/claude-opus-4-5": { "deprecation_date": "2026-11-24", + "successor_model": "vertex_ai/claude-opus-5-5", "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_creation_input_token_cost_batches": 3.125e-06, @@ -52456,6 +53197,7 @@ }, "vertex_ai/claude-sonnet-4-5": { "deprecation_date": "2026-09-29", + "successor_model": "vertex_ai/claude-sonnet-5-5", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, @@ -52743,6 +53485,8 @@ }, "vertex_ai/gemini-2.5-flash-image": { "deprecation_date": "2027-03-15", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -52799,6 +53543,7 @@ "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 3.6e-07, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, @@ -52813,6 +53558,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, @@ -52848,6 +53594,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_priority": 2.16e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, @@ -52869,6 +53616,7 @@ "cache_read_input_token_cost_flex": 2.5e-08, "cache_read_input_token_cost_priority": 9e-08, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.00056, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -52881,6 +53629,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "output_cost_per_token_flex": 1.5e-06, @@ -52909,6 +53658,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "supports_reasoning": false, @@ -52938,6 +53688,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, @@ -53018,17 +53769,28 @@ }, "vertex_ai/gemini-3.1-flash-lite": { "cache_read_input_audio_token_cost": 5e-08, - "deprecation_date": "2027-05-07", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_batches": 2.5e-08, + "cache_read_input_audio_token_cost_flex": 2.5e-08, + "cache_read_input_audio_token_cost_priority": 9e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-07", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 5e-07, + "input_cost_per_audio_token_above_200k_tokens": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 9e-07, + "input_cost_per_audio_token_batches": 2.5e-07, + "input_cost_per_audio_token_flex": 2.5e-07, + "input_cost_per_audio_token_priority": 9e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, - "input_cost_per_audio_token_priority": 9e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65536, @@ -53039,7 +53801,10 @@ "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, "regional_endpoint_uplift_multiplier": 1.1, + "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -53057,6 +53822,7 @@ "supports_audio_input": true, "supports_audio_output": false, "supports_function_calling": true, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -53068,19 +53834,11 @@ "supports_video_input": true, "supports_vision": true, "supports_web_search": true, - "supports_native_streaming": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014, - "search_context_size_high": 0.014 - }, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, - "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014, - "input_cost_per_audio_token_batches": 2.5e-07 + "web_search_billing_unit": "per_query" }, "vertex_ai/gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_batches": 1.5e-08, "cache_read_input_token_cost_flex": 1.5e-08, @@ -53496,6 +54254,7 @@ "vertex_ai/zai-org/glm-4.7-maas": { "cache_read_input_token_cost": 6e-08, "deprecation_date": "2026-10-21", + "successor_model": "vertex_ai/zai-org/glm-5.2-maas", "input_cost_per_token": 6e-07, "litellm_provider": "vertex_ai-zai_models", "max_input_tokens": 200000, @@ -53514,6 +54273,7 @@ "vertex_ai/zai-org/glm-5-maas": { "cache_read_input_token_cost": 1e-07, "deprecation_date": "2026-10-21", + "successor_model": "vertex_ai/zai-org/glm-5.2-maas", "input_cost_per_token": 1e-06, "litellm_provider": "vertex_ai-zai_models", "max_input_tokens": 200000, @@ -53933,6 +54693,7 @@ }, "vertex_ai/veo-3.1-generate-001": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -53948,6 +54709,7 @@ }, "vertex_ai/veo-3.1-fast-generate-001": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -54150,7 +54912,7 @@ "voyage/voyage-lite-01": { "input_cost_per_token": 1e-07, "litellm_provider": "voyage", - "max_input_tokens": 4096, + "max_input_tokens": 4000, "mode": "embedding", "output_cost_per_token": 0.0 }, @@ -54166,7 +54928,8 @@ "litellm_provider": "voyage", "max_input_tokens": 32000, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "supports_embedding_image_input": true }, "wandb/openai/gpt-oss-120b": { "supports_reasoning": true, @@ -54598,6 +55361,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "source": "https://developers.openai.com/api/docs/pricing" }, "xai/grok-4.20-multi-agent-beta-0309": { @@ -54861,6 +55626,8 @@ }, "zai.glm-4.7": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "zai.glm-5", "input_cost_per_token": 6e-07, "input_cost_per_token_batches": 3e-07, "input_cost_per_token_flex": 3e-07, @@ -54905,6 +55672,7 @@ }, "zai.glm-4.7-flash": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7e-08, "input_cost_per_token_batches": 3.5e-08, "input_cost_per_token_flex": 3.5e-08, @@ -55218,6 +55986,7 @@ }, "openai/sora-2": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -55232,6 +56001,7 @@ }, "openai/sora-2-pro": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -55247,6 +56017,7 @@ "openai/sora-2-pro-high-res": { "litellm_provider": "openai", "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, "source": "https://platform.openai.com/docs/api-reference/videos", @@ -58913,6 +59684,8 @@ }, "gpt-4o-mini-tts-2025-12-15": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -58933,6 +59706,8 @@ }, "gpt-4o-mini-transcribe-2025-03-20": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-4o-mini-transcribe-2025-12-15", "input_cost_per_audio_token": 1.25e-06, "input_cost_per_second": 5e-05, "input_cost_per_token": 1.25e-06, @@ -58948,6 +59723,8 @@ }, "gpt-4o-mini-transcribe-2025-12-15": { "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_audio_token": 1.25e-06, "input_cost_per_second": 5e-05, "input_cost_per_token": 1.25e-06, @@ -58969,6 +59746,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -58996,6 +59774,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -59047,6 +59826,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-realtime-whisper": { @@ -59068,6 +59849,7 @@ }, "sora-2": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -59082,6 +59864,7 @@ }, "sora-2-pro": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -59097,6 +59880,7 @@ "sora-2-pro-high-res": { "litellm_provider": "openai", "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, "source": "https://developers.openai.com/api/docs/pricing", @@ -59113,6 +59897,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 8e-06, "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token": 5e-06, @@ -59128,48 +59914,6 @@ "/v1/images/edits" ] }, - "gemini-2.0-flash-exp-image-generation": { - "deprecation_date": "2025-11-14", - "input_cost_per_token": 0.0, - "litellm_provider": "gemini", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "mode": "image_generation", - "output_cost_per_image": 0.039, - "output_cost_per_token": 0.0, - "source": "https://ai.google.dev/pricing", - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_vision": true - }, - "gemini/gemini-2.0-flash-exp-image-generation": { - "deprecation_date": "2025-11-14", - "input_cost_per_token": 0.0, - "litellm_provider": "gemini", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "mode": "image_generation", - "output_cost_per_image": 0.039, - "output_cost_per_token": 0.0, - "source": "https://ai.google.dev/pricing", - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_vision": true, - "tpm": 250000, - "rpm": 10 - }, "gemini-2.5-flash-native-audio-latest": { "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, @@ -59222,6 +59966,8 @@ }, "gemini-2.5-flash-native-audio-preview-12-2025": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -59253,6 +59999,8 @@ }, "gemini-3.1-flash-live-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_image_token": 1e-06, "input_cost_per_token": 7.5e-07, @@ -59410,6 +60158,8 @@ }, "gemini/gemini-2.5-flash-native-audio-preview-12-2025": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -59444,6 +60194,8 @@ }, "gemini/gemini-3.1-flash-live-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_image_token": 1e-06, "input_cost_per_token": 7.5e-07, @@ -59483,6 +60235,8 @@ }, "gemini/gemini-3.1-flash-tts-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", @@ -59552,6 +60306,8 @@ }, "gemini-2.5-flash-preview-tts": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -63773,6 +64529,7 @@ }, "claude-mythos-preview": { "deprecation_date": "2026-06-09", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", "supports_anthropic_compaction": true, "cache_creation_input_token_cost": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 2e-05, @@ -64449,6 +65206,7 @@ "fireworks_ai/glm-5p2-fast": { "cache_read_input_token_cost": 2.1e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/glm-5p3-fast", "input_cost_per_token": 2.1e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, @@ -64718,6 +65476,7 @@ "fireworks_ai/accounts/fireworks/routers/glm-5p2-fast": { "cache_read_input_token_cost": 2.1e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/accounts/fireworks/routers/glm-5p3-fast", "input_cost_per_token": 2.1e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, @@ -64809,7 +65568,7 @@ "supports_vision": false }, "novita/deepseek/deepseek-v4-pro-0813": { - "cache_read_input_token_cost": 1.3200000000000002e-07, + "cache_read_input_token_cost": 4.4e-08, "input_cost_per_token": 1.32e-06, "litellm_provider": "novita", "max_input_tokens": 1048576, @@ -64921,13 +65680,13 @@ "supports_vision": false }, "novita/mindai/macaron-v1-venti": { - "cache_read_input_token_cost": 3e-07, - "input_cost_per_token": 1.5e-06, + "cache_read_input_token_cost": 1.65e-07, + "input_cost_per_token": 8.25e-07, "litellm_provider": "novita", "max_input_tokens": 1048576, "max_output_tokens": 131072, "mode": "chat", - "output_cost_per_token": 4.5e-06, + "output_cost_per_token": 2.475e-06, "source": "https://novita.ai/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -65030,13 +65789,13 @@ "supports_vision": false }, "novita/mindai/macaron-v1-tall": { - "cache_read_input_token_cost": 8e-08, - "input_cost_per_token": 4.5000000000000003e-07, + "cache_read_input_token_cost": 4.4e-08, + "input_cost_per_token": 2.475e-07, "litellm_provider": "novita", "max_input_tokens": 262144, "max_output_tokens": 32768, "mode": "chat", - "output_cost_per_token": 2.6e-06, + "output_cost_per_token": 1.43e-06, "source": "https://novita.ai/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -66461,9 +67220,9 @@ "deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731": { "max_output_tokens": 1048576, "max_input_tokens": 1048576, - "input_cost_per_token": 8e-08, + "input_cost_per_token": 6e-08, "output_cost_per_token": 1.8e-07, - "cache_read_input_token_cost": 1.6e-08, + "cache_read_input_token_cost": 1.5e-08, "supports_prompt_caching": true, "litellm_provider": "deepinfra", "mode": "chat", @@ -66842,8 +67601,8 @@ "deepinfra/google/gemma-4-31B-it": { "max_output_tokens": 262144, "max_input_tokens": 262144, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 3.8e-07, + "input_cost_per_token": 2e-07, + "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_function_calling": true, @@ -67000,10 +67759,12 @@ }, "xai/grok-imagine-image-quality": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67017,10 +67778,12 @@ }, "xai/grok-imagine-image-quality-20260403": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67034,10 +67797,12 @@ }, "xai/grok-imagine-image-quality-latest": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67051,10 +67816,12 @@ }, "xai/grok-imagine-image-pro": { "deprecation_date": "2026-05-15", + "deprecation_source": "https://docs.x.ai/developers/migration/may-15-retirement", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67172,6 +67939,8 @@ }, "xai/grok-voice-transcribe-1.0": { "deprecation_date": "2026-10-02", + "deprecation_source": "https://docs.x.ai/docs/release-notes", + "successor_model": "xai/grok-voice-transcribe-2.0", "input_cost_per_second": 2.778e-05, "litellm_provider": "xai", "metadata": { @@ -68037,6 +68806,7 @@ }, "together_ai/nvidia/NVIDIA-Nemotron-Nano-9B-v2": { "deprecation_date": "2026-02-25", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 6e-08, "output_cost_per_token": 2.5e-07, "litellm_provider": "together_ai", @@ -68107,6 +68877,7 @@ "azure_ai/kimi-k2.7-code": { "cache_read_input_token_cost": 1.9e-07, "deprecation_date": "2027-04-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 9.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -68142,6 +68913,7 @@ "supports_reasoning": true, "supports_tool_choice": true, "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/deepseek-v4-1-flash-is-coming-to-microsoft-foundry/4556431" }, "azure_ai/MAI-Image-2.6": { @@ -68250,6 +69022,8 @@ }, "azure_ai/Cohere-command-a-plus-05-2026": { "deprecation_date": "2026-10-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "coherelabs-command-a-plus-05-2026-fp81", "input_cost_per_token": 8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -68283,6 +69057,8 @@ }, "azure_ai/deepseek-r1": { "deprecation_date": "2026-08-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-pro", "input_cost_per_token": 1.35e-06, "output_cost_per_token": 5.4e-06, "litellm_provider": "azure_ai", @@ -68292,6 +69068,8 @@ }, "azure_ai/deepseek-v3-0324": { "deprecation_date": "2026-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-flash", "input_cost_per_token": 1.14e-06, "output_cost_per_token": 4.56e-06, "litellm_provider": "azure_ai", @@ -68301,6 +69079,8 @@ }, "azure_ai/deepseek-v3.1": { "deprecation_date": "2026-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-flash", "input_cost_per_token": 1.23e-06, "output_cost_per_token": 4.94e-06, "litellm_provider": "azure_ai", @@ -68310,6 +69090,8 @@ }, "azure_ai/grok-3": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4", "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, "litellm_provider": "azure_ai", @@ -68319,6 +69101,8 @@ }, "azure_ai/grok-3-mini": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-reasoning", "input_cost_per_token": 2.5e-07, "output_cost_per_token": 1.27e-06, "litellm_provider": "azure_ai", @@ -68328,6 +69112,8 @@ }, "azure_ai/grok-4-fast-non-reasoning": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-non-reasoning", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -68337,6 +69123,8 @@ }, "azure_ai/grok-4-fast-reasoning": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-reasoning", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -68346,12 +69134,18 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 262144, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.88e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 5.04e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_native_structured_output": true, "supports_system_messages": true, @@ -68362,13 +69156,20 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, + "input_cost_per_token_batches": 1.2e-07, + "input_cost_per_token_flex": 1.2e-07, + "input_cost_per_token_priority": 4.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_system_messages": true, "supports_vision": true, "supports_audio_input": false, @@ -68377,13 +69178,20 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.76e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 4.8e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_system_messages": true, "supports_audio_input": false, "supports_function_calling": true, @@ -68392,12 +69200,18 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b": { "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 7.8e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 3.9e-07, + "output_cost_per_token_flex": 3.9e-07, + "output_cost_per_token_priority": 1.365e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_reasoning": true, "supports_system_messages": true, @@ -68409,12 +69223,18 @@ "bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 3.6e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68428,12 +69248,18 @@ "bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68554,7 +69380,10 @@ "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-gov-west-1" - } + }, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-west-1/index.json" }, "bedrock/us-gov-west-1/anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -68636,12 +69465,18 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 262144, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.88e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 5.04e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_function_calling": true, "supports_native_structured_output": true, "supports_system_messages": true, @@ -68652,13 +69487,20 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, + "input_cost_per_token_batches": 1.2e-07, + "input_cost_per_token_flex": 1.2e-07, + "input_cost_per_token_priority": 4.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_system_messages": true, "supports_vision": true, "supports_audio_input": false, @@ -68667,13 +69509,20 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.76e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 4.8e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_system_messages": true, "supports_audio_input": false, "supports_function_calling": true, @@ -68682,12 +69531,18 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-super-3-120b": { "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 7.8e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 3.9e-07, + "output_cost_per_token_flex": 3.9e-07, + "output_cost_per_token_priority": 1.365e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_function_calling": true, "supports_reasoning": true, "supports_system_messages": true, @@ -68699,12 +69554,18 @@ "bedrock/us-gov-east-1/openai.gpt-oss-20b-1:0": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 3.6e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68718,12 +69579,18 @@ "bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68829,7 +69696,10 @@ "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-east-1/index.json" }, "bedrock/us-gov-east-1/anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -68924,16 +69794,32 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 2.7e-06, - "input_cost_per_token_above_272k_tokens": 5.4e-06, - "cache_creation_input_token_cost": 3.375e-06, - "cache_creation_input_token_cost_above_272k_tokens": 6.75e-06, - "cache_read_input_token_cost": 2.7e-07, - "cache_read_input_token_cost_above_272k_tokens": 5.4e-07, - "output_cost_per_token": 1.62e-05, - "output_cost_per_token_above_272k_tokens": 2.43e-05, + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_creation_input_token_cost_above_272k_tokens_flex": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.32e-05, + "cache_creation_input_token_cost_flex": 1.65e-06, + "cache_creation_input_token_cost_priority": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.056e-06, + "cache_read_input_token_cost_flex": 1.32e-07, + "cache_read_input_token_cost_priority": 5.28e-07, + "input_cost_per_token_above_272k_tokens_flex": 2.64e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.056e-05, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 5.28e-06, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05, + "output_cost_per_token_above_272k_tokens_flex": 1.188e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.752e-05, + "output_cost_per_token_flex": 7.92e-06, + "output_cost_per_token_priority": 3.168e-05, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-terra.html", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_web_search": true }, "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-luna": { @@ -68961,16 +69847,32 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 2.7e-07, - "input_cost_per_token_above_272k_tokens": 5.4e-07, - "cache_creation_input_token_cost": 3.375e-07, - "cache_creation_input_token_cost_above_272k_tokens": 6.75e-07, - "cache_read_input_token_cost": 2.7e-08, - "cache_read_input_token_cost_above_272k_tokens": 5.4e-08, - "output_cost_per_token": 1.62e-06, - "output_cost_per_token_above_272k_tokens": 2.43e-06, + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.32e-06, + "cache_creation_input_token_cost_flex": 1.65e-07, + "cache_creation_input_token_cost_priority": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.056e-07, + "cache_read_input_token_cost_flex": 1.32e-08, + "cache_read_input_token_cost_priority": 5.28e-08, + "input_cost_per_token_above_272k_tokens_flex": 2.64e-07, + "input_cost_per_token_above_272k_tokens_priority": 1.056e-06, + "input_cost_per_token_flex": 1.32e-07, + "input_cost_per_token_priority": 5.28e-07, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.188e-06, + "output_cost_per_token_above_272k_tokens_priority": 4.752e-06, + "output_cost_per_token_flex": 7.92e-07, + "output_cost_per_token_priority": 3.168e-06, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-luna.html", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_web_search": true }, "bedrock_mantle/us-gov-west-1/openai.gpt-5.4": { @@ -68997,9 +69899,9 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 3.375e-06, - "cache_read_input_token_cost": 3.375e-07, - "output_cost_per_token": 2.025e-05, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-54.html", "supports_web_search": true @@ -69019,20 +69921,36 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "input_cost_per_token": 1.5e-06, "output_cost_per_token": 3e-06, - "cache_read_input_token_cost": 2.4e-07 + "cache_read_input_token_cost": 2.4e-07, + "cache_read_input_token_cost_batches": 1.2e-07, + "cache_read_input_token_cost_flex": 1.2e-07, + "cache_read_input_token_cost_priority": 4.2e-07, + "input_cost_per_token_batches": 7.5e-07, + "input_cost_per_token_flex": 7.5e-07, + "input_cost_per_token_priority": 2.625e-06, + "output_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_flex": 1.5e-06, + "output_cost_per_token_priority": 5.25e-06 }, "bedrock_mantle/us-gov-west-1/xai.grok-4.6": { "use_openai_responses_path": true, "input_cost_per_token": 2.64e-06, "output_cost_per_token": 7.92e-06, "cache_read_input_token_cost": 6.6e-07, + "cache_read_input_token_cost_flex": 3.3e-07, + "cache_read_input_token_cost_priority": 1.155e-06, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 4.62e-06, "litellm_provider": "bedrock_mantle", "max_input_tokens": 500000, "max_output_tokens": 500000, "mode": "chat", + "output_cost_per_token_flex": 3.96e-06, + "output_cost_per_token_priority": 1.386e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69046,11 +69964,17 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-e2b": { "input_cost_per_token": 4.8e-08, + "input_cost_per_token_flex": 2.4e-08, + "input_cost_per_token_priority": 8.4e-08, "output_cost_per_token": 9.6e-08, "litellm_provider": "bedrock_mantle", "max_input_tokens": 128000, "max_output_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 4.8e-08, + "output_cost_per_token_flex": 4.8e-08, + "output_cost_per_token_priority": 1.68e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69064,11 +69988,18 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-26b-a4b": { "input_cost_per_token": 1.56e-07, + "input_cost_per_token_batches": 7.8e-08, + "input_cost_per_token_flex": 7.8e-08, + "input_cost_per_token_priority": 2.73e-07, "output_cost_per_token": 4.8e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 256000, "max_output_tokens": 256000, "mode": "chat", + "output_cost_per_token_batches": 2.4e-07, + "output_cost_per_token_flex": 2.4e-07, + "output_cost_per_token_priority": 8.4e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69082,11 +70013,18 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-31b": { "input_cost_per_token": 1.68e-07, + "input_cost_per_token_batches": 8.4e-08, + "input_cost_per_token_flex": 8.4e-08, + "input_cost_per_token_priority": 2.94e-07, "output_cost_per_token": 4.8e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 256000, "max_output_tokens": 256000, "mode": "chat", + "output_cost_per_token_batches": 2.4e-07, + "output_cost_per_token_flex": 2.4e-07, + "output_cost_per_token_priority": 8.4e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69101,12 +70039,18 @@ "bedrock_mantle/us-gov-west-1/openai.gpt-oss-20b": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "output_cost_per_token": 3.6e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69120,12 +70064,18 @@ "bedrock_mantle/us-gov-west-1/openai.gpt-oss-120b": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "output_cost_per_token": 7.2e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69241,10 +70191,17 @@ "input_cost_per_token": 2.64e-06, "output_cost_per_token": 7.92e-06, "cache_read_input_token_cost": 6.6e-07, + "cache_read_input_token_cost_flex": 3.3e-07, + "cache_read_input_token_cost_priority": 1.155e-06, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 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"together_ai/mistralai/Mistral-Small-24B-Instruct-2501": { "deprecation_date": "2026-04-02", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, @@ -70533,6 +71401,7 @@ }, "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1": { "deprecation_date": "2026-04-16", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, @@ -70544,6 +71413,7 @@ "together_ai/moonshotai/Kimi-K2.6": { "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-08-19", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.2e-06, "litellm_provider": "together_ai", "max_input_tokens": 262144, @@ -70555,6 +71425,7 @@ "together_ai/moonshotai/Kimi-K2.7-Code": { "cache_read_input_token_cost": 1.9e-07, "deprecation_date": "2026-08-27", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 9.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, @@ -70563,19 +71434,10 @@ "output_cost_per_token": 4e-06, "source": "https://api.together.ai/v1/models" }, - "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF": { - "deprecation_date": "2025-08-28", - "input_cost_per_token": 8.8e-07, - "litellm_provider": "together_ai", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 8.8e-07, - "source": "https://api.together.ai/v1/models" - }, "together_ai/nvidia/nemotron-3-ultra-550b-a55b": { "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-08-27", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 512288, @@ -70586,16 +71448,19 @@ }, "together_ai/openai/gpt-oss-20b": { "deprecation_date": "2026-09-15", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 5e-08, "litellm_provider": "together_ai", "max_input_tokens": 131072, "max_output_tokens": 131072, "mode": "chat", "output_cost_per_token": 2e-07, - "source": "https://api.together.ai/v1/models" + "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/Qwen/Qwen3.5-9B" }, "together_ai/zai-org/GLM-4.5-Air-FP8": { "deprecation_date": "2026-04-02", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, @@ -70606,6 +71471,7 @@ }, "together_ai/zai-org/GLM-4.7": { "deprecation_date": "2026-04-02", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 4.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 202752, @@ -70616,6 +71482,7 @@ }, "together_ai/zai-org/GLM-5": { "deprecation_date": "2026-06-22", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 202752, @@ -70627,6 +71494,7 @@ "together_ai/zai-org/GLM-5.1": { "cache_read_input_token_cost": 2.6e-07, "deprecation_date": "2026-07-10", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.4e-06, "litellm_provider": "together_ai", "max_input_tokens": 202752, @@ -70637,6 +71505,7 @@ }, "azure/eu/codex-mini": { "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 4.13e-07, "input_cost_per_token": 1.65e-06, "litellm_provider": "azure", @@ -70653,6 +71522,8 @@ }, "azure/eu/gpt-4.1": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -70672,6 +71543,8 @@ }, "azure/eu/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -70691,6 +71564,8 @@ }, "azure/eu/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -70701,7 +71576,9 @@ "source": 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"2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1-codex", "cache_read_input_token_cost": 1.38e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -70742,7 +71623,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_batches": 1.375e-08, "cache_read_input_token_cost_priority": 4.95e-08, @@ -70763,6 +71646,8 @@ }, "azure/eu/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "cache_read_input_token_cost": 5.5e-09, "cache_read_input_token_cost_batches": 2.75e-09, "input_cost_per_token": 5.5e-08, @@ -70780,6 +71665,8 @@ }, "azure/eu/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2-pro", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -70795,6 +71682,7 @@ }, "azure/eu/gpt-5.1-codex-max": { "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -70804,6 +71692,8 @@ }, "azure/eu/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_batches": 9.625e-08, "cache_read_input_token_cost_priority": 3.85e-07, @@ -70824,6 +71714,8 @@ }, "azure/eu/gpt-5.2-codex": { "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.3-codex", "cache_read_input_token_cost": 1.925e-07, "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", @@ -70847,6 +71739,7 @@ }, "azure/eu/gpt-5.3-codex": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_priority": 3.85e-07, "input_cost_per_token": 1.925e-06, @@ -70864,6 +71757,7 @@ }, "azure/eu/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 8.25e-08, "cache_read_input_token_cost_batches": 4.125e-08, "cache_read_input_token_cost_priority": 1.65e-07, @@ -70884,6 +71778,7 @@ }, "azure/eu/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_batches": 1.1e-08, "input_cost_per_token": 2.2e-07, @@ -70901,6 +71796,7 @@ }, "azure/eu/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 3.3e-05, "input_cost_per_token_above_272k_tokens": 6.6e-05, "input_cost_per_token_above_272k_tokens_batches": 3.3e-05, @@ -71074,6 +71970,7 @@ }, "azure/eu/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -71092,6 +71989,7 @@ }, "azure/eu/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -71110,6 +72008,8 @@ }, "azure/eu/o3-deep-research": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 2.75e-06, "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -71119,6 +72019,8 @@ }, "azure/eu/o4-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "cache_read_input_token_cost": 3.03e-07, "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, @@ -71137,6 +72039,8 @@ }, "azure/eu/o4-mini-2025-04-16": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "cache_read_input_token_cost": 3.03e-07, "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, @@ -71155,6 +72059,7 @@ }, "azure/eu/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71162,6 +72067,7 @@ }, "azure/eu/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "mode": "embedding", @@ -71169,6 +72075,7 @@ }, "azure/eu/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71218,6 +72125,7 @@ }, "azure/us/codex-mini": { "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 4.13e-07, "input_cost_per_token": 1.65e-06, "litellm_provider": "azure", @@ -71234,6 +72142,8 @@ }, "azure/us/gpt-4.1": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -71253,6 +72163,8 @@ }, "azure/us/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -71272,6 +72184,8 @@ }, "azure/us/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -71282,7 +72196,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-4o-2024-05-13": { - "deprecation_date": "2026-12-09", + "deprecation_date": "2027-01-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "input_cost_per_token": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "litellm_provider": "azure", @@ -71295,6 +72211,8 @@ }, "azure/us/gpt-5": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -71315,6 +72233,8 @@ }, "azure/us/gpt-5-codex": { "deprecation_date": "2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1-codex", "cache_read_input_token_cost": 1.38e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -71323,7 +72243,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_batches": 1.375e-08, "cache_read_input_token_cost_priority": 4.95e-08, @@ -71344,6 +72266,8 @@ }, "azure/us/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 5.5e-09, "cache_read_input_token_cost_batches": 2.75e-09, "input_cost_per_token": 5.5e-08, @@ -71361,6 +72285,8 @@ }, "azure/us/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2-pro", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -71376,6 +72302,7 @@ }, "azure/us/gpt-5.1-codex-max": { "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -71385,6 +72312,8 @@ }, "azure/us/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_batches": 9.625e-08, "cache_read_input_token_cost_priority": 3.85e-07, @@ -71405,6 +72334,8 @@ }, "azure/us/gpt-5.2-codex": { "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.3-codex", "cache_read_input_token_cost": 1.925e-07, "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", @@ -71428,6 +72359,7 @@ }, "azure/us/gpt-5.3-codex": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_priority": 3.85e-07, "input_cost_per_token": 1.925e-06, @@ -71445,6 +72377,7 @@ }, "azure/us/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 8.25e-08, "cache_read_input_token_cost_batches": 4.125e-08, "cache_read_input_token_cost_priority": 1.65e-07, @@ -71465,6 +72398,7 @@ }, "azure/us/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_batches": 1.1e-08, "input_cost_per_token": 2.2e-07, @@ -71482,6 +72416,7 @@ }, "azure/us/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 3.3e-05, "input_cost_per_token_above_272k_tokens": 6.6e-05, "input_cost_per_token_above_272k_tokens_batches": 3.3e-05, @@ -71511,6 +72446,8 @@ }, "azure/us/o3-deep-research": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 2.75e-06, "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -71520,6 +72457,7 @@ }, "azure/us/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71527,6 +72465,7 @@ }, "azure/us/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "mode": "embedding", @@ -71534,6 +72473,7 @@ }, "azure/us/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71544,6 +72484,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -73964,6 +74906,7 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "deprecation_date": "2026-05-15", + "deprecation_source": "https://docs.x.ai/developers/migration/may-15-retirement", "input_cost_per_image_token": 1e-06, "input_cost_per_token": 1e-06, "input_cost_per_token_above_200k_tokens": 2e-06, @@ -73974,6 +74917,7 @@ "output_cost_per_token": 2e-06, "output_cost_per_token_above_200k_tokens": 4e-06, "source": "https://api.x.ai/v1/language-models", + "successor_model": "xai/grok-build-0.1", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -74063,6 +75007,8 @@ }, "vertex_ai/gemini-2.0-flash": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "input_cost_per_audio_token": 1e-06, "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_character": 3.75e-08, @@ -74076,6 +75022,8 @@ }, "vertex_ai/gemini-2.0-flash-lite": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "input_cost_per_audio_token": 7.5e-08, "input_cost_per_audio_token_batches": 3.75e-08, "input_cost_per_character": 1.875e-08, @@ -74127,6 +75075,8 @@ }, "vertex_ai/veo-3.0-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74142,6 +75092,8 @@ }, "vertex_ai/veo-3.0-fast-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-fast-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74158,6 +75110,8 @@ }, "vertex_ai/veo-2.0-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74505,12 +75459,14 @@ "input_cost_per_token": 5.5e-06, "cache_read_input_token_cost": 5.5e-07, "output_cost_per_token": 2.75e-05, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-northeast-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "ap-southeast-4" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "apac.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -74682,6 +75638,7 @@ }, "deepseek.r1-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-r1.html", "input_cost_per_token": 1.35e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -75107,7 +76064,7 @@ "supports_function_calling": true, "input_cost_per_token": 2e-06, "output_cost_per_token": 1e-05, - "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost": 1e-07, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "perplexity/anthropic/claude-fable-5-1": { @@ -75155,6 +76112,9 @@ "output_cost_per_token": 5e-05, "cache_read_input_token_cost": 1e-06, "cache_creation_input_token_cost": 1.25e-05, + "input_cost_per_token_above_272k_tokens": 2e-05, + "output_cost_per_token_above_272k_tokens": 7.5e-05, + "cache_read_input_token_cost_above_272k_tokens": 2e-06, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "perplexity/openai/gpt-6-luna": { @@ -75533,6 +76493,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -75757,6 +76718,8 @@ }, "azure_ai/kimi-k2-thinking": { "deprecation_date": "2026-03-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", + "successor_model": "azure_ai/kimi-k2.5", "input_cost_per_token": 6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -75858,6 +76821,7 @@ "azure/model-router": { "comment": "The flat $0.14 per M input tokens is the router's own fee, the routed model is priced on top of it", "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.4e-07, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -75869,6 +76833,7 @@ }, "azure/whisper": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_second": 0.0001, "litellm_provider": "azure", "mode": "audio_transcription", @@ -75930,6 +76895,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 7.5e-06, "output_cost_per_token_batches": 3.75e-06, "output_cost_per_token_priority": 1.35e-05, @@ -75976,6 +76942,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 7.5e-06, "output_cost_per_token_batches": 3.75e-06, "output_cost_per_token_priority": 1.35e-05, @@ -76622,6 +77589,9 @@ "input_cost_per_token": 1e-07, "output_cost_per_token": 5e-07, "cache_read_input_token_cost": 1e-08, + "input_cost_per_token_above_100k_tokens": 5e-07, + "output_cost_per_token_above_100k_tokens": 2.5e-06, + "cache_read_input_token_cost_above_100k_tokens": 5e-08, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "us-gov.anthropic.claude-haiku-5-5": { @@ -77310,6 +78280,7 @@ "litellm_provider": "xai", "mode": "realtime", "source": "https://docs.x.ai/developers/pricing", + "successor_model": "xai/grok-voice-3.0", "supported_endpoints": [ "/v1/realtime" ], @@ -77393,6 +78364,7 @@ "cache_creation_input_token_cost_above_1hr": 1.6e-05, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 8e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -77504,9 +78476,10 @@ "max_output_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1e-06, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/reflection%2Fbeam", + "source": "https://tokenfactory.nebius.com/api/public/models_info", "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 5e-08 }, "baseten/deepseek-ai/DeepSeek-V4.1-Flash-1017": { "cache_read_input_token_cost": 7e-09, @@ -78837,6 +79810,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "bedrock/claude_platform/claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "bedrock", "max_input_tokens": 200000, @@ -79049,6 +80024,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": {"search_context_size_high":0.01,"search_context_size_low":0.01,"search_context_size_medium":0.01}, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -80954,6 +81930,7 @@ "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.003e-08, "deprecation_date": "2026-11-19", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 3.0002e-07, "input_dbu_cost_per_token": 4.286e-06, "litellm_provider": "databricks", @@ -80981,7 +81958,8 @@ "input_cost_per_token_priority": 5.25e-07, "output_cost_per_token_priority": 2.1e-06, "cache_read_input_token_cost_priority": 5.25e-08, - "cache_creation_input_token_cost_priority": 5.25e-07 + "cache_creation_input_token_cost_priority": 5.25e-07, + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017" }, "databricks/databricks-deepseek-v4-1-flash-1017": { "cache_creation_input_token_cost": 3.0002e-07, @@ -81433,6 +82411,8 @@ "databricks/databricks-kimi-k2-7": { "cache_creation_input_token_cost": 9.4997e-07, "cache_read_input_token_cost": 1.8998e-07, + "deprecation_date": "2026-10-30", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/retired-models-policy", "input_cost_per_token": 9.4997e-07, "input_dbu_cost_per_token": 1.3571e-05, "litellm_provider": "databricks", @@ -81445,6 +82425,7 @@ "output_cost_per_token": 4.00001e-06, "output_dbu_cost_per_token": 5.7143e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "successor_model": "databricks/databricks-kimi-k3", "supported_modalities": [ "text", "image" @@ -81531,6 +82512,23 @@ "supports_tool_choice": true, "supports_vision": true }, + "fireworks_ai/accounts/fireworks/routers/deepseek-v4p1-flash-1017-ultrafast": { + "cache_read_input_token_cost": 4.8e-08, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 9.6e-06, + "source": "https://docs.fireworks.ai/serverless/pricing.md", + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/routers/deepseek-v4p1-flash-1017-spot": { "cache_read_input_token_cost": 4.2e-09, "input_cost_per_token": 2.1e-07, @@ -81625,17 +82623,17 @@ "supports_native_streaming": true }, "fireworks_ai/accounts/fireworks/models/ultramarine-1-flash-us": { - "cache_read_input_token_cost": 4.5e-08, - "cache_read_input_token_cost_priority": 5.625e-08, - "input_cost_per_token": 2.25e-07, - "input_cost_per_token_priority": 2.8125e-07, + "cache_read_input_token_cost": 3e-08, + "cache_read_input_token_cost_priority": 3.75e-08, + "input_cost_per_token": 1.5e-07, + "input_cost_per_token_priority": 1.875e-07, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, "max_output_tokens": 1048576, "mode": "chat", - "output_cost_per_token": 7.5e-07, - "output_cost_per_token_priority": 9.375e-07, - "source": "https://docs.fireworks.ai/serverless/pricing", + "output_cost_per_token": 5e-07, + "output_cost_per_token_priority": 6.25e-07, + "source": "https://docs.fireworks.ai/serverless/pricing.md", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -81725,6 +82723,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": {"search_context_size_high":0.01,"search_context_size_low":0.01,"search_context_size_medium":0.01}, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -81777,5 +82776,3109 @@ "supports_response_schema": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true + }, + "nebius/deepseek-ai/DeepSeek-V4.1-Flash-1017": { + "max_input_tokens": 1048576, + "input_cost_per_token": 3e-07, + "cache_read_input_token_cost": 6e-09, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "nebius/meta-models/muse-spark-open": { + "max_input_tokens": 1048576, + "input_cost_per_token": 1.25e-06, + "cache_read_input_token_cost": 1.5e-07, + "output_cost_per_token": 4.25e-06, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_video_input": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "nebius/MiniMaxAI/MiniMax-M3.1-Flash": { + "max_input_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "cache_read_input_token_cost": 1.5e-08, + "output_cost_per_token": 6e-07, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "nebius/nvidia/Nemotron-3.5-Super-VL": { + "max_input_tokens": 1048576, + "input_cost_per_token": 3e-07, + "cache_read_input_token_cost": 6e-08, + "output_cost_per_token": 9e-07, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_video_input": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "empiriolabs/aplomb-1": { + "litellm_provider": "empiriolabs", + "mode": "chat", + "max_input_tokens": 1000000, + "input_cost_per_token": 2e-08, + "output_cost_per_token": 0.0, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses", + 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true, + "supports_vision": true + }, + "baseten/moonshotai/Kimi-K3.1-Pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "baseten", + "max_input_tokens": 1000000, + "max_output_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1e-05, + "source": "https://inference.baseten.co/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true } } diff --git a/litellm/models/plugin_upload.py b/litellm/models/plugin_upload.py new file mode 100644 index 00000000000..41fbf901a03 --- /dev/null +++ b/litellm/models/plugin_upload.py @@ -0,0 +1,29 @@ +from collections.abc import Mapping +from dataclasses import dataclass +from typing import Protocol + + +@dataclass(frozen=True, slots=True) +class PluginUpload: + name: str + version: str | None + description: str | None + manifest: Mapping[str, object] + content: bytes + created_by: str | None + + +@dataclass(frozen=True, slots=True) +class PluginArchiveMetadata: + name: str + enabled: bool + source: Mapping[str, str] + manifest_json: str | None + + +class PluginUploadStore(Protocol): + async def create(self, upload: PluginUpload) -> str | None: ... + + async def get_metadata(self, name: str) -> PluginArchiveMetadata | None: ... + + async def get_content(self, metadata: PluginArchiveMetadata) -> bytes | None: ... diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index 40ea9d15ff4..00fee0a0d5a 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -5250,13 +5250,13 @@ "generateContent": false } }, - "empiriolabs": { - "display_name": "EmpirioLabs (`empiriolabs`)", - "url": "https://docs.litellm.ai/docs/providers/empiriolabs", + "wally": { + "display_name": "Wally (`wally`)", + "url": "https://docs.litellm.ai/docs/providers/wally", "endpoints": { - "chat_completions": false, - "responses": false, - "text_completion": false, + "chat_completions": true, + "responses": true, + "text_completion": true, "embeddings": false, "image_generations": false, "image_edits": false, @@ -5269,11 +5269,37 @@ "realtime": false, "video_generations": false, "batches": false, - "messages": false, - "interactions": false, + "messages": true, + "interactions": true, "systemone": false, - "a2a": false, - "generateContent": false + "a2a": true, + "generateContent": true + } + }, + "empiriolabs": { + "display_name": "EmpirioLabs AI (`empiriolabs`)", + "url": "https://docs.litellm.ai/docs/providers/empiriolabs", + "endpoints": { + "chat_completions": true, + "responses": true, + "text_completion": true, + "embeddings": true, + "image_generations": false, + "image_edits": false, + "audio_transcriptions": false, + "audio_speech": false, + "rerank": false, + "ocr": false, + "search": false, + 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text-destructive",children:(0,r.jsx)(m.f,{className:"h-5 w-5 shrink-0"})})]})]},t)})})]})})})})]})}):null}],863679)}]); \ No newline at end of file diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json index 5cc1c53d396..ab85b6f8842 100644 --- a/litellm/proxy/_lazy_openapi_snapshot.json +++ b/litellm/proxy/_lazy_openapi_snapshot.json @@ -7454,6 +7454,31 @@ "claude_code_marketplace": { "components": { "schemas": { + "Body_upload_plugin_claude_code_plugins_upload_post": { + "properties": { + "file": { + "contentMediaType": "application/octet-stream", + "description": "Skill ZIP containing SKILL.md and supporting files, up to 10 MiB", + "title": "File", + "type": "string" + }, + "metadata": { + "contentMediaType": "application/json", + "contentSchema": { + "$ref": "#/components/schemas/UploadPluginRequest" + }, + "description": "JSON skill metadata without a source URL", + "title": "Metadata", + "type": "string" + } + }, + "required": [ + "file", + "metadata" + ], + "title": "Body_upload_plugin_claude_code_plugins_upload_post", + "type": "object" + }, "HTTPValidationError": { "properties": { "detail": { @@ -8057,6 +8082,137 @@ "title": "UpdatePluginRequest", "type": "object" }, + "UploadPluginRequest": { + "properties": { + "author": { + "anyOf": [ + { + "$ref": "#/components/schemas/PluginAuthor" + }, + { + "type": "null" + } + ], + "description": "Plugin author" + }, + "category": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Plugin category", + "title": "Category" + }, + "description": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Plugin description", + "title": "Description" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Skill domain (e.g., 'Productivity')", + "title": "Domain" + }, + "homepage": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Plugin homepage URL", + "title": "Homepage" + }, + "installation_preference": { + "anyOf": [ + { + "enum": [ + "available", + "auto_install", + "required" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Emitted as installationPreference on this plugin's marketplace.json entry. Needs an archive source, the only plugins Claude Desktop lists from a marketplace.json URL, and 'auto_install' or 'required' also need its sha256. Claude Desktop honors the mark only when the archive and marketplace.json share its inference gateway origin", + "title": "Installation Preference" + }, + "keywords": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "description": "Search keywords", + "title": "Keywords" + }, + "name": { + "description": "Skill name (kebab-case)", + "pattern": "^[a-z0-9-]+$", + "title": "Name", + "type": "string" + }, + "namespace": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Skill namespace within domain (e.g., 'workflows')", + "title": "Namespace" + }, + "version": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "1.0.0", + "description": "Semantic version", + "title": "Version" + } + }, + "required": [ + "name" + ], + "title": "UploadPluginRequest", + "type": "object" + }, "ValidationError": { "properties": { "ctx": { @@ -8242,6 +8398,53 @@ ] } }, + "/claude-code/plugins/upload": { + "post": { + "description": "Create a skill and its uploaded ZIP atomically. Duplicate names return 409.", + "operationId": "upload_plugin_claude_code_plugins_upload_post", + "requestBody": { + "content": { + "multipart/form-data": { + "schema": { + "$ref": "#/components/schemas/Body_upload_plugin_claude_code_plugins_upload_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/RegisterPluginResponse" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Upload Plugin", + "tags": [ + "claude_code_marketplace" + ] + } + }, "/claude-code/plugins/{plugin_name}": { "delete": { "description": "Delete a plugin from the marketplace.\n\nRequires a proxy admin API key.\n\nParameters:\n - plugin_name: The name of the plugin to delete", @@ -8388,6 +8591,59 @@ ] } }, + "/claude-code/plugins/{plugin_name}/archive/{digest}.zip": { + "get": { + "description": "Download an uploaded ZIP under the same visibility policy as its skill.", + "operationId": "download_plugin_archive_claude_code_plugins__plugin_name__archive__digest__zip_get", + "parameters": [ + { + "in": "path", + "name": "plugin_name", + "required": true, + "schema": { + "title": "Plugin Name", + "type": "string" + } + }, + { + "in": "path", + "name": "digest", + "required": true, + "schema": { + "title": "Digest", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/zip": { + "schema": { + "format": "binary", + "type": "string" + } + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "summary": "Download Plugin Archive", + "tags": [ + "claude_code_marketplace" + ] + } + }, "/claude-code/plugins/{plugin_name}/disable": { "post": { "description": "Disable a plugin without deleting it.\n\nRequires a proxy admin API key.\n\nParameters:\n - plugin_name: The name of the plugin to disable", @@ -19977,7 +20233,7 @@ "type": "null" } ], - "description": "Regex pattern applied to the tool's function name", + "description": "Regex pattern applied to the tool's name, or to its type when the tool has no name", "title": "Tool Name" }, "tool_type": { diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index fc1e7128e10..9d84770b288 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -38,7 +38,6 @@ from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( validate_no_callback_env_reference, ) from litellm.proxy.mcp_server.stdio_gate import MCP_STDIO_DISABLED_MESSAGE, is_mcp_stdio_enabled -from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings from litellm.types.agents import ( AgentCaller as AgentCaller, # noqa: PLC0414 # public re-export ) @@ -467,6 +466,8 @@ class LiteLLMRoutes(enum.Enum): "/v1/skills/{skill_id}", "/claude-code/marketplace.json", "/claude-code/plugins", + "/claude-code/plugins/upload", + "/claude-code/plugins/{plugin_name}/archive/{digest}.zip", "/claude-code/plugins/{plugin_name}", ] @@ -2479,22 +2480,6 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase): Documents all the fields supported by `general_settings` in config.yaml """ - accounting_capture: UsageCaptureSettings | None = Field( - None, - description="Opt-in durable usage capture. Legacy spend updates remain active. Requires local Prisma writes.", - ) - accounting_sync_ack: bool = Field( - False, - description="Wait for usage commit on foreground non-stream chat, text, embedding and Responses requests. " - "Other requests remain supported with asynchronous usage capture.", - ) - accounting_sync_ack_timeout_seconds: float = Field( - 20.0, gt=0, description="Maximum response wait for usage commit, including the batch flush interval" - ) - accounting_sync_ack_failure_policy: Literal["fail_closed", "log"] = Field( - "fail_closed", description="Return 503 or log the persistence failure after the response wait expires" - ) - request_tag_mappings: Sequence[RequestTagMapping] | None = Field( None, description="Map W3C baggage values to request tags. Exact value/tag rules take precedence over tag_prefix fallbacks.", @@ -4796,11 +4781,14 @@ class UnregisteredJWTClientBehavior(str, enum.Enum): - auto_register: Automatically create a new virtual key and mapping on first encounter. The new key has no budget/model restrictions; admins can tighten it later via /jwt_client/update. + - link_only: Map an eligible existing user key or return HTTP 403, without + creating a key. Proxy admins authenticate without a mapped key. """ FALLBACK_TEAM_MAPPING = "fallback_team_mapping" REJECT = "reject" AUTO_REGISTER = "auto_register" + LINK_ONLY = "link_only" class JWTIssuerConfig(LiteLLMBaseModel): @@ -5038,7 +5026,12 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase): "What to do when virtual_key_claim_field is set but the JWT claim value " "has no registered mapping. 'fallback_team_mapping' (default): fall through " "to team-based JWT auth. 'reject': return HTTP 403. " - "'auto_register': auto-create a virtual key and mapping on first encounter." + "'auto_register': auto-create a virtual key and mapping on first encounter. " + "'link_only': map an eligible existing user key or return HTTP 403, without creating a key. " + "link_only ignores auto_register_map_existing_key; user provisioning still follows user_id_upsert. " + "Existing-key selection ignores allowed_routes; JWTs inherit the selected key's permissions, " + "including management routes, with request-time key restrictions still enforced. " + "Unmapped proxy admins authenticate directly in auto_register and link_only modes." ), ) auto_register_map_existing_key: bool = Field( @@ -5048,8 +5041,10 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase): "field is the user_id_jwt_field or user_email_jwt_field, the JWT claim is mapped to a virtual key the " "JWT-resolved user already owns instead of minting a new one. If the user owns several, the most recently created key in the " "JWT-resolved team (or with no team when the JWT resolves none) is chosen among keys that never " - "expire, are not blocked, are not Admin UI session keys, were not minted by auto_register, and " - "have no allowed_routes or include llm_api_routes. Otherwise a new key is minted as usual." + "expire, are not blocked, are not Admin UI session keys, and were not minted by auto_register. " + "allowed_routes does not affect key selection. JWTs inherit the selected key's permissions, " + "including management routes; request-time key restrictions still apply. " + "Otherwise a new key is minted as usual." ), ) routing_overrides: list[JWTRoutingOverride] | None = Field( diff --git a/litellm/proxy/agent_endpoints/a2a_endpoints.py b/litellm/proxy/agent_endpoints/a2a_endpoints.py index 3dfaa7f213e..f0a4d47f1e4 100644 --- a/litellm/proxy/agent_endpoints/a2a_endpoints.py +++ b/litellm/proxy/agent_endpoints/a2a_endpoints.py @@ -26,6 +26,7 @@ from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.core_helpers import as_str_mapping from litellm.litellm_core_utils.url_utils import SSRFError, validate_url from litellm.llms.a2a.common_utils import resolve_a2a_hop_auth_header +from litellm.llms.azure.common_utils import caused_by_entra_rejection from litellm.llms.custom_httpx.sse_lines import aiter_sse_lines from litellm.proxy._types import ProxyException, UserAPIKeyAuth from litellm.proxy.a2a.agent_card import point_agent_card_at_proxy @@ -1202,6 +1203,8 @@ async def invoke_agent_a2a( pass if isinstance(e, litellm.BadRequestError): return _jsonrpc_error(body.get("id"), -32602, e.message, 400) + if isinstance(e, litellm.AuthenticationError) and caused_by_entra_rejection(e): + return _jsonrpc_error(body.get("id"), -32000, e.message, 401) return _jsonrpc_error(body.get("id"), -32603, f"Internal error: {e}", 500) diff --git a/litellm/proxy/analytics_endpoints/cache_activity.py b/litellm/proxy/analytics_endpoints/cache_activity.py index 8a3f71e656e..17e250a2dbe 100644 --- a/litellm/proxy/analytics_endpoints/cache_activity.py +++ b/litellm/proxy/analytics_endpoints/cache_activity.py @@ -2,31 +2,22 @@ import asyncio import json from collections.abc import Sequence from datetime import datetime -from typing import Final, Protocol +from typing import TYPE_CHECKING, Final from pydantic import TypeAdapter from litellm.proxy._types import LiteLLMRoutes from litellm.proxy.auth.authorization import ReadScope, read_scope_bind_params +from litellm.proxy.utils import query_raw_within_statement_timeout from litellm.types.llms.base import LiteLLMBaseModel +if TYPE_CHECKING: + from litellm.proxy.utils import PrismaClient + UNKNOWN_CALL_TYPE: Final = "Unknown" INFO_ROUTES_JSON: Final = json.dumps(LiteLLMRoutes.info_routes.value) -class _SupportsQueryRaw(Protocol): - """The single database operation the cache-activity queries issue.""" - - async def query_raw(self, query: str, *args: object) -> Sequence[object]: ... - - -class _SupportsRawQueryDb(Protocol): - """A prisma client handle, narrowed to the raw-query surface used here.""" - - @property - def db(self) -> _SupportsQueryRaw: ... - - class CacheActivityGroup(LiteLLMBaseModel): call_type: str api_requests: int @@ -174,7 +165,7 @@ def compute_totals(groups: Sequence[CacheActivityGroup]) -> CacheActivityTotals: async def get_cache_activity( - prisma_client: _SupportsRawQueryDb, + prisma_client: "PrismaClient", read_scope: ReadScope, start_date: datetime, end_date: datetime, @@ -185,14 +176,46 @@ async def get_cache_activity( models_json: Final = json.dumps(list(models)) scope_params: Final = read_scope_bind_params(read_scope) group_rows, error_rows, key_alias_rows, model_rows = await asyncio.gather( - prisma_client.db.query_raw( - GROUPS_SQL, start_date, end_date, key_aliases_json, models_json, INFO_ROUTES_JSON, *scope_params + query_raw_within_statement_timeout( + prisma_client, + GROUPS_SQL, + start_date, + end_date, + key_aliases_json, + models_json, + INFO_ROUTES_JSON, + *scope_params, + what="Cache activity", ), - prisma_client.db.query_raw( - ERROR_BREAKDOWN_SQL, start_date, end_date, key_aliases_json, models_json, INFO_ROUTES_JSON, *scope_params + query_raw_within_statement_timeout( + prisma_client, + ERROR_BREAKDOWN_SQL, + start_date, + end_date, + key_aliases_json, + models_json, + INFO_ROUTES_JSON, + *scope_params, + what="Cache activity", + ), + query_raw_within_statement_timeout( + prisma_client, + KEY_ALIAS_OPTIONS_SQL, + start_date, + end_date, + INFO_ROUTES_JSON, + *scope_params, + what="Cache activity", + ), + query_raw_within_statement_timeout( + prisma_client, + MODEL_OPTIONS_SQL, + start_date, + end_date, + INFO_ROUTES_JSON, + *scope_params, + what="Cache activity", ), - prisma_client.db.query_raw(KEY_ALIAS_OPTIONS_SQL, start_date, end_date, INFO_ROUTES_JSON, *scope_params), - prisma_client.db.query_raw(MODEL_OPTIONS_SQL, start_date, end_date, INFO_ROUTES_JSON, *scope_params), ) groups: Final = _groups_adapter.validate_python(group_rows or []) return CacheActivityResponse( diff --git a/litellm/proxy/anthropic_endpoints/answered_status.py b/litellm/proxy/anthropic_endpoints/answered_status.py new file mode 100644 index 00000000000..8a3469f9119 --- /dev/null +++ b/litellm/proxy/anthropic_endpoints/answered_status.py @@ -0,0 +1,30 @@ +from typing import Final, TypeVar + +from litellm.exceptions import InternalServerError, MidStreamFallbackError + +_Raised = TypeVar("_Raised", bound=Exception) + + +def _provider_status_code(exc: Exception) -> int | None: + unwrapped = exc # rebind-ok: the recursion gate requires iterative wrapper traversal + while isinstance(unwrapped, MidStreamFallbackError) and unwrapped.original_exception is not None: + unwrapped = unwrapped.original_exception + match unwrapped: + case InternalServerError(provider_status_code=int() as provider_status): + return provider_status + case _: + return None + + +def _copy_carrying(exc: _Raised, attribute: str, value: int | str) -> _Raised: + answered: Final = type(exc).__new__(type(exc), *exc.args) + answered.__dict__.update({**exc.__dict__, attribute: value}) + answered.__cause__ = exc.__cause__ + answered.__context__ = exc.__context__ + answered.__suppress_context__ = exc.__suppress_context__ + return answered.with_traceback(exc.__traceback__) + + +def with_answered_status(exc: Exception) -> Exception: + provider_status_code: Final = _provider_status_code(exc) + return exc if provider_status_code is None else _copy_carrying(exc, "status_code", provider_status_code) diff --git a/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_marketplace.py b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_marketplace.py index fc82686947d..b8c90fe7e93 100644 --- a/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_marketplace.py +++ b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_marketplace.py @@ -18,29 +18,40 @@ Endpoints: /claude-code/plugins/{name} - DELETE - Delete a plugin (proxy admin only) """ +import asyncio +import hashlib import json import re from collections.abc import Mapping, Sequence from datetime import datetime, timezone from types import MappingProxyType from typing import Annotated, Final, Protocol, TypedDict, get_args -from urllib.parse import urlsplit -from fastapi import APIRouter, Depends, HTTPException, Request -from fastapi.responses import JSONResponse -from pydantic import TypeAdapter +from fastapi import APIRouter, Depends, File, Form, HTTPException, Request, UploadFile +from fastapi.responses import JSONResponse, Response +from pydantic import Json, TypeAdapter from typing_extensions import ReadOnly from litellm._logging import verbose_proxy_logger +from litellm.litellm_core_utils.url_utils import is_https_url_with_host +from litellm.models.plugin_upload import PluginUpload from litellm.proxy._types import CommonProxyErrors, ProxyErrorTypes, ProxyException, UserAPIKeyAuth from litellm.proxy.anthropic_endpoints.claude_code_endpoints.claude_code_skill_access import ( SkillVisibility, resolve_skill_visibility, ) +from litellm.proxy.anthropic_endpoints.claude_code_endpoints.skill_upload_archive import ( + MAX_SKILL_UPLOAD_BYTES, + build_uploaded_skill_archive, +) from litellm.proxy.auth.auth_exception_handler import DB_UNAVAILABLE_FALLBACK_USER_ID from litellm.proxy.auth.user_api_key_auth import user_api_key_auth -from litellm.proxy.common_utils.claude_code_marketplace_auth import marketplace_auth_required +from litellm.proxy.common_utils.claude_code_marketplace_auth import ( + marketplace_auth_required, + marketplace_key_header_present, +) from litellm.proxy.common_utils.resource_ownership import is_proxy_admin +from litellm.proxy.deps.repositories import PluginUploadRepositoryDep from litellm.repositories.table_repositories import ClaudeCodePluginRepository from litellm.types.proxy.auth.special_headers import SpecialHeaders from litellm.types.proxy.claude_code_endpoints import ( @@ -52,6 +63,7 @@ from litellm.types.proxy.claude_code_endpoints import ( RegisterPluginRequest, RegisterPluginResponse, UpdatePluginRequest, + UploadPluginRequest, ) router: Final = APIRouter() @@ -148,6 +160,117 @@ async def _marketplace_caller(request: Request, key: str | None) -> UserAPIKeyAu return None +@router.post( + "/claude-code/plugins/upload", + tags=["Claude Code Marketplace"], + response_model=RegisterPluginResponse, +) +async def upload_plugin( + request: Request, + uploads: PluginUploadRepositoryDep, + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], + file: Annotated[UploadFile, File(description="Skill ZIP containing SKILL.md and supporting files, up to 10 MiB")], + metadata: Annotated[Json[UploadPluginRequest], Form(description="JSON skill metadata without a source URL")], +) -> RegisterPluginResponse: + """Create a skill and its uploaded ZIP atomically. Duplicate names return 409.""" + from litellm.proxy.utils import get_custom_url + + _require_proxy_admin(user_api_key_dict) + if uploads is None: + raise _error_response(500, CommonProxyErrors.db_not_connected_error.value) + content: Final = await file.read(MAX_SKILL_UPLOAD_BYTES + 1) + if len(content) > MAX_SKILL_UPLOAD_BYTES: + raise _error_response(413, "Skill ZIP must be 10 MiB or smaller") + archive: Final = await asyncio.to_thread(build_uploaded_skill_archive, content, metadata.name) + if archive is None: + raise _error_response( + 400, "Upload a valid ZIP with SKILL.md at the root or inside one skill folder, and safe supporting files" + ) + digest: Final = hashlib.sha256(archive).hexdigest() + source: Final = { + "source": "archive", + "url": get_custom_url( + request_base_url=str(request.base_url), + route=f"claude-code/plugins/{metadata.name}/archive/{digest}.zip", + ), + "sha256": digest, + } + spec: Final = RegisterPluginRequest.model_validate({**metadata.model_dump(), "source": source}) + _validate_installation_preference(spec) + plugin_id: Final = await uploads.create( + PluginUpload( + name=spec.name, + version=spec.version, + description=spec.description, + manifest=_build_plugin_manifest(spec.name, spec), + content=archive, + created_by=user_api_key_dict.user_id, + ) + ) + if plugin_id is None: + raise _name_conflict_error(spec.name) + return RegisterPluginResponse( + status="success", + action="created", + plugin=PluginResponse( + id=plugin_id, + name=spec.name, + version=spec.version, + description=spec.description, + source=source, + installation_preference=spec.installation_preference, + enabled=True, + ), + ) + + +@router.get( + "/claude-code/plugins/{plugin_name}/archive/{digest}.zip", + tags=["Claude Code Marketplace"], + response_class=Response, + responses={200: {"content": {"application/zip": {"schema": {"type": "string", "format": "binary"}}}}}, +) +async def download_plugin_archive( + request: Request, + plugin_name: str, + digest: str, + uploads: PluginUploadRepositoryDep, +) -> Response: + """Download an uploaded ZIP under the same visibility policy as its skill.""" + from litellm.proxy.utils import get_custom_url + + caller: Final = ( + await _header_key_caller(request) + if marketplace_key_header_present(request) + else await _marketplace_caller(request, None) + ) + visibility: Final = await resolve_skill_visibility(caller) + if uploads is None: + raise _error_response(500, CommonProxyErrors.db_not_connected_error.value) + archive: Final = await uploads.get_metadata(plugin_name) + expected_url: Final = get_custom_url( + request_base_url=str(request.base_url), route=f"claude-code/plugins/{plugin_name}/archive/{digest}.zip" + ) + if ( + archive is None + or not visibility.allows(archive) + or archive.source != {"source": "archive", "url": expected_url, "sha256": digest} + ): + raise _error_response(404, "Skill archive not found") + content: Final = await uploads.get_content(archive) + if content is None: + raise _error_response(404, "Skill archive not found") + return Response( + content=content, + media_type="application/zip", + headers={ + "Content-Disposition": f'attachment; filename="{archive.name}.zip"', + "Cache-Control": "private, no-store", + "X-Content-Type-Options": "nosniff", + }, + ) + + @router.get( "/claude-code/marketplace.json", tags=["Claude Code Marketplace"], @@ -254,14 +377,6 @@ _VALID_GIT_SUBDIR_PATH_RE: Final = re.compile(r"^[a-zA-Z0-9][a-zA-Z0-9._-]*(/[a- _VALID_SHA256_RE: Final = re.compile(r"^[0-9a-fA-F]{64}$") -def _is_https_url_with_host(url: str) -> bool: - try: - parts: Final = urlsplit(url) - except ValueError: - return False - return parts.scheme == "https" and bool(parts.hostname) - - def _validate_plugin_source(source: Mapping[str, str]) -> None: """Validate plugin source format, raising HTTPException on invalid input.""" source_type: Final = source.get("source") @@ -298,7 +413,7 @@ def _validate_plugin_source(source: Mapping[str, str]) -> None: }, ) elif source_type == "archive": - if not _is_https_url_with_host(source.get("url", "")): + if not is_https_url_with_host(source.get("url", "")): raise HTTPException( status_code=400, detail={ @@ -680,18 +795,19 @@ async def update_plugin( prisma_client: Final = await get_prisma_client() - _validate_plugin_source(request.source) - _validate_installation_preference(request) - - existing: Final[_PluginRecord | None] = await ClaudeCodePluginRepository(prisma_client).table.find_unique( - where={"name": plugin_name} - ) + repository: Final = ClaudeCodePluginRepository(prisma_client, use_writer=True) + existing: Final[_PluginRecord | None] = await repository.table.find_unique(where={"name": plugin_name}) if not existing: raise _error_response(404, f"Plugin '{plugin_name}' not found") + current_manifest: Final = MANIFEST_FIELDS.validate_json(existing.manifest_json or "{}") + if request.source != current_manifest.get("source"): + _validate_plugin_source(request.source) + _validate_installation_preference(request) + manifest: Final[Mapping[str, object]] = _build_plugin_manifest(plugin_name, request) - plugin: Final[_PluginRecord | None] = await ClaudeCodePluginRepository(prisma_client).table.update( + plugin: Final[_PluginRecord | None] = await repository.table.update( where={"name": plugin_name}, data={ "version": request.version, diff --git a/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_skill_access.py b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_skill_access.py index 9e5b55e7e36..4775498dec3 100644 --- a/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_skill_access.py +++ b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/claude_code_skill_access.py @@ -19,8 +19,11 @@ if TYPE_CHECKING: class _SkillRecord(Protocol): - name: str - enabled: bool + @property + def name(self) -> str: ... + + @property + def enabled(self) -> bool: ... def _skills_of(permission: LiteLLM_ObjectPermissionTable | None) -> frozenset[str]: diff --git a/litellm/proxy/anthropic_endpoints/claude_code_endpoints/skill_upload_archive.py b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/skill_upload_archive.py new file mode 100644 index 00000000000..48af819b673 --- /dev/null +++ b/litellm/proxy/anthropic_endpoints/claude_code_endpoints/skill_upload_archive.py @@ -0,0 +1,30 @@ +import json +import re +import struct +import zipfile +from typing import Final + +from litellm.proxy.discovery_endpoints.agent_skills_archive import pack_skill_archive, read_skill_archive_members + +MAX_SKILL_UPLOAD_BYTES: Final = 10 * 1024 * 1024 +MAX_SKILL_ARCHIVE_COMPRESSION_RATIO: Final = 50 + + +def build_uploaded_skill_archive(content: bytes, name: str) -> bytes | None: + if len(content) > MAX_SKILL_UPLOAD_BYTES or re.fullmatch(r"[a-z0-9-]+", name) is None: + return None + try: + skill: Final = read_skill_archive_members(content, for_upload=True) + if skill is None: + return None + members: Final = ((".claude-plugin/plugin.json", json.dumps({"name": name}).encode(), 0o644),) + tuple( + (f"skills/{name}/{path}", data, mode) for path, data, mode in skill + ) + compressed: Final = pack_skill_archive(members) + return ( + pack_skill_archive(members, zipfile.ZIP_STORED) + if sum(len(data) for _, data, _ in members) > len(compressed) * MAX_SKILL_ARCHIVE_COMPRESSION_RATIO + else compressed + ) + except (struct.error, UnicodeEncodeError): + return None diff --git a/litellm/proxy/anthropic_endpoints/endpoints.py b/litellm/proxy/anthropic_endpoints/endpoints.py index baa6602b042..7e990e3c71b 100644 --- a/litellm/proxy/anthropic_endpoints/endpoints.py +++ b/litellm/proxy/anthropic_endpoints/endpoints.py @@ -16,12 +16,14 @@ from litellm.anthropic_interface.exceptions import ( AnthropicExceptionMapping, ) from litellm.integrations.custom_guardrail import ModifyResponseException +from litellm.litellm_core_utils.agentic_followup_failure import is_agentic_followup_failure from litellm.litellm_core_utils.tool_fields import first_validation_error from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler from litellm.llms.anthropic.messages.context_management import ( AnthropicContextManagementError, ) from litellm.proxy._types import * +from litellm.proxy.anthropic_endpoints.answered_status import with_answered_status from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_request_processing import ( ProxyBaseLLMRequestProcessing, @@ -48,6 +50,7 @@ from litellm.proxy.common_utils.openai_error_payload import ( ) from litellm.proxy.common_utils.token_count_log import rejected_token_count from litellm.proxy.common_utils.token_count_settings import count_source_fields +from litellm.proxy.hooks.proxy_track_cost_callback import failure_row_response_cost from litellm.proxy.litellm_pre_call_utils import ( forwarded_anthropic_oauth_token, should_auto_drop_params_for_agentic_cli, @@ -225,15 +228,18 @@ async def anthropic_response( request_id=request.headers.get("x-request-id"), ) return JSONResponse(status_code=e.status_code, content=body) - except Exception as e: + except Exception as raised: + answered: Final = with_answered_status(raised) await proxy_logging_obj.post_call_failure_hook( - user_api_key_dict=user_api_key_dict, original_exception=e, request_data=base_llm_response_processor.data + user_api_key_dict=user_api_key_dict, + original_exception=answered, + request_data=base_llm_response_processor.data, ) - log_llm_api_exception(e, base_llm_response_processor.litellm_call_id) + log_llm_api_exception(answered, base_llm_response_processor.litellm_call_id) - if isinstance(e, ProxyException): + if isinstance(answered, ProxyException): return _anthropic_error_json_response( - with_litellm_call_id(e, base_llm_response_processor.litellm_call_id), request + with_litellm_call_id(answered, base_llm_response_processor.litellm_call_id), request ) # Extract model_id from request metadata (same as success path) @@ -247,23 +253,27 @@ async def anthropic_response( call_id=base_llm_response_processor.litellm_call_id, model_id=model_id, version=version, - response_cost=0, + response_cost=( + failure_row_response_cost(base_llm_response_processor.data) + if is_agentic_followup_failure(answered) + else 0 + ), model_region=getattr(user_api_key_dict, "allowed_model_region", ""), request_data=data, - timeout=getattr(e, "timeout", None), + timeout=getattr(answered, "timeout", None), litellm_logging_obj=None, ) - if isinstance(e, HTTPException): - return _anthropic_error_json_response(proxy_exception_from_http_exception(e, headers), request) + if isinstance(answered, HTTPException): + return _anthropic_error_json_response(proxy_exception_from_http_exception(answered, headers), request) - error_msg: Final = f"{e}" + error_msg: Final = f"{answered}" return _anthropic_error_json_response( ProxyException( - message=getattr(e, "message", error_msg), - type=openai_error_type(e, error_status_code(e, 500)), - param=openai_error_param(e), - code=error_status_code(e, 500), + message=getattr(answered, "message", error_msg), + type=openai_error_type(answered, error_status_code(answered, 500)), + param=openai_error_param(answered), + code=error_status_code(answered, 500), headers=headers, ), request, @@ -383,8 +393,9 @@ async def count_tokens( except ProxyException as e: return _anthropic_error_json_response(e, request) except Exception as e: - log_llm_api_exception(e, litellm_call_id) - return _anthropic_error_json_response(proxy_exception_from_route_error(e), request) + answered: Final = with_answered_status(e) + log_llm_api_exception(answered, litellm_call_id) + return _anthropic_error_json_response(proxy_exception_from_route_error(answered), request) else: return { "input_tokens": token_response.total_tokens, diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index 9d98e2c9973..af60a315c60 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -574,7 +574,6 @@ def is_model_cost_zero(model: str | list[str] | None, llm_router: RegistryReader return False continue try: - # Use the router registry's group info directly for better reliability model_group_info = llm_router.registry().group(model_name, include_hidden=True) if model_group_info is None: diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 1dd0647d25f..82135cb1b74 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -13,6 +13,7 @@ import re import secrets from collections.abc import Mapping, Sequence from datetime import datetime, timezone +from enum import Enum from types import MappingProxyType from typing import Final, NamedTuple, Protocol, Union, cast @@ -1576,10 +1577,14 @@ _JWT_AUTH_DISABLED_HINT = ( ) +class _JWTKeyMappingFailure(Enum): + NO_ELIGIBLE_KEY = "JWT Key Mapping: No eligible existing virtual key for this identity. Access denied." + + class _PendingAutoRegister(NamedTuple): """ Signal returned by ``_resolve_jwt_to_virtual_key`` when the JWT's claim is - unmapped and ``unregistered_jwt_client_behavior`` is AUTO_REGISTER. + unmapped and ``unregistered_jwt_client_behavior`` is AUTO_REGISTER or LINK_ONLY. The caller MUST run standard ``JWTAuthManager.auth_builder`` to apply RBAC, scope mappings, ``custom_validate``, and ``user_allowed_email_domain`` @@ -1593,23 +1598,22 @@ class _PendingAutoRegister(NamedTuple): claim_value: str cache_key: str jwt_issuer: str | None = None + behavior: UnregisteredJWTClientBehavior = UnregisteredJWTClientBehavior.AUTO_REGISTER def _claim_identifies_user(jwt_handler: JWTHandler, claim_field: str, jwt_issuer: str | None) -> bool: - if not jwt_handler.litellm_jwtauth.auto_register_map_existing_key: - return False if jwt_handler.litellm_jwtauth.is_user_identity_claim(claim_field, jwt_issuer): return True verbose_proxy_logger.warning( - "JWT Key Mapping (auto_register_map_existing_key): claim '%s' is not the user_id or user_email JWT field " - "and may be shared by several users, so a new key is minted instead of reusing one the user owns.", + "JWT Key Mapping: claim '%s' is not the user_id or user_email JWT field " + "and may be shared by several users, so existing user keys cannot be linked automatically.", claim_field, ) return False async def _reusable_key_hash_for_user(prisma_client: PrismaClient, user_id: str, team_id: str | None) -> str | None: - key: Final = await VerificationTokenRepository(prisma_client).find_newest_reusable_llm_api_key(user_id, team_id) + key: Final = await VerificationTokenRepository(prisma_client).find_newest_reusable_key(user_id, team_id) return None if key is None else key.token @@ -1628,11 +1632,13 @@ async def _auto_register_jwt_mapping( org_id: str | None = None, end_user_id: str | None = None, agent_id: str | None = None, -) -> UserAPIKeyAuth | None: + behavior: UnregisteredJWTClientBehavior = UnregisteredJWTClientBehavior.AUTO_REGISTER, +) -> UserAPIKeyAuth | _JWTKeyMappingFailure | None: """ Auto-register: create a new virtual key + mapping for an unrecognised JWT claim value, or point the mapping at a key the resolved user already owns - when ``auto_register_map_existing_key`` is set. ``team_id`` and ``user_id`` + when ``auto_register_map_existing_key`` is set. LINK_ONLY always searches + for an existing key and rejects when none is eligible. ``team_id`` and ``user_id`` must come from a successful ``JWTAuthManager.auth_builder`` run — they encode the JWT identity AFTER RBAC/scope/custom_validate/email-domain policy has been enforced. The key @@ -1650,11 +1656,21 @@ async def _auto_register_jwt_mapping( generate_key_helper_fn, ) + link_only: Final = behavior == UnregisteredJWTClientBehavior.LINK_ONLY + reuse_existing: Final = link_only or jwt_handler.litellm_jwtauth.auto_register_map_existing_key existing_token_hash: Final = ( - await _reusable_key_hash_for_user(prisma_client, user_id, team_id) - if user_id is not None and _claim_identifies_user(jwt_handler, virtual_key_claim_field, jwt_issuer) + await _reusable_key_hash_for_user( + prisma_client, + user_id, + team_id, + ) + if reuse_existing + and user_id is not None + and _claim_identifies_user(jwt_handler, virtual_key_claim_field, jwt_issuer) else None ) + if link_only and existing_token_hash is None: + return _JWTKeyMappingFailure.NO_ELIGIBLE_KEY minted: Final = existing_token_hash is None if existing_token_hash is not None: token_hash = existing_token_hash @@ -1691,8 +1707,8 @@ async def _auto_register_jwt_mapping( "jwt_claim_name": virtual_key_claim_field, "jwt_claim_value": claim_value, "token": token_hash, - "created_by": "auto_register", - "updated_by": "auto_register", + "created_by": behavior.value, + "updated_by": behavior.value, } ) except Exception as e: @@ -1704,8 +1720,8 @@ async def _auto_register_jwt_mapping( # the DB and the cleartext is in memory on this request. Delete it # so orphans don't accumulate under sustained concurrency. verbose_proxy_logger.debug( - "JWT Key Mapping (auto_register): unique conflict on create — " - "deleting orphaned virtual key and fetching winner's mapping for %s='%s'.", + "JWT Key Mapping (%s): unique conflict on create, fetching winner's mapping for %s='%s'.", + behavior.value, virtual_key_claim_field, claim_value, ) @@ -1735,7 +1751,7 @@ async def _auto_register_jwt_mapping( raise HTTPException( status_code=503, detail=( - "JWT Key Mapping: AUTO_REGISTER race resolution failed — " + f"JWT Key Mapping: {behavior.value.upper()} race resolution failed — " "winner's mapping was concurrently removed. Retry the request." ), ) @@ -1749,7 +1765,8 @@ async def _auto_register_jwt_mapping( ) verbose_proxy_logger.info( - "JWT Key Mapping (auto_register): %s virtual key for %s='%s'.", + "JWT Key Mapping (%s): %s virtual key for %s='%s'.", + behavior.value, "created new" if minted else "mapped existing", virtual_key_claim_field, claim_value, @@ -1821,7 +1838,7 @@ async def _resolve_jwt_to_virtual_key( - ``UserAPIKeyAuth``: a resolved virtual key (cache hit or DB hit). The caller may use this directly; JWT policy has been enforced previously (at key-creation time or, for cached results, before caching). - - ``_PendingAutoRegister``: claim is unmapped and behavior is AUTO_REGISTER. + - ``_PendingAutoRegister``: claim is unmapped and behavior is AUTO_REGISTER or LINK_ONLY. The caller MUST run ``JWTAuthManager.auth_builder`` to enforce JWT policy (RBAC, scope, custom_validate, email-domain), then invoke ``_auto_register_jwt_mapping`` with the validated team_id/user_id. @@ -1829,7 +1846,7 @@ async def _resolve_jwt_to_virtual_key( The caller falls through to standard team-based JWT auth (which itself enforces full JWT policy via auth_builder). - Raises HTTPException: REJECT policy hit, missing claim under - REJECT/AUTO_REGISTER, or other policy violations. + REJECT/AUTO_REGISTER/LINK_ONLY, or other policy violations. """ raw_issuer: Final = jwt_claims.get(JWTHandler.LITELLM_JWT_ISSUER_CLAIM) normalized_issuer: Final = raw_issuer if isinstance(raw_issuer, str) else None @@ -1856,6 +1873,7 @@ async def _resolve_jwt_to_virtual_key( if behavior in ( UnregisteredJWTClientBehavior.REJECT, UnregisteredJWTClientBehavior.AUTO_REGISTER, + UnregisteredJWTClientBehavior.LINK_ONLY, ): raise HTTPException( status_code=403, @@ -1887,7 +1905,7 @@ async def _resolve_jwt_to_virtual_key( status_code=403, detail=f"JWT Key Mapping: No registered mapping for {virtual_key_claim_field}='{claim_value}'. Access denied.", ) - if behavior == UnregisteredJWTClientBehavior.AUTO_REGISTER: + if behavior in (UnregisteredJWTClientBehavior.AUTO_REGISTER, UnregisteredJWTClientBehavior.LINK_ONLY): # Stale sentinel written under a prior fallback_team_mapping config — # evict it and defer auto-register to after auth_builder runs. Raise # the same 500 as the fresh-path AUTO_REGISTER branch when there is @@ -1897,7 +1915,7 @@ async def _resolve_jwt_to_virtual_key( raise HTTPException( status_code=500, detail=( - "JWT Key Mapping: AUTO_REGISTER requires a database connection. " + f"JWT Key Mapping: {behavior.value.upper()} requires a database connection. " "Configure a database or change unregistered_jwt_client_behavior." ), ) @@ -1907,6 +1925,7 @@ async def _resolve_jwt_to_virtual_key( claim_value=str(claim_value), cache_key=cache_key, jwt_issuer=normalized_issuer, + behavior=behavior, ) return None elif cached_mapping is not None: @@ -1967,12 +1986,12 @@ async def _resolve_jwt_to_virtual_key( detail=f"JWT Key Mapping: No registered mapping for {virtual_key_claim_field}='{claim_value}'. Access denied.", ) - if behavior == UnregisteredJWTClientBehavior.AUTO_REGISTER: + if behavior in (UnregisteredJWTClientBehavior.AUTO_REGISTER, UnregisteredJWTClientBehavior.LINK_ONLY): if prisma_client is None: raise HTTPException( status_code=500, detail=( - "JWT Key Mapping: AUTO_REGISTER requires a database connection. " + f"JWT Key Mapping: {behavior.value.upper()} requires a database connection. " "Configure a database or change unregistered_jwt_client_behavior." ), ) @@ -1985,6 +2004,7 @@ async def _resolve_jwt_to_virtual_key( claim_value=str(claim_value), cache_key=cache_key, jwt_issuer=normalized_issuer, + behavior=behavior, ) # FALLBACK_TEAM_MAPPING (default): cache the miss and return None so the @@ -2477,10 +2497,13 @@ async def user_api_key_auth_builder( org_id=org_id, end_user_id=end_user_id, agent_id=agent_id, + behavior=pending_auto_register.behavior, ) if pending_auto_register is not None and prisma_client is not None else None ) + if isinstance(auto_registered, _JWTKeyMappingFailure): + raise HTTPException(status_code=403, detail=auto_registered.value) if auto_registered is not None: auto_registered.jwt_claims = jwt_claims auto_registered.user_email = user_email @@ -2496,7 +2519,13 @@ async def user_api_key_auth_builder( falls_through_to_key_checks: Final = ( auto_registered is not None - and jwt_handler.litellm_jwtauth.auto_register_map_existing_key + and ( + jwt_handler.litellm_jwtauth.auto_register_map_existing_key + or ( + pending_auto_register is not None + and pending_auto_register.behavior == UnregisteredJWTClientBehavior.LINK_ONLY + ) + ) and master_key is not None ) if not falls_through_to_key_checks: diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 5c538c0f6bd..17e6d3ee411 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -55,6 +55,7 @@ from litellm.constants import ( from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.otel.runtime import phase_event from litellm.litellm_core_utils.add_retry_fallback_headers import get_hidden_params_dict +from litellm.litellm_core_utils.agentic_followup_failure import is_agentic_followup_failure from litellm.litellm_core_utils.bug_report import ( allowlisted, bug_report_notice, @@ -132,13 +133,13 @@ from litellm.proxy.common_utils.sse_keepalive import ( from litellm.proxy.dd_span_tagger import DDSpanTagger from litellm.proxy.guardrails.auto_router_compression import arm_pre_call as _arm_auto_router_compression from litellm.proxy.hooks.parallel_request_limiter_v3 import get_or_create_request_stash +from litellm.proxy.hooks.proxy_track_cost_callback import failure_row_response_cost from litellm.proxy.native_compaction import with_proxy_compaction_executor from litellm.proxy.route_llm_request import ( endpoint_unsupported_by_model, route_request, ) from litellm.proxy.spend_tracking.cost_finalization import finalize_proxy_response_cost, prepare_response_cost -from litellm.proxy.spend_tracking.usage_capture import capture_proxy_response from litellm.proxy.telemetry.request_context import bind_call_id as bind_telemetry_call_id from litellm.proxy.utils import ( # noqa: F401 # legacy module exports ProxyLogging, @@ -272,6 +273,7 @@ if TYPE_CHECKING: ProxyConfig = _ProxyConfig else: ProxyConfig = Any +from litellm.proxy.anthropic_endpoints.answered_status import with_answered_status from litellm.proxy.anthropic_endpoints.streaming_model_restamp import ( AnthropicStreamModelRestamper, ) @@ -1272,6 +1274,21 @@ def _sse_error_frames(error_obj: Mapping[str, object]) -> tuple[str, str]: return f"data: {json.dumps({'error': error_obj}, default=str)}\n\n", "data: [DONE]\n\n" +def _openai_late_failure_frames(exc: Exception) -> tuple[str, ...]: + _, error_obj = sse_error_payload(exc) + return _sse_error_frames(error_obj) + + +def _anthropic_late_failure_frames(exc: Exception) -> tuple[str, ...]: + answered: Final = proxy_exception_from_route_error(with_answered_status(exc)) + return ( + anthropic_error_sse_frame( + status_code=error_status_code(answered, status.HTTP_500_INTERNAL_SERVER_ERROR), + raw_message=answered.message, + ), + ) + + def _sse_stream_headers(headers: Mapping[str, str]) -> Mapping[str, str]: """`headers` plus the two that stop reverse proxies from buffering SSE (issue #28384).""" return MappingProxyType({**headers, **_TTFT_KEEPALIVE_HEADERS}) @@ -1520,6 +1537,7 @@ async def open_sse_before_first_byte( ping_interval_seconds: float | str | None, media_type: str = "text/event-stream", on_late_failure: Callable[[Exception], Awaitable[HTTPException | None]] | None = None, + late_failure_frames: Callable[[Exception], tuple[str, ...]] = _openai_late_failure_frames, ) -> _LateResponseT | StreamingResponse: """Write SSE keepalive comments while the upstream LLM call is still in flight. @@ -1565,8 +1583,7 @@ async def open_sse_before_first_byte( # would never fire and the failure would go unaudited. The hook # also gets to sanitize what reaches the client, by returning or # raising a replacement, so its answer decides the frame. - _, error_obj = sse_error_payload(await _sanitized_late_failure(exc, on_late_failure)) - for frame in _sse_error_frames(error_obj): + for frame in late_failure_frames(await _sanitized_late_failure(exc, on_late_failure)): yield frame.encode() return async for chunk in _relay_late_response(produced): @@ -1887,6 +1904,13 @@ def _response_cost_header_value(hidden_params: Mapping[str, object]) -> float | def cost_headers( response_cost: float | str | None, litellm_logging_obj: LiteLLMLoggingObj | None ) -> Mapping[str, str | None]: + if ( + response_cost == 0 + and litellm_logging_obj is not None + and litellm_logging_obj.prepared_cost is not None + and not litellm_logging_obj.prepared_cost.price_known + ): + return {} cost_breakdown: Final = _get_cost_breakdown_from_logging_obj( litellm_logging_obj=litellm_logging_obj, response_cost=response_cost ) @@ -2975,13 +2999,14 @@ class ProxyBaseLLMRequestProcessing: idle. See ``open_sse_before_first_byte``; unwrapped unless an operator sets ``litellm_settings.sse_keepalive_ping_interval_seconds``. """ + answers_as_anthropic: Final = route_type == "anthropic_messages" async def _audit_late_failure(exc: Exception) -> HTTPException | None: # Once a keepalive is on the wire this can no longer raise, so the # caller's `except` never runs its own post_call_failure_hook. return await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, - original_exception=exc, + original_exception=with_answered_status(exc) if answers_as_anthropic else exc, request_data=self.data, dispatched_call_type=self.route_type, ) @@ -3010,6 +3035,7 @@ class ProxyBaseLLMRequestProcessing: ), ping_interval_seconds=ttft_keepalive_interval(self.data, llm_router), on_late_failure=_audit_late_failure, + late_failure_frames=_anthropic_late_failure_frames if answers_as_anthropic else _openai_late_failure_frames, ) async def _process_llm_request( @@ -3370,9 +3396,6 @@ class ProxyBaseLLMRequestProcessing: ) ), ) - await capture_proxy_response( - logging_obj, response, background=_REQUEST_FIELDS.validate_python(self.data).get("background") is True - ) except (Exception, asyncio.CancelledError) as exc: _exception_raised = True if logging_obj.prepared_cost is not None: @@ -4377,6 +4400,7 @@ class ProxyBaseLLMRequestProcessing: """Raises ProxyException (OpenAI API compatible) if an exception is raised""" log_llm_api_exception(e, self.litellm_call_id) billing_logging_obj: Final = _REQUEST_FIELDS.validate_python(self.data).get("litellm_logging_obj") + failed_in_agentic_follow_up: Final = is_agentic_followup_failure(e) # Allow callbacks to transform the error response transformed_exception: Final = await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, @@ -4413,7 +4437,9 @@ class ProxyBaseLLMRequestProcessing: model_id=model_id, version=version, response_cost=( - _litellm_logging_obj.finalized_cost.amount + failure_row_response_cost(self.data) + if failed_in_agentic_follow_up + else _litellm_logging_obj.finalized_cost.amount if isinstance(_litellm_logging_obj, LiteLLMLoggingObj) and _litellm_logging_obj.finalized_cost is not None else 0 @@ -4634,6 +4660,7 @@ class ProxyBaseLLMRequestProcessing: request: Request | None = None, flush_tail: Callable[[], bytes] | None = None, seal_open_frame: Callable[[bytes], str] | None = None, + answer_status: Callable[[Exception], Exception] | None = None, ) -> AsyncGenerator[str, None]: """ Shared streaming data generator: runs proxy iterator hook, per-chunk hook, @@ -4649,6 +4676,9 @@ class ProxyBaseLLMRequestProcessing: relays raw upstream bytes, so an upstream that hangs up mid-frame leaves the client inside an open frame, where an error frame would be swallowed or misparsed instead of raised. + + ``answer_status`` gives a failure the status the route answers it with + before the failure hook records it. """ verbose_proxy_logger.debug("inside generator") # Resolve per-stream (not per-chunk) whether the heavy per-chunk path @@ -4745,6 +4775,7 @@ class ProxyBaseLLMRequestProcessing: verbose_proxy_logger.exception( "litellm.proxy.proxy_server.async_data_generator(): Exception occured - %s", e ) + answered: Final = e if answer_status is None else answer_status(e) already_reported: Final = ( isinstance(stream_logging_obj, LiteLLMLoggingObj) and not stream_logging_obj.claim_stream_failure_report() @@ -4754,24 +4785,25 @@ class ProxyBaseLLMRequestProcessing: if already_reported else await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, - original_exception=e, + original_exception=answered, request_data=request_data, ) ) - if transformed_exception is not None: - e = transformed_exception + failure: Final = answered if transformed_exception is None else transformed_exception verbose_proxy_logger.debug( "An error occurred: %s", - e, + failure, ) - if isinstance(e, HTTPException): - raise e - stream_error_status: Final = error_status_code(e, status.HTTP_500_INTERNAL_SERVER_ERROR) + if isinstance(failure, HTTPException): + if failure is e: + raise + raise failure from e + stream_error_status: Final = error_status_code(failure, status.HTTP_500_INTERNAL_SERVER_ERROR) proxy_exception: Final = ProxyException( - message=redact_internal_details_from_client_message(getattr(e, "message", str(e))), - type=openai_error_type(e, stream_error_status), - param=openai_error_param(e), + message=redact_internal_details_from_client_message(getattr(failure, "message", str(failure))), + type=openai_error_type(failure, stream_error_status), + param=openai_error_param(failure), code=stream_error_status, ) stream_completed = True @@ -4827,6 +4859,7 @@ class ProxyBaseLLMRequestProcessing: request=request, flush_tail=None if restamper is None else restamper.flush, seal_open_frame=seal_open_sse_frame, + answer_status=with_answered_status, ) @overload diff --git a/litellm/proxy/common_utils/claude_code_marketplace_auth.py b/litellm/proxy/common_utils/claude_code_marketplace_auth.py index 0cfb3061744..2cf9ae03327 100644 --- a/litellm/proxy/common_utils/claude_code_marketplace_auth.py +++ b/litellm/proxy/common_utils/claude_code_marketplace_auth.py @@ -1,10 +1,25 @@ from typing import Final +from fastapi import Request from pydantic import TypeAdapter, ValidationError +from litellm.types.proxy.auth.special_headers import SpecialHeaders + _AUTH_REQUIRED_SETTING: Final[TypeAdapter[bool | None]] = TypeAdapter(bool | None) +def marketplace_key_header_present(request: Request) -> bool: + """Recognize header credentials before an optional-auth archive chooses anonymity.""" + from litellm.proxy.proxy_server import general_settings_view + + configured_header: Final = general_settings_view().get("litellm_key_header_name") + return ( + SpecialHeaders.openai_authorization.value in request.headers + or SpecialHeaders.custom_litellm_api_key.value in request.headers + or (isinstance(configured_header, str) and configured_header in request.headers) + ) + + def marketplace_auth_required() -> bool: from litellm.proxy.proxy_server import general_settings_view diff --git a/litellm/proxy/common_utils/fips.py b/litellm/proxy/common_utils/fips.py new file mode 100644 index 00000000000..1f8404fc37c --- /dev/null +++ b/litellm/proxy/common_utils/fips.py @@ -0,0 +1,89 @@ +from collections.abc import Mapping +from typing import Final + +from litellm.secret_managers.main import str_to_bool + +FIPS_MODE_SETTING: Final = "fips_mode" +_SETTING: Final = f"general_settings.{FIPS_MODE_SETTING}" + + +class FipsModeError(Exception): + pass + + +def _fips_mode_setting(general_settings: Mapping[str, object]) -> bool | None: + """general_settings.fips_mode as a boolean: False when the key is absent, None when it is not a boolean, including + a key with no value.""" + if FIPS_MODE_SETTING not in general_settings: + return False + value: Final = general_settings[FIPS_MODE_SETTING] + if isinstance(value, bool): + return value + return str_to_bool(value) if isinstance(value, str) else None + + +def openssl_enforces_fips() -> bool: + """Python's OpenSSL runs with FIPS-only defaults and itself refuses MD5.""" + try: + import _hashlib + + if _hashlib.get_fips_mode() != 1: + return False + except (ImportError, AttributeError, ValueError): + return False + try: + _hashlib.new("md5", b"", usedforsecurity=True) + except ValueError: + return True + return False + + +def cryptography_enforces_fips() -> bool: + """The cryptography library's own OpenSSL runs with FIPS-only defaults and its FIPS provider can hash SHA-256.""" + try: + from cryptography.exceptions import InternalError, UnsupportedAlgorithm + from cryptography.hazmat.bindings._rust import openssl as rust_openssl + from cryptography.hazmat.primitives import hashes + except ImportError: + return False + try: + if not rust_openssl.is_fips_enabled(): + return False + hashes.Hash(hashes.SHA256()).finalize() + except (AttributeError, InternalError, UnsupportedAlgorithm): + return False + return True + + +def fips_mode_refusal(general_settings: Mapping[str, object]) -> str | None: + """Why the proxy must not start: general_settings.fips_mode is not a boolean, or is on but Python's or the + cryptography library's OpenSSL does not enforce FIPS. None when it may start.""" + setting: Final = _fips_mode_setting(general_settings) + if setting is None: + value: Final = general_settings.get(FIPS_MODE_SETTING) + if value is None: + return ( + f"{_SETTING} has no value: it is empty, or an os.environ/ reference to a variable that is not set. " + "Set it to true or false, or remove it." + ) + return f"{_SETTING}={value!r} is not a boolean. Set it to true or false, or remove it." + if not setting: + return None + if not openssl_enforces_fips(): + return ( + f"{_SETTING} is on but OpenSSL does not enforce FIPS. Run the proxy on an OpenSSL with its FIPS " + f"provider active and default_properties = fips=yes, or turn {_SETTING} off." + ) + if not cryptography_enforces_fips(): + return ( + f"{_SETTING} is on but the cryptography library's OpenSSL has no working FIPS provider. Build it " + "against the system OpenSSL (pip install --no-binary cryptography cryptography), or turn " + f"{_SETTING} off." + ) + return None + + +def enforce_fips_mode(general_settings: Mapping[str, object]) -> None: + refusal: Final = fips_mode_refusal(general_settings) + if refusal is not None: + raise FipsModeError(refusal) diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index baf586acef6..e210c337922 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -207,10 +207,10 @@ def _parse_form_metadata(raw_metadata: str) -> object: """The JSON body path rejects a lone surrogate escape (``"\\ud800"``) with a 400 because no provider or database can encode it. ``json.loads`` accepts the escape, so the multipart ``metadata`` field gets the same check.""" - parsed: Final[object] = json.loads(raw_metadata) try: + parsed: Final[object] = json.loads(raw_metadata) json.dumps(parsed, ensure_ascii=False).encode("utf-8") - except UnicodeEncodeError as e: + except (json.JSONDecodeError, UnicodeEncodeError) as e: raise ProxyException( message=f"Invalid form payload: metadata {e}", type="invalid_request_error", @@ -220,6 +220,11 @@ def _parse_form_metadata(raw_metadata: str) -> object: return parsed +def _reraise_request_size_error(error: Exception) -> None: + if isinstance(error, HTTPException) and error.status_code == status.HTTP_413_CONTENT_TOO_LARGE: + raise error + + async def _read_form_data(request: Request) -> FormData: try: form_data: Final = await request.form() @@ -227,6 +232,7 @@ async def _read_form_data(request: Request) -> FormData: except ClientDisconnect: raise except Exception as e: + _reraise_request_size_error(e) # ``request.form()`` raises on malformed multipart (missing # boundary, malformed chunk encoding, …). Surface as 400 so # the auth-time pre-read does not silently cache ``{}`` while @@ -377,6 +383,7 @@ async def read_request_body(request: Request | None) -> dict: raise disconnected from e except Exception as e: # Catch unexpected errors to avoid crashes + _reraise_request_size_error(e) verbose_proxy_logger.exception("Unexpected error reading request body - %s", e) return {} @@ -814,10 +821,6 @@ def populate_request_with_path_params(request_data: dict, request: Request) -> d request_data["vector_store_ids"] = [value] continue request_data.setdefault(key, value) - verbose_proxy_logger.debug( - "populate_request_with_path_params: Found path_params, vector_store_ids=%s", - request_data.get("vector_store_ids"), - ) return request_data # Fallback: parse the URL path directly to extract vector_store_id @@ -853,7 +856,3 @@ def _add_vector_store_id_from_path(request_data: dict, request: Request) -> None existing_ids.append(vector_store_id) else: request_data["vector_store_ids"] = [vector_store_id] - verbose_proxy_logger.debug( - "populate_request_with_path_params: Updated request_data with vector_store_ids=%s", - request_data.get("vector_store_ids"), - ) diff --git a/litellm/proxy/common_utils/model_deprecation.py b/litellm/proxy/common_utils/model_deprecation.py index 5c764d1a33e..b86ebd830f9 100644 --- a/litellm/proxy/common_utils/model_deprecation.py +++ b/litellm/proxy/common_utils/model_deprecation.py @@ -32,6 +32,7 @@ class _ResolvedDeprecation: litellm_model: str | None litellm_provider: str | None copied_from_another_row: bool = False + successor_model: str | None = None def _parse_deprecation_date(raw_value: object) -> date | None: @@ -58,10 +59,11 @@ def _cost_map_lookup(model_key: str | None, catalog: Mapping[str, object]) -> _R entry: Final = litellm.model_cost.get(model_key) if not isinstance(entry, Mapping): return None - parsed: Final = _parse_deprecation_date(entry.get("deprecation_date")) + fields: Final[Mapping[str, object]] = entry + parsed: Final = _parse_deprecation_date(fields.get("deprecation_date")) if parsed is None: return None - provider: Final = entry.get("litellm_provider") + provider: Final = fields.get("litellm_provider") return _ResolvedDeprecation( deprecation_date=parsed, litellm_model=model_key, @@ -69,6 +71,7 @@ def _cost_map_lookup(model_key: str | None, catalog: Mapping[str, object]) -> _R copied_from_another_row=isinstance(entry, AliasedCostEntry) and "deprecation_date" not in entry.registered_fields and model_key not in catalog, + successor_model=_successor_model(fields.get("successor_model")), ) @@ -127,15 +130,6 @@ def _resolve_deployment_deprecation( litellm_params: Final = _mapping_field(deployment, "litellm_params") model_key: Final = _as_model_key(litellm_params.get("model")) - override: Final = _parse_deprecation_date(model_info.get("deprecation_date")) - if override is not None: - override_provider: Final = model_info.get("litellm_provider") - return _ResolvedDeprecation( - deprecation_date=override, - litellm_model=model_key, - litellm_provider=override_provider if isinstance(override_provider, str) else None, - ) - provider: Final = _deployment_provider(litellm_params) base_model: Final = _as_model_key(model_info.get("base_model")) model_name: Final = model_key.split("/", 1)[1] if model_key is not None and "/" in model_key else model_key @@ -150,7 +144,18 @@ def _resolve_deployment_deprecation( if pick is not None ) own_rows: Final = tuple(pick for pick in picks if not pick.copied_from_another_row) - return min(own_rows or picks, key=lambda pick: pick.deprecation_date, default=None) + from_catalog: Final = min(own_rows or picks, key=lambda pick: pick.deprecation_date, default=None) + + override: Final = _parse_deprecation_date(model_info.get("deprecation_date")) + if override is None: + return from_catalog + override_provider: Final = model_info.get("litellm_provider") + return _ResolvedDeprecation( + deprecation_date=override, + litellm_model=model_key, + litellm_provider=override_provider if isinstance(override_provider, str) else None, + successor_model=from_catalog.successor_model if from_catalog is not None else None, + ) def _classify(days_until: int, warn_within_days: int) -> DeprecationStatus: @@ -182,7 +187,7 @@ def _build_info( today: date, warn_within_days: int, catalog: Mapping[str, object], -) -> ModelDeprecationInfo | None: +) -> _DeploymentInfo | None: model_name: Final = deployment.get("model_name") if not isinstance(model_name, str) or not model_name: return None @@ -192,21 +197,40 @@ def _build_info( return None days_until: Final = (resolved.deprecation_date - today).days - return ModelDeprecationInfo( - model_name=model_name, - litellm_model=resolved.litellm_model, - deprecation_date=resolved.deprecation_date, - days_until_deprecation=days_until, - status=_classify(days_until, warn_within_days), - litellm_provider=resolved.litellm_provider, - successor_model=_checked_successor(model_name, _mapping_field(deployment, "model_info"), llm_router), + configured: Final = _checked_successor(model_name, _mapping_field(deployment, "model_info"), llm_router) + return _DeploymentInfo( + info=ModelDeprecationInfo( + model_name=model_name, + litellm_model=resolved.litellm_model, + deprecation_date=resolved.deprecation_date, + days_until_deprecation=days_until, + status=_classify(days_until, warn_within_days), + litellm_provider=resolved.litellm_provider, + successor_model=configured or resolved.successor_model, + ), + configured_successor=configured is not None, ) -def _merge_group(group: Sequence[ModelDeprecationInfo]) -> ModelDeprecationInfo: - """Keep the group's first deployment in config order, naming the first successor any of its deployments names""" - first: Final = group[0] - successors: Final = tuple(model.successor_model for model in group if model.successor_model is not None) +@dataclass(frozen=True, slots=True) +class _DeploymentInfo: + info: ModelDeprecationInfo + configured_successor: bool + + +def _merge_group(group: Sequence[_DeploymentInfo]) -> ModelDeprecationInfo: + """Keep the group's first deployment in config order, naming the first successor any of its deployments names, + a successor configured in model_info ahead of one the cost map supplies""" + first: Final = group[0].info + tiers: Final = tuple( + tuple( + deployment.info.successor_model + for deployment in group + if deployment.configured_successor is configured and deployment.info.successor_model is not None + ) + for configured in (True, False) + ) + successors: Final = next((tier for tier in tiers if tier), ()) if not successors: return first if len(frozenset(successors)) > 1: @@ -220,13 +244,13 @@ def _merge_group(group: Sequence[ModelDeprecationInfo]) -> ModelDeprecationInfo: def _dedupe( - models: Sequence[ModelDeprecationInfo], + models: Sequence[_DeploymentInfo], ) -> tuple[ModelDeprecationInfo, ...]: """Report a model group carrying the same date on several deployments once""" - ordered: Final = sorted(models, key=lambda model: (model.model_name, model.deprecation_date)) + ordered: Final = sorted(models, key=lambda model: (model.info.model_name, model.info.deprecation_date)) return tuple( _merge_group(tuple(group)) - for _, group in groupby(ordered, key=lambda model: (model.model_name, model.deprecation_date)) + for _, group in groupby(ordered, key=lambda model: (model.info.model_name, model.info.deprecation_date)) ) diff --git a/litellm/proxy/credential_endpoints/endpoints.py b/litellm/proxy/credential_endpoints/endpoints.py index 66bd9d97c91..ae9b84eeb05 100644 --- a/litellm/proxy/credential_endpoints/endpoints.py +++ b/litellm/proxy/credential_endpoints/endpoints.py @@ -902,8 +902,6 @@ def update_db_credential( # update model info if encrypted_credential.credential_info: """Update credential info""" - if "credential_info" not in merged_credential.credential_info: - merged_credential.credential_info = {} merged_credential.credential_info.update(encrypted_credential.credential_info) return merged_credential diff --git a/litellm/proxy/db/accounting_ledger.py b/litellm/proxy/db/accounting_ledger.py deleted file mode 100644 index 0a9c5db1619..00000000000 --- a/litellm/proxy/db/accounting_ledger.py +++ /dev/null @@ -1,352 +0,0 @@ -from __future__ import annotations - -import hashlib -import json -from collections.abc import AsyncGenerator, Callable -from contextlib import AbstractAsyncContextManager, asynccontextmanager -from dataclasses import dataclass -from datetime import datetime, timedelta, timezone -from typing import TYPE_CHECKING, Final, Literal, TypeAlias - -from pydantic import AwareDatetime, ConfigDict, JsonValue, TypeAdapter -from typing_extensions import assert_never - -from litellm.accounting import CostEvent, DeliveryTarget, EventKind, InboundUsageEvent, OutboundUsageEvent -from litellm.proxy.db.db_span import db_span -from litellm.proxy.db.routing_prisma_wrapper import writer_wrapper - -if TYPE_CHECKING: - from prisma import Prisma - - from litellm.proxy.utils import PrismaClient - -AccountingEvent: TypeAlias = InboundUsageEvent | OutboundUsageEvent | CostEvent -_JSON: Final = TypeAdapter(dict[str, JsonValue]) -_INSTANT: Final[TypeAdapter[datetime]] = TypeAdapter(AwareDatetime) -_STRINGS: Final = TypeAdapter(tuple[dict[str, str], ...]) -_TIMEOUT: Final = timedelta(seconds=10) - - -class AccountingConflictError(ValueError): - pass - - -@dataclass(frozen=True, slots=True) -class UsageWrite: - event: InboundUsageEvent | OutboundUsageEvent - envelope: str - targets: tuple[DeliveryTarget, ...] = () - - -_Transaction: TypeAlias = AbstractAsyncContextManager["Prisma"] - - -@dataclass(frozen=True, slots=True) -class _AcceptedEvent: - event_id: str - kind: EventKind - source_event_id: str | None - occurred_at: datetime - payload: str - payload_hash: str - targets: tuple[str, ...] - schema_version: int - version: int - - -def _json(value: object) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False) - - -def _prepare( - event: AccountingEvent, - targets: tuple[DeliveryTarget, ...], - envelope: str, - schema_version: int, - version: int, -) -> _AcceptedEvent: - if type(schema_version) is not int or type(version) is not int or min(schema_version, version) < 1: - raise ValueError("Accounting versions must be positive integers") - validated: Final = type(event).model_validate_json(event.model_dump_json()) - payload: Final = _JSON.validate_json(envelope) - if payload.get("event") != validated.model_dump(mode="json"): - raise ValueError("Accounting envelope must contain the matching public event") - destinations: Final = tuple(sorted(frozenset(DeliveryTarget(target).value for target in targets))) - serialized: Final = _json(payload) - identity: Final = _json((schema_version, version, destinations, payload)) - kind: Final = ( - EventKind.COST - if isinstance(validated, CostEvent) - else EventKind.INBOUND_USAGE - if isinstance(validated, InboundUsageEvent) - else EventKind.OUTBOUND_USAGE - ) - return _AcceptedEvent( - event_id=validated.event_id, - kind=kind, - source_event_id=validated.usage_event_id if isinstance(validated, CostEvent) else None, - occurred_at=validated.occurred_at if isinstance(validated, CostEvent) else validated.ended_at, - payload=serialized, - payload_hash=hashlib.sha256(identity.encode()).hexdigest(), - targets=destinations, - schema_version=schema_version, - version=version, - ) - - -_INSERT_EVENT: Final = """ -INSERT INTO "LiteLLM_AccountingEvent" - (event_id, kind, source_event_id, schema_version, payload_hash, occurred_at, payload) -VALUES ($1, $2::"AccountingEventKind", $3, $4::int, $5, $6::timestamp, $7::jsonb) -ON CONFLICT (event_id) DO NOTHING RETURNING event_id -""" -_INSERT_DELIVERIES: Final = """ -INSERT INTO "LiteLLM_AccountingDelivery" (event_id, target, version) -SELECT $1, value::"AccountingDeliveryTarget", $3::int FROM jsonb_array_elements_text($2::jsonb) -""" -_LOCK_SOURCE: Final = """ -SELECT event_id FROM "LiteLLM_AccountingEvent" -WHERE event_id = $1 AND kind = 'outbound_usage' FOR UPDATE -""" -_EXISTING_COST: Final = """ -SELECT event_id FROM "LiteLLM_AccountingEvent" WHERE kind = 'cost' AND source_event_id = $1 -""" -_LOCK_EVENT: Final = """ -SELECT payload_hash FROM "LiteLLM_AccountingEvent" WHERE event_id = $1 FOR UPDATE -""" -_INSERT_USAGE_BATCH: Final = """ -INSERT INTO "LiteLLM_AccountingEvent" - (event_id, kind, schema_version, payload_hash, occurred_at, payload) -SELECT event_id, kind::"AccountingEventKind", 1, payload_hash, occurred_at::timestamp, payload -FROM jsonb_to_recordset($1::jsonb) AS records( - event_id text, kind text, payload_hash text, occurred_at text, payload jsonb) -ORDER BY event_id -ON CONFLICT (event_id) DO NOTHING RETURNING event_id -""" -_LOCK_USAGE_BATCH: Final = """ -SELECT event_id, payload_hash FROM "LiteLLM_AccountingEvent" -WHERE event_id IN (SELECT value FROM jsonb_array_elements_text($1::jsonb)) -ORDER BY event_id FOR UPDATE -""" -_LOAD_USAGE_BATCH: Final = """ -SELECT event.payload, ARRAY( - SELECT target::text FROM "LiteLLM_AccountingDelivery" delivery - WHERE delivery.event_id = event.event_id ORDER BY target::text -) AS targets -FROM "LiteLLM_AccountingEvent" event -WHERE event.event_id IN (SELECT value FROM jsonb_array_elements_text($1::jsonb)) - AND event.kind = 'outbound_usage' AND event.schema_version = 1 -""" -_INSERT_USAGE_DELIVERIES: Final = """ -INSERT INTO "LiteLLM_AccountingDelivery" (event_id, target, version) -SELECT records.event_id, destination.value::"AccountingDeliveryTarget", 1 -FROM jsonb_to_recordset($1::jsonb) AS records(event_id text, targets jsonb) -CROSS JOIN LATERAL jsonb_array_elements_text(records.targets) AS destination -""" -_COMPLETED_FAMILY: Final = """ -root.source_event_id IS NULL AND root.kind IN ('inbound_usage', 'outbound_usage') -AND root.recorded_at < $1::timestamp -AND (root.kind = 'inbound_usage' OR EXISTS ( - SELECT 1 FROM "LiteLLM_AccountingEvent" AS cost WHERE cost.source_event_id = root.event_id AND cost.kind = 'cost' -)) -AND NOT EXISTS ( - SELECT 1 FROM "LiteLLM_AccountingEvent" AS member - WHERE (member.event_id = root.event_id OR (member.kind = 'cost' AND member.source_event_id = root.event_id)) - AND (member.recorded_at >= $1::timestamp - OR EXISTS (SELECT 1 FROM "LiteLLM_AccountingDelivery" AS delivery - WHERE delivery.event_id = member.event_id AND delivery.state != 'applied')) -) -""" -_RETENTION_CANDIDATES: Final = ( - 'SELECT root.event_id FROM "LiteLLM_AccountingEvent" AS root WHERE ' - + _COMPLETED_FAMILY - + " ORDER BY root.recorded_at, root.event_id LIMIT $2::int FOR UPDATE OF root SKIP LOCKED" -) -_ELIGIBLE_GROUP: Final = ( - 'SELECT root.event_id FROM "LiteLLM_AccountingEvent" AS root WHERE root.event_id = $2 AND ' + _COMPLETED_FAMILY -) -_DELETE_DELIVERIES: Final = """ -DELETE FROM "LiteLLM_AccountingDelivery" WHERE event_id IN ( - SELECT event_id FROM "LiteLLM_AccountingEvent" WHERE event_id = $1 OR (kind = 'cost' AND source_event_id = $1) -) -""" -_DELETE_EVENTS: Final = """ -DELETE FROM "LiteLLM_AccountingEvent" WHERE event_id = $1 OR (kind = 'cost' AND source_event_id = $1) -""" - - -_Conflict: TypeAlias = Literal["missing_source", "competing_cost", "identity_conflict"] - - -async def _accept(db: Prisma, record: _AcceptedEvent) -> Literal["inserted", "duplicate"] | _Conflict: - if record.source_event_id is not None: - source: Final = _STRINGS.validate_python(await db.query_raw(_LOCK_SOURCE, record.source_event_id)) - if not source: - return "missing_source" - costs: Final = _STRINGS.validate_python(tuple(await db.query_raw(_EXISTING_COST, record.source_event_id))) - if any(cost["event_id"] != record.event_id for cost in costs): - return "competing_cost" - inserted: Final = _STRINGS.validate_python( - await db.query_raw( - _INSERT_EVENT, - record.event_id, - record.kind.value, - record.source_event_id, - record.schema_version, - record.payload_hash, - record.occurred_at.astimezone(timezone.utc).isoformat(), - record.payload, - ) - ) - if not inserted: - existing: Final = _STRINGS.validate_python(tuple(await db.query_raw(_LOCK_EVENT, record.event_id))) - if not existing or existing[0]["payload_hash"] != record.payload_hash: - return "identity_conflict" - return "duplicate" - await db.execute_raw(_INSERT_DELIVERIES, record.event_id, _json(record.targets), record.version) - return "inserted" - - -async def _delete_completed(db: Prisma, event_id: str, cutoff: str) -> int: - if not _STRINGS.validate_python(await db.query_raw(_ELIGIBLE_GROUP, cutoff, event_id)): - return 0 - await db.execute_raw(_DELETE_DELIVERIES, event_id) - return await db.execute_raw(_DELETE_EVENTS, event_id) - - -class AccountingLedger: - def __init__(self, transaction: Callable[[], _Transaction]) -> None: - self._transaction: Final = transaction - - @classmethod - def for_client(cls, client: PrismaClient) -> AccountingLedger: - from prisma.client import TransactionManager - - managers: Final = TypeAdapter(TransactionManager, config=ConfigDict(arbitrary_types_allowed=True)) - - @asynccontextmanager - async def transaction() -> AsyncGenerator[Prisma]: - primary: Final = writer_wrapper(client.db) - manager: Final = managers.validate_python(primary.tx(timeout=_TIMEOUT)) - async with db_span("accounting_ledger", "LiteLLM_AccountingEvent"), manager as db: - await db.execute_raw("SET LOCAL statement_timeout = 5000") - await db.execute_raw("SET LOCAL lock_timeout = 1000") - yield db - - return cls(transaction) - - async def insert_usage_batch(self, writes: tuple[UsageWrite, ...]) -> None: - records: Final = tuple(_prepare(write.event, write.targets, write.envelope, 1, 1) for write in writes) - if not records: - return - unique: Final = {record.event_id: record for record in records} - if any(record.payload_hash != unique[record.event_id].payload_hash for record in records): - raise AccountingConflictError("Batch contains conflicting copies of a usage identity") - batch: Final = _json( - tuple( - { - "event_id": record.event_id, - "kind": record.kind.value, - "payload_hash": record.payload_hash, - "occurred_at": record.occurred_at.astimezone(timezone.utc).isoformat(), - "payload": _JSON.validate_json(record.payload), - } - for record in unique.values() - ) - ) - async with self._transaction() as db: - inserted: Final = _STRINGS.validate_python(await db.query_raw(_INSERT_USAGE_BATCH, batch)) - accepted: Final = _STRINGS.validate_python(await db.query_raw(_LOCK_USAGE_BATCH, _json(tuple(unique)))) - if len(accepted) != len(unique) or any( - row["payload_hash"] != unique[row["event_id"]].payload_hash for row in accepted - ): - raise AccountingConflictError("Usage identity conflicts with accepted content or delivery set") - deliveries: Final = _json( - tuple({"event_id": row["event_id"], "targets": unique[row["event_id"]].targets} for row in inserted) - ) - await db.execute_raw(_INSERT_USAGE_DELIVERIES, deliveries) - - async def load_usage_batch(self, event_ids: tuple[str, ...]) -> tuple[UsageWrite, ...]: - if not event_ids: - return () - async with self._transaction() as db: - rows: Final = TypeAdapter(tuple[dict[str, JsonValue], ...]).validate_python( - await db.query_raw(_LOAD_USAGE_BATCH, _json(event_ids)) - ) - return tuple( - UsageWrite( - OutboundUsageEvent.model_validate_json(_json(_JSON.validate_python(row["payload"])["event"])), - _json(row["payload"]), - TypeAdapter(tuple[DeliveryTarget, ...]).validate_python(row["targets"]), - ) - for row in rows - ) - - async def insert_event( - self, - event: AccountingEvent, - targets: tuple[DeliveryTarget, ...], - *, - envelope: str, - schema_version: int = 1, - version: int = 1, - ) -> Literal["inserted", "duplicate"]: - """Accept T5/T6's serialized envelope, containing its public event under `event`. - - The immutable acceptance hash includes the target set and delivery version. - Changing either for an accepted event conflicts. T7 must provide a separate - add-target or new-version workflow that preserves receipts and prevents - duplicate contributions; redelivery cannot silently create or reset work. - - The Prisma adapter opens one interactive transaction per event, including - two SET LOCAL statements and a source-row lock for costs. I/O is async, - but awaiting this method inline adds commit latency per event, potentially - several times per request. T5 owns bounded asynchronous capture and an - explicit batching strategy; looping over this method does not batch commits. - Success acknowledges commit, independently of HTTP response acknowledgement. - """ - record: Final = _prepare(event, targets, envelope, schema_version, version) - async with self._transaction() as db: - result: Final = await _accept(db, record) - match result: - case "inserted" | "duplicate": - return result - case "missing_source": - raise AccountingConflictError("Cost source must be a durably accepted outbound usage event") - case "competing_cost": - raise AccountingConflictError("Usage event already has an authoritative cost") - case "identity_conflict": - raise AccountingConflictError("Event identity conflicts with its accepted content or delivery set") - case _: - assert_never(result) - - async def purge_completed( - self, - *, - redelivery_before: datetime | None = None, - recovery_before: datetime | None = None, - limit: int = 100, - ) -> int: - """Delete at most `limit` completed families once both retention owners permit it. - - T8 must supply a coherent recovery watermark including outstanding holds. - Omitting either watermark disables deletion. Each usage row and its cost - remain together so a retained charge never loses its source or vice versa. - Applied receipts are deleted with the family, ending its retained dedup - protection. The caller must guarantee events recorded before - redelivery_before cannot be redelivered through supported paths. - T8's recovery_before must also protect checkpoints, - outstanding holds and unfinished settlements; age alone is insufficient. - """ - if type(limit) is not int or not 1 <= limit <= 1000: - raise ValueError("Accounting retention batch size must be between 1 and 1000") - if redelivery_before is None or recovery_before is None: - return 0 - cutoff: Final = min( - _INSTANT.validate_python(redelivery_before).astimezone(timezone.utc), - _INSTANT.validate_python(recovery_before).astimezone(timezone.utc), - ).isoformat() - async with self._transaction() as db: - roots: Final = _STRINGS.validate_python(tuple(await db.query_raw(_RETENTION_CANDIDATES, cutoff, limit))) - deleted: Final = tuple([await _delete_completed(db, root["event_id"], cutoff) for root in roots]) - return sum(deleted) diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index d9fb236632b..35e7ae1c579 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -34,6 +34,7 @@ from litellm.constants import ( DB_SPEND_UPDATE_JOB_NAME, INTERNAL_CALL_ORIGIN_METADATA_KEY, ) +from litellm.litellm_core_utils.batch_utils import BATCH_COST_REQUEST_ID_SUFFIX from litellm.litellm_core_utils.core_helpers import finite_cost, single_log_line from litellm.litellm_core_utils.litellm_logging import coerce_model_access_groups from litellm.litellm_core_utils.safe_json_loads import safe_json_loads @@ -92,6 +93,7 @@ from litellm.proxy.spend_tracking.spend_log_error_logger import spend_log_error from litellm.repositories.prisma_protocols import BatchTable from litellm.responses.streaming_iterator import spawn_logging_task from litellm.types.utils import CallTypes +from litellm.types.videos.utils import VIDEO_COST_REQUEST_ID_SUFFIX, VIDEO_STATUS_CALL_TYPES if TYPE_CHECKING: from litellm.proxy.db.autorouter_session_rollup import AutoRouterTurnTransaction @@ -158,23 +160,53 @@ def _org_member_transaction_key(org_id: str, user_id: str) -> str: return f"organization_id::{quote(org_id, safe='')}::user_id::{quote(user_id, safe='')}" -def _is_batch_cost_row(payload: SpendLogsPayload) -> bool: - return payload.get("call_type") == CallTypes.aretrieve_batch.value and payload.get("status") == "success" +async def _track_guardrail_usage_of_cost_row( + payload: SpendLogsPayload, prisma_client: "PrismaClient", disable_spend_logs: bool +) -> None: + """A job's cost row skips the spend log queue, so its guardrail runs are counted here instead of at the flush.""" + if disable_spend_logs: + return + from litellm.proxy.guardrails.usage_tracking import process_spend_logs_guardrail_usage + + try: + await process_spend_logs_guardrail_usage(prisma_client=prisma_client, logs_to_process=(payload,)) + except Exception as e: # noqa: BLE001 # guardrail usage is best effort, as at the spend log flush + verbose_proxy_logger.warning( + "Guardrail usage tracking failed for spend row %s (non-fatal): %s", payload["request_id"], e + ) -_BATCH_COST_CLAIM_FIELDS: Final = frozenset({"request_id", "call_type", "spend", "startTime", "endTime", "status"}) +def _is_settle_once_cost_row(payload: SpendLogsPayload) -> bool: + """A poll of a job whose cost is charged once, by the first poll that sees it, however often it is polled. + + A batch's cost is known only when a retrieve sees it finished (LIT-7048). A video's cost is + charged at create by the built-in providers, but a custom provider may report it on the + status poll that sees the video completed. Only a row carrying the job's cost suffix settles + the job, so a poll that pays only its guardrails, under an id that client call ids cannot + turn into the job's, is charged like any other request. + """ + if payload.get("status") != "success": + return False + call_type: Final = payload.get("call_type") + request_id: Final = payload["request_id"] + if call_type == CallTypes.aretrieve_batch.value: + return request_id.endswith(BATCH_COST_REQUEST_ID_SUFFIX) + return call_type in VIDEO_STATUS_CALL_TYPES and request_id.endswith(VIDEO_COST_REQUEST_ID_SUFFIX) -def _batch_cost_row_to_write(payload: SpendLogsPayload, disable_spend_logs: bool) -> Mapping[str, object]: - """Reduce a batch's cost row to what tells the retrieves apart when logging is off. +_COST_CLAIM_FIELDS: Final = frozenset({"request_id", "call_type", "spend", "startTime", "endTime", "status"}) - A proxy run with spend logs disabled still needs one row per batch to charge it once, + +def _cost_row_to_write(payload: SpendLogsPayload, disable_spend_logs: bool) -> Mapping[str, object]: + """Reduce a job's cost row to what tells its polls apart when logging is off. + + A proxy run with spend logs disabled still needs one row per job to charge it once, so the row is written either way, but it carries no request of its own: no metadata, no requester IP, no key, model, or token counts (LIT-7048). """ if disable_spend_logs is False: return payload - return MappingProxyType({field: value for field, value in payload.items() if field in _BATCH_COST_CLAIM_FIELDS}) + return MappingProxyType({field: value for field, value in payload.items() if field in _COST_CLAIM_FIELDS}) class _SpendIncrement(TypedDict): @@ -613,7 +645,7 @@ class DBSpendUpdateWriter: ) -> bool: """Record the request's spend, answering whether its cost still needs charging. - False only for a batch retrieve whose cost row another retrieve already wrote, + False only for a batch or video poll whose cost row another poll already wrote, so the caller leaves the key, team, and user counters alone (LIT-7048). """ from litellm.proxy.proxy_server import ( @@ -755,24 +787,24 @@ class DBSpendUpdateWriter: disable_spend_logs: bool, llm_router: "Router | None" = None, ) -> bool: - if prisma_client is not None and _is_batch_cost_row(payload): - return await self._claim_batch_cost_spend_log( + if prisma_client is not None and _is_settle_once_cost_row(payload): + return await self._claim_settle_once_cost_row( payload=payload, prisma_client=prisma_client, disable_spend_logs=disable_spend_logs ) if disable_spend_logs is False: await self._insert_spend_log_to_db(payload=payload, prisma_client=prisma_client, llm_router=llm_router) return True - async def _claim_batch_cost_spend_log( + async def _claim_settle_once_cost_row( self, payload: SpendLogsPayload, prisma_client: "PrismaClient", disable_spend_logs: bool ) -> bool: - """Write the batch's cost row now, or learn that another retrieve already did. + """Write the job's cost row now, or learn that another poll already did. - Every retrieve of one batch shares this row, so the insert that lands first owns + Every poll of one batch or video shares this row, so the insert that lands first owns the charge and every later one finds the row and charges nothing (LIT-7048). Only - a row that recorded a charge counts: a failed retrieve, a request whose client - picked the batch id as its call id, and the $0 row an older proxy left behind - while the batch was still running all leave the charge to be made. + a row that recorded a charge counts: a failed poll, a request whose client + picked the job id as its call id, and the $0 row a poll left behind + while the job was still running all leave the charge to be made. """ from litellm.repositories.table_repositories import SpendLogsRepository @@ -783,7 +815,7 @@ class DBSpendUpdateWriter: ) row: Final = spend_log_row_with_retained_metadata( - _batch_cost_row_to_write(payload, disable_spend_logs), configured_spend_logs_metadata_fields() + _cost_row_to_write(payload, disable_spend_logs), configured_spend_logs_metadata_fields() ) spend_logs: Final = SpendLogsRepository(prisma_client).table try: @@ -791,35 +823,40 @@ class DBSpendUpdateWriter: data=[prisma_client.jsonify_object(row)], skip_duplicates=True, ) - if claimed == 1: - return True - existing: Final = await spend_logs.find_unique(where={"request_id": request_id}) + existing: Final = None if claimed == 1 else await spend_logs.find_unique(where={"request_id": request_id}) except Exception as e: # noqa: BLE001 # prisma raises its own hierarchy; an unreachable DB queues the row like any other spend log verbose_proxy_logger.warning( - "Could not claim spend row %s for a batch's cost, queueing it: %s", request_id, e + "Could not claim spend row %s for a job's cost, queueing it: %s", request_id, e ) await self._insert_spend_log_to_db(payload=prisma_client.jsonify_object(row), prisma_client=prisma_client) return True - if existing is None or existing.call_type != CallTypes.aretrieve_batch.value or existing.status != "success": + if claimed == 1: + await _track_guardrail_usage_of_cost_row(payload, prisma_client, disable_spend_logs) + return True + if existing is None or existing.call_type != payload["call_type"] or existing.status != "success": verbose_proxy_logger.warning( - "Spend row %s belongs to a %s request, so this batch's cost is charged without a row of its own", + "Spend row %s belongs to a %s request, so this job's cost is charged without a row of its own", request_id, getattr(existing, "call_type", None), ) return True if existing.spend > 0: - verbose_proxy_logger.debug("Cost tracking skipped: spend row %s already charged this batch", request_id) + verbose_proxy_logger.debug("Cost tracking skipped: spend row %s already charged this job", request_id) return False - return await self._take_over_uncharged_batch_cost_row(payload=payload, prisma_client=prisma_client, row=row) + taken_over: Final = await self._take_over_uncharged_cost_row( + payload=payload, prisma_client=prisma_client, row=row + ) + if taken_over: + await _track_guardrail_usage_of_cost_row(payload, prisma_client, disable_spend_logs) + return taken_over - async def _take_over_uncharged_batch_cost_row( + async def _take_over_uncharged_cost_row( self, payload: SpendLogsPayload, prisma_client: "PrismaClient", row: Mapping[str, object] ) -> bool: - """Take the batch's cost row over from the poll that left it charging nothing. + """Take the job's cost row over from the poll that left it charging nothing. - A pre-upgrade proxy wrote that row every time it polled the batch while it was still - running, so the charge is still to be made and the row still has to end up carrying - it. The row stops matching the moment it carries a charge, so it is one retrieve that + A poll that saw the job still running wrote that row at $0, so the charge is still to be + made and the row still has to end up carrying it. The row stops matching the moment it carries a charge, so it is one poll that takes it over and charges, and every later one reads the charge and charges nothing. """ from litellm.repositories.table_repositories import SpendLogsRepository @@ -827,7 +864,7 @@ class DBSpendUpdateWriter: request_id: Final = payload["request_id"] if payload["spend"] <= 0: verbose_proxy_logger.debug( - "Cost tracking skipped: this batch costs nothing and spend row %s says so", request_id + "Cost tracking skipped: this job costs nothing and spend row %s says so", request_id ) return False try: @@ -837,18 +874,18 @@ class DBSpendUpdateWriter: ), where={ "request_id": request_id, - "call_type": CallTypes.aretrieve_batch.value, + "call_type": payload["call_type"], "status": "success", "spend": 0.0, }, ) except Exception as e: # noqa: BLE001 # prisma raises its own hierarchy; the next retrieve takes the row over verbose_proxy_logger.warning( - "Could not take over spend row %s, leaving this batch's cost to the next retrieve: %s", request_id, e + "Could not take over spend row %s, leaving this job's cost to the next poll: %s", request_id, e ) return False if taken_over == 0: - verbose_proxy_logger.debug("Cost tracking skipped: spend row %s already charged this batch", request_id) + verbose_proxy_logger.debug("Cost tracking skipped: spend row %s already charged this job", request_id) return False return True diff --git a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py index 170eb450339..29f727ea41b 100644 --- a/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py +++ b/litellm/proxy/db/db_transaction_queue/spend_log_cleanup.py @@ -598,7 +598,7 @@ class SpendLogCleanup: rows: Final = _STORAGE_ROWS.validate_python( await tx.query_raw(trusted_sql(_STORAGE_SQL), list(_SPEND_LOG_TABLES)) ) - except Exception as e: # noqa: BLE001 - a failed measurement skips the size pass, it must not fail the run + except Exception as e: # noqa: BLE001 # a failed measurement skips the size pass, it must not fail the run verbose_proxy_logger.warning("Could not measure spend-log storage: %s: %s", type(e).__name__, e) return None if not rows: @@ -666,7 +666,7 @@ class SpendLogCleanup: await tx.query_raw(trusted_sql(_LOAD_SIZE_STATE_SQL), SPEND_LOG_SIZE_CLEANUP_STATE_PARAM) ) return SizeCleanupState.model_validate_json(rows[0].state_json) if rows else None - except Exception as e: # noqa: BLE001 - an unreadable count restarts from the catalog, it must not fail the run + except Exception as e: # noqa: BLE001 # an unreadable count restarts from the catalog, it must not fail the run verbose_proxy_logger.warning("Could not read the spend-log size cleanup state: %s: %s", type(e).__name__, e) return None @@ -678,7 +678,7 @@ class SpendLogCleanup: await tx.execute_raw( trusted_sql(_SAVE_SIZE_STATE_SQL), SPEND_LOG_SIZE_CLEANUP_STATE_PARAM, state.model_dump_json() ) - except Exception as e: # noqa: BLE001 - a lost save costs one re-seed from the catalog, it must not fail the run + except Exception as e: # noqa: BLE001 # a lost save costs one re-seed from the catalog, it must not fail the run verbose_proxy_logger.warning("Could not save the spend-log size cleanup state: %s: %s", type(e).__name__, e) async def _size_step_cutoff(self, prisma_client: PrismaClient, deadline: float, rows: int) -> datetime | None: diff --git a/litellm/proxy/db/exception_handler.py b/litellm/proxy/db/exception_handler.py index 13a14e95e56..8fbc8708f8c 100644 --- a/litellm/proxy/db/exception_handler.py +++ b/litellm/proxy/db/exception_handler.py @@ -34,6 +34,8 @@ _CONNECTION_CAPACITY_PHRASES: Final = ( _PRISMA_POOL_TIMEOUT_CODE: Final = "P2024" _LOCK_TIMEOUT_SQLSTATE: Final = "55P03" _LOCK_TIMEOUT_PHRASE: Final = "canceling statement due to lock timeout" +_STATEMENT_TIMEOUT_SQLSTATE: Final = "57014" +_STATEMENT_TIMEOUT_PHRASE: Final = "canceling statement due to statement timeout" def exception_chain(e: BaseException) -> Iterator[BaseException]: @@ -302,6 +304,17 @@ class PrismaDBExceptionHandler: return True return _LOCK_TIMEOUT_PHRASE in str(e).lower() + @staticmethod + def is_statement_timeout_error(e: Exception) -> bool: + """True iff Postgres cancelled a statement for exceeding ``statement_timeout`` (SQLSTATE 57014).""" + import prisma + + if not isinstance(e, _exception_types(prisma.errors.PrismaError)): + return False + if PrismaDBExceptionHandler.postgres_sqlstate(e) == _STATEMENT_TIMEOUT_SQLSTATE: + return True + return _STATEMENT_TIMEOUT_PHRASE in str(e).lower() + @staticmethod def postgres_sqlstate(e: Exception) -> str | None: """The SQLSTATE Postgres attached to a failed statement, as prisma surfaces it, or None.""" diff --git a/litellm/proxy/db/model_usage_rollup.py b/litellm/proxy/db/model_usage_rollup.py index 4566b10b260..e6fbc299504 100644 --- a/litellm/proxy/db/model_usage_rollup.py +++ b/litellm/proxy/db/model_usage_rollup.py @@ -88,14 +88,14 @@ def _internal_call_origin(metadata: str) -> object: return decoded.get(INTERNAL_CALL_ORIGIN_METADATA_KEY) -def _date_from_start_time(start_time: datetime | str) -> str | None: +def date_from_start_time(start_time: datetime | str) -> str | None: if isinstance(start_time, datetime): return start_time.date().isoformat() return start_time[:10] if len(start_time) >= 10 else None def build_model_usage_transaction(payload: SpendLogsPayload) -> ModelUsageTransaction | None: - date: Final = _date_from_start_time(payload["startTime"]) + date: Final = date_from_start_time(payload["startTime"]) origin: Final = _internal_call_origin(payload["metadata"]) if date is None or (origin and origin != BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN): return None diff --git a/litellm/proxy/deps/repositories.py b/litellm/proxy/deps/repositories.py index e37ecfd582a..8f929119dee 100644 --- a/litellm/proxy/deps/repositories.py +++ b/litellm/proxy/deps/repositories.py @@ -4,10 +4,12 @@ from fastapi import Depends from litellm.models.deployment_warning import DeploymentWarningIgnoreStore from litellm.models.gateway_activity import GatewayActivityStore +from litellm.models.plugin_upload import PluginUploadStore from litellm.models.user_banner import UserBannerStore from litellm.proxy.deps.database import DatabaseDep from litellm.repositories.deployment_warning_ignore_repository import DeploymentWarningIgnoreRepository from litellm.repositories.gateway_activity_repository import GatewayActivityRepository +from litellm.repositories.plugin_upload_repository import PluginUploadRepository from litellm.repositories.user_banner_repository import UserBannerRepository @@ -34,3 +36,10 @@ async def get_gateway_activity_repository(database: DatabaseDep) -> GatewayActiv GatewayActivityRepositoryDep: TypeAlias = Annotated[ GatewayActivityStore | None, Depends(get_gateway_activity_repository) ] + + +async def get_plugin_upload_repository(database: DatabaseDep) -> PluginUploadStore | None: + return None if database is None else PluginUploadRepository(database.prisma) + + +PluginUploadRepositoryDep: TypeAlias = Annotated[PluginUploadStore | None, Depends(get_plugin_upload_repository)] diff --git a/litellm/proxy/discovery_endpoints/agent_skills_archive.py b/litellm/proxy/discovery_endpoints/agent_skills_archive.py index 94a0c3e2cf1..dd78896725f 100644 --- a/litellm/proxy/discovery_endpoints/agent_skills_archive.py +++ b/litellm/proxy/discovery_endpoints/agent_skills_archive.py @@ -8,8 +8,11 @@ SHA-256 digest published in the index reproducible for identical uploads. import hashlib import io +import lzma import re +import stat import zipfile +import zlib from collections.abc import Mapping from dataclasses import dataclass from types import MappingProxyType @@ -23,7 +26,7 @@ MAX_ARCHIVE_ENTRIES: Final = 1000 SKILL_MANIFEST_FILENAME: Final = "SKILL.md" _ZIP_ENTRY_TIMESTAMP: Final = (1980, 1, 1, 0, 0, 0) -_ZIP_ENTRY_PERMISSIONS: Final = 0o644 << 16 +_ZIP_ENTRY_MODE: Final = 0o644 _FRONTMATTER_PATTERN: Final = re.compile(r"^---\s*\n(.*?)\n---\s*(?:\n|$)", re.DOTALL) _WINDOWS_DRIVE_PATTERN: Final = re.compile(r"^[A-Za-z]:") _EMPTY_FRONTMATTER: Final[Mapping[str, object]] = MappingProxyType({}) @@ -38,19 +41,26 @@ class SkillArchive: declared_description: str | None -def build_skill_archive(stored_content: bytes) -> SkillArchive | None: - """Return the installable archive for an upload, or None when it holds no root SKILL.md.""" +def read_skill_archive_members( + stored_content: bytes, *, for_upload: bool = False +) -> tuple[tuple[str, bytes, int], ...] | None: try: with zipfile.ZipFile(io.BytesIO(stored_content)) as uploaded: - members: Final = _flattened_members(uploaded) - except (zipfile.BadZipFile, OSError, RuntimeError): + return _flattened_members(uploaded, for_upload=for_upload) + except (zipfile.BadZipFile, OSError, RuntimeError, EOFError, lzma.LZMAError, zlib.error): return None + +def build_skill_archive(stored_content: bytes) -> SkillArchive | None: + """Return the installable archive for an upload, or None when it holds no root SKILL.md.""" + members: Final = read_skill_archive_members(stored_content) if members is None: return None - frontmatter: Final = _manifest_frontmatter(next(data for name, data in members if name == SKILL_MANIFEST_FILENAME)) - content: Final = _repack(members) + frontmatter: Final = _manifest_frontmatter( + next(data for name, data, _ in members if name == SKILL_MANIFEST_FILENAME) + ) + content: Final = pack_skill_archive(members) return SkillArchive( content=content, digest=f"sha256:{hashlib.sha256(content).hexdigest()}", @@ -59,24 +69,58 @@ def build_skill_archive(stored_content: bytes) -> SkillArchive | None: ) -def _flattened_members(uploaded: zipfile.ZipFile) -> tuple[tuple[str, bytes], ...] | None: - infos: Final = tuple(info for info in uploaded.infolist() if not info.is_dir()) +def _flattened_members(uploaded: zipfile.ZipFile, *, for_upload: bool) -> tuple[tuple[str, bytes, int], ...] | None: + infos: Final = tuple(info for info in uploaded.infolist() if for_upload or not info.is_dir()) if not infos or len(infos) > MAX_ARCHIVE_ENTRIES: return None if sum(info.file_size for info in infos) > MAX_ARCHIVE_UNPACKED_BYTES: return None - normalized: Final = tuple((info, _normalized_path(info.filename)) for info in infos) + normalized: Final = tuple( + (info, _normalized_path(info.orig_filename if for_upload else info.filename)) for info in infos + ) if any(path is None for _, path in normalized): return None + if for_upload and ( + len(frozenset(path for _, path in normalized)) != len(normalized) + or any(not _safe_upload_entry(info) for info in infos) + ): + return None - prefix: Final = _common_root_prefix(tuple(path for _, path in normalized if path is not None)) - flattened: Final = tuple((info, path[len(prefix) :]) for info, path in normalized if path is not None) + kept: Final = tuple( + (info, path) + for info, path in normalized + if path is not None and not info.is_dir() and (not for_upload or not _is_macos_metadata(path)) + ) + prefix: Final = _common_root_prefix(tuple(path for _, path in kept)) + flattened: Final = tuple((info, path[len(prefix) :]) for info, path in kept) names: Final = frozenset(name for _, name in flattened) if SKILL_MANIFEST_FILENAME not in names or len(names) != len(flattened): return None - return tuple((name, uploaded.read(info)) for info, name in sorted(flattened, key=lambda member: member[1])) + return tuple( + (name, uploaded.read(info), _entry_mode(info, preserve_executable_bits=for_upload)) + for info, name in sorted(flattened, key=lambda member: member[1]) + ) + + +def _entry_mode(info: zipfile.ZipInfo, *, preserve_executable_bits: bool) -> int: + if not preserve_executable_bits or info.create_system != 3: + return _ZIP_ENTRY_MODE + mode: Final = info.external_attr >> 16 + if stat.S_IFMT(mode) not in (0, stat.S_IFREG): + return _ZIP_ENTRY_MODE + return _ZIP_ENTRY_MODE | (mode & 0o111) + + +def _safe_upload_entry(info: zipfile.ZipInfo) -> bool: + return not info.flag_bits & 1 and ( + info.create_system != 3 or stat.S_IFMT(info.external_attr >> 16) in (0, stat.S_IFREG, stat.S_IFDIR) + ) + + +def _is_macos_metadata(path: str) -> bool: + return path.split("/", 1)[0] == "__MACOSX" or path.rsplit("/", 1)[-1] == ".DS_Store" def _common_root_prefix(paths: tuple[str, ...]) -> str: @@ -117,16 +161,16 @@ def _frontmatter_text(frontmatter: Mapping[str, object], key: str) -> str | None return value.strip() or None -def _zip_entry(name: str) -> zipfile.ZipInfo: +def _zip_entry(name: str, mode: int) -> zipfile.ZipInfo: entry: Final = zipfile.ZipInfo(filename=name, date_time=_ZIP_ENTRY_TIMESTAMP) - entry.compress_type = zipfile.ZIP_DEFLATED - entry.external_attr = _ZIP_ENTRY_PERMISSIONS + entry.create_system = 3 + entry.external_attr = mode << 16 return entry -def _repack(members: tuple[tuple[str, bytes], ...]) -> bytes: +def pack_skill_archive(members: tuple[tuple[str, bytes, int], ...], compression: int = zipfile.ZIP_DEFLATED) -> bytes: buffer: Final = io.BytesIO() - with zipfile.ZipFile(buffer, mode="w", compression=zipfile.ZIP_DEFLATED) as repacked: - for name, data in members: - repacked.writestr(_zip_entry(name), data) + with zipfile.ZipFile(buffer, mode="w") as repacked: + for name, data, mode in members: + repacked.writestr(_zip_entry(name, mode), data, compress_type=compression) return buffer.getvalue() diff --git a/litellm/proxy/enterprise_billing/billing_metrics.py b/litellm/proxy/enterprise_billing/billing_metrics.py index 166ece28b83..b6f69e8233c 100644 --- a/litellm/proxy/enterprise_billing/billing_metrics.py +++ b/litellm/proxy/enterprise_billing/billing_metrics.py @@ -2,29 +2,33 @@ Push-based OTLP metering for enterprise litellm deployments. Owns a dedicated OpenTelemetry meter provider and an OTLP/HTTP exporter -authenticated to our global collector with a TLS client certificate. The -collector front end terminates mutual TLS: the client certificate presented -here is validated against our CA at the edge, and the verified subject is -what identifies the deployment. It is intentionally isolated from the global -meter provider so the customer's own OTEL metrics are untouched and ours -never leak into their backend. +authenticated to our global collector with a Cognito bearer token. It is +intentionally isolated from the global meter provider so the customer's own +OTEL metrics are untouched and ours never leak into their backend. -The deployment's identity rides on the TLS client certificate, not on the -payload; the secret license key is never sent as an attribute or header. +The secret license key is never sent as an attribute or header. """ import os -import tempfile -from dataclasses import dataclass -from typing import TYPE_CHECKING, Final, Optional +import threading +import time +from collections.abc import Callable +from dataclasses import dataclass, field +from types import MappingProxyType +from typing import TYPE_CHECKING, Final, TypedDict +import requests from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter from opentelemetry.metrics import Counter from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader from opentelemetry.sdk.resources import Resource +from pydantic import BaseModel, ConfigDict +from requests.auth import AuthBase +from typing_extensions import ReadOnly, Unpack from litellm._logging import verbose_proxy_logger +from litellm.litellm_core_utils.url_utils import is_https_url_with_host from litellm.proxy.middleware.billable_request_metrics_middleware import ( BillableCategory, ) @@ -33,23 +37,15 @@ if TYPE_CHECKING: from litellm.proxy._types import EnterpriseLicenseData ENDPOINT_ENV: Final = "LITELLM_BILLING_METRICS_ENDPOINT" -CLIENT_CERT_ENV: Final = "LITELLM_BILLING_METRICS_CLIENT_CERT" -CLIENT_KEY_ENV: Final = "LITELLM_BILLING_METRICS_CLIENT_KEY" -CA_CERT_ENV: Final = "LITELLM_BILLING_METRICS_CA_CERT" +CLIENT_ID_ENV: Final = "LITELLM_BILLING_METRICS_CLIENT_ID" +CLIENT_SECRET_ENV: Final = "LITELLM_BILLING_METRICS_CLIENT_SECRET" +TOKEN_URL_ENV: Final = "LITELLM_BILLING_METRICS_TOKEN_URL" EXPORT_INTERVAL_ENV: Final = "LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS" DEFAULT_EXPORT_INTERVAL_MS: Final = 60_000 SHUTDOWN_FLUSH_TIMEOUT_MS: Final = 5_000 +_TOKEN_REFRESH_SKEW_SECONDS: Final = 60 _METRICS_PATH: Final = "/v1/metrics" -# The cert env vars take a path or the PEM itself. Secret stores that inject -# values as env content cannot mount them as files, so inline PEM is written out. -_PEM_PREFIX: Final = "-----BEGIN" -_PEM_DIR_PREFIX: Final = "litellm-billing-mtls-" -_PEM_FILE_MODE: Final = 0o600 -_CLIENT_CERT_FILENAME: Final = "client.crt" -_CLIENT_KEY_FILENAME: Final = "client.key" -_CA_CERT_FILENAME: Final = "ca.crt" - METRIC_NAME: Final = "litellm.enterprise.billable_requests" METER_NAME: Final = "litellm.enterprise.billing" @@ -59,21 +55,28 @@ AttributeValue = str | int @dataclass(frozen=True, slots=True) class BillingMetricsConfig: endpoint: str - client_cert_path: str - client_key_path: str - ca_cert_path: str | None + token_url: str + client_id: str + client_secret: str = field(repr=False) export_interval_ms: int litellm_version: str license_id: str | None +class _BillingMetricsOAuthToken(BaseModel): + model_config = ConfigDict(frozen=True, strict=True) + + access_token: str + expires_in: int + + def _metrics_endpoint(endpoint: str) -> str: """The OTLP/HTTP metric exporter wants the full URL including the signal path.""" trimmed: Final = endpoint.rstrip("/") return trimmed if trimmed.endswith(_METRICS_PATH) else f"{trimmed}{_METRICS_PATH}" -def _resource_attributes(config: BillingMetricsConfig) -> dict[str, AttributeValue]: +def resource_attributes(config: BillingMetricsConfig) -> dict[str, AttributeValue]: base: Final[dict[str, AttributeValue]] = { "service.name": "litellm-proxy", "litellm.version": config.litellm_version, @@ -82,7 +85,7 @@ def _resource_attributes(config: BillingMetricsConfig) -> dict[str, AttributeVal return {**base, **license_attr} -def _billable_attributes( +def billable_attributes( category: BillableCategory, route: str, status_code: int, model_id: str | None ) -> dict[str, AttributeValue]: base: Final[dict[str, AttributeValue]] = { @@ -94,23 +97,95 @@ def _billable_attributes( return {**base, **model_attr} -def build_mtls_meter_provider(config: BillingMetricsConfig) -> MeterProvider: - """OTLP/HTTP exporter presenting a TLS client certificate. +class BillingMetricsBearerAuth(AuthBase): + def __init__( + self, + *, + client_id: str, + client_secret: str, + token_url: str, + clock: Callable[[], float] = time.monotonic, + ) -> None: + self._client_id: Final = client_id + self._client_secret: Final = client_secret + self._token_url: Final = token_url + self._clock: Final = clock + self._lock: Final = threading.Lock() + self._cached_token: str | None = None + self._refresh_at: float = 0.0 - The collector's load balancer terminates mutual TLS and validates the client - certificate against our CA. Server verification uses the system trust store - (the collector presents a public web-PKI certificate); ca_cert_path overrides - it only for private/test collectors. - """ - exporter: Final = OTLPMetricExporter( - endpoint=_metrics_endpoint(config.endpoint), - # None -> exporter falls back to the system trust store. - certificate_file=config.ca_cert_path, - client_certificate_file=config.client_cert_path, - client_key_file=config.client_key_path, + def __call__(self, request: requests.PreparedRequest) -> requests.PreparedRequest: + token: Final = self._get_token() + authenticated_request: Final = request.copy() + authenticated_request.headers["Authorization"] = f"Bearer {token}" + return authenticated_request + + def invalidate(self) -> None: + with self._lock: + self._cached_token = None + self._refresh_at = 0.0 + + def _get_token(self) -> str: + with self._lock: + now: Final = self._clock() + if self._cached_token is not None and now < self._refresh_at: + return self._cached_token + token, cache_seconds = self._fetch_token() + self._cached_token = token + self._refresh_at = now + cache_seconds + return token + + def _fetch_token(self) -> tuple[str, float]: + try: + # The OTel periodic exporter's background thread makes blocking token I/O safe here. + response: Final = requests.post( + self._token_url, + auth=(self._client_id, self._client_secret), + data={"grant_type": "client_credentials", "scope": "telemetry/write"}, + timeout=10, + ) + response.raise_for_status() + token_response: Final = _BillingMetricsOAuthToken.model_validate(response.json()) + return token_response.access_token, max(0.0, float(token_response.expires_in) - _TOKEN_REFRESH_SKEW_SECONDS) + except (requests.RequestException, ValueError): + raise requests.RequestException("Billing metrics token request failed") from None + + +class _SessionHookKwargs(TypedDict, total=False): + stream: ReadOnly[object] + timeout: ReadOnly[object] + verify: ReadOnly[object] + cert: ReadOnly[object] + proxies: ReadOnly[object] + + +class _BillingMetricsBearerSession(requests.Session): + def __init__(self, auth: BillingMetricsBearerAuth) -> None: + super().__init__() + self._bearer_auth: Final = auth + self.auth = auth + self.hooks = {"response": [self._invalidate_token_on_unauthorized]} + + def _invalidate_token_on_unauthorized( + self, + response: requests.Response, + **kwargs: Unpack[_SessionHookKwargs], # kwargs-ok: requests hooks forward send options + ) -> requests.Response: + if response.status_code == 401: + self._bearer_auth.invalidate() + return response + + +def build_bearer_meter_provider(config: BillingMetricsConfig) -> MeterProvider: + auth: Final = BillingMetricsBearerAuth( + client_id=config.client_id, + client_secret=config.client_secret, + token_url=config.token_url, ) + session: Final = _BillingMetricsBearerSession(auth) + exporter: Final = OTLPMetricExporter(endpoint=_metrics_endpoint(config.endpoint), session=session) reader: Final = PeriodicExportingMetricReader(exporter, export_interval_millis=config.export_interval_ms) - return MeterProvider(metric_readers=[reader], resource=Resource.create(_resource_attributes(config))) + return MeterProvider(metric_readers=[reader], resource=Resource.create(resource_attributes(config))) class BillingMetricsRecorder: @@ -125,7 +200,7 @@ class BillingMetricsRecorder: ) def record(self, *, category: BillableCategory, route: str, status_code: int, model_id: str | None) -> None: - self._counter.add(1, _billable_attributes(category, route, status_code, model_id)) + self._counter.add(1, billable_attributes(category, route, status_code, model_id)) def shutdown(self) -> None: """Final flush + exporter-thread stop. Without this, up to one export @@ -146,115 +221,34 @@ def _export_interval_ms() -> int: return DEFAULT_EXPORT_INTERVAL_MS -@dataclass(frozen=True, slots=True) -class _CredentialPaths: - client_cert_path: str - client_key_path: str - ca_cert_path: str | None - - -def _is_pem_content(value: str) -> bool: - return value.lstrip().startswith(_PEM_PREFIX) - - -def _write_pem(directory: str, filename: str, pem: str) -> str: - path: Final = os.path.join(directory, filename) - with open(path, "w", encoding="utf-8") as handle: - handle.write(pem if pem.endswith("\n") else f"{pem}\n") - os.chmod(path, _PEM_FILE_MODE) - return path - - -def _resolve_credential_paths(*, client_cert: str, client_key: str, ca_cert: str | None) -> _CredentialPaths: - """ - Accept either a filesystem path or inline PEM content for each credential. - - Secret stores that inject values as environment content rather than mounted - files (ECS tasks reading AWS Secrets Manager, Cloud Run reading Secret - Manager) can only deliver the certificate as a string. The OTLP exporter - takes paths, so inline PEM is written to a private directory once, when the - recorder is built. Raises OSError if that write fails; the caller disables - metering rather than propagating. - """ - inline: Final = tuple(value for value in (client_cert, client_key, ca_cert) if value and _is_pem_content(value)) - if not inline: - return _CredentialPaths(client_cert, client_key, ca_cert) - - # mkdtemp is 0o700, so the 0o600 key file it holds is unreachable by other users. - directory: Final = tempfile.mkdtemp(prefix=_PEM_DIR_PREFIX) - return _CredentialPaths( - client_cert_path=( - _write_pem(directory, _CLIENT_CERT_FILENAME, client_cert) if _is_pem_content(client_cert) else client_cert - ), - client_key_path=( - _write_pem(directory, _CLIENT_KEY_FILENAME, client_key) if _is_pem_content(client_key) else client_key - ), - ca_cert_path=( - _write_pem(directory, _CA_CERT_FILENAME, ca_cert) if ca_cert and _is_pem_content(ca_cert) else ca_cert - ), - ) - - def load_billing_metrics_config( - *, license_data: Optional["EnterpriseLicenseData"], litellm_version: str + *, license_data: "EnterpriseLicenseData | None", litellm_version: str ) -> BillingMetricsConfig | None: - endpoint: Final = os.getenv(ENDPOINT_ENV) - client_cert: Final = os.getenv(CLIENT_CERT_ENV) - client_key: Final = os.getenv(CLIENT_KEY_ENV) - # Optional: only for private/test collectors whose server cert is not on the - # public web PKI. The production collector needs no CA override. - ca_cert: Final = os.getenv(CA_CERT_ENV) - - missing: Final = [ - name - for name, value in ( - (ENDPOINT_ENV, endpoint), - (CLIENT_CERT_ENV, client_cert), - (CLIENT_KEY_ENV, client_key), - ) - if not value - ] - if not endpoint or not client_cert or not client_key: + env: Final = MappingProxyType( + {name: os.getenv(name) or "" for name in (ENDPOINT_ENV, CLIENT_ID_ENV, CLIENT_SECRET_ENV, TOKEN_URL_ENV)} + ) + missing: Final = tuple(name for name, value in env.items() if not value) + if missing: verbose_proxy_logger.warning( "Enterprise billing metrics disabled: licensed deployment missing config (%s)", ", ".join(missing), ) return None - try: - paths: Final = _resolve_credential_paths(client_cert=client_cert, client_key=client_key, ca_cert=ca_cert) - except OSError as exc: - verbose_proxy_logger.warning( - "Enterprise billing metrics disabled: could not write inline certificate content to disk: %s", exc - ) - return None - - # Report the variable names, never their values. A value that is neither a - # readable path nor recognizable PEM is still secret material, and this - # warning would otherwise copy a client key straight into the proxy logs. - unreadable: Final = [ + insecure_urls: Final = tuple( env_name - for env_name, path in ( - (CLIENT_CERT_ENV, paths.client_cert_path), - (CLIENT_KEY_ENV, paths.client_key_path), - (CA_CERT_ENV, paths.ca_cert_path), - ) - if path and not os.path.isfile(path) - ] - if unreadable: - verbose_proxy_logger.warning( - "Enterprise billing metrics disabled: %s did not resolve to a readable certificate file. " - "Set each to a file path, or to inline PEM content beginning with '%s'.", - ", ".join(unreadable), - _PEM_PREFIX, - ) + for env_name, url in ((ENDPOINT_ENV, env[ENDPOINT_ENV]), (TOKEN_URL_ENV, env[TOKEN_URL_ENV])) + if not is_https_url_with_host(url) + ) + if insecure_urls: + verbose_proxy_logger.warning("Enterprise billing metrics disabled: %s must use HTTPS", insecure_urls[0]) return None return BillingMetricsConfig( - endpoint=endpoint, - client_cert_path=paths.client_cert_path, - client_key_path=paths.client_key_path, - ca_cert_path=paths.ca_cert_path, + endpoint=env[ENDPOINT_ENV], + client_id=env[CLIENT_ID_ENV], + client_secret=env[CLIENT_SECRET_ENV], + token_url=env[TOKEN_URL_ENV], export_interval_ms=_export_interval_ms(), litellm_version=litellm_version, license_id=(license_data or {}).get("user_id"), @@ -282,7 +276,7 @@ _ACTIVE_RECORDER: Final = _ActiveRecorderRegistry() def build_billing_metrics_recorder( - *, premium: bool, license_data: Optional["EnterpriseLicenseData"], litellm_version: str + *, premium: bool, license_data: "EnterpriseLicenseData | None", litellm_version: str ) -> BillingMetricsRecorder | None: """Build the recorder, or None when the deployment is not licensed or metering is unconfigured.""" if not premium: @@ -296,14 +290,12 @@ def build_billing_metrics_recorder( return None try: - recorder: Final = BillingMetricsRecorder(build_mtls_meter_provider(config)) + provider: Final = build_bearer_meter_provider(config) + recorder: Final = BillingMetricsRecorder(provider) except Exception as exc: # noqa: BLE001 -- metering must never break proxy startup verbose_proxy_logger.warning("Enterprise billing metrics disabled: failed to initialize exporter: %s", exc) return None _ACTIVE_RECORDER.set(recorder) - # The only positive signal that this component meters. Without it, a silent - # return above is indistinguishable from a working exporter in the logs, and - # a component that carries the cert but no license would look healthy. verbose_proxy_logger.info( "Enterprise billing metrics enabled: exporting to %s every %d ms", config.endpoint, diff --git a/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py b/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py index 4d910cde603..9b9e0949a1a 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/compresr/compresr.py @@ -22,7 +22,7 @@ import json import time from collections import Counter, OrderedDict from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Final, Literal, Protocol, TypeGuard +from typing import TYPE_CHECKING, ClassVar, Final, Literal, Protocol, TypeGuard from urllib.parse import urlparse import httpx @@ -507,6 +507,8 @@ class _CompressionResult: class CompresrGuardrail(CustomGuardrail): + server_fulfilled_tool_names: ClassVar[frozenset[str]] = frozenset({COMPRESR_RETRIEVE_TOOL_NAME}) + def __init__( self, api_base: str | None = None, diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py index 44705c4d166..a2688312b38 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py @@ -32,7 +32,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" additional_provider_specific_params=getattr(litellm_params, "additional_provider_specific_params", {}), unreachable_fallback=getattr(litellm_params, "unreachable_fallback", "fail_closed"), fail_on_error=getattr(litellm_params, "fail_on_error", True), - forward_messages_as_sent=getattr(litellm_params, "forward_messages_as_sent", False), + forward_messages_as_sent=litellm_params.forward_messages_as_sent, extra_headers=getattr(litellm_params, "extra_headers", None), guardrail_name=guardrail.get("guardrail_name", ""), event_hook=litellm_params.mode, diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index d69fe4068e0..4880444ada1 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -504,8 +504,7 @@ class NomaGuardrail(CustomGuardrail): messages[i]["content"] = anonymized_content break - def _replace_llm_response_content(self, response: LLMResponse, anonymized_content: str, position: int): - """Replace the content of the choice at ``position`` with its anonymized version.""" + def _replace_llm_response_content(self, response: LLMResponse, anonymized_content: str, position: int) -> None: if not isinstance(response, litellm.ModelResponse): return diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py index 5f9dc4a7a31..5f99b1a4611 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -1,13 +1,16 @@ import json import re -from collections.abc import AsyncGenerator, AsyncIterable, Mapping, Sequence -from typing import Any, Final, Literal, TypedDict +from collections.abc import AsyncGenerator, AsyncIterable, Callable, Iterable, Mapping, Sequence +from dataclasses import dataclass +from functools import partial, reduce +from itertools import chain, groupby +from types import MappingProxyType +from typing import Any, Final, Literal, TypeAlias, TypedDict, TypeVar from fastapi import HTTPException from pydantic import TypeAdapter from typing_extensions import ReadOnly, Required -from litellm import ChatCompletionToolParam from litellm._logging import verbose_proxy_logger from litellm.caching.dual_cache import DualCache from litellm.completion_extras.litellm_responses_transformation.transformation import ( @@ -19,6 +22,9 @@ from litellm.integrations.custom_guardrail import ( CustomGuardrail, log_guardrail_information, ) +from litellm.litellm_core_utils.core_helpers import as_str_mapping, attribute_or_none +from litellm.litellm_core_utils.tool_fields import declared_function_name, qualified_member_name +from litellm.llms.base_llm.guardrail_translation.utils import anthropic_tool_names from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.common_utils.callback_utils import ( add_guardrail_to_applied_guardrails_header, @@ -30,7 +36,10 @@ from litellm.proxy.guardrails.anthropic_sse import ( is_raw_sse_stream, is_sse_error_stream, ) +from litellm.responses.additional_tools import ADDITIONAL_TOOLS_INPUT_ITEM_TYPE from litellm.types.guardrails import GuardrailEventHooks, LitellmParams +from litellm.types.llms.anthropic import ANTHROPIC_HOSTED_TOOLS +from litellm.types.llms.vertex_ai import VertexToolName from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import ( PermissionError, ToolPermissionRule, @@ -48,8 +57,48 @@ from litellm.types.utils import ( ) GUARDRAIL_NAME: Final = "tool_permission" +_MCP_TOOLSET_TYPE: Final = "mcp_toolset" +_HOSTED_SERVER_NAME_FIELDS: Final = MappingProxyType({"mcp": "server_label", _MCP_TOOLSET_TYPE: "mcp_server_name"}) +_TOOL_CARRYING_INPUT_TYPES: Final = frozenset({"tool_search_output", ADDITIONAL_TOOLS_INPUT_ITEM_TYPE}) +_DECLARATION_KEYS: Final = ("functionDeclarations", "function_declarations") +_NON_TOOL_PART_KEYS: Final = frozenset({*_DECLARATION_KEYS, "cachePoint", "type"}) +_TOOL_CONFIG_KEYS: Final = ("toolConfig", "tool_config") +_FUNCTION_CALLING_CONFIG_KEYS: Final = ("functionCallingConfig", "function_calling_config") +_ALLOWED_FUNCTION_NAMES_KEYS: Final = ("allowedFunctionNames", "allowed_function_names") +_FORCED_FUNCTION_CALLING_MODES: Final = frozenset({"ANY", "VALIDATED"}) +_NAME_IDENTIFIED_TYPES: Final = frozenset({"function", "custom", "namespace", *_HOSTED_SERVER_NAME_FIELDS}) +_TEXT_EDITOR_TOOL_NAMES: Final = frozenset({"str_replace_editor", "str_replace_based_edit_tool"}) +_TYPE_VERSION_SUFFIX: Final = re.compile(r"_\d{8}$") +_GEMINI_TYPED_HOSTED_TOOLS: Final = ("computer_use", "web_search", "web_search_preview") +_NAMED_CHOICE_TYPES: Final = frozenset({"tool", "function", "custom", "allowed_tools", "any", "auto", "none"}) _DECODED_ARGUMENTS: Final = TypeAdapter(object) _REQUEST_DATA: Final = TypeAdapter(object) +_ToolTarget: TypeAlias = tuple[str | None, str | None] +_JudgedCalls: TypeAlias = Mapping[str, tuple[tuple[str, str], ...]] +_GroupedT: Final = TypeVar("_GroupedT") + + +def _snake_case(name: str) -> str: + return re.sub(r"(? str: + head, *rest = name.split("_") + return head + "".join(part.title() for part in rest) + + +_GEMINI_HOSTED_TOOL_KEYS: Final = frozenset( + chain.from_iterable((_snake_case(tool.value), _camel_case(tool.value)) for tool in VertexToolName) +) + + +def _name_identifies_tool(name: str, tool_type: str) -> bool: + stem: Final = _TYPE_VERSION_SUFFIX.sub("", tool_type) + return ( + tool_type in _NAME_IDENTIFIED_TYPES + or name == stem + or (stem == ANTHROPIC_HOSTED_TOOLS.TEXT_EDITOR.value and name in _TEXT_EDITOR_TOOL_NAMES) + ) def _object_mapping(value: object) -> Mapping[str, object] | None: @@ -68,6 +117,356 @@ def _function_tool_name(tool: object) -> str | None: return name if isinstance(name, str) else None +def _non_empty_string(value: object) -> str | None: + return value if isinstance(value, str) and value else None + + +def _request_tool_names(tool: object) -> tuple[str, ...]: + fields: Final = _object_mapping(tool) + custom: Final = ( + _object_mapping(fields.get("custom")) if fields is not None and fields.get("type") == "custom" else None + ) + custom_name: Final = _non_empty_string(custom.get("name")) if custom is not None else None + server_field: Final = _HOSTED_SERVER_NAME_FIELDS.get(str(fields.get("type"))) if fields is not None else None + server_name: Final = ( + _non_empty_string(fields.get(server_field)) if fields is not None and server_field is not None else None + ) + extra_names: Final = tuple(name for name in (_function_name(tool), custom_name, server_name) if name is not None) + return tuple(dict.fromkeys(anthropic_tool_names(tool) + extra_names)) + + +def _function_name(tool: object) -> str | None: + fields: Final = _object_mapping(tool) + function: Final = _object_mapping(fields.get("function")) if fields is not None else None + return _non_empty_string(function.get("name")) if function is not None else None + + +def _namespace_function_targets(tool: object) -> tuple[_ToolTarget, ...]: + function_name: Final = _function_name(tool) + return ((function_name, "function"),) if function_name is not None else () + + +def _gemini_hosted_key_targets(tool: object) -> tuple[_ToolTarget, ...]: + fields: Final = _object_mapping(tool) + if fields is None or fields.get("type") in _GEMINI_TYPED_HOSTED_TOOLS: + return () + keys: Final = tuple(key for key in fields if key != "type") + return (_part_target(fields, keys[0]),) if len(keys) == 1 and keys[0] in _GEMINI_HOSTED_TOOL_KEYS else () + + +def _is_namespace(tool: object) -> bool: + fields: Final = _object_mapping(tool) + return fields is not None and fields.get("type") == "namespace" + + +def _namespace_members(tool: object) -> Sequence[object]: + fields: Final = _object_mapping(tool) + members: Final = _object_list(fields.get("tools")) if fields is not None and _is_namespace(tool) else None + return members or () + + +def _returned_namespace(tool_call: ChatCompletionMessageToolCall) -> str | None: + tags: Final = as_str_mapping(attribute_or_none(tool_call, "provider_specific_fields")) + return _non_empty_string(tags.get("namespace")) if tags is not None else None + + +def _judged_call_names( + tool_call: ChatCompletionMessageToolCall, returned_name: str, judged_names: _JudgedCalls +) -> tuple[tuple[str, str], ...]: + namespace: Final = _returned_namespace(tool_call) + if namespace is not None: + return ((qualified_member_name(namespace, returned_name), returned_name),) + return judged_names.get(returned_name, ()) + + +def _pair_namespace(qualified: str, bare: str) -> str | None: + suffix: Final = qualified_member_name("", bare) + if qualified == bare or not qualified.endswith(suffix): + return None + return _non_empty_string(qualified.removesuffix(suffix)) + + +def _qualified_target(namespace: str | None, target: _ToolTarget) -> _ToolTarget: + name, tool_type = target + return (qualified_member_name(namespace, name), tool_type) if namespace is not None and name is not None else target + + +def _member_name(member: object) -> str | None: + fields: Final = _object_mapping(member) + return _non_empty_string(fields.get("name")) if fields is not None else None + + +def _declaration_targets(declaration: object) -> tuple[_ToolTarget, ...]: + name: Final = _member_name(declaration) + return ((name, "function"),) if name is not None else () + + +@dataclass(frozen=True, slots=True) +class _MemberGroup: + key: str + members: Sequence[object] + targets_of: Callable[[object], tuple[_ToolTarget, ...]] + + +def _member_group( + tool: object, namespace_member_targets: Callable[[str | None, object], tuple[_ToolTarget, ...]] +) -> _MemberGroup | None: + fields: Final = _object_mapping(tool) + if fields is None: + return None + members: Final = _namespace_members(tool) + if members: + return _MemberGroup("tools", members, partial(namespace_member_targets, _member_name(tool))) + key: Final = next((key for key in _DECLARATION_KEYS if _object_list(fields.get(key))), None) + declarations: Final = _object_list(fields.get(key)) if key is not None else None + return _MemberGroup(key, declarations, _declaration_targets) if key is not None and declarations else None + + +def _keyed_tool_fields(tool: object) -> Mapping[str, object] | None: + fields: Final = _object_mapping(tool) + if fields is None or isinstance(fields.get("type"), str) or _request_tool_names(tool): + return None + return fields + + +def _part_keys(fields: Mapping[str, object]) -> tuple[str, ...]: + return tuple(key for key in fields if key not in _NON_TOOL_PART_KEYS) + + +def _part_target(fields: Mapping[str, object], key: str) -> _ToolTarget: + return (None if key in _GEMINI_HOSTED_TOOL_KEYS else _member_name(fields.get(key)), key) + + +def _input_item_tools(item: object) -> Sequence[object] | None: + fields: Final = _object_mapping(item) + carries_tools: Final = fields is not None and _non_empty_string(fields.get("type")) in _TOOL_CARRYING_INPUT_TYPES + return _object_list(fields.get("tools")) if fields is not None and carries_tools else None + + +def _request_list(data: object, key: str) -> Sequence[object] | None: + fields: Final = _object_mapping(data) + return _object_list(fields.get(key)) if fields is not None else None + + +def _input_items(data: object) -> Sequence[object]: + return _request_list(data, "input") or () + + +def _tools_in_items(items: Sequence[object]) -> tuple[object, ...]: + return tuple(chain.from_iterable(_input_item_tools(item) or () for item in items)) + + +def _carries_tools(items: Sequence[object]) -> bool: + return any(_input_item_tools(item) is not None for item in items) + + +def _pair_key(pair: tuple[str, object]) -> str: + return pair[0] + + +def _grouped_values(pairs: Iterable[tuple[str, _GroupedT]]) -> Mapping[str, tuple[_GroupedT, ...]]: + groups: Final = groupby(sorted(pairs, key=_pair_key), key=_pair_key) + return MappingProxyType({key: tuple(dict.fromkeys(value for _, value in group)) for key, group in groups}) + + +def _request_layers(data: object) -> tuple[Mapping[str, object], ...]: + fields: Final = _object_mapping(data) + extra_body: Final = _object_mapping(fields.get("extra_body")) if fields is not None else None + return tuple(layer for layer in (fields, extra_body) if layer is not None) + + +def _layer_sent_tools(layer: Mapping[str, object]) -> tuple[object, ...]: + return ( + *(_request_list(layer, "tools") or ()), + *(_request_list(layer, "functions") or ()), + *_tools_in_items(_input_items(layer)), + ) + + +def _sent_tools(data: object) -> tuple[object, ...]: + return tuple(chain.from_iterable(map(_layer_sent_tools, _request_layers(data)))) + + +def _sent_fields(data: object) -> Mapping[str, object]: + return dict(chain.from_iterable(layer.items() for layer in _request_layers(data))) + + +@dataclass(frozen=True, slots=True) +class _KeptLayer: + fields: Mapping[str, object] + tools: tuple[object, ...] | None + functions: tuple[object, ...] | None + mcp_servers: tuple[object, ...] | None + input: tuple[object, ...] | None + + def sent_tools(self) -> tuple[object, ...]: + kept_input: Final = _input_items(self.fields) if self.input is None else self.input + return (*(self.tools or ()), *(self.functions or ()), *_tools_in_items(kept_input)) + + def kept_lists(self) -> dict[str, object]: + lists: Final = ( + ("tools", self.tools), + ("functions", self.functions), + ("mcp_servers", self.mcp_servers), + ("input", self.input), + ) + return {key: list(kept) for key, kept in lists if kept is not None} + + def overlaid_by(self, over: "_KeptLayer") -> "_KeptLayer": + return _KeptLayer( + fields={**self.fields, **over.fields}, + tools=over.tools if "tools" in over.fields else self.tools, + functions=over.functions if "functions" in over.fields else self.functions, + mcp_servers=over.mcp_servers if "mcp_servers" in over.fields else self.mcp_servers, + input=over.input if "input" in over.fields else self.input, + ) + + +def _mcp_server_target(server: object) -> _ToolTarget: + fields: Final = _object_mapping(server) + return (_non_empty_string(fields.get("name")) if fields is not None else None, _MCP_TOOLSET_TYPE) + + +def _reference_name(part: object) -> str | None: + fields: Final = _object_mapping(part) + is_reference: Final = fields is not None and fields.get("type") == "tool_reference" + return _non_empty_string(fields.get("tool_name")) if fields is not None and is_reference else None + + +def _references_one_of(part: object, names: frozenset[str]) -> bool: + name: Final = _reference_name(part) + return name is not None and name in names + + +def _without_references_to(parts: Sequence[object], names: frozenset[str]) -> Sequence[object]: + removed: Final = [name for name in map(_reference_name, parts) if name is not None and name in names] + kept: Final = [part for part in parts if not _references_one_of(part, names)] + if kept or not removed: + return kept + return [{"type": "text", "text": f"Permission denied: tool {', '.join(removed)} is not available"}] + + +def _tool_result_without_references(fields: Mapping[str, object], names: frozenset[str]) -> object: + parts: Final = _object_list(fields.get("content")) + return {**fields, "content": _without_references_to(parts, names)} if parts else fields + + +def _search_result_without_references(fields: Mapping[str, object], names: frozenset[str]) -> object: + result: Final = _object_mapping(fields.get("content")) + references: Final = _object_list(result.get("tool_references")) if result is not None else None + if result is None or references is None: + return fields + kept: Final = [reference for reference in references if not _references_one_of(reference, names)] + return {**fields, "content": {**result, "tool_references": kept}} + + +def _without_references_in_block(block: object, names: frozenset[str]) -> object: + fields: Final = _object_mapping(block) + if fields is None: + return block + match fields.get("type"): + case "tool_result": + return _tool_result_without_references(fields, names) + case "tool_search_tool_result": + return _search_result_without_references(fields, names) + case _: + return block + + +def _without_references_in_message(message: object, names: frozenset[str]) -> object: + fields: Final = _object_mapping(message) + content: Final = _object_list(fields.get("content")) if fields is not None else None + if fields is None or content is None: + return message + if fields.get("role") == "tool": + return {**fields, "content": _without_references_to(content, names)} + return {**fields, "content": [_without_references_in_block(block, names) for block in content]} + + +def _allowed_tool_entries(tool_choice: Mapping[str, object]) -> Sequence[object] | None: + if tool_choice.get("type") != "allowed_tools": + return None + nested: Final = _object_mapping(tool_choice.get("allowed_tools")) + return _object_list((nested if nested is not None else tool_choice).get("tools")) + + +def _is_removed_entry(entry: object, removed_names: frozenset[str], removed_types: frozenset[str]) -> bool: + names: Final = _request_tool_names(entry) + if names: + return any(name in removed_names for name in names) + fields: Final = _object_mapping(entry) + entry_type: Final = _non_empty_string(fields.get("type")) if fields is not None else None + return entry_type is not None and entry_type in removed_types + + +def _with_allowed_tool_entries(tool_choice: Mapping[str, object], entries: list[object]) -> dict[str, object]: + nested: Final = _object_mapping(tool_choice.get("allowed_tools")) + if nested is None: + return {**tool_choice, "tools": entries} + return {**tool_choice, "allowed_tools": {**nested, "tools": entries}} + + +def _is_denied_target( + target: _ToolTarget, denied_targets: frozenset[_ToolTarget], denied_names: frozenset[str] +) -> bool: + return target in denied_targets or target[0] in denied_names + + +def _kept_members(group: _MemberGroup | None, is_denied: Callable[[_ToolTarget], bool]) -> tuple[object, ...]: + if group is None: + return () + return tuple(member for member in group.members if not any(map(is_denied, group.targets_of(member)))) + + +def _none_tool_choice(tool_choice: object) -> object: + choice: Final = _object_mapping(tool_choice) + return {"type": "none"} if choice is not None and choice.get("type") in ("tool", "any") else "none" + + +def _requires_a_tool(tool_choice: object) -> bool: + choice: Final = _object_mapping(tool_choice) + return tool_choice == "required" or (choice is not None and choice.get("type") == "any") + + +def _kept_allowed_function_names(config: Mapping[str, object], removed_names: frozenset[str]) -> Mapping[str, object]: + listed: Final = tuple((key, _object_list(config.get(key))) for key in _ALLOWED_FUNCTION_NAMES_KEYS) + return { + key: [name for name in names if _non_empty_string(name) not in removed_names] + for key, names in listed + if names is not None + } + + +def _rewritten_function_calling_config( + config: Mapping[str, object], removed_names: frozenset[str], functions_remain: bool +) -> Mapping[str, object]: + kept_names: Final = _kept_allowed_function_names(config, removed_names) + had_names: Final = any(_object_list(config.get(key)) for key in _ALLOWED_FUNCTION_NAMES_KEYS) + every_listed_name_removed: Final = had_names and not any(kept_names.values()) + forces_a_function: Final = str(config.get("mode", "")).upper() in _FORCED_FUNCTION_CALLING_MODES + if not every_listed_name_removed and (functions_remain or not forces_a_function): + return {**config, **kept_names} + return {**{key: value for key, value in config.items() if key not in _ALLOWED_FUNCTION_NAMES_KEYS}, "mode": "NONE"} + + +def _rewritten_tool_config_field( + key: str, value: object, removed_names: frozenset[str], functions_remain: bool +) -> object: + config: Final = _object_mapping(value) + if key not in _FUNCTION_CALLING_CONFIG_KEYS or config is None: + return value + return _rewritten_function_calling_config(config, removed_names, functions_remain) + + +def _rewritten_tool_config(tool_config: object, removed_names: frozenset[str], functions_remain: bool) -> object: + fields: Final = _object_mapping(tool_config) + if fields is None: + return tool_config + return { + key: _rewritten_tool_config_field(key, value, removed_names, functions_remain) for key, value in fields.items() + } + + class _ToolPermissionRuleFields(TypedDict, total=False): """The config-file shape a :class:`ToolPermissionRule` is built from.""" @@ -258,7 +657,7 @@ class ToolPermissionGuardrail(CustomGuardrail): name_required: Final = rule.tool_name is not None type_required: Final = rule.tool_type is not None - name_matched: Final = self._matches_regex(name_pattern, tool_name) if name_required else True + name_matched: Final = self._matches_regex(name_pattern, tool_name or tool_type) if name_required else True type_matched: Final = self._matches_regex(type_pattern, tool_type) if type_required else True overall_match: Final = name_matched and type_matched @@ -320,6 +719,72 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.debug(message) return is_allowed, None, message + def _is_name_rule_for(self, rule: ToolPermissionRule, tool_name: str, tool_type: str | None) -> bool: + return rule.tool_name is not None and self._rule_matches_tool(rule, tool_name=tool_name, tool_type=tool_type)[0] + + def _decides_call( + self, rule: ToolPermissionRule, tool_name: str, tool_call: ChatCompletionMessageToolCall | None + ) -> bool: + if tool_call is None or not rule.allowed_param_patterns: + return True + arguments, parse_error = self._parse_tool_call_arguments(tool_call) + if parse_error or not arguments: + return True + return self._patterns_match_for_rule(arguments=arguments, rule=rule, tool_name=tool_name)[0] + + def _first_name_rule( + self, tool_name: str, tool_type: str | None, tool_call: ChatCompletionMessageToolCall | None + ) -> ToolPermissionRule | None: + return next( + ( + rule + for rule in self.rules + if self._is_name_rule_for(rule, tool_name, tool_type) and self._decides_call(rule, tool_name, tool_call) + ), + None, + ) + + def _narrower_name( + self, + name: str, + other: str, + tool_type: str | None, + tool_call: ChatCompletionMessageToolCall | None, + ) -> str: + rule: Final = self._first_name_rule(name, tool_type, tool_call) + other_rule: Final = self._first_name_rule(other, tool_type, tool_call) + if other_rule is None or other_rule is rule: + return name + if rule is None or self._is_name_rule_for(rule, other, tool_type): + return other + if self._is_name_rule_for(other_rule, name, tool_type): + return name + return other if other_rule.decision == "deny" else name + + def _deciding_member_name( + self, + qualified: str, + bare: str, + tool_type: str | None, + tool_call: ChatCompletionMessageToolCall | None = None, + ) -> str: + member: Final = self._narrower_name(qualified, bare, tool_type, tool_call) + namespace: Final = _pair_namespace(qualified, bare) + return member if namespace is None else self._narrower_name(member, namespace, tool_type, tool_call) + + def _check_target_permission( + self, target: _ToolTarget, bare_names: Mapping[str, tuple[str, ...]] + ) -> tuple[bool, str | None, str | None]: + name, tool_type = target + candidates: Final = tuple(sorted(bare_names.get(name, ()))) if name is not None else () + if name is None or not candidates: + return self._check_tool_permission(name, tool_type) + results: Final = tuple( + self._check_tool_permission(self._deciding_member_name(name, bare_name, tool_type), tool_type) + for bare_name in candidates + ) + return next((result for result in results if not result[0]), results[0]) + def _parse_tool_call_arguments( self, tool_call: ChatCompletionMessageToolCall ) -> tuple[Mapping[str, object] | None, str | None]: @@ -411,9 +876,9 @@ class ToolPermissionGuardrail(CustomGuardrail): return True, None def _get_permission_for_tool_call( - self, tool_call: ChatCompletionMessageToolCall + self, tool_call: ChatCompletionMessageToolCall, judged_name: str | None = None ) -> tuple[bool, str | None, str | None]: - tool_name: Final = tool_call.function.name if tool_call.function else None + tool_name: Final = judged_name or (tool_call.function.name if tool_call.function else None) tool_type: Final = getattr(tool_call, "type", None) if not tool_name and not tool_type: return self.default_action == "allow", None, None @@ -556,10 +1021,46 @@ class ToolPermissionGuardrail(CustomGuardrail): tool_call for block in content if (tool_call := self._anthropic_tool_use_to_tool_call(block)) is not None ) + def _returned_call_names(self, data: Mapping[str, object]) -> _JudgedCalls: + sent_tools: Final = _sent_tools(_REQUEST_DATA.validate_python(data)) + member_names: Final = tuple(chain.from_iterable(map(self._bare_member_names, sent_tools))) + if not member_names: + return MappingProxyType({}) + top_level_tools: Final = (tool for tool in sent_tools if not _is_namespace(tool)) + top_level_names: Final = frozenset(name for name in map(declared_function_name, top_level_tools) if name) + listed: Final = frozenset(name for name, _ in self._collect_request_tools(data) if name is not None) + own_names: Final = frozenset(bare for _, bare in member_names if bare in listed) | top_level_names + returned_members: Final = _grouped_values( + chain( + ((bare, (name, bare)) for name, bare in member_names if bare not in top_level_names), + ((name, (name, bare)) for name, bare in member_names if name not in top_level_names), + ) + ) + return MappingProxyType( + { + name: (*members, *(((name, name),) if name in own_names else ())) + for name, members in returned_members.items() + } + ) + + def _judged_name(self, tool_call: ChatCompletionMessageToolCall, qualified: str, bare: str) -> str: + return qualified if qualified == bare else self._deciding_member_name(qualified, bare, "function", tool_call) + + def _judge_tool_call( + self, tool_call: ChatCompletionMessageToolCall, judged_names: _JudgedCalls + ) -> tuple[bool, str | None, str | None]: + returned_name: Final = tool_call.function.name if tool_call.function else None + names: Final = _judged_call_names(tool_call, returned_name, judged_names) if returned_name is not None else () + if returned_name is None or not names: + return self._get_permission_for_tool_call(tool_call) + judged: Final = dict.fromkeys(self._judged_name(tool_call, qualified, bare) for qualified, bare in names) + results: Final = tuple(self._get_permission_for_tool_call(tool_call, name) for name in judged) + return next((result for result in results if not result[0]), results[0]) + def _evaluate_tool_calls( - self, tool_calls: Sequence[ChatCompletionMessageToolCall] + self, tool_calls: Sequence[ChatCompletionMessageToolCall], judged_names: _JudgedCalls ) -> tuple[tuple[ChatCompletionMessageToolCall, PermissionError], ...]: - checked: Final = tuple((tool_call, *self._get_permission_for_tool_call(tool_call)) for tool_call in tool_calls) + checked: Final = tuple((tool_call, *self._judge_tool_call(tool_call, judged_names)) for tool_call in tool_calls) for _tool_call, is_allowed, _rule_id, message in checked: if not is_allowed and message is not None: @@ -619,112 +1120,311 @@ class ToolPermissionGuardrail(CustomGuardrail): if not any(_is_tool_use_block(block) for block in kept_blocks): response["stop_reason"] = "end_turn" # rebind-ok: dropping every tool_use ends the turn - def _get_request_tool_name(self, tool: object) -> tuple[str | None, str | None]: - tool_type: Final = self._get_mapping_value(tool, "type") - if tool_type != "function": - return None, tool_type + def _unqualified_member_targets(self, member: object) -> tuple[_ToolTarget, ...]: + fields: Final = _object_mapping(member) + if fields is None: + return () + targets: Final = self._get_request_tool_targets(member) + own_name: Final = _member_name(member) + judged_names: Final = frozenset(name for name, _ in targets) + own_target: Final = ( + ((own_name, _non_empty_string(fields.get("type"))),) + if own_name is not None and own_name not in judged_names + else () + ) + return targets + own_target - function: Final = self._get_mapping_value(tool, "function") - tool_name: Final = self._get_mapping_value(function, "name") - return tool_name, tool_type + def _namespace_member_targets(self, namespace: str | None, member: object) -> tuple[_ToolTarget, ...]: + return tuple(_qualified_target(namespace, target) for target in self._unqualified_member_targets(member)) - def _get_legacy_function_name(self, function: object) -> str | None: - return self._get_mapping_value(function, "name") + def _bare_member_names(self, tool: object) -> tuple[tuple[str, str], ...]: + namespace: Final = _member_name(tool) + members: Final = _namespace_members(tool) + if namespace is None or not members: + return () + targets: Final = chain.from_iterable(map(self._unqualified_member_targets, members)) + return tuple((qualified_member_name(namespace, name), name) for name, _ in targets if name is not None) - def _get_named_tool_choice(self, data: dict) -> str | None: - tool_choice: Final = data.get("tool_choice") + def _get_request_tool_targets(self, tool: object) -> tuple[_ToolTarget, ...]: + group: Final = _member_group(tool, self._namespace_member_targets) + member_targets: Final = ( + tuple(chain.from_iterable(map(group.targets_of, group.members))) if group is not None else () + ) + keyed: Final = _keyed_tool_fields(tool) + part_targets: Final = tuple(_part_target(keyed, key) for key in _part_keys(keyed)) if keyed is not None else () + return member_targets + part_targets + self._own_tool_targets(tool) + + def _own_tool_targets(self, tool: object) -> tuple[_ToolTarget, ...]: + if _keyed_tool_fields(tool) is not None: + return () + named: Final = _namespace_function_targets(tool) if _namespace_members(tool) else self._named_tool_targets(tool) + return named + _gemini_hosted_key_targets(tool) + + def _named_tool_targets(self, tool: object) -> tuple[_ToolTarget, ...]: + raw_type: Final = self._get_mapping_value(tool, "type") + tool_type: Final = raw_type if isinstance(raw_type, str) else None + anthropic_custom: Final = ( + tool_type == "custom" + and self._get_mapping_value(tool, "input_schema") is not None + and _object_mapping(self._get_mapping_value(tool, "custom")) is None + ) + normalized_type: Final = "function" if tool_type is None or anthropic_custom else tool_type + names: Final = _request_tool_names(tool) + named_targets: Final = tuple((name, normalized_type) for name in names) + if names and all(_name_identifies_tool(name, normalized_type) for name in names): + return named_targets + if names: + return (*named_targets, (None, normalized_type)) + return ((None, tool_type),) if tool_type is not None else () + + def _get_named_tool_choice(self, tool_choice: object) -> str | None: if not tool_choice or tool_choice in ("auto", "none", "required"): return None if isinstance(tool_choice, str): return tool_choice - if self._get_mapping_value(tool_choice, "type") != "function": + choice: Final = _object_mapping(tool_choice) + if choice is None: return None - return self._get_mapping_value(self._get_mapping_value(tool_choice, "function"), "name") + choice_type: Final = choice.get("type") + server_field: Final = _HOSTED_SERVER_NAME_FIELDS.get(str(choice_type)) + if server_field is not None: + return _non_empty_string(choice.get(server_field)) + if choice_type not in ("tool", "function", "custom"): + return None + nested: Final = _object_mapping(choice.get(str(choice_type))) + nested_name: Final = _non_empty_string(nested.get("name")) if nested is not None else None + return nested_name or _non_empty_string(choice.get("name")) - def _get_named_function_call(self, data: dict) -> str | None: + def _get_named_function_call(self, data: Mapping[str, object]) -> str | None: function_call: Final = data.get("function_call") if not function_call or function_call in ("auto", "none"): return None if isinstance(function_call, str): return function_call - return self._get_mapping_value(function_call, "name") + return _non_empty_string(self._get_mapping_value(function_call, "name")) - def _collect_request_tools(self, data: dict) -> list[tuple[str, str | None]]: - request_tools: Final[list[tuple[str, str | None]]] = [] + def _listed_layer_targets(self, layer: Mapping[str, object]) -> tuple[_ToolTarget, ...]: + return ( + *chain.from_iterable(map(self._get_request_tool_targets, _request_list(layer, "tools") or ())), + *map(_mcp_server_target, _request_list(layer, "mcp_servers") or ()), + *chain.from_iterable(map(self._legacy_function_targets, _request_list(layer, "functions") or ())), + ) - for tool in data.get("tools") or []: - tool_name, tool_type = self._get_request_tool_name(tool) - if tool_name is not None: - request_tools.append((tool_name, tool_type)) + def _forced_layer_names(self, layer: Mapping[str, object]) -> tuple[str | None, ...]: + return (self._get_named_tool_choice(layer.get("tool_choice")), self._get_named_function_call(layer)) - for function in data.get("functions") or []: - function_name = self._get_legacy_function_name(function) - if function_name is not None: - request_tools.append((function_name, "function")) + def _collect_request_tools(self, data: Mapping[str, object]) -> tuple[_ToolTarget, ...]: + request: Final = _REQUEST_DATA.validate_python(data) + layers: Final = _request_layers(request) + listed: Final = tuple(chain.from_iterable(map(self._listed_layer_targets, layers))) + listed_names: Final = frozenset(name for name, _ in listed) + forced: Final = chain.from_iterable(map(self._forced_layer_names, layers)) + replayed: Final = map(_function_tool_name, replayed_tool_search_tools(request)) + input_tools: Final = _tools_in_items(tuple(chain.from_iterable(map(_input_items, layers)))) + return ( + *listed, + *((name, "function") for name in forced if name is not None and name not in listed_names), + *((name, "function") for name in replayed if name is not None), + *chain.from_iterable(map(self._get_request_tool_targets, input_tools)), + ) - for forced_tool_name in ( - self._get_named_tool_choice(data), - self._get_named_function_call(data), - ): - if forced_tool_name is not None: - request_tools.append((forced_tool_name, "function")) + def _legacy_function_targets(self, function: object) -> tuple[_ToolTarget, ...]: + targets: Final = self._get_request_tool_targets(function) + as_functions: Final = tuple((name, "function") for name, _ in targets if name is not None) + return tuple(dict.fromkeys(targets + as_functions)) - replayed: Final = map(_function_tool_name, replayed_tool_search_tools(_REQUEST_DATA.validate_python(data))) - request_tools.extend((name, "function") for name in replayed if name is not None) + def _kept_layer( + self, + layer: Mapping[str, object], + denied_targets: frozenset[_ToolTarget], + denied_names: frozenset[str], + ) -> _KeptLayer: + is_denied: Final = partial(_is_denied_target, denied_targets=denied_targets, denied_names=denied_names) + tools: Final = _request_list(layer, "tools") + functions: Final = _request_list(layer, "functions") + mcp_servers: Final = _request_list(layer, "mcp_servers") + return _KeptLayer( + fields=layer, + tools=None if tools is None else self._kept_tool_list(tools, denied_targets, denied_names), + functions=None if functions is None else self._kept_tool_list(functions, denied_targets, denied_names), + mcp_servers=( + None + if mcp_servers is None + else tuple(server for server in mcp_servers if not is_denied(_mcp_server_target(server))) + ), + input=self._kept_input_list(_input_items(layer), denied_targets, denied_names), + ) - return request_tools + def _rewritten_layer_fields( + self, + kept: _KeptLayer, + rewrite_choice: Callable[[object], object], + removed_names: frozenset[str], + functions_remain: bool, + ) -> dict[str, object]: + layer: Final = kept.fields + choice: Final = layer.get("tool_choice") + relaxes_function_call: Final = self._get_named_function_call(layer) in removed_names + return { + **kept.kept_lists(), + **({"tool_choice": rewrite_choice(choice)} if choice is not None else {}), + **({"function_call": "none"} if relaxes_function_call else {}), + **{ + key: _rewritten_tool_config(layer[key], removed_names, functions_remain) + for key in _TOOL_CONFIG_KEYS + if key in layer + }, + } + + def _tool_names_in(self, tools: Sequence[object]) -> frozenset[str]: + targets: Final = chain.from_iterable(self._get_request_tool_targets(tool) for tool in tools) + return frozenset(name for name, _ in targets if name is not None) + + def _referable_names_in(self, tools: Sequence[object]) -> frozenset[str]: + member_names: Final = chain.from_iterable(map(self._bare_member_names, tools)) + namespace_names: Final = (_member_name(tool) for tool in tools if _namespace_members(tool)) + return ( + self._tool_names_in(tools) + | frozenset(bare for _, bare in member_names) + | frozenset(name for name in namespace_names if name is not None) + ) + + def _kept_tools( + self, + tool: object, + denied_targets: frozenset[_ToolTarget], + denied_names: frozenset[str], + ) -> tuple[object, ...]: + is_denied: Final = partial(_is_denied_target, denied_targets=denied_targets, denied_names=denied_names) + group: Final = _member_group(tool, self._namespace_member_targets) + keyed: Final = _keyed_tool_fields(tool) + fields: Final = _object_mapping(tool) + if fields is None or (group is None and keyed is None): + return () if any(map(is_denied, self._get_request_tool_targets(tool))) else (tool,) + own_targets: Final = self._own_tool_targets(tool) + if any(map(is_denied, own_targets)): + return () + kept_members: Final = _kept_members(group, is_denied) + denied_keys: Final = ( + frozenset(key for key in _part_keys(keyed) if is_denied(_part_target(keyed, key))) + if keyed is not None + else frozenset[str]() + ) + if not denied_keys and len(kept_members) == len(group.members if group is not None else ()): + return (tool,) + emptied_group: Final = frozenset({group.key}) if group is not None and not kept_members else frozenset[str]() + dropped_keys: Final = denied_keys | emptied_group + kept_fields: Final = { + key: list(kept_members) if group is not None and key == group.key else value + for key, value in fields.items() + if key not in dropped_keys + } + own_name_left: Final = any(name is not None for name, _ in own_targets) + parts_left: Final = bool(kept_members) or own_name_left or (keyed is not None and bool(_part_keys(kept_fields))) + return (kept_fields,) if parts_left else () + + def _kept_tool_list( + self, + tools: Sequence[object], + denied_targets: frozenset[_ToolTarget], + denied_names: frozenset[str], + ) -> tuple[object, ...]: + return tuple(chain.from_iterable(self._kept_tools(tool, denied_targets, denied_names) for tool in tools)) + + def _kept_input_list( + self, + items: Sequence[object], + denied_targets: frozenset[_ToolTarget], + denied_names: frozenset[str], + ) -> tuple[object, ...] | None: + if not _carries_tools(items): + return None + return tuple(chain.from_iterable(self._kept_input_items(item, denied_targets, denied_names) for item in items)) + + def _kept_input_items( + self, + item: object, + denied_targets: frozenset[_ToolTarget], + denied_names: frozenset[str], + ) -> tuple[object, ...]: + fields: Final = _object_mapping(item) + tools: Final = _input_item_tools(item) + if fields is None or tools is None: + return (item,) + kept: Final = self._kept_tool_list(tools, denied_targets, denied_names) + emptied: Final = bool(tools) and not kept and fields.get("type") == ADDITIONAL_TOOLS_INPUT_ITEM_TYPE + return () if emptied else ({**fields, "tools": list(kept)},) + + def _tool_types_in(self, tools: Sequence[object]) -> frozenset[str]: + targets: Final = chain.from_iterable(self._get_request_tool_targets(tool) for tool in tools) + return frozenset(tool_type for _, tool_type in targets if tool_type is not None) + + def _rewritten_tool_choice( + self, tool_choice: object, removed_names: frozenset[str], removed_types: frozenset[str], tools_remain: bool + ) -> object: + named: Final = self._get_named_tool_choice(tool_choice) + if named is not None and named in removed_names: + return _none_tool_choice(tool_choice) + choice: Final = _object_mapping(tool_choice) + hosted_type: Final = _non_empty_string(choice.get("type")) if choice is not None else None + if hosted_type not in _NAMED_CHOICE_TYPES and hosted_type in removed_types: + return _none_tool_choice(tool_choice) + allowed: Final = _allowed_tool_entries(choice) if choice is not None else None + if choice is not None and allowed is not None: + kept: Final = [entry for entry in allowed if not _is_removed_entry(entry, removed_names, removed_types)] + return _with_allowed_tool_entries(choice, kept) if kept and tools_remain else _none_tool_choice(choice) + if not tools_remain and (named is not None or _requires_a_tool(tool_choice)): + return _none_tool_choice(tool_choice) + return tool_choice def _modify_request_with_permission_errors( self, data: dict, - denied_tool_names: list[str], + denied_targets: frozenset[_ToolTarget], ): """ - Modify the request to replace denied tool_calls blocks with error results - - Args: - data: The model request to modify - denied_tools: List of (tool_use, error) tuples for denied tools + Remove denied tools from the request, and relax any tool_choice that would still require one """ - if not denied_tool_names: + if not denied_targets: return data - verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_tool_names)) + verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_targets)) - # Create a mapping of tool_use_id to error result - error_tool_names: Final = set() - for tool_use in denied_tool_names: - error_tool_names.add(tool_use) - - tools: Final[list[ChatCompletionToolParam] | None] = data.get("tools") - if tools is not None: - new_tools: Final = [] - for tool in tools: - tool_name, tool_type = self._get_request_tool_name(tool) - if tool_type == "function" and tool_name in error_tool_names: - continue - new_tools.append(tool) - data["tools"] = new_tools - - functions: Final = data.get("functions") - if functions is not None: - data["functions"] = [ - function for function in functions if self._get_legacy_function_name(function) not in error_tool_names - ] - - named_tool_choice: Final = self._get_named_tool_choice(data) - if named_tool_choice in error_tool_names: - data["tool_choice"] = "none" - - named_function_call: Final = self._get_named_function_call(data) - if named_function_call in error_tool_names: - data["function_call"] = "none" - - messages: Final = without_tool_search_items_loading( - _REQUEST_DATA.validate_python(data), frozenset(denied_tool_names) + denied_names: Final = frozenset(name for name, _ in denied_targets if name is not None) + request: Final = _REQUEST_DATA.validate_python(data) + kept_layers: Final = tuple( + self._kept_layer(layer, denied_targets, denied_names) for layer in _request_layers(request) ) + + sent_tools: Final = _layer_sent_tools(_sent_fields(request)) + kept_sent: Final = reduce(_KeptLayer.overlaid_by, kept_layers) + kept_sent_tools: Final = kept_sent.sent_tools() + removed_names: Final = denied_names | ( + self._referable_names_in(sent_tools) - self._referable_names_in(kept_sent_tools) + ) + kept_types: Final = self._tool_types_in(kept_sent_tools) + removed_types: Final = self._tool_types_in(sent_tools) - kept_types + + tools_remain: Final = bool(kept_sent_tools) or bool(kept_sent.mcp_servers) + rewrite_choice: Final = partial( + self._rewritten_tool_choice, + removed_names=removed_names, + removed_types=removed_types, + tools_remain=tools_remain, + ) + rewritten: Final = tuple( + self._rewritten_layer_fields(kept, rewrite_choice, removed_names, "function" in kept_types) + for kept in kept_layers + ) + data.update(rewritten[0]) + if len(rewritten) > 1 and rewritten[1]: + data["extra_body"] = {**kept_layers[1].fields, **rewritten[1]} # rebind-ok: rewrite mode edits in place + + messages: Final = without_tool_search_items_loading(_REQUEST_DATA.validate_python(data), denied_names) if messages is not None: - data["messages"] = messages # rebind-ok: rewrite mode edits the request in place + data["messages"] = [ # rebind-ok: rewrite mode edits the request in place + _without_references_in_message(message, removed_names) for message in messages + ] return data @@ -843,25 +1543,29 @@ class ToolPermissionGuardrail(CustomGuardrail): ) return data - # Check permissions for each tool - denied_tool_names: Final = [] - for tool_name, tool_type in new_tools: - is_allowed, _, message = self._check_tool_permission(tool_name, tool_type) + member_names: Final = tuple( + chain.from_iterable(map(self._bare_member_names, _sent_tools(_REQUEST_DATA.validate_python(data)))) + ) + bare_names: Final = _grouped_values(member_names) + decisions: Final = tuple((target, self._check_target_permission(target, bare_names)) for target in new_tools) + denied: Final = tuple( + (target, message) + for target, (is_allowed, _, message) in decisions + if not is_allowed and message is not None + ) + for _, message in denied: + verbose_proxy_logger.info("Tool Permission Guardrail: %s", message) + if denied and self.on_disallowed_action == "block": + raise HTTPException( + status_code=400, + detail={ + "error": "Violated guardrail policy", + "detection_message": denied[0][1], + }, + ) - if not is_allowed and message is not None: - verbose_proxy_logger.info("Tool Permission Guardrail: %s", message) - if self.on_disallowed_action == "block": - raise HTTPException( - status_code=400, - detail={ - "error": "Violated guardrail policy", - "detection_message": message, - }, - ) - denied_tool_names.append(tool_name) - - if denied_tool_names: - data = self._modify_request_with_permission_errors(data, denied_tool_names) + if denied: + self._modify_request_with_permission_errors(data, frozenset(target for target, _ in denied)) verbose_proxy_logger.debug("Tool Permission Guardrail Pre-Call Hook: All tools allowed") @@ -908,7 +1612,7 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.debug("Tool Permission Guardrail: Found %s tool calls", len(tool_calls)) - denied_tools: Final = self._evaluate_tool_calls(tool_calls) + denied_tools: Final = self._evaluate_tool_calls(tool_calls, self._returned_call_names(data)) if not denied_tools: verbose_proxy_logger.debug("Tool Permission Guardrail Post-Call Hook: All tools allowed") @@ -932,7 +1636,7 @@ class ToolPermissionGuardrail(CustomGuardrail): Args: user_api_key_dict: User API key information (unused but required by interface) response: The model response to check - request_data: The model request (unused but required by interface) + request_data: The model request """ # Import here to avoid circular imports @@ -949,7 +1653,9 @@ class ToolPermissionGuardrail(CustomGuardrail): stream_chunk_builder(chunks=all_chunks) if not is_raw_sse_stream(all_chunks) else None ) if isinstance(assembled_model_response, ModelResponse): - denied_tools = self._check_assembled_stream(assembled_model_response) + denied_tools = self._check_assembled_stream( + assembled_model_response, self._returned_call_names(request_data) + ) if denied_tools: self._modify_response_with_permission_errors(assembled_model_response, denied_tools) @@ -975,18 +1681,23 @@ class ToolPermissionGuardrail(CustomGuardrail): yield chunk return - for sse_chunk in self._anthropic_stream_to_deliver(anthropic_response, all_chunks): + for sse_chunk in self._anthropic_stream_to_deliver( + anthropic_response, all_chunks, self._returned_call_names(request_data) + ): yield sse_chunk def _anthropic_stream_to_deliver( - self, anthropic_response: ModelResponse, source_chunks: Sequence[object] + self, + anthropic_response: ModelResponse, + source_chunks: Sequence[object], + judged_names: _JudgedCalls, ) -> Sequence[object]: """The frames the client gets: on a failed upstream only what the check read, never the upstream's own. Assembly reads past an upstream error, and a client can read a frame of a broken stream that assembly does not, so forwarding those frames would pass a tool call nobody checked. """ - denials: Final = self._check_assembled_stream(anthropic_response) + denials: Final = self._check_assembled_stream(anthropic_response, judged_names) if denials: self._modify_response_with_permission_errors(anthropic_response, denials) elif not anthropic_sse_error_events(source_chunks): @@ -994,7 +1705,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return anthropic_sse_chunks_from_response(anthropic_response, source_chunks, keep_explanations=True) def _check_assembled_stream( - self, assembled: ModelResponse + self, assembled: ModelResponse, judged_names: _JudgedCalls ) -> tuple[tuple[ChatCompletionMessageToolCall, PermissionError], ...]: verbose_proxy_logger.debug("Tool Permission Guardrail: Checking response") tool_calls: Final = self._extract_tool_calls_from_response(assembled) @@ -1002,7 +1713,7 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.debug("Tool Permission Guardrail: No tool uses found") return () verbose_proxy_logger.debug("Tool Permission Guardrail: Found %s tool calls", len(tool_calls)) - denied_tools: Final = self._evaluate_tool_calls(tool_calls) + denied_tools: Final = self._evaluate_tool_calls(tool_calls, judged_names) if not denied_tools: verbose_proxy_logger.debug("Tool Permission Guardrail Post-Call Hook: All tools allowed") return denied_tools diff --git a/litellm/proxy/hooks/max_budget_per_session_limiter.py b/litellm/proxy/hooks/max_budget_per_session_limiter.py index 002aeb145a5..636345a7ef7 100644 --- a/litellm/proxy/hooks/max_budget_per_session_limiter.py +++ b/litellm/proxy/hooks/max_budget_per_session_limiter.py @@ -26,6 +26,14 @@ from litellm._logging import verbose_proxy_logger from litellm.caching.redis_cache import log_redis_failure from litellm.exceptions import RateLimitType from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.batch_utils import ( + ChargeOnceJob, + charged_poll_cost, + defers_batch_cost, + job_charged_once_marker_key, + job_to_charge_once, +) +from litellm.litellm_core_utils.core_helpers import finite_cost from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.common_utils.proxy_rate_limit_error import ProxyRateLimitError from litellm.proxy.hooks.rate_limiter_utils import resolve_llm_provider_for_rate_limit @@ -44,6 +52,7 @@ _CACHE_AMOUNT_ADAPTER: Final[TypeAdapter[str | bytes | int | float]] = TypeAdapt _OPTIONAL_CACHE_AMOUNT_ADAPTER: Final[TypeAdapter[str | bytes | int | float | None]] = TypeAdapter( str | bytes | int | float | None, config=ConfigDict(strict=True, hide_input_in_errors=True) ) +_CALLBACK_FIELDS: Final = TypeAdapter(dict[str, object]) # Redis Lua script for atomic float increment with TTL. @@ -63,6 +72,30 @@ end return new_val """ +MAX_BUDGET_SESSION_CHARGE_JOB_ONCE_SCRIPT: Final = """ +local spend_key = KEYS[1] +local marker_key = KEYS[2] +local ttl = tonumber(ARGV[3]) + +local polls = redis.call('INCR', marker_key) +redis.call('EXPIRE', marker_key, ttl) +local amount = ARGV[2] +if polls == 1 then + amount = ARGV[1] +end +if tonumber(amount) <= 0 then + return '0' +end + +local existed = redis.call('EXISTS', spend_key) +redis.call('INCRBYFLOAT', spend_key, amount) +if existed == 0 then + redis.call('EXPIRE', spend_key, ttl) +end + +return amount +""" + # Default TTL for session budget counters (1 hour) DEFAULT_MAX_BUDGET_PER_SESSION_TTL: Final = 3600 @@ -87,12 +120,15 @@ class _PROXY_MaxBudgetPerSessionHandler(CustomLogger): ) ) - if self.internal_usage_cache.dual_cache.redis_cache is not None: - self.increment_script = self.internal_usage_cache.dual_cache.redis_cache.async_register_script( - MAX_BUDGET_SESSION_INCREMENT_SCRIPT - ) - else: - self.increment_script = None + redis_cache: Final = self.internal_usage_cache.dual_cache.redis_cache + self.increment_script = ( + redis_cache.async_register_script(MAX_BUDGET_SESSION_INCREMENT_SCRIPT) if redis_cache is not None else None + ) + self.charge_job_once_script = ( + redis_cache.async_register_script(MAX_BUDGET_SESSION_CHARGE_JOB_ONCE_SCRIPT) + if redis_cache is not None + else None + ) @with_service_target("session_budgets") async def async_pre_call_hook( @@ -140,7 +176,7 @@ class _PROXY_MaxBudgetPerSessionHandler(CustomLogger): return None @with_service_target("session_budgets") - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time) -> None: + async def async_log_success_event(self, kwargs, response_obj: object, start_time, end_time) -> None: """ After a successful LLM call, increment the session spend by the response cost. """ @@ -168,15 +204,23 @@ class _PROXY_MaxBudgetPerSessionHandler(CustomLogger): if max_budget is None: return - response_cost: Final = kwargs.get("response_cost") or 0.0 + callback_fields: Final = _CALLBACK_FIELDS.validate_python(kwargs) + response_cost: Final = finite_cost(charged_poll_cost(callback_fields)) or 0.0 if response_cost <= 0: return + if defers_batch_cost(callback_fields, response_obj): + return + cache_key: Final = self._make_cache_key(str(session_id)) - await self._increment_spend(cache_key, float(response_cost)) + job: Final = job_to_charge_once(callback_fields, response_obj, response_cost) + if job is None: + await self._increment_spend(cache_key, response_cost) + else: + await self._charge_job_once(cache_key, job, response_cost) verbose_proxy_logger.debug( - "MaxBudgetPerSessionHandler: incremented session %s spend by %.6f", + "MaxBudgetPerSessionHandler: charged session %s for a call that cost %.6f", session_id, response_cost, ) @@ -221,6 +265,31 @@ class _PROXY_MaxBudgetPerSessionHandler(CustomLogger): def _make_cache_key(self, session_id: str) -> str: return f"{{session_budget:{session_id}}}:spend" + async def _charge_job_once(self, cache_key: str, job: ChargeOnceJob, response_cost: float) -> None: + marker_key: Final = job_charged_once_marker_key(spend_key=cache_key, job=job) + if self.charge_job_once_script is not None: + try: + await self.charge_job_once_script( + keys=[cache_key, marker_key], + args=[str(response_cost), str(job.repeat_poll_cost), self.ttl], + ) + except Exception as e: + log_redis_failure( + verbose_proxy_logger, + logging.WARNING, + "MaxBudgetPerSessionHandler: Redis job charge failed, falling back to in-memory", + e, + ) + else: + return + + polls: Final = await self.internal_usage_cache.dual_cache.in_memory_cache.async_increment( + key=marker_key, value=1, ttl=self.ttl + ) + charge: Final = response_cost if polls == 1 else job.repeat_poll_cost + if charge > 0: + await self._in_memory_increment_spend(cache_key, charge) + @with_service_target("session_budgets") async def _get_current_spend(self, cache_key: str) -> float: """Read current accumulated spend for a session.""" diff --git a/litellm/proxy/hooks/model_max_budget_limiter.py b/litellm/proxy/hooks/model_max_budget_limiter.py index 1c10826124e..889fb9fb373 100644 --- a/litellm/proxy/hooks/model_max_budget_limiter.py +++ b/litellm/proxy/hooks/model_max_budget_limiter.py @@ -19,7 +19,12 @@ from litellm._internal_context import with_service_target from litellm._logging import verbose_proxy_logger from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import Span -from litellm.litellm_core_utils.batch_utils import batch_charged_once_marker_key, batch_id_to_charge_once +from litellm.litellm_core_utils.batch_utils import ( + ChargeOnceJob, + defers_batch_cost, + job_charged_once_marker_key, + job_to_charge_once, +) from litellm.litellm_core_utils.duration_parser import duration_in_seconds from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup from litellm.llms.bedrock.common_utils import get_bedrock_base_model, get_bedrock_cross_region_inference_regions @@ -1208,18 +1213,17 @@ class _PROXY_VirtualKeyModelMaxBudgetLimiter(RouterBudgetLimiting): ) return - batch_id: Final = batch_id_to_charge_once( - call_type=kwargs.get("call_type"), - response_obj=response_obj, - response_cost=response_cost, - ) + if defers_batch_cost(kwargs, response_obj): + return + + job: Final = job_to_charge_once(kwargs=kwargs, response_obj=response_obj, response_cost=response_cost) for entity_type, entity_id, resolved in resolved_budgets: await self._charge_entity( entity_type=entity_type, entity_id=entity_id, resolved=resolved, response_cost=response_cost, - batch_id=batch_id, + job=job, ) if self.dual_cache.redis_cache is not None: @@ -1236,7 +1240,7 @@ class _PROXY_VirtualKeyModelMaxBudgetLimiter(RouterBudgetLimiting): entity_id: str | None, resolved: ResolvedModelBudget, response_cost: float, - batch_id: str | None, + job: ChargeOnceJob | None, ) -> None: budget_duration: Final = resolved.budget_config.budget_duration if budget_duration is None: @@ -1247,11 +1251,19 @@ class _PROXY_VirtualKeyModelMaxBudgetLimiter(RouterBudgetLimiting): budget_model=resolved.budget_model, budget_duration=budget_duration, ) - if batch_id is not None and not await self._claim_batch_charge( + if job is not None and not await self._claim_job_charge( spend_key=spend_key, - batch_id=batch_id, + job=job, ttl_seconds=duration_in_seconds(budget_duration), ): + if job.repeat_poll_cost > 0: + await self._charge_entity( + entity_type=entity_type, + entity_id=entity_id, + resolved=resolved, + response_cost=job.repeat_poll_cost, + job=None, + ) return start_time_key: Final = model_budget_start_time_cache_key( entity_type=entity_type, @@ -1362,8 +1374,8 @@ class _PROXY_VirtualKeyModelMaxBudgetLimiter(RouterBudgetLimiting): spend=response_cost, ) - async def _claim_batch_charge(self, spend_key: str, batch_id: str, ttl_seconds: int) -> bool: - marker_key: Final = batch_charged_once_marker_key(spend_key=spend_key, batch_id=batch_id) + async def _claim_job_charge(self, spend_key: str, job: ChargeOnceJob, ttl_seconds: int) -> bool: + marker_key: Final = job_charged_once_marker_key(spend_key=spend_key, job=job) polls: Final = await self.dual_cache.async_increment_cache( key=marker_key, value=1, ttl=ttl_seconds, refresh_ttl=True ) diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 1cbe5eb39e8..5c2f6bab650 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -55,7 +55,6 @@ from litellm.litellm_core_utils.add_retry_fallback_headers import ( ensure_response_additional_headers, response_has_hidden_params, ) -from litellm.litellm_core_utils.audio_utils.utils import estimate_audio_tokens from litellm.litellm_core_utils.core_helpers import as_str_mapping from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_str_from_messages, @@ -64,7 +63,11 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( from litellm.litellm_core_utils.ptu_pricing import is_ptu_cost_attribution_enabled from litellm.litellm_core_utils.ptu_shares import PTUTeamCeiling from litellm.litellm_core_utils.routing_common_utils import resolve_model_group_alias -from litellm.litellm_core_utils.token_counter import estimate_image_tokens, offload_token_count +from litellm.litellm_core_utils.token_counter import ( + estimate_media_block_tokens, + offload_token_count, + split_embedding_input_tokens, +) from litellm.llms.azure.ptu_capacity import normalized_tokens from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.auth_utils import ( @@ -156,6 +159,12 @@ def _resolve_media_models(router: "Router | None", model: str, team_id: str | No return tuple(dict.fromkeys(router.registry().names(model, kind="litellm_base_models", team_id=team_id))) or (model,) +def _media_models( + resolver: Callable[[str, str | None], tuple[str, ...]] | None, model: str, team_id: str | None +) -> tuple[str, ...]: + return resolver(model, team_id) if resolver is not None else (model,) + + def _resolve_media_models_via_proxy_router(model: str, team_id: str | None) -> tuple[str, ...]: from litellm.proxy.proxy_server import llm_router @@ -1378,20 +1387,28 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): ) media_source: Final = messages if messages is not None else original_input + is_embedding_call: Final = call_type in EMBEDDING_API_CALL_TYPES projected, media = ( split_media_for_tpm(media_source) if include_media and (isinstance(messages, list) or call_type in RESPONSES_API_CALL_TYPES) else (original_input, ()) ) - input_text: Final = projected if call_type in RESPONSES_API_CALL_TYPES else original_input fallback_model: Final = request_model if isinstance(request_model, str) else None resolved_models: Final = ( - media_model_resolver(fallback_model or "", team_id) - if media and media_model_resolver is not None - else (fallback_model or "",) + _media_models(media_model_resolver, fallback_model or "", team_id) if media else (fallback_model or "",) ) - media_tokens: Final = sum( - self._estimate_media_block_tokens(block, resolved_models, fallback_model) for block in media + embedding_text, embedding_media_tokens = ( + split_embedding_input_tokens( + original_input, + lambda: _media_models(media_model_resolver, fallback_model or "", team_id), + fallback_model, + ) + if include_media and is_embedding_call + else (original_input, 0) + ) + input_text: Final = projected if call_type in RESPONSES_API_CALL_TYPES else embedding_text + media_tokens: Final = embedding_media_tokens + sum( + estimate_media_block_tokens(block, resolved_models, fallback_model) for block in media ) total_chars: Final = ( @@ -1436,24 +1453,6 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): return estimated_input_tokens, max_tokens_estimate * self.get_output_candidate_count(data, call_type) - @staticmethod - def _estimate_media_block_tokens( - block: Mapping[str, object], models: tuple[str, ...], fallback_model: str | None - ) -> int: - if block.get("type") == "input_audio": - audio: Final = _callback_kwargs_mapping(block.get("input_audio")) - data: Final = audio.get("data") - return estimate_audio_tokens(data if isinstance(data, str) else None, models, fallback_model=fallback_model) - image: Final = _callback_kwargs_mapping(block.get("image_url")) - url: Final = image.get("url") - detail: Final = image.get("detail") - return estimate_image_tokens( - models, - url if isinstance(url, str) else "", - detail if isinstance(detail, str) else "auto", - fallback_model=fallback_model, - ) - def _is_redis_cluster(self) -> bool: """ Check if the dual cache is using Redis cluster. @@ -4098,12 +4097,20 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): None if is_responses_request else self._translate_google_genai_native_request(data, call_type) ) is_embedding_request: Final = self._is_embedding_request(data, call_type) - embedding_text: Final = data.get("input") if is_embedding_request else None + request: Final = as_str_mapping(data) + raw_embedding_input: Final = request.get("input") if is_embedding_request and request is not None else None pretokenized_input_tokens: Final = ( - self._count_pretokenized_embedding_input(embedding_text) if is_embedding_request else None + self._count_pretokenized_embedding_input(raw_embedding_input) if is_embedding_request else None ) if pretokenized_input_tokens is not None: return pretokenized_input_tokens + embedding_text, embedding_media_tokens = ( + split_embedding_input_tokens( + raw_embedding_input, lambda: self._media_model_resolver(model or "", team_id), model + ) + if is_embedding_request + else (None, 0) + ) prompt: Final = data.get("prompt") fallback_text: Final = prompt if prompt is not None else data.get("input") @@ -4153,12 +4160,16 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): for tokenizer_model in (self._counted_models_resolver(model, team_id) if model else ("",)) ), ) - return estimate + audio_token_estimate + return estimate + audio_token_estimate + embedding_media_tokens except Exception: # noqa: BLE001 # tokenizer failures degrade to the cheap estimate if call_type in RERANK_API_CALL_TYPES and isinstance(selected_text, str): return max(0, len(selected_text) // DEFAULT_CHARS_PER_TOKEN) estimated_input_tokens, _ = self._estimate_input_and_output_tokens( - data=data, call_type=call_type, include_media=False + data=data, + call_type=call_type, + include_media=is_embedding_request, + team_id=team_id, + media_model_resolver=self._media_model_resolver, ) return estimated_input_tokens + audio_token_estimate diff --git a/litellm/proxy/hooks/proxy_track_cost_callback.py b/litellm/proxy/hooks/proxy_track_cost_callback.py index 8a015d396d0..90887422567 100644 --- a/litellm/proxy/hooks/proxy_track_cost_callback.py +++ b/litellm/proxy/hooks/proxy_track_cost_callback.py @@ -4,6 +4,7 @@ from collections.abc import Callable, Generator, Mapping, Sequence from contextlib import contextmanager from contextvars import ContextVar from datetime import datetime, timezone +from functools import partial from types import MappingProxyType from typing import TYPE_CHECKING, Final, Protocol, TypeAlias, cast @@ -11,10 +12,10 @@ from pydantic import TypeAdapter, ValidationError import litellm from litellm._logging import verbose_proxy_logger -from litellm.batches.batch_utils import batch_cost_is_final from litellm.caching.redis_batch import flush_post_call_redis_batches from litellm.constants import BACKGROUND_INTERACTION_COST_POLLING_ENABLED, BILLED_RETRY_ATTEMPT_METADATA_KEY from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.batch_utils import defers_batch_cost, repeat_poll_cost from litellm.litellm_core_utils.core_helpers import ( budget_reservation_from_metadata, finite_cost, @@ -24,7 +25,6 @@ from litellm.litellm_core_utils.core_helpers import ( ) from litellm.litellm_core_utils.litellm_logging import ( StandardLoggingPayloadSetup, - UsageCaptureObservation, UsageObservation, ) from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import guardrail_information_cost @@ -67,7 +67,6 @@ from litellm.proxy.spend_tracking.spend_tracking_utils import ( # noqa: F401 # sanitize_error_information_for_spend_logs, # pyright: ignore[reportUnusedImport] # backwards-compatible package export should_store_prompts_and_responses_in_spend_logs, ) -from litellm.proxy.spend_tracking.usage_capture import CapturedUsage, capture_callback, captured_callback_kwargs from litellm.proxy.utils import ( REJECTED_BEFORE_REQUEST_SETUP, REJECTED_BEFORE_REQUEST_SETUP_KEY, @@ -77,7 +76,6 @@ from litellm.proxy.utils import ( from litellm.types.router import Deployment from litellm.types.utils import ( CallTypes, - LiteLLMBatch, StandardLoggingPayload, ) from litellm.utils import get_end_user_id_for_cost_tracking @@ -225,6 +223,32 @@ def _guardrail_info_lifted_from_litellm_metadata( return {"standard_logging_guardrail_information": guardrail_info} +def _failure_row_guardrail_information(request_data: Mapping[str, object]) -> object: + metadata: Final = _str_keyed_fields(request_data.get("metadata")) + lifted: Final = _guardrail_info_lifted_from_litellm_metadata( + _str_keyed_fields(request_data.get("litellm_metadata")), + guardrail_info_already_set="standard_logging_guardrail_information" in metadata, + ) + return {**metadata, **lifted}.get("standard_logging_guardrail_information") + + +def failure_row_response_cost(request_data: Mapping[str, object]) -> float: + stashed_cost: Final = request_data.get("response_cost") + recovered_stream_cost: Final = ( + max(float(stashed_cost), 0.0) + if isinstance(request_data.get("combined_usage_object"), litellm.Usage) + and isinstance(stashed_cost, (int, float)) + else 0.0 + ) + finalized_failure: Final = _str_keyed_fields(request_data.get("standard_logging_object")) + finalized_amount: Final = finalized_failure.get("response_cost") + return ( + float(finalized_amount) + if finalized_failure.get("cost_finalized") is True and isinstance(finalized_amount, (int, float)) + else recovered_stream_cost + guardrail_information_cost(_failure_row_guardrail_information(request_data)) + ) + + def _redaction_opt_in_lifted_from_litellm_metadata( litellm_metadata: ObjectMapping, *, caller_set_headers: bool ) -> ObjectMapping: @@ -349,26 +373,13 @@ def _attempt_spend_row_kwargs( "combined_usage_object": attempt.usage, "response_cost": attempt.response_cost, "_accounting_usage_observed": UsageObservation.ESTIMATED, - "_accounting_usage_snapshot": UsageCaptureObservation.from_usage( - attempt.usage, UsageObservation.ESTIMATED, attempt.response_cost - ), - "_accounting_prior_captures": (), } row.pop("billed_failed_attempts", None) - row.pop("_accounting_capture", None) call_id: Final = request_data.get("litellm_call_id") or litellm_params.get("litellm_call_id") if isinstance(call_id, str): row["litellm_call_id"] = f"{call_id}{suffix}" row["parent_request_id"] = call_id metadata["parent_request_id"] = call_id - captured_attempts: Final = request_data.get("_accounting_billed_attempts") - if isinstance(captured_attempts, tuple): - for captured in TypeAdapter(tuple[object, ...]).validate_python(captured_attempts): - if ( - isinstance(captured, CapturedUsage) - and captured.payload["litellm_call_id"] == row["litellm_call_id"] - ): - row["_accounting_capture"] = captured standard_logging_object: Final = request_data.get("standard_logging_object") if isinstance(standard_logging_object, dict): payload: Final = cast("Mapping[str, object]", standard_logging_object) # cast-ok: StandardLoggingPayload dict @@ -404,7 +415,7 @@ class _ProxyDBLogger(CustomLogger): async def async_log_success_event( self, kwargs: ObjectMapping, response_obj: object, start_time: datetime, end_time: datetime ) -> None: - if self.spend_event_producer is None or not is_offloadable_success(response_obj): + if self.spend_event_producer is None or not is_offloadable_success(response_obj, kwargs.get("call_type")): await self._PROXY_track_cost_callback(dict(kwargs), response_obj, start_time, end_time) return event: Final = build_spend_event( @@ -454,9 +465,6 @@ class _ProxyDBLogger(CustomLogger): "Failed to invalidate budget reservation counters after failure release failed" ) - capture_metadata: Final = await self._try_capture_failure_usage( - request_data, original_exception, user_api_key_dict - ) if completed_cost and tracking_disabled: return @@ -493,10 +501,8 @@ class _ProxyDBLogger(CustomLogger): _error_information.pop("traceback", None) _metadata["error_information"] = _error_information - _metadata = ( # rebind-ok: enriched identity is shared with the earlier immutable capture - {**_metadata, **capture_metadata} - if capture_metadata is not None - else await ProxyDBLogger._enrich_failure_metadata_unless_db_stalled( + _metadata = ( # rebind-ok: enrich identity before building failure spend rows + await ProxyDBLogger._enrich_failure_metadata_unless_db_stalled( metadata=_metadata, original_exception=original_exception ) ) @@ -577,19 +583,7 @@ class _ProxyDBLogger(CustomLogger): # recovered cost onto request_data (the usage rides along in # ``combined_usage_object`` for the token columns), so attribute the # real partial spend to this failure row instead of zero. - recovered_stream_cost: Final = ( - max(float(request_data.get("response_cost") or 0.0), 0.0) - if isinstance(request_data.get("combined_usage_object"), litellm.Usage) - else 0.0 - ) - finalized_failure: Final = _str_keyed_fields(request_data.get("standard_logging_object")) - finalized_amount: Final = finalized_failure.get("response_cost") - recovered_response_cost: Final = ( - float(finalized_amount) - if finalized_failure.get("cost_finalized") is True and isinstance(finalized_amount, (int, float)) - else recovered_stream_cost - + guardrail_information_cost(existing_metadata.get("standard_logging_guardrail_information")) - ) + recovered_response_cost: Final = failure_row_response_cost(request_data) failure_snapshot_with_late_guardrails: Final = _failure_snapshot_with_guardrails_recorded_after_it( request_data.get("standard_logging_object"), @@ -615,106 +609,6 @@ class _ProxyDBLogger(CustomLogger): ), ) - async def _failure_capture_metadata( - self, request_data: Mapping[str, object], exception: Exception, auth: UserAPIKeyAuth - ) -> Mapping[str, object] | None: - from litellm.proxy.proxy_server import usage_capture - - if usage_capture.meter is None: - return None - captured_identity: Final = ( - _batch_accounting_for(request_data.get("litellm_call_id")) is not None - and request_data.get(REJECTED_BEFORE_REQUEST_SETUP_KEY) is not REJECTED_BEFORE_REQUEST_SETUP - ) - current_identity: Final = { - **LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=auth), - "user_api_key": LiteLLMProxyRequestSetup.get_logged_api_key(auth), - } - metadata: Final = ( - { - **dict.fromkeys(current_identity), - **{ - key: value - for key, value in _REQUEST_DATA.validate_python( - get_litellm_metadata_from_kwargs(dict(request_data)) - ).items() - if key in current_identity or key == "tags" - }, - } - if captured_identity - else current_identity - ) - return await self._enrich_failure_metadata_unless_db_stalled( - metadata=metadata, original_exception=exception, resolve_missing_key_identity=not captured_identity - ) - - async def _try_capture_failure_usage( - self, - request_data: dict[str, object], # mutable-ok: receipt handoff shared with the legacy writer - original_exception: Exception, - auth: UserAPIKeyAuth, - ) -> Mapping[str, object] | None: - try: - metadata: Final = await self._failure_capture_metadata(request_data, original_exception, auth) - self._capture_failure_usage(request_data, original_exception, auth, metadata) - except Exception: # noqa: BLE001 # capture failure must not suppress legacy billing or reservation release - verbose_proxy_logger.exception("Usage capture failed on the request failure path") - return None - return metadata - - def _capture_failure_usage( - self, - request_data: dict[str, object], # mutable-ok: receipt handoff shared with the legacy writer - original_exception: Exception, - auth: UserAPIKeyAuth, - metadata: Mapping[str, object] | None = None, - ) -> None: - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy.db.exception_handler import exception_chain - from litellm.proxy.proxy_server import usage_capture - from litellm.proxy.spend_tracking.usage_capture import UsageAcknowledgementError - - if any(isinstance(error, UsageAcknowledgementError) for error in exception_chain(original_exception)): - return - usage_meter: Final = usage_capture.meter - - if usage_meter is None: - return - logging_obj: Final = request_data.get("litellm_logging_obj") - details: Final[Mapping[str, object]] = ( - _str_keyed_fields(logging_obj.model_call_details) if isinstance(logging_obj, Logging) else {} - ) - params: Final = _str_keyed_fields(request_data.get("litellm_params") or details.get("litellm_params")) - failure_data: Final = { - **details, - **request_data, - "litellm_params": { - **params, - "metadata": { - **_str_keyed_fields(params.get("metadata")), - **( - metadata - if metadata is not None - else { - **LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=auth), - "user_api_key": LiteLLMProxyRequestSetup.get_logged_api_key(auth), - } - ), - "status": "failure", - }, - }, - } - start: Final = logging_obj.start_time if isinstance(logging_obj, Logging) else request_data.get("start_time") - if not isinstance(start, datetime): - return - end: Final = datetime.now(timezone.utc) - captured: Final = tuple( - capture_callback(usage_meter, row, original_exception, start, end) - for _, row in _failure_spend_rows(failure_data, 0.0, None, self._deployment_lookup) - ) - request_data["_accounting_capture"] = captured[0] # rebind-ok: share one receipt with the legacy writer - request_data["_accounting_billed_attempts"] = captured[1:] # rebind-ok: share attempt receipts - async def _write_failure_spend_rows( self, user_api_key_dict: UserAPIKeyAuth, @@ -900,20 +794,11 @@ class _ProxyDBLogger(CustomLogger): resolve_missing_key_identity=str(kwargs.get("call_type")) not in _CAPTURED_IDENTITY_CALL_TYPES, ) _write_spend_metadata_to_kwargs(kwargs=kwargs, metadata=metadata) - kwargs.update(await captured_callback_kwargs(kwargs, completion_response, start_time, end_time)) budget_reservation: Final = _get_budget_reservation_from_metadata(metadata=metadata) if accounting is not None and (budget_reservation is not None or litellm.apply_search_free_tier): accounting.write_started = True - if ( - isinstance(completion_response, LiteLLMBatch) - and kwargs.get("call_type") == CallTypes.aretrieve_batch.value - and not batch_cost_is_final(completion_response) - ): - verbose_proxy_logger.debug( - "Cost tracking deferred for batch %s still in status %s", - completion_response.id, - completion_response.status, - ) + if defers_batch_cost(kwargs, completion_response): + verbose_proxy_logger.debug("Cost tracking deferred for a batch whose cost is not final yet") await _release_budget_reservation(budget_reservation=budget_reservation) return user_id: Final = cast(str | None, metadata.get("user_api_key_user_id", None)) @@ -975,25 +860,28 @@ class _ProxyDBLogger(CustomLogger): agent_id=metadata.get("billing_agent_id") or metadata.get("agent_id"), ): ## UPDATE DATABASE - charged: Final = await _update_database_and_spend_counters( - proxy_logging_obj=proxy_logging_obj, - increment_spend_counters=increment_spend_counters, - user_api_key=user_api_key, - user_id=user_id, - end_user_id=end_user_id, - team_id=team_id, - org_id=org_id, + charged_cost: Final = await _charge_request_cost( + partial( + _update_database_and_spend_counters, + proxy_logging_obj=proxy_logging_obj, + increment_spend_counters=increment_spend_counters, + user_api_key=user_api_key, + user_id=user_id, + end_user_id=end_user_id, + team_id=team_id, + org_id=org_id, + completion_response=completion_response, + start_time=start_time, + end_time=end_time, + request_tags=tags, + model_access_groups=model_access_groups, + project_id=project_id, + ), kwargs=kwargs, - completion_response=completion_response, - start_time=start_time, - end_time=end_time, response_cost=response_cost, budget_reservation=budget_reservation, - request_tags=tags, - model_access_groups=model_access_groups, - project_id=project_id, ) - if not charged: + if charged_cost is None: return # update cache (fire-and-forget for backward compat: @@ -1003,7 +891,7 @@ class _ProxyDBLogger(CustomLogger): token=user_api_key, user_id=user_id, end_user_id=end_user_id, - response_cost=response_cost, + response_cost=charged_cost, team_id=team_id, parent_otel_span=parent_otel_span, tags=tags, @@ -1014,7 +902,7 @@ class _ProxyDBLogger(CustomLogger): token=user_api_key, key_alias=key_alias, end_user_id=end_user_id, - response_cost=response_cost, + response_cost=charged_cost, max_budget=end_user_max_budget, ) elif budget_reservation is not None: @@ -1308,6 +1196,50 @@ def _get_request_tags_for_cost_tracking( return None +class _ChargeSpend(Protocol): + async def __call__( + self, + *, + kwargs: dict[str, object], # mutable-ok: update_database takes the logging kwargs dict itself + response_cost: float, + budget_reservation: dict[str, object] | None, # mutable-ok: reconcile stamps the shared reservation dict + ) -> bool: ... + + +async def _charge_request_cost( + charge_spend: _ChargeSpend, + kwargs: dict[str, object], # mutable-ok: the first charge passes the caller's logging kwargs dict on unchanged + response_cost: float, + budget_reservation: dict[str, object] | None, # mutable-ok: reconcile stamps the shared reservation dict +) -> float | None: + """What the request charged, since a poll of a job an earlier poll charged still pays for its own guardrails.""" + if await charge_spend(kwargs=kwargs, response_cost=response_cost, budget_reservation=budget_reservation): + return response_cost + standard_logging_payload: Final = kwargs.get("standard_logging_object") + cost_after_job: Final = repeat_poll_cost(kwargs.get("call_type"), response_cost, standard_logging_payload) + if cost_after_job <= 0: + return None + charged: Final = await charge_spend( + kwargs={ + **kwargs, + "response_cost": cost_after_job, + **( + { + "standard_logging_object": { + **_OBJECT_MAPPING.validate_python(standard_logging_payload), + "response_cost": cost_after_job, + } + } + if isinstance(standard_logging_payload, Mapping) + else {} + ), + }, + response_cost=cost_after_job, + budget_reservation=None, + ) + return cost_after_job if charged else None + + class _IncrementSpendCounters(Protocol): """The ``increment_spend_counters`` coroutine :func:`_update_database_and_spend_counters` awaits.""" diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py index 54a2d4062d2..d2aac9cae38 100644 --- a/litellm/proxy/management_endpoints/internal_user_endpoints.py +++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py @@ -47,7 +47,7 @@ from litellm.proxy.auth.password_policy import ( ) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.auth_cache_invalidation_pubsub import evict_and_broadcast -from litellm.proxy.common_utils.openai_error_payload import proxy_exception_for +from litellm.proxy.common_utils.openai_error_payload import openai_shaped_proxy_exception, proxy_exception_for from litellm.proxy.common_utils.user_api_key_cache import ( AUTH_OBJECTS_TARGET, object_permission_cache_key, @@ -1048,6 +1048,7 @@ async def _check_user_info_v2_access( 1. Proxy admins / proxy admin viewers can access any user 2. User can access their own info 3. Team admins can access info of users in their teams + 4. Org admins can access info of users in their organizations Raises on unexpected DB errors so they surface as 500s, not silent 404s. """ @@ -1090,9 +1091,33 @@ async def _check_user_info_v2_access( if team.team_id in (target_user.teams or []): return target_user + if user_api_key_dict.user_id is not None and await _target_in_caller_admin_org( + prisma_client=prisma_client, + caller_user_id=user_api_key_dict.user_id, + target_user_id=target_user_id, + ): + return await _fetch_target_user() + return None +async def _target_in_caller_admin_org( + prisma_client: "PrismaClient", + caller_user_id: str, + target_user_id: str, +) -> bool: + admin_memberships: Final = await prisma_client.db.litellm_organizationmembership.find_many( + where={"user_id": caller_user_id, "user_role": LitellmUserRoles.ORG_ADMIN.value} + ) + if not admin_memberships: + return False + admin_org_ids: Final = {membership.organization_id for membership in admin_memberships} + target_memberships: Final = await prisma_client.db.litellm_organizationmembership.find_many( + where={"user_id": target_user_id} + ) + return any(membership.organization_id in admin_org_ids for membership in target_memberships) + + @router.get( "/v2/user/info", tags=["Internal User management"], @@ -1197,6 +1222,8 @@ async def user_info_v2( ), ) except Exception as e: + if isinstance(e, HTTPException) and e.status_code < 500: + raise openai_shaped_proxy_exception(e, str(e.detail), e.status_code) verbose_proxy_logger.exception("litellm.proxy.proxy_server.user_info_v2(): Exception occured - %s", e) raise handle_exception_on_proxy(e) diff --git a/litellm/proxy/management_endpoints/prompt_caching_requests.py b/litellm/proxy/management_endpoints/prompt_caching_requests.py index 2f00d263d84..d52c0255b08 100644 --- a/litellm/proxy/management_endpoints/prompt_caching_requests.py +++ b/litellm/proxy/management_endpoints/prompt_caching_requests.py @@ -14,9 +14,7 @@ from litellm.proxy.spend_tracking.savings import ( marks_gateway_injection, prompt_caching_savings_for_request, ) -from litellm.proxy.spend_tracking.spend_tracking_utils import ( - _query_raw_rows, # pyright: ignore[reportPrivateUsage] # existing typed spend-query adapter; rows validated below -) +from litellm.proxy.utils import query_raw_within_statement_timeout from litellm.types.integrations.anthropic_cache_control_hook import GATEWAY_INJECTED_CACHE_METADATA_KEY from litellm.types.llms.base import LiteLLMBaseModel from litellm.types.management_endpoints.prompt_caching_requests import ( @@ -158,7 +156,7 @@ async def get_prompt_caching_requests( else cursor_start_time ) rows: Final = _REQUEST_ROWS.validate_python( - await _query_raw_rows( + await query_raw_within_statement_timeout( prisma_client, prompt_caching_requests_sql(filter), start.isoformat(), @@ -166,8 +164,8 @@ async def get_prompt_caching_requests( page_size + 1, cursor_time.isoformat() if cursor_time is not None else None, cursor_request_id, + what="Prompt caching requests", ) - or () ) def current_router() -> "Router | None": diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index f505406fe0e..54b9b26123e 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -224,7 +224,12 @@ from litellm.proxy.management_helpers.utils import ( add_new_member, management_endpoint_wrapper, ) -from litellm.proxy.utils import PrismaClient, ProxyLogging, handle_exception_on_proxy +from litellm.proxy.utils import ( + PrismaClient, + ProxyLogging, + handle_exception_on_proxy, + query_raw_within_statement_timeout, +) from litellm.repositories.budget_repository import BudgetRepository from litellm.repositories.chunked_in import delete_many_in, find_many_in from litellm.repositories.daily_activity_buckets import api_key_from_bucket @@ -7473,6 +7478,9 @@ class _TeamUserSpendDbRow(TypedDict): failed_requests: ReadOnly[int] +_TEAM_USER_SPEND_ROWS: Final = TypeAdapter(tuple[_TeamUserSpendDbRow, ...]) + + @router.get( "/team/spend/by_user", response_model=TeamUserSpendResponse, @@ -7523,12 +7531,16 @@ async def get_team_spend_by_user( own_user_only: Final = scope.api_key_filter is not None user_param: Final = (user_api_key_dict.user_id or "",) if own_user_only else () async with db_span("team_user_spend", "LiteLLM_SpendLogs"): - rows: Final[Sequence[_TeamUserSpendDbRow]] = await prisma_client.db.query_raw( - _team_user_spend_sql(team_count=len(scoped_team_ids), restrict_to_user=own_user_only), - start_date, - end_date, - *scoped_team_ids, - *user_param, + rows: Final = _TEAM_USER_SPEND_ROWS.validate_python( + await query_raw_within_statement_timeout( + prisma_client, + _team_user_spend_sql(team_count=len(scoped_team_ids), restrict_to_user=own_user_only), + start_date, + end_date, + *scoped_team_ids, + *user_param, + what="Spend per user", + ) ) results: Final = tuple( TeamUserSpendRow( diff --git a/litellm/proxy/management_helpers/user_list_sorting.py b/litellm/proxy/management_helpers/user_list_sorting.py index d3d2e323cb0..407a188ff1a 100644 --- a/litellm/proxy/management_helpers/user_list_sorting.py +++ b/litellm/proxy/management_helpers/user_list_sorting.py @@ -15,6 +15,10 @@ class _UserId(BaseModel): _USER_IDS: Final = TypeAdapter(tuple[_UserId, ...]) +def _csv_values(raw: str | None) -> tuple[str, ...]: + return tuple(value.strip() for value in (raw or "").split(",") if value.strip()) + + async def sorted_user_ids( prisma_client: PrismaClient, *, @@ -30,9 +34,9 @@ async def sorted_user_ids( organization_ids: str | None, search: str | None, ) -> Sequence[str]: - ids: Final = tuple(value.strip() for value in (user_ids or "").split(",") if value.strip()) - sso_ids: Final = tuple(value.strip() for value in (sso_user_ids or "").split(",") if value.strip()) - org_ids: Final = tuple(value.strip() for value in (organization_ids or "").split(",") if value.strip()) + ids: Final = _csv_values(user_ids) + sso_ids: Final = _csv_values(sso_user_ids) + org_ids: Final = _csv_values(organization_ids) expression: Final = ( "CASE WHEN u.metadata->'scim_active' = 'false'::jsonb THEN 1 ELSE 0 END" if sort_by == "status" diff --git a/litellm/proxy/mcp_server/mcp_server_manager.py b/litellm/proxy/mcp_server/mcp_server_manager.py index eacba25257e..013336e1d5d 100644 --- a/litellm/proxy/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/mcp_server/mcp_server_manager.py @@ -367,7 +367,7 @@ _UpstreamSessionKey: TypeAlias = tuple[ str, str, str, - tuple[MCPTransport, MCPUpstreamProtocol, float, bool, bool, bool], + tuple[MCPTransport, MCPUpstreamProtocol, float, bool, bool, bool, bool], ] @@ -6253,9 +6253,9 @@ class MCPServerManager: "server_name": server_name, "mcp_rate_limit_server_name": server.alias or server.server_name or server.name, "user_api_key_auth": checked_auth, - "user_api_key_user_id": (getattr(checked_auth, "user_id", None) if checked_auth else None), - "user_api_key_team_id": (getattr(checked_auth, "team_id", None) if checked_auth else None), - "user_api_key_end_user_id": (getattr(checked_auth, "end_user_id", None) if checked_auth else None), + "user_api_key_user_id": checked_auth.user_id if checked_auth else None, + "user_api_key_team_id": checked_auth.team_id if checked_auth else None, + "user_api_key_end_user_id": checked_auth.end_user_id if checked_auth else None, "user_api_key_hash": (getattr(checked_auth, "api_key_hash", None) if checked_auth else None), "incoming_bearer_token": caller_bearer.get_secret_value() if caller_bearer is not None else None, "headers": logging_safe_mcp_headers(raw_headers), diff --git a/litellm/proxy/mcp_server/upstream.py b/litellm/proxy/mcp_server/upstream.py index 96a1c2db9ba..3dd245947db 100644 --- a/litellm/proxy/mcp_server/upstream.py +++ b/litellm/proxy/mcp_server/upstream.py @@ -231,6 +231,7 @@ async def prepare_upstream_client( server_url="", transport_type=transport, protocol_version=protocol_version, + skip_tool_output_validation=server.skip_tool_output_validation, auth_type=server.auth_type, auth_value=auth_value, timeout=timeout, @@ -265,6 +266,7 @@ async def prepare_upstream_client( server_url=server.url or "", transport_type=transport, protocol_version=protocol_version, + skip_tool_output_validation=server.skip_tool_output_validation, upstream_transport=upstream_transport, auth_type=server.auth_type, timeout=timeout, @@ -308,6 +310,7 @@ async def prepare_upstream_client( server_url=server.url or "", transport_type=transport, protocol_version=protocol_version, + skip_tool_output_validation=server.skip_tool_output_validation, upstream_transport=upstream_transport, auth_type=server.auth_type, auth_value=auth_value, diff --git a/litellm/proxy/middleware/request_body_decompression_middleware.py b/litellm/proxy/middleware/request_body_decompression_middleware.py index 3e144d7bf4c..2c36a621387 100644 --- a/litellm/proxy/middleware/request_body_decompression_middleware.py +++ b/litellm/proxy/middleware/request_body_decompression_middleware.py @@ -20,7 +20,11 @@ from litellm.constants import ( ) from litellm.proxy.auth.auth_utils import get_request_route from litellm.proxy.common_utils.anthropic_error_payload import proxy_error_type, route_error_content -from litellm.proxy.middleware.request_size_limit_middleware import MaxRequestSizeGetter, send_request_too_large +from litellm.proxy.middleware.request_size_limit_middleware import ( + MaxRequestSizeGetter, + effective_max_request_size_mb, + send_request_too_large, +) RFC_9659_ZSTD_WINDOW_LOG_MAX: Final = 23 GZIP_MAGIC: Final = b"\x1f\x8b" @@ -337,6 +341,7 @@ class RequestBodyDecompressionMiddleware: max_decompression_ratio: int = MAX_REQUEST_BODY_DECOMPRESSION_RATIO, max_decoded_bodies_in_flight_mb: float = MAX_DECOMPRESSED_REQUEST_BODIES_IN_FLIGHT_MB, codecs: Mapping[str, Codec] = AVAILABLE_CODECS, + route_max_request_size_mb: Mapping[tuple[str, str], float] | None = None, ) -> None: self.app = app self.get_max_request_size_mb: Final = get_max_request_size_mb @@ -344,6 +349,7 @@ class RequestBodyDecompressionMiddleware: self.max_decompression_ratio: Final = max_decompression_ratio self.budget: Final = _DecodedBodyBudget(int(max_decoded_bodies_in_flight_mb * 1024 * 1024)) self.codecs: Final = codecs + self.route_max_request_size_mb: Final = MappingProxyType(dict(route_max_request_size_mb or {})) async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: coding: Final = _coding_for(scope) @@ -378,7 +384,10 @@ class RequestBodyDecompressionMiddleware: async def _decode_then_call( self, scope: Scope, receive: Receive, send: Send, codec: Codec, prefix: _Prefix ) -> None: - max_mb: Final = _effective_max_mb(self.max_decompressed_body_size_mb, self.get_max_request_size_mb()) + request_max_mb: Final = effective_max_request_size_mb( + scope, self.get_max_request_size_mb(), self.route_max_request_size_mb + ) + max_mb: Final = _effective_max_mb(self.max_decompressed_body_size_mb, request_max_mb) limits: Final = _Limits(max_mb=max_mb, max_ratio=self.max_decompression_ratio) match await _receive_body(scope, receive, prefix, limits.max_bytes): case _Body(data=data): diff --git a/litellm/proxy/middleware/request_size_limit_middleware.py b/litellm/proxy/middleware/request_size_limit_middleware.py index 15bbf05bd01..f2883168d65 100644 --- a/litellm/proxy/middleware/request_size_limit_middleware.py +++ b/litellm/proxy/middleware/request_size_limit_middleware.py @@ -1,7 +1,10 @@ import json -from collections.abc import Callable +from collections.abc import Callable, Mapping +from types import MappingProxyType from typing import Final +from fastapi import HTTPException +from starlette._utils import get_route_path from starlette.types import ASGIApp, Message, Receive, Scope, Send from litellm.proxy.common_utils.anthropic_error_payload import route_error_content @@ -10,8 +13,22 @@ MaxRequestSizeGetter = Callable[[], int | float | None] RequestSizeLimitEnabledGetter = Callable[[], bool] -class RequestEntityTooLarge(Exception): - pass +class RequestEntityTooLarge(HTTPException): + def __init__(self, max_request_size_mb: float) -> None: + super().__init__(status_code=413, detail=f"Request size is too large. Max size is {max_request_size_mb} MB") + + +def effective_max_request_size_mb( + scope: Scope, + configured: float | None, + route_limits: Mapping[tuple[str, str], float], +) -> float | None: + global_limit: Final = configured if configured is not None and configured > 0 else None + method: Final = scope.get("method") + route_limit: Final = route_limits.get((method, get_route_path(scope))) if isinstance(method, str) else None + if route_limit is None: + return global_limit + return min(route_limit, global_limit) if global_limit is not None else route_limit class RequestSizeLimitMiddleware: @@ -28,19 +45,29 @@ class RequestSizeLimitMiddleware: app: ASGIApp, get_max_request_size_mb: MaxRequestSizeGetter, is_request_size_limit_enabled: RequestSizeLimitEnabledGetter, + route_max_request_size_mb: Mapping[tuple[str, str], float] | None = None, ) -> None: self.app = app self.get_max_request_size_mb = get_max_request_size_mb self.is_request_size_limit_enabled = is_request_size_limit_enabled + self.route_max_request_size_mb = MappingProxyType(dict(route_max_request_size_mb or {})) + + def _max_request_size_mb(self, scope: Scope) -> float | None: + configured: Final = self.get_max_request_size_mb() + return effective_max_request_size_mb( + scope, + configured if self.is_request_size_limit_enabled() else None, + self.route_max_request_size_mb, + ) async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: if scope["type"] != "http": await self.app(scope, receive, send) return - max_request_size_mb: Final = self.get_max_request_size_mb() + max_request_size_mb: Final = self._max_request_size_mb(scope) max_request_size_bytes: Final = _mb_to_bytes(max_request_size_mb) - if max_request_size_bytes is None or not self.is_request_size_limit_enabled(): + if max_request_size_bytes is None or max_request_size_mb is None: await self.app(scope, receive, send) return @@ -61,7 +88,7 @@ class RequestSizeLimitMiddleware: received_body_bytes += len(message.get("body", b"")) if received_body_bytes > max_request_size_bytes: - raise RequestEntityTooLarge + raise RequestEntityTooLarge(max_request_size_mb) return message async def tracking_send(message: Message) -> None: diff --git a/litellm/proxy/openai_files_endpoints/common_utils.py b/litellm/proxy/openai_files_endpoints/common_utils.py index c0d81abeba7..18ce18da936 100644 --- a/litellm/proxy/openai_files_endpoints/common_utils.py +++ b/litellm/proxy/openai_files_endpoints/common_utils.py @@ -21,8 +21,8 @@ from typing import ( from pydantic import TypeAdapter from typing_extensions import NotRequired, ReadOnly, TypedDict -from litellm.batches.batch_utils import batch_cost_is_final from litellm.constants import MAX_FILE_LIST_LIMIT +from litellm.litellm_core_utils.batch_utils import batch_cost_is_final from litellm.proxy._types import ProxyException from litellm.repositories.managed_file_repository import ManagedFileRepository from litellm.repositories.table_repositories import ( diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index be9f80694b8..627164abb93 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -31,7 +31,7 @@ from typing_extensions import ReadOnly, TypedDict import litellm from litellm import get_llm_provider -from litellm._logging import verbose_proxy_logger +from litellm._logging import redact_internal_details_from_client_message, verbose_proxy_logger from litellm._uuid import uuid from litellm.constants import ( ALLOWED_VERTEX_AI_PASSTHROUGH_HEADERS, @@ -58,6 +58,7 @@ from litellm.llms.anthropic.common_utils import ( anthropic_pass_through_api_base, merge_anthropic_beta_headers, ) +from litellm.llms.azure.common_utils import entra_rejection_in from litellm.llms.azure.passthrough.transformation import ( foreign_azure_deployment, is_azure_body_model_inference_endpoint, @@ -1417,13 +1418,12 @@ async def handle_bedrock_count_tokens( if deployment is not None: model_litellm_params: Final = deployment.get("litellm_params", {}) - # Get the resolved model ID from the configuration if "model" in model_litellm_params: resolved_model = model_litellm_params["model"] # Copy all litellm_params - BaseAWSLLM will handle AWS credential discovery for key, value in model_litellm_params.items(): - if key != "user_api_key_dict": # Don't overwrite user_api_key_dict + if key != "user_api_key_dict": litellm_params[key] = value verbose_proxy_logger.debug("Count tokens litellm_params: %s", litellm_params) @@ -2382,6 +2382,16 @@ async def _relay_router_model( ) except httpx.HTTPStatusError as upstream_error: return await _relay_upstream_response(upstream_error.response) + except Exception as error: + rejection: Final = entra_rejection_in(error) + if rejection is None: + raise + raise ProxyException( + message=redact_internal_details_from_client_message(rejection.message), + type=ProxyErrorTypes.auth_error, + param=None, + code=rejection.status_code, + ) from error if not is_streaming_request: return await _relay_upstream_response(cast(httpx.Response, result)) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_websocket_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_websocket_passthrough_logging_handler.py index f3bad54ffb2..90eb76cc9d8 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_websocket_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_websocket_passthrough_logging_handler.py @@ -158,9 +158,7 @@ def _responses_turn(event: Mapping[str, object], session_model: str) -> _Complet return _CompletedTurn( model=ended.response.model or session_model, response=ended.response, - usage=ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( # pyright: ignore[reportPrivateUsage] # the one shared Responses usage converter - ended.response.usage - ), + usage=ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(ended.response.usage), ) diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 0fbfa0b06a7..0c89627e74d 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -23,7 +23,7 @@ from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import StartupOutputSettings, print_startup_message, print_startup_warning, terminal_text -from litellm.constants import DEFAULT_NUM_WORKERS_LITELLM_PROXY +from litellm.constants import DEFAULT_MAX_REQUEST_HEADER_SIZE, DEFAULT_NUM_WORKERS_LITELLM_PROXY from litellm.proxy.db.pgbouncer import ( PgBouncerError, PgBouncerSettings, @@ -409,6 +409,7 @@ class ProxyInitializationHelpers: log_config: str | None = None, keepalive_timeout: int | None = None, timeout_worker_healthcheck: int | None = None, + max_request_header_size: int | None = None, ) -> dict: """ Get the arguments for `uvicorn` worker @@ -427,6 +428,9 @@ class ProxyInitializationHelpers: "port": port, "server_header": False, "timeout_graceful_shutdown": GracefulShutdownManager.get_timeout(), + "h11_max_incomplete_event_size": ( + max_request_header_size if max_request_header_size is not None else DEFAULT_MAX_REQUEST_HEADER_SIZE + ), } if log_config is not None: print_startup_message(f"Using log_config: {log_config}") @@ -680,6 +684,7 @@ class ProxyInitializationHelpers: ssl_keyfile_path: str, max_requests_before_restart: int | None = None, max_requests_before_restart_jitter: int | None = None, + max_request_header_size: int | None = None, ): """ Run litellm with `gunicorn` @@ -754,7 +759,7 @@ class ProxyInitializationHelpers: gunicorn_options: Final = { "bind": f"{host}:{port}", "workers": num_workers, # default is 1 - "worker_class": build_graceful_shutdown_uvicorn_worker(graceful_shutdown_timeout), + "worker_class": build_graceful_shutdown_uvicorn_worker(graceful_shutdown_timeout, max_request_header_size), "graceful_timeout": math.ceil(graceful_shutdown_timeout) + GRACEFUL_SHUTDOWN_TEARDOWN_HEADROOM_SECONDS, "preload": True, # Add the preload flag, "accesslog": "-", # Log to stdout @@ -1108,6 +1113,18 @@ class ProxyInitializationHelpers: help="Set the uvicorn keepalive timeout in seconds (uvicorn timeout_keep_alive parameter)", envvar="KEEPALIVE_TIMEOUT", ) +@click.option( + "--max_request_header_size", + default=None, + show_default=f"{DEFAULT_MAX_REQUEST_HEADER_SIZE} bytes with h11", + type=click.IntRange(min=1), + help=( + "Maximum bytes buffered for an incomplete HTTP request header block. " + "Applies when uvicorn or --run_gunicorn uses h11. Parser selection is unchanged; httptools ignores this setting. " + "Ignored under --run_hypercorn / --run_granian." + ), + envvar="MAX_REQUEST_HEADER_SIZE", +) @click.option( "--timeout_worker_healthcheck", default=None, @@ -1253,6 +1270,7 @@ def run_server( check_migrations_against_existing_data: bool, keepalive_timeout, timeout_worker_healthcheck, + max_request_header_size: int | None, max_requests_before_restart: int | None, max_requests_before_restart_jitter: int | None, limit_concurrency: int | None, @@ -1747,6 +1765,7 @@ def run_server( log_config=log_config, keepalive_timeout=keepalive_timeout, timeout_worker_healthcheck=(timeout_worker_healthcheck if running_uvicorn else None), + max_request_header_size=max_request_header_size, ) # Optional: recycle uvicorn workers after N requests if max_requests_before_restart is not None: @@ -1821,6 +1840,7 @@ def run_server( ssl_keyfile_path=ssl_keyfile_path, max_requests_before_restart=max_requests_before_restart, max_requests_before_restart_jitter=max_requests_before_restart_jitter, + max_request_header_size=max_request_header_size, ) elif run_hypercorn is True: ProxyInitializationHelpers.init_hypercorn_server( diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 6fcf789dc1b..87bc78730b5 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -530,6 +530,7 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( encrypt_value_helper, ) from litellm.proxy.common_utils.error_body_call_id import JSON_OBJECT, error_body_call_id, with_call_id +from litellm.proxy.common_utils.fips import enforce_fips_mode from litellm.proxy.common_utils.healthy_model_filter import ( get_hidden_unhealthy_model_names, is_healthy_only_listing_default, @@ -876,8 +877,6 @@ from litellm.proxy.spend_tracking.spend_event_producer import ( SpendEventProducer, build_spend_event_producer, ) -from litellm.proxy.spend_tracking.usage_capture import UsageBatchWriter, UsageCaptureRuntime -from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings from litellm.proxy.telemetry.endpoints import router as telemetry_router from litellm.proxy.telemetry.middleware import TelemetryMiddleware from litellm.proxy.telemetry.runtime import TelemetryRuntime @@ -1391,7 +1390,6 @@ async def _flush_spend_around_logging_drain_on_shutdown( finally: await worker.stop(discard_pending=True, timeout=GracefulShutdownManager.remaining(2.0, clock=clock)) await drain_tasks(GracefulShutdownManager.remaining(5.0, clock=clock)) - await usage_capture.stop(GracefulShutdownManager.remaining(clock=clock)) await flush_spend() @@ -1673,6 +1671,8 @@ async def _proxy_lifespan(app: FastAPI, resources: AsyncExitStack) -> AsyncGener if decoded_worker_config is not None: await initialize_from_worker_config(decoded_worker_config) + enforce_fips_mode(general_settings_view()) + async with AsyncExitStack() as boot_database: enforce_master_key_boot_verdict( await with_stored_secrets_counted( @@ -2928,9 +2928,15 @@ def configured_max_request_size_mb() -> int | float | None: return MAX_REQUEST_SIZE_MB_SETTING.validate_python(general_settings.get("max_request_size_mb")) +_ROUTE_MAX_REQUEST_SIZE_MB: Final[Mapping[tuple[str, str], float]] = MappingProxyType( + {("POST", "/claude-code/plugins/upload"): 11} +) + + app.add_middleware( RequestBodyDecompressionMiddleware, get_max_request_size_mb=lambda: configured_max_request_size_mb() if premium_user is True else None, + route_max_request_size_mb=_ROUTE_MAX_REQUEST_SIZE_MB, ) app.add_middleware( CORSMiddleware, @@ -3300,39 +3306,9 @@ def load_from_azure_key_vault(use_azure_key_vault: bool = False): spend_event_producer: SpendEventProducer | None = None -usage_capture: Final = UsageCaptureRuntime() - - -def _configure_usage_capture() -> None: - settings: Final = _GENERAL_SETTINGS_VIEW.validate_python(general_settings) - capture_settings: Final = settings.get("accounting_capture") - if settings.get("accounting_sync_ack") and capture_settings is None: - raise ValueError("accounting_sync_ack requires accounting_capture") - if capture_settings is None: - if usage_capture.meter is not None: - raise ValueError("Disabling accounting_capture requires a worker restart") - return - if ( - prisma_client is None - or CollectorSettings().enabled - or os.getenv("SPEND_LOGS_URL") - or litellm.apply_search_free_tier - ): - raise ValueError( - "Usage capture requires local Prisma writes and disabled collector, remote writer and search free tier" - ) - from litellm.proxy.db.accounting_ledger import AccountingLedger - - ledger: Final = AccountingLedger.for_client(prisma_client) - writer: Final = UsageBatchWriter(ledger.insert_usage_batch, ledger.load_usage_batch) - usage_capture.start( - writer.write, UsageCaptureSettings.model_validate(capture_settings), load=ledger.load_usage_batch - ) - def cost_tracking(): global prisma_client, spend_event_producer - _configure_usage_capture() if prisma_client is not None: from litellm.integrations.shadow_eval_logger import ShadowEvalLogger @@ -13910,7 +13886,7 @@ async def audio_transcriptions( api_base=api_base, version=version, response_cost=response_cost, - model_region=getattr(user_api_key_dict, "allowed_model_region", ""), + model_region=user_api_key_dict.allowed_model_region, call_id=response_call_id, request_data=data, hidden_params=hidden_params, @@ -17000,9 +16976,6 @@ async def _apply_search_filter_to_models( team_public_model_name: Final = (m.get("model_info") or {}).get("team_public_model_name") or "" if search_lower in team_public_model_name.lower(): return True - # Also match the underlying LiteLLM model name (e.g. - # "openrouter/deepseek/deepseek-chat"), so users can find - # deployments by typing the provider or the upstream model id. litellm_params: Final[Mapping[str, object]] = m.get("litellm_params") or MappingProxyType({}) litellm_model: Final = litellm_params.get("model") return isinstance(litellm_model, str) and search_lower in litellm_model.lower() @@ -18611,8 +18584,9 @@ async def model_deprecations( Reads `deprecation_date` metadata from `model_prices_and_context_window.json` (and any per-deployment `model_info.deprecation_date` overrides) for the - models configured on this proxy. An entry also names its `successor_model` - when the deployment sets `model_info.successor_model`. + models configured on this proxy. An entry also names its `successor_model`: + `model_info.successor_model` when the deployment sets one, else the + replacement the cost map records from the provider's deprecation notice. Parameters: warn_within_days: Window (in days) used to bucket "imminent" models, @@ -22416,6 +22390,7 @@ app.add_middleware( RequestSizeLimitMiddleware, get_max_request_size_mb=configured_max_request_size_mb, is_request_size_limit_enabled=lambda: premium_user is True, + route_max_request_size_mb=_ROUTE_MAX_REQUEST_SIZE_MB, ) app.add_middleware( AdmissionControlMiddleware, diff --git a/litellm/proxy/public_endpoints/provider_create_fields.json b/litellm/proxy/public_endpoints/provider_create_fields.json index aa7554c4a5a..5ded2fa5f5e 100644 --- a/litellm/proxy/public_endpoints/provider_create_fields.json +++ b/litellm/proxy/public_endpoints/provider_create_fields.json @@ -1553,6 +1553,34 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "EMPIRIOLABS", + "provider_display_name": "EmpirioLabs AI", + "litellm_provider": "empiriolabs", + "credential_fields": [ + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://api.empiriolabs.ai/v1", + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "api_key", + "label": "API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + } + ], + "default_model_placeholder": "empiriolabs/aplomb-1" + }, { "provider": "EMPOWER", "provider_display_name": "Empower", @@ -3567,6 +3595,34 @@ ], "default_model_placeholder": "reka/reka-flash" }, + { + "provider": "WALLY", + "provider_display_name": "Wally", + "litellm_provider": "wally", + "credential_fields": [ + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://inference.runanywhere.ai/v1", + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, + { + "key": "api_key", + "label": "API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + } + ], + "default_model_placeholder": "wally/glm-5.3-flash" + }, { "provider": "Sail", "provider_display_name": "Sail", diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py index 3f76bcf69ad..c4cd7c2d411 100644 --- a/litellm/proxy/response_api_endpoints/endpoints.py +++ b/litellm/proxy/response_api_endpoints/endpoints.py @@ -2162,7 +2162,7 @@ _WS_FRAME: Final = TypeAdapter(dict[str, object]) _GUARDRAIL_INFORMATION: Final = TypeAdapter(list[dict[str, object]]) _PII_TOKENS: Final = TypeAdapter(dict[str, str]) _GUARDRAIL_INFORMATION_KEY: Final = "standard_logging_guardrail_information" -_GUARDED_FRAME_FIELDS: Final = ("input", "instructions", "tools") +_GUARDED_FRAME_FIELDS: Final = ("input", "instructions", "tools", "tool_choice", "extra_body") _CLIENT_METADATA_KEYS: Final = ("metadata", "litellm_metadata") _CLIENT_GUARDRAIL_SELECTORS: Final = ("guardrails", "guardrail_config", "policies") _METADATA_GUARDRAIL_SELECTORS: Final = ("guardrails", "guardrail_config") diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index 179b741fe7c..c007b047087 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -1615,9 +1615,18 @@ model LiteLLM_ClaudeCodePluginTable { updated_at DateTime? @default(now()) @updatedAt created_by String? + uploaded_archive LiteLLM_ClaudeCodePluginUpload? + @@map("LiteLLM_ClaudeCodePluginTable") } +model LiteLLM_ClaudeCodePluginUpload { + plugin_name String @id + content Bytes + plugin LiteLLM_ClaudeCodePluginTable @relation(fields: [plugin_name], references: [name], onDelete: Cascade, onUpdate: Cascade) +} + + // User/team-scoped memory store with a GLOBAL unique key. // `value` is a string (typically markdown/text meant for LLM context); // `metadata` is an optional JSON envelope for structured tags without schema changes. diff --git a/litellm/proxy/shutdown/graceful_uvicorn_worker.py b/litellm/proxy/shutdown/graceful_uvicorn_worker.py index 6ae5ae60c3f..0bbbfe0a4a9 100644 --- a/litellm/proxy/shutdown/graceful_uvicorn_worker.py +++ b/litellm/proxy/shutdown/graceful_uvicorn_worker.py @@ -1,10 +1,21 @@ from uvicorn.workers import UvicornWorker +from litellm.constants import DEFAULT_MAX_REQUEST_HEADER_SIZE -def build_graceful_shutdown_uvicorn_worker(timeout_graceful_shutdown: float) -> type[UvicornWorker]: + +def build_graceful_shutdown_uvicorn_worker( + timeout_graceful_shutdown: float, + max_request_header_size: int | None = None, +) -> type[UvicornWorker]: class GracefulShutdownUvicornWorker(UvicornWorker): def __init__(self, *args: object) -> None: - self.CONFIG_KWARGS = {**UvicornWorker.CONFIG_KWARGS, "timeout_graceful_shutdown": timeout_graceful_shutdown} + self.CONFIG_KWARGS = { + **UvicornWorker.CONFIG_KWARGS, + "timeout_graceful_shutdown": timeout_graceful_shutdown, + "h11_max_incomplete_event_size": ( + max_request_header_size if max_request_header_size is not None else DEFAULT_MAX_REQUEST_HEADER_SIZE + ), + } super().__init__(*args) return GracefulShutdownUvicornWorker diff --git a/litellm/proxy/spend_tracking/session_page.py b/litellm/proxy/spend_tracking/session_page.py new file mode 100644 index 00000000000..810afbfa7b7 --- /dev/null +++ b/litellm/proxy/spend_tracking/session_page.py @@ -0,0 +1,221 @@ +"""Which sessions a logs page shows and how many match, read from the newest rows instead of every row. Reads grow +until one settles both, so the result is what grouping every row would give.""" + +import math +from collections.abc import Awaitable, Callable, Sequence +from dataclasses import dataclass +from typing import Final, Protocol, TypeAlias + +from typing_extensions import ReadOnly, TypedDict + +MCP_CALL_TYPES_SQL: Final = "('call_mcp_tool', 'list_mcp_tools')" +AGENT_CALL_TYPE_SQL: Final = "'asend_message'" +SESSION_KEY_EXPR: Final = "COALESCE(NULLIF(session_id, ''), request_id)" +SESSION_REPRESENTATIVE_ORDER_SQL: Final = ( + f"(call_type = {AGENT_CALL_TYPE_SQL}) DESC, " + f'CASE WHEN call_type = {AGENT_CALL_TYPE_SQL} THEN "endTime" END DESC NULLS LAST, ' + f'call_type IN {MCP_CALL_TYPES_SQL}, "startTime" DESC, request_id' +) + + +class _ReadRow(TypedDict): + session_key: ReadOnly[str | None] + api_key: ReadOnly[str | None] + last_activity: ReadOnly[str | None] + settled: ReadOnly[bool | None] + rows_read: ReadOnly[int] + session_count: ReadOnly[int] + + +class _SupportsQueryRaw(Protocol): + async def query_raw(self, query: str, *args: object) -> Sequence[_ReadRow]: ... + + +@dataclass(frozen=True, slots=True) +class Session: + key: str + api_key: str + last_activity: str + + +@dataclass(frozen=True, slots=True) +class SessionPage: + sessions: tuple[Session, ...] + has_more: bool + next_cursor: str | None + total: int + total_is_capped: bool + + +@dataclass(frozen=True, slots=True) +class _Request: + cursor: tuple[str, str, str] | None + offset: int + page_limit: int + sort_desc: bool + status: str | None + cap: int + + def first_read_size(self) -> int | None: + """Every row when oldest first, or when filtered by outcome, which any of a session's rows can decide. Otherwise + the fewest that could settle a page: a session past the cap and an older row.""" + return self.cap + 2 if self.sort_desc and self.status is None else None + + +@dataclass(frozen=True, slots=True) +class _Read: + candidates: tuple[Session, ...] + last_settled: bool + rows_read: int + session_count: int + saw_every_row: bool + + def settles(self, request: _Request) -> bool: + page_settled: Final = ( + request.page_limit <= 0 + or self.saw_every_row + or (len(self.candidates) > request.page_limit and self.last_settled) + ) + return page_settled and (self.saw_every_row or self.session_count > request.cap) + + def next_size(self, request: _Request) -> int: + rows_per_session: Final = self.rows_read / self.session_count + return max(2 * self.rows_read, math.ceil(1.25 * (request.cap + 1) * rows_per_session)) + + +_Reader: TypeAlias = Callable[[int | None], Awaitable[_Read]] + + +def _parse_session_cursor(session_cursor: str | None) -> tuple[str, str, str] | None: + if session_cursor is None or session_cursor.count("|") < 2: + return None + last_activity, _, rest = session_cursor.partition("|") + api_key, _, session_key = rest.partition("|") + if not last_activity or not session_key: + return None + return (last_activity, session_key, api_key) + + +async def read_session_page( + db: _SupportsQueryRaw, + *, + where_clause: str, + params: Sequence[object], + status: str | None, + page: int, + page_size: int, + sort_desc: bool, + session_cursor: str | None, + cap: int, +) -> SessionPage: + """One page of sessions after ``session_cursor``, or after ``(page - 1) * page_size`` sessions, keeping only those + whose outcome is ``status`` when given. ``where_clause`` reads ``params`` as ``$1`` onward.""" + cursor: Final = _parse_session_cursor(session_cursor) + offset: Final = (page - 1) * page_size if cursor is None else 0 + request: Final = _Request(cursor, offset, min(page_size, cap - offset), sort_desc, status, cap) + return await _session_page(_sql_reader(db, where_clause, params, request), request) + + +async def _session_page(read: _Reader, request: _Request) -> SessionPage: + last = await read(request.first_read_size()) # rebind-ok: each read replaces the last until one settles the page + while not last.settles(request): + last = await read(last.next_size(request)) + has_more: Final = len(last.candidates) > request.page_limit + sessions: Final = last.candidates[: request.page_limit] + final: Final = sessions[-1] if has_more and sessions else None + return SessionPage( + sessions=sessions, + has_more=has_more, + next_cursor=f"{final.last_activity}|{final.api_key}|{final.key}" if final else None, + total=min(last.session_count, request.cap), + total_is_capped=last.session_count > request.cap, + ) + + +def _sessions_sql(request: _Request, status_index: int) -> str: + """Each session's last activity, from its rows in ``scanned``, keeping only sessions whose outcome is the status.""" + if request.status is None: + return ( + 'SELECT session_key, api_key, MAX("startTime") AS last_activity FROM scanned GROUP BY session_key, api_key' + ) + return f""" + SELECT session_key, api_key, last_activity + FROM ( + SELECT DISTINCT ON (session_key, api_key) + session_key, + api_key, + status, + MAX("startTime") OVER (PARTITION BY session_key, api_key) AS last_activity + FROM scanned + ORDER BY session_key, api_key, {SESSION_REPRESENTATIVE_ORDER_SQL} + ) AS outcomes + WHERE COALESCE(status, 'success') = ${status_index} + """ + + +def _sql_reader(db: _SupportsQueryRaw, where_clause: str, params: Sequence[object], request: _Request) -> _Reader: + status_index: Final = len(params) + 1 + status_args: Final = () if request.status is None else (request.status,) + cursor_index: Final = status_index + len(status_args) + after_cursor: Final = ( + f"WHERE (last_activity, session_key, api_key) {'<' if request.sort_desc else '>'} " + f"(${cursor_index}::timestamp, ${cursor_index + 1}, ${cursor_index + 2})" + if request.cursor + else "" + ) + outcome_columns: Final = ', status, call_type, "endTime", request_id' if request.status is not None else "" + direction: Final = "DESC" if request.sort_desc else "ASC" + page_rows: Final = request.page_limit + 1 if request.page_limit > 0 else 0 + skip: Final = f"OFFSET {request.offset}" if page_rows and request.offset else "" + + async def read(size: int | None) -> _Read: + # LEFT JOIN keeps the summary row when no session is on the page. + rows: Final = await db.query_raw( + f""" + WITH scanned AS ( + SELECT {SESSION_KEY_EXPR} AS session_key, api_key, "startTime"{outcome_columns} + FROM "LiteLLM_SpendLogs" + WHERE {where_clause} + {f'ORDER BY "startTime" DESC LIMIT {size}' if size is not None else ""} + ), + sessions AS ({_sessions_sql(request, status_index)}), + summary AS ( + SELECT COUNT(*) AS rows_read, + MIN("startTime") AS oldest, + (SELECT COUNT(*) FROM sessions) AS session_count + FROM scanned + ) + SELECT page.session_key, + page.api_key, + page.last_activity::text AS last_activity, + page.last_activity > summary.oldest AS settled, + summary.rows_read, + summary.session_count + FROM summary + LEFT JOIN LATERAL ( + SELECT session_key, api_key, last_activity + FROM sessions + {after_cursor} + ORDER BY last_activity {direction}, session_key {direction}, api_key {direction} + LIMIT {page_rows} {skip} + ) AS page ON TRUE + ORDER BY page.last_activity {direction}, page.session_key {direction}, page.api_key {direction} + """, + *params, + *status_args, + *(request.cursor or ()), + ) + rows_read: Final = int(rows[0]["rows_read"]) + return _Read( + candidates=tuple( + Session(str(row["session_key"]), str(row["api_key"]), str(row["last_activity"])) + for row in rows + if row["session_key"] is not None + ), + last_settled=bool(rows[-1]["settled"]), + rows_read=rows_read, + session_count=int(rows[0]["session_count"]), + saw_every_row=size is None or rows_read < size, + ) + + return read diff --git a/litellm/proxy/spend_tracking/spend_event.py b/litellm/proxy/spend_tracking/spend_event.py index 031e84896b2..d054519cb9c 100644 --- a/litellm/proxy/spend_tracking/spend_event.py +++ b/litellm/proxy/spend_tracking/spend_event.py @@ -28,6 +28,7 @@ from litellm.proxy.db.spend_log_tool_index import response_tool_call_names from litellm.types.interactions import InteractionsAPIResponse from litellm.types.llms.base import LiteLLMBaseModel from litellm.types.utils import LiteLLMBatch, Usage +from litellm.types.videos.utils import is_video_status_call_type SPEND_EVENT_VERSION: Final = 1 CACHE_OFF_KEY: Final = "Cache OFF" @@ -205,9 +206,10 @@ class SpendEventDecodeError: reason: str -def is_offloadable_success(response_obj: object) -> bool: - """Batch retrieves and interaction polls branch on the concrete response class, so they stay in-process.""" - return not isinstance(response_obj, _NOT_OFFLOADED_RESPONSE_TYPES) +def is_offloadable_success(response_obj: object, call_type: object) -> bool: + """Batch retrieves and interaction polls branch on the concrete response class, and a video status poll on + the status and video id the event leaves out, so they stay in-process.""" + return not isinstance(response_obj, _NOT_OFFLOADED_RESPONSE_TYPES) and not is_video_status_call_type(call_type) def _json_fallback(value: object) -> str: diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py index 89200792c0b..fcb9eb4a280 100644 --- a/litellm/proxy/spend_tracking/spend_management_endpoints.py +++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py @@ -3,6 +3,7 @@ import collections import json import os from collections.abc import Mapping, Sequence +from dataclasses import dataclass from datetime import date, datetime, timedelta, timezone from functools import partial from itertools import groupby @@ -52,6 +53,13 @@ from litellm.proxy.auth.authorization_dependencies import ( from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.openai_error_payload import error_status_code, openai_error_param, openai_error_type from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup +from litellm.proxy.spend_tracking.session_page import ( + AGENT_CALL_TYPE_SQL, + MCP_CALL_TYPES_SQL, + SESSION_KEY_EXPR, + SESSION_REPRESENTATIVE_ORDER_SQL, + read_session_page, +) from litellm.proxy.spend_tracking.spend_capture_rate import ( ProviderBillingCredentialMissing, ProviderBillingRequestFailed, @@ -65,7 +73,7 @@ from litellm.proxy.spend_tracking.spend_tracking_utils import ( get_spend_by_team, get_spend_by_team_and_customer, ) -from litellm.proxy.utils import handle_exception_on_proxy +from litellm.proxy.utils import handle_exception_on_proxy, query_raw_within_statement_timeout from litellm.repositories.prisma_protocols import TableActions from litellm.repositories.table_repositories import SpendLogsRepository from litellm.repositories.team_repository import TeamRepository @@ -87,23 +95,15 @@ router: Final = APIRouter() SPEND_LOGS_PAGINATION_COUNT_CAP: Final = 10000 _DECODED_JSON: Final = TypeAdapter(object) -_SESSION_KEY_EXPR: Final = "COALESCE(NULLIF(session_id, ''), request_id)" -_SESSION_GROUP_KEY_SQL: Final = f"{_SESSION_KEY_EXPR}, api_key" -_MCP_CALL_TYPES_SQL: Final = "('call_mcp_tool', 'list_mcp_tools')" -_AGENT_CALL_TYPE_SQL: Final = "'asend_message'" -_SESSION_REPRESENTATIVE_ORDER_SQL: Final = ( - f"(call_type = {_AGENT_CALL_TYPE_SQL}) DESC, " - f'CASE WHEN call_type = {_AGENT_CALL_TYPE_SQL} THEN "endTime" END DESC NULLS LAST, ' - f'call_type IN {_MCP_CALL_TYPES_SQL}, "startTime" DESC, request_id' -) +_SESSION_GROUP_KEY_SQL: Final = f"{SESSION_KEY_EXPR}, api_key" _BATCH_CALL_TYPES_SQL: Final = "('acreate_batch', 'create_batch', 'aretrieve_batch', 'retrieve_batch')" _SPAN_TYPE_SQL_CONDITIONS: Final[Mapping[str, str]] = MappingProxyType( { - "mcp": f"call_type IN {_MCP_CALL_TYPES_SQL}", - "agent": f"call_type = {_AGENT_CALL_TYPE_SQL}", + "mcp": f"call_type IN {MCP_CALL_TYPES_SQL}", + "agent": f"call_type = {AGENT_CALL_TYPE_SQL}", "batch": f"call_type IN {_BATCH_CALL_TYPES_SQL}", "llm": ( - f"(call_type NOT IN {_MCP_CALL_TYPES_SQL} AND call_type != {_AGENT_CALL_TYPE_SQL} " + f"(call_type NOT IN {MCP_CALL_TYPES_SQL} AND call_type != {AGENT_CALL_TYPE_SQL} " f"AND call_type NOT IN {_BATCH_CALL_TYPES_SQL})" ), } @@ -268,6 +268,20 @@ async def _query_raw_or_none(prisma_client: PrismaClient, sql_query: str, *args: return await _query_raw(prisma_client, sql_query, *args) +async def _query_spend_logs(prisma_client: PrismaClient, sql_query: str, *args: object) -> Sequence[_RowT]: + return cast( # cast-ok: callers declare the row shape their SELECT list produces, as with _query_raw + "Sequence[_RowT]", await query_raw_within_statement_timeout(prisma_client, sql_query, *args) + ) + + +@dataclass(frozen=True, slots=True) +class _SpendLogsReader: + prisma_client: PrismaClient + + async def query_raw(self, query: str, *args: object) -> Sequence[Mapping[str, object]]: + return await query_raw_within_statement_timeout(self.prisma_client, query, *args) + + class _TeamTable(Protocol): """The subset of the Prisma team table API this module uses.""" @@ -1567,10 +1581,7 @@ async def get_global_spend_report( ORDER BY total_cost DESC; """ - db_response = await _query_raw_or_none(prisma_client, sql_query, start_date_obj, end_date_obj, api_key) - if db_response is None: - return [] - + db_response = await _query_spend_logs(prisma_client, sql_query, start_date_obj, end_date_obj, api_key) return db_response elif internal_user_id is not None: verbose_proxy_logger.debug("Getting /spend for internal_user_id: %s", internal_user_id) @@ -1610,12 +1621,9 @@ async def get_global_spend_report( ORDER BY total_cost DESC; """ - db_response = await _query_raw_or_none( + db_response = await _query_spend_logs( prisma_client, sql_query, start_date_obj, end_date_obj, internal_user_id ) - if db_response is None: - return [] - return db_response elif team_id is not None and customer_id is not None: return await get_spend_by_team_and_customer( @@ -1677,10 +1685,7 @@ async def get_global_spend_report( group_by_day; """ - db_response = await _query_raw_or_none(prisma_client, sql_query, start_date_obj, end_date_obj) - if db_response is None: - return [] - + db_response = await _query_spend_logs(prisma_client, sql_query, start_date_obj, end_date_obj) return db_response elif group_by == "api_key": sql_query = """ @@ -1718,12 +1723,11 @@ async def get_global_spend_report( ORDER BY total_cost DESC; """ - db_response = await _query_raw_or_none(prisma_client, sql_query, start_date_obj, end_date_obj) - if db_response is None: - return [] - + db_response = await _query_spend_logs(prisma_client, sql_query, start_date_obj, end_date_obj) return db_response + except HTTPException: + raise except Exception as e: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, @@ -1967,7 +1971,7 @@ async def get_key_spend_report( caller_value=LiteLLMProxyRequestSetup.get_logged_api_key(user_api_key_dict), scope_name="api_key", ) - db_response: Sequence[Mapping[str, object]] | None = await _query_raw_or_none( + db_response: Sequence[Mapping[str, object]] = await _query_spend_logs( prisma_client, _scoped_spend_report_sql(scope_column="api_key"), start_date_obj, @@ -2009,7 +2013,7 @@ async def get_user_spend_report( caller_value=user_api_key_dict.user_id, scope_name="internal_user_id", ) - db_response: Sequence[Mapping[str, object]] | None = await _query_raw_or_none( + db_response: Sequence[Mapping[str, object]] = await _query_spend_logs( prisma_client, _scoped_spend_report_sql(scope_column="user"), start_date_obj, @@ -2051,7 +2055,7 @@ async def get_team_spend_report( caller_value=user_api_key_dict.team_id, scope_name="team_id", ) - db_response: Sequence[Mapping[str, object]] | None = await _query_raw_or_none( + db_response: Sequence[Mapping[str, object]] = await _query_spend_logs( prisma_client, _scoped_spend_report_sql(scope_column="team_id"), start_date_obj, @@ -2093,7 +2097,7 @@ async def get_organization_spend_report( organization_id=organization_id, prisma_client=prisma_client, ) - db_response: Sequence[Mapping[str, object]] | None = await _query_raw_or_none( + db_response: Sequence[Mapping[str, object]] = await _query_spend_logs( prisma_client, _ORG_SPEND_REPORT_SQL, start_date_obj, @@ -2948,23 +2952,6 @@ async def ui_view_spend_logs( sql_params.append(json.dumps(used_client_oauth_token)) p += 1 - if status_filter is not None and group_by_session is True and not is_search_lookup: - session_filter_conditions: Final = " AND ".join(sql_conditions) or "TRUE" - sql_conditions.append( - f"""({_SESSION_GROUP_KEY_SQL}) IN ( - SELECT session_key, api_key FROM ( - SELECT DISTINCT ON ({_SESSION_GROUP_KEY_SQL}) - {_SESSION_KEY_EXPR} AS session_key, api_key, status - FROM "LiteLLM_SpendLogs" - WHERE {session_filter_conditions} - ORDER BY {_SESSION_GROUP_KEY_SQL}, {_SESSION_REPRESENTATIVE_ORDER_SQL} - ) AS session_outcomes - WHERE COALESCE(status, 'success') = ${p} - )""" - ) - sql_params.append(status_filter) - p += 1 - if ( group_by_session is True and not is_v2 @@ -2977,12 +2964,30 @@ async def ui_view_spend_logs( sql_conditions=sql_conditions, sql_params=sql_params, next_param_index=p, + status_filter=status_filter, page=page, page_size=page_size, sort_desc=order_direction != "asc", session_cursor=session_cursor, ) + if status_filter is not None and group_by_session is True and not is_search_lookup: + session_filter_conditions: Final = " AND ".join(sql_conditions) or "TRUE" + sql_conditions.append( + f"""({_SESSION_GROUP_KEY_SQL}) IN ( + SELECT session_key, api_key FROM ( + SELECT DISTINCT ON ({_SESSION_GROUP_KEY_SQL}) + {SESSION_KEY_EXPR} AS session_key, api_key, status + FROM "LiteLLM_SpendLogs" + WHERE {session_filter_conditions} + ORDER BY {_SESSION_GROUP_KEY_SQL}, {SESSION_REPRESENTATIVE_ORDER_SQL} + ) AS session_outcomes + WHERE COALESCE(status, 'success') = ${p} + )""" + ) + sql_params.append(status_filter) + p += 1 + # Build the ORDER BY expression. ttft_ms is computed from # completionStartTime - startTime; non-streaming rows (where # completionStartTime is null or equals endTime) yield NULL, so we @@ -3017,7 +3022,7 @@ async def ui_view_spend_logs( LIMIT ${p} ) AS bounded_matches """ - count_rows: Final[Sequence[_SpendLogsCountRow] | None] = await _query_raw_or_none( + count_rows: Final[Sequence[_SpendLogsCountRow]] = await _query_spend_logs( prisma_client, count_query, *sql_params, SPEND_LOGS_PAGINATION_COUNT_CAP + 1 ) raw_total: Final = int(count_rows[0]["total_count"]) if count_rows else 0 @@ -3031,7 +3036,7 @@ async def ui_view_spend_logs( {_SPEND_LOG_LIST_COLUMNS} FROM "LiteLLM_SpendLogs" WHERE {joined_conditions} - ORDER BY {_SESSION_GROUP_KEY_SQL}, {_SESSION_REPRESENTATIVE_ORDER_SQL} + ORDER BY {_SESSION_GROUP_KEY_SQL}, {SESSION_REPRESENTATIVE_ORDER_SQL} ) AS session_representatives ORDER BY {exact_request_id_first}{_order_expr} {_sql_dir}{_nulls_clause}, request_id LIMIT ${p} OFFSET ${p + 1} @@ -3048,7 +3053,7 @@ async def ui_view_spend_logs( ) sql_params.extend([page_size, skip]) - data: Final[Sequence[Mapping[str, object]]] = await _query_raw(prisma_client, sql_query, *sql_params) + data: Final[Sequence[Mapping[str, object]]] = await _query_spend_logs(prisma_client, sql_query, *sql_params) if request_id is not None and not is_v2 and not is_admin_view: await _assert_user_owns_fetched_spend_rows( @@ -3080,22 +3085,6 @@ async def ui_view_spend_logs( raise handle_exception_on_proxy(e) -class _SessionPageRow(TypedDict): - session_key: ReadOnly[str] - api_key: ReadOnly[str] - last_activity: ReadOnly[str] - - -def _parse_session_cursor(session_cursor: str | None) -> tuple[str, str, str] | None: - if session_cursor is None or session_cursor.count("|") < 2: - return None - last_activity, _, rest = session_cursor.partition("|") - api_key, _, session_key = rest.partition("|") - if not last_activity or not session_key: - return None - return (last_activity, session_key, api_key) - - async def _fetch_session_representatives( prisma_client: "PrismaClient", where_clause: str, @@ -3104,19 +3093,21 @@ async def _fetch_session_representatives( session_keys: Sequence[tuple[str, str]], ) -> list[dict[str, object]]: # mutable-ok: _build_ui_spend_logs_response writes session counts onto each row """Fetch the final agent outcome, or newest non-MCP row, of each ``(session_key, api_key)`` session, in ``session_keys`` order.""" + # Lets Postgres use the session_id and request_id indexes. rep_query: Final = f""" SELECT * FROM ( SELECT DISTINCT ON ({_SESSION_GROUP_KEY_SQL}) {_SPEND_LOG_LIST_COLUMNS} FROM "LiteLLM_SpendLogs" WHERE {where_clause} + AND (session_id = ANY(${next_param_index}::text[]) OR request_id = ANY(${next_param_index}::text[])) AND ({_SESSION_GROUP_KEY_SQL}) IN ( SELECT * FROM unnest(${next_param_index}::text[], ${next_param_index + 1}::text[]) ) - ORDER BY {_SESSION_GROUP_KEY_SQL}, {_SESSION_REPRESENTATIVE_ORDER_SQL} + ORDER BY {_SESSION_GROUP_KEY_SQL}, {SESSION_REPRESENTATIVE_ORDER_SQL} ) AS session_representatives """ - rep_rows: Final[Sequence[dict[str, object]]] = await _query_raw( # mutable-ok: rows are enriched in place + rep_rows: Final[Sequence[dict[str, object]]] = await _query_spend_logs( # mutable-ok: rows are enriched in place prisma_client, rep_query, *sql_params, @@ -3129,117 +3120,32 @@ async def _fetch_session_representatives( return [rep_by_key[key] for key in session_keys if key in rep_by_key] -async def _count_grouped_sessions( - prisma_client: "PrismaClient", - where_clause: str, - sql_params: Sequence[object], - next_param_index: int, -) -> tuple[int, bool]: - """Count the sessions matching the filter, returning ``(total, total_is_capped)`` bounded by the count cap.""" - count_query: Final = f""" - SELECT COUNT(*) AS total_count - FROM ( - SELECT 1 - FROM "LiteLLM_SpendLogs" - WHERE {where_clause} - GROUP BY {_SESSION_GROUP_KEY_SQL} - LIMIT ${next_param_index} - ) AS bounded_sessions - """ - count_rows: Final[Sequence[_SpendLogsCountRow]] = await _query_raw( - prisma_client, count_query, *sql_params, SPEND_LOGS_PAGINATION_COUNT_CAP + 1 - ) - raw_total: Final = int(count_rows[0]["total_count"]) if count_rows else 0 - return ( - (SPEND_LOGS_PAGINATION_COUNT_CAP, True) if raw_total > SPEND_LOGS_PAGINATION_COUNT_CAP else (raw_total, False) - ) - - async def _ui_session_grouped_spend_logs( prisma_client: "PrismaClient", sql_conditions: Sequence[str], sql_params: Sequence[object], next_param_index: int, + status_filter: str | None, page: int, page_size: int, sort_desc: bool, session_cursor: str | None, ) -> Mapping[str, object]: - """ - One row per session, keyset-paginated by session last activity. - - Sessions are derived on the fly from ``LiteLLM_SpendLogs`` (no extra - table): rows sharing a ``session_id`` and ``api_key`` form a session, rows - without a session id are singletons keyed by ``request_id``. A page is the - next ``page_size`` sessions ordered by ``(MAX(startTime), session_key, - api_key)``, resumed from the ``session_cursor`` keyset - ``'||'`` instead of an OFFSET, so - page depth does not degrade the query plan. A request for ``page > 1`` - without a cursor (the UI jumping straight to the last page, or back to a - page it never walked through) falls back to ``OFFSET (page - 1) * - page_size``, trimmed to the end of the ``SPEND_LOGS_PAGINATION_COUNT_CAP`` - window the capped ``total`` promises, so a page never runs past that total - and one starting at or past it returns no rows without a query. Each session is represented - by its final agent outcome (or newest non-MCP row), enriched by ``_build_ui_spend_logs_response`` - exactly like the flat listing, and the response carries - ``next_session_cursor`` / ``has_more`` while ``total`` counts sessions - (capped like the flat total). A page that runs out of sessions while still - holding some is itself the end of the list, so its ``total`` is - ``offset + len(page)`` and the grouped count query is skipped; a page that - starts past the end says nothing about the total, so that one is counted. - """ + """One row per session, keyset-paginated by last activity. ``read_session_page`` picks and counts the sessions, + and each is shown by its final agent outcome or newest non-MCP row.""" where_clause: Final = " AND ".join(sql_conditions) if sql_conditions else "TRUE" - cmp_op: Final = "<" if sort_desc else ">" - direction: Final = "DESC" if sort_desc else "ASC" - - cursor: Final = _parse_session_cursor(session_cursor) - having_clause: Final = ( - f'HAVING (MAX("startTime"), {_SESSION_GROUP_KEY_SQL}) {cmp_op} ' - f"(${next_param_index}::timestamp, ${next_param_index + 1}, ${next_param_index + 2})" - if cursor - else "" + session_page: Final = await read_session_page( + _SpendLogsReader(prisma_client), # pyright: ignore[reportArgumentType] # rows are the TypedDict the session SQL selects + where_clause=where_clause, + params=sql_params, + status=status_filter, + page=page, + page_size=page_size, + sort_desc=sort_desc, + session_cursor=session_cursor, + cap=SPEND_LOGS_PAGINATION_COUNT_CAP, ) - cursor_params: Final[tuple[object, ...]] = cursor if cursor else () - limit_index: Final = next_param_index + len(cursor_params) - offset: Final = (page - 1) * page_size if cursor is None else 0 - page_limit: Final = min(page_size, SPEND_LOGS_PAGINATION_COUNT_CAP - offset) - offset_params: Final[tuple[int, ...]] = (offset,) if offset and page_limit > 0 else () - offset_clause: Final = f"OFFSET ${limit_index + 1}" if offset_params else "" - - page_query: Final = f""" - SELECT {_SESSION_KEY_EXPR} AS session_key, - api_key, - MAX("startTime")::text AS last_activity - FROM "LiteLLM_SpendLogs" - WHERE {where_clause} - GROUP BY {_SESSION_GROUP_KEY_SQL} - {having_clause} - ORDER BY MAX("startTime") {direction}, {_SESSION_KEY_EXPR} {direction}, api_key {direction} - LIMIT ${limit_index} {offset_clause} - """ - page_rows: Final[Sequence[_SessionPageRow]] = ( - () - if page_limit <= 0 - else await _query_raw(prisma_client, page_query, *sql_params, *cursor_params, page_limit + 1, *offset_params) - ) - - has_more: Final = len(page_rows) > page_limit - visible_rows: Final = page_rows[:page_limit] - next_cursor: Final = ( - f"{visible_rows[-1]['last_activity']}|{visible_rows[-1]['api_key']}|{visible_rows[-1]['session_key']}" - if has_more and visible_rows - else None - ) - - page_starts_inside_the_list: Final = offset == 0 or len(page_rows) > 0 - page_ends_the_list: Final = cursor is None and page_limit > 0 and not has_more and page_starts_inside_the_list - total_records, total_is_capped = ( - (offset + len(page_rows), False) - if page_ends_the_list - else await _count_grouped_sessions(prisma_client, where_clause, sql_params, next_param_index) - ) - - session_keys: Final = tuple((row["session_key"], row["api_key"]) for row in visible_rows) + session_keys: Final = tuple((session.key, session.api_key) for session in session_page.sessions) data: Final[list[dict[str, object]]] = ( # mutable-ok: _build_ui_spend_logs_response writes onto each row await _fetch_session_representatives( prisma_client=prisma_client, @@ -3253,18 +3159,17 @@ async def _ui_session_grouped_spend_logs( ) _hydrate_spend_log_metadata(data) - total_pages: Final = (total_records + page_size - 1) // page_size response: Final[Mapping[str, object]] = await _build_ui_spend_logs_response( prisma_client, data, - total_records, + session_page.total, page, page_size, - total_pages, + (session_page.total + page_size - 1) // page_size, enrich_session_counts=True, - total_is_capped=total_is_capped, + total_is_capped=session_page.total_is_capped, ) - return {**response, "next_session_cursor": next_cursor, "has_more": has_more} + return {**response, "next_session_cursor": session_page.next_cursor, "has_more": session_page.has_more} class RequestResponsePayload(NamedTuple): @@ -4705,7 +4610,7 @@ async def _build_ui_spend_logs_response( if (row.get("api_key") if isinstance(row, dict) else getattr(row, "api_key", None)) is not None } ) - rows: Final[Sequence[_SessionSpendRow]] = await _query_raw( + rows: Final[Sequence[_SessionSpendRow]] = await _query_spend_logs( prisma_client, f""" SELECT s.*, COALESCE(m.session_models, ARRAY[]::text[]) AS session_models @@ -4720,16 +4625,16 @@ async def _build_ui_spend_logs_response( ) ), 0)::bigint AS session_total_duration_ms, COUNT(*) FILTER ( - WHERE call_type IN {_MCP_CALL_TYPES_SQL} + WHERE call_type IN {MCP_CALL_TYPES_SQL} )::int AS mcp_tool_call_count, COALESCE(SUM(spend) FILTER ( - WHERE call_type IN {_MCP_CALL_TYPES_SQL} + WHERE call_type IN {MCP_CALL_TYPES_SQL} ), 0)::double precision AS mcp_tool_call_spend, COUNT(*) FILTER (WHERE LOWER(cache_hit) = 'true')::int AS session_cache_hit_count, COUNT(*) FILTER ( - WHERE call_type NOT IN {_MCP_CALL_TYPES_SQL} AND call_type != {_AGENT_CALL_TYPE_SQL} + WHERE call_type NOT IN {MCP_CALL_TYPES_SQL} AND call_type != {AGENT_CALL_TYPE_SQL} )::int AS session_llm_count, - COUNT(*) FILTER (WHERE call_type = {_AGENT_CALL_TYPE_SQL})::int AS session_agent_count, + COUNT(*) FILTER (WHERE call_type = {AGENT_CALL_TYPE_SQL})::int AS session_agent_count, COALESCE(SUM(prompt_tokens), 0)::bigint AS session_total_prompt_tokens, COALESCE(SUM(completion_tokens), 0)::bigint AS session_total_completion_tokens, COALESCE(SUM(total_tokens), 0)::bigint AS session_total_tokens @@ -4776,7 +4681,7 @@ async def _build_ui_spend_logs_response( if row.get("session_id") and row.get("api_key") is not None for models in (TypeAdapter(list[str]).validate_python(row.get("session_models") or ()),) } - except PrismaError: + except (PrismaError, HTTPException): verbose_proxy_logger.debug( "Failed to enrich session spend aggregates for spend logs UI", exc_info=True, diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py index a9478a1f889..85a7f870eb4 100644 --- a/litellm/proxy/spend_tracking/spend_tracking_utils.py +++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py @@ -3,7 +3,7 @@ import json import os import re import secrets -from collections.abc import Callable, Mapping, Sequence +from collections.abc import Mapping, Sequence from datetime import datetime, timezone from datetime import datetime as dt from functools import reduce @@ -38,6 +38,12 @@ from litellm.constants import ( ) from litellm.integrations.otel.model.metadata import caller_session_id from litellm.integrations.otel.model.trace_controls import TraceControls +from litellm.litellm_core_utils.batch_utils import ( + BATCH_COST_REQUEST_ID_SUFFIX, + charged_poll_cost, + charged_video_id, + pays_only_its_guardrail, +) from litellm.litellm_core_utils.classifier_logging import ( CLASSIFIER_AUDIT_FIELDS, classifier_audit_fields, @@ -58,7 +64,6 @@ from litellm.litellm_core_utils.internal_call_metadata import ( ) from litellm.litellm_core_utils.litellm_logging import ( StandardLoggingPayloadSetup, - UsageObservation, coerce_model_access_groups, is_valid_sha256_hash, request_model_access_groups_from_litellm_params, @@ -75,7 +80,7 @@ from litellm.proxy._types import ( ) from litellm.proxy.route_llm_request import ProxyModelNotFoundError from litellm.proxy.spend_tracking.spend_log_error_logger import spend_log_error -from litellm.proxy.utils import PrismaClient, hash_token +from litellm.proxy.utils import PrismaClient, hash_token, query_raw_within_statement_timeout from litellm.responses.litellm_completion_transformation.compaction_items import ( compaction_summary, summary_only_compaction_content, @@ -99,6 +104,7 @@ from litellm.types.utils import ( VectorStoreSearchResponse, batch_chat_usage, ) +from litellm.types.videos.utils import VIDEO_COST_REQUEST_ID_SUFFIX from litellm.utils import get_end_user_id_for_cost_tracking if TYPE_CHECKING: @@ -343,7 +349,7 @@ def _get_spend_logs_metadata( return clean_metadata -BATCH_COST_REQUEST_ID_SUFFIX: Final = "_batch_cost" +JOB_POLL_GUARDRAIL_COST_REQUEST_ID_SUFFIX: Final = "_guardrail_cost" def _cost_row_request_id_suffix(call_type: str, metadata: Mapping[str, object], kwargs: Mapping[str, object]) -> str: @@ -371,6 +377,13 @@ def get_spend_logs_id( ) if resolved_id is None: return None + response_cost: Final = charged_poll_cost(kwargs) + charged_video: Final = charged_video_id(call_type, kwargs, resolved_id, response_cost) + if charged_video is not None: + return f"{charged_video}{VIDEO_COST_REQUEST_ID_SUFFIX}" + call_id: Final = kwargs.get("litellm_call_id") + if pays_only_its_guardrail(call_type, kwargs, response_cost) and isinstance(call_id, str) and call_id: + return f"{call_id}{JOB_POLL_GUARDRAIL_COST_REQUEST_ID_SUFFIX}" return f"{resolved_id}{_cost_row_request_id_suffix(call_type, metadata, kwargs)}" @@ -592,7 +605,6 @@ def get_logging_payload( start_time: datetime, end_time: datetime, llm_router: "Router | None" = None, - capture_usage: Callable[[SpendLogsPayload, str | None], None] | None = None, ) -> SpendLogsPayload: if kwargs is None: kwargs = {} @@ -649,10 +661,12 @@ def get_logging_payload( usage = _usage # A request that failed mid-stream has no usable response_obj usage, and a stream - # continued on a fallback only has the fallback's. combined_usage_object holds the - # usage of every call the client's answer took, so it wins in both cases. + # continued on a fallback or a success that bills a failed follow-up only has its own. + # combined_usage_object holds the usage of every call the row bills, so it wins in each case. _combined_usage: Final = kwargs.get("combined_usage_object") - if (not usage or "continued_partial_usage" in kwargs) and isinstance(_combined_usage, litellm.Usage): + if ( + not usage or "continued_partial_usage" in kwargs or "failed_attempts_billed_on_success" in kwargs + ) and isinstance(_combined_usage, litellm.Usage): usage = _combined_usage.model_dump() id = get_spend_logs_id(call_type or "acompletion", response_obj_dict, kwargs, metadata) @@ -980,14 +994,6 @@ def get_logging_payload( payload.get("spend"), ) - if capture_usage is not None: - observed_usage: Final = ( - StandardLoggingPayloadSetup.get_usage_as_dict({"usage": usage}) - if usage and kwargs.get("_accounting_usage_observed") is not UsageObservation.UNKNOWN - else None - ) - capture_usage(payload, safe_dumps(observed_usage) if observed_usage is not None else None) - # Explicitly clear large intermediate objects to reduce memory pressure del response_obj_dict, usage, clean_metadata, additional_usage_values @@ -1092,7 +1098,7 @@ async def _query_raw_rows( sql_query: str, *args: object, ) -> Sequence[Mapping[str, object]] | None: - return await prisma_client.db.query_raw(sql_query, *args) + return await query_raw_within_statement_timeout(prisma_client, sql_query, *args) async def get_spend_by_team( diff --git a/litellm/proxy/spend_tracking/usage_capture.py b/litellm/proxy/spend_tracking/usage_capture.py deleted file mode 100644 index 19480a66678..00000000000 --- a/litellm/proxy/spend_tracking/usage_capture.py +++ /dev/null @@ -1,1084 +0,0 @@ -from __future__ import annotations - -import asyncio -import json -import re -from collections.abc import Awaitable, Callable, Iterator, Mapping -from dataclasses import dataclass, replace -from datetime import datetime, timezone -from decimal import Decimal -from hashlib import sha256 -from itertools import chain, groupby, islice, repeat -from typing import TYPE_CHECKING, Final - -from pydantic import BaseModel, JsonValue, TypeAdapter - -from litellm._logging import verbose_proxy_logger -from litellm.accounting import ( - Dimension, - DimensionName, - InboundUsageEvent, - Metric, - MetricName, - OutboundUsageEvent, - Outcome, -) -from litellm.constants import BILLED_RETRY_ATTEMPT_METADATA_KEY, INTERNAL_CALL_ORIGIN_METADATA_KEY -from litellm.proxy.spend_tracking.usage_meter import ( - DurableUsageMeter, - UsageCaptureError, - UsageCaptureSettings, - usage_delivery_targets, -) -from litellm.types.utils import CallTypes, CustomPricingLiteLLMParams - -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy._types import SpendLogsPayload - - -from litellm.proxy.db.accounting_ledger import AccountingConflictError, UsageWrite - - -class UsageAcknowledgementError(UsageCaptureError): - def __init__(self, cause: Exception) -> None: - super().__init__("Usage persistence did not acknowledge commit") - self.__cause__ = cause - - -@dataclass(frozen=True, slots=True) -class CapturedUsage: - payload: SpendLogsPayload - writes: tuple[UsageWrite, ...] - receipt: asyncio.Future[None] - - -_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue]) - - -_FIELDS: Final = TypeAdapter(dict[str, object]) - - -_INSTANT: Final = TypeAdapter(datetime) - - -_DIMENSIONS: Final = ( - ("api_key", DimensionName.KEY), - ("user", DimensionName.USER), - ("team_id", DimensionName.TEAM), - ("organization_id", DimensionName.ORGANIZATION), - ("end_user", DimensionName.END_USER), - ("model", DimensionName.MODEL), - ("model_group", DimensionName.MODEL_GROUP), - ("model_id", DimensionName.DEPLOYMENT), - ("custom_llm_provider", DimensionName.PROVIDER), - ("mcp_namespaced_tool_name", DimensionName.TOOL), - ("agent_id", DimensionName.AGENT), -) - - -_USAGE_METRICS: Final = tuple( - metric - for metric in MetricName - if metric not in (MetricName.SPEND, MetricName.REQUESTS, MetricName.SUCCESSFUL_REQUESTS, MetricName.FAILED_REQUESTS) -) - - -def _fields(value: object) -> Mapping[str, object]: - return _FIELDS.validate_python(value) if isinstance(value, Mapping) else {} - - -def _text(value: object) -> str | None: - return value if isinstance(value, str) and value else None - - -def _utc_time(value: datetime | str) -> datetime: - parsed: Final = _INSTANT.validate_python(value) - return parsed.replace(tzinfo=timezone.utc) if parsed.tzinfo is None else parsed.astimezone(timezone.utc) - - -def _json(value: object) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), allow_nan=False) - - -def _usage_metrics(usage: Mapping[str, JsonValue] | None) -> tuple[Metric, ...] | None: - if usage is None: - return None - return tuple( - Metric(name=metric, value=Decimal(str(value))) - for metric in _USAGE_METRICS - if isinstance(value := usage.get(metric.value), (int, float)) and not isinstance(value, bool) - ) - - -@dataclass(frozen=True, slots=True) -class UsageCaptureContext: - caller_id: str | None - attempt_id: str - parent_request_id: str | None - retry_of_attempt_id: str | None - job_id: str | None - charge_eligible: bool - request_count_eligible: bool - ownership: str - pricing: str - finalized_cost: str | None = None - allocation: str | None = None - outcome: Outcome | None = None - started_at: datetime | None = None - ended_at: datetime | None = None - phase: str = "execution" - extra_dimensions: tuple[Dimension, ...] = () - provider_timestamps: str = "{}" - usage_source: str = "measured" - included_attempt_ids: tuple[str, ...] = () - related_attempt_ids: tuple[str, ...] = () - - -def _database_usage_write(event: InboundUsageEvent | OutboundUsageEvent, envelope: Mapping[str, object]) -> UsageWrite: - from sys import maxsize - - from litellm.litellm_core_utils.safe_json_dumps import safe_json_structure - - normalized: Final = _FIELDS.validate_python(safe_json_structure(envelope, max_depth=maxsize)) - if normalized.get("event") != event.model_dump(mode="json"): - raise UsageCaptureError("Usage event text cannot be normalized without changing identity or attribution") - serialized: Final = _json(normalized) - _JSON_OBJECT.validate_json(serialized) - return UsageWrite(event, serialized, usage_delivery_targets(event.dimensions)) - - -def usage_writes( - payload: SpendLogsPayload, usage_json: str | None, context: UsageCaptureContext -) -> tuple[UsageWrite, ...]: - fields: Final = _FIELDS.validate_python(payload) - tags: Final = TypeAdapter(tuple[str, ...]).validate_json(payload["request_tags"]) - dimensions: Final = ( - *(Dimension(name=name, value=value) for field, name in _DIMENSIONS if (value := _text(fields.get(field)))), - *(Dimension(name=DimensionName.TAG, value=tag) for tag in dict.fromkeys(tags)), - *context.extra_dimensions, - ) - outcome: Final = context.outcome or (Outcome.FAILED if payload["status"] == "failure" else Outcome.SUCCEEDED) - started: Final = context.started_at or _utc_time(payload["startTime"]) - ended: Final = context.ended_at or _utc_time(payload["endTime"]) - usage: Final = None if usage_json is None else _JSON_OBJECT.validate_json(usage_json) - outbound: Final = OutboundUsageEvent( - event_id=context.attempt_id, - attempt_id=context.attempt_id, - request_id=context.caller_id, - retry_of_attempt_id=context.retry_of_attempt_id, - call_type=CallTypes(payload["call_type"]), - started_at=started, - ended_at=ended, - outcome=outcome, - dimensions=dimensions, - metrics=_usage_metrics(None if context.usage_source == "unknown" else usage), - ) - envelope: Final = { - "event": outbound.model_dump(mode="json"), - "usage": usage, - "usage_source": "unknown" if usage is None else context.usage_source, - "included_attempt_ids": context.included_attempt_ids, - "related_attempt_ids": context.related_attempt_ids, - "pricing": _JSON_OBJECT.validate_json(context.pricing), - "charge_eligible": context.charge_eligible, - "request_count_eligible": context.request_count_eligible, - "ownership": context.ownership, - "parent_request_id": context.parent_request_id, - "job_id": context.job_id, - "phase": context.phase, - "provider_timestamps": _JSON_OBJECT.validate_json(context.provider_timestamps), - "finalized_cost": None - if context.finalized_cost is None - else _JSON_OBJECT.validate_json(context.finalized_cost), - "allocation": None if context.allocation is None else _JSON_OBJECT.validate_json(context.allocation), - } - outbound_write: Final = _database_usage_write(outbound, envelope) - if not context.request_count_eligible or context.caller_id is None: - return (outbound_write,) - inbound: Final = InboundUsageEvent( - event_id=f"{context.caller_id}:inbound", - request_id=context.caller_id, - call_type=outbound.call_type, - started_at=started, - ended_at=ended, - outcome=outcome, - dimensions=dimensions, - metrics=( - Metric(name=MetricName.REQUESTS, value=Decimal(1)), - Metric(name=MetricName.SUCCESSFUL_REQUESTS, value=Decimal(int(outcome is Outcome.SUCCEEDED))), - Metric(name=MetricName.FAILED_REQUESTS, value=Decimal(int(outcome is not Outcome.SUCCEEDED))), - ), - ) - return ( - _database_usage_write(inbound, {"event": inbound.model_dump(mode="json")}), - outbound_write, - ) - - -_INCLUDED_IN_PARENT: Final = object() - - -_PRICING_FIELDS: Final = frozenset( - { - "service_tier", - "data_residency", - "vertex_location", - "region_name", - "base_model", - "custom_pricing", - "input_cost_per_token", - "output_cost_per_token", - "input_cost_per_second", - "output_cost_per_second", - "input_cost_per_character", - "output_cost_per_character", - "size", - "quality", - "n", - "duration", - "seconds", - "search_context_size", - "additional_response_cost", - } -) - - -def _selected_pricing(fields: Mapping[str, object]) -> Mapping[str, JsonValue]: - return _JSON_OBJECT.validate_python( - { - key: value - for key, value in fields.items() - if key in _PRICING_FIELDS or key in CustomPricingLiteLLMParams.model_fields - } - ) - - -def _response_fields(response: object) -> Mapping[str, object]: - if isinstance(response, BaseModel): - return _FIELDS.validate_python(response.model_dump()) - return _fields(response) - - -def _job_time(response: Mapping[str, object], key: str) -> datetime | None: - stamp: Final = response.get(key) - if isinstance(stamp, (int, float)) and not isinstance(stamp, bool): - return datetime.fromtimestamp(stamp, tz=timezone.utc) - return None - - -def capture_context(payload: SpendLogsPayload, kwargs: Mapping[str, object], response: object) -> UsageCaptureContext: - from dataclasses import asdict - - from litellm.litellm_core_utils.hidden_params import get_hidden_params - from litellm.litellm_core_utils.internal_call_metadata import is_background_response, is_unbilled_non_inference_call - from litellm.litellm_core_utils.litellm_logging import ( - Logging, - UsageCaptureObservation, - UsageObservation, - mask_api_base_credentials, - ) - from litellm.litellm_core_utils.llm_cost_calc.finalization import FinalizedCost - from litellm.proxy.hooks.search_free_tier import SearchAllowanceAllocation - from litellm.proxy.spend_tracking.spend_tracking_utils import get_request_model_access_groups - - metadata: Final = _JSON_OBJECT.validate_json(payload["metadata"]) - params: Final = _fields(kwargs.get("litellm_params")) - request_metadata: Final = _fields(params.get("litellm_metadata") or params.get("metadata")) - standard: Final = _fields(kwargs.get("standard_logging_object")) - logging_obj: Final = kwargs.get("litellm_logging_obj") - raw_response: Final = _response_fields(response) - hidden: Final = _fields(get_hidden_params(response)) - tool: Final = _fields(kwargs.get("mcp_tool_call_metadata")) - tool_pricing: Final = _fields(tool.get("mcp_server_cost_info")) - caller: Final = payload["litellm_call_id"] - cached: Final = payload["cache_hit"].lower() == "true" - included_in_parent: Final = kwargs.get("_accounting_internal_aggregate") is _INCLUDED_IN_PARENT - billed_id: Final = payload["request_id"] - terminal: Final = raw_response.get("status") in { - "completed", - "complete", - "failed", - "cancelled", - "expired", - "incomplete", - } - deferred: Final = billed_id.endswith(("_batch_cost", "_background_cost")) and terminal - management: Final = ( - (billed_id.endswith(("_batch_cost", "_background_cost")) and not terminal) - or (is_background_response(response) and not terminal) - or is_unbilled_non_inference_call(payload["call_type"], request_metadata, response) - ) - attempt: Final = ( - f"{caller}_cache_hit" - if cached and caller - else f"{caller}:status" - if management - else f"{billed_id}:included" - if included_in_parent - else billed_id - ) - if not attempt or attempt == "None" or (cached and not caller): - raise UsageCaptureError("Usage capture requires a stable execution identity") - retry: Final = metadata.get(BILLED_RETRY_ATTEMPT_METADATA_KEY) is True - origin: Final = metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY) - parent: Final = _text(kwargs.get("parent_request_id")) or _text(request_metadata.get("parent_request_id")) - finalized_value: Final = ( - logging_obj.finalized_cost if isinstance(logging_obj, Logging) else kwargs.get("_accounting_finalized_cost") - ) - finalized: Final = finalized_value if isinstance(finalized_value, FinalizedCost) else None - allocation_value: Final = ( - logging_obj.cost_allowance_allocation - if isinstance(logging_obj, Logging) - else kwargs.get("_accounting_allocation") - ) - allocation: Final = allocation_value if isinstance(allocation_value, SearchAllowanceAllocation) else None - pricing_snapshot: Final = kwargs.get("_accounting_pricing_snapshot") - observation: Final = kwargs.get("_accounting_usage_snapshot") - captured_pricing: Final = { - "model_map_information": ( - _JSON_OBJECT.validate_json(pricing_snapshot.model_map_information) - if isinstance(pricing_snapshot, PricingCapture) - else metadata.get("model_map_information") - ), - "cost_breakdown": metadata.get("cost_breakdown"), - **( - _JSON_OBJECT.validate_json(pricing_snapshot.inputs) - if isinstance(pricing_snapshot, PricingCapture) - else _pricing_inputs(kwargs, logging_obj, response) - ), - "tool": { - "name": tool.get("name"), - "cost_per_query": _fields(tool_pricing.get("tool_name_to_cost_per_query")).get( - _text(tool.get("name")) or "", tool_pricing.get("default_cost_per_query") - ), - } - if tool - else None, - "response_inputs": { - "audio_transcription_duration": hidden.get("audio_transcription_duration", raw_response.get("duration")), - "billed_units": _fields(raw_response.get("meta")).get("billed_units"), - "batch_models": hidden.get("batch_models"), - "batch_successful_requests": hidden.get("batch_successful_requests"), - "batch_failed_requests": hidden.get("batch_failed_requests"), - "service_tier": raw_response.get("service_tier"), - "region_name": hidden.get("region_name"), - }, - "reported_cost": observation.response_cost - if isinstance(observation, UsageCaptureObservation) - else standard.get( - "response_cost", kwargs.get("response_cost", _fields(get_hidden_params(response)).get("response_cost")) - ), - "adjustments": ( - _JSON_OBJECT.validate_json(pricing_snapshot.adjustments) - if isinstance(pricing_snapshot, PricingCapture) - else None - ), - } - return UsageCaptureContext( - caller_id=None if deferred else parent if retry or included_in_parent else caller, - attempt_id=attempt, - parent_request_id=parent, - retry_of_attempt_id=_text(request_metadata.get("retry_of_attempt_id")), - job_id=_text(raw_response.get("id")) if deferred or management else None, - charge_eligible=not cached and not included_in_parent and not management, - request_count_eligible=bool(caller) and not origin and not retry and not deferred and not included_in_parent, - ownership="included_in_parent" - if included_in_parent - else "aggregate" - if kwargs.get("additional_response_cost") is not None - else "attempt", - pricing=_json(captured_pricing), - finalized_cost=_json( - { - "response_cost": finalized.amount, - "cost_breakdown": finalized.logging_breakdown(), - "allowance_discount": finalized.allowance_discount, - } - ) - if finalized is not None - else ( - _json({"response_cost": standard.get("response_cost"), "cost_breakdown": standard.get("cost_breakdown")}) - if standard.get("cost_finalized") is True - else None - ), - allocation=_json(asdict(allocation)) if allocation is not None else None, - started_at=_job_time(raw_response, "created_at") if deferred else None, - ended_at=( - _job_time(raw_response, "completed_at") - or _job_time(raw_response, "failed_at") - or _job_time(raw_response, "cancelled_at") - or _job_time(raw_response, "expired_at") - ) - if deferred - else None, - outcome=( - Outcome.CANCELLED - if raw_response.get("status") == "cancelled" - or isinstance(response, asyncio.CancelledError) - or getattr(response, "status_code", None) == 499 - else Outcome.FAILED - if raw_response.get("status") in {"failed", "expired", "incomplete"} or isinstance(response, BaseException) - else None - ), - phase="terminal" if deferred else "management" if management else "execution", - usage_source="estimated" - if kwargs.get("_accounting_usage_observed") is UsageObservation.ESTIMATED - else "measured", - included_attempt_ids=TypeAdapter(tuple[str, ...]).validate_python( - kwargs.get("_accounting_included_attempt_ids", ()) - ), - related_attempt_ids=tuple(write.event.event_id for write in _prior_writes(kwargs)), - provider_timestamps=_json( - { - key: raw_response.get(key) - for key in ("created_at", "completed_at", "failed_at", "cancelled_at", "expired_at") - if raw_response.get(key) is not None - } - ) - if deferred - else "{}", - extra_dimensions=( - *( - (Dimension(name=DimensionName.TOOL, value=tool_name),) - if not payload.get("mcp_namespaced_tool_name") - and (tool_name := _text(tool.get("namespaced_tool_name"))) - else () - ), - *( - (Dimension(name=DimensionName.API_BASE, value=mask_api_base_credentials(api_base)),) - if (api_base := _text(payload["api_base"])) - else () - ), - *( - Dimension(name=DimensionName.MODEL_ACCESS_GROUP, value=group) - for group in get_request_model_access_groups(kwargs) - ), - *( - (Dimension(name=DimensionName.PROJECT, value=project),) - if (project := _text(request_metadata.get("user_api_key_project_id"))) - else () - ), - *( - (Dimension(name=DimensionName.SERVICE_TIER, value=tier),) - if (tier := _text(raw_response.get("service_tier") or kwargs.get("service_tier"))) - else () - ), - ), - ) - - -class _CaptureBuilder: - def __init__(self, kwargs: Mapping[str, object], response: object) -> None: - self.kwargs: Final = kwargs - self.response: Final = response - self.writes: tuple[UsageWrite, ...] = () - - def observe(self, payload: SpendLogsPayload, usage: str | None) -> None: - from litellm.litellm_core_utils.litellm_logging import UsageCaptureObservation, UsageObservation - - snapshot: Final = self.kwargs.get("_accounting_usage_snapshot") - if isinstance(snapshot, UsageCaptureError): - raise snapshot - effective: Final = snapshot.usage_json if isinstance(snapshot, UsageCaptureObservation) else usage - parsed: Final = _JSON_OBJECT.validate_json(effective) if effective is not None else {} - batch: Final = _fields(parsed.get("batch_usage_by_model")) - unknown: Final = (bool(batch) or bool(parsed.get("batch_unknown_usage_requests"))) and all( - value is None for value in batch.values() - ) - observed: Final = ( - _json( - { - "batch_usage_by_model": batch, - "batch_unknown_usage_requests": parsed.get("batch_unknown_usage_requests"), - **( - {"batch_usage_by_request": parsed["batch_usage_by_request"]} - if "batch_usage_by_request" in parsed - else {} - ), - } - ) - if unknown - else effective - ) - context: Final = capture_context(payload, self.kwargs, self.response) - provenance: Final = ( - "unknown" - if unknown - else "estimated" - if isinstance(snapshot, UsageCaptureObservation) and snapshot.provenance is UsageObservation.ESTIMATED - else context.usage_source - ) - self.writes = usage_writes(payload, observed, replace(context, usage_source=provenance)) - - -def _prior_captures(kwargs: Mapping[str, object]) -> tuple[CapturedUsage, ...]: - prior: Final = kwargs.get("_accounting_prior_captures", ()) - return ( - tuple( - item for item in TypeAdapter(tuple[object, ...]).validate_python(prior) if isinstance(item, CapturedUsage) - ) - if isinstance(prior, tuple) - else () - ) - - -def _prior_writes(kwargs: Mapping[str, object]) -> tuple[UsageWrite, ...]: - return tuple(chain.from_iterable(item.writes for item in _prior_captures(kwargs))) - - -def capture_callback( - meter: DurableUsageMeter, - kwargs: Mapping[str, object], - response: object, - start_time: datetime, - end_time: datetime, -) -> CapturedUsage: - from litellm.proxy.db.db_spend_update_writer import get_llm_router - from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload - - existing: Final = kwargs.get("_accounting_capture") - if isinstance(existing, CapturedUsage): - return existing - builder: Final = _CaptureBuilder(kwargs, response) - payload: Final = get_logging_payload( - dict(kwargs), response, start_time, end_time, llm_router=get_llm_router(), capture_usage=builder.observe - ) - prerequisites: Final = tuple(item.receipt for item in _prior_captures(kwargs)) - return CapturedUsage(payload, builder.writes, meter.submit(builder.writes, prerequisites=prerequisites)) - - -def _batch_rows(response: object) -> tuple[Mapping[str, JsonValue], ...]: - usage: Final = _response_fields(_response_fields(response).get("usage")) - rows: Final = usage.get("batch_usage_by_request") - return TypeAdapter(tuple[dict[str, JsonValue], ...]).validate_python(rows) if rows else () - - -def _batch_root(write: UsageWrite, rows: tuple[Mapping[str, JsonValue], ...]) -> UsageWrite: - event: Final = write.event.model_copy(update={"metrics": None}) - envelope: Final = _JSON_OBJECT.validate_json(write.envelope) - return _database_usage_write( - event, - { - **envelope, - "event": event.model_dump(mode="json"), - "usage": { - "batch_row_count": len(rows), - "batch_rows_sha256": sha256(_json(rows).encode()).hexdigest(), - "batch_charge_eligible": envelope["charge_eligible"], - }, - "usage_source": "unknown", - "charge_eligible": False, - "ownership": "aggregate", - }, - ) - - -def _batch_row_write(root: UsageWrite, row: Mapping[str, JsonValue], index: int) -> UsageWrite: - envelope: Final = _JSON_OBJECT.validate_json(root.envelope) - pricing: Final = _JSON_OBJECT.validate_python(envelope["pricing"]) - model: Final = _text(row["model"]) - usage: Final = _JSON_OBJECT.validate_python(row["usage"]) if row["usage"] is not None else None - identity: Final = f"{root.event.event_id}:row:{index}" - event: Final = OutboundUsageEvent.model_validate( - { - **root.event.model_dump(), - "event_id": identity, - "attempt_id": identity, - "metrics": _usage_metrics(usage), - "dimensions": ( - *(dimension for dimension in root.event.dimensions if dimension.name is not DimensionName.MODEL), - *((Dimension(name=DimensionName.MODEL, value=model),) if model else ()), - ), - } - ) - return _database_usage_write( - event, - { - **envelope, - "event": event.model_dump(mode="json"), - "usage": usage, - "usage_source": "unknown" if usage is None else "measured", - "charge_eligible": _fields(envelope["usage"])["batch_charge_eligible"], - "ownership": "attempt", - "parent_request_id": root.event.event_id, - "finalized_cost": None, - "allocation": None, - "pricing": { - **pricing, - "model_map_information": _fields(pricing.get("batch_models")).get(model or ""), - "batch_models": {}, - "reported_cost": None, - "cost_breakdown": None, - "response_inputs": { - key: value - for key, value in _fields(pricing.get("response_inputs")).items() - if key not in {"batch_models", "batch_successful_requests"} - }, - }, - }, - ) - - -@dataclass(frozen=True, slots=True) -class _BatchCapture: - payload: SpendLogsPayload - root: UsageWrite - rows: bytes - prerequisites: tuple[asyncio.Future[None], ...] - - -def _prepare_batch_capture( - kwargs: Mapping[str, object], response: object, start_time: datetime, end_time: datetime -) -> _BatchCapture: - from litellm.proxy.db.db_spend_update_writer import get_llm_router - from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload - - rows: Final = tuple(sorted(_batch_rows(response), key=_json)) - builder: Final = _CaptureBuilder(kwargs, response) - payload: Final = get_logging_payload( - dict(kwargs), response, start_time, end_time, llm_router=get_llm_router(), capture_usage=builder.observe - ) - if len(builder.writes) != 1 or _terminal_facts(builder.writes[0]) is None: - raise UsageCaptureError("Batch rows require one terminal job observation") - return _BatchCapture( - payload, - _batch_root(builder.writes[0], rows), - b"\n".join(_json((row, sum(1 for _ in group))).encode("utf-8") for row, group in groupby(map(_json, rows))), - tuple(prior.receipt for prior in _prior_captures(kwargs)), - ) - - -def _batch_groups(rows: bytes) -> Iterator[tuple[str, int]]: - return (TypeAdapter(tuple[str, int]).validate_json(match.group()) for match in re.finditer(rb"[^\n]+", rows)) - - -def _batch_writes(root: UsageWrite, rows: bytes) -> Iterator[UsageWrite]: - expanded: Final = chain.from_iterable(repeat(row, count) for row, count in _batch_groups(rows)) - return (_batch_row_write(root, _JSON_OBJECT.validate_json(row), index) for index, row in enumerate(expanded)) - - -def _batch_write_bound(root: UsageWrite, rows: bytes) -> int: - last_index: Final = sum(count for _, count in _batch_groups(rows)) - 1 - return max( - len(root.envelope.encode("utf-8")), - max( - len(_batch_row_write(root, _JSON_OBJECT.validate_json(row), last_index).envelope.encode("utf-8")) - for row, _ in _batch_groups(rows) - ), - ) - - -def _batch_capacity( - meter: DurableUsageMeter, root: UsageWrite, rows: bytes, *, extra_bytes: int = 0 -) -> tuple[int, int, int]: - retained: Final = len(rows) + len(root.envelope.encode("utf-8")) + extra_bytes - bound: Final = _batch_write_bound(root, rows) - chunk_bytes: Final = max( - bound, - min( - bound * min(meter.settings.batch_size, sum(count for _, count in _batch_groups(rows))), - meter.settings.max_pending_bytes // 4, - (meter.settings.max_pending_bytes - retained) // 2, - ), - ) - return retained + chunk_bytes, chunk_bytes, bound - - -async def _persist_batch_capture( - meter: DurableUsageMeter, - load: Callable[[tuple[str, ...]], Awaitable[tuple[UsageWrite, ...]]], - root: UsageWrite, - rows: bytes, - prerequisites: tuple[asyncio.Future[None], ...], - chunk_bytes: int, - reservation: object | None = None, -) -> None: - for receipt in prerequisites: - await asyncio.shield(receipt) - await meter.record_writes((root,)) - accepted: Final = await load((root.event.event_id,)) - if len(accepted) != 1: - raise UsageCaptureError("Committed batch snapshot could not be loaded") - frozen: Final = _reuse_terminal_snapshot(root, accepted[0]) - retained, capacity, bound = _batch_capacity(meter, frozen, rows, extra_bytes=len(root.envelope.encode("utf-8"))) - if reservation is not None: - meter.resize_producer(reservation, retained, capacity) - write_capacity: Final = capacity if reservation is not None else chunk_bytes - if bound > write_capacity: - raise UsageCaptureError("Batch observation exceeds reserved producer write capacity") - group_size: Final = min(meter.settings.batch_size, max(1, write_capacity // bound)) - remaining: Final = _batch_writes(frozen, rows) - while group := tuple(islice(remaining, group_size)): - await meter.record_writes(group) - - -async def capture_batch_callback( - meter: DurableUsageMeter, - load: Callable[[tuple[str, ...]], Awaitable[tuple[UsageWrite, ...]]] | None, - kwargs: Mapping[str, object], - response: object, - start_time: datetime, - end_time: datetime, -) -> CapturedUsage: - if load is None: - raise UsageCaptureError("Batch capture requires the existing ledger snapshot reader") - capture: Final = _prepare_batch_capture(kwargs, response, start_time, end_time) - await _persist_batch_capture( - meter, load, capture.root, capture.rows, capture.prerequisites, meter.settings.max_pending_bytes - ) - receipt: Final[asyncio.Future[None]] = asyncio.get_running_loop().create_future() - receipt.set_result(None) - return CapturedUsage(capture.payload, (capture.root,), receipt) - - -async def captured_callback_kwargs( - kwargs: Mapping[str, object], response: object, start_time: datetime | None, end_time: datetime | None -) -> Mapping[str, object]: - from litellm.proxy.proxy_server import usage_capture - - usage_meter: Final = usage_capture.meter - - if usage_meter is None: - return kwargs - if start_time is None or end_time is None: - verbose_proxy_logger.error("Usage capture requires callback start and end timestamps") - return kwargs - try: - captured: Final = ( - usage_capture.submit_batch(kwargs, response, start_time, end_time) - if _batch_rows(response) - else capture_callback(usage_meter, kwargs, response, start_time, end_time) - ) - except Exception: # noqa: BLE001 # capture failure must not suppress legacy billing - verbose_proxy_logger.exception("Usage capture failed before legacy cost tracking") - return kwargs - return {**kwargs, "_accounting_capture": captured} - - -async def capture_proxy_response(logging_obj: object, response: object, *, background: bool = False) -> None: - from fastapi import HTTPException - - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy.proxy_server import general_settings, usage_capture - - settings: Final = _FIELDS.validate_python(general_settings) - usage_meter: Final = usage_capture.meter - - if usage_meter is None or not isinstance(logging_obj, Logging): - return - sync_ack: Final = ( - settings.get("accounting_sync_ack") is True - and logging_obj.stream is not True - and not background - and logging_obj.call_type in {"acompletion", "atext_completion", "aembedding", "aresponses"} - ) - try: - capture_logging_usage(usage_meter, logging_obj, response, logging_obj.start_time, datetime.now(timezone.utc)) - captured: Final = logging_obj.model_call_details.get("_accounting_capture") - if sync_ack: - if not isinstance(captured, CapturedUsage): - raise UsageCaptureError("Usage capture was not admitted") - timeout: Final = TypeAdapter(float).validate_python( - settings.get("accounting_sync_ack_timeout_seconds", 20.0) - ) - await asyncio.wait_for(asyncio.shield(captured.receipt), timeout=timeout) - except Exception as error: # noqa: BLE001 # the configured response policy owns every capture failure - verbose_proxy_logger.exception("Usage capture failed before the response boundary") - if sync_ack and settings.get("accounting_sync_ack_failure_policy", "fail_closed") == "fail_closed": - acknowledgement: Final = UsageAcknowledgementError(error) - raise HTTPException(503, str(acknowledgement)) from acknowledgement - - -def capture_logging_usage( - meter: DurableUsageMeter, - logging_obj: object, - response: object, - start_time: datetime, - end_time: datetime, - freeze_only: bool = False, -) -> CapturedUsage | UsageCaptureError | None: - from litellm._internal_context import internal_parent_request_id, is_internal_call - from litellm.batches.batch_utils import batch_cost_is_final - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import LiteLLMBatch - - if not isinstance(logging_obj, Logging): - return - internal: Final = is_internal_call.get() - if isinstance(response, LiteLLMBatch) and batch_cost_is_final(response) and response.usage is None: - return - try: - if not internal and isinstance(logging_obj.model_call_details.get("_accounting_capture"), CapturedUsage): - return capture_callback(meter, logging_obj.model_call_details, response, start_time, end_time) - normalized_result: Final = logging_obj.usage_capture_response(response) - prepared: Final = logging_obj.prepared_cost - normalized: Final = ( - {**_response_fields(normalized_result), "usage": prepared.usage.model_dump()} - if prepared is not None - else normalized_result - ) - if normalized is None: - return - details: Final = _FIELDS.validate_python(logging_obj.model_call_details) - params: Final = _fields(details.get("litellm_params")) - metadata: Final = _fields(params.get("litellm_metadata") or params.get("metadata")) - if metadata.get("batch_ignore_default_logging") is True: - return - existing_pricing: Final = details.get("_accounting_pricing_snapshot") - pricing: Final = ( - existing_pricing - if isinstance(existing_pricing, PricingCapture) and not internal - else _capture_pricing(logging_obj, normalized, details) - ) - if freeze_only or _batch_rows(normalized): - if not internal: - logging_obj.record_usage_pricing(pricing) - return - captured: Final = capture_callback( - meter, - { - **details, - "litellm_logging_obj": logging_obj, - "_accounting_capture": None if internal else details.get("_accounting_capture"), - "_accounting_pricing_snapshot": pricing, - "_accounting_internal_aggregate": _INCLUDED_IN_PARENT if internal else None, - "parent_request_id": internal_parent_request_id.get() if internal else details.get("parent_request_id"), - }, - normalized, - start_time, - end_time, - ) - if not internal: - logging_obj.record_usage_capture(captured) - except Exception as error: # noqa: BLE001 # failed capture must not prevent normal logging and legacy charging - verbose_proxy_logger.exception("Usage capture failed at the execution boundary") - return UsageCaptureError(str(error)) - else: - return captured - - -@dataclass(frozen=True, slots=True) -class PricingCapture: - model_map_information: str - adjustments: str - inputs: str - - -def _capture_pricing(logging_obj: Logging, normalized: object, details: Mapping[str, object]) -> PricingCapture: - import litellm - from litellm.litellm_core_utils.cost_discount import resolve_cost_discount - from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup - - params: Final = _fields(details.get("litellm_params")) - prepared: Final = logging_obj.prepared_cost - provider: Final = _text(details.get("custom_llm_provider")) - builtins: Final = logging_obj.standard_built_in_tools_params - pricing: Final = ( - prepared.payload["model_map_information"] - if prepared is not None - else StandardLoggingPayloadSetup.get_model_cost_information( - base_model=logging_obj.get_deployment_model_for_cost(), - custom_pricing=bool(params.get("custom_pricing")), - custom_llm_provider=_text(details.get("custom_llm_provider")), - init_response_obj=normalized, - api_base=_text(params.get("api_base")), - router_model_id=logging_obj.get_router_model_id(), - ) - ) - adjustments: Final = _json( - { - "discount": resolve_cost_discount( - litellm.cost_discount_config, - provider, - logging_obj.get_deployment_model_for_cost(), - _text(params.get("region_name") or params.get("vertex_location")), - ), - "margin": litellm.cost_margin_config.get(provider or "", litellm.cost_margin_config.get("global")), - "web_search_options": ( - _selected_pricing(_fields(builtins.get("web_search_options"))) - if builtins.get("web_search_options") is not None - else None - ), - "file_search": builtins.get("file_search") is not None, - } - ) - batch_usage: Final = _fields(_fields(_response_fields(normalized).get("usage")).get("batch_usage_by_model")) - return PricingCapture( - _json(pricing), - adjustments, - _json( - { - **_pricing_inputs(details, logging_obj, normalized), - "batch_models": { - model: StandardLoggingPayloadSetup.get_model_cost_information( - base_model=model, - custom_pricing=bool(params.get("custom_pricing")), - custom_llm_provider=provider, - init_response_obj={"model": model}, - api_base=_text(params.get("api_base")), - router_model_id=logging_obj.get_router_model_id(), - ) - for model in batch_usage - }, - } - ), - ) - - -def _pricing_inputs(kwargs: Mapping[str, object], logging_obj: object, response: object) -> Mapping[str, object]: - from litellm.litellm_core_utils.litellm_logging import Logging - - return { - "request": _selected_pricing(kwargs), - "deployment": _selected_pricing(_fields(kwargs.get("litellm_params"))), - "optional_params": _selected_pricing(_fields(kwargs.get("optional_params"))), - "logging": logging_obj.usage_capture_pricing(response) if isinstance(logging_obj, Logging) else None, - "model_info": _selected_pricing( - _fields( - _fields( - _fields(kwargs.get("litellm_params")).get("litellm_metadata") - or _fields(kwargs.get("litellm_params")).get("metadata") - ).get("model_info") - ) - ), - } - - -def _terminal_facts(write: UsageWrite) -> Mapping[str, JsonValue] | None: - payload: Final = _JSON_OBJECT.validate_json(write.envelope) - if payload.get("phase") != "terminal": - return None - event: Final = _JSON_OBJECT.validate_python(payload["event"]) - return { - **{ - key: value - for key, value in payload.items() - if key not in {"event", "pricing", "finalized_cost", "allocation"} - }, - "event": {key: value for key, value in event.items() if key not in {"started_at", "ended_at"}}, - } - - -def _reuse_terminal_snapshot(candidate: UsageWrite, accepted: UsageWrite) -> UsageWrite: - facts: Final = _terminal_facts(candidate) - if facts is None or facts != _terminal_facts(accepted) or set(candidate.targets) != set(accepted.targets): - raise AccountingConflictError("Repeated terminal job disagrees with its accepted usage or attribution") - return accepted - - -class UsageBatchWriter: - def __init__( - self, - insert: Callable[[tuple[UsageWrite, ...]], Awaitable[None]], - load: Callable[[tuple[str, ...]], Awaitable[tuple[UsageWrite, ...]]], - ) -> None: - self._insert: Final = insert - self._load: Final = load - - async def _restore(self, writes: tuple[UsageWrite, ...]) -> tuple[UsageWrite, ...]: - terminal: Final = tuple(write for write in writes if _terminal_facts(write) is not None) - if not terminal: - return writes - identities: Final = tuple(dict.fromkeys(write.event.event_id for write in terminal)) - stored: Final = await self._load(identities) - # Re-read the first accepted observation and pricing before the ledger's unchanged id/hash check. - snapshots: Final = {write.event.event_id: write for write in (*reversed(terminal), *stored)} - return tuple( - _reuse_terminal_snapshot(write, snapshots[write.event.event_id]) - if write.event.event_id in snapshots - else write - for write in writes - ) - - async def write(self, writes: tuple[UsageWrite, ...]) -> None: - restored: Final = await self._restore(writes) - try: - await self._insert(restored) - except AccountingConflictError: - if not any(_terminal_facts(write) is not None for write in writes): - raise - await self._insert(await self._restore(writes)) - - -class UsageCaptureRuntime: - def __init__(self) -> None: - self._batches: Final[set[asyncio.Task[None]]] = set() # mutable-ok: owned producer lifecycle - self._stopping = False - self.meter: DurableUsageMeter | None = None - self.load: Callable[[tuple[str, ...]], Awaitable[tuple[UsageWrite, ...]]] | None = None - - def start( - self, - write_batch: Callable[[tuple[UsageWrite, ...]], Awaitable[None]], - settings: UsageCaptureSettings, - *, - load: Callable[[tuple[str, ...]], Awaitable[tuple[UsageWrite, ...]]] | None = None, - ) -> None: - from litellm.litellm_core_utils.litellm_logging import Logging - - if self.meter is not None: - if self.meter.settings != settings: - raise ValueError("Changing accounting_capture requires a worker restart") - return - meter: Final = DurableUsageMeter(write_batch, settings) - self.meter = meter - self.load = load - self._stopping = False - Logging.usage_capture_callback = lambda logger, response, start, end, freeze: capture_logging_usage( - meter, logger, response, start, end, freeze - ) - - def submit_batch( - self, kwargs: Mapping[str, object], response: object, start_time: datetime, end_time: datetime - ) -> CapturedUsage: - meter: Final = self.meter - load: Final = self.load - if meter is None or load is None or self._stopping: - raise UsageCaptureError("Batch capture requires an active ledger writer and snapshot reader") - if len(self._batches) >= meter.settings.max_pending_records: - raise UsageCaptureError("Batch capture producer capacity exhausted") - capture: Final = _prepare_batch_capture(kwargs, response, start_time, end_time) - retained, chunk_bytes, _ = _batch_capacity( - meter, capture.root, capture.rows, extra_bytes=len(capture.root.envelope.encode("utf-8")) - ) - reservation: Final = meter.reserve_producer(retained, chunk_bytes) - task: Final = asyncio.create_task( - _persist_batch_capture( - meter, load, capture.root, capture.rows, capture.prerequisites, chunk_bytes, reservation - ), - name="accounting batch capture", - ) - task.add_done_callback(lambda _: meter.release_producer(reservation)) - self._batches.add(task) - task.add_done_callback(self._batch_finished) - return CapturedUsage(capture.payload, (capture.root,), task) - - def _batch_finished(self, task: asyncio.Task[None]) -> None: - self._batches.discard(task) - if task.cancelled(): - verbose_proxy_logger.error("Batch usage capture stopped before complete persistence") - elif (error := task.exception()) is not None: - verbose_proxy_logger.error("Batch usage capture failed", exc_info=error) - - async def stop(self, timeout: float) -> None: - from litellm.litellm_core_utils.litellm_logging import Logging - - if self.meter is None: - return - self._stopping = True - deadline: Final = asyncio.get_running_loop().time() + timeout - try: - if self._batches: - await asyncio.wait(tuple(self._batches), timeout=max(0, timeout)) - finally: - remaining: Final = tuple(self._batches) - for task in remaining: - task.cancel() - await asyncio.gather(*remaining, return_exceptions=True) - try: - await self.meter.stop(max(0, deadline - asyncio.get_running_loop().time())) - finally: - Logging.usage_capture_callback = None - self.meter = None - self.load = None diff --git a/litellm/proxy/spend_tracking/usage_meter.py b/litellm/proxy/spend_tracking/usage_meter.py deleted file mode 100644 index 431e4c2c3c3..00000000000 --- a/litellm/proxy/spend_tracking/usage_meter.py +++ /dev/null @@ -1,330 +0,0 @@ -from __future__ import annotations - -import asyncio -from collections import deque -from collections.abc import Awaitable, Callable, Mapping -from concurrent.futures import Future -from dataclasses import dataclass -from itertools import accumulate, chain, count, takewhile -from threading import Lock -from typing import Final - -from pydantic import BaseModel, ConfigDict, Field - -from litellm._logging import verbose_proxy_logger -from litellm.accounting import ( - DeliveryTarget, - Dimension, - DimensionName, - InboundUsageEvent, - OutboundUsageEvent, -) -from litellm.proxy.db.accounting_ledger import AccountingConflictError, UsageWrite - -_DAILY_TARGETS: Final = ( - (DimensionName.KEY, DeliveryTarget.DAILY_USER_SPEND), - (DimensionName.USER, DeliveryTarget.DAILY_USER_SPEND), - (DimensionName.TEAM, DeliveryTarget.DAILY_TEAM_SPEND), - (DimensionName.ORGANIZATION, DeliveryTarget.DAILY_ORGANIZATION_SPEND), - (DimensionName.END_USER, DeliveryTarget.DAILY_END_USER_SPEND), - (DimensionName.AGENT, DeliveryTarget.DAILY_AGENT_SPEND), - (DimensionName.TAG, DeliveryTarget.DAILY_TAG_SPEND), - (DimensionName.MODEL, DeliveryTarget.DAILY_MODEL_USAGE), - (DimensionName.TOOL, DeliveryTarget.DAILY_TOOL_SPEND), -) - - -def usage_delivery_targets(dimensions: tuple[Dimension, ...]) -> tuple[DeliveryTarget, ...]: - names: Final = frozenset(dimension.name for dimension in dimensions) - return tuple(dict.fromkeys(target for dimension, target in _DAILY_TARGETS if dimension in names)) - - -class UsageCaptureSettings(BaseModel): - model_config = ConfigDict(frozen=True, extra="forbid") - - batch_size: int = Field(default=1000, ge=2, le=1000) - flush_interval_seconds: float = Field(default=5.0, gt=0, le=10) - max_pending_bytes: int = Field(default=16 * 1024 * 1024, ge=1024) - max_pending_records: int = Field(default=10000, ge=2) - - -class UsageCaptureError(RuntimeError): - pass - - -@dataclass(frozen=True, slots=True) -class _Pending: - writes: tuple[UsageWrite, ...] - size: int - committed: Future[None] - finished: asyncio.Event - prerequisites: tuple[asyncio.Future[None], ...] - - -async def _wait_for_batch(wakeup: asyncio.Event, timeout: float) -> None: - try: - await asyncio.wait_for(wakeup.wait(), timeout) - except asyncio.TimeoutError: - return - - -def _report_failure(receipt: Future[None]) -> None: - if not receipt.cancelled() and receipt.exception() is not None: - verbose_proxy_logger.error("Usage capture did not acknowledge commit", exc_info=receipt.exception()) - - -def _retryable_persistence_failure(error: Exception) -> bool: - from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler, exception_chain - - outage: Final = PrismaDBExceptionHandler.find_database_service_unavailable_error_in_chain(error) - return (outage is not None and not PrismaDBExceptionHandler.is_permanent_database_fault(outage)) or any( - isinstance(cause, Exception) - and ( - PrismaDBExceptionHandler.is_deadlock_error(cause) - or PrismaDBExceptionHandler.is_lock_timeout_error(cause) - or PrismaDBExceptionHandler.postgres_sqlstate(cause) == "40001" - ) - for cause in exception_chain(error) - ) - - -def _isolatable_persistence_failure(error: Exception) -> bool: - from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler, exception_chain - - return isinstance(error, AccountingConflictError) or any( - isinstance(cause, Exception) and (PrismaDBExceptionHandler.postgres_sqlstate(cause) or "").startswith("22") - for cause in exception_chain(error) - ) - - -class DurableUsageMeter: - def __init__( - self, - write_batch: Callable[[tuple[UsageWrite, ...]], Awaitable[None]], - settings: UsageCaptureSettings, - *, - wait_for_batch: Callable[[asyncio.Event, float], Awaitable[None]] = _wait_for_batch, - retry_delay: Callable[[float], Awaitable[None]] = asyncio.sleep, - ) -> None: - self._write_batch: Final = write_batch - self.settings: Final = settings - self._wait_for_batch: Final = wait_for_batch - self._retry_delay: Final = retry_delay - self._loop: Final = asyncio.get_running_loop() - self._admission: Final = Lock() - self._queue: Final[deque[_Pending]] = deque() # mutable-ok: one bounded producer/consumer queue - self._wakeup: Final = asyncio.Event() - self._capacity_changed: Final = asyncio.Event() - self._reservations: Mapping[object, tuple[int, int]] = {} - self._reserved_bytes = 0 - self._task: asyncio.Task[None] | None = None - self._closed = False - self.pending_bytes = 0 - self.pending_records = 0 - - def submit( - self, writes: tuple[UsageWrite, ...], *, prerequisites: tuple[asyncio.Future[None], ...] = () - ) -> asyncio.Future[None]: - size: Final = sum(len(write.envelope.encode("utf-8")) for write in writes) - with self._admission: - if ( - self._closed - or not writes - or len(writes) > self.settings.batch_size - or self.pending_bytes + size > self.settings.max_pending_bytes - or self.pending_records + len(writes) > self.settings.max_pending_records - ): - raise UsageCaptureError("Usage capture admission rejected: closed, empty or capacity exhausted") - receipt: Final = Future[None]() - receipt.set_running_or_notify_cancel() - receipt.add_done_callback(_report_failure) - self._queue.append(_Pending(writes, size, receipt, asyncio.Event(), prerequisites)) - self.pending_bytes += size - self.pending_records += len(writes) - self._loop.call_soon_threadsafe(self._ensure_worker) - wrapped: Final = asyncio.wrap_future(receipt, loop=self._loop) - wrapped.add_done_callback(lambda done: None if done.cancelled() else done.exception()) - return wrapped - - def reserve_producer(self, size: int, headroom: int) -> object: - token: Final = object() - self.resize_producer(token, size, headroom) - return token - - def resize_producer(self, token: object, size: int, headroom: int) -> None: - with self._admission: - previous: Final = self._reservations.get(token, (0, 0))[0] - delta: Final = size - previous - others: Final = (value[1] for key, value in self._reservations.items() if key is not token) - required: Final = max((headroom, *others)) - if ( - self._closed - or self.pending_bytes + delta > self.settings.max_pending_bytes - or self._reserved_bytes + delta + required > self.settings.max_pending_bytes - ): - raise UsageCaptureError("Batch capture producer byte capacity exhausted") - self._reservations = {**self._reservations, token: (size, headroom)} - self._reserved_bytes += delta - self.pending_bytes += delta - self._capacity_changed.set() - - def release_producer(self, token: object) -> None: - with self._admission: - size, _ = self._reservations[token] - self._reservations = {key: value for key, value in self._reservations.items() if key is not token} - self._reserved_bytes -= size - self.pending_bytes -= size - self._capacity_changed.set() - - def _ensure_worker(self) -> None: - if self.pending_records >= self.settings.batch_size or self._queue and self._queue[0].prerequisites: - self._wakeup.set() - if self._task is None or self._task.done(): - self._task = asyncio.create_task(self._run(), name="accounting usage capture") - - async def record(self, event: InboundUsageEvent | OutboundUsageEvent) -> None: - envelope: Final = '{"event":' + event.model_dump_json() + "}" - await asyncio.shield(self.submit((UsageWrite(event, envelope, usage_delivery_targets(event.dimensions)),))) - - async def record_writes(self, writes: tuple[UsageWrite, ...]) -> None: - size: Final = sum(len(write.envelope.encode("utf-8")) for write in writes) - capacity: Final = self.settings.max_pending_bytes - self._reserved_bytes - if len(writes) > min(self.settings.batch_size, self.settings.max_pending_records) or ( - size > capacity and len(writes) > 1 - ): - middle: Final = len(writes) // 2 - await self.record_writes(writes[:middle]) - await self.record_writes(writes[middle:]) - return - if size > capacity: - raise UsageCaptureError("One usage observation exceeds capture capacity") - while (receipt := self._submit_available(writes)) is None: - self._wakeup.set() - await self._capacity_changed.wait() - self._wakeup.set() - await asyncio.shield(receipt) - - def _submit_available(self, writes: tuple[UsageWrite, ...]) -> asyncio.Future[None] | None: - self._capacity_changed.clear() - try: - receipt: Final = self.submit(writes) - except UsageCaptureError: - if self._closed or not self.pending_records or not writes: - raise - return None - return receipt - - async def flush(self) -> None: - self._ensure_worker() - self._wakeup.set() - if self._task is not None: - await asyncio.shield(self._task) - - async def close(self) -> None: - with self._admission: - self._closed = True - await self.flush() - - async def stop(self, timeout: float) -> None: - with self._admission: - self._closed = True - try: - await asyncio.wait_for(self.close(), timeout) - except asyncio.TimeoutError: - if self._task is not None: - self._task.cancel() - await asyncio.gather(self._task, return_exceptions=True) - while self._queue: - self._finish( - self._take_batch(), UsageCaptureError("Usage capture stopped before commit acknowledgement") - ) - verbose_proxy_logger.error("Usage capture shutdown deadline exhausted; commit acknowledgement incomplete") - - def _take_batch(self) -> tuple[_Pending, ...]: - with self._admission: - if self._queue[0].prerequisites: - return (self._queue.popleft(),) - independent: Final = takewhile(lambda item: not item.prerequisites, self._queue) - sizes: Final = accumulate(len(item.writes) for item in independent) - count: Final = sum(1 for _ in takewhile(lambda size: size <= self.settings.batch_size, sizes)) - return tuple(self._queue.popleft() for _ in range(count)) - - async def _persist(self, writes: tuple[UsageWrite, ...]) -> None: - for attempt in count(): - try: - await self._write_batch(writes) - except Exception as error: # noqa: BLE001 # retry only classified database failures, preserving frozen writes - if not _retryable_persistence_failure(error): - raise - verbose_proxy_logger.warning("Usage persistence unavailable; retaining admitted batch for retry") - await self._retry_delay(min(10.0, 0.2 * 2 ** min(attempt, 6))) - else: - return - - async def _commit(self, batch: tuple[_Pending, ...]) -> None: - writes: Final = tuple(chain.from_iterable(item.writes for item in batch)) - try: - if batch[0].prerequisites: - outcomes: Final = await asyncio.gather( - *(asyncio.shield(receipt) for receipt in batch[0].prerequisites), return_exceptions=True - ) - for outcome in outcomes: - if isinstance(outcome, BaseException): - raise UsageCaptureError("Usage dependency did not acknowledge commit") from outcome - await self._persist(writes) - except asyncio.CancelledError: - self._finish(batch, UsageCaptureError("Usage commit acknowledgement interrupted")) - raise - except Exception as error: # noqa: BLE001 # isolate data faults, surface systemic failures to all producers - if len(batch) > 1 and _isolatable_persistence_failure(error): - try: - for item in batch: - await self._commit((item,)) - except asyncio.CancelledError: - self._finish(batch, UsageCaptureError("Usage commit acknowledgement interrupted")) - raise - return - self._finish( - batch, - UsageCaptureError("Usage identity conflicts with accepted content") - if isinstance(error, AccountingConflictError) - else error, - ) - else: - self._finish(batch, None) - - def _finish(self, batch: tuple[_Pending, ...], error: Exception | None) -> None: - for item in batch: - if item.finished.is_set(): - continue - item.finished.set() - with self._admission: - self.pending_bytes -= item.size - self.pending_records -= len(item.writes) - self._capacity_changed.set() - if item.committed.done(): - continue - if error is None: - item.committed.set_result(None) - else: - item.committed.set_exception(error) - - async def _run(self) -> None: - try: - while self._queue: - await self._wait_for_batch(self._wakeup, self.settings.flush_interval_seconds) - self._wakeup.clear() - await self._commit(self._take_batch()) - if ( - self._closed - or self.pending_records >= self.settings.batch_size - or self._queue - and self._queue[0].prerequisites - ): - self._wakeup.set() - except (Exception, asyncio.CancelledError): # noqa: BLE001 # reject queued receipts if the writer exits abnormally - while self._queue: - self._finish( - self._take_batch(), UsageCaptureError("Usage capture stopped before commit acknowledgement") - ) - raise diff --git a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py index 6d31fa3f226..e68008135aa 100644 --- a/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py +++ b/litellm/proxy/ui_crud_endpoints/proxy_setting_endpoints.py @@ -6,6 +6,7 @@ from collections import Counter from collections.abc import Mapping, MutableMapping, Sequence from types import MappingProxyType from typing import ( + TYPE_CHECKING, Annotated, Final, Literal, @@ -16,7 +17,6 @@ from typing import ( from urllib.parse import urlparse from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile -from prisma.errors import PrismaError from pydantic import BaseModel, ConfigDict, JsonValue, TypeAdapter, ValidationError, create_model, field_validator from pydantic.fields import FieldInfo, PydanticUndefined from typing_extensions import NotRequired, ReadOnly, TypedDict @@ -63,6 +63,9 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( SSOConfig, ) +if TYPE_CHECKING: + from prisma.errors import PrismaError + router: Final = APIRouter() _DECODED_JSON: Final = TypeAdapter(object) @@ -1260,7 +1263,9 @@ def _parse_environment_variables_param(param_value: str | Mapping[str, object] | async def _load_environment_variables_param( repo: ConfigRepository, -) -> Mapping[str, object] | None | PrismaError | ValueError: +) -> "Mapping[str, object] | None | PrismaError | ValueError": + from prisma.errors import PrismaError + try: entry: Final = await _config_param_db(repo).find_unique(where={"param_name": "environment_variables"}) param_value: Final = None if entry is None else _parse_environment_variables_param(entry.param_value) @@ -1314,6 +1319,8 @@ async def update_sso_settings( detail={"error": "Set `'STORE_MODEL_IN_DB='True'` in your env to enable this feature."}, ) + from prisma.errors import PrismaError + # Read the existing SSO row first so the audit log captures a real # before/after diff. Stored values are encrypted; decrypt them so the # before-snapshot has the same shape as after_value, and rely on @@ -1353,7 +1360,7 @@ async def update_sso_settings( if isinstance(environment_variables_param, Mapping) else MappingProxyType({}) ) - stored_values: Final = {k: v for k, v in (before_sso_data or {}).items() if v is not None} + stored_values: Final = {k: v for k, v in (before_sso_data or {}).items() if v is not None and v != ""} fallback_values: Final = {**legacy_env_values, **stored_values} submitted: Final = sso_config.model_dump(include=sso_config.model_fields_set) sso_data: Final = { diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 837433fd8cc..145c8e38229 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -28,7 +28,7 @@ from dataclasses import dataclass, field from datetime import date, datetime, timedelta, timezone from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText -from functools import partial +from functools import partial, reduce from itertools import takewhile from operator import attrgetter from types import MappingProxyType @@ -66,6 +66,7 @@ from litellm.constants import ( REDACTED_BY_LITELLM, REDIS_SPEND_LOGS_BUFFER_DEQUEUE_COUNT, SPEND_LOG_QUEUE_MAX_BYTES, + SPEND_LOG_READ_STATEMENT_TIMEOUT_SECONDS, SPEND_LOG_WRITE_BATCH_MAX_BYTES, SPEND_LOG_WRITE_BATCH_MAX_ROWS, VLLM_CHOICE_FIELDS, @@ -167,7 +168,6 @@ from litellm.litellm_core_utils.env_utils import get_env_float, get_env_int from litellm.litellm_core_utils.litellm_logging import ( BilledRoundsDispatch, Logging, - UsageObservation, deployment_pricing_model_info, ) from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token @@ -1680,14 +1680,17 @@ _LOG_DB_METRICS_CALL_TYPES: Final = frozenset(("get_data", "insert_data", "updat _EXCEPTION_CHAIN_MAX_DEPTH: Final = 16 +def _extended_exception_chain(chain: tuple[BaseException, ...], iteration: int) -> tuple[BaseException, ...]: + if len(chain) != iteration + 1: + return chain + following: Final = chain[-1].__cause__ or chain[-1].__context__ + if following is None or following in chain: + return chain + return (*chain, following) + + def _exception_chain(exception: BaseException) -> tuple[BaseException, ...]: - chain: Final[list[BaseException]] = [exception] # mutable-ok: built once, returned frozen - for _ in range(_EXCEPTION_CHAIN_MAX_DEPTH): - following = chain[-1].__cause__ or chain[-1].__context__ - if following is None or following in chain: - break - chain.append(following) - return tuple(chain) + return reduce(_extended_exception_chain, range(_EXCEPTION_CHAIN_MAX_DEPTH), (exception,)) def _timed_out_reading_from_provider(exception: Exception) -> bool: @@ -1801,39 +1804,6 @@ def _failure_fields_to_lift( if _prompt_estimate is not None else () ) - from litellm.litellm_core_utils.litellm_logging import UsageCaptureObservation - - _recorded_observation: Final[object] = _model_call_details.get("_accounting_usage_observed") - _current_usage: Final = ( - _current_failure_usage_to_lift( - _model_call_details, - request_data, - _model_call_details.get("current_attempt_api_call_start_time", _first_handoff) is not None, - _billed, - _deployment_id, - _base_model, - _estimate_call_type, - ) - if Logging.usage_capture_callback is not None - and (_billed or _model_call_details.get("combined_usage_object") is not None) - else None - ) - _current_observation: Final = ( - _recorded_observation - if isinstance(_recorded_observation, UsageObservation) - else UsageObservation.UNKNOWN - if _current_usage is None - else UsageObservation.KNOWN - if _model_call_details.get("combined_usage_object") is not None - else UsageObservation.ESTIMATED - ) - _capture_observation: Final = UsageCaptureObservation.from_usage( - _current_usage[0] if _current_usage is not None and isinstance(_current_usage[0], Usage) else None, - _current_observation, - float(_current_usage[1]) - if _current_usage is not None and isinstance(_current_usage[1], (int, float)) - else None, - ) _entries: Final = ( ("start_time", _model_call_details.get("start_time")), ("first_api_call_start_time", _first_handoff), @@ -1842,16 +1812,12 @@ def _failure_fields_to_lift( ("response_cost", None if _usage_to_lift is None else (_usage_to_lift[1] or 0.0)), ("standard_logging_object", _model_call_details.get("standard_logging_object")), ("billed_failed_attempts", _earlier_attempts or None), - ("_accounting_capture", _model_call_details.get("_accounting_capture")), ("_accounting_pricing_snapshot", _model_call_details.get("_accounting_pricing_snapshot")), ("_accounting_finalized_cost", _logging_obj.finalized_cost if isinstance(_logging_obj, Logging) else None), ( "_accounting_allocation", _logging_obj.cost_allowance_allocation if isinstance(_logging_obj, Logging) else None, ), - ("_accounting_usage_observed", _current_observation if Logging.usage_capture_callback is not None else None), - ("_accounting_usage_snapshot", _capture_observation if Logging.usage_capture_callback is not None else None), - ("_accounting_prior_captures", _model_call_details.get("_accounting_prior_captures")), ) return MappingProxyType({key: value for key, value in _entries if value is not None}) @@ -5309,6 +5275,39 @@ _deprecated_key_cache: Final[LimitedSizeOrderedDict] = LimitedSizeOrderedDict(ma _DEPRECATED_KEY_CACHE_TTL_SECONDS: Final = 60 _PRISMA_DEFAULT_TX_TIMEOUT: Final = timedelta(seconds=5) _PRISMA_DEFAULT_TX_MAX_WAIT: Final = timedelta(seconds=2) +_SPEND_LOG_READ_STATEMENT_TIMEOUT: Final = timedelta(seconds=SPEND_LOG_READ_STATEMENT_TIMEOUT_SECONDS) +_STATEMENT_TIMEOUT_TRANSACTION_GRACE: Final = timedelta(seconds=5) + + +class _ReadTransactionClient(Protocol): + @property + def read_db(self) -> PrismaWrapper: ... + + +async def query_raw_within_statement_timeout( + prisma_client: _ReadTransactionClient, + sql: str, + *params: object, + what: str = "Spend logs", + timeout: timedelta = _SPEND_LOG_READ_STATEMENT_TIMEOUT, +) -> Sequence[Mapping[str, object]]: + """One raw read on the read target that Postgres cancels at ``timeout``. + + Prisma's HTTP client gives up on a statement after 30s anyway, so without + this the Admin UI's error is the same but Postgres keeps running the scan. + A cancelled read surfaces as a 503 asking for a narrower date range. + """ + try: + async with prisma_client.read_db.tx(timeout=timeout + _STATEMENT_TIMEOUT_TRANSACTION_GRACE) as transaction: + await transaction.execute_raw(f"SET LOCAL statement_timeout = {int(timeout / timedelta(milliseconds=1))}") + return await transaction.query_raw(sql, *params) + except Exception as e: + if not PrismaDBExceptionHandler.is_statement_timeout_error(e): + raise + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail={"error": f"{what} took longer than {int(timeout.total_seconds())}s. Pick a shorter date range"}, + ) from e async def _lookup_deprecated_key( diff --git a/litellm/repositories/plugin_upload_repository.py b/litellm/repositories/plugin_upload_repository.py new file mode 100644 index 00000000000..99f1f76abb5 --- /dev/null +++ b/litellm/repositories/plugin_upload_repository.py @@ -0,0 +1,71 @@ +import json +from types import MappingProxyType +from typing import TYPE_CHECKING, Final, cast # noqa: TID251 # Prisma dynamically exposes its typed table actions + +from pydantic import TypeAdapter + +from litellm.models.plugin_upload import PluginArchiveMetadata, PluginUpload +from litellm.repositories.prisma_protocols import TableActions +from litellm.repositories.table_repositories import ClaudeCodePluginRepository + +if TYPE_CHECKING: + from prisma import models as prisma_models + + from litellm.proxy.utils import PrismaClient + +_MANIFEST: Final = TypeAdapter(dict[str, object]) +_SOURCE: Final = TypeAdapter(dict[str, str]) + + +class PluginUploadRepository: + def __init__(self, prisma_client: "PrismaClient") -> None: + self._plugins = ClaudeCodePluginRepository(prisma_client, use_writer=True).table + self._archives = cast( # cast-ok: Prisma exposes generated table actions through an untyped delegate + TableActions["prisma_models.LiteLLM_ClaudeCodePluginUpload"], + prisma_client.writer_db.litellm_claudecodepluginupload, + ) + + async def create(self, upload: PluginUpload) -> str | None: + from prisma import Base64 + from prisma.errors import UniqueViolationError + + try: + plugin: Final = await self._plugins.create( + data={ + "name": upload.name, + "version": upload.version, + "description": upload.description, + "manifest_json": json.dumps(dict(upload.manifest)), + "created_by": upload.created_by, + "uploaded_archive": {"create": {"content": Base64.encode(upload.content)}}, + } + ) + except UniqueViolationError: + return None + return plugin.id + + async def get_metadata(self, name: str) -> PluginArchiveMetadata | None: + plugin: Final = await self._plugins.find_unique(where={"name": name}) + if plugin is None: + return None + manifest: Final = _MANIFEST.validate_json(plugin.manifest_json or "{}") + return PluginArchiveMetadata( + name=plugin.name, + enabled=plugin.enabled, + source=MappingProxyType(_SOURCE.validate_python(manifest.get("source", {}))), + manifest_json=plugin.manifest_json, + ) + + async def get_content(self, metadata: PluginArchiveMetadata) -> bytes | None: + archive: Final = await self._archives.find_first( + where={ + "plugin_name": metadata.name, + "plugin": { + "is": { + "manifest_json": metadata.manifest_json, + "enabled": metadata.enabled, + } + }, + } + ) + return None if archive is None else archive.content.decode() diff --git a/litellm/repositories/verification_token_repository.py b/litellm/repositories/verification_token_repository.py index b20fb47306e..fb1f1c89e69 100644 --- a/litellm/repositories/verification_token_repository.py +++ b/litellm/repositories/verification_token_repository.py @@ -124,9 +124,7 @@ class VerificationTokenRepository(BaseRepository[LiteLLM_VerificationToken]): records: Final[Sequence[PrismaVerificationToken]] = await self.table.find_many(where={"user_id": user_id}) return self._to_model_list(records) - async def find_newest_reusable_llm_api_key( - self, user_id: str, team_id: str | None - ) -> LiteLLM_VerificationToken | None: + async def find_newest_reusable_key(self, user_id: str, team_id: str | None) -> LiteLLM_VerificationToken | None: records: Final[Sequence[PrismaVerificationToken]] = await self.table.find_many( where={ "user_id": user_id, @@ -140,12 +138,6 @@ class VerificationTokenRepository(BaseRepository[LiteLLM_VerificationToken]): {"team_id": {"not": UI_SESSION_TOKEN_TEAM_ID}}, ] }, - { - "OR": [ - {"allowed_routes": {"is_empty": True}}, - {"allowed_routes": {"has": "llm_api_routes"}}, - ] - }, ], }, order={"created_at": "desc"}, diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 3de63538f9a..e9f9eb85d25 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -759,9 +759,7 @@ def _api_base_kwarg(kwargs: Mapping[str, object]) -> str | None: return api_base if isinstance(api_base, str) else None -def _dispatched_model_name( - model: str, custom_llm_provider: str, api_base: str | None, litellm_params: GenericLiteLLMParams | None = None -) -> str: +def _dispatched_model_name(model: str, custom_llm_provider: str) -> str: from litellm.litellm_core_utils.get_llm_provider_logic import resolve_model_and_provider_for_metadata provider_model, _ = resolve_model_and_provider_for_metadata(model=model, custom_llm_provider=custom_llm_provider) @@ -774,7 +772,6 @@ def _will_bridge_to_chat_completions( use_chat_completions_api: bool, model_info: object, api_base: str | None, - litellm_params: GenericLiteLLMParams | None = None, ) -> bool: """``_bridges_to_chat_completions`` for callers running before the provider config is resolved. @@ -791,7 +788,7 @@ def _will_bridge_to_chat_completions( return True return _bridges_to_chat_completions( _resolve_responses_api_provider_config( - _dispatched_model_name(normalized_model[0], custom_llm_provider, api_base, litellm_params), + _dispatched_model_name(normalized_model[0], custom_llm_provider), custom_llm_provider, model_info, api_base, @@ -816,7 +813,6 @@ def provider_stores_the_conversation(model: str, custom_llm_provider: str | None bool(kwargs.get("use_chat_completions_api")), kwargs.get("model_info"), api_base, - litellm_params, ) @@ -999,7 +995,6 @@ async def aresponses( bool(kwargs.get("use_chat_completions_api")), kwargs.get("model_info"), _api_base_kwarg(kwargs), - GenericLiteLLMParams(**kwargs), ), ): _stamp_injection_points_for_the_hook(kwargs, model, custom_llm_provider, input, tools) @@ -1175,7 +1170,6 @@ def _apply_prompt_management_to_responses_call( use_chat_completions_api, kwargs.get("model_info"), _api_base_kwarg(kwargs), - GenericLiteLLMParams(**kwargs), ), ): _stamp_injection_points_for_the_hook(kwargs, model, custom_llm_provider, input, tools) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index da8ff2cd207..249c6370b86 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -1031,9 +1031,7 @@ class BaseResponsesAPIStreamingIterator: ) if estimate is None: return - usage: Final = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( # pyright: ignore[reportPrivateUsage] # same shared transform the success path above uses - estimate - ) + usage: Final = ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(estimate) served_tier: Final[object] = getattr(self._created_response, "service_tier", None) assembled: Final = ModelResponse( model=self.model, usage=usage, service_tier=served_tier if isinstance(served_tier, str) else None @@ -1892,7 +1890,7 @@ def stamp_responses_usage_cost( return if isinstance(bill, bool) or not isinstance(bill, (int, float)): return - if bill > 0 or isinstance(getattr(usage_obj, "cost", None), (int, float)): + if bill > 0 or isinstance(usage_obj.cost, (int, float)): stamp_litellm_bill_on_usage(response_obj, usage_obj, float(bill)) diff --git a/litellm/router_api.py b/litellm/router_api.py index 3007b5345c7..f22e9b1f351 100644 --- a/litellm/router_api.py +++ b/litellm/router_api.py @@ -1402,6 +1402,9 @@ class RoutingContext: def deployment_params(self, request: Mapping[str, object], deployment: "Deployment | None") -> Mapping[str, object]: return self._router._params_sent_to_deployment(request, deployment) + def default_litellm_params(self) -> Mapping[str, object]: + return self._router._active_default_litellm_params() + def settings(self) -> RouterSettings: return self._router.settings() diff --git a/litellm/router_python_core.py b/litellm/router_python_core.py index 3301e0bed3e..c3ba4548b9b 100644 --- a/litellm/router_python_core.py +++ b/litellm/router_python_core.py @@ -110,6 +110,7 @@ from litellm.litellm_core_utils.add_retry_fallback_headers import ( replace_complexity_router_headers, response_total_token_count, ) +from litellm.litellm_core_utils.agentic_followup_failure import is_agentic_followup_failure from litellm.litellm_core_utils.asyncify import run_async_function from litellm.litellm_core_utils.batch_utils import ( batch_create_routing, @@ -138,7 +139,7 @@ from litellm.litellm_core_utils.core_helpers import ( from litellm.litellm_core_utils.coroutine_checker import coroutine_checker from litellm.litellm_core_utils.credential_accessor import CredentialAccessor, reject_api_key_secret_reference from litellm.litellm_core_utils.dd_tracing import tracer -from litellm.litellm_core_utils.deployment_pricing import deployment_pricing_models +from litellm.litellm_core_utils.deployment_pricing import deployment_backend_models, deployment_pricing_models from litellm.litellm_core_utils.get_litellm_params import SUPPLEMENTAL_KWARGS_KEYS from litellm.litellm_core_utils.get_llm_provider_logic import ( declared_authenticating_provider, @@ -308,6 +309,7 @@ from litellm.router_utils.cooldown_handlers import ( get_cooldown_deployment_info, get_cooldown_deployments, is_background_response_poll_not_found, + is_caller_entra_credential_failure, is_caller_scoped_auth_failure, is_caller_timeout_408, is_failure_outside_selected_deployment, @@ -482,11 +484,13 @@ from litellm.types.services import ServiceTypes from litellm.types.utils import ( AUTOROUTER_CLASSIFIER_CALL_ORIGIN, COST_MAP_LOOKUP_KEY, + DEPLOYMENT_SCOPED_TOKEN_LIMIT_FIELDS, PROMPT_QUOTING_ROUTING_DECISION_FIELDS, CustomPricingLiteLLMParams, GenericBudgetConfigType, LiteLLMBatch, LlmProviders, + LlmProvidersSet, ModelInfo, ModelResponseStream, StandardLoggingPayload, @@ -509,9 +513,11 @@ from litellm.utils import ( TextCompletionResponse, _restore_correlation_context_if_supported, # pyright: ignore[reportPrivateUsage] # same task-local cleanup as the SDK wrapper aresolve_api_key_secret_reference, + catalog_with_runtime_registrations, function_setup, get_llm_provider, get_non_default_completion_params, + get_potential_model_names, get_secret, get_utc_datetime, is_generalized_model_info, @@ -3840,11 +3846,6 @@ class PythonCore(DeprecatedRouterMembers): priced_partial._hidden_params = { # pyright: ignore[reportPrivateUsage] # no public accessor key: value for key, value in get_hidden_params_dict(partial_response).items() if key != "response_cost" } - if LiteLLMLogging.usage_capture_callback is not None: - from litellm._internal_context import internal_sub_call - - with internal_sub_call(cut_logging.litellm_call_id): - cut_logging.capture_usage_before_logging(priced_partial) return ContinuedPartial( usage=BaseTokenUsageProcessor.combine_usage_objects( usage_objects=[partial_usage, inherited_usage] @@ -4043,6 +4044,8 @@ class PythonCore(DeprecatedRouterMembers): if continuation_output is not None else None ) + if cut_partial is not None: + model_response.logging_obj.mark_stash_billed_by_continuation() if continue_after_content: emitted_tokens: Final = int( getattr(complete_response_object_usage, "completion_tokens", 0) or 0 @@ -5212,8 +5215,19 @@ class PythonCore(DeprecatedRouterMembers): # _update_kwargs_with_deployment and stamped into kwargs["model_info"]; stamping # the static shared deployment's id instead would let one tenant's bad credentials # cool down the deployment every other tenant sharing this config relies on. + if getattr(exception, "failed_deployment_id", None): + return effective_model_info: Final = kwargs.get("model_info") or deployment.get("model_info") or MappingProxyType({}) self._set_failed_deployment_id_on_exception(exception, MappingProxyType({"model_info": effective_model_info})) + configured_deployment_id: Final = _MODEL_INFO_ADAPTER.validate_python(effective_model_info).get( + "original_model_id" + ) + if isinstance(configured_deployment_id, str) and configured_deployment_id: + exception.failed_configured_deployment_id = configured_deployment_id # pyright: ignore[reportAttributeAccessIssue] # rebind-ok: the exception carries the failure to _maybe_run_weighted_failover + + def _stamp_failed_attempt(self, exception: Exception, kwargs: Mapping[str, object], *, picked: bool) -> None: + if picked: + self._stamp_failed_deployment_id_with_effective_model_info(exception, MappingProxyType({}), kwargs) @staticmethod def _stamp_retry_skip_deployment_id(exception: Exception, kwargs: Mapping[str, object]) -> None: @@ -6100,9 +6114,10 @@ class PythonCore(DeprecatedRouterMembers): def _image_generation(self, prompt: str, model: str, **kwargs): model_name: Final = "" + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _image_generation()- model: %s", model) - deployment: Final = self.get_available_deployment( + deployment = self.get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6138,6 +6153,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def aimage_generation(self, prompt: str, model: str, **kwargs): @@ -6163,11 +6181,12 @@ class PythonCore(DeprecatedRouterMembers): async def _aimage_generation(self, prompt: str, model: str, **kwargs): model_name = model start_time: Final = datetime.now() # noqa: DTZ005 # SDK logging uses naive local timestamps + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _image_generation()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) try: - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6228,6 +6247,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def atranscription(self, file: FileTypes, model: str, **kwargs): @@ -6274,10 +6296,11 @@ class PythonCore(DeprecatedRouterMembers): async def _atranscription(self, file: FileTypes, model: str, **kwargs): model_name: Final = model + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _atranscription()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6311,6 +6334,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def aspeech(self, model: str, input: str, voice: str | None = None, **kwargs): @@ -6367,10 +6393,11 @@ class PythonCore(DeprecatedRouterMembers): async def _aspeech(self, model: str, input: str, voice: str | None = None, **kwargs): model_name: Final = model + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _aspeech()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6404,6 +6431,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def arerank(self, model: str, **kwargs): @@ -6429,9 +6459,10 @@ class PythonCore(DeprecatedRouterMembers): async def _arerank(self, model: str, **kwargs): model_name = None + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _rerank()- model: %s", model) - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, specific_deployment=kwargs.pop("specific_deployment", None), request_kwargs=kwargs, @@ -6463,6 +6494,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e def text_completion( @@ -6544,10 +6578,11 @@ class PythonCore(DeprecatedRouterMembers): raise e async def _atext_completion(self, model: str, prompt: str, **kwargs): + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _atext_completion()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": prompt}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6583,6 +6618,9 @@ class PythonCore(DeprecatedRouterMembers): if model is not None: self.fail_calls[model] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def aadapter_completion( @@ -6614,10 +6652,11 @@ class PythonCore(DeprecatedRouterMembers): raise e async def _aadapter_completion(self, adapter_id: str, model: str, **kwargs): + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _aadapter_completion()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=[{"role": "user", "content": "default text"}], specific_deployment=kwargs.pop("specific_deployment", None), @@ -6653,6 +6692,9 @@ class PythonCore(DeprecatedRouterMembers): if model is not None: self.fail_calls[model] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def _asearch_with_fallbacks(self, original_function: Callable, **kwargs) -> object: @@ -7382,6 +7424,8 @@ class PythonCore(DeprecatedRouterMembers): last_error = e # rebind-ok: the newest failure is what the fallback chain and the caller see for attempt in range(attempted_retries_for_request(initial_kwargs), budget): trigger = _mid_stream_retry_trigger(last_error) + if is_agentic_followup_failure(trigger): + return _StreamRetriesExhausted(last_error) if not policy_applies and not self._stream_should_retry( trigger, healthy_deployments, all_deployments, initial_kwargs ): @@ -7639,6 +7683,7 @@ class PythonCore(DeprecatedRouterMembers): """ handler_name: Final = original_function.__name__ metadata_variable_name: Final = get_router_metadata_variable_name(function_name="generic_api_call") + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _generic_api_call() - handler: %s, model: %s", handler_name, model) self._update_kwargs_before_fallbacks( @@ -7647,7 +7692,7 @@ class PythonCore(DeprecatedRouterMembers): metadata_variable_name=metadata_variable_name, ) with self._video_call_pin(handler_name, kwargs), self._route_capability_pin(handler_name): - deployment: Final = self.get_available_deployment( + deployment = self.get_available_deployment( # rebind-ok: set on success, see pre-init model=model, messages=kwargs.get("messages", None), input=kwargs.get("input", None), @@ -7693,6 +7738,9 @@ class PythonCore(DeprecatedRouterMembers): verbose_router_logger.info("%s(model=%s)\x1b[31m Exception %s\x1b[0m", handler_name, model, e) if model is not None: self.fail_calls[model] += 1 + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e def embedding( @@ -7714,10 +7762,11 @@ class PythonCore(DeprecatedRouterMembers): def _embedding(self, input: str | list[object], model: str, **kwargs): model_name = None + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside embedding()- model: %s", model) with embedding_deployment_selection(): - deployment: Final = self.get_available_deployment( + deployment = self.get_available_deployment( # rebind-ok: set on success, see pre-init model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), @@ -7774,6 +7823,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e async def aembedding( @@ -7803,11 +7855,12 @@ class PythonCore(DeprecatedRouterMembers): async def _aembedding(self, input: str | list[object], model: str, **kwargs): model_name = None + deployment = None # rebind-ok: pre-init so the except block can stamp a failure with no deployment picked try: verbose_router_logger.debug("Inside _aembedding()- model: %s", model) parent_otel_span: Final = get_parent_otel_span_from_kwargs(kwargs) with embedding_deployment_selection(): - deployment: Final = await self._core_async_get_available_deployment( + deployment = await self._core_async_get_available_deployment( # rebind-ok: set on success, see pre-init model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), @@ -7854,6 +7907,9 @@ class PythonCore(DeprecatedRouterMembers): if model_name is not None: self.fail_calls[model_name] += 1 self._stamp_retry_skip_deployment_id(e, kwargs) + self._stamp_failed_attempt( + e, _REQUEST_KWARGS_ADAPTER.validate_python(kwargs), picked=deployment is not None + ) raise e #### FILES API #### @@ -9092,8 +9148,9 @@ class PythonCore(DeprecatedRouterMembers): kwargs[metadata_variable_name] = meta if not isinstance(meta, dict): return None + configured_failed_id: Final[str | None] = getattr(exception, "failed_configured_deployment_id", None) prev_excluded: Final = set(meta.get("_failover_excluded_ids") or []) - excluded: Final = prev_excluded | {failed_id} + excluded: Final = prev_excluded | {configured_failed_id or failed_id} all_ids: Final = { (d.get("model_info") or {}).get("id") @@ -9611,7 +9668,7 @@ class PythonCore(DeprecatedRouterMembers): response = add_retry_headers_to_response(response=response, attempted_retries=0, max_retries=None) return response except Exception as e: - if is_guardrail_intervention(e): + if is_guardrail_intervention(e) or is_agentic_followup_failure(e): raise current_attempt = None original_exception = e @@ -9706,6 +9763,7 @@ class PythonCore(DeprecatedRouterMembers): # Update retry tracking metadata before each retry attempt _metadata["attempted_retries"] = current_attempt + 1 _metadata["max_retries"] = num_retries + carry_superseded_attempt_usage(kwargs) # if the function call is successful, no exception will be raised and we'll break out of the loop response = await self.make_call(original_function, *args, **kwargs) if coroutine_checker.is_async_callable(response): # async errors are often returned as coroutines @@ -9719,6 +9777,8 @@ class PythonCore(DeprecatedRouterMembers): return response except Exception as e: + if is_agentic_followup_failure(e): + raise # Always track the latest error so we raise the most # recent exception instead of the first one. original_exception = e @@ -10263,8 +10323,9 @@ class PythonCore(DeprecatedRouterMembers): return False exception_status: Final = getattr(exception, "status_code", "") + failure_call_details: Final = _CALL_KWARGS_ADAPTER.validate_python(kwargs) - if is_caller_timeout_408(kwargs, exception_status): + if is_caller_timeout_408(failure_call_details, exception_status): verbose_router_logger.debug( "Router: Exiting 'deployment_callback_on_failure' without cooldown. " "A timeout the caller set caused this 408, not the deployment's health." @@ -10288,6 +10349,18 @@ class PythonCore(DeprecatedRouterMembers): "Caller-scoped OAuth authentication failed, not the deployment." ) return False + if is_caller_entra_credential_failure( + caller_failure_deployment, + self._active_default_litellm_params(), + failure_call_details, + exception_status, + exception, + ): + verbose_router_logger.debug( + "Router: Exiting 'deployment_callback_on_failure' without cooldown. " + "Entra ID rejected a credential the caller sent, not the deployment's own." + ) + return False exception_headers: Final = exception_mapping_utils._get_response_headers(original_exception=exception) @@ -12171,6 +12244,25 @@ class PythonCore(DeprecatedRouterMembers): return (backend_key, backend_key.replace("responses/", "")) return (backend_key,) + @staticmethod + def _catalog_entry_has_token_limit(catalog_entry: Mapping[str, object], field: str) -> bool: + return catalog_entry.get(field) is not None or ( + field == "max_tokens" and catalog_entry.get("max_output_tokens") is not None + ) + + @staticmethod + def _shared_backend_model_info_for_keys( + catalog: Mapping[str, Mapping[str, object]], + backend_keys: tuple[str, ...], + shared_model_info: Mapping[str, object], + ) -> Mapping[str, object]: + catalog_token_limit_fields: Final = frozenset( + field + for field in DEPLOYMENT_SCOPED_TOKEN_LIMIT_FIELDS + if any(PythonCore._catalog_entry_has_token_limit(catalog.get(key) or {}, field) for key in backend_keys) + ) + return {field: value for field, value in shared_model_info.items() if field not in catalog_token_limit_fields} + @staticmethod def _deployment_model_cost_payload(deployment: Deployment) -> dict[str, object]: # mutable-ok: cost-map entry """The ``model_info`` a deployment contributes to ``litellm.model_cost``. @@ -12283,6 +12375,31 @@ class PythonCore(DeprecatedRouterMembers): ## OLD MODEL REGISTRATION ## Kept to prevent breaking changes backend_keys: Final = PythonCore._backend_cost_map_keys(model=model, custom_llm_provider=custom_llm_provider) + model_provider_prefix: Final = model.partition("/")[0] + catalog_provider: Final = ( + custom_llm_provider + if custom_llm_provider is not None + else model_provider_prefix + if model_provider_prefix in LlmProvidersSet + else None + ) + potential_names: Final = ( + get_potential_model_names(model, catalog_provider) if catalog_provider is not None else None + ) + catalog_backend_keys: Final = ( + ( + *backend_keys, + model, + potential_names["split_model"], + potential_names["stripped_model_name"], + potential_names["combined_model_name"], + potential_names["combined_stripped_model_name"], + potential_names["provider_prefixed_model_name"], + potential_names["region_free_combined_model_name"], + ) + if potential_names is not None + else (*backend_keys, model) + ) # For the shared backend key, keep only cost-map schema fields # (minus custom pricing) so that one deployment's pricing overrides @@ -12290,7 +12407,12 @@ class PythonCore(DeprecatedRouterMembers): # don't pollute another deployment sharing the same backend model # name. Each deployment's full model_info is already stored under # its unique model_id above. - shared_model_info: Final = shared_backend_model_info(model_info) + catalog: Final = catalog_with_runtime_registrations() + shared_model_info: Final = PythonCore._shared_backend_model_info_for_keys( + catalog=catalog, + backend_keys=catalog_backend_keys, + shared_model_info=shared_backend_model_info(model_info), + ) python_host.invoke("register_model")( model_cost={_key: keep_registered_mode(_key, shared_model_info) for _key in backend_keys}, persist_across_reloads=False, @@ -14879,6 +15001,7 @@ class PythonCore(DeprecatedRouterMembers): input: str | list[object] | None, request_kwargs: Mapping[str, object] | None = None, tokenizer_model: str = "", + deployments: Sequence[Mapping[str, object]] = (), ) -> int: """ Count input tokens for context-window pre-call checks. @@ -14913,11 +15036,15 @@ class PythonCore(DeprecatedRouterMembers): if input is not None: from openai.types.responses.response_create_params import ResponseInputParam + from litellm.litellm_core_utils.token_counter import split_embedding_input_tokens from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, ) - typed_input: Final = cast(str | ResponseInputParam, input) # cast-ok: str | list matches transform input + text_input, _ = split_embedding_input_tokens(input, partial(deployment_backend_models, deployments)) + typed_input: Final = cast( # cast-ok: str | list matches transform input + str | ResponseInputParam, text_input + ) input_messages: Final = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages( input=typed_input, responses_api_request={"instructions": instructions} if instructions is not None else {}, @@ -14985,6 +15112,7 @@ class PythonCore(DeprecatedRouterMembers): self.served_openai_models(model) or served_openai_models(healthy_deployments) ) or "", + deployments=healthy_deployments, ) except Exception as e: # noqa: BLE001 # best-effort: an uncountable prompt must not fail the request verbose_router_logger.error( @@ -15015,6 +15143,7 @@ class PythonCore(DeprecatedRouterMembers): self.served_openai_models(model) or served_openai_models(deployments) ) or "", + deployments=deployments, ) except Exception as e: verbose_router_logger.error( diff --git a/litellm/router_strategy/adaptive_router/signals.py b/litellm/router_strategy/adaptive_router/signals.py index 72e8d27d2bf..c7fffd05632 100644 --- a/litellm/router_strategy/adaptive_router/signals.py +++ b/litellm/router_strategy/adaptive_router/signals.py @@ -198,7 +198,7 @@ def _detect_loop(history: list[str], new_calls: Sequence[Mapping[str, object]]) return False -_EXHAUSTION_STATUSES: Final = {408, 413, 429, 503, 504} +_EXHAUSTION_STATUSES: Final = {408, 413, 429, 503, 504, 529} _EXHAUSTION_KEYWORDS: Final = ( "context length", diff --git a/litellm/router_strategy/auto_router/litellm_encoder.py b/litellm/router_strategy/auto_router/litellm_encoder.py index 743a0b97fd2..a402f9c8ecf 100644 --- a/litellm/router_strategy/auto_router/litellm_encoder.py +++ b/litellm/router_strategy/auto_router/litellm_encoder.py @@ -30,7 +30,7 @@ class CustomDenseEncoder(DenseEncoder): litellm_router_instance: ClassVar = RenamedRouterAttribute() # deprecated: the router attribute before ``ctx`` @renamed_router_param("litellm_router_instance") - def __init__(self, ctx: Optional["RoutingContext"] = None, **kwargs): + def __init__(self, ctx: Optional["RoutingContext"] = None, **kwargs) -> None: if "ctx" in kwargs: ctx = kwargs.pop("ctx") @@ -55,7 +55,7 @@ class LiteLLMRouterEncoder(CustomDenseEncoder, AsymmetricDenseMixin): model_name: str, score_threshold: float | None = None, max_input_chars: int = 0, - ): + ) -> None: """Initialize the LiteLLMEncoder. :param ctx: The routing context of the owning router. diff --git a/litellm/router_strategy/budget_limiter.py b/litellm/router_strategy/budget_limiter.py index 1d502dd8d95..98dd39b40e3 100644 --- a/litellm/router_strategy/budget_limiter.py +++ b/litellm/router_strategy/budget_limiter.py @@ -36,7 +36,12 @@ from litellm._logging import verbose_router_logger from litellm.caching.caching import DualCache from litellm.caching.redis_cache import RedisCache, RedisPipelineIncrementOperation, log_redis_failure from litellm.integrations.custom_logger import CustomLogger, Span -from litellm.litellm_core_utils.batch_utils import batch_charged_once_marker_key, batch_id_to_charge_once +from litellm.litellm_core_utils.batch_utils import ( + ChargeOnceJob, + defers_batch_cost, + job_charged_once_marker_key, + job_to_charge_once, +) from litellm.litellm_core_utils.core_helpers import ( get_metadata_variable_name_from_kwargs, ) @@ -52,7 +57,7 @@ from litellm.types.utils import BudgetConfig as GenericBudgetInfo DEFAULT_REDIS_SYNC_INTERVAL: Final = 1 _CALLBACK_FIELDS: Final = TypeAdapter(dict[str, object]) -_BATCH_CHARGE_VALUES: Final = TypeAdapter(tuple[object, float]) +_JOB_CHARGE_VALUES: Final = TypeAdapter(tuple[object, float]) class _LiteLLMParamsDictView: @@ -545,13 +550,16 @@ class RouterBudgetLimiting(CustomLogger): if standard_logging_payload is None: raise ValueError("standard_logging_payload is required") - batch_response, response_cost = _BATCH_CHARGE_VALUES.validate_python( + polled_response, response_cost = _JOB_CHARGE_VALUES.validate_python( (response_obj, _CALLBACK_FIELDS.validate_python(standard_logging_payload).get("response_cost", 0)) ) model_id: Final[str] = str(standard_logging_payload.get("model_id", "")) custom_llm_provider: Final[str | None] = standard_logging_payload.get("custom_llm_provider") - batch_id: Final = batch_id_to_charge_once(callback_fields.get("call_type"), batch_response, response_cost) + if defers_batch_cost(callback_fields, polled_response): + return + + job: Final = job_to_charge_once(callback_fields, polled_response, response_cost) budget_config: Final = ( self._get_budget_config_for_provider(custom_llm_provider) if custom_llm_provider is not None else None @@ -565,7 +573,7 @@ class RouterBudgetLimiting(CustomLogger): spend_key=spend_key, start_time_key=start_time_key, response_cost=response_cost, - batch_id=batch_id, + job=job, ) deployment_budget_config: Final = self._get_budget_config_for_deployment(model_id) @@ -578,7 +586,7 @@ class RouterBudgetLimiting(CustomLogger): spend_key=deployment_spend_key, start_time_key=deployment_start_time_key, response_cost=response_cost, - batch_id=batch_id, + job=job, ) request_tags: Final = get_tags_from_request_kwargs( @@ -596,7 +604,7 @@ class RouterBudgetLimiting(CustomLogger): spend_key=_tag_spend_key, start_time_key=_tag_start_time_key, response_cost=response_cost, - batch_id=batch_id, + job=job, ) async def _increment_spend_for_key( @@ -605,14 +613,21 @@ class RouterBudgetLimiting(CustomLogger): spend_key: str, start_time_key: str, response_cost: float, - batch_id: str | None = None, + job: ChargeOnceJob | None = None, ): if budget_config.budget_duration is None: return current_time: Final = self._clock() new_window_seconds: Final = budget_window_seconds(current_time, budget_config.budget_duration) - if batch_id is not None and not await self._claim_batch_charge(spend_key, batch_id, new_window_seconds): + if job is not None and not await self._claim_job_charge(spend_key, job, new_window_seconds): + if job.repeat_poll_cost > 0: + await self._increment_spend_for_key( + budget_config=budget_config, + spend_key=spend_key, + start_time_key=start_time_key, + response_cost=job.repeat_poll_cost, + ) return budget_start: Final = await self._get_or_set_budget_start_time( @@ -639,8 +654,8 @@ class RouterBudgetLimiting(CustomLogger): verbose_router_logger.debug("Incremented spend for %s by %s", spend_key, response_cost) - async def _claim_batch_charge(self, spend_key: str, batch_id: str, ttl_seconds: int) -> bool: - marker_key: Final = batch_charged_once_marker_key(spend_key=spend_key, batch_id=batch_id) + async def _claim_job_charge(self, spend_key: str, job: ChargeOnceJob, ttl_seconds: int) -> bool: + marker_key: Final = job_charged_once_marker_key(spend_key=spend_key, job=job) redis_cache: Final = self.dual_cache.redis_cache if redis_cache is not None: polls: Final = await redis_cache.async_increment(key=marker_key, value=1, ttl=ttl_seconds, refresh_ttl=True) diff --git a/litellm/router_strategy/lowest_cost.py b/litellm/router_strategy/lowest_cost.py index 3f0a5412b62..6a8757f6e6e 100644 --- a/litellm/router_strategy/lowest_cost.py +++ b/litellm/router_strategy/lowest_cost.py @@ -2,16 +2,22 @@ # picks based on response time (for streaming, this is time to first token) from collections.abc import Mapping from datetime import datetime +from functools import partial from typing import Final import litellm -from litellm import ModelResponse, token_counter, verbose_logger +from litellm import ModelResponse, verbose_logger from litellm._internal_context import with_service_target from litellm._logging import verbose_router_logger from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.batch_utils import is_batch_retrieve_call_type -from litellm.litellm_core_utils.deployment_pricing import deployment_pricing_models, str_keyed +from litellm.litellm_core_utils.deployment_pricing import ( + deployment_backend_models, + deployment_pricing_models, + str_keyed, +) +from litellm.litellm_core_utils.token_counter import embedding_aware_token_count def _deployment_cost_map_rates(deployment: object) -> tuple[float | None, float | None]: @@ -235,7 +241,9 @@ class LowestCostLoggingHandler(CustomLogger): } try: - input_tokens = token_counter(messages=messages, text=input) + input_tokens = embedding_aware_token_count( + messages, input, partial(deployment_backend_models, healthy_deployments) + ) except Exception: input_tokens = 0 diff --git a/litellm/router_strategy/lowest_latency.py b/litellm/router_strategy/lowest_latency.py index c352934db6c..981fb5341dc 100644 --- a/litellm/router_strategy/lowest_latency.py +++ b/litellm/router_strategy/lowest_latency.py @@ -3,18 +3,21 @@ import random from collections.abc import Sequence from datetime import datetime, timedelta +from functools import partial from math import ceil, isfinite from typing import TYPE_CHECKING, Any, Final from pydantic import Field import litellm -from litellm import ModelResponse, token_counter, verbose_logger +from litellm import ModelResponse, verbose_logger from litellm._internal_context import with_service_target from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.batch_utils import is_batch_retrieve_call_type from litellm.litellm_core_utils.core_helpers import get_parent_otel_span_from_kwargs, safe_divide_seconds +from litellm.litellm_core_utils.deployment_pricing import deployment_backend_models +from litellm.litellm_core_utils.token_counter import embedding_aware_token_count from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.llms.openai import ResponsesAPIResponse from litellm.types.utils import LiteLLMPydanticObjectBase @@ -439,7 +442,9 @@ class LowestLatencyLoggingHandler(CustomLogger): } try: - input_tokens = token_counter(messages=messages, text=input) + input_tokens = embedding_aware_token_count( + messages, input, partial(deployment_backend_models, healthy_deployments) + ) except Exception: input_tokens = 0 diff --git a/litellm/router_strategy/lowest_tpm_rpm.py b/litellm/router_strategy/lowest_tpm_rpm.py index 9636d7f009d..e9769c6f595 100644 --- a/litellm/router_strategy/lowest_tpm_rpm.py +++ b/litellm/router_strategy/lowest_tpm_rpm.py @@ -2,14 +2,16 @@ # identifies lowest tpm deployment import traceback from datetime import datetime +from functools import partial from typing import Final -from litellm import token_counter from litellm._internal_context import with_service_target from litellm._logging import verbose_router_logger from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.batch_utils import is_batch_retrieve_call_type +from litellm.litellm_core_utils.deployment_pricing import deployment_backend_models +from litellm.litellm_core_utils.token_counter import embedding_aware_token_count from litellm.types.utils import LiteLLMPydanticObjectBase from litellm.utils import print_verbose @@ -174,7 +176,9 @@ class LowestTPMLoggingHandler(CustomLogger): verbose_router_logger.debug("tpm_key=%s, tpm_dict: %s, rpm_dict: %s", tpm_key, tpm_dict, rpm_dict) try: - input_tokens = token_counter(messages=messages, text=input) + input_tokens = embedding_aware_token_count( + messages, input, partial(deployment_backend_models, healthy_deployments) + ) except Exception: input_tokens = 0 verbose_router_logger.debug("input_tokens=%s", input_tokens) diff --git a/litellm/router_strategy/lowest_tpm_rpm_v2.py b/litellm/router_strategy/lowest_tpm_rpm_v2.py index 0719b30d36d..0574c3d7f69 100644 --- a/litellm/router_strategy/lowest_tpm_rpm_v2.py +++ b/litellm/router_strategy/lowest_tpm_rpm_v2.py @@ -5,18 +5,20 @@ from collections.abc import Iterator, Mapping, Sequence from contextlib import contextmanager from contextvars import ContextVar from dataclasses import dataclass +from functools import partial from typing import TYPE_CHECKING, Any, Final import httpx import litellm -from litellm import token_counter from litellm._internal_context import with_service_target from litellm._logging import verbose_logger, verbose_router_logger from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.batch_utils import is_batch_retrieve_call_type from litellm.litellm_core_utils.core_helpers import get_parent_otel_span_from_kwargs +from litellm.litellm_core_utils.deployment_pricing import deployment_backend_models +from litellm.litellm_core_utils.token_counter import embedding_aware_token_count from litellm.types.router import RouterErrors from litellm.types.utils import LiteLLMPydanticObjectBase, StandardLoggingPayload from litellm.utils import get_utc_datetime, print_verbose @@ -422,7 +424,9 @@ class LowestTPMLoggingHandler_v2(BaseRoutingStrategy, CustomLogger): rpm_dict[rpm_keys[idx].split(":")[0]] = rpm_values[idx] try: - input_tokens = token_counter(messages=messages, text=input) + input_tokens = embedding_aware_token_count( + messages, input, partial(deployment_backend_models, healthy_deployments) + ) except Exception: input_tokens = 0 verbose_router_logger.debug("input_tokens=%s", input_tokens) diff --git a/litellm/router_utils/client_initalization_utils.py b/litellm/router_utils/client_initalization_utils.py index fdcead4a6b9..b7bb6d80ad4 100644 --- a/litellm/router_utils/client_initalization_utils.py +++ b/litellm/router_utils/client_initalization_utils.py @@ -50,7 +50,7 @@ class MaxParallelRequestsLimit: class InitalizeCachedClient: @staticmethod @renamed_router_param("litellm_router_instance") - def set_max_parallel_requests_client(ctx: "RoutingContext", model: dict): + def set_max_parallel_requests_client(ctx: "RoutingContext", model: dict) -> None: litellm_params: Final = model.get("litellm_params", {}) model_id: Final = model["model_info"]["id"] rpm: Final = litellm_params.get("rpm", None) diff --git a/litellm/router_utils/cooldown_handlers.py b/litellm/router_utils/cooldown_handlers.py index e50d5bb47a6..7a8a645ad13 100644 --- a/litellm/router_utils/cooldown_handlers.py +++ b/litellm/router_utils/cooldown_handlers.py @@ -847,6 +847,45 @@ def is_stream_stall(exception: BaseException) -> bool: return isinstance(exception, litellm.Timeout) and isinstance(exception.__cause__, MidStreamFallbackError) +def is_caller_entra_credential_failure( + deployment: Deployment | None, + router_defaults: Mapping[str, object], + model_call_details: Mapping[str, object], + exception_status: str | int, + exception: BaseException, +) -> bool: + if cast_exception_status_to_int(exception_status) != 401 or deployment is None: + return False + + from litellm.llms.azure.common_utils import caused_by_entra_rejection + + if not caused_by_entra_rejection(exception): + return False + from litellm.llms.azure_ai.common_utils import AZURE_ENTRA_LITELLM_PARAM_KEYS, azure_entra_environment_defaults + + call_params: Final = _CREDENTIAL_VALUES_ADAPTER.validate_python(model_call_details.get("litellm_params") or {}) + + credential_name: Final = deployment.litellm_params.litellm_credential_name + configured_by_precedence: Final[tuple[Mapping[str, object], ...]] = ( + router_defaults, + _CREDENTIAL_VALUES_ADAPTER.validate_python(deployment.litellm_params.model_dump(exclude_none=True)), + _CREDENTIAL_VALUES_ADAPTER.validate_python( + CredentialAccessor.get_credential_values(credential_name) if credential_name is not None else {} + ), + ) + environment: Final = azure_entra_environment_defaults() + return any( + call_params.get(key) is not None + and (call_params.get(key) or environment.get(key)) + != (_first_configured(configured_by_precedence, key) or environment.get(key)) + for key in AZURE_ENTRA_LITELLM_PARAM_KEYS + ) + + +def _first_configured(sources: tuple[Mapping[str, object], ...], key: str) -> object: + return next((value for source in sources if (value := source.get(key)) is not None), None) + + def is_caller_scoped_auth_failure(deployment: Deployment | None, exception_status: str | int) -> bool: if cast_exception_status_to_int(exception_status) not in (401, 403) or deployment is None: return False diff --git a/litellm/router_utils/fallback_event_handlers.py b/litellm/router_utils/fallback_event_handlers.py index c929cca44be..e8f832b4b2d 100644 --- a/litellm/router_utils/fallback_event_handlers.py +++ b/litellm/router_utils/fallback_event_handlers.py @@ -28,6 +28,7 @@ from litellm.router_utils.cooldown_handlers import ( _first_present, # pyright: ignore[reportPrivateUsage] - shared internal helper, used across router_utils cast_exception_status_to_int, is_advisor_orchestration_failure, + is_caller_entra_credential_failure, is_caller_scoped_auth_failure, is_caller_timeout_408, is_stream_stall, @@ -132,6 +133,14 @@ def _trigger_cooldown_for_failed_deployment( deployment_id, ) return + if is_caller_entra_credential_failure( + deployment, ctx.default_litellm_params(), model_call_details, exception_status, exception + ): + verbose_router_logger.debug( + "Not triggering cooldown for fallback deployment %s: Entra ID rejected a credential the caller sent.", + deployment_id, + ) + return # Priority: deployment config > response header > router default, matching # Router.deployment_callback_on_failure's precedence for the primary path. diff --git a/litellm/router_utils/pre_call_checks/io_token_rate_limit_check.py b/litellm/router_utils/pre_call_checks/io_token_rate_limit_check.py index 80e1f3fe29c..f887c4551f2 100644 --- a/litellm/router_utils/pre_call_checks/io_token_rate_limit_check.py +++ b/litellm/router_utils/pre_call_checks/io_token_rate_limit_check.py @@ -20,6 +20,7 @@ from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final import httpx +from pydantic import TypeAdapter import litellm from litellm import token_counter @@ -28,6 +29,7 @@ from litellm._logging import verbose_router_logger from litellm.caching.dual_cache import DualCache from litellm.caching.redis_cache import RedisCache, RedisCircuitBreakerOpenError from litellm.constants import NO_MAX_TOKENS_TPM_RESERVATION_FRACTION +from litellm.litellm_core_utils.core_helpers import coerce_token_limit from litellm.litellm_core_utils.internal_call_metadata import ( IO_TOKEN_RESERVATION_KEYS, ITPM_CACHE_KEY, @@ -41,6 +43,8 @@ from litellm.router_utils.routing_read_batch import ROUTER_USAGE_TARGET from litellm.types.router import RouterCacheEnum, RouterErrors from litellm.utils import get_utc_datetime +_MODEL_INFO_ADAPTER: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object]) + if TYPE_CHECKING: from opentelemetry.trace import Span as _Span @@ -227,8 +231,20 @@ def _deployment_output_cap(request_kwargs: Mapping[str, object] | None, deployme ) -def _default_otpm_reservation(model_name: str, otpm_limit: int) -> int: - model_max: Final = _model_max_output_tokens(model_name) if model_name else None +def _default_otpm_reservation(deployment: Mapping[str, object], model_name: str, otpm_limit: int) -> int: + model_info: Final[object] = deployment.get("model_info") + configured_model_max: Final = ( + coerce_token_limit(_MODEL_INFO_ADAPTER.validate_python(model_info).get("max_output_tokens")) + if isinstance(model_info, Mapping) + else None + ) + model_max: Final = ( + max(0, configured_model_max) + if configured_model_max is not None + else _model_max_output_tokens(model_name) + if model_name + else None + ) share_of_limit: Final = max(1, int(otpm_limit * NO_MAX_TOKENS_TPM_RESERVATION_FRACTION)) return min(4096 if model_max is None else model_max, share_of_limit) @@ -242,7 +258,7 @@ def _otpm_reservation( from_request: Final = _explicit_max_tokens(request_kwargs) if from_request is not None: return from_request - default: Final = _default_otpm_reservation(model_name, otpm_limit) + default: Final = _default_otpm_reservation(deployment, model_name, otpm_limit) from_deployment: Final = _deployment_output_cap(request_kwargs, deployment) return default if from_deployment is None else min(from_deployment, default) diff --git a/litellm/router_utils/pre_call_checks/model_rate_limit_check.py b/litellm/router_utils/pre_call_checks/model_rate_limit_check.py index f41d19cd62c..a3da0ae1674 100644 --- a/litellm/router_utils/pre_call_checks/model_rate_limit_check.py +++ b/litellm/router_utils/pre_call_checks/model_rate_limit_check.py @@ -134,7 +134,7 @@ class ModelRateLimitingCheck(CustomLogger): this check actively enforces those limits across ALL routing strategies. """ - def __init__(self, dual_cache: DualCache, clock: Callable[[], datetime] = get_utc_datetime): + def __init__(self, dual_cache: DualCache, clock: Callable[[], datetime] = get_utc_datetime) -> None: self.dual_cache = dual_cache self.clock = clock # model_ids already warned about itpm/otpm + tpm/rpm on the same deployment, diff --git a/litellm/router_utils/search_api_router.py b/litellm/router_utils/search_api_router.py index aa2b29988dc..17a56e62e41 100644 --- a/litellm/router_utils/search_api_router.py +++ b/litellm/router_utils/search_api_router.py @@ -52,7 +52,7 @@ class SearchAPIRouter: @staticmethod @renamed_router_param("router_instance") - async def update_router_search_tools(ctx: "RoutingContext", search_tools: list): + async def update_router_search_tools(ctx: "RoutingContext", search_tools: list) -> None: """ Update the router with search tools from the database. @@ -143,7 +143,6 @@ class SearchAPIRouter: # Set up kwargs for the fallback system kwargs["model"] = search_tool_name # Use model field for compatibility with fallback system kwargs["original_generic_function"] = original_function - # Bind ctx to the helper method using partial kwargs["original_function"] = partial( SearchAPIRouter.async_search_with_fallbacks_helper, litellm_routing_ctx=ctx, diff --git a/litellm/rust_bridge/_native.pyi b/litellm/rust_bridge/_native.pyi index 8177e4a5cc9..954815f6a4f 100644 --- a/litellm/rust_bridge/_native.pyi +++ b/litellm/rust_bridge/_native.pyi @@ -296,32 +296,3 @@ class _SecretManagerRuntime: timeout: float | httpx.Timeout | None = None, primary_secret_name: str | None = None, ) -> Future[JsonValue]: ... - -@final -class NativeCacheHandle: - def __new__(cls, _uninstantiable: Never, /) -> Never: ... - @staticmethod - def memory( - *, - ttl: float = 600.0, - capacity: int = 200, - max_entry_bytes: int = 4194304, - ) -> NativeCacheHandle: ... - @staticmethod - def redis( - url: str, - *, - namespace: str, - ttl: float = 600.0, - max_entry_bytes: int = 4194304, - ) -> NativeCacheHandle: ... - def get(self, key: str) -> object: ... - def set(self, key: str, value: object, *, ttl: float | None = None) -> None: ... - def async_get(self, key: str) -> Future[object]: ... - def async_set(self, key: str, value: object, *, ttl: float | None = None) -> Future[None]: ... - def async_set_many(self, entries: Sequence[tuple[str, object]], *, ttl: float | None = None) -> Future[None]: ... - def flush(self) -> None: ... - def async_flush(self) -> Future[None]: ... - def ping(self) -> Future[bool]: ... - def disconnect(self) -> Future[None]: ... - def delete(self, keys: Sequence[str]) -> Future[None]: ... diff --git a/litellm/text_completions/streaming_iterator.py b/litellm/text_completions/streaming_iterator.py index 6bba56a5afe..2b5b4d52274 100644 --- a/litellm/text_completions/streaming_iterator.py +++ b/litellm/text_completions/streaming_iterator.py @@ -31,7 +31,7 @@ class TextCompletionStreamWrapper: def __aiter__(self): return self - def convert_to_text_completion_object(self, chunk: ModelResponse | ModelResponseStream): + def convert_to_text_completion_object(self, chunk: ModelResponse | ModelResponseStream) -> TextCompletionResponse: try: response: Final = TextCompletionResponse() response["id"] = chunk.get("id", None) diff --git a/litellm/types/integrations/custom_logger.py b/litellm/types/integrations/custom_logger.py index 77e75dea030..14ed719a880 100644 --- a/litellm/types/integrations/custom_logger.py +++ b/litellm/types/integrations/custom_logger.py @@ -74,14 +74,13 @@ class AgenticLoopSafetyError(ValueError): Covers both rails: the bounded-loop cap (``max_agentic_loops``) and the repeated tool-call fingerprint cycle break. Subclasses ``ValueError`` so callers that already catch the broader type keep working. - - Only the anthropic messages loop raises this today. The chat completions - loop in ``litellm_core_utils/chat_completion_agentic_loop.py`` still raises - a plain ``ValueError`` from its own copy of the same rails, so catching - this type alone will not cover that surface until it is moved over. """ +class AgenticLoopLimitError(AgenticLoopSafetyError): + pass + + class StandardCustomLoggerInitParams(LiteLLMBaseModel): """ Params for initializing a CustomLogger. diff --git a/litellm/types/mcp_server/mcp_server_manager.py b/litellm/types/mcp_server/mcp_server_manager.py index 0d39b0a0616..1d3d5911eb1 100644 --- a/litellm/types/mcp_server/mcp_server_manager.py +++ b/litellm/types/mcp_server/mcp_server_manager.py @@ -23,6 +23,8 @@ from litellm.types.mcp import ( def _validate_mcp_protocol_metadata(value: dict[str, object]) -> dict[str, object]: if "protocol_version" in value: TypeAdapter[MCPUpstreamProtocol](MCPUpstreamProtocol).validate_python(value["protocol_version"]) + if "skip_tool_output_validation" in value: + TypeAdapter[bool](bool).validate_python(value["skip_tool_output_validation"], strict=True) return value @@ -297,6 +299,10 @@ class MCPServer(LiteLLMBaseModel): """ return self.oauth2_flow == "client_credentials" + @property + def skip_tool_output_validation(self) -> bool: + return (self.mcp_info or {}).get("skip_tool_output_validation") is True + @model_validator(mode="after") def resolve_protocol_version(self) -> Self: if "protocol_version" not in self.model_fields_set and self.mcp_info is not None: diff --git a/litellm/types/proxy/claude_code_endpoints.py b/litellm/types/proxy/claude_code_endpoints.py index 19e40aa5c5d..49dd8b9ef91 100644 --- a/litellm/types/proxy/claude_code_endpoints.py +++ b/litellm/types/proxy/claude_code_endpoints.py @@ -4,7 +4,7 @@ Claude Code Marketplace endpoint types for LiteLLM Proxy from typing import Final, Literal, TypeAlias -from pydantic import AliasChoices, Field, TypeAdapter, model_validator +from pydantic import AliasChoices, ConfigDict, Field, TypeAdapter, model_validator from litellm.types.llms.base import LiteLLMBaseModel @@ -27,20 +27,11 @@ class PluginOwner(LiteLLMBaseModel): email: str | None = Field(None, description="Owner email") -class PluginSpec(LiteLLMBaseModel): +class PluginMetadata(LiteLLMBaseModel): """Mutable fields shared by plugin create and update requests.""" - source: dict[str, str] = Field( - ..., - description=( - "Plugin source reference. Supported formats:\n" - "- GitHub: {'source': 'github', 'repo': 'org/repo'}\n" - "- Git URL: {'source': 'url', 'url': 'https://github.com/org/repo.git'}\n" - "- Git Subdir: {'source': 'git-subdir', 'url': 'https://github.com/org/repo.git', 'path': 'plugins/plugin-name'}\n" - "- Zip archive on any https host (e.g. S3): " - "{'source': 'archive', 'url': 'https://bucket.s3.amazonaws.com/plugin.zip', 'sha256': ''}" - ), - ) + model_config = ConfigDict(frozen=True) + version: str | None = Field("1.0.0", description="Semantic version") description: str | None = Field(None, description="Plugin description") author: PluginAuthor | None = Field(None, description="Plugin author") @@ -75,6 +66,20 @@ class PluginSpec(LiteLLMBaseModel): } +class PluginSpec(PluginMetadata): + source: dict[str, str] = Field( + ..., + description=( + "Plugin source reference. Supported formats:\n" + "- GitHub: {'source': 'github', 'repo': 'org/repo'}\n" + "- Git URL: {'source': 'url', 'url': 'https://github.com/org/repo.git'}\n" + "- Git Subdir: {'source': 'git-subdir', 'url': 'https://github.com/org/repo.git', 'path': 'plugins/plugin-name'}\n" + "- Zip archive on any https host (e.g. S3): " + "{'source': 'archive', 'url': 'https://bucket.s3.amazonaws.com/plugin.zip', 'sha256': ''}" + ), + ) + + class RegisterPluginRequest(PluginSpec): """ Request body for registering a plugin in the marketplace. @@ -90,6 +95,10 @@ class RegisterPluginRequest(PluginSpec): ) +class UploadPluginRequest(PluginMetadata): + name: str = Field(..., pattern=r"^[a-z0-9-]+$", description="Skill name (kebab-case)") + + class UpdatePluginRequest(PluginSpec): """ Request body for replacing an existing plugin. diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py index 6f521d1fd1b..99ff7953ee2 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -16,7 +16,7 @@ class ToolPermissionRule(LiteLLMBaseModel): id: str = Field(description="Unique identifier for the rule") tool_name: str | None = Field( default=None, - description="Regex pattern applied to the tool's function name", + description="Regex pattern applied to the tool's name, or to its type when the tool has no name", ) tool_type: str | None = Field( default=None, diff --git a/litellm/types/proxy/model_deprecation.py b/litellm/types/proxy/model_deprecation.py index 8645f89b65d..3699f01a684 100644 --- a/litellm/types/proxy/model_deprecation.py +++ b/litellm/types/proxy/model_deprecation.py @@ -34,7 +34,10 @@ class ModelDeprecationInfo(LiteLLMBaseModel): litellm_provider: str | None = Field(default=None, description="The provider this model belongs to.") successor_model: str | None = Field( default=None, - description=("The model to migrate to, as set in model_info.successor_model on the deployment."), + description=( + "The model to migrate to: model_info.successor_model on the deployment when set, " + "else the successor_model the cost map names for the deprecated model." + ), ) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 57f4d971eb3..4c47489e08f 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -383,6 +383,10 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): input_cost_per_character: float | None # only for vertex ai models input_cost_per_audio_token: float | None input_cost_per_audio_token_flex: ReadOnly[float | None] + input_cost_per_audio_token_above_200k_tokens: ReadOnly[float | None] + input_cost_per_audio_token_above_200k_tokens_flex: ReadOnly[float | None] + input_cost_per_audio_token_above_200k_tokens_priority: ReadOnly[float | None] + input_cost_per_audio_token_above_200k_tokens_batches: ReadOnly[float | None] input_cost_per_audio_token_priority: ReadOnly[float | None] input_cost_per_token_above_128k_tokens: float | None # only for vertex ai models input_cost_per_token_above_200k_tokens: float | None # only for vertex ai gemini-2.5-pro models @@ -408,10 +412,12 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): input_cost_per_token_batches: float | None input_cost_per_video_token_batches: ReadOnly[float | None] input_cost_per_token_above_200k_tokens_batches: ReadOnly[float | None] + input_cost_per_token_above_200k_tokens_flex: ReadOnly[float | None] input_cost_per_token_above_100k_tokens_batches: ReadOnly[float | None] input_cost_per_token_above_272k_tokens_batches: ReadOnly[float | None] output_cost_per_token_batches: float | None output_cost_per_token_above_200k_tokens_batches: ReadOnly[float | None] + output_cost_per_token_above_200k_tokens_flex: ReadOnly[float | None] output_cost_per_token_above_100k_tokens_batches: ReadOnly[float | None] output_cost_per_token_above_272k_tokens_batches: ReadOnly[float | None] output_cost_per_token: Required[float | None] @@ -425,6 +431,9 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): regional_processing_uplift_multiplier_us: ( float | None ) # OpenAI US data-residency uplift multiplier applied to all token costs (e.g. 1.10 = +10%) + fedramp_uplift_multiplier: ReadOnly[ + float | None + ] # OpenAI FedRAMP endpoint (gov.api.openai.com) uplift multiplier applied to all token costs (e.g. 1.10 = +10%) regional_endpoint_uplift_multiplier: ReadOnly[ float | None ] # Vertex AI non-global (regional) endpoint uplift multiplier applied to all token costs (e.g. 1.10 = +10%) @@ -444,6 +453,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): output_cost_per_pixel: ReadOnly[float | None] output_cost_per_image_token: float | None output_cost_per_image_token_batches: ReadOnly[float | None] + output_cost_per_image_token_priority: ReadOnly[float | None] output_cost_per_video_token: float | None # for gemini omni models with video output output_vector_size: int | None output_cost_per_reasoning_token: float | None @@ -4173,6 +4183,10 @@ class CustomPricingLiteLLMParams(MirroredPricingParams): input_cost_per_character_above_128k_tokens: float | None = None input_cost_per_audio_token: float | None = None input_cost_per_audio_token_flex: float | None = None + input_cost_per_audio_token_above_200k_tokens: float | None = None + input_cost_per_audio_token_above_200k_tokens_flex: float | None = None + input_cost_per_audio_token_above_200k_tokens_priority: float | None = None + input_cost_per_audio_token_above_200k_tokens_batches: float | None = None input_cost_per_audio_token_priority: float | None = None input_cost_per_token_cache_hit: float | None = None input_cost_per_token_above_128k_tokens: float | None = None @@ -4183,6 +4197,7 @@ class CustomPricingLiteLLMParams(MirroredPricingParams): input_cost_per_token_above_272k_tokens_flex: float | None = None input_cost_per_token_above_272k_tokens_ultrafast: float | None = None input_cost_per_token_above_200k_tokens_batches: float | None = None + input_cost_per_token_above_200k_tokens_flex: float | None = None input_cost_per_token_above_100k_tokens_batches: float | None = None input_cost_per_token_above_272k_tokens_batches: float | None = None input_cost_per_query: float | None = None @@ -4212,12 +4227,14 @@ class CustomPricingLiteLLMParams(MirroredPricingParams): output_cost_per_token_above_272k_tokens_flex: float | None = None output_cost_per_token_above_272k_tokens_ultrafast: float | None = None output_cost_per_token_above_200k_tokens_batches: float | None = None + output_cost_per_token_above_200k_tokens_flex: float | None = None output_cost_per_token_above_100k_tokens_batches: float | None = None output_cost_per_token_above_272k_tokens_batches: float | None = None output_cost_per_character_above_128k_tokens: float | None = None output_cost_per_image: float | None = None output_cost_per_image_token: float | None = None output_cost_per_image_token_batches: float | None = None + output_cost_per_image_token_priority: float | None = None output_cost_per_video_token: float | None = None output_cost_per_reasoning_token: float | None = None output_cost_per_reasoning_token_flex: float | None = None @@ -4244,6 +4261,7 @@ class CustomPricingLiteLLMParams(MirroredPricingParams): annotation_cost_per_page_batches: float | None = None regional_processing_uplift_multiplier_eu: float | None = None regional_processing_uplift_multiplier_us: float | None = None + fedramp_uplift_multiplier: float | None = None regional_endpoint_uplift_multiplier: float | None = None @classmethod @@ -4284,6 +4302,10 @@ DEPLOYMENT_SCOPED_CAPABILITY_FIELDS: Final[frozenset[str]] = frozenset( } ) +DEPLOYMENT_SCOPED_TOKEN_LIMIT_FIELDS: Final[frozenset[str]] = frozenset( + {"max_input_tokens", "max_output_tokens", "max_tokens"} +) + DEPLOYMENT_SCOPED_TOKEN_COUNT_FIELDS: Final[frozenset[str]] = frozenset( { "token_count_framing", @@ -4628,6 +4650,7 @@ class LlmProviders(str, Enum): STRANDS_DECIDER = "strands_decider" LAYA = "laya" BESPOKE = "bespoke" + EMPIRIOLABS = "empiriolabs" EU_JEV = "eu_jev" MISTRAL = "mistral" MILVUS = "milvus" @@ -4756,6 +4779,7 @@ class LlmProviders(str, Enum): SAIL = "sail" REKA = "reka" STREAMLAKE = "streamlake" + WALLY = "wally" LITELLM_AGENT = "litellm_agent" CURSOR = "cursor" BEDROCK_MANTLE = "bedrock_mantle" @@ -5241,11 +5265,13 @@ class DataResidency(Enum): Inferred from the OpenAI api_base host (eu.api.openai.com -> EU, us.api.openai.com -> US). Used to apply the regional-processing cost uplift (see ``regional_processing_uplift_multiplier_`` - on ModelInfo). + on ModelInfo). gov.api.openai.com maps to FEDRAMP, which applies + ``fedramp_uplift_multiplier`` instead. """ US = "us" EU = "eu" + FEDRAMP = "fedramp" LLMResponseTypes = ( diff --git a/litellm/types/videos/utils.py b/litellm/types/videos/utils.py index 0fc2b8ede1b..240e36ecdf6 100644 --- a/litellm/types/videos/utils.py +++ b/litellm/types/videos/utils.py @@ -17,10 +17,11 @@ VIDEO_ID_PREFIX: Final = "video_" VIDEO_ID_SERVED_MODEL_PREFIX: Final = "served_model:" VIDEO_ID_DEPLOYMENT_PREFIX: Final = "deployment_id:" VIDEO_ID_REQUESTED_MODEL_PREFIX: Final = "requested_model:" -VIDEO_ID_CALL_TYPES: Final = frozenset( +VIDEO_STATUS_CALL_TYPES: Final = frozenset({"avideo_status", "video_status"}) +VIDEO_COST_REQUEST_ID_SUFFIX: Final = "_video_cost" +POLLED_VIDEO_STATUS_KWARG: Final = "polled_video_status" +VIDEO_ID_CALL_TYPES: Final = VIDEO_STATUS_CALL_TYPES | frozenset( { - "avideo_status", - "video_status", "avideo_content", "video_content", "avideo_remix", @@ -31,6 +32,12 @@ VIDEO_ID_CALL_TYPES: Final = frozenset( "video_extension", } ) + + +def is_video_status_call_type(call_type: object) -> bool: + return isinstance(call_type, str) and call_type in VIDEO_STATUS_CALL_TYPES + + CHARACTER_ID_PREFIX: Final = "character_" CHARACTER_ID_TEMPLATE: Final = "litellm:custom_llm_provider:{};model_id:{};character_id:{}" @@ -94,6 +101,27 @@ def _trailing_part(parts: Sequence[str], prefix: str) -> str | None: return next((part.removeprefix(prefix) or None for part in parts[3:] if part.startswith(prefix)), None) +def _decoded_video_id_payload(encoded_video_id: str) -> str | None: + if not encoded_video_id.startswith(VIDEO_ID_PREFIX): + return None + try: + decoded_id: Final = base64.b64decode( + _add_base64_padding(encoded_video_id.removeprefix(VIDEO_ID_PREFIX)).encode("utf-8") + ).decode("utf-8") + except ValueError as e: + verbose_logger.debug("Error decoding video_id '%s': %s", encoded_video_id, e) + return None + return decoded_id if ";" in decoded_id else None + + +def canonical_video_id(video_id: str) -> str: + """One spelling per video, since a client may poll a LiteLLM video id with or without its base64 padding.""" + payload: Final = _decoded_video_id_payload(video_id) + if payload is None: + return video_id + return f"{VIDEO_ID_PREFIX}{base64.b64encode(payload.encode('utf-8')).decode('utf-8')}" + + def decode_video_id_with_provider(encoded_video_id: str) -> DecodedVideoId: """Decode provider and model_id from encoded video_id.""" if not encoded_video_id: @@ -103,26 +131,8 @@ def decode_video_id_with_provider(encoded_video_id: str) -> DecodedVideoId: video_id=encoded_video_id, ) - if not encoded_video_id.startswith(VIDEO_ID_PREFIX): - return DecodedVideoId( - custom_llm_provider=None, - model_id=None, - video_id=encoded_video_id, - ) - - try: - cleaned_id = encoded_video_id.replace(VIDEO_ID_PREFIX, "") - cleaned_id = _add_base64_padding(cleaned_id) - decoded_id: Final = base64.b64decode(cleaned_id.encode("utf-8")).decode("utf-8") - except Exception as e: - verbose_logger.debug("Error decoding video_id '%s': %s", encoded_video_id, e) - return DecodedVideoId( - custom_llm_provider=None, - model_id=None, - video_id=encoded_video_id, - ) - - if ";" not in decoded_id: + decoded_id: Final = _decoded_video_id_payload(encoded_video_id) + if decoded_id is None: return DecodedVideoId( custom_llm_provider=None, model_id=None, diff --git a/litellm/utils.py b/litellm/utils.py index 319d32f0cbd..e27bbb9f0cf 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -83,6 +83,7 @@ from litellm.constants import ( PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO, TOOL_CHOICE_OBJECT_TOKEN_COUNT, ) +from litellm.litellm_core_utils.agentic_followup_failure import is_agentic_followup_failure from litellm.litellm_core_utils.allowed_tools_tool_choice import ( CHAT_ALLOWED_TOOLS_LAYOUT, parse_chat_allowed_tools_tool_choice, @@ -1635,7 +1636,6 @@ def dispatch_success_logging( is_litellm_internal_call: bool, ) -> None: if is_litellm_internal_call: - logging_obj.capture_usage_before_logging(result, start_time, end_time) return _schedule_async_success_logging( @@ -1674,14 +1674,9 @@ def _schedule_async_success_logging( if not logging_obj.claim_async_success_log(): return - if logging_obj.defer_async_logging and not is_completion_with_fallbacks: - logging_obj.capture_usage_before_logging(result, start_time, end_time, freeze_only=True) - def _enqueue_async_logging() -> None: from litellm.responses.streaming_iterator import spawn_logging_task - if not is_completion_with_fallbacks: - logging_obj.capture_usage_before_logging(result, start_time, end_time) if logging_obj.defer_async_logging: logging_obj.handle_sync_success_callbacks_for_async_calls( result=result, start_time=start_time, end_time=end_time @@ -2279,7 +2274,6 @@ def client(original_function): # LOG SUCCESS - handle streaming success logging in the _next_ object, remove `handle_success` once it's deprecated verbose_logger.info("Wrapper: Completed Call, calling success_handler") - logging_obj.capture_usage_before_logging(TypeAdapter(object).validate_python(result), start_time, end_time) if not is_internal_call.get(): # Copy the current context to propagate it to the background thread # This is essential for OpenTelemetry span context propagation @@ -2312,7 +2306,7 @@ def client(original_function): is_completion_litellm_router_call: Final = _is_litellm_router_call(kwargs, is_async=False) if ( - num_retries and not is_completion_litellm_router_call + num_retries and not is_completion_litellm_router_call and not is_agentic_followup_failure(e) ): # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying if ( isinstance(e, openai.APIError) @@ -2346,7 +2340,7 @@ def client(original_function): is_responses_litellm_router_call: Final = _is_litellm_router_call(kwargs, is_async=False) if ( - num_retries and not is_responses_litellm_router_call + num_retries and not is_responses_litellm_router_call and not is_agentic_followup_failure(e) ): # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying if ( isinstance(e, openai.APIError) @@ -2645,7 +2639,10 @@ def client(original_function): is_acompletion_litellm_router_call: Final = _is_litellm_router_call(kwargs, is_async=True) if ( - num_retries and not is_acompletion_litellm_router_call and not isinstance(e, ImportError) + num_retries + and not is_acompletion_litellm_router_call + and not isinstance(e, ImportError) + and not is_agentic_followup_failure(e) ): # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying try: litellm.num_retries = None # set retries to None to prevent infinite loops @@ -2676,7 +2673,10 @@ def client(original_function): is_aresponses_litellm_router_call: Final = _is_litellm_router_call(kwargs, is_async=True) if ( - num_retries and not is_aresponses_litellm_router_call and not isinstance(e, ImportError) + num_retries + and not is_aresponses_litellm_router_call + and not isinstance(e, ImportError) + and not is_agentic_followup_failure(e) ): # only enter this if call is not from litellm router/proxy. router has it's own logic for retrying try: litellm.num_retries = None # set retries to None to prevent infinite loops @@ -7445,6 +7445,18 @@ def get_model_info_helper( input_cost_per_text_message=_model_info.get("input_cost_per_text_message", None), input_cost_per_audio_token=_model_info.get("input_cost_per_audio_token", None), input_cost_per_audio_token_flex=_model_info.get("input_cost_per_audio_token_flex", None), + input_cost_per_audio_token_above_200k_tokens=_model_info.get( + "input_cost_per_audio_token_above_200k_tokens", None + ), + input_cost_per_audio_token_above_200k_tokens_flex=_model_info.get( + "input_cost_per_audio_token_above_200k_tokens_flex", None + ), + input_cost_per_audio_token_above_200k_tokens_priority=_model_info.get( + "input_cost_per_audio_token_above_200k_tokens_priority", None + ), + input_cost_per_audio_token_above_200k_tokens_batches=_model_info.get( + "input_cost_per_audio_token_above_200k_tokens_batches", None + ), input_cost_per_audio_token_priority=_model_info.get("input_cost_per_audio_token_priority", None), input_cost_per_image_token=_model_info.get("input_cost_per_image_token", None), input_cost_per_video_token=_model_info.get("input_cost_per_video_token", None), @@ -7458,6 +7470,9 @@ def get_model_info_helper( input_cost_per_token_above_200k_tokens_batches=_model_info.get( "input_cost_per_token_above_200k_tokens_batches" ), + input_cost_per_token_above_200k_tokens_flex=_model_info.get( + "input_cost_per_token_above_200k_tokens_flex", None + ), input_cost_per_token_above_272k_tokens_batches=_model_info.get( "input_cost_per_token_above_272k_tokens_batches" ), @@ -7465,6 +7480,9 @@ def get_model_info_helper( output_cost_per_token_above_200k_tokens_batches=_model_info.get( "output_cost_per_token_above_200k_tokens_batches" ), + output_cost_per_token_above_200k_tokens_flex=_model_info.get( + "output_cost_per_token_above_200k_tokens_flex", None + ), output_cost_per_token_above_272k_tokens_batches=_model_info.get( "output_cost_per_token_above_272k_tokens_batches" ), @@ -7479,6 +7497,7 @@ def get_model_info_helper( regional_processing_uplift_multiplier_us=_model_info.get( "regional_processing_uplift_multiplier_us", None ), + fedramp_uplift_multiplier=_model_info.get("fedramp_uplift_multiplier", None), regional_endpoint_uplift_multiplier=_model_info.get("regional_endpoint_uplift_multiplier", None), output_cost_per_audio_token=_model_info.get("output_cost_per_audio_token", None), output_cost_per_character=_model_info.get("output_cost_per_character", None), @@ -7526,6 +7545,7 @@ def get_model_info_helper( output_cost_per_pixel=_model_info.get("output_cost_per_pixel", None), output_cost_per_image_token=_model_info.get("output_cost_per_image_token", None), output_cost_per_image_token_batches=_model_info.get("output_cost_per_image_token_batches", None), + output_cost_per_image_token_priority=_model_info.get("output_cost_per_image_token_priority", None), output_cost_per_video_token=_model_info.get("output_cost_per_video_token", None), output_vector_size=_model_info.get("output_vector_size", None), citation_cost_per_token=_model_info.get("citation_cost_per_token", None), @@ -10276,6 +10296,7 @@ class ProviderConfigManager: if provider == LlmProviders.BESPOKE: return litellm.BespokeDecisionsConfig() if provider in ( + LlmProviders.EMPIRIOLABS, LlmProviders.OPENAI, LlmProviders.AZURE, LlmProviders.AZURE_AI, @@ -10283,6 +10304,7 @@ class ProviderConfigManager: LlmProviders.PARALLEL_AI, ): configs: Final = { + LlmProviders.EMPIRIOLABS: litellm.EmpirioLabsDecisionsConfig, LlmProviders.OPENAI: litellm.OpenAIDecisionsConfig, LlmProviders.AZURE: litellm.AzureDecisionsConfig, LlmProviders.AZURE_AI: litellm.AzureAIDecisionsConfig, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 974f1bb0c8b..25c7c3593ed 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -3,6 +3,8 @@ "cache_creation_input_audio_token_cost": 4e-07, "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, + "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -42,6 +44,8 @@ "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, + "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -204,6 +208,7 @@ }, "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -353,6 +358,7 @@ }, "ai21.jamba-1-5-large-v1:0": { "deprecation_date": "2026-11-26", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-ai21-labs-jamba-1-5-large.md", "input_cost_per_token": 2e-06, "litellm_provider": "bedrock", "max_input_tokens": 256000, @@ -363,6 +369,7 @@ }, "ai21.jamba-1-5-mini-v1:0": { "deprecation_date": "2026-11-26", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-ai21-labs-jamba-1-5-mini.md", "input_cost_per_token": 2e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, @@ -532,6 +539,7 @@ }, "amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -540,6 +548,7 @@ }, "us.amazon.nova-canvas-v1:0": { "deprecation_date": "2026-09-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "litellm_provider": "bedrock", "max_input_tokens": 2600, "mode": "image_generation", @@ -548,6 +557,7 @@ }, "us.writer.palmyra-x4-v1:0": { "deprecation_date": "2027-03-09", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x4.md", "input_cost_per_token": 2.5e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -560,6 +570,7 @@ }, "us.writer.palmyra-x5-v1:0": { "deprecation_date": "2027-03-21", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x5.md", "input_cost_per_token": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -572,6 +583,7 @@ }, "writer.palmyra-x4-v1:0": { "deprecation_date": "2027-03-09", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x4.md", "input_cost_per_token": 2.5e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -584,6 +596,7 @@ }, "writer.palmyra-x5-v1:0": { "deprecation_date": "2027-03-21", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-writer-palmyra-x5.md", "input_cost_per_token": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -964,6 +977,7 @@ }, "twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -978,6 +992,7 @@ }, "us.twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -992,6 +1007,7 @@ }, "eu.twelvelabs.marengo-embed-2-7-v1:0": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_query": 7e-05, "input_cost_per_video_per_second": 0.0007, "input_cost_per_audio_per_second": 0.00014, @@ -1045,6 +1061,7 @@ }, "twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1054,6 +1071,7 @@ }, "us.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1063,6 +1081,7 @@ }, "eu.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1072,6 +1091,7 @@ }, "global.twelvelabs.pegasus-1-2-v1:0": { "deprecation_date": "2027-03-22", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-twelvelabs-pegasus-v1-2.md", "input_cost_per_video_per_second": 0.00049, "output_cost_per_token": 7.5e-06, "litellm_provider": "bedrock", @@ -1175,7 +1195,8 @@ "supports_native_streaming": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "token_count_model": {"model":"anthropic.claude-haiku-4-5-20251001-v1:0","litellm_provider":"bedrock"} }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -1275,6 +1296,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -2135,12 +2157,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.5e-06, + "output_cost_per_token_batches": 1.25e-05 }, "anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2221,12 +2245,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "global.anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.5e-06, + "output_cost_per_token_batches": 1.25e-05 }, "global.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2307,12 +2333,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-east-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-east-1" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "us.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2393,12 +2421,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/eu-west-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "eu-west-1" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "eu.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2474,12 +2504,14 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-southeast-2/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "ap-southeast-4" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "au.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -2560,7 +2592,9 @@ "supports_output_config": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/" + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-northeast-1/index.json", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "jp.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -3397,6 +3431,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -3440,6 +3476,8 @@ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -3674,6 +3712,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "apac.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -3729,6 +3769,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "au.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -3762,6 +3804,7 @@ }, "azure/ada": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -3772,6 +3815,7 @@ "azure/codex-mini": { "cache_read_input_token_cost": 3.75e-07, "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-06, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -3799,19 +3843,10 @@ "supports_tool_choice": true, "supports_vision": true }, - "azure/command-r-plus": { - "deprecation_date": "2025-06-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true - }, "azure_ai/claude-haiku-4-5": { "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-haiku-5-5", "cache_creation_input_token_cost": 1.25e-06, "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_read_input_token_cost": 1e-07, @@ -3835,6 +3870,8 @@ }, "azure_ai/claude-opus-4-5": { "deprecation_date": "2026-11-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-opus-5-5", "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -3859,6 +3896,7 @@ }, "azure_ai/claude-opus-4-6": { "deprecation_date": "2027-02-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "supports_legacy_thinking": true, "input_cost_per_token": 5e-06, @@ -3890,6 +3928,7 @@ }, "azure_ai/claude-opus-4-7": { "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, "output_cost_per_token": 2.5e-05, @@ -3921,6 +3960,7 @@ }, "azure_ai/claude-fable-5": { "deprecation_date": "2027-12-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3949,7 +3989,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true, + "supports_max_reasoning_effort": false, "prompt_cache_min_tokens": 512 }, "azure_ai/claude-fable-5-1": { @@ -3983,12 +4023,44 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true, + "supports_max_reasoning_effort": false, "prompt_cache_min_tokens": 512, - "deprecation_date": "2027-12-05" + "deprecation_date": "2027-12-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement" + }, + "azure_ai/claude-mythos-5-1": { + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": false, + "prompt_cache_min_tokens": 512, + "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/claude-models" }, "azure_ai/claude-opus-5": { "deprecation_date": "2027-07-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -4057,6 +4129,7 @@ }, "azure_ai/claude-opus-4-8": { "deprecation_date": "2027-09-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -4089,6 +4162,8 @@ }, "azure_ai/claude-sonnet-4-5": { "deprecation_date": "2026-11-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/claude-sonnet-5-5", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -4112,6 +4187,7 @@ }, "azure_ai/claude-sonnet-5": { "deprecation_date": "2027-06-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -4144,6 +4220,7 @@ }, "azure_ai/claude-sonnet-4-6": { "deprecation_date": "2027-02-10", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "supports_adaptive_thinking": true, "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, @@ -4204,6 +4281,7 @@ }, "azure_ai/gpt-oss-120b": { "deprecation_date": "2027-09-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.5e-07, "output_cost_per_token": 6e-07, "litellm_provider": "azure_ai", @@ -4459,6 +4537,7 @@ }, "azure_ai/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -4506,6 +4585,7 @@ "azure_ai/gpt-chat-latest": { "cache_read_input_token_cost": 5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -4540,6 +4620,7 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_image_token": 8e-06, "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", @@ -4555,6 +4636,7 @@ "azure_ai/codex-mini": { "cache_read_input_token_cost": 3.75e-07, "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 200000, @@ -4584,6 +4666,7 @@ }, "azure_ai/whisper": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_second": 0.0001, "litellm_provider": "azure_ai", "mode": "audio_transcription", @@ -4637,6 +4720,7 @@ }, "azure_ai/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -4683,6 +4767,8 @@ }, "azure_ai/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_priority": 5e-07, @@ -4730,6 +4816,8 @@ }, "azure_ai/gpt-5.4-2026-03-05": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_priority": 5e-07, @@ -4777,6 +4865,7 @@ }, "azure_ai/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "cache_read_input_token_cost_priority": 6e-06, @@ -4822,6 +4911,7 @@ }, "azure_ai/gpt-5.4-pro-2026-03-05": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "cache_read_input_token_cost_priority": 6e-06, @@ -4867,6 +4957,7 @@ }, "azure_ai/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, @@ -4908,6 +4999,7 @@ }, "azure_ai/gpt-5.4-mini-2026-03-17": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, @@ -4949,6 +5041,7 @@ }, "azure_ai/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, @@ -4990,6 +5083,7 @@ }, "azure_ai/gpt-5.4-nano-2026-03-17": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, @@ -5031,6 +5125,7 @@ }, "azure_ai/model_router": { "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, "litellm_provider": "azure_ai", @@ -5042,6 +5137,7 @@ }, "azure_ai/model-router": { "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, "litellm_provider": "azure_ai", @@ -5053,6 +5149,8 @@ }, "azure/eu/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "input_cost_per_token_batches": 1.375e-06, @@ -5072,6 +5170,8 @@ }, "azure/eu/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -5091,6 +5191,8 @@ }, "azure/eu/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -5111,6 +5213,7 @@ "azure/eu/gpt-4o-mini": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -5134,6 +5237,7 @@ "azure/eu/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -5172,26 +5276,6 @@ "supports_system_messages": true, "supports_tool_choice": true }, - "azure/eu/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2.2e-05, - "cache_read_input_token_cost": 2.75e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.00011, - "input_cost_per_token": 5.5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.00022, - "output_cost_per_token": 2.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/eu/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_audio_token_cost": 2.5e-06, "cache_read_input_token_cost": 2.75e-06, @@ -5223,6 +5307,8 @@ "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1", "input_cost_per_token": 1.375e-06, "input_cost_per_token_batches": 6.875e-07, "input_cost_per_token_priority": 2.75e-06, @@ -5266,7 +5352,9 @@ "azure/eu/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_priority": 4.95e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "input_cost_per_token": 2.75e-07, "input_cost_per_token_batches": 1.375e-07, "input_cost_per_token_priority": 4.95e-07, @@ -5308,7 +5396,9 @@ "cache_read_input_token_cost_batches": 1.375e-08 }, "azure/eu/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -5356,6 +5446,8 @@ }, "azure/eu/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2-codex", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -5387,6 +5479,7 @@ }, "azure/eu/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", @@ -5419,6 +5512,8 @@ "azure/eu/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "input_cost_per_token": 5.5e-08, "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", @@ -5460,6 +5555,7 @@ "azure/eu/o1": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -5478,6 +5574,7 @@ "azure/eu/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -5512,6 +5609,8 @@ "azure/eu/o3-mini": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -5529,6 +5628,8 @@ "azure/eu/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -5546,6 +5647,7 @@ "azure/global-standard/gpt-4o-2024-08-06": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", @@ -5565,6 +5667,7 @@ "azure/global-standard/gpt-4o-2024-11-20": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", @@ -5582,6 +5685,7 @@ }, "azure/global-standard/gpt-4o-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -5601,6 +5705,8 @@ }, "azure/global/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/global/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -5620,6 +5726,8 @@ }, "azure/global/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/global/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -5638,7 +5746,8 @@ "supports_vision": true }, "azure/global/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -5686,6 +5795,7 @@ }, "azure/global/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -5717,6 +5827,7 @@ }, "azure/global/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 2.5e-07, "litellm_provider": "azure", @@ -5775,29 +5886,6 @@ "supports_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-35-turbo-16k": { - "deprecation_date": "2025-04-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 16385, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 4e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, - "azure/gpt-35-turbo-16k-0613": { - "deprecation_date": "2025-04-30", - "input_cost_per_token": 3e-06, - "litellm_provider": "azure", - "max_input_tokens": 16385, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 4e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-35-turbo-instruct": { "input_cost_per_token": 1.65e-06, "litellm_provider": "azure_text", @@ -5816,18 +5904,6 @@ "output_cost_per_token": 2.2e-06, "source": "https://azure.microsoft.com/en-us/pricing/details/azure-openai/" }, - "azure/gpt-4": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 3e-05, - "litellm_provider": "azure", - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-4-0125-preview": { "input_cost_per_token": 1e-05, "litellm_provider": "azure", @@ -5839,18 +5915,6 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-4-0613": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 3e-05, - "litellm_provider": "azure", - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_tool_choice": true - }, "azure/gpt-4-1106-preview": { "input_cost_per_token": 1e-05, "litellm_provider": "azure", @@ -5862,28 +5926,6 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "azure/gpt-4-32k": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 6e-05, - "litellm_provider": "azure", - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, - "azure/gpt-4-32k-0613": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 6e-05, - "litellm_provider": "azure", - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_tool_choice": true - }, "azure/gpt-4-turbo": { "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -5922,6 +5964,8 @@ }, "azure/gpt-4.1": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, @@ -5964,6 +6008,8 @@ }, "azure/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, @@ -6006,6 +6052,8 @@ }, "azure/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, @@ -6048,6 +6096,8 @@ }, "azure/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, @@ -6090,6 +6140,8 @@ }, "azure/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -6123,6 +6175,8 @@ }, "azure/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -6154,28 +6208,10 @@ "supports_tool_choice": true, "supports_vision": true }, - "azure/gpt-4.5-preview": { - "cache_read_input_token_cost": 3.75e-05, - "deprecation_date": "2025-07-14", - "input_cost_per_token": 7.5e-05, - "input_cost_per_token_batches": 3.75e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 16384, - "mode": "chat", - "output_cost_per_token": 0.00015, - "output_cost_per_token_batches": 7.5e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/legacy-models", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "azure/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6194,7 +6230,9 @@ "supports_vision": true }, "azure/gpt-4o-2024-05-13": { - "deprecation_date": "2026-12-09", + "deprecation_date": "2027-01-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "azure", @@ -6212,6 +6250,8 @@ }, "azure/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6231,6 +6271,8 @@ }, "azure/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -6250,6 +6292,7 @@ }, "azure/gpt-audio": { "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6283,6 +6326,7 @@ }, "azure/gpt-audio-2025-08-28": { "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6315,6 +6359,7 @@ }, "azure/gpt-audio-1.5": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6348,6 +6393,7 @@ }, "azure/gpt-audio-1.5-2026-02-23": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6382,6 +6428,7 @@ }, "azure/gpt-audio-mini": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "azure", @@ -6414,6 +6461,7 @@ }, "azure/gpt-audio-mini-2025-10-06": { "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "azure", @@ -6478,6 +6526,7 @@ }, "azure/gpt-4o-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -6503,6 +6552,7 @@ "azure/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 7.5e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "litellm_provider": "azure", @@ -6579,6 +6629,7 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6616,6 +6667,7 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-03-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6652,6 +6704,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1-2026-07-07", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6689,6 +6743,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1-2026-07-07", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6726,6 +6782,7 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -6766,6 +6823,7 @@ "cache_read_input_image_token_cost": 8e-08, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-06-25", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6805,6 +6863,7 @@ "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6841,6 +6900,7 @@ "cache_read_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -6874,6 +6934,7 @@ }, "azure/gpt-4o-mini-transcribe": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -6887,6 +6948,7 @@ }, "azure/gpt-4o-mini-tts": { "deprecation_date": "2027-06-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 6e-07, "litellm_provider": "azure", "mode": "audio_speech", @@ -6904,26 +6966,6 @@ "audio" ] }, - "azure/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2e-05, - "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.0001, - "input_cost_per_token": 5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.0002, - "output_cost_per_token": 2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, "input_cost_per_audio_token": 4e-05, @@ -6952,6 +6994,7 @@ }, "azure/gpt-4o-transcribe": { "deprecation_date": "2026-10-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 6e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -6966,6 +7009,7 @@ }, "azure/gpt-4o-transcribe-diarize": { "deprecation_date": "2027-04-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_audio_token": 6e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -7004,7 +7048,9 @@ "azure/gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -7052,6 +7098,8 @@ "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-codex", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", @@ -7086,6 +7134,7 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.5e-07, "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", @@ -7118,6 +7167,8 @@ }, "azure/gpt-5": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -7164,6 +7215,8 @@ "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_priority": 2.5e-06, @@ -7207,6 +7260,8 @@ "azure/gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.1-codex", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7236,7 +7291,9 @@ "supports_vision": true }, "azure/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, @@ -7282,7 +7339,9 @@ "azure/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 4.5e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-mini", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, @@ -7325,6 +7384,8 @@ }, "azure/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "cache_read_input_token_cost": 5e-09, "cache_read_input_token_cost_batches": 2.5e-09, "input_cost_per_token": 5e-08, @@ -7367,6 +7428,8 @@ "azure/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4-nano", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", @@ -7407,6 +7470,8 @@ }, "azure/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-pro", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", @@ -7442,7 +7507,9 @@ "supports_vision": true }, "azure/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_batches": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, @@ -7491,6 +7558,8 @@ "azure/gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -7515,6 +7584,8 @@ "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7536,6 +7607,8 @@ "azure/gpt-5.2-chat": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7556,6 +7629,8 @@ "azure/gpt-5.3-chat": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7576,6 +7651,8 @@ "azure/us/gpt-5.1-chat": { "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7597,6 +7674,8 @@ "azure/us/gpt-5.2-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7617,6 +7696,8 @@ "azure/us/gpt-5.3-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7637,6 +7718,8 @@ "azure/eu/gpt-5.1-chat": { "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7658,6 +7741,8 @@ "azure/eu/gpt-5.2-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7678,6 +7763,8 @@ "azure/eu/gpt-5.3-chat": { "cache_read_input_token_cost": 1.925e-07, "deprecation_date": "2026-06-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-chat-latest", "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", "max_input_tokens": 111616, @@ -7695,38 +7782,10 @@ "supports_response_schema": true, "supports_tool_choice": true }, - "azure/us/o1-preview": { - "cache_read_input_token_cost": 8.25e-06, - "deprecation_date": "2025-07-28", - "input_cost_per_token": 1.65e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6.6e-05, - "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_prompt_caching": true - }, - "azure/eu/o1-preview": { - "cache_read_input_token_cost": 8.25e-06, - "deprecation_date": "2025-07-28", - "input_cost_per_token": 1.65e-05, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6.6e-05, - "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", - "supported_endpoints": [ - "/v1/chat/completions" - ], - "supports_prompt_caching": true - }, "azure/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.2-codex", "cache_read_input_token_cost": 1.25e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", @@ -7759,6 +7818,7 @@ "azure/gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7789,6 +7849,7 @@ }, "azure/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 2.5e-07, "litellm_provider": "azure", @@ -7820,6 +7881,8 @@ }, "azure/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4", "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_batches": 8.75e-08, "cache_read_input_token_cost_priority": 3.5e-07, @@ -7867,6 +7930,8 @@ "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.4", "input_cost_per_token": 1.75e-06, "input_cost_per_token_batches": 8.75e-07, "input_cost_per_token_priority": 3.5e-06, @@ -7911,6 +7976,8 @@ "azure/gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.3-codex", "input_cost_per_token": 1.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -7943,6 +8010,7 @@ "cache_read_input_token_cost": 1.75e-07, "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.75e-06, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", @@ -8056,6 +8124,8 @@ }, "azure/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.5e-07, @@ -8113,6 +8183,8 @@ }, "azure/us/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.5", "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.75e-07, @@ -8162,6 +8234,8 @@ }, "azure/eu/gpt-5.4": { "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.5", "cache_read_input_token_cost": 2.75e-07, "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens_batches": 2.75e-07, @@ -8216,6 +8290,8 @@ "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5", "input_cost_per_token": 2.5e-06, "input_cost_per_token_above_272k_tokens": 5e-06, "input_cost_per_token_priority": 5e-06, @@ -8271,6 +8347,8 @@ "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.5", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -8320,6 +8398,8 @@ "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.5", "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, "output_cost_per_token": 1.65e-05, @@ -8366,6 +8446,8 @@ }, "azure/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5-pro", "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "input_cost_per_token": 3e-05, @@ -8417,6 +8499,8 @@ "cache_read_input_token_cost": 3e-06, "cache_read_input_token_cost_above_272k_tokens": 6e-06, "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.5-pro", "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, "input_cost_per_token_above_272k_tokens_flex": 3e-05, @@ -8529,6 +8613,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-sol", "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, @@ -8592,6 +8678,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-sol", "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, @@ -8655,6 +8743,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6.1-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, @@ -8718,6 +8808,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6.1-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, @@ -8781,6 +8873,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, @@ -8844,6 +8938,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, @@ -8907,11 +9003,7 @@ "mode": "chat", "output_cost_per_token": 5e-05, "output_cost_per_token_above_272k_tokens": 7.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", "supported_endpoints": [ "/v1/chat/completions", @@ -8943,7 +9035,11 @@ "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-05, "cache_read_input_token_cost_above_272k_tokens_flex": 1e-06, "input_cost_per_token_above_272k_tokens_flex": 1e-05, - "output_cost_per_token_above_272k_tokens_flex": 3.75e-05 + "output_cost_per_token_above_272k_tokens_flex": 3.75e-05, + "input_cost_per_token_flex": 5e-06, + "output_cost_per_token_flex": 2.5e-05, + "cache_read_input_token_cost_flex": 5e-07, + "cache_creation_input_token_cost_flex": 6.25e-06 }, "azure/gpt-6-astra-2026-09-03": { "cache_creation_input_token_cost": 1.25e-05, @@ -8959,11 +9055,7 @@ "mode": "chat", "output_cost_per_token": 5e-05, "output_cost_per_token_above_272k_tokens": 7.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", "supported_endpoints": [ "/v1/chat/completions", @@ -8995,7 +9087,11 @@ "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-05, "cache_read_input_token_cost_above_272k_tokens_flex": 1e-06, "input_cost_per_token_above_272k_tokens_flex": 1e-05, - "output_cost_per_token_above_272k_tokens_flex": 3.75e-05 + "output_cost_per_token_above_272k_tokens_flex": 3.75e-05, + "input_cost_per_token_flex": 5e-06, + "output_cost_per_token_flex": 2.5e-05, + "cache_read_input_token_cost_flex": 5e-07, + "cache_creation_input_token_cost_flex": 6.25e-06 }, "azure/gpt-6-luna": { "cache_creation_input_token_cost": 1.25e-07, @@ -9011,12 +9107,8 @@ "mode": "chat", "output_cost_per_token": 5e-07, "output_cost_per_token_above_272k_tokens": 7.5e-07, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, - "source": "https://azure.microsoft.com/en-us/blog/gpt-6-astra-sol-and-luna-for-production-agents-in-microsoft-foundry/", + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -9043,7 +9135,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-07, + "cache_creation_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-08, + "cache_read_input_token_cost_flex": 5e-09, + "input_cost_per_token_above_272k_tokens_flex": 1e-07, + "input_cost_per_token_flex": 5e-08, + "output_cost_per_token_above_272k_tokens_flex": 3.75e-07, + "output_cost_per_token_flex": 2.5e-07 }, "azure/gpt-6-luna-2026-09-22": { "cache_creation_input_token_cost": 1.25e-07, @@ -9059,12 +9159,8 @@ "mode": "chat", "output_cost_per_token": 5e-07, "output_cost_per_token_above_272k_tokens": 7.5e-07, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, - "source": "https://azure.microsoft.com/en-us/blog/gpt-6-astra-sol-and-luna-for-production-agents-in-microsoft-foundry/", + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -9091,7 +9187,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "cache_creation_input_token_cost_above_272k_tokens_flex": 1.25e-07, + "cache_creation_input_token_cost_flex": 6.25e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-08, + "cache_read_input_token_cost_flex": 5e-09, + "input_cost_per_token_above_272k_tokens_flex": 1e-07, + "input_cost_per_token_flex": 5e-08, + "output_cost_per_token_above_272k_tokens_flex": 3.75e-07, + "output_cost_per_token_flex": 2.5e-07 }, "azure/gpt-6-sol": { "cache_creation_input_token_cost": 2.5e-06, @@ -9143,7 +9247,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 1e-07, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6-sol-2026-09-22": { "cache_creation_input_token_cost": 2.5e-06, @@ -9195,7 +9307,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 1e-07, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6.1-terra": { "cache_creation_input_token_cost": 1.25e-06, @@ -9258,11 +9378,7 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_272k_tokens": 1.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models", "supported_endpoints": [ "/v1/chat/completions", @@ -9290,7 +9406,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 5e-08, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/gpt-6.1-sol-2026-09-29": { "cache_creation_input_token_cost": 2.5e-06, @@ -9306,11 +9430,7 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_272k_tokens": 1.5e-05, - "search_context_cost_per_query": { - "search_context_size_high": 0.014, - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014 - }, + "search_context_cost_per_query": {"search_context_size_high":0.014,"search_context_size_low":0.014,"search_context_size_medium":0.014}, "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/models", "supported_endpoints": [ "/v1/chat/completions", @@ -9338,7 +9458,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 5e-06, + "cache_read_input_token_cost_flex": 5e-08, + "cache_creation_input_token_cost_flex": 1.25e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "output_cost_per_token_above_272k_tokens_flex": 7.5e-06, + "cache_read_input_token_cost_above_272k_tokens_flex": 1e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06 }, "azure/us/gpt-6.1-terra": { "cache_creation_input_token_cost": 1.375e-06, @@ -9628,6 +9756,7 @@ "azure/gpt-chat-latest": { "cache_read_input_token_cost": 5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -9760,6 +9889,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6-sol", "input_cost_per_token": 4.4e-06, "input_cost_per_token_above_272k_tokens": 8.8e-06, "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, @@ -9815,6 +9946,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6.1-terra", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, @@ -9870,6 +10003,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-6-luna", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, @@ -10066,6 +10201,7 @@ "azure/us/gpt-chat-latest": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -10162,6 +10298,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6-sol", "input_cost_per_token": 4.4e-06, "input_cost_per_token_above_272k_tokens": 8.8e-06, "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, @@ -10217,6 +10355,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6.1-terra", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, @@ -10272,6 +10412,8 @@ "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-6-luna", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, @@ -10319,6 +10461,8 @@ }, "azure/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5e-07, @@ -10379,6 +10523,8 @@ }, "azure/us/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5.5e-07, @@ -10433,6 +10579,8 @@ }, "azure/eu/gpt-5.5": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_above_272k_tokens_batches": 5.5e-07, @@ -10534,6 +10682,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5e-06, "input_cost_per_token_above_272k_tokens_flex": 5e-06, "input_cost_per_token_batches": 2.5e-06, @@ -10544,6 +10694,8 @@ }, "azure/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1.25e-06, @@ -10647,6 +10799,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "output_cost_per_token_above_272k_tokens_batches": 2.475e-05, @@ -10655,6 +10809,8 @@ }, "azure/us/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, @@ -10752,6 +10908,8 @@ "supports_vision": true, "supports_web_search": true, "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "input_cost_per_token_above_272k_tokens_batches": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "output_cost_per_token_above_272k_tokens_batches": 2.475e-05, @@ -10760,6 +10918,8 @@ }, "azure/eu/gpt-5.5-2026-04-24": { "deprecation_date": "2027-10-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "cache_read_input_token_cost_priority": 1.375e-06, @@ -10896,6 +11056,7 @@ }, "azure/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_batches": 3.75e-08, "cache_read_input_token_cost_flex": 3.75e-08, @@ -10950,6 +11111,7 @@ "cache_read_input_token_cost_flex": 3.75e-08, "cache_read_input_token_cost_priority": 1.5e-07, "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -10998,6 +11160,7 @@ }, "azure/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_batches": 1e-08, "cache_read_input_token_cost_flex": 1e-08, @@ -11048,6 +11211,7 @@ "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -11096,6 +11260,8 @@ "cache_read_input_image_token_cost": 2.5e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-1.5", "input_cost_per_image_token": 1e-05, "input_cost_per_token": 5e-06, "litellm_provider": "azure", @@ -11130,6 +11296,7 @@ }, "azure/high/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.59263611e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11140,6 +11307,7 @@ }, "azure/high/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11150,6 +11318,7 @@ }, "azure/high/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "azure", "mode": "image_generation", @@ -11160,6 +11329,7 @@ }, "azure/low/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0490417e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11170,6 +11340,7 @@ }, "azure/low/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11180,6 +11351,7 @@ }, "azure/low/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11190,6 +11362,7 @@ }, "azure/medium/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11200,6 +11373,7 @@ }, "azure/medium/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11210,6 +11384,7 @@ }, "azure/medium/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11222,6 +11397,7 @@ "cache_read_input_image_token_cost": 2.5e-07, "cache_read_input_token_cost": 2e-07, "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_image_token": 2.5e-06, "input_cost_per_token": 2e-06, "litellm_provider": "azure", @@ -11236,6 +11412,8 @@ "azure/gpt-image-1.5": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2026-12-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-2", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11253,6 +11431,8 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-12-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-image-2", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -11268,6 +11448,7 @@ "azure/gpt-image-2": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11285,6 +11466,7 @@ "azure/gpt-image-2.5-flare": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-09-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11302,6 +11484,7 @@ "azure/gpt-image-2.5-sunburst": { "cache_read_input_image_token_cost": 2e-06, "deprecation_date": "2027-09-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, @@ -11320,6 +11503,7 @@ "cache_read_input_image_token_cost": 2e-06, "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-10-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 5e-06, "input_cost_per_image_token": 8e-06, "litellm_provider": "azure", @@ -11335,6 +11519,7 @@ }, "azure/low/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0751953125e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11345,6 +11530,7 @@ }, "azure/low/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0751953125e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11355,6 +11541,7 @@ }, "azure/low/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 2.0345052083e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11365,6 +11552,7 @@ }, "azure/medium/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 8.056640625e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11375,6 +11563,7 @@ }, "azure/medium/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 8.056640625e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11385,6 +11574,7 @@ }, "azure/medium/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 7.9752604167e-09, "litellm_provider": "azure", "mode": "image_generation", @@ -11395,6 +11585,7 @@ }, "azure/high/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.173828125e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11405,6 +11596,7 @@ }, "azure/high/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.173828125e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11415,6 +11607,7 @@ }, "azure/high/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_pixel": 3.1575520833e-08, "litellm_provider": "azure", "mode": "image_generation", @@ -11443,6 +11636,7 @@ }, "azure/o1": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 7.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "azure", @@ -11460,6 +11654,7 @@ "azure/o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", @@ -11512,6 +11707,7 @@ }, "azure/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -11549,6 +11745,7 @@ }, "azure/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -11587,6 +11784,8 @@ "azure/o3-deep-research": { "cache_read_input_token_cost": 2.5e-06, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -11623,6 +11822,8 @@ }, "azure/o3-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, @@ -11642,6 +11843,8 @@ "azure/o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", @@ -11658,6 +11861,8 @@ }, "azure/o3-pro": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "azure", @@ -11693,6 +11898,8 @@ }, "azure/o3-pro-2025-06-10": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "azure", @@ -11728,6 +11935,8 @@ }, "azure/o4-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "cache_read_input_token_cost": 2.75e-07, "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, @@ -11766,6 +11975,8 @@ "azure/o4-mini-2025-04-16": { "cache_read_input_token_cost": 2.75e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", @@ -11817,6 +12028,7 @@ }, "azure/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11826,6 +12038,7 @@ }, "azure/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11835,6 +12048,7 @@ }, "azure/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "max_input_tokens": 8192, @@ -11880,6 +12094,8 @@ }, "azure/us/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -11922,6 +12138,8 @@ }, "azure/us/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -11964,6 +12182,8 @@ }, "azure/us/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -11997,6 +12217,8 @@ }, "azure/us/gpt-4o-2024-08-06": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "input_cost_per_token_batches": 1.375e-06, @@ -12016,6 +12238,8 @@ }, "azure/us/gpt-4o": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -12035,6 +12259,8 @@ }, "azure/us/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_creation_input_token_cost": 1.38e-06, "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, @@ -12055,6 +12281,7 @@ "azure/us/gpt-4o-mini": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -12078,6 +12305,7 @@ "azure/us/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.65e-07, "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", @@ -12116,26 +12344,6 @@ "supports_system_messages": true, "supports_tool_choice": true }, - "azure/us/gpt-4o-realtime-preview-2024-10-01": { - "cache_creation_input_audio_token_cost": 2.2e-05, - "cache_read_input_token_cost": 2.75e-06, - "deprecation_date": "2025-03-26", - "input_cost_per_audio_token": 0.00011, - "input_cost_per_token": 5.5e-06, - "litellm_provider": "azure", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "realtime", - "output_cost_per_audio_token": 0.00022, - "output_cost_per_token": 2.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_audio_input": true, - "supports_audio_output": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, "azure/us/gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_audio_token_cost": 2.5e-06, "cache_read_input_token_cost": 2.75e-06, @@ -12167,6 +12375,8 @@ "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "input_cost_per_token": 1.375e-06, "input_cost_per_token_batches": 6.875e-07, "input_cost_per_token_priority": 2.75e-06, @@ -12210,7 +12420,9 @@ "azure/us/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_priority": 4.95e-08, - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "input_cost_per_token": 2.75e-07, "input_cost_per_token_batches": 1.375e-07, "input_cost_per_token_priority": 4.95e-07, @@ -12254,6 +12466,8 @@ "azure/us/gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "input_cost_per_token": 5.5e-08, "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", @@ -12293,7 +12507,9 @@ "cache_read_input_token_cost_batches": 2.75e-09 }, "azure/us/gpt-5.1": { - "deprecation_date": "2027-05-15", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -12341,6 +12557,8 @@ }, "azure/us/gpt-5.1-codex": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2-codex", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -12372,6 +12590,7 @@ }, "azure/us/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", @@ -12404,6 +12623,7 @@ "azure/us/o1": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -12422,6 +12642,7 @@ "azure/us/o1-2024-12-17": { "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -12455,6 +12676,7 @@ }, "azure/us/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -12492,6 +12714,7 @@ }, "azure/us/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -12530,6 +12753,8 @@ "azure/us/o3-mini": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12547,6 +12772,8 @@ "azure/us/o3-mini-2025-01-31": { "cache_read_input_token_cost": 6.05e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12564,6 +12791,8 @@ "azure/us/o4-mini": { "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12589,6 +12818,8 @@ "azure/us/o4-mini-2025-04-16": { "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-terra", "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", @@ -12620,6 +12851,8 @@ }, "azure_ai/Cohere-embed-v3-english": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 512, @@ -12631,6 +12864,8 @@ }, "azure_ai/Cohere-embed-v3-multilingual": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 512, @@ -12642,6 +12877,7 @@ }, "azure_ai/Codestral-2501": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 3e-07, "litellm_provider": "azure_ai", "max_input_tokens": 256000, @@ -12663,6 +12899,7 @@ }, "azure_ai/FLUX-1.1-pro": { "deprecation_date": "2027-01-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 5000, "mode": "image_generation", @@ -12674,6 +12911,7 @@ }, "azure_ai/FLUX.1-Kontext-pro": { "deprecation_date": "2027-01-05", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 5000, "mode": "image_generation", @@ -12685,6 +12923,7 @@ }, "azure_ai/flux.2-pro": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "max_input_tokens": 32000, "mode": "image_generation", @@ -12696,6 +12935,7 @@ }, "azure_ai/FLUX.2-flex": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_pixel": 5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 32000, @@ -12746,6 +12986,7 @@ }, "azure_ai/FW-GLM-5": { "deprecation_date": "2027-07-01", + "successor_model": "azure_ai/FW-GLM-5.2", "cache_read_input_token_cost": 2.2e-07, "input_cost_per_token": 1.1e-06, "litellm_provider": "azure_ai", @@ -12762,6 +13003,7 @@ }, "azure_ai/FW-GLM-5.1": { "deprecation_date": "2027-07-01", + "successor_model": "azure_ai/FW-GLM-5.2", "cache_read_input_token_cost": 2.86e-07, "input_cost_per_token": 1.54e-06, "litellm_provider": "azure_ai", @@ -13019,6 +13261,8 @@ "/v1/images/edits" ], "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/MAI-Image-2.6", "max_input_tokens": 32000 }, "azure_ai/MAI-Image-2.5-Flash": { @@ -13034,10 +13278,13 @@ "/v1/images/edits" ], "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/MAI-Image-2.6-Flash", "max_input_tokens": 32000 }, "azure_ai/MAI-Image-2.5-Pro": { "deprecation_date": "2026-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_image_token": 8e-06, "input_cost_per_token": 5e-06, "litellm_provider": "azure_ai", @@ -13077,6 +13324,7 @@ }, "azure_ai/Llama-3.3-70B-Instruct": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 7.1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13090,6 +13338,7 @@ }, "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13104,6 +13353,7 @@ }, "azure_ai/Llama-4-Scout-17B-16E-Instruct": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 10000000, @@ -13116,149 +13366,10 @@ "supports_tool_choice": true, "supports_vision": true }, - "azure_ai/Meta-Llama-3-70B-Instruct": { - "deprecation_date": "2025-06-30", - "input_cost_per_token": 1.1e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "mode": "chat", - "output_cost_per_token": 3.7e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, - "azure_ai/Meta-Llama-3.1-70B-Instruct": { - "deprecation_date": "2025-06-30", - "input_cost_per_token": 2.68e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 2048, - "mode": "chat", - "output_cost_per_token": 3.54e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, - "azure_ai/Phi-3-medium-128k-instruct": { - "deprecation_date": 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"2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 1.25e-07, "litellm_provider": "azure_ai", "max_input_tokens": 16384, @@ -13273,6 +13384,8 @@ }, "azure_ai/Phi-4-mini-instruct": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13285,6 +13398,8 @@ }, "azure_ai/Phi-4-multimodal-instruct": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_audio_token": 4e-06, "input_cost_per_token": 8e-08, "litellm_provider": "azure_ai", @@ -13300,6 +13415,8 @@ }, "azure_ai/Phi-4-mini-reasoning": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13312,6 +13429,8 @@ }, "azure_ai/Phi-4-reasoning": { "deprecation_date": "2027-01-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "microsoft--phi-4", "input_cost_per_token": 1.25e-07, "litellm_provider": "azure_ai", "max_input_tokens": 32768, @@ -13345,6 +13464,7 @@ }, "azure_ai/Cohere-parse-v5": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "litellm_provider": "azure_ai", "mode": "ocr", "ocr_cost_per_page": 0.0015, @@ -13355,6 +13475,8 @@ }, "azure_ai/cohere-command-a": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "coherelabs-command-a-plus-05-2026-fp81", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13395,6 +13517,7 @@ }, "azure_ai/MAI-DS-R1": { "deprecation_date": "2026-02-27", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "input_cost_per_token": 1.35e-06, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13406,30 +13529,10 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "azure_ai/cohere-rerank-v3-english": { - "deprecation_date": "2025-06-30", - "input_cost_per_query": 0.002, - "input_cost_per_token": 0.0, - "litellm_provider": "azure_ai", - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "mode": "rerank", - "output_cost_per_token": 0.0, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models" - }, - "azure_ai/cohere-rerank-v3-multilingual": { - "deprecation_date": "2025-06-30", - "input_cost_per_query": 0.002, - "input_cost_per_token": 0.0, - "litellm_provider": "azure_ai", - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "mode": "rerank", - "output_cost_per_token": 0.0, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models" - }, "azure_ai/cohere-rerank-v4.0-pro": { "deprecation_date": "2027-09-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "Cohere-rerank-v5-pro", "input_cost_per_query": 0.0025, "input_cost_per_token": 0.0, "litellm_provider": "azure_ai", @@ -13441,6 +13544,8 @@ }, "azure_ai/cohere-rerank-v4.0-fast": { "deprecation_date": "2027-09-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "Cohere-rerank-v5-fast", "input_cost_per_query": 0.002, "input_cost_per_token": 0.0, "litellm_provider": "azure_ai", @@ -13452,6 +13557,8 @@ }, "azure_ai/deepseek-v3.2": { "deprecation_date": "2027-11-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4.1-flash", "input_cost_per_token": 5.8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13468,6 +13575,8 @@ }, "azure_ai/deepseek-v3.2-speciale": { "deprecation_date": "2027-11-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-pro", "input_cost_per_token": 5.8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13482,20 +13591,9 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "azure_ai/deepseek-v3": { - "deprecation_date": "2025-08-31", - "input_cost_per_token": 1.14e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 4.56e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/deepseek-v4-pro": { "deprecation_date": "2028-02-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.74e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13512,6 +13610,8 @@ }, "azure_ai/deepseek-v4-flash": { "deprecation_date": "2028-02-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/DeepSeek-V4-Flash-0731", "input_cost_per_token": 1.9e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13529,6 +13629,7 @@ "azure_ai/DeepSeek-V4-Flash-0731": { "cache_read_input_token_cost": 1.4e-08, "deprecation_date": "2026-12-03", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 4.4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -13544,6 +13645,8 @@ }, "azure_ai/embed-v-4-0": { "deprecation_date": "2027-06-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/Cohere-Embed-V5-Fast", "input_cost_per_token": 1.2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13563,6 +13666,8 @@ }, "azure_ai/grok-4": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4.3", "input_cost_per_token": 3e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13621,6 +13726,7 @@ "azure_ai/grok-4-20-reasoning": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13640,6 +13746,7 @@ "azure_ai/grok-4-20-non-reasoning": { "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-06", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 1.25e-06, "litellm_provider": "azure_ai", "max_input_tokens": 262000, @@ -13657,6 +13764,7 @@ }, "azure_ai/grok-4-1-fast-non-reasoning": { "deprecation_date": "2027-05-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -13672,6 +13780,7 @@ }, "azure_ai/grok-4-1-fast-reasoning": { "deprecation_date": "2027-05-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -13692,6 +13801,7 @@ }, "azure_ai/grok-code-fast-1": { "deprecation_date": "2027-01-31", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 2e-07, "litellm_provider": "azure_ai", "max_input_tokens": 256000, @@ -13707,6 +13817,7 @@ }, "azure_ai/jais-30b-chat": { "deprecation_date": "2026-01-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "input_cost_per_token": 0.0032, "litellm_provider": "azure_ai", "max_input_tokens": 8192, @@ -13716,20 +13827,9 @@ "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", "supports_pdf_input": false }, - "azure_ai/jamba-instruct": { - "deprecation_date": "2025-03-01", - "input_cost_per_token": 5e-07, - "litellm_provider": "azure_ai", - "max_input_tokens": 70000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7e-07, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/kimi-k2.5": { "deprecation_date": "2027-01-26", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -13747,6 +13847,7 @@ }, "azure_ai/kimi-k2.6": { "deprecation_date": "2027-04-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 9.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -13771,6 +13872,8 @@ }, "azure_ai/ministral-3b": { "deprecation_date": "2026-12-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "ministral-ministral-4b-instruct", "input_cost_per_token": 4e-08, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13782,32 +13885,6 @@ "supports_pdf_input": false, "supports_tool_choice": true }, - "azure_ai/mistral-large": { - "deprecation_date": "2025-04-15", - "input_cost_per_token": 4e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, - "azure_ai/mistral-large-2407": { - "deprecation_date": "2025-05-13", - "input_cost_per_token": 2e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 128000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 6e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/mistral-large-latest": { "input_cost_per_token": 2e-06, "litellm_provider": "azure_ai", @@ -13835,6 +13912,8 @@ }, "azure_ai/mistral-medium-2505": { "deprecation_date": "2026-12-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/mistral-medium-3-5", "input_cost_per_token": 4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13848,6 +13927,8 @@ }, "azure_ai/mistral-nemo": { "deprecation_date": "2026-01-30", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", + "successor_model": "azure_ai/mistral-small-2503", "input_cost_per_token": 1.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 131072, @@ -13858,21 +13939,10 @@ "supports_function_calling": true, "supports_pdf_input": false }, - "azure_ai/mistral-small": { - "deprecation_date": "2025-07-31", - "input_cost_per_token": 1e-06, - "litellm_provider": "azure_ai", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 3e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", - "supports_function_calling": true, - "supports_pdf_input": false, - "supports_tool_choice": true - }, "azure_ai/mistral-small-2503": { "deprecation_date": "2027-02-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "mistralai-mistral-small-4-119b-2603", "input_cost_per_token": 1e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -13886,6 +13956,8 @@ }, "babbage-002": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, "litellm_provider": "text-completion-openai", @@ -14050,6 +14122,7 @@ }, "bedrock/ap-northeast-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14094,6 +14167,7 @@ }, "bedrock/ap-northeast-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.3e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -14146,6 +14220,7 @@ }, "bedrock/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -14179,6 +14254,7 @@ }, "bedrock/ap-south-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.18e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14189,6 +14265,7 @@ }, "bedrock/ap-south-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14221,6 +14298,7 @@ }, "bedrock/ap-south-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14265,6 +14343,7 @@ }, "bedrock/ap-south-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.1e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -14357,6 +14436,7 @@ }, "bedrock/ap-southeast-3/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-minimax-minimax-m2-1.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_flex": 1.8e-07, "litellm_provider": "bedrock", @@ -14427,6 +14507,7 @@ }, "bedrock/ca-central-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.05e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14437,6 +14518,7 @@ }, "bedrock/ca-central-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14469,6 +14551,7 @@ }, "bedrock/eu-north-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14612,6 +14695,7 @@ }, "bedrock/eu-central-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14673,6 +14757,7 @@ }, "bedrock/eu-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.86e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14683,6 +14768,7 @@ }, "bedrock/eu-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14693,6 +14779,7 @@ }, "bedrock/eu-west-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -14754,6 +14841,7 @@ }, "bedrock/eu-west-2/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 3.45e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14764,6 +14852,7 @@ }, "bedrock/eu-west-2/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14774,6 +14863,7 @@ }, "bedrock/eu-west-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4.7e-07, "input_cost_per_token_batches": 2.35e-07, "input_cost_per_token_flex": 2.35e-07, @@ -14861,6 +14951,7 @@ }, "bedrock/eu-west-3/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 2e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -14882,6 +14973,7 @@ }, "bedrock/eu-west-3/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 5.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -14893,6 +14985,7 @@ }, "bedrock/eu-south-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-minimax-minimax-m2-1.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_flex": 1.8e-07, "litellm_provider": "bedrock", @@ -14963,6 +15056,7 @@ }, "bedrock/sa-east-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 4.45e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -14973,6 +15067,7 @@ }, "bedrock/sa-east-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 5e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15005,6 +15100,7 @@ }, "bedrock/sa-east-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3.6e-07, "input_cost_per_token_batches": 1.8e-07, "input_cost_per_token_flex": 1.8e-07, @@ -15049,6 +15145,7 @@ }, "bedrock/sa-east-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 7.3e-07, "input_cost_per_token_flex": 3.6e-07, "litellm_provider": "bedrock", @@ -15180,6 +15277,7 @@ }, "bedrock/us-east-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15190,6 +15288,7 @@ }, "bedrock/us-east-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15200,6 +15299,7 @@ }, "bedrock/us-east-1/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15221,6 +15321,7 @@ }, "bedrock/us-east-1/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15254,6 +15355,7 @@ }, "bedrock/us-east-1/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15297,6 +15399,7 @@ }, "bedrock/us-east-1/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -15375,6 +15478,7 @@ }, "bedrock/us-east-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15419,6 +15523,7 @@ }, "bedrock/us-east-2/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -15493,7 +15598,7 @@ "output_vector_size": 1536 }, "bedrock/us-gov-east-1/amazon.titan-embed-text-v2:0": { - "input_cost_per_token": 2e-07, + "input_cost_per_token": 3e-08, "litellm_provider": "bedrock", "max_input_tokens": 8192, "mode": "embedding", @@ -15529,6 +15634,8 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "bedrock/us-gov-east-1/anthropic.claude-sonnet-5-5", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15559,6 +15666,7 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15578,7 +15686,8 @@ "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "bedrock/us-gov-east-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 2.65e-06, @@ -15606,8 +15715,10 @@ "input_cost_per_image": 7.2e-05, "input_cost_per_video_per_second": 0.00084, "input_cost_per_audio_per_second": 0.000168, + "input_cost_per_token_batches": 8.1e-08, "output_cost_per_token": 0.0, "output_vector_size": 3072, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_embedding_image_input": true, "supports_image_input": true, "supports_video_input": true, @@ -15616,11 +15727,14 @@ "bedrock/us-gov-west-1/amazon.nova-lite-v1:0": { "cache_read_input_token_cost": 1.8e-08, "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, "litellm_provider": "bedrock", "max_input_tokens": 300000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 2.88e-07, + "output_cost_per_token_batches": 1.44e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -15631,11 +15745,14 @@ "bedrock/us-gov-west-1/amazon.nova-micro-v1:0": { "cache_read_input_token_cost": 1.05e-08, "input_cost_per_token": 4.2e-08, + "input_cost_per_token_batches": 2.1e-08, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 1.68e-07, + "output_cost_per_token_batches": 8.4e-08, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_prompt_caching": true, "supports_response_schema": true, @@ -15643,12 +15760,21 @@ }, "bedrock/us-gov-west-1/amazon.nova-pro-v1:0": { "cache_read_input_token_cost": 2.4e-07, + "cache_read_input_token_cost_flex": 1.2e-07, + "cache_read_input_token_cost_priority": 4.2e-07, "input_cost_per_token": 9.6e-07, + "input_cost_per_token_batches": 4.8e-07, + "input_cost_per_token_flex": 4.8e-07, + "input_cost_per_token_priority": 1.68e-06, "litellm_provider": "bedrock", "max_input_tokens": 300000, "max_output_tokens": 10000, "mode": "chat", "output_cost_per_token": 3.84e-06, + "output_cost_per_token_batches": 1.92e-06, + "output_cost_per_token_flex": 1.92e-06, + "output_cost_per_token_priority": 6.72e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -15701,6 +15827,8 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "bedrock/us-gov-west-1/anthropic.claude-sonnet-5-5", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15731,6 +15859,7 @@ "cache_creation_input_token_cost_above_1hr": 7.2e-06, "cache_read_input_token_cost": 3.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "input_cost_per_token": 3.6e-06, "litellm_provider": "bedrock", "supports_tool_search": true, @@ -15750,10 +15879,12 @@ "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "bedrock/us-gov-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8000, @@ -15765,6 +15896,7 @@ }, "bedrock/us-gov-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8000, @@ -15776,6 +15908,7 @@ }, "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15786,6 +15919,7 @@ }, "bedrock/us-west-1/meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -15875,6 +16009,7 @@ }, "bedrock/us-west-2/mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15896,6 +16031,7 @@ }, "bedrock/us-west-2/mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -15929,6 +16065,7 @@ }, "bedrock/us-west-2/minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -15972,6 +16109,7 @@ }, "bedrock/us-west-2/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-moonshot-ai-kimi-k2-thinking.html", "input_cost_per_token": 6e-07, "input_cost_per_token_flex": 3e-07, "litellm_provider": "bedrock", @@ -16144,17 +16282,6 @@ "supports_function_calling": true, "supports_tool_choice": true }, - "cerebras/llama3.1-70b": { - "deprecation_date": "2025-01-17", - "input_cost_per_token": 6e-07, - "litellm_provider": "cerebras", - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 6e-07, - "supports_function_calling": true, - "supports_tool_choice": true - }, "cerebras/llama3.1-8b": { "deprecation_date": "2026-05-27", "input_cost_per_token": 1e-07, @@ -16228,6 +16355,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 0.0001, "source": "https://developers.openai.com/api/docs/pricing" }, @@ -16293,6 +16422,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", + "successor_model": "claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -16326,6 +16457,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", + "successor_model": "claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -16494,6 +16627,7 @@ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", "litellm_provider": "bedrock", "supports_tool_search": true, "max_input_tokens": 200000, @@ -16513,7 +16647,8 @@ "prompt_cache_min_tokens": 1024, "input_cost_per_token_batches": 1.5e-06, "output_cost_per_token_batches": 7.5e-06, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md" + "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "token_count_model": {"model":"anthropic.claude-sonnet-4-5-20250929-v1:0","litellm_provider":"bedrock"} }, "claude-opus-4-5-20251101": { "supports_web_search": true, @@ -17705,12 +17840,15 @@ }, "computer-use-preview": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/responses" ], @@ -17734,12 +17872,15 @@ }, "computer-use-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/responses" ], @@ -17764,6 +17905,7 @@ "deepseek-chat": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "litellm_provider": "deepseek", "max_input_tokens": 131072, @@ -17785,6 +17927,7 @@ "deepseek-reasoner": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "litellm_provider": "deepseek", "max_input_tokens": 131072, @@ -21815,6 +21958,7 @@ "cache_creation_input_token_cost": 3.74997e-06, "cache_read_input_token_cost": 3.0002e-07, "deprecation_date": "2026-10-09", + "successor_model": "databricks/databricks-claude-sonnet-5", "input_cost_per_token": 2.99999e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -21938,6 +22082,7 @@ "cache_creation_input_token_cost": 1.4e-07, "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-11-05", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.4e-07, "input_dbu_cost_per_token": 2e-06, "litellm_provider": "databricks", @@ -21951,6 +22096,7 @@ "output_cost_per_token": 2.8e-07, "output_dbu_cost_per_token": 4e-06, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "supported_modalities": [ "text" ], @@ -21964,14 +22110,19 @@ "supports_vision": false }, "databricks/system.ai.deepseek-v4-flash-0731": { + "deprecation_date": "2026-11-05", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/retired-models-policy", "litellm_provider": "databricks", "mode": "chat", + "source": "https://docs.databricks.com/aws/en/machine-learning/retired-models-policy", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "token_count_framing": 97 }, "databricks/databricks-deepseek-v4-pro-0813": { "cache_creation_input_token_cost": 1.31999e-06, "cache_read_input_token_cost": 1.3202e-07, "deprecation_date": "2026-10-30", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.31999e-06, "input_dbu_cost_per_token": 1.8857e-05, "litellm_provider": "databricks", @@ -21984,6 +22135,7 @@ "output_cost_per_token": 3.95997e-06, "output_dbu_cost_per_token": 5.6571e-05, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-deepseek-v4-1-flash-1017", "supported_modalities": [ "text" ], @@ -22000,6 +22152,7 @@ "cache_creation_input_token_cost": 3.0002e-07, "cache_read_input_token_cost": 3.0002e-08, "deprecation_date": "2026-10-02", + "successor_model": "databricks/databricks-gemini-3-5-flash", "input_cost_per_token": 3.0001999999999996e-07, "input_dbu_cost_per_token": 4.285999999999999e-06, "litellm_provider": "databricks", @@ -22269,7 +22422,7 @@ }, "mode": "chat", "output_cost_per_token": 3.74997e-06, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22287,7 +22440,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-7-flash": { "cache_creation_input_token_cost": 7.4998e-07, @@ -22301,7 +22456,7 @@ }, "mode": "chat", "output_cost_per_token": 3.74997e-06, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22319,7 +22474,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-6-flash": { "cache_creation_input_token_cost": 7.4998e-07, @@ -22335,7 +22492,7 @@ "mode": "chat", "output_cost_per_token": 3.74997e-06, "output_dbu_cost_per_token": 5.3571e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22354,7 +22511,9 @@ "input_cost_per_token_priority": 1.35002e-06, "output_cost_per_token_priority": 6.75003e-06, "cache_read_input_token_cost_priority": 1.3503e-07, - "cache_creation_input_token_cost_priority": 1.35002e-06 + "cache_creation_input_token_cost_priority": 1.35002e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-5-flash": { "cache_creation_input_token_cost": 1.87502e-06, @@ -22370,7 +22529,7 @@ "mode": "chat", "output_cost_per_token": 1.124998e-05, "output_dbu_cost_per_token": 0.000160714, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22388,7 +22547,9 @@ "input_cost_per_token_priority": 3.37498e-06, "output_cost_per_token_priority": 2.025002e-05, "cache_read_input_token_cost_priority": 3.3747e-07, - "cache_creation_input_token_cost_priority": 3.37498e-06 + "cache_creation_input_token_cost_priority": 3.37498e-06, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemini-3-5-flash-lite": { "cache_creation_input_token_cost": 3.7499e-07, @@ -22404,7 +22565,7 @@ "mode": "chat", "output_cost_per_token": 3.12501e-06, "output_dbu_cost_per_token": 4.4643e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "supported_modalities": [ "text", "image", @@ -22424,7 +22585,9 @@ "input_cost_per_token_priority": 6.7501e-07, "output_cost_per_token_priority": 5.62499e-06, "cache_read_input_token_cost_priority": 6.748e-08, - "cache_creation_input_token_cost_priority": 6.7501e-07 + "cache_creation_input_token_cost_priority": 6.7501e-07, + "supports_audio_input": true, + "supports_video_input": true }, "databricks/databricks-gemma-3-12b": { "cache_creation_input_token_cost": 1.5001e-07, @@ -23038,6 +23201,7 @@ "cache_creation_input_token_cost": 1.00002e-06, "cache_read_input_token_cost": 1.7003e-07, "deprecation_date": "2026-10-30", + "deprecation_source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", @@ -23049,6 +23213,7 @@ "output_cost_per_token": 4.04999e-06, "output_dbu_cost_per_token": 5.7857e-05, "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "successor_model": "databricks/databricks-glm-5-3", "supported_modalities": [ "text", "image" @@ -23273,6 +23438,8 @@ }, "davinci-002": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "text-completion-openai", @@ -24167,7 +24334,7 @@ "max_output_tokens": 163840, "input_cost_per_token": 5e-07, "output_cost_per_token": 2.15e-06, - "cache_read_input_token_cost": 4e-07, + "cache_read_input_token_cost": 3.5e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, @@ -24241,7 +24408,7 @@ "max_output_tokens": 163840, "input_cost_per_token": 2.5e-07, "output_cost_per_token": 9.5e-07, - "cache_read_input_token_cost": 2.16e-07, + "cache_read_input_token_cost": 1.3e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, @@ -24614,6 +24781,7 @@ "cache_creation_input_token_cost": 0.0, "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", @@ -24666,6 +24834,7 @@ "deepseek/deepseek-reasoner": { "cache_read_input_token_cost": 2.8e-08, "deprecation_date": "2026-07-24", + "deprecation_source": "https://api-docs.deepseek.com/updates", "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", @@ -24719,6 +24888,7 @@ }, "deepseek.v3-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-v3-1.html", "input_cost_per_token": 5.8e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -25528,6 +25698,7 @@ "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 1.1e-06, "deprecation_date": "2026-10-15", + "successor_model": "eu.anthropic.claude-haiku-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -25645,6 +25816,7 @@ "supports_vision": true, "prompt_cache_min_tokens": 1024, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "token_count_model": { "model": "anthropic.claude-opus-4-1-20250805-v1:0", "litellm_provider": "bedrock" @@ -25679,6 +25851,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "eu.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -25722,6 +25896,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "eu.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -25755,6 +25931,7 @@ }, "eu.meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1.3e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -25767,6 +25944,7 @@ }, "eu.meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.9e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -28346,6 +28524,7 @@ "cache_read_input_token_cost": 1.4e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/accounts/fireworks/models/glm-5p3", "input_cost_per_token": 1.4e-06, "input_cost_per_token_priority": 1.75e-06, "litellm_provider": "fireworks_ai", @@ -28721,6 +28900,7 @@ "cache_read_input_token_cost": 1.4e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/glm-5p3", "input_cost_per_token": 1.4e-06, "input_cost_per_token_priority": 1.75e-06, "litellm_provider": "fireworks_ai", @@ -29104,6 +29284,8 @@ }, "ft:babbage-002": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.6e-06, "input_cost_per_token_batches": 8e-07, "litellm_provider": "text-completion-openai", @@ -29116,6 +29298,8 @@ }, "ft:davinci-002": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.2e-05, "input_cost_per_token_batches": 6e-06, "litellm_provider": "text-completion-openai", @@ -29128,6 +29312,8 @@ }, "ft:gpt-3.5-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "openai", @@ -29135,6 +29321,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_system_messages": true, @@ -29142,45 +29329,57 @@ }, "ft:gpt-3.5-turbo-0125": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-3.5-turbo-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "ft:gpt-3.5-turbo", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 4096, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-3.5-turbo-1106": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, + "fedramp_uplift_multiplier": 1.1, "supports_system_messages": true, "supports_tool_choice": true }, "ft:gpt-4-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing", "supports_function_calling": true, "supports_system_messages": true, @@ -29190,6 +29389,8 @@ "cache_read_input_token_cost": 1.875e-06, "cache_read_input_token_cost_batches": 9e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3.75e-06, "input_cost_per_token_batches": 2.225e-06, "litellm_provider": "openai", @@ -29197,6 +29398,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.25e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29211,12 +29413,15 @@ "ft:gpt-4o-2024-11-20": { "cache_creation_input_token_cost": 1.875e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -29229,6 +29434,8 @@ "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "litellm_provider": "openai", @@ -29236,6 +29443,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29250,6 +29458,8 @@ "cache_read_input_token_cost": 7.5e-07, "cache_read_input_token_cost_batches": 5e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 3e-06, "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "openai", @@ -29257,6 +29467,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29270,6 +29481,8 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_batches": 1e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 8e-07, "input_cost_per_token_batches": 4e-07, "litellm_provider": "openai", @@ -29277,6 +29490,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 3.2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.6e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29290,6 +29504,8 @@ "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_batches": 2.5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_batches": 1e-07, "litellm_provider": "openai", @@ -29297,6 +29513,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29310,6 +29527,8 @@ "cache_read_input_token_cost": 1e-06, "cache_read_input_token_cost_batches": 5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 4e-06, "input_cost_per_token_batches": 2e-06, "litellm_provider": "openai", @@ -29317,6 +29536,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 1.6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -29328,19 +29548,38 @@ }, "gemini-2.5-flash": { "cache_read_input_audio_token_cost": 1e-07, - "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_flex": 3e-08, "cache_read_input_token_cost_priority": 5.4e-08, + "deprecation_date": "2026-10-20", + "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 3e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, + "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, "mode": "chat", "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, + "output_cost_per_token_batches": 1.25e-06, + "output_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_priority": 4.5e-06, + "search_context_cost_per_query": {"search_context_size_low":0.035,"search_context_size_medium":0.035,"search_context_size_high":0.035}, + "search_free_requests": {"count":1500,"period":"day","shared_group":"gemini-2-flash"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.8-flash", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29357,6 +29596,7 @@ ], "supports_audio_output": false, "supports_function_calling": true, + "supports_image_size": false, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29366,26 +29606,12 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - }, - "search_free_requests": {"count": 1500, "period": "day", "shared_group": "gemini-2-flash"}, - "google_maps_grounding_cost_per_query": 0.025, - "input_cost_per_audio_token_batches": 5e-07, - "input_cost_per_token_batches": 1.5e-07, - "input_cost_per_token_flex": 1.5e-07, - "input_cost_per_token_priority": 5.4e-07, - "input_cost_per_audio_token_priority": 1.8e-06, - "output_cost_per_token_batches": 1.25e-06, - "output_cost_per_token_flex": 1.25e-06, - "output_cost_per_token_priority": 4.5e-06, - "supports_image_size": false + "supports_web_search": true }, "gemini-2.5-flash-image": { "deprecation_date": "2027-03-15", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -29441,6 +29667,7 @@ "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 3.6e-07, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, @@ -29455,6 +29682,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, @@ -29509,6 +29737,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_priority": 2.16e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, @@ -29549,6 +29778,7 @@ "cache_read_input_token_cost_flex": 2.5e-08, "cache_read_input_token_cost_priority": 9e-08, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.00056, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -29561,6 +29791,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "output_cost_per_token_flex": 1.5e-06, @@ -29607,6 +29838,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", @@ -29655,6 +29887,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, @@ -29735,17 +29968,28 @@ }, "gemini-3.1-flash-lite": { "cache_read_input_audio_token_cost": 5e-08, - "deprecation_date": "2027-05-07", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_batches": 2.5e-08, + "cache_read_input_audio_token_cost_flex": 2.5e-08, + "cache_read_input_audio_token_cost_priority": 9e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-07", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 5e-07, + "input_cost_per_audio_token_above_200k_tokens": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 9e-07, + "input_cost_per_audio_token_batches": 2.5e-07, + "input_cost_per_audio_token_flex": 2.5e-07, + "input_cost_per_audio_token_priority": 9e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, - "input_cost_per_audio_token_priority": 9e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65536, @@ -29755,7 +29999,10 @@ "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, + "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.5-flash-lite", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29773,6 +30020,7 @@ "supports_audio_input": true, "supports_audio_output": false, "supports_function_calling": true, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29784,19 +30032,11 @@ "supports_video_input": true, "supports_vision": true, "supports_web_search": true, - "supports_native_streaming": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014, - "search_context_size_high": 0.014 - }, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, - "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014, - "input_cost_per_audio_token_batches": 2.5e-07 + "web_search_billing_unit": "per_query" }, "gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_batches": 1.5e-08, "cache_read_input_token_cost_flex": 1.5e-08, @@ -29892,19 +30132,38 @@ }, "gemini-2.5-flash-lite": { "cache_read_input_audio_token_cost": 3e-08, - "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_priority": 5.4e-08, "cache_read_input_token_cost": 1e-08, "cache_read_input_token_cost_flex": 1e-08, "cache_read_input_token_cost_priority": 1.8e-08, + "deprecation_date": "2026-10-20", + "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_audio_token": 3e-07, + "input_cost_per_audio_token_above_200k_tokens": 3e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-08, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-08, + "input_cost_per_audio_token_above_200k_tokens_priority": 5.4e-07, + "input_cost_per_audio_token_batches": 5e-08, + "input_cost_per_audio_token_flex": 5e-08, + "input_cost_per_audio_token_priority": 5.4e-07, "input_cost_per_token": 1e-07, + "input_cost_per_token_batches": 5e-08, + "input_cost_per_token_flex": 5e-08, + "input_cost_per_token_priority": 1.8e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, "mode": "chat", "output_cost_per_reasoning_token": 4e-07, "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "output_cost_per_token_flex": 2e-07, + "output_cost_per_token_priority": 7.2e-07, + "search_context_cost_per_query": {"search_context_size_low":0.035,"search_context_size_medium":0.035,"search_context_size_high":0.035}, + "search_free_requests": {"count":1500,"period":"day","shared_group":"gemini-2-flash"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "gemini-3.8-flash", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -29921,6 +30180,7 @@ ], "supports_audio_output": false, "supports_function_calling": true, + "supports_image_size": false, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -29930,23 +30190,7 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - }, - "search_free_requests": {"count": 1500, "period": "day", "shared_group": "gemini-2-flash"}, - "google_maps_grounding_cost_per_query": 0.025, - "input_cost_per_audio_token_batches": 5e-08, - "input_cost_per_token_batches": 5e-08, - "input_cost_per_token_flex": 5e-08, - "input_cost_per_token_priority": 1.8e-07, - "input_cost_per_audio_token_priority": 5.4e-07, - "output_cost_per_token_batches": 2e-07, - "output_cost_per_token_flex": 2e-07, - "output_cost_per_token_priority": 7.2e-07, - "supports_image_size": false + "supports_web_search": true }, "gemini-2.5-flash-lite-preview-09-2025": { "cache_read_input_token_cost": 1e-08, @@ -30042,6 +30286,8 @@ }, "gemini-live-2.5-flash-native-audio": { "deprecation_date": "2026-12-13", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", @@ -30175,6 +30421,8 @@ }, "gemini-2.5-pro": { "deprecation_date": "2026-10-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, "cache_creation_input_token_cost_above_200k_tokens": 2.5e-07, @@ -30223,11 +30471,13 @@ "search_free_requests": {"count": 10000, "period": "day", "shared_group": "gemini-2.5-pro"}, "google_maps_grounding_cost_per_query": 0.025, "input_cost_per_token_above_200k_tokens_batches": 1.25e-06, + "input_cost_per_token_above_200k_tokens_flex": 1.25e-06, "input_cost_per_token_above_200k_tokens_priority": 4.5e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, "input_cost_per_token_priority": 2.25e-06, "output_cost_per_token_above_200k_tokens_batches": 7.5e-06, + "output_cost_per_token_above_200k_tokens_flex": 7.5e-06, "output_cost_per_token_above_200k_tokens_priority": 2.7e-05, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, @@ -30243,6 +30493,7 @@ "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "input_cost_per_token_above_200k_tokens_batches": 2e-06, + "input_cost_per_token_above_200k_tokens_flex": 2e-06, "input_cost_per_token_above_200k_tokens_priority": 7.2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -30255,6 +30506,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_batches": 9e-06, + "output_cost_per_token_above_200k_tokens_flex": 9e-06, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, @@ -30402,11 +30654,17 @@ }, "vertex_ai/gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_priority": 9e-08, "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 1e-06, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -30421,7 +30679,7 @@ "output_cost_per_token_flex": 1.5e-06, "output_cost_per_token_priority": 5.4e-06, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -30455,6 +30713,8 @@ "vertex_ai/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, "deprecation_date": "2027-05-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "input_cost_per_token": 1.5e-06, @@ -30566,6 +30826,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -30628,6 +30890,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2027-01-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -30659,7 +30923,7 @@ "prompt_cache_min_tokens": 4096.0, "regional_endpoint_uplift_multiplier": 1.1, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -30789,6 +31053,7 @@ "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "input_cost_per_token_above_200k_tokens_batches": 2e-06, + "input_cost_per_token_above_200k_tokens_flex": 2e-06, "input_cost_per_token_above_200k_tokens_priority": 7.2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -30801,6 +31066,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_batches": 9e-06, + "output_cost_per_token_above_200k_tokens_flex": 9e-06, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, @@ -31039,6 +31305,8 @@ }, "gemini-embedding-001": { "deprecation_date": "2028-05-20", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-embedding-2", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 1.2e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -31063,6 +31331,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_embedding_image_input": true, "uses_embed_content": true }, "gemini-embedding-2": { @@ -31080,6 +31349,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31098,6 +31368,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31116,6 +31387,7 @@ "output_cost_per_token": 0, "output_vector_size": 3072, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supports_embedding_image_input": true, "supports_multimodal": true, "uses_embed_content": true }, @@ -31133,6 +31405,8 @@ }, "gemini/gemini-embedding-001": { "deprecation_date": "2028-05-14", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-embedding-2", "input_cost_per_token": 1.5e-07, "litellm_provider": "gemini", "max_input_tokens": 2048, @@ -31161,12 +31435,15 @@ "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supports_audio_input": true, + "supports_embedding_image_input": true, "supports_multimodal": true, "supports_vision": true, "tpm": 10000000 }, "gemini/gemini-3-pro-image-preview": { "deprecation_date": "2026-06-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3-pro-image", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -31215,6 +31492,8 @@ }, "gemini/gemini-3.1-flash-image-preview": { "deprecation_date": "2026-06-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-nano-banana-2.1", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -31263,6 +31542,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-05-25", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.1-flash-lite", "input_cost_per_audio_token": 5e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, @@ -31320,6 +31601,8 @@ "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini/gemini-embedding-2-preview": { + "deprecation_date": "2026-08-10", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", "input_cost_per_audio_token": 6.5e-06, "input_cost_per_audio_token_batches": 3.25e-06, "input_cost_per_image_token": 4.5e-07, @@ -31335,7 +31618,9 @@ "output_vector_size": 3072, "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", + "successor_model": "gemini/gemini-embedding-2", "supports_audio_input": true, + "supports_embedding_image_input": true, "supports_multimodal": true, "supports_vision": true, "tpm": 10000000 @@ -31422,6 +31707,8 @@ "cache_read_input_token_cost_flex": 3e-08, "cache_read_input_token_cost_priority": 5.4e-08, "deprecation_date": "2026-12-01", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.5-flash-lite", "input_cost_per_audio_token": 1e-06, "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_token": 3e-07, @@ -31481,6 +31768,8 @@ "gemini/gemini-2.5-flash-image": { "cache_read_input_token_cost": 3e-08, "deprecation_date": "2027-03-15", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -31708,6 +31997,8 @@ }, "gemini/deep-research-pro-preview-12-2025": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/deep-research-preview-04-2026", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -31925,6 +32216,8 @@ }, "gemini/gemini-2.5-flash-preview-tts": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -32006,11 +32299,15 @@ "supports_url_context": true, "cache_read_input_token_cost_above_200k_tokens_batches": 2.5e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash", "input_cost_per_token_above_200k_tokens_batches": 1.25e-06, "output_cost_per_token_above_200k_tokens_batches": 7.5e-06 }, "gemini/gemini-2.5-computer-use-preview-10-2025": { "deprecation_date": "2026-07-28", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash", "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, "litellm_provider": "gemini", @@ -32049,6 +32346,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-05-07", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.5-flash-lite", "input_cost_per_audio_token": 5e-07, "input_cost_per_audio_token_flex": 2.5e-07, "input_cost_per_token": 2.5e-07, @@ -32514,7 +32813,9 @@ "supports_video_input": true, "supports_vision": true, "tpm": 800000, - "deprecation_date": "2026-10-22" + "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash" }, "gemini/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -32650,11 +32951,17 @@ }, "gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, + "cache_read_input_audio_token_cost_priority": 1.8e-07, "cache_read_input_token_cost": 5e-08, "cache_read_input_token_cost_priority": 9e-08, "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_above_200k_tokens": 1e-06, + "input_cost_per_audio_token_above_200k_tokens_batches": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 1.8e-06, "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_audio_token_flex": 5e-07, "input_cost_per_audio_token_priority": 1.8e-06, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -32670,7 +32977,7 @@ "output_cost_per_token_flex": 1.5e-06, "output_cost_per_token_priority": 5.4e-06, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -32706,13 +33013,13 @@ "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, "litellm_provider": "vertex_ai-language-models", - "max_input_tokens": 1048576, - "max_output_tokens": 65535, + "max_input_tokens": 131072, + "max_output_tokens": 57920, "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "output_cost_per_video_token": 1.75e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/omni-flash-preview", "supported_endpoints": [ "/v1/chat/completions" ], @@ -32730,11 +33037,14 @@ "supports_reasoning": true, "supports_system_messages": true, "supports_video_input": true, - "supports_vision": true + "supports_vision": true, + "deprecation_date": "2026-11-06" }, "gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, "deprecation_date": "2027-05-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "cache_read_input_token_cost": 1.5e-07, "cache_read_input_token_cost_batches": 7.5e-08, "input_cost_per_audio_token": 1.5e-06, @@ -32846,6 +33156,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2026-11-19", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -32908,6 +33220,8 @@ "output_cost_per_token_priority": 6.75e-06, "cache_read_input_token_cost_priority": 1.35e-07, "deprecation_date": "2027-01-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "gemini-3.8-flash", "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, @@ -32938,7 +33252,7 @@ "output_cost_per_token_priority": 6.75e-06, "prompt_cache_min_tokens": 4096.0, "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", @@ -33026,6 +33340,8 @@ "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_audio_token": 7e-07, "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 5e-07, @@ -33278,6 +33594,8 @@ }, "gemini/veo-3.1-fast-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33294,6 +33612,8 @@ }, "gemini/veo-3.1-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33309,6 +33629,8 @@ }, "gemini/veo-3.1-lite-generate-preview": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-omni-1.1-flash", "litellm_provider": "gemini", "max_input_tokens": 1024, "mode": "video_generation", @@ -33644,6 +33966,7 @@ }, "github_copilot/gpt-4.1": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "litellm_provider": "github_copilot", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -33656,6 +33979,7 @@ }, "github_copilot/gpt-4.1-2025-04-14": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "litellm_provider": "github_copilot", "max_input_tokens": 128000, "max_output_tokens": 16384, @@ -33757,6 +34081,7 @@ "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, "deprecation_date": "2026-10-19", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "input_cost_per_token": 2.5e-06, "input_cost_per_token_above_272k_tokens": 5e-06, "litellm_provider": "github_copilot", @@ -33799,6 +34124,7 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "deprecation_date": "2026-10-19", + "deprecation_source": "https://raw.githubusercontent.com/github/docs/main/data/tables/copilot/model-deprecation-history.yml", "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, "litellm_provider": "github_copilot", @@ -34652,6 +34978,7 @@ }, "google.gemma-3-12b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 9e-08, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_flex": 5e-08, @@ -34673,6 +35000,7 @@ }, "google.gemma-3-27b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.3e-07, "input_cost_per_token_batches": 1.2e-07, "input_cost_per_token_flex": 1.2e-07, @@ -34694,6 +35022,7 @@ }, "google.gemma-3-4b-it": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4e-08, "input_cost_per_token_batches": 2e-08, "input_cost_per_token_flex": 2e-08, @@ -34728,6 +35057,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "global.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -34763,6 +35094,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "global.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -34851,12 +35184,15 @@ }, "gpt-3.5-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 5e-07, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -34865,6 +35201,8 @@ }, "gpt-3.5-turbo-0125": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "openai", @@ -34872,6 +35210,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 7.5e-07, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34882,6 +35221,8 @@ }, "gpt-3.5-turbo-1106": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -34889,6 +35230,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34899,18 +35241,22 @@ }, "gpt-3.5-turbo-16k": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_token": 3e-06, "litellm_provider": "openai", "max_input_tokens": 16385, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-06, + "fedramp_uplift_multiplier": 1.1, "supports_prompt_caching": true, "supports_system_messages": true, "supports_tool_choice": true }, "gpt-3.5-turbo-instruct": { "deprecation_date": "2026-09-28", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.5e-06, "litellm_provider": "text-completion-openai", "max_input_tokens": 8192, @@ -34929,12 +35275,15 @@ }, "gpt-4": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "litellm_provider": "openai", "max_input_tokens": 8192, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_prompt_caching": true, "supports_system_messages": true, @@ -34942,6 +35291,8 @@ }, "gpt-4-0613": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 3e-05, "input_cost_per_token_batches": 1.5e-05, "litellm_provider": "openai", @@ -34949,6 +35300,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -34958,12 +35310,15 @@ }, "gpt-4-1106-preview": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -34972,12 +35327,15 @@ }, "gpt-4-turbo": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -34988,6 +35346,8 @@ }, "gpt-4-turbo-2024-04-09": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1e-05, "input_cost_per_token_batches": 5e-06, "litellm_provider": "openai", @@ -34995,6 +35355,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1.5e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -35009,6 +35370,8 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_priority": 3.5e-06, @@ -35017,6 +35380,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_priority": 1.4e-05, "search_context_cost_per_query": { @@ -35052,6 +35416,8 @@ "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-06, "input_cost_per_token_priority": 3.5e-06, "input_cost_per_token_batches": 1e-06, @@ -35060,6 +35426,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.4e-05, "output_cost_per_token_batches": 4e-06, "search_context_cost_per_query": { @@ -35095,6 +35462,8 @@ "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, "input_cost_per_token_priority": 7e-07, @@ -35103,6 +35472,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8e-07, "output_cost_per_token_priority": 2.8e-06, "search_context_cost_per_query": { @@ -35138,6 +35508,8 @@ "cache_read_input_token_cost": 1e-07, "cache_read_input_token_cost_priority": 1.75e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 4e-07, "input_cost_per_token_priority": 7e-07, "input_cost_per_token_batches": 2e-07, @@ -35146,6 +35518,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 1.6e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-06, "output_cost_per_token_batches": 8e-07, "search_context_cost_per_query": { @@ -35181,6 +35554,8 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_priority": 2e-07, @@ -35189,6 +35564,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_priority": 8e-07, "source": "https://developers.openai.com/api/docs/pricing", @@ -35218,6 +35594,8 @@ "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_priority": 5e-08, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 1e-07, "input_cost_per_token_priority": 2e-07, "input_cost_per_token_batches": 5e-08, @@ -35226,6 +35604,7 @@ "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 8e-07, "output_cost_per_token_batches": 2e-07, "source": "https://developers.openai.com/api/docs/pricing", @@ -35255,6 +35634,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, "input_cost_per_token_priority": 4.25e-06, @@ -35263,6 +35644,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_priority": 1.7e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -35277,6 +35659,8 @@ }, "gpt-4o-2024-05-13": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "input_cost_per_token_priority": 8.75e-06, @@ -35285,6 +35669,7 @@ "max_output_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 7.5e-06, "output_cost_per_token_priority": 2.625e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -35300,6 +35685,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_priority": 4.25e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35308,6 +35695,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.7e-05, "output_cost_per_token_batches": 5e-06, "source": "https://developers.openai.com/api/docs/pricing", @@ -35324,6 +35712,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_priority": 2.125e-06, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2.5e-06, "input_cost_per_token_priority": 4.25e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35332,6 +35722,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1.7e-05, "output_cost_per_token_batches": 5e-06, "source": "https://developers.openai.com/api/docs/pricing", @@ -35346,6 +35737,8 @@ }, "gpt-4o-audio-preview-2024-12-17": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35363,6 +35756,8 @@ }, "gpt-4o-audio-preview-2025-06-03": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35380,6 +35775,8 @@ }, "gpt-audio": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35450,6 +35847,8 @@ }, "gpt-audio-2025-08-28": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -35487,6 +35886,8 @@ }, "gpt-audio-mini": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35524,6 +35925,8 @@ }, "gpt-audio-mini-2025-10-06": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35594,12 +35997,16 @@ "supports_tool_choice": true, "supports_vision": false, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-audio-1.5", "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-4o-mini": { "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.25e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "input_cost_per_token_priority": 2.5e-07, @@ -35608,6 +36015,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "output_cost_per_token_priority": 1e-06, "supports_function_calling": true, @@ -35629,6 +36037,8 @@ "cache_read_input_token_cost": 7.5e-08, "cache_read_input_token_cost_priority": 1.25e-07, "deprecation_date": "2027-01-14", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 1.5e-07, "input_cost_per_token_priority": 2.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -35637,6 +36047,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 1e-06, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { @@ -35656,6 +36067,7 @@ }, "gpt-4o-mini-audio-preview-2024-12-17": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -35675,6 +36087,7 @@ "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -35699,6 +36112,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35717,6 +36131,8 @@ }, "gpt-4o-mini-search-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "cache_read_input_token_cost": 7.5e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -35725,6 +36141,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 6e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-07, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35754,11 +36171,15 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 5e-05, "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-4o-mini-tts": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -35779,6 +36200,8 @@ }, "gpt-4o-mini-tts-2025-03-20": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -35806,6 +36229,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35824,6 +36248,8 @@ }, "gpt-4o-search-preview-2025-03-11": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "input_cost_per_token_batches": 1.25e-06, @@ -35832,6 +36258,7 @@ "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "search_context_cost_per_query": { "search_context_size_high": 0.025, @@ -35861,6 +36288,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_second": 0.0001, "source": "https://developers.openai.com/api/docs/pricing" }, @@ -35869,6 +36298,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -35890,6 +36321,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_token": 5e-06, "litellm_provider": "openai", "mode": "image_generation", @@ -36013,6 +36446,8 @@ }, "low/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36025,6 +36460,8 @@ }, "low/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36037,6 +36474,8 @@ }, "low/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36049,6 +36488,8 @@ }, "medium/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.034, "litellm_provider": "openai", "mode": "image_generation", @@ -36061,6 +36502,8 @@ }, "medium/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.05, "litellm_provider": "openai", "mode": "image_generation", @@ -36073,6 +36516,8 @@ }, "medium/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.05, "litellm_provider": "openai", "mode": "image_generation", @@ -36085,6 +36530,8 @@ }, "high/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.133, "litellm_provider": "openai", "mode": "image_generation", @@ -36097,6 +36544,8 @@ }, "high/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.2, "litellm_provider": "openai", "mode": "image_generation", @@ -36109,6 +36558,8 @@ }, "high/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.2, "litellm_provider": "openai", "mode": "image_generation", @@ -36121,6 +36572,8 @@ }, "standard/1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36133,6 +36586,8 @@ }, "standard/1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36145,6 +36600,8 @@ }, "standard/1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36157,6 +36614,8 @@ }, "1024-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.009, "litellm_provider": "openai", "mode": "image_generation", @@ -36169,6 +36628,8 @@ }, "1024-x-1536/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36181,6 +36642,8 @@ }, "1536-x-1024/gpt-image-1.5": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.013, "litellm_provider": "openai", "mode": "image_generation", @@ -36201,7 +36664,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "low/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36213,7 +36678,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "low/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36225,7 +36692,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.034, @@ -36237,7 +36706,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -36249,7 +36720,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "medium/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -36261,7 +36734,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.133, @@ -36273,7 +36748,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -36285,7 +36762,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "high/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -36297,7 +36776,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -36309,7 +36790,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36321,7 +36804,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "standard/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36333,7 +36818,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -36345,7 +36832,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36357,7 +36846,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -36369,7 +36860,9 @@ ], "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-12-01" + "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst" }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, @@ -36377,6 +36870,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, @@ -36386,6 +36881,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, "output_cost_per_token_priority": 2e-05, @@ -36432,12 +36928,15 @@ "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5-codex", "supported_endpoints": [ "/v1/responses" @@ -36472,6 +36971,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2e-05, "reasoning_contexts": [ "auto", @@ -36517,17 +37017,22 @@ "source": "https://developers.openai.com/api/docs/pricing", "supports_xhigh_reasoning_effort": false, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol" }, "gpt-5.1-codex-mini": { "cache_read_input_token_cost": 2.5e-08, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex-mini", "supported_endpoints": [ "/v1/responses" @@ -36552,12 +37057,15 @@ "gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex-max", "supported_endpoints": [ "/v1/responses" @@ -36583,12 +37091,15 @@ "gpt-5.1-codex": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 400000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-codex", "supported_endpoints": [ "/v1/responses" @@ -36614,12 +37125,15 @@ "gpt-5.1-chat-latest": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.1-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -36648,6 +37162,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "openai", @@ -36655,6 +37171,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2e-05, "reasoning_contexts": [ "auto", @@ -36713,6 +37230,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -36764,12 +37282,15 @@ "gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.2-codex", "supported_endpoints": [ "/v1/responses" @@ -36795,12 +37316,15 @@ "gpt-5.2-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-08-10", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.2-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -36835,6 +37359,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -36891,6 +37416,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8.4e-05, "reasoning_contexts": [ "auto", @@ -36939,6 +37465,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 8.4e-05, "reasoning_contexts": [ "auto", @@ -37045,6 +37572,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37138,6 +37666,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37239,6 +37768,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37331,6 +37861,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37421,6 +37952,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37513,6 +38045,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37604,6 +38137,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37648,10 +38182,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/chat/completions", @@ -37686,10 +38221,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/chat/completions", @@ -37724,10 +38260,11 @@ "input_cost_per_token": 1.25e-05, "input_cost_per_token_above_272k_tokens": 2.5e-05, "litellm_provider": "openai", - "max_input_tokens": 400000, + "max_input_tokens": 272000, "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 7.5e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 0.0001125, "supported_endpoints": [ "/v1/responses" @@ -37765,6 +38302,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 3e-05, "supported_endpoints": [ "/v1/chat/completions", @@ -37803,6 +38341,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_above_272k_tokens": 3e-05, "supported_endpoints": [ "/v1/responses" @@ -37836,6 +38375,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-05, + "fedramp_uplift_multiplier": 1.1, "reasoning_effort_levels": [ "medium" ], @@ -37904,6 +38444,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -37974,6 +38515,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38036,6 +38578,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38095,6 +38638,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38162,6 +38706,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38227,6 +38772,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38284,6 +38830,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38344,6 +38891,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38406,6 +38954,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38468,6 +39017,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38527,6 +39077,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38560,13 +39111,17 @@ "source": "https://developers.openai.com/api/docs/pricing", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna" }, "gpt-5.4-nano-2026-03-17": { "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_batches": 1e-08, "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-luna", "input_cost_per_token": 2e-07, "input_cost_per_token_flex": 1e-07, "input_cost_per_token_batches": 1e-07, @@ -38587,6 +39142,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -38623,6 +39179,8 @@ }, "gpt-5-pro": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -38630,6 +39188,7 @@ "max_output_tokens": 272000, "mode": "responses", "output_cost_per_token": 0.00012, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-05, "reasoning_contexts": [ "auto", @@ -38672,6 +39231,8 @@ }, "gpt-5-pro-2025-10-06": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -38679,6 +39240,7 @@ "max_output_tokens": 272000, "mode": "responses", "output_cost_per_token": 0.00012, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 6e-05, "reasoning_contexts": [ "auto", @@ -38725,6 +39287,8 @@ "cache_read_input_token_cost_flex": 6.25e-08, "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "input_cost_per_token_batches": 6.25e-07, "input_cost_per_token_flex": 6.25e-07, @@ -38734,6 +39298,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 5e-06, "output_cost_per_token_flex": 5e-06, "output_cost_per_token_priority": 2e-05, @@ -38780,12 +39345,15 @@ "gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.2-chat-latest", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -38815,12 +39383,15 @@ "gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -38853,6 +39424,7 @@ "max_output_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_priority": 2.8e-05, "reasoning_contexts": [ "auto", @@ -38892,17 +39464,22 @@ "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false, - "deprecation_date": "2027-04-01" + "deprecation_date": "2027-04-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-6-sol" }, "gpt-5.3-chat-latest": { "cache_read_input_token_cost": 1.75e-07, "deprecation_date": "2026-08-10", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.75e-06, "litellm_provider": "openai", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/gpt-5.3-chat-latest", "supported_endpoints": [ "/v1/chat/completions", @@ -38931,6 +39508,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, @@ -38940,6 +39519,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1e-06, "output_cost_per_token_flex": 1e-06, "output_cost_per_token_priority": 3.6e-06, @@ -38989,6 +39569,8 @@ "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, @@ -38998,6 +39580,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 1e-06, "output_cost_per_token_flex": 1e-06, "output_cost_per_token_priority": 3.6e-06, @@ -39046,6 +39629,8 @@ "cache_read_input_token_cost_batches": 2.5e-09, "cache_read_input_token_cost_flex": 2.5e-09, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "input_cost_per_token_flex": 2.5e-08, @@ -39054,6 +39639,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_flex": 2e-07, "reasoning_contexts": [ @@ -39101,6 +39687,8 @@ "cache_read_input_token_cost_batches": 2.5e-09, "cache_read_input_token_cost_flex": 2.5e-09, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-luna", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 2.5e-08, "input_cost_per_token_flex": 2.5e-08, @@ -39109,6 +39697,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2e-07, "output_cost_per_token_flex": 2e-07, "reasoning_contexts": [ @@ -39156,6 +39745,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 1e-05, "input_cost_per_image_token_batches": 5e-06, "input_cost_per_token": 5e-06, @@ -39174,6 +39765,8 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_batches": 1e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 2.5e-06, "input_cost_per_image_token_batches": 1.25e-06, "input_cost_per_token": 2e-06, @@ -39193,6 +39786,8 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -39375,6 +39970,8 @@ "cache_read_input_image_token_cost": 8e-08, "cache_read_input_token_cost": 6e-08, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_audio_token": 1e-05, "input_cost_per_image_token": 8e-07, "input_cost_per_token": 6e-07, @@ -39410,6 +40007,8 @@ "cache_read_input_image_token_cost": 5e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -39733,16 +40332,6 @@ "supports_response_schema": true, "supports_vision": true }, - "groq/llama-guard-3-8b": { - "deprecation_date": "2025-06-06", - "input_cost_per_token": 2e-07, - "litellm_provider": "groq", - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 2e-07, - "source": "https://console.groq.com/docs/model/llama-guard-3-8b" - }, "groq/meta-llama/llama-prompt-guard-2-22m": { "input_cost_per_token": 3e-08, "litellm_provider": "groq", @@ -39889,6 +40478,8 @@ }, "high/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.167, "input_cost_per_pixel": 1.59263611e-07, "litellm_provider": "openai", @@ -39900,6 +40491,8 @@ }, "high/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.25, "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "openai", @@ -39911,6 +40504,8 @@ }, "high/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.25, "input_cost_per_pixel": 1.58945719e-07, "litellm_provider": "openai", @@ -40288,6 +40883,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "jp.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -41450,6 +42047,8 @@ }, "low/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.011, "input_cost_per_pixel": 1.0490417e-08, "litellm_provider": "openai", @@ -41461,6 +42060,8 @@ }, "low/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.016, "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "openai", @@ -41472,6 +42073,8 @@ }, "low/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.016, "input_cost_per_pixel": 1.0172526e-08, "litellm_provider": "openai", @@ -41495,6 +42098,8 @@ }, "medium/1024-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.042, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41506,6 +42111,8 @@ }, "medium/1024-x-1536/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.063, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41517,6 +42124,8 @@ }, "medium/1536-x-1024/gpt-image-1": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.063, "input_cost_per_pixel": 4.0054321e-08, "litellm_provider": "openai", @@ -41528,6 +42137,8 @@ }, "low/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.005, "litellm_provider": "openai", "mode": "image_generation", @@ -41537,6 +42148,8 @@ }, "low/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.006, "litellm_provider": "openai", "mode": "image_generation", @@ -41546,6 +42159,8 @@ }, "low/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.006, "litellm_provider": "openai", "mode": "image_generation", @@ -41555,6 +42170,8 @@ }, "medium/1024-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.011, "litellm_provider": "openai", "mode": "image_generation", @@ -41564,6 +42181,8 @@ }, "medium/1024-x-1536/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.015, "litellm_provider": "openai", "mode": "image_generation", @@ -41573,6 +42192,8 @@ }, "medium/1536-x-1024/gpt-image-1-mini": { "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image": 0.015, "litellm_provider": "openai", "mode": "image_generation", @@ -41620,6 +42241,7 @@ }, "meta.llama3-1-405b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.4e-06, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41632,6 +42254,7 @@ }, "meta.llama3-1-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock", @@ -41646,6 +42269,7 @@ }, "meta.llama3-1-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.2e-07, "input_cost_per_token_batches": 1.1e-07, "litellm_provider": "bedrock", @@ -41660,6 +42284,7 @@ }, "meta.llama3-2-11b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41673,6 +42298,7 @@ }, "meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41685,6 +42311,7 @@ }, "meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41697,6 +42324,7 @@ }, "meta.llama3-2-90b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -41710,6 +42338,7 @@ }, "meta.llama3-3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock_converse", @@ -41724,6 +42353,7 @@ }, "meta.llama3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-70b-instruct.md", "input_cost_per_token": 2.65e-06, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -41734,6 +42364,7 @@ }, "meta.llama3-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-8b-instruct.md", "input_cost_per_token": 3e-07, "litellm_provider": "bedrock", "max_input_tokens": 8192, @@ -41744,6 +42375,7 @@ }, "meta.llama4-maverick-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-maverick-17b-instruct.md", "input_cost_per_token": 2.4e-07, "input_cost_per_token_batches": 1.2e-07, "litellm_provider": "bedrock_converse", @@ -41766,6 +42398,7 @@ }, "meta.llama4-scout-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-scout-17b-instruct.md", "input_cost_per_token": 1.7e-07, "input_cost_per_token_batches": 8.5e-08, "litellm_provider": "bedrock_converse", @@ -42108,6 +42741,7 @@ }, "minimax.minimax-m2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -42130,6 +42764,7 @@ }, "minimax.minimax-m2.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, @@ -42350,6 +42985,7 @@ }, "mistral.devstral-2-123b": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, "input_cost_per_token_flex": 2e-07, @@ -42456,6 +43092,7 @@ }, "mistral.mistral-7b-instruct-v0:2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mistral-7b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -42521,6 +43158,7 @@ }, "mistral.mixtral-8x7b-instruct-v0:1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-mistral-ai-mixtral-8x7b-instruct.md", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 32000, @@ -42532,6 +43170,7 @@ }, "mistral.voxtral-mini-3b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4e-08, "input_cost_per_token_batches": 2e-08, "input_cost_per_token_flex": 2e-08, @@ -42551,6 +43190,7 @@ }, "mistral.voxtral-small-24b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_flex": 5e-08, @@ -42570,6 +43210,7 @@ }, "bedrock/ap-southeast-2/mistral.voxtral-mini-3b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 4.12e-08, "input_cost_per_token_batches": 2.06e-08, "input_cost_per_token_flex": 2.06e-08, @@ -42589,6 +43230,7 @@ }, "bedrock/ap-southeast-2/mistral.voxtral-small-24b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.03e-07, "input_cost_per_token_batches": 5.15e-08, "input_cost_per_token_flex": 5.15e-08, @@ -42608,6 +43250,7 @@ }, "bedrock/ap-northeast-1/mistral.voxtral-mini-3b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 5e-08, "input_cost_per_token_batches": 3e-08, "input_cost_per_token_flex": 3e-08, @@ -42627,6 +43270,7 @@ }, "bedrock/ap-northeast-1/mistral.voxtral-small-24b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.2e-07, "input_cost_per_token_batches": 6e-08, "input_cost_per_token_flex": 6e-08, @@ -42646,6 +43290,7 @@ }, "bedrock/eu-west-2/mistral.voxtral-mini-3b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-08, "input_cost_per_token_batches": 3e-08, "input_cost_per_token_flex": 3e-08, @@ -42665,6 +43310,7 @@ }, "bedrock/eu-west-2/mistral.voxtral-small-24b-2507": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.6e-07, "input_cost_per_token_batches": 8e-08, "input_cost_per_token_flex": 8e-08, @@ -43057,6 +43703,7 @@ 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+ "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 6e-08 }, "nebius/moonshotai/Kimi-K2.6": { "max_input_tokens": 262144, @@ -44567,7 +45224,8 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_vision": true, - "source": "https://tokenfactory.nebius.com/models/catalog/image2text/moonshotai%2FKimi-K2.6" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1.6e-07 }, "nebius/moonshotai/Kimi-K2.7-Code": { "max_input_tokens": 262144, @@ -44579,10 +45237,11 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_vision": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/moonshotai%2FKimi-K2.7-Code" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1.5e-07 }, "nebius/moonshotai/Kimi-K3": { - "max_input_tokens": 1024000, + "max_input_tokens": 1048576, "max_output_tokens": 1024000, "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, @@ -44591,7 +45250,8 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_vision": true, - "source": "https://tokenfactory.nebius.com/models/catalog/image2text/moonshotai%2FKimi-K3" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 3e-07 }, "nebius/NousResearch/Hermes-4-405B": { "max_input_tokens": 131072, @@ -44602,7 +45262,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/NousResearch%2FHermes-4-405B" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 2e-07 }, "nebius/NousResearch/Hermes-4-70B": { "max_input_tokens": 131072, @@ -44646,7 +45307,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": 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}, "nebius/nvidia/Nemotron-3_5-Lightning": { "max_input_tokens": 1048576, @@ -44691,7 +45355,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/nvidia%2FNemotron-3_5-Lightning" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 3e-08 }, "nebius/openai/gpt-oss-120b": { "max_input_tokens": 131072, @@ -44702,7 +45367,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/openai%2Fgpt-oss-120b" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1e-07 }, "nebius/openbmb/MiniCPM-V-4_5": { "max_input_tokens": 32000, @@ -44712,7 +45378,8 @@ "litellm_provider": "nebius", "mode": "chat", "supports_vision": true, - "source": "https://tokenfactory.nebius.com/models/catalog/image2text/openbmb%2FMiniCPM-V-4_5" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1.3e-07 }, "nebius/Qwen/Qwen3-235B-A22B-Instruct-2507": { "max_input_tokens": 262144, @@ -44722,7 +45389,8 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/Qwen%2FQwen3-235B-A22B-Instruct-2507" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 5e-08 }, "nebius/Qwen/Qwen3-30B-A3B-Instruct-2507": { "max_input_tokens": 262144, @@ -44732,7 +45400,8 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/Qwen%2FQwen3-30B-A3B-Instruct-2507" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1e-07 }, "nebius/Qwen/Qwen3-Next-80B-A3B-Thinking": { "max_input_tokens": 128000, @@ -44754,7 +45423,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/Qwen%2FQwen3.5-397B-A17B" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1.2e-07 }, "nebius/Qwen/Qwen3.8-27B": { "input_cost_per_token": 4.5e-07, @@ -44762,9 +45432,10 @@ "max_input_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/Qwen%2FQwen3.8-27B", + "source": "https://tokenfactory.nebius.com/api/public/models_info", "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 1e-07 }, "nebius/zai-org/GLM-5.1": { "max_input_tokens": 202752, @@ -44775,7 +45446,8 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/zai-org%2FGLM-5.1" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 2.6e-07 }, "nebius/zai-org/GLM-5.2": { "max_input_tokens": 1048576, @@ -44786,21 +45458,23 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/zai-org%2FGLM-5.2" + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "cache_read_input_token_cost": 1.4e-07 }, "nebius/zai-org/GLM-5.3": { "input_cost_per_token": 1.4e-06, "litellm_provider": "nebius", - "max_input_tokens": 1024000, + "max_input_tokens": 1048576, "max_output_tokens": 1024000, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/zai-org%2FGLM-5.3", + "source": "https://tokenfactory.nebius.com/api/public/models_info", "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 2.6e-07 }, "nebius/zai-org/GLM-5.3-Flash": { - "max_input_tokens": 1024000, + "max_input_tokens": 1048576, "max_output_tokens": 1024000, "input_cost_per_token": 1.5e-07, "output_cost_per_token": 5e-07, @@ -44808,8 +45482,9 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/zai-org%2FGLM-5.3-Flash", - "supports_vision": true + "source": "https://tokenfactory.nebius.com/api/public/models_info", + "supports_vision": true, + "cache_read_input_token_cost": 3e-08 }, "nebius/BAAI/bge-en-icl": { "max_input_tokens": 32768, @@ -44845,6 +45520,7 @@ }, "nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2e-07, "input_cost_per_token_batches": 1e-07, "input_cost_per_token_flex": 1e-07, @@ -44866,6 +45542,7 @@ }, "nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-08, "input_cost_per_token_batches": 3e-08, "input_cost_per_token_flex": 3e-08, @@ -44932,6 +45609,7 @@ "o1": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-10-23", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -44939,6 +45617,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -44954,6 +45633,8 @@ "o1-2024-12-17": { "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "openai", @@ -44961,6 +45642,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -44975,6 +45657,8 @@ }, "o1-pro": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -44982,6 +45666,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 0.0006, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 0.0003, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -45008,6 +45693,8 @@ }, "o1-pro-2025-03-19": { "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 0.00015, "input_cost_per_token_batches": 7.5e-05, "litellm_provider": "openai", @@ -45015,6 +45702,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 0.0006, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 0.0003, "source": "https://developers.openai.com/api/docs/pricing", "supported_endpoints": [ @@ -45044,6 +45732,7 @@ "cache_read_input_token_cost_flex": 2.5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2026-12-11", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -45053,6 +45742,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_flex": 4e-06, "output_cost_per_token_priority": 1.4e-05, @@ -45100,6 +45790,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o3-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45121,10 +45812,14 @@ "supports_tool_choice": false, "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-07-23" + "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol" }, "o3-deep-research-2025-06-26": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "cache_read_input_token_cost": 2.5e-06, "input_cost_per_token": 1e-05, "litellm_provider": "openai", @@ -45132,6 +45827,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 4e-05, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o3-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45159,6 +45855,8 @@ "cache_read_input_token_cost_flex": 2.5e-07, "cache_read_input_token_cost_priority": 8.75e-07, "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, @@ -45168,6 +45866,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-06, "output_cost_per_token_flex": 4e-06, "output_cost_per_token_priority": 1.4e-05, @@ -45210,6 +45909,8 @@ "o3-mini": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "openai", @@ -45217,6 +45918,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -45230,6 +45932,8 @@ "o3-mini-2025-01-31": { "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "openai", @@ -45237,6 +45941,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "source": "https://developers.openai.com/api/docs/pricing", "supports_function_calling": true, @@ -45249,6 +45954,8 @@ }, "o3-pro": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "openai", @@ -45256,6 +45963,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-05, "reasoning_contexts": [ "auto", @@ -45293,6 +46001,8 @@ }, "o3-pro-2025-06-10": { "deprecation_date": "2026-12-11", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "input_cost_per_token": 2e-05, "input_cost_per_token_batches": 1e-05, "litellm_provider": "openai", @@ -45300,6 +46010,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-05, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 4e-05, "reasoning_contexts": [ "auto", @@ -45340,6 +46051,8 @@ "cache_read_input_token_cost_flex": 1.38e-07, "cache_read_input_token_cost_priority": 5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "input_cost_per_token_flex": 5.5e-07, @@ -45349,6 +46062,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "output_cost_per_token_flex": 2.2e-06, "output_cost_per_token_priority": 8e-06, @@ -45376,6 +46090,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o4-mini-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45397,10 +46112,14 @@ "supports_tool_choice": false, "supports_vision": true, "supports_pdf_input": true, - "deprecation_date": "2026-07-23" + "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol" }, "o4-mini-deep-research-2025-06-26": { "deprecation_date": "2026-07-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-sol", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "litellm_provider": "openai", @@ -45408,6 +46127,7 @@ "max_output_tokens": 100000, "mode": "responses", "output_cost_per_token": 8e-06, + "fedramp_uplift_multiplier": 1.1, "source": "https://developers.openai.com/api/docs/models/o4-mini-deep-research", "supported_endpoints": [ "/v1/batch", @@ -45435,6 +46155,8 @@ "cache_read_input_token_cost_flex": 1.38e-07, "cache_read_input_token_cost_priority": 5e-07, "deprecation_date": "2026-10-23", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-5.6-terra", "input_cost_per_token": 1.1e-06, "input_cost_per_token_batches": 5.5e-07, "input_cost_per_token_flex": 5.5e-07, @@ -45444,6 +46166,7 @@ "max_output_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "fedramp_uplift_multiplier": 1.1, "output_cost_per_token_batches": 2.2e-06, "output_cost_per_token_flex": 2.2e-06, "output_cost_per_token_priority": 8e-06, @@ -45733,6 +46456,7 @@ "mode": "embedding", "output_vector_size": 1024, "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", + "supports_embedding_image_input": true, "supports_vision": true }, "oci/cohere.command-a-reasoning-08-2025": { @@ -47248,6 +47972,7 @@ }, "qwen.qwen3-coder-480b-a35b-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-qwen-qwen3-coder-480b-a35b-instruct.html", "input_cost_per_token": 4.5e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 262144, @@ -47279,6 +48004,7 @@ }, "qwen.qwen3-coder-30b-a3b-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, "input_cost_per_token_priority": 2.625e-07, @@ -48858,6 +49584,7 @@ }, "text-embedding-004": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -48869,6 +49596,7 @@ }, "text-embedding-005": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -48939,6 +49667,7 @@ }, "text-multilingual-embedding-002": { "deprecation_date": "2027-04-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -49053,6 +49782,7 @@ }, "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo": { "deprecation_date": "2026-10-22", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.04e-06, "litellm_provider": "together_ai", "max_input_tokens": 131072, @@ -49060,6 +49790,7 @@ "mode": "chat", "output_cost_per_token": 1.04e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/meta-models/Muse-Glimmer-30B", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -49119,6 +49850,7 @@ "together_ai/MiniMaxAI/MiniMax-M3": { "cache_read_input_token_cost": 6e-08, "deprecation_date": "2026-11-06", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 3e-07, "litellm_provider": "together_ai", "max_input_tokens": 524288, @@ -49126,6 +49858,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/MiniMaxAI/MiniMax-M3.1-Flash", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49202,6 +49935,7 @@ "together_ai/Qwen/Qwen3.8-2.4T-A95B": { "cache_read_input_token_cost": 2.5e-07, "deprecation_date": "2026-11-04", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, @@ -49209,6 +49943,7 @@ "mode": "chat", "output_cost_per_token": 6e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/Qwen/Qwen3.7-Max", "supports_prompt_caching": true }, "together_ai/arize-ai/qwen-2-1.5b-instruct": { @@ -49223,6 +49958,7 @@ "together_ai/deepseek-ai/DeepSeek-V4-Flash-0731": { "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-10-22", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.4e-07, "litellm_provider": "together_ai", "max_input_tokens": 1048576, @@ -49230,6 +49966,7 @@ "mode": "chat", "output_cost_per_token": 2.8e-07, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/deepseek-ai/DeepSeek-V4.1-Flash-1017", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49314,6 +50051,7 @@ "together_ai/thinkingmachines/Inkling": { "cache_read_input_token_cost": 1.7e-07, "deprecation_date": "2026-11-06", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 524288, @@ -49321,6 +50059,7 @@ "mode": "chat", "output_cost_per_token": 4.05e-06, "source": "https://api.together.ai/v1/models", + "successor_model": "together_ai/meta-models/Muse-Spark-Open-816B-A42B", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -49378,6 +50117,8 @@ }, "tts-1": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_character": 1.5e-05, "litellm_provider": "openai", "mode": "audio_speech", @@ -49388,6 +50129,8 @@ }, "tts-1-hd": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_character": 3e-05, "litellm_provider": "openai", "mode": "audio_speech", @@ -49618,6 +50361,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2027-01-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -49656,6 +50400,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "us.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -49698,6 +50444,8 @@ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.44e-05, "cache_read_input_token_cost_above_200k_tokens": 7.2e-07, "deprecation_date": "2027-04-08", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-sonnet-4-5.md", + "successor_model": "us-gov.anthropic.claude-sonnet-5-5", "litellm_provider": "bedrock_converse", "supports_tool_search": true, "max_input_tokens": 200000, @@ -49813,7 +50561,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-west-1/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -49835,7 +50583,9 @@ "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-gov-west-1" - } + }, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05 }, "us-gov.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -49934,6 +50684,7 @@ }, "us-gov.nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -49949,6 +50700,7 @@ }, "us-gov.nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -50204,6 +50956,8 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "deprecation_date": "2026-10-14", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "us.anthropic.claude-sonnet-5", "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -50238,6 +50992,7 @@ }, "us.deepseek.r1-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-r1.html", "input_cost_per_token": 1.35e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -50290,6 +51045,7 @@ }, "us.meta.llama3-1-405b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.4e-06, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50302,6 +51058,7 @@ }, "us.meta.llama3-1-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock", @@ -50316,6 +51073,7 @@ }, "us.meta.llama3-1-8b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 2.2e-07, "input_cost_per_token_batches": 1.1e-07, "litellm_provider": "bedrock", @@ -50330,6 +51088,7 @@ }, "us.meta.llama3-2-11b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.6e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50343,6 +51102,7 @@ }, "us.meta.llama3-2-1b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-1b-instruct.md", "input_cost_per_token": 1e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50355,6 +51115,7 @@ }, "us.meta.llama3-2-3b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-3-2-3b-instruct.md", "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50367,6 +51128,7 @@ }, "us.meta.llama3-2-90b-instruct-v1:0": { "deprecation_date": "2026-07-07", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, @@ -50380,6 +51142,7 @@ }, "us.meta.llama3-3-70b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7.2e-07, "input_cost_per_token_batches": 3.6e-07, "litellm_provider": "bedrock_converse", @@ -50394,6 +51157,7 @@ }, "us.meta.llama4-maverick-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-maverick-17b-instruct.md", "input_cost_per_token": 2.4e-07, "input_cost_per_token_batches": 1.2e-07, "litellm_provider": "bedrock_converse", @@ -50416,6 +51180,7 @@ }, "us.meta.llama4-scout-17b-instruct-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-meta-llama-4-scout-17b-instruct.md", "input_cost_per_token": 1.7e-07, "input_cost_per_token_batches": 8.5e-08, "litellm_provider": "bedrock_converse", @@ -51676,6 +52441,7 @@ }, "vertex_ai/claude-haiku-4-5": { "deprecation_date": "2026-10-15", + "successor_model": "vertex_ai/claude-haiku-5-5", "cache_creation_input_token_cost": 1.25e-06, "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_creation_input_token_cost_batches": 6.25e-07, @@ -51795,32 +52561,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "vertex_ai/claude-3-opus": { - "deprecation_date": "2025-08-01", - "input_cost_per_token": 1.5e-05, - "litellm_provider": "vertex_ai-anthropic_models", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_assistant_prefill": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "vertex_ai/claude-3-opus@20240229": { - "deprecation_date": "2025-08-01", - "input_cost_per_token": 1.5e-05, - "litellm_provider": "vertex_ai-anthropic_models", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_assistant_prefill": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "vertex_ai/claude-3-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "vertex_ai-anthropic_models", @@ -51847,6 +52587,7 @@ }, "vertex_ai/claude-opus-4-5": { "deprecation_date": "2026-11-24", + "successor_model": "vertex_ai/claude-opus-5-5", "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_creation_input_token_cost_batches": 3.125e-06, @@ -52456,6 +53197,7 @@ }, "vertex_ai/claude-sonnet-4-5": { "deprecation_date": "2026-09-29", + "successor_model": "vertex_ai/claude-sonnet-5-5", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, @@ -52743,6 +53485,8 @@ }, "vertex_ai/gemini-2.5-flash-image": { "deprecation_date": "2027-03-15", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.1-flash-lite-image", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -52799,6 +53543,7 @@ "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 3.6e-07, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, @@ -52813,6 +53558,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, @@ -52848,6 +53594,7 @@ "output_cost_per_image": 0.134, "output_cost_per_image_token": 0.00012, "output_cost_per_image_token_batches": 6e-05, + "output_cost_per_image_token_priority": 0.000216, "output_cost_per_token": 1.2e-05, "output_cost_per_token_priority": 2.16e-05, "output_cost_per_token_above_200k_tokens": 1.8e-05, @@ -52869,6 +53616,7 @@ "cache_read_input_token_cost_flex": 2.5e-08, "cache_read_input_token_cost_priority": 9e-08, "deprecation_date": "2027-05-28", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "input_cost_per_image": 0.00056, "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -52881,6 +53629,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "output_cost_per_token_flex": 1.5e-06, @@ -52909,6 +53658,7 @@ "output_cost_per_image": 0.0672, "output_cost_per_image_token": 6e-05, "output_cost_per_image_token_batches": 3e-05, + "output_cost_per_image_token_priority": 0.000108, "output_cost_per_token": 3e-06, "output_cost_per_token_batches": 1.5e-06, "supports_reasoning": false, @@ -52938,6 +53688,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "output_cost_per_token_flex": 7.5e-07, @@ -53018,17 +53769,28 @@ }, "vertex_ai/gemini-3.1-flash-lite": { "cache_read_input_audio_token_cost": 5e-08, - "deprecation_date": "2027-05-07", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "cache_read_input_audio_token_cost_batches": 2.5e-08, + "cache_read_input_audio_token_cost_flex": 2.5e-08, + "cache_read_input_audio_token_cost_priority": 9e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_batches": 1.25e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, + "deprecation_date": "2027-05-07", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_audio_token": 5e-07, + "input_cost_per_audio_token_above_200k_tokens": 5e-07, + "input_cost_per_audio_token_above_200k_tokens_batches": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_flex": 2.5e-07, + "input_cost_per_audio_token_above_200k_tokens_priority": 9e-07, + "input_cost_per_audio_token_batches": 2.5e-07, + "input_cost_per_audio_token_flex": 2.5e-07, + "input_cost_per_audio_token_priority": 9e-07, "input_cost_per_token": 2.5e-07, "input_cost_per_token_batches": 1.25e-07, "input_cost_per_token_flex": 1.25e-07, "input_cost_per_token_priority": 4.5e-07, - "input_cost_per_audio_token_priority": 9e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65536, @@ -53039,7 +53801,10 @@ "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, "regional_endpoint_uplift_multiplier": 1.1, + "search_context_cost_per_query": {"search_context_size_low":0.014,"search_context_size_medium":0.014,"search_context_size_high":0.014}, + "search_free_requests": {"count":5000,"period":"month","shared_group":"gemini-3"}, "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -53057,6 +53822,7 @@ "supports_audio_input": true, "supports_audio_output": false, "supports_function_calling": true, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, @@ -53068,19 +53834,11 @@ "supports_video_input": true, "supports_vision": true, "supports_web_search": true, - "supports_native_streaming": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.014, - "search_context_size_medium": 0.014, - "search_context_size_high": 0.014 - }, - "search_free_requests": {"count": 5000, "period": "month", "shared_group": "gemini-3"}, - "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014, - "input_cost_per_audio_token_batches": 2.5e-07 + "web_search_billing_unit": "per_query" }, "vertex_ai/gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "cache_read_input_token_cost": 3e-08, "cache_read_input_token_cost_batches": 1.5e-08, "cache_read_input_token_cost_flex": 1.5e-08, @@ -53496,6 +54254,7 @@ "vertex_ai/zai-org/glm-4.7-maas": { "cache_read_input_token_cost": 6e-08, "deprecation_date": "2026-10-21", + "successor_model": "vertex_ai/zai-org/glm-5.2-maas", "input_cost_per_token": 6e-07, "litellm_provider": "vertex_ai-zai_models", "max_input_tokens": 200000, @@ -53514,6 +54273,7 @@ "vertex_ai/zai-org/glm-5-maas": { "cache_read_input_token_cost": 1e-07, "deprecation_date": "2026-10-21", + "successor_model": "vertex_ai/zai-org/glm-5.2-maas", "input_cost_per_token": 1e-06, "litellm_provider": "vertex_ai-zai_models", "max_input_tokens": 200000, @@ -53933,6 +54693,7 @@ }, "vertex_ai/veo-3.1-generate-001": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -53948,6 +54709,7 @@ }, "vertex_ai/veo-3.1-fast-generate-001": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -54150,7 +54912,7 @@ "voyage/voyage-lite-01": { "input_cost_per_token": 1e-07, "litellm_provider": "voyage", - "max_input_tokens": 4096, + "max_input_tokens": 4000, "mode": "embedding", "output_cost_per_token": 0.0 }, @@ -54166,7 +54928,8 @@ "litellm_provider": "voyage", "max_input_tokens": 32000, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "supports_embedding_image_input": true }, "wandb/openai/gpt-oss-120b": { "supports_reasoning": true, @@ -54598,6 +55361,8 @@ "/v1/audio/transcriptions" ], "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "source": "https://developers.openai.com/api/docs/pricing" }, "xai/grok-4.20-multi-agent-beta-0309": { @@ -54861,6 +55626,8 @@ }, "zai.glm-4.7": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "zai.glm-5", "input_cost_per_token": 6e-07, "input_cost_per_token_batches": 3e-07, "input_cost_per_token_flex": 3e-07, @@ -54905,6 +55672,7 @@ }, "zai.glm-4.7-flash": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 7e-08, "input_cost_per_token_batches": 3.5e-08, "input_cost_per_token_flex": 3.5e-08, @@ -55218,6 +55986,7 @@ }, "openai/sora-2": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -55232,6 +56001,7 @@ }, "openai/sora-2-pro": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -55247,6 +56017,7 @@ "openai/sora-2-pro-high-res": { "litellm_provider": "openai", "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, "source": "https://platform.openai.com/docs/api-reference/videos", @@ -58913,6 +59684,8 @@ }, "gpt-4o-mini-tts-2025-12-15": { "deprecation_date": "2027-01-06", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "input_cost_per_token": 6e-07, "litellm_provider": "openai", "mode": "audio_speech", @@ -58933,6 +59706,8 @@ }, "gpt-4o-mini-transcribe-2025-03-20": { "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-4o-mini-transcribe-2025-12-15", "input_cost_per_audio_token": 1.25e-06, "input_cost_per_second": 5e-05, "input_cost_per_token": 1.25e-06, @@ -58948,6 +59723,8 @@ }, "gpt-4o-mini-transcribe-2025-12-15": { "deprecation_date": "2027-02-26", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-live-transcribe", "input_cost_per_audio_token": 1.25e-06, "input_cost_per_second": 5e-05, "input_cost_per_token": 1.25e-06, @@ -58969,6 +59746,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -58996,6 +59774,7 @@ "max_output_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -59047,6 +59826,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "deprecation_date": "2027-01-20", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-realtime-2.1-mini", "source": "https://developers.openai.com/api/docs/pricing" }, "gpt-realtime-whisper": { @@ -59068,6 +59849,7 @@ }, "sora-2": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -59082,6 +59864,7 @@ }, "sora-2-pro": { "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "litellm_provider": "openai", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, @@ -59097,6 +59880,7 @@ "sora-2-pro-high-res": { "litellm_provider": "openai", "deprecation_date": "2026-09-24", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, "source": "https://developers.openai.com/api/docs/pricing", @@ -59113,6 +59897,8 @@ "cache_read_input_token_cost": 1.25e-06, "cache_read_input_token_cost_batches": 6.3e-07, "deprecation_date": "2026-12-01", + "deprecation_source": "https://developers.openai.com/api/docs/deprecations", + "successor_model": "gpt-image-2.5-sunburst", "input_cost_per_image_token": 8e-06, "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token": 5e-06, @@ -59128,48 +59914,6 @@ "/v1/images/edits" ] }, - "gemini-2.0-flash-exp-image-generation": { - "deprecation_date": "2025-11-14", - "input_cost_per_token": 0.0, - "litellm_provider": "gemini", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "mode": "image_generation", - "output_cost_per_image": 0.039, - "output_cost_per_token": 0.0, - "source": "https://ai.google.dev/pricing", - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_vision": true - }, - "gemini/gemini-2.0-flash-exp-image-generation": { - "deprecation_date": "2025-11-14", - "input_cost_per_token": 0.0, - "litellm_provider": "gemini", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "mode": "image_generation", - "output_cost_per_image": 0.039, - "output_cost_per_token": 0.0, - "source": "https://ai.google.dev/pricing", - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_vision": true, - "tpm": 250000, - "rpm": 10 - }, "gemini-2.5-flash-native-audio-latest": { "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, @@ -59222,6 +59966,8 @@ }, "gemini-2.5-flash-native-audio-preview-12-2025": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -59253,6 +59999,8 @@ }, "gemini-3.1-flash-live-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_image_token": 1e-06, "input_cost_per_token": 7.5e-07, @@ -59410,6 +60158,8 @@ }, "gemini/gemini-2.5-flash-native-audio-preview-12-2025": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -59444,6 +60194,8 @@ }, "gemini/gemini-3.1-flash-live-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-live", "input_cost_per_audio_token": 3e-06, "input_cost_per_image_token": 1e-06, "input_cost_per_token": 7.5e-07, @@ -59483,6 +60235,8 @@ }, "gemini/gemini-3.1-flash-tts-preview": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 1e-06, "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", @@ -59552,6 +60306,8 @@ }, "gemini-2.5-flash-preview-tts": { "deprecation_date": "2026-11-17", + "deprecation_source": "https://ai.google.dev/gemini-api/docs/deprecations", + "successor_model": "gemini/gemini-3.8-flash-tts", "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", @@ -63773,6 +64529,7 @@ }, "claude-mythos-preview": { "deprecation_date": "2026-06-09", + "deprecation_source": "https://platform.claude.com/docs/en/about-claude/model-deprecations", "supports_anthropic_compaction": true, "cache_creation_input_token_cost": 1.25e-05, "cache_creation_input_token_cost_above_1hr": 2e-05, @@ -64449,6 +65206,7 @@ "fireworks_ai/glm-5p2-fast": { "cache_read_input_token_cost": 2.1e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/glm-5p3-fast", "input_cost_per_token": 2.1e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, @@ -64718,6 +65476,7 @@ "fireworks_ai/accounts/fireworks/routers/glm-5p2-fast": { "cache_read_input_token_cost": 2.1e-07, "deprecation_date": "2026-09-25", + "successor_model": "fireworks_ai/accounts/fireworks/routers/glm-5p3-fast", "input_cost_per_token": 2.1e-06, "litellm_provider": "fireworks_ai", "max_input_tokens": 1048576, @@ -64809,7 +65568,7 @@ "supports_vision": false }, "novita/deepseek/deepseek-v4-pro-0813": { - "cache_read_input_token_cost": 1.3200000000000002e-07, + "cache_read_input_token_cost": 4.4e-08, "input_cost_per_token": 1.32e-06, "litellm_provider": "novita", "max_input_tokens": 1048576, @@ -64921,13 +65680,13 @@ "supports_vision": false }, "novita/mindai/macaron-v1-venti": { - "cache_read_input_token_cost": 3e-07, - "input_cost_per_token": 1.5e-06, + "cache_read_input_token_cost": 1.65e-07, + "input_cost_per_token": 8.25e-07, "litellm_provider": "novita", "max_input_tokens": 1048576, "max_output_tokens": 131072, "mode": "chat", - "output_cost_per_token": 4.5e-06, + "output_cost_per_token": 2.475e-06, "source": "https://novita.ai/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -65030,13 +65789,13 @@ "supports_vision": false }, "novita/mindai/macaron-v1-tall": { - "cache_read_input_token_cost": 8e-08, - "input_cost_per_token": 4.5000000000000003e-07, + "cache_read_input_token_cost": 4.4e-08, + "input_cost_per_token": 2.475e-07, "litellm_provider": "novita", "max_input_tokens": 262144, "max_output_tokens": 32768, "mode": "chat", - "output_cost_per_token": 2.6e-06, + "output_cost_per_token": 1.43e-06, "source": "https://novita.ai/pricing", "supports_function_calling": true, "supports_prompt_caching": true, @@ -66461,9 +67220,9 @@ "deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731": { "max_output_tokens": 1048576, "max_input_tokens": 1048576, - "input_cost_per_token": 8e-08, + "input_cost_per_token": 6e-08, "output_cost_per_token": 1.8e-07, - "cache_read_input_token_cost": 1.6e-08, + "cache_read_input_token_cost": 1.5e-08, "supports_prompt_caching": true, "litellm_provider": "deepinfra", "mode": "chat", @@ -66842,8 +67601,8 @@ "deepinfra/google/gemma-4-31B-it": { "max_output_tokens": 262144, "max_input_tokens": 262144, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 3.8e-07, + "input_cost_per_token": 2e-07, + "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_function_calling": true, @@ -67000,10 +67759,12 @@ }, "xai/grok-imagine-image-quality": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67017,10 +67778,12 @@ }, "xai/grok-imagine-image-quality-20260403": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67034,10 +67797,12 @@ }, "xai/grok-imagine-image-quality-latest": { "deprecation_date": "2026-11-02", + "deprecation_source": "https://docs.x.ai/developers/migration/imagine-image-quality-nov-2", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67051,10 +67816,12 @@ }, "xai/grok-imagine-image-pro": { "deprecation_date": "2026-05-15", + "deprecation_source": "https://docs.x.ai/developers/migration/may-15-retirement", "input_cost_per_image": 0.05, "litellm_provider": "xai", "mode": "image_generation", "source": "https://docs.x.ai/docs/models", + "successor_model": "xai/grok-imagine-image-2.0", "supported_endpoints": [ "/v1/images/generations" ], @@ -67172,6 +67939,8 @@ }, "xai/grok-voice-transcribe-1.0": { "deprecation_date": "2026-10-02", + "deprecation_source": "https://docs.x.ai/docs/release-notes", + "successor_model": "xai/grok-voice-transcribe-2.0", "input_cost_per_second": 2.778e-05, "litellm_provider": "xai", "metadata": { @@ -68037,6 +68806,7 @@ }, "together_ai/nvidia/NVIDIA-Nemotron-Nano-9B-v2": { "deprecation_date": "2026-02-25", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 6e-08, "output_cost_per_token": 2.5e-07, "litellm_provider": "together_ai", @@ -68107,6 +68877,7 @@ "azure_ai/kimi-k2.7-code": { "cache_read_input_token_cost": 1.9e-07, "deprecation_date": "2027-04-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 9.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -68142,6 +68913,7 @@ "supports_reasoning": true, "supports_tool_choice": true, "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/deepseek-v4-1-flash-is-coming-to-microsoft-foundry/4556431" }, "azure_ai/MAI-Image-2.6": { @@ -68250,6 +69022,8 @@ }, "azure_ai/Cohere-command-a-plus-05-2026": { "deprecation_date": "2026-10-16", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "coherelabs-command-a-plus-05-2026-fp81", "input_cost_per_token": 8e-07, "litellm_provider": "azure_ai", "max_input_tokens": 128000, @@ -68283,6 +69057,8 @@ }, "azure_ai/deepseek-r1": { "deprecation_date": "2026-08-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-pro", "input_cost_per_token": 1.35e-06, "output_cost_per_token": 5.4e-06, "litellm_provider": "azure_ai", @@ -68292,6 +69068,8 @@ }, "azure_ai/deepseek-v3-0324": { "deprecation_date": "2026-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-flash", "input_cost_per_token": 1.14e-06, "output_cost_per_token": 4.56e-06, "litellm_provider": "azure_ai", @@ -68301,6 +69079,8 @@ }, "azure_ai/deepseek-v3.1": { "deprecation_date": "2026-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/deepseek-v4-flash", "input_cost_per_token": 1.23e-06, "output_cost_per_token": 4.94e-06, "litellm_provider": "azure_ai", @@ -68310,6 +69090,8 @@ }, "azure_ai/grok-3": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4", "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, "litellm_provider": "azure_ai", @@ -68319,6 +69101,8 @@ }, "azure_ai/grok-3-mini": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-reasoning", "input_cost_per_token": 2.5e-07, "output_cost_per_token": 1.27e-06, "litellm_provider": "azure_ai", @@ -68328,6 +69112,8 @@ }, "azure_ai/grok-4-fast-non-reasoning": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-non-reasoning", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -68337,6 +69123,8 @@ }, "azure_ai/grok-4-fast-reasoning": { "deprecation_date": "2026-05-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", + "successor_model": "azure_ai/grok-4-1-fast-reasoning", "input_cost_per_token": 2e-07, "output_cost_per_token": 5e-07, "litellm_provider": "azure_ai", @@ -68346,12 +69134,18 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 262144, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.88e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 5.04e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_native_structured_output": true, "supports_system_messages": true, @@ -68362,13 +69156,20 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, + "input_cost_per_token_batches": 1.2e-07, + "input_cost_per_token_flex": 1.2e-07, + "input_cost_per_token_priority": 4.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_system_messages": true, "supports_vision": true, "supports_audio_input": false, @@ -68377,13 +69178,20 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.76e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 4.8e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_system_messages": true, "supports_audio_input": false, "supports_function_calling": true, @@ -68392,12 +69200,18 @@ }, "bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b": { "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 7.8e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 3.9e-07, + "output_cost_per_token_flex": 3.9e-07, + "output_cost_per_token_priority": 1.365e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_function_calling": true, "supports_reasoning": true, "supports_system_messages": true, @@ -68409,12 +69223,18 @@ "bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 3.6e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68428,12 +69248,18 @@ "bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68554,7 +69380,10 @@ "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "us-gov-west-1" - } + }, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-west-1/index.json" }, "bedrock/us-gov-west-1/anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -68636,12 +69465,18 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 262144, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.88e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 5.04e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_function_calling": true, "supports_native_structured_output": true, "supports_system_messages": true, @@ -68652,13 +69487,20 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-12b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", "input_cost_per_token": 2.4e-07, + "input_cost_per_token_batches": 1.2e-07, + "input_cost_per_token_flex": 1.2e-07, + "input_cost_per_token_priority": 4.2e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-12b-v2-vl-bf16.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_system_messages": true, "supports_vision": true, "supports_audio_input": false, @@ -68667,13 +69509,20 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-nano-9b-v2": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", "input_cost_per_token": 7.2e-08, + "input_cost_per_token_batches": 3.6e-08, + "input_cost_per_token_flex": 3.6e-08, + "input_cost_per_token_priority": 1.32e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.76e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-nvidia-nvidia-nemotron-nano-9b-v2.html", + "output_cost_per_token_batches": 1.44e-07, + "output_cost_per_token_flex": 1.44e-07, + "output_cost_per_token_priority": 4.8e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_system_messages": true, "supports_audio_input": false, "supports_function_calling": true, @@ -68682,12 +69531,18 @@ }, "bedrock/us-gov-east-1/nvidia.nemotron-super-3-120b": { "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 256000, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 7.8e-07, - "source": "https://aws.amazon.com/bedrock/pricing/", + "output_cost_per_token_batches": 3.9e-07, + "output_cost_per_token_flex": 3.9e-07, + "output_cost_per_token_priority": 1.365e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supports_function_calling": true, "supports_reasoning": true, "supports_system_messages": true, @@ -68699,12 +69554,18 @@ "bedrock/us-gov-east-1/openai.gpt-oss-20b-1:0": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 3.6e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68718,12 +69579,18 @@ "bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "litellm_provider": "bedrock", "max_input_tokens": 128000, "max_output_tokens": 16384, "mode": "chat", "output_cost_per_token": 7.2e-07, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions" ], @@ -68829,7 +69696,10 @@ "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/us-gov-east-1/index.json" }, "bedrock/us-gov-east-1/anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -68924,16 +69794,32 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 2.7e-06, - "input_cost_per_token_above_272k_tokens": 5.4e-06, - "cache_creation_input_token_cost": 3.375e-06, - "cache_creation_input_token_cost_above_272k_tokens": 6.75e-06, - "cache_read_input_token_cost": 2.7e-07, - "cache_read_input_token_cost_above_272k_tokens": 5.4e-07, - "output_cost_per_token": 1.62e-05, - "output_cost_per_token_above_272k_tokens": 2.43e-05, + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_creation_input_token_cost_above_272k_tokens_flex": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.32e-05, + "cache_creation_input_token_cost_flex": 1.65e-06, + "cache_creation_input_token_cost_priority": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.056e-06, + "cache_read_input_token_cost_flex": 1.32e-07, + "cache_read_input_token_cost_priority": 5.28e-07, + "input_cost_per_token_above_272k_tokens_flex": 2.64e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.056e-05, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 5.28e-06, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05, + "output_cost_per_token_above_272k_tokens_flex": 1.188e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.752e-05, + "output_cost_per_token_flex": 7.92e-06, + "output_cost_per_token_priority": 3.168e-05, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-terra.html", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_web_search": true }, "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-luna": { @@ -68961,16 +69847,32 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 2.7e-07, - "input_cost_per_token_above_272k_tokens": 5.4e-07, - "cache_creation_input_token_cost": 3.375e-07, - "cache_creation_input_token_cost_above_272k_tokens": 6.75e-07, - "cache_read_input_token_cost": 2.7e-08, - "cache_read_input_token_cost_above_272k_tokens": 5.4e-08, - "output_cost_per_token": 1.62e-06, - "output_cost_per_token_above_272k_tokens": 2.43e-06, + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.32e-06, + "cache_creation_input_token_cost_flex": 1.65e-07, + "cache_creation_input_token_cost_priority": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.056e-07, + "cache_read_input_token_cost_flex": 1.32e-08, + "cache_read_input_token_cost_priority": 5.28e-08, + "input_cost_per_token_above_272k_tokens_flex": 2.64e-07, + "input_cost_per_token_above_272k_tokens_priority": 1.056e-06, + "input_cost_per_token_flex": 1.32e-07, + "input_cost_per_token_priority": 5.28e-07, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.188e-06, + "output_cost_per_token_above_272k_tokens_priority": 4.752e-06, + "output_cost_per_token_flex": 7.92e-07, + "output_cost_per_token_priority": 3.168e-06, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-luna.html", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supports_web_search": true }, "bedrock_mantle/us-gov-west-1/openai.gpt-5.4": { @@ -68997,9 +69899,9 @@ "supports_tool_choice": true, "supports_xhigh_reasoning_effort": true, "supports_vision": true, - "input_cost_per_token": 3.375e-06, - "cache_read_input_token_cost": 3.375e-07, - "output_cost_per_token": 2.025e-05, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05, "search_context_cost_per_query": {"search_context_size_high":0.0144,"search_context_size_low":0.0144,"search_context_size_medium":0.0144}, "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-54.html", "supports_web_search": true @@ -69019,20 +69921,36 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "source": "https://aws.amazon.com/bedrock/pricing/", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "input_cost_per_token": 1.5e-06, "output_cost_per_token": 3e-06, - "cache_read_input_token_cost": 2.4e-07 + "cache_read_input_token_cost": 2.4e-07, + "cache_read_input_token_cost_batches": 1.2e-07, + "cache_read_input_token_cost_flex": 1.2e-07, + "cache_read_input_token_cost_priority": 4.2e-07, + "input_cost_per_token_batches": 7.5e-07, + "input_cost_per_token_flex": 7.5e-07, + "input_cost_per_token_priority": 2.625e-06, + "output_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_flex": 1.5e-06, + "output_cost_per_token_priority": 5.25e-06 }, "bedrock_mantle/us-gov-west-1/xai.grok-4.6": { "use_openai_responses_path": true, "input_cost_per_token": 2.64e-06, "output_cost_per_token": 7.92e-06, "cache_read_input_token_cost": 6.6e-07, + "cache_read_input_token_cost_flex": 3.3e-07, + "cache_read_input_token_cost_priority": 1.155e-06, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 4.62e-06, "litellm_provider": "bedrock_mantle", "max_input_tokens": 500000, "max_output_tokens": 500000, "mode": "chat", + "output_cost_per_token_flex": 3.96e-06, + "output_cost_per_token_priority": 1.386e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69046,11 +69964,17 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-e2b": { "input_cost_per_token": 4.8e-08, + "input_cost_per_token_flex": 2.4e-08, + "input_cost_per_token_priority": 8.4e-08, "output_cost_per_token": 9.6e-08, "litellm_provider": "bedrock_mantle", "max_input_tokens": 128000, "max_output_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 4.8e-08, + "output_cost_per_token_flex": 4.8e-08, + "output_cost_per_token_priority": 1.68e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69064,11 +69988,18 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-26b-a4b": { "input_cost_per_token": 1.56e-07, + "input_cost_per_token_batches": 7.8e-08, + "input_cost_per_token_flex": 7.8e-08, + "input_cost_per_token_priority": 2.73e-07, "output_cost_per_token": 4.8e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 256000, "max_output_tokens": 256000, "mode": "chat", + "output_cost_per_token_batches": 2.4e-07, + "output_cost_per_token_flex": 2.4e-07, + "output_cost_per_token_priority": 8.4e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69082,11 +70013,18 @@ }, "bedrock_mantle/us-gov-west-1/google.gemma-4-31b": { "input_cost_per_token": 1.68e-07, + "input_cost_per_token_batches": 8.4e-08, + "input_cost_per_token_flex": 8.4e-08, + "input_cost_per_token_priority": 2.94e-07, "output_cost_per_token": 4.8e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 256000, "max_output_tokens": 256000, "mode": "chat", + "output_cost_per_token_batches": 2.4e-07, + "output_cost_per_token_flex": 2.4e-07, + "output_cost_per_token_priority": 8.4e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "use_openai_responses_path": true, "supported_endpoints": [ "/v1/chat/completions", @@ -69101,12 +70039,18 @@ "bedrock_mantle/us-gov-west-1/openai.gpt-oss-20b": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "output_cost_per_token": 3.6e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69120,12 +70064,18 @@ "bedrock_mantle/us-gov-west-1/openai.gpt-oss-120b": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "output_cost_per_token": 7.2e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-west-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69241,10 +70191,17 @@ "input_cost_per_token": 2.64e-06, "output_cost_per_token": 7.92e-06, "cache_read_input_token_cost": 6.6e-07, + "cache_read_input_token_cost_flex": 3.3e-07, + "cache_read_input_token_cost_priority": 1.155e-06, + "input_cost_per_token_flex": 1.32e-06, + "input_cost_per_token_priority": 4.62e-06, "litellm_provider": "bedrock_mantle", "max_input_tokens": 500000, "max_output_tokens": 500000, "mode": "chat", + "output_cost_per_token_flex": 3.96e-06, + "output_cost_per_token_priority": 1.386e-05, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69259,12 +70216,18 @@ "bedrock_mantle/us-gov-east-1/openai.gpt-oss-20b": { "supports_vision": false, "input_cost_per_token": 8.4e-08, + "input_cost_per_token_batches": 4.2e-08, + "input_cost_per_token_flex": 4.2e-08, + "input_cost_per_token_priority": 1.47e-07, "output_cost_per_token": 3.6e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-20b.html", + "output_cost_per_token_batches": 1.8e-07, + "output_cost_per_token_flex": 1.8e-07, + "output_cost_per_token_priority": 6.3e-07, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69278,12 +70241,18 @@ "bedrock_mantle/us-gov-east-1/openai.gpt-oss-120b": { "supports_vision": false, "input_cost_per_token": 1.8e-07, + "input_cost_per_token_batches": 9e-08, + "input_cost_per_token_flex": 9e-08, + "input_cost_per_token_priority": 3.15e-07, "output_cost_per_token": 7.2e-07, "litellm_provider": "bedrock_mantle", "max_input_tokens": 131072, "max_output_tokens": 16384, "mode": "chat", - "source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-oss-120b.html", + "output_cost_per_token_batches": 3.6e-07, + "output_cost_per_token_flex": 3.6e-07, + "output_cost_per_token_priority": 1.26e-06, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrock/current/us-gov-east-1/index.json", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -69296,6 +70265,7 @@ }, "bedrock_mantle/deepseek.v3.1": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 5.8e-07, "input_cost_per_token_batches": 2.9e-07, "input_cost_per_token_flex": 2.9e-07, @@ -69317,6 +70287,7 @@ }, "bedrock_mantle/moonshotai.kimi-k2-thinking": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 6e-07, "input_cost_per_token_batches": 3e-07, "input_cost_per_token_flex": 3e-07, @@ -69381,6 +70352,7 @@ }, "bedrock_mantle/qwen.qwen3-coder-30b-a3b-instruct": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, 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"https://docs.perplexity.ai/docs/agent-api/models" }, + "perplexity/perplexity/nemotron-3.5-super-120b-a12b": { + "litellm_provider": "perplexity", + "mode": "responses", + "input_cost_per_token": 2e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 4e-08, + "source": "https://docs.perplexity.ai/docs/agent-api/models" + }, "perplexity/perplexity/nemotron-3-ultra-550b-a55b": { "litellm_provider": "perplexity", "mode": "responses", @@ -70104,12 +71093,12 @@ "input_cost_per_token": 1.5e-06, "litellm_provider": "vertex_ai", "max_output_tokens": 57920, - "max_input_tokens": 57920, + "max_input_tokens": 131072, "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "output_cost_per_video_token": 1.75e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/omni-1-1-flash", "supported_endpoints": [ 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"together_ai/mistralai/Mistral-Small-24B-Instruct-2501": { "deprecation_date": "2026-04-02", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, @@ -70533,6 +71401,7 @@ }, "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1": { "deprecation_date": "2026-04-16", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 32768, @@ -70544,6 +71413,7 @@ "together_ai/moonshotai/Kimi-K2.6": { "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-08-19", + "deprecation_source": "https://docs.together.ai/docs/deprecations", "input_cost_per_token": 1.2e-06, "litellm_provider": "together_ai", "max_input_tokens": 262144, @@ -70555,6 +71425,7 @@ "together_ai/moonshotai/Kimi-K2.7-Code": { "cache_read_input_token_cost": 1.9e-07, "deprecation_date": "2026-08-27", + "deprecation_source": 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"cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -70672,6 +71543,8 @@ }, "azure/eu/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -70691,6 +71564,8 @@ }, "azure/eu/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -70701,7 +71576,9 @@ "source": 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"2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.1-codex", "cache_read_input_token_cost": 1.38e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -70742,7 +71623,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-mini", "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_batches": 1.375e-08, "cache_read_input_token_cost_priority": 4.95e-08, @@ -70763,6 +71646,8 @@ }, "azure/eu/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4-nano", "cache_read_input_token_cost": 5.5e-09, "cache_read_input_token_cost_batches": 2.75e-09, "input_cost_per_token": 5.5e-08, @@ -70780,6 +71665,8 @@ }, "azure/eu/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.2-pro", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -70795,6 +71682,7 @@ }, "azure/eu/gpt-5.1-codex-max": { "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -70804,6 +71692,8 @@ }, "azure/eu/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.4", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_batches": 9.625e-08, "cache_read_input_token_cost_priority": 3.85e-07, @@ -70824,6 +71714,8 @@ }, "azure/eu/gpt-5.2-codex": { "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.3-codex", "cache_read_input_token_cost": 1.925e-07, "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", @@ -70847,6 +71739,7 @@ }, "azure/eu/gpt-5.3-codex": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_priority": 3.85e-07, "input_cost_per_token": 1.925e-06, @@ -70864,6 +71757,7 @@ }, "azure/eu/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 8.25e-08, "cache_read_input_token_cost_batches": 4.125e-08, "cache_read_input_token_cost_priority": 1.65e-07, @@ -70884,6 +71778,7 @@ }, "azure/eu/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_batches": 1.1e-08, "input_cost_per_token": 2.2e-07, @@ -70901,6 +71796,7 @@ }, "azure/eu/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 3.3e-05, "input_cost_per_token_above_272k_tokens": 6.6e-05, "input_cost_per_token_above_272k_tokens_batches": 3.3e-05, @@ -71074,6 +71970,7 @@ }, "azure/eu/o3": { "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -71092,6 +71989,7 @@ }, "azure/eu/o3-2025-04-16": { "deprecation_date": "2026-11-19", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, @@ -71110,6 +72008,8 @@ }, "azure/eu/o3-deep-research": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-sol", "cache_read_input_token_cost": 2.75e-06, "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -71119,6 +72019,8 @@ }, "azure/eu/o4-mini": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "cache_read_input_token_cost": 3.03e-07, "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, @@ -71137,6 +72039,8 @@ }, "azure/eu/o4-mini-2025-04-16": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/eu/gpt-5.6-terra", "cache_read_input_token_cost": 3.03e-07, "input_cost_per_token": 1.21e-06, "input_cost_per_token_batches": 6.05e-07, @@ -71155,6 +72059,7 @@ }, "azure/eu/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71162,6 +72067,7 @@ }, "azure/eu/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "mode": "embedding", @@ -71169,6 +72075,7 @@ }, "azure/eu/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71218,6 +72125,7 @@ }, "azure/us/codex-mini": { "deprecation_date": "2026-11-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 4.13e-07, "input_cost_per_token": 1.65e-06, "litellm_provider": "azure", @@ -71234,6 +72142,8 @@ }, "azure/us/gpt-4.1": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, @@ -71253,6 +72163,8 @@ }, "azure/us/gpt-4.1-mini": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 1.1e-07, "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, @@ -71272,6 +72184,8 @@ }, "azure/us/gpt-4.1-nano": { "deprecation_date": "2027-04-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, "input_cost_per_token_batches": 5.5e-08, @@ -71282,7 +72196,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-4o-2024-05-13": { - "deprecation_date": "2026-12-09", + "deprecation_date": "2027-01-14", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "input_cost_per_token": 5.5e-06, "input_cost_per_token_batches": 2.75e-06, "litellm_provider": "azure", @@ -71295,6 +72211,8 @@ }, "azure/us/gpt-5": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1", "cache_read_input_token_cost": 1.375e-07, "cache_read_input_token_cost_batches": 6.875e-08, "cache_read_input_token_cost_priority": 2.75e-07, @@ -71315,6 +72233,8 @@ }, "azure/us/gpt-5-codex": { "deprecation_date": "2027-03-17", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.1-codex", "cache_read_input_token_cost": 1.38e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -71323,7 +72243,9 @@ "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5-mini": { - "deprecation_date": "2027-02-09", + "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-mini", "cache_read_input_token_cost": 2.75e-08, "cache_read_input_token_cost_batches": 1.375e-08, "cache_read_input_token_cost_priority": 4.95e-08, @@ -71344,6 +72266,8 @@ }, "azure/us/gpt-5-nano": { "deprecation_date": "2027-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4-nano", "cache_read_input_token_cost": 5.5e-09, "cache_read_input_token_cost_batches": 2.75e-09, "input_cost_per_token": 5.5e-08, @@ -71361,6 +72285,8 @@ }, "azure/us/gpt-5-pro": { "deprecation_date": "2027-04-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.2-pro", "input_cost_per_token": 1.65e-05, "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", @@ -71376,6 +72302,7 @@ }, "azure/us/gpt-5.1-codex-max": { "deprecation_date": "2027-05-18", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.375e-07, "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", @@ -71385,6 +72312,8 @@ }, "azure/us/gpt-5.2": { "deprecation_date": "2027-06-08", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.4", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_batches": 9.625e-08, "cache_read_input_token_cost_priority": 3.85e-07, @@ -71405,6 +72334,8 @@ }, "azure/us/gpt-5.2-codex": { "deprecation_date": "2027-07-13", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.3-codex", "cache_read_input_token_cost": 1.925e-07, "input_cost_per_token": 1.925e-06, "litellm_provider": "azure", @@ -71428,6 +72359,7 @@ }, "azure/us/gpt-5.3-codex": { "deprecation_date": "2027-08-24", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 1.925e-07, "cache_read_input_token_cost_priority": 3.85e-07, "input_cost_per_token": 1.925e-06, @@ -71445,6 +72377,7 @@ }, "azure/us/gpt-5.4-mini": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 8.25e-08, "cache_read_input_token_cost_batches": 4.125e-08, "cache_read_input_token_cost_priority": 1.65e-07, @@ -71465,6 +72398,7 @@ }, "azure/us/gpt-5.4-nano": { "deprecation_date": "2027-09-21", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_batches": 1.1e-08, "input_cost_per_token": 2.2e-07, @@ -71482,6 +72416,7 @@ }, "azure/us/gpt-5.4-pro": { "deprecation_date": "2027-09-07", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 3.3e-05, "input_cost_per_token_above_272k_tokens": 6.6e-05, "input_cost_per_token_above_272k_tokens_batches": 3.3e-05, @@ -71511,6 +72446,8 @@ }, "azure/us/o3-deep-research": { "deprecation_date": "2026-11-19", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/us/gpt-5.6-sol", "cache_read_input_token_cost": 2.75e-06, "input_cost_per_token": 1.1e-05, "litellm_provider": "azure", @@ -71520,6 +72457,7 @@ }, "azure/us/text-embedding-3-large": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.43e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71527,6 +72465,7 @@ }, "azure/us/text-embedding-3-small": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 2.2e-08, "litellm_provider": "azure", "mode": "embedding", @@ -71534,6 +72473,7 @@ }, "azure/us/text-embedding-ada-002": { "deprecation_date": "2028-02-09", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.1e-07, "litellm_provider": "azure", "mode": "embedding", @@ -71544,6 +72484,8 @@ "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-01-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", + "successor_model": "azure/gpt-realtime-2.1", "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image_token": 5e-06, "input_cost_per_token": 4e-06, @@ -73964,6 +74906,7 @@ "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "deprecation_date": "2026-05-15", + "deprecation_source": "https://docs.x.ai/developers/migration/may-15-retirement", "input_cost_per_image_token": 1e-06, "input_cost_per_token": 1e-06, "input_cost_per_token_above_200k_tokens": 2e-06, @@ -73974,6 +74917,7 @@ "output_cost_per_token": 2e-06, "output_cost_per_token_above_200k_tokens": 4e-06, "source": "https://api.x.ai/v1/language-models", + "successor_model": "xai/grok-build-0.1", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -74063,6 +75007,8 @@ }, "vertex_ai/gemini-2.0-flash": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "input_cost_per_audio_token": 1e-06, "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_character": 3.75e-08, @@ -74076,6 +75022,8 @@ }, "vertex_ai/gemini-2.0-flash-lite": { "deprecation_date": "2026-06-01", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/gemini-3.5-flash-lite", "input_cost_per_audio_token": 7.5e-08, "input_cost_per_audio_token_batches": 3.75e-08, "input_cost_per_character": 1.875e-08, @@ -74127,6 +75075,8 @@ }, "vertex_ai/veo-3.0-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74142,6 +75092,8 @@ }, "vertex_ai/veo-3.0-fast-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-fast-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74158,6 +75110,8 @@ }, "vertex_ai/veo-2.0-generate-001": { "deprecation_date": "2026-06-30", + "deprecation_source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions", + "successor_model": "vertex_ai/veo-3.1-generate-001", "litellm_provider": "vertex_ai-video-models", "max_input_tokens": 1024, "mode": "video_generation", @@ -74505,12 +75459,14 @@ "input_cost_per_token": 5.5e-06, "cache_read_input_token_cost": 5.5e-07, "output_cost_per_token": 2.75e-05, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/ap-northeast-1/index.json", "token_count_model": { "model": "anthropic.claude-opus-5", "litellm_provider": "bedrock_mantle", "region": "ap-southeast-4" - } + }, + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.375e-05 }, "apac.anthropic.claude-opus-5-5": { "bedrock_converse_supports_strict_tools": false, @@ -74682,6 +75638,7 @@ }, "deepseek.r1-v1:0": { "deprecation_date": "2027-03-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-deepseek-deepseek-r1.html", "input_cost_per_token": 1.35e-06, "litellm_provider": "bedrock_converse", "max_input_tokens": 128000, @@ -75107,7 +76064,7 @@ "supports_function_calling": true, "input_cost_per_token": 2e-06, "output_cost_per_token": 1e-05, - "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost": 1e-07, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "perplexity/anthropic/claude-fable-5-1": { @@ -75155,6 +76112,9 @@ "output_cost_per_token": 5e-05, "cache_read_input_token_cost": 1e-06, "cache_creation_input_token_cost": 1.25e-05, + "input_cost_per_token_above_272k_tokens": 2e-05, + "output_cost_per_token_above_272k_tokens": 7.5e-05, + "cache_read_input_token_cost_above_272k_tokens": 2e-06, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "perplexity/openai/gpt-6-luna": { @@ -75533,6 +76493,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -75757,6 +76718,8 @@ }, "azure_ai/kimi-k2-thinking": { "deprecation_date": "2026-03-29", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models", + "successor_model": "azure_ai/kimi-k2.5", "input_cost_per_token": 6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, @@ -75858,6 +76821,7 @@ "azure/model-router": { "comment": "The flat $0.14 per M input tokens is the router's own fee, the routed model is priced on top of it", "deprecation_date": "2027-05-20", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_token": 1.4e-07, "litellm_provider": "azure", "max_input_tokens": 200000, @@ -75869,6 +76833,7 @@ }, "azure/whisper": { "deprecation_date": "2026-12-15", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "input_cost_per_second": 0.0001, "litellm_provider": "azure", "mode": "audio_transcription", @@ -75930,6 +76895,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 7.5e-06, "output_cost_per_token_batches": 3.75e-06, "output_cost_per_token_priority": 1.35e-05, @@ -75976,6 +76942,7 @@ "output_cost_per_image": 0.0336, "output_cost_per_image_token": 3e-05, "output_cost_per_image_token_batches": 1.5e-05, + "output_cost_per_image_token_priority": 5.4e-05, "output_cost_per_token": 7.5e-06, "output_cost_per_token_batches": 3.75e-06, "output_cost_per_token_priority": 1.35e-05, @@ -76622,6 +77589,9 @@ "input_cost_per_token": 1e-07, "output_cost_per_token": 5e-07, "cache_read_input_token_cost": 1e-08, + "input_cost_per_token_above_100k_tokens": 5e-07, + "output_cost_per_token_above_100k_tokens": 2.5e-06, + "cache_read_input_token_cost_above_100k_tokens": 5e-08, "source": "https://docs.perplexity.ai/docs/agent-api/models" }, "us-gov.anthropic.claude-haiku-5-5": { @@ -77310,6 +78280,7 @@ "litellm_provider": "xai", "mode": "realtime", "source": "https://docs.x.ai/developers/pricing", + "successor_model": "xai/grok-voice-3.0", "supported_endpoints": [ "/v1/realtime" ], @@ -77393,6 +78364,7 @@ "cache_creation_input_token_cost_above_1hr": 1.6e-05, "cache_read_input_token_cost": 4e-07, "deprecation_date": "2027-10-01", + "deprecation_source": "https://learn.microsoft.com/en-us/azure/foundry/concepts/model-lifecycle-retirement", "input_cost_per_token": 8e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, @@ -77504,9 +78476,10 @@ "max_output_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1e-06, - "source": "https://tokenfactory.nebius.com/models/catalog/text2text/reflection%2Fbeam", + "source": "https://tokenfactory.nebius.com/api/public/models_info", "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 5e-08 }, "baseten/deepseek-ai/DeepSeek-V4.1-Flash-1017": { "cache_read_input_token_cost": 7e-09, @@ -78837,6 +79810,8 @@ "cache_creation_input_token_cost_batches": 1.875e-06, "cache_read_input_token_cost_batches": 1.5e-07, "deprecation_date": "2026-11-30", + "deprecation_source": "https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle-legacy.html", + "successor_model": "bedrock/claude_platform/claude-sonnet-5-5", "input_cost_per_token_batches": 1.5e-06, "litellm_provider": "bedrock", "max_input_tokens": 200000, @@ -79049,6 +80024,7 @@ ], "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, + "fedramp_uplift_multiplier": 1.1, "search_context_cost_per_query": 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+ "max_output_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 9.6e-06, + "source": "https://docs.fireworks.ai/serverless/pricing.md", + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/routers/deepseek-v4p1-flash-1017-spot": { "cache_read_input_token_cost": 4.2e-09, "input_cost_per_token": 2.1e-07, @@ -81625,17 +82623,17 @@ "supports_native_streaming": true }, "fireworks_ai/accounts/fireworks/models/ultramarine-1-flash-us": { - "cache_read_input_token_cost": 4.5e-08, - "cache_read_input_token_cost_priority": 5.625e-08, - "input_cost_per_token": 2.25e-07, - "input_cost_per_token_priority": 2.8125e-07, + "cache_read_input_token_cost": 3e-08, + "cache_read_input_token_cost_priority": 3.75e-08, + "input_cost_per_token": 1.5e-07, + 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true, "supports_xhigh_reasoning_effort": true + }, + "nebius/deepseek-ai/DeepSeek-V4.1-Flash-1017": { + "max_input_tokens": 1048576, + "input_cost_per_token": 3e-07, + "cache_read_input_token_cost": 6e-09, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "nebius/meta-models/muse-spark-open": { + "max_input_tokens": 1048576, + "input_cost_per_token": 1.25e-06, + "cache_read_input_token_cost": 1.5e-07, + "output_cost_per_token": 4.25e-06, + "litellm_provider": "nebius", + "mode": "chat", + "supports_vision": true, + "supports_video_input": true, + "supports_reasoning": true, + "supports_function_calling": true, + "source": "https://tokenfactory.nebius.com/api/public/models_info" + }, + "nebius/MiniMaxAI/MiniMax-M3.1-Flash": { + "max_input_tokens": 1048576, + "input_cost_per_token": 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"https://ie-api.gmicloud.ai/v1/artifact/get_public_artifacts" + }, + "gmi/zai-org/GLM-5.1-FP8": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "gmi", + "max_input_tokens": 202752, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://ie-api.gmicloud.ai/v1/artifact/get_public_artifacts" + }, + "gmi/zai-org/GLM-5.2-FP8": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "gmi", + "max_input_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://ie-api.gmicloud.ai/v1/artifact/get_public_artifacts" + }, + "gmi/zai-org/GLM-5.3": { + "cache_read_input_token_cost": 1.3e-07, + "input_cost_per_token": 7e-07, + "litellm_provider": "gmi", + "max_input_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 2.2e-06, + "source": "https://ie-api.gmicloud.ai/v1/artifact/get_public_artifacts" + }, + "gmi/zai-org/GLM-5.3-Flash": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 7.5e-08, + "litellm_provider": "gmi", + "max_input_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 2.5e-07, + "source": "https://ie-api.gmicloud.ai/v1/artifact/get_public_artifacts" + }, + "fireworks_ai/accounts/fireworks/models/kimi-k3p1-pro": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_priority": 3e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_priority": 3e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1e-05, + "output_cost_per_token_priority": 1.5e-05, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "source": "https://api.fireworks.ai/v1/serverless/models?format=nested", + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "baseten/moonshotai/Kimi-K3.1-Pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "baseten", + "max_input_tokens": 1000000, + "max_output_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1e-05, + "source": "https://inference.baseten.co/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true } } diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index 40ea9d15ff4..00fee0a0d5a 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -5250,13 +5250,13 @@ "generateContent": false } }, - "empiriolabs": { - "display_name": "EmpirioLabs (`empiriolabs`)", - "url": "https://docs.litellm.ai/docs/providers/empiriolabs", + "wally": { + "display_name": "Wally (`wally`)", + "url": "https://docs.litellm.ai/docs/providers/wally", "endpoints": { - "chat_completions": false, - "responses": false, - "text_completion": false, + "chat_completions": true, + "responses": true, + "text_completion": true, "embeddings": false, "image_generations": false, "image_edits": false, @@ -5269,11 +5269,37 @@ "realtime": false, "video_generations": false, "batches": false, - "messages": false, - "interactions": false, + "messages": true, + "interactions": true, "systemone": false, - "a2a": false, - "generateContent": false + "a2a": true, + "generateContent": true + } + }, + "empiriolabs": { + "display_name": "EmpirioLabs AI (`empiriolabs`)", + "url": "https://docs.litellm.ai/docs/providers/empiriolabs", + "endpoints": { + "chat_completions": true, + "responses": true, + "text_completion": true, + "embeddings": true, + "image_generations": false, + "image_edits": false, + "audio_transcriptions": false, + "audio_speech": false, + "rerank": false, + "ocr": false, + "search": false, + "moderations": false, + "realtime": false, + "video_generations": false, + "batches": false, + "messages": true, + "interactions": true, + "systemone": false, + "a2a": true, + "generateContent": true } }, "openinfer": { diff --git a/pyproject.toml b/pyproject.toml index 7a653b28f81..f2774e87b7b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -266,6 +266,7 @@ proxy-dev = [ "azure-identity==1.25.2", "a2a-sdk==1.1.0", "backports-zstd==1.8.0; python_version < '3.14'", + "httptools>=0.7.1,<0.8", ] ci = [ "psutil==7.2.2", diff --git a/schema.prisma b/schema.prisma index 179b741fe7c..c007b047087 100644 --- a/schema.prisma +++ b/schema.prisma @@ -1615,9 +1615,18 @@ model LiteLLM_ClaudeCodePluginTable { updated_at DateTime? @default(now()) @updatedAt created_by String? + uploaded_archive LiteLLM_ClaudeCodePluginUpload? + @@map("LiteLLM_ClaudeCodePluginTable") } +model LiteLLM_ClaudeCodePluginUpload { + plugin_name String @id + content Bytes + plugin LiteLLM_ClaudeCodePluginTable @relation(fields: [plugin_name], references: [name], onDelete: Cascade, onUpdate: Cascade) +} + + // User/team-scoped memory store with a GLOBAL unique key. // `value` is a string (typically markdown/text meant for LLM context); // `metadata` is an optional JSON envelope for structured tags without schema changes. diff --git a/scripts/check_mcp_sdk_install.py b/scripts/check_mcp_sdk_install.py index f2c2891bfd8..f5261bc22f8 100644 --- a/scripts/check_mcp_sdk_install.py +++ b/scripts/check_mcp_sdk_install.py @@ -13,6 +13,7 @@ IMPORTED_MODULES: Final[tuple[str, ...]] = ( "litellm.proxy.mcp_server.server", "litellm.proxy.mcp_server.mcp_server_manager", "litellm.proxy.mcp_server.rest_endpoints", + "litellm.proxy.proxy_server", ) diff --git a/scripts/check_openapi_docs.py b/scripts/check_openapi_docs.py index e29cac29212..219ef912952 100755 --- a/scripts/check_openapi_docs.py +++ b/scripts/check_openapi_docs.py @@ -18,7 +18,7 @@ from collections.abc import Iterator, Mapping from typing import Final, NamedTuple GATED_METHODS: Final = frozenset({"post"}) -SCHEMA_MEDIA_TYPES: Final = ("application/json", "application/sdp") +SCHEMA_MEDIA_TYPES: Final = ("application/json", "application/sdp", "multipart/form-data") class Violation(NamedTuple): diff --git a/scripts/check_schema_compatibility.py b/scripts/check_schema_compatibility.py index 898c65dff2c..b8e9ac3c07a 100644 --- a/scripts/check_schema_compatibility.py +++ b/scripts/check_schema_compatibility.py @@ -52,14 +52,20 @@ def check_breaking_changes(old_schema: Schema, new_schema: Schema) -> tuple[str, return tuple(changes()) -def main() -> int: - parser: Final = argparse.ArgumentParser() - parser.add_argument("--base-ref", required=True) - options: Final = parser.parse_args() - base_ref: Final = TypeAdapter(str).validate_python(options.base_ref) - base: Final = subprocess.run( +def _merge_base(base_ref: str) -> str: + return subprocess.run( ("git", "merge-base", "HEAD", base_ref), capture_output=True, text=True, check=True ).stdout.strip() + + +def main() -> int: + parser: Final = argparse.ArgumentParser() + source: Final = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--base-ref", help="compare against the merge base of HEAD and this ref (needs history)") + source.add_argument("--base-commit", help="compare against this commit as is (works in a shallow clone)") + options: Final = TypeAdapter(dict[str, str | None]).validate_python(vars(parser.parse_args())) + base_commit: Final = options["base_commit"] + base: Final = base_commit if base_commit else _merge_base(options["base_ref"] or "") previous: Final = subprocess.run( ("git", "show", f"{base}:schema.prisma"), capture_output=True, text=True, check=True ).stdout diff --git a/terraform/litellm/aws/README.md b/terraform/litellm/aws/README.md index bedb6f4e98b..6f0450c0091 100644 --- a/terraform/litellm/aws/README.md +++ b/terraform/litellm/aws/README.md @@ -212,33 +212,23 @@ orthogonal to the OTLP variables above; their credentials still go in ### Enterprise billing metrics -License-gated request metering is opt-in and gated entirely on -`billing_metrics_endpoint`. Empty (default) and no billing env is added to -the container, so existing deployments are unchanged. Set it and both -gateway and backend export billable-request counts over OTLP/HTTP, -authenticating to the collector with the mTLS client certificate issued for -your deployment. - -The proxy accepts the certificate, key, and CA bundle as either a file path -or literal PEM content. This stack takes the PEM, writes each one to its own -Secrets Manager entry, grants the task-execution role -`secretsmanager:GetSecretValue` on them, and injects them as -`LITELLM_BILLING_METRICS_CLIENT_CERT` / `_CLIENT_KEY` (and `_CA_CERT` when -set), so no volume mount is needed on Fargate. +License-gated request metering uses Cognito bearer authentication. Set the +endpoint, client ID, and token URL as plain values, and provide the client +secret through the sensitive Terraform variable. The client secret is stored +in Secrets Manager and injected into both gateway and backend tasks ```hcl billing_metrics_endpoint = "https://telemetry.litellm.ai/v1/metrics" +billing_metrics_client_id = "cognito-client-id" +billing_metrics_token_url = "https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token" ``` ```bash -export TF_VAR_billing_metrics_client_cert_pem="$(cat client.crt)" -export TF_VAR_billing_metrics_client_key_pem="$(cat client.key)" +export TF_VAR_billing_metrics_client_secret="cognito-client-secret" ``` -`billing_metrics_ca_cert_pem` is only for private or test collectors whose -CA is not in the system trust store; leave it empty against -`telemetry.litellm.ai`. Metering requires an enterprise license, so pair -this with `litellm_license`. To tune the export cadence, set +The client ID and client secret are required when the endpoint is set +Metering also requires an enterprise license. To tune the export cadence, set `LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS` through `gateway_extra_env` / `backend_extra_env` diff --git a/terraform/litellm/aws/ecs.tf b/terraform/litellm/aws/ecs.tf index 412e6c423f5..dec7f599ced 100644 --- a/terraform/litellm/aws/ecs.tf +++ b/terraform/litellm/aws/ecs.tf @@ -79,32 +79,19 @@ locals { { name = "OTEL_HEADERS", valueFrom = var.otel_headers_secret_arn }, ] : [] - # Enterprise request metering, gated on billing_metrics_endpoint. The - # endpoint rides in as a plain env var; the mTLS material is stored in - # Secrets Manager (secrets.tf) and injected as PEM-valued env vars, which - # the proxy accepts in place of file paths. Each PEM is wired only when the - # operator supplied it, so an empty ca_cert_pem falls back to the system - # trust store. - billing_metrics_enabled = var.billing_metrics_endpoint != "" - billing_metrics_client_cert_enabled = local.billing_metrics_enabled && var.billing_metrics_client_cert_pem != "" - billing_metrics_client_key_enabled = local.billing_metrics_enabled && var.billing_metrics_client_key_pem != "" - billing_metrics_ca_cert_enabled = local.billing_metrics_enabled && var.billing_metrics_ca_cert_pem != "" + # Enterprise request metering, gated on billing_metrics_endpoint. + billing_metrics_enabled = var.billing_metrics_endpoint != "" + billing_metrics_client_secret_enabled = local.billing_metrics_enabled && var.billing_metrics_client_secret != "" billing_metrics_env = local.billing_metrics_enabled ? [ { name = "LITELLM_BILLING_METRICS_ENDPOINT", value = var.billing_metrics_endpoint }, + { name = "LITELLM_BILLING_METRICS_CLIENT_ID", value = var.billing_metrics_client_id }, + { name = "LITELLM_BILLING_METRICS_TOKEN_URL", value = var.billing_metrics_token_url }, ] : [] - billing_metrics_secrets = concat( - local.billing_metrics_client_cert_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CLIENT_CERT", valueFrom = aws_secretsmanager_secret.billing_metrics_client_cert[0].arn }, - ] : [], - local.billing_metrics_client_key_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CLIENT_KEY", valueFrom = aws_secretsmanager_secret.billing_metrics_client_key[0].arn }, - ] : [], - local.billing_metrics_ca_cert_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CA_CERT", valueFrom = aws_secretsmanager_secret.billing_metrics_ca_cert[0].arn }, - ] : [], - ) + billing_metrics_secrets = local.billing_metrics_client_secret_enabled ? [ + { name = "LITELLM_BILLING_METRICS_CLIENT_SECRET", valueFrom = aws_secretsmanager_secret.billing_metrics_client_secret[0].arn }, + ] : [] managed_db_env = var.create_database ? [ { name = "IAM_TOKEN_DB_AUTH", value = "true" }, @@ -338,27 +325,12 @@ locals { # ---------- Gateway ---------- resource "aws_ecs_task_definition" "gateway" { - # Metering needs a client certificate AND its key. Each secret is created only - # when its own PEM is supplied, so an endpoint set with a missing key would - # otherwise apply cleanly and leave the proxy logging "missing config" and - # never exporting. ca_cert_pem stays optional: empty means fall back to the - # system trust store. - # - # The guard lives here, on an unconditional resource, rather than on the cert - # secret: that secret is count-gated on the cert itself, so it has zero - # instances in exactly the case this must catch. Adding count or for_each to - # this resource would silently stop the guard from evaluating. - # - # endpoint cert key -> result - # "" any any -> metering off, no secrets created - # set set set -> metering on - # set any-missing -> plan fails here lifecycle { precondition { condition = var.billing_metrics_endpoint == "" || ( - var.billing_metrics_client_cert_pem != "" && var.billing_metrics_client_key_pem != "" + var.billing_metrics_client_id != "" && var.billing_metrics_client_secret != "" ) - error_message = "billing_metrics_client_cert_pem and billing_metrics_client_key_pem are both required when billing_metrics_endpoint is set." + error_message = "billing_metrics_client_id and billing_metrics_client_secret are required when billing_metrics_endpoint is set." } precondition { @@ -465,9 +437,7 @@ resource "aws_ecs_service" "gateway" { aws_secretsmanager_secret_version.license, aws_secretsmanager_secret_version.database_url, aws_secretsmanager_secret_version.redis_url, - aws_secretsmanager_secret_version.billing_metrics_client_cert, - aws_secretsmanager_secret_version.billing_metrics_client_key, - aws_secretsmanager_secret_version.billing_metrics_ca_cert, + aws_secretsmanager_secret_version.billing_metrics_client_secret, ] tags = local.tags @@ -481,9 +451,9 @@ resource "aws_ecs_task_definition" "backend" { lifecycle { precondition { condition = var.billing_metrics_endpoint == "" || ( - var.billing_metrics_client_cert_pem != "" && var.billing_metrics_client_key_pem != "" + var.billing_metrics_client_id != "" && var.billing_metrics_client_secret != "" ) - error_message = "billing_metrics_client_cert_pem and billing_metrics_client_key_pem are both required when billing_metrics_endpoint is set." + error_message = "billing_metrics_client_id and billing_metrics_client_secret are required when billing_metrics_endpoint is set." } } @@ -566,9 +536,7 @@ resource "aws_ecs_service" "backend" { aws_secretsmanager_secret_version.ui_password, aws_secretsmanager_secret_version.database_url, aws_secretsmanager_secret_version.redis_url, - aws_secretsmanager_secret_version.billing_metrics_client_cert, - aws_secretsmanager_secret_version.billing_metrics_client_key, - aws_secretsmanager_secret_version.billing_metrics_ca_cert, + aws_secretsmanager_secret_version.billing_metrics_client_secret, ] tags = local.tags diff --git a/terraform/litellm/aws/iam.tf b/terraform/litellm/aws/iam.tf index 3c55f07b02a..a653bd82d31 100644 --- a/terraform/litellm/aws/iam.tf +++ b/terraform/litellm/aws/iam.tf @@ -53,9 +53,7 @@ data "aws_iam_policy_document" "secrets_access" { [aws_secretsmanager_secret.master_key.arn], aws_secretsmanager_secret.license[*].arn, aws_secretsmanager_secret.ui_password[*].arn, - aws_secretsmanager_secret.billing_metrics_client_cert[*].arn, - aws_secretsmanager_secret.billing_metrics_client_key[*].arn, - aws_secretsmanager_secret.billing_metrics_ca_cert[*].arn, + aws_secretsmanager_secret.billing_metrics_client_secret[*].arn, aws_secretsmanager_secret.database_url[*].arn, aws_secretsmanager_secret.redis_url[*].arn, local.extra_secret_arns, diff --git a/terraform/litellm/aws/secrets.tf b/terraform/litellm/aws/secrets.tf index 921bae4d827..6821b8b43dc 100644 --- a/terraform/litellm/aws/secrets.tf +++ b/terraform/litellm/aws/secrets.tf @@ -75,59 +75,21 @@ resource "aws_secretsmanager_secret_version" "ui_password" { secret_string = var.ui_password } -# Billing-metrics mTLS material — only created when metering is enabled -# (billing_metrics_endpoint non-empty) and the operator supplied the PEM. -# The task-execution role gets GetSecretValue via iam.tf, and gateway + -# backend pick the env vars up through shared_secrets in ecs.tf. -resource "aws_secretsmanager_secret" "billing_metrics_client_cert" { - count = local.billing_metrics_client_cert_enabled ? 1 : 0 +resource "aws_secretsmanager_secret" "billing_metrics_client_secret" { + count = local.billing_metrics_client_secret_enabled ? 1 : 0 - name = "${local.name}-billing-metrics-client-cert" - description = "LITELLM_BILLING_METRICS_CLIENT_CERT for gateway + backend." + name = "${local.name}-billing-metrics-client-secret" + description = "LITELLM_BILLING_METRICS_CLIENT_SECRET for gateway + backend." recovery_window_in_days = 0 tags = local.tags } -resource "aws_secretsmanager_secret_version" "billing_metrics_client_cert" { - count = local.billing_metrics_client_cert_enabled ? 1 : 0 +resource "aws_secretsmanager_secret_version" "billing_metrics_client_secret" { + count = local.billing_metrics_client_secret_enabled ? 1 : 0 - secret_id = aws_secretsmanager_secret.billing_metrics_client_cert[0].id - secret_string = var.billing_metrics_client_cert_pem -} - -resource "aws_secretsmanager_secret" "billing_metrics_client_key" { - count = local.billing_metrics_client_key_enabled ? 1 : 0 - - name = "${local.name}-billing-metrics-client-key" - description = "LITELLM_BILLING_METRICS_CLIENT_KEY for gateway + backend." - recovery_window_in_days = 0 - - tags = local.tags -} - -resource "aws_secretsmanager_secret_version" "billing_metrics_client_key" { - count = local.billing_metrics_client_key_enabled ? 1 : 0 - - secret_id = aws_secretsmanager_secret.billing_metrics_client_key[0].id - secret_string = var.billing_metrics_client_key_pem -} - -resource "aws_secretsmanager_secret" "billing_metrics_ca_cert" { - count = local.billing_metrics_ca_cert_enabled ? 1 : 0 - - name = "${local.name}-billing-metrics-ca-cert" - description = "LITELLM_BILLING_METRICS_CA_CERT for gateway + backend." - recovery_window_in_days = 0 - - tags = local.tags -} - -resource "aws_secretsmanager_secret_version" "billing_metrics_ca_cert" { - count = local.billing_metrics_ca_cert_enabled ? 1 : 0 - - secret_id = aws_secretsmanager_secret.billing_metrics_ca_cert[0].id - secret_string = var.billing_metrics_ca_cert_pem + secret_id = aws_secretsmanager_secret.billing_metrics_client_secret[0].id + secret_string = var.billing_metrics_client_secret } resource "aws_secretsmanager_secret" "db_master_password" { diff --git a/terraform/litellm/aws/variables.tf b/terraform/litellm/aws/variables.tf index 9b7a279a907..be7656c2d48 100644 --- a/terraform/litellm/aws/variables.tf +++ b/terraform/litellm/aws/variables.tf @@ -764,13 +764,7 @@ variable "otel_headers_secret_arn" { # ---------- Enterprise billing metrics ---------- # -# License-gated request metering. Opt-in and gated entirely on -# billing_metrics_endpoint: leave it empty (the default) and nothing -# metering-related lands in the container env. Set it and gateway + backend -# export billable-request counts over OTLP/HTTP, authenticating to the -# collector with an mTLS client cert. The proxy accepts the cert, key, and CA -# as either a file path or literal PEM content, so on Fargate they are -# injected straight from Secrets Manager as env vars and no volume is needed. +# License-gated request metering. The client secret is stored in Secrets Manager. variable "billing_metrics_endpoint" { description = <<-EOT @@ -784,44 +778,34 @@ variable "billing_metrics_endpoint" { default = "" } -variable "billing_metrics_client_cert_pem" { +variable "billing_metrics_client_id" { description = <<-EOT - PEM content of the mTLS client certificate issued for this deployment. - When billing_metrics_endpoint is set, the stack stores this in a - `-litellm--billing-metrics-client-cert` Secrets Manager - entry, grants the task-execution role GetSecretValue on it, and exposes - it to gateway + backend as LITELLM_BILLING_METRICS_CLIENT_CERT. Required - whenever metering is enabled. + Cognito OAuth2 client ID exposed to the gateway and backend as + LITELLM_BILLING_METRICS_CLIENT_ID. Required when billing_metrics_endpoint + is set. + EOT + type = string + default = "" +} + +variable "billing_metrics_client_secret" { + description = <<-EOT + Cognito OAuth2 client secret stored in Secrets Manager and exposed to + the gateway and backend as LITELLM_BILLING_METRICS_CLIENT_SECRET. + Required when billing_metrics_endpoint is set. EOT type = string default = "" sensitive = true } -variable "billing_metrics_client_key_pem" { +variable "billing_metrics_token_url" { description = <<-EOT - PEM content of the private key matching - billing_metrics_client_cert_pem. Stored in a - `-litellm--billing-metrics-client-key` Secrets Manager - entry and exposed as LITELLM_BILLING_METRICS_CLIENT_KEY. Required - whenever metering is enabled. + Cognito OAuth2 client-credentials token endpoint exposed to gateway and + backend as LITELLM_BILLING_METRICS_TOKEN_URL. EOT type = string - default = "" - sensitive = true -} - -variable "billing_metrics_ca_cert_pem" { - description = <<-EOT - PEM content of the CA bundle used to verify the metering collector. - Only needed for private or test collectors whose CA is not in the - system trust store; telemetry.litellm.ai is publicly trusted, so leave - this empty for production. When set, it is exposed as - LITELLM_BILLING_METRICS_CA_CERT. - EOT - type = string - default = "" - sensitive = true + default = "https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token" } # ---------- Collector sidecar ---------- diff --git a/terraform/litellm/gcp/README.md b/terraform/litellm/gcp/README.md index 3227dcac179..9e21ba14543 100644 --- a/terraform/litellm/gcp/README.md +++ b/terraform/litellm/gcp/README.md @@ -210,33 +210,23 @@ Behavior matches the AWS stack 1:1; the only naming differences are ### Enterprise billing metrics -License-gated request metering is opt-in and gated entirely on -`billing_metrics_endpoint`. Empty (default) and no billing env is added to -the container, so existing deployments are unchanged. Set it and both -gateway and backend export billable-request counts over OTLP/HTTP, -authenticating to the collector with the mTLS client certificate issued for -your deployment. - -The proxy accepts the certificate, key, and CA bundle as either a file path -or literal PEM content. This stack takes the PEM, writes each one to its own -Secret Manager entry, grants the runtime service account -`roles/secretmanager.secretAccessor` on them, and injects them as Cloud Run -secret env vars `LITELLM_BILLING_METRICS_CLIENT_CERT` / `_CLIENT_KEY` (and -`_CA_CERT` when set), so no volume mount is needed. +License-gated request metering uses Cognito bearer authentication. Set the +endpoint, client ID, and token URL as plain values, and provide the client +secret through the sensitive Terraform variable. The client secret is stored +in Secret Manager and injected into both gateway and backend services ```hcl billing_metrics_endpoint = "https://telemetry.litellm.ai/v1/metrics" +billing_metrics_client_id = "cognito-client-id" +billing_metrics_token_url = "https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token" ``` ```bash -export TF_VAR_billing_metrics_client_cert_pem="$(cat client.crt)" -export TF_VAR_billing_metrics_client_key_pem="$(cat client.key)" +export TF_VAR_billing_metrics_client_secret="cognito-client-secret" ``` -`billing_metrics_ca_cert_pem` is only for private or test collectors whose -CA is not in the system trust store; leave it empty against -`telemetry.litellm.ai`. Metering requires an enterprise license, so pair -this with `litellm_license`. To tune the export cadence, set +The client ID and client secret are required when the endpoint is set +Metering also requires an enterprise license. To tune the export cadence, set `LITELLM_BILLING_METRICS_EXPORT_INTERVAL_MS` through `gateway_extra_env` / `backend_extra_env` diff --git a/terraform/litellm/gcp/cloudrun.tf b/terraform/litellm/gcp/cloudrun.tf index d0b32a367d6..07bf68b3a62 100644 --- a/terraform/litellm/gcp/cloudrun.tf +++ b/terraform/litellm/gcp/cloudrun.tf @@ -62,32 +62,19 @@ locals { { name = "OTEL_HEADERS", secret = var.otel_headers_secret, version = "latest" }, ] : [] - # Enterprise request metering, gated on billing_metrics_endpoint. The - # endpoint rides in as a plain env var; the mTLS material lives in Secret - # Manager (secrets.tf) and is injected as PEM-valued env vars, which the - # proxy accepts in place of file paths. Each PEM is wired only when the - # operator supplied it, so an empty ca_cert_pem falls back to the system - # trust store. - billing_metrics_enabled = var.billing_metrics_endpoint != "" - billing_metrics_client_cert_enabled = local.billing_metrics_enabled && var.billing_metrics_client_cert_pem != "" - billing_metrics_client_key_enabled = local.billing_metrics_enabled && var.billing_metrics_client_key_pem != "" - billing_metrics_ca_cert_enabled = local.billing_metrics_enabled && var.billing_metrics_ca_cert_pem != "" + # Enterprise request metering, gated on billing_metrics_endpoint. + billing_metrics_enabled = var.billing_metrics_endpoint != "" + billing_metrics_client_secret_enabled = local.billing_metrics_enabled && var.billing_metrics_client_secret != "" billing_metrics_env_kv = local.billing_metrics_enabled ? [ { name = "LITELLM_BILLING_METRICS_ENDPOINT", value = var.billing_metrics_endpoint }, + { name = "LITELLM_BILLING_METRICS_CLIENT_ID", value = var.billing_metrics_client_id }, + { name = "LITELLM_BILLING_METRICS_TOKEN_URL", value = var.billing_metrics_token_url }, ] : [] - billing_metrics_env_secrets = concat( - local.billing_metrics_client_cert_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CLIENT_CERT", secret = google_secret_manager_secret.billing_metrics_client_cert[0].id, version = "latest" }, - ] : [], - local.billing_metrics_client_key_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CLIENT_KEY", secret = google_secret_manager_secret.billing_metrics_client_key[0].id, version = "latest" }, - ] : [], - local.billing_metrics_ca_cert_enabled ? [ - { name = "LITELLM_BILLING_METRICS_CA_CERT", secret = google_secret_manager_secret.billing_metrics_ca_cert[0].id, version = "latest" }, - ] : [], - ) + billing_metrics_env_secrets = local.billing_metrics_client_secret_enabled ? [ + { name = "LITELLM_BILLING_METRICS_CLIENT_SECRET", secret = google_secret_manager_secret.billing_metrics_client_secret[0].id, version = "latest" }, + ] : [] # Cloud Run v2 secret env vars use value_source.secret_key_ref pointing at a # secret resource ID. Shared between gateway and backend (the migrations @@ -481,9 +468,7 @@ resource "google_cloud_run_v2_service" "gateway" { google_secret_manager_secret_iam_member.license, google_secret_manager_secret_iam_member.extras, google_secret_manager_secret_iam_member.otel_headers, - google_secret_manager_secret_iam_member.billing_metrics_client_cert, - google_secret_manager_secret_iam_member.billing_metrics_client_key, - google_secret_manager_secret_iam_member.billing_metrics_ca_cert, + google_secret_manager_secret_iam_member.billing_metrics_client_secret, google_secret_manager_secret_iam_member.metrics_run_monitoring, google_project_iam_member.runtime_metric_writer, google_storage_bucket_iam_member.proxy_config_runtime, @@ -603,9 +588,7 @@ resource "google_cloud_run_v2_service" "backend" { google_secret_manager_secret_iam_member.ui_password, google_secret_manager_secret_iam_member.extras, google_secret_manager_secret_iam_member.otel_headers, - google_secret_manager_secret_iam_member.billing_metrics_client_cert, - google_secret_manager_secret_iam_member.billing_metrics_client_key, - google_secret_manager_secret_iam_member.billing_metrics_ca_cert, + google_secret_manager_secret_iam_member.billing_metrics_client_secret, google_storage_bucket_iam_member.proxy_config_runtime, google_sql_user.app, terraform_data.migration, diff --git a/terraform/litellm/gcp/iam.tf b/terraform/litellm/gcp/iam.tf index 5c2bc38bc12..1e0a5fe9626 100644 --- a/terraform/litellm/gcp/iam.tf +++ b/terraform/litellm/gcp/iam.tf @@ -10,9 +10,9 @@ resource "google_service_account" "runtime" { lifecycle { precondition { condition = !var.create_runtime || var.billing_metrics_endpoint == "" || ( - var.billing_metrics_client_cert_pem != "" && var.billing_metrics_client_key_pem != "" + var.billing_metrics_client_id != "" && var.billing_metrics_client_secret != "" ) - error_message = "billing_metrics_client_cert_pem and billing_metrics_client_key_pem are both required when billing_metrics_endpoint is set and create_runtime is true." + error_message = "billing_metrics_client_id and billing_metrics_client_secret are required when billing_metrics_endpoint is set and create_runtime is true." } } } @@ -91,28 +91,10 @@ resource "google_secret_manager_secret_iam_member" "otel_headers" { member = "serviceAccount:${google_service_account.runtime.email}" } -# Billing-metrics mTLS accessors — only created when request metering is -# enabled and the matching PEM was supplied. -resource "google_secret_manager_secret_iam_member" "billing_metrics_client_cert" { - count = local.billing_metrics_client_cert_enabled ? 1 : 0 +resource "google_secret_manager_secret_iam_member" "billing_metrics_client_secret" { + count = local.billing_metrics_client_secret_enabled ? 1 : 0 - secret_id = google_secret_manager_secret.billing_metrics_client_cert[0].id - role = "roles/secretmanager.secretAccessor" - member = "serviceAccount:${google_service_account.runtime.email}" -} - -resource "google_secret_manager_secret_iam_member" "billing_metrics_client_key" { - count = local.billing_metrics_client_key_enabled ? 1 : 0 - - secret_id = google_secret_manager_secret.billing_metrics_client_key[0].id - role = "roles/secretmanager.secretAccessor" - member = "serviceAccount:${google_service_account.runtime.email}" -} - -resource "google_secret_manager_secret_iam_member" "billing_metrics_ca_cert" { - count = local.billing_metrics_ca_cert_enabled ? 1 : 0 - - secret_id = google_secret_manager_secret.billing_metrics_ca_cert[0].id + secret_id = google_secret_manager_secret.billing_metrics_client_secret[0].id role = "roles/secretmanager.secretAccessor" member = "serviceAccount:${google_service_account.runtime.email}" } diff --git a/terraform/litellm/gcp/secrets.tf b/terraform/litellm/gcp/secrets.tf index b3b656721ab..f3636b6e8e8 100644 --- a/terraform/litellm/gcp/secrets.tf +++ b/terraform/litellm/gcp/secrets.tf @@ -64,59 +64,21 @@ resource "google_secret_manager_secret_version" "ui_password" { secret_data = var.ui_password } -# Billing-metrics mTLS material — only created when metering is enabled -# (billing_metrics_endpoint non-empty) and the operator supplied the PEM. -# The runtime SA gets accessor permission via iam.tf, and gateway + backend -# pick the env vars up through billing_metrics_env_secrets in cloudrun.tf. -resource "google_secret_manager_secret" "billing_metrics_client_cert" { - count = local.billing_metrics_client_cert_enabled ? 1 : 0 +resource "google_secret_manager_secret" "billing_metrics_client_secret" { + count = local.billing_metrics_client_secret_enabled ? 1 : 0 - secret_id = "${local.name}-billing-metrics-client-cert" + secret_id = "${local.name}-billing-metrics-client-secret" labels = local.labels replication { auto {} } } -resource "google_secret_manager_secret_version" "billing_metrics_client_cert" { - count = local.billing_metrics_client_cert_enabled ? 1 : 0 +resource "google_secret_manager_secret_version" "billing_metrics_client_secret" { + count = local.billing_metrics_client_secret_enabled ? 1 : 0 - secret = google_secret_manager_secret.billing_metrics_client_cert[0].id - secret_data = var.billing_metrics_client_cert_pem -} - -resource "google_secret_manager_secret" "billing_metrics_client_key" { - count = local.billing_metrics_client_key_enabled ? 1 : 0 - - secret_id = "${local.name}-billing-metrics-client-key" - labels = local.labels - replication { - auto {} - } -} - -resource "google_secret_manager_secret_version" "billing_metrics_client_key" { - count = local.billing_metrics_client_key_enabled ? 1 : 0 - - secret = google_secret_manager_secret.billing_metrics_client_key[0].id - secret_data = var.billing_metrics_client_key_pem -} - -resource "google_secret_manager_secret" "billing_metrics_ca_cert" { - count = local.billing_metrics_ca_cert_enabled ? 1 : 0 - - secret_id = "${local.name}-billing-metrics-ca-cert" - labels = local.labels - replication { - auto {} - } -} - -resource "google_secret_manager_secret_version" "billing_metrics_ca_cert" { - count = local.billing_metrics_ca_cert_enabled ? 1 : 0 - - secret = google_secret_manager_secret.billing_metrics_ca_cert[0].id - secret_data = var.billing_metrics_ca_cert_pem + secret = google_secret_manager_secret.billing_metrics_client_secret[0].id + secret_data = var.billing_metrics_client_secret } resource "google_secret_manager_secret" "metrics_run_monitoring" { diff --git a/terraform/litellm/gcp/variables.tf b/terraform/litellm/gcp/variables.tf index 6161e893e02..e40ee36fa18 100644 --- a/terraform/litellm/gcp/variables.tf +++ b/terraform/litellm/gcp/variables.tf @@ -602,14 +602,7 @@ variable "gateway_metrics_collector_image" { # ---------- Enterprise billing metrics ---------- # -# License-gated request metering. Opt-in and gated entirely on -# billing_metrics_endpoint: leave it empty (the default) and nothing -# metering-related is added to the container env. Set it and gateway + -# backend export billable-request counts over OTLP/HTTP, authenticating to -# the collector with an mTLS client cert. The proxy accepts the cert, key, -# and CA as either a file path or literal PEM content, so on Cloud Run they -# are injected straight from Secret Manager as env vars and no volume is -# needed. +# License-gated request metering. The client secret is stored in Secret Manager. variable "billing_metrics_endpoint" { description = <<-EOT @@ -623,44 +616,34 @@ variable "billing_metrics_endpoint" { default = "" } -variable "billing_metrics_client_cert_pem" { +variable "billing_metrics_client_id" { description = <<-EOT - PEM content of the mTLS client certificate issued for this deployment. - When billing_metrics_endpoint is set, the stack stores this in a - `-litellm--billing-metrics-client-cert` Secret Manager - entry, grants the runtime SA accessor on it, and exposes it to gateway + - backend as LITELLM_BILLING_METRICS_CLIENT_CERT. Required whenever - metering is enabled. + Cognito OAuth2 client ID exposed to the gateway and backend as + LITELLM_BILLING_METRICS_CLIENT_ID. Required when billing_metrics_endpoint + is set. + EOT + type = string + default = "" +} + +variable "billing_metrics_client_secret" { + description = <<-EOT + Cognito OAuth2 client secret stored in Secret Manager and exposed to + the gateway and backend as LITELLM_BILLING_METRICS_CLIENT_SECRET. + Required when billing_metrics_endpoint is set. EOT type = string default = "" sensitive = true } -variable "billing_metrics_client_key_pem" { +variable "billing_metrics_token_url" { description = <<-EOT - PEM content of the private key matching - billing_metrics_client_cert_pem. Stored in a - `-litellm--billing-metrics-client-key` Secret Manager entry - and exposed as LITELLM_BILLING_METRICS_CLIENT_KEY. Required whenever - metering is enabled. + Cognito OAuth2 client-credentials token endpoint exposed to gateway and + backend as LITELLM_BILLING_METRICS_TOKEN_URL. EOT type = string - default = "" - sensitive = true -} - -variable "billing_metrics_ca_cert_pem" { - description = <<-EOT - PEM content of the CA bundle used to verify the metering collector. - Only needed for private or test collectors whose CA is not in the - system trust store; telemetry.litellm.ai is publicly trusted, so leave - this empty for production. When set, it is exposed as - LITELLM_BILLING_METRICS_CA_CERT. - EOT - type = string - default = "" - sensitive = true + default = "https://litellm-prod.auth.us-west-1.amazoncognito.com/oauth2/token" } # ---------- Collector sidecar ---------- diff --git a/tests/_support/mcp_count_tool_upstream.py b/tests/_support/mcp_count_tool_upstream.py new file mode 100644 index 00000000000..f1aa61c7dba --- /dev/null +++ b/tests/_support/mcp_count_tool_upstream.py @@ -0,0 +1,42 @@ +import json +import queue +from collections.abc import Callable, Mapping +from typing import Final + +import httpx2 + +COUNT_OUTPUT_SCHEMA: Final = {"type": "object", "properties": {"n": {"type": "integer"}}, "required": ["n"]} + + +def count_tool_upstream( + structured: Mapping[str, object], methods_seen: "queue.SimpleQueue[str]" +) -> Callable[[httpx2.Request], httpx2.Response]: + """An MCP upstream with one tool, `count`, whose declared output schema wants an integer `n`.""" + + def respond(request: httpx2.Request) -> httpx2.Response: + if request.method != "POST": + return httpx2.Response(200) + payload: Final = json.loads(request.content) + methods_seen.put(payload["method"]) + if "id" not in payload: + return httpx2.Response(202) + results: Final[Mapping[str, Mapping[str, object]]] = { + "initialize": { + "protocolVersion": payload["params"].get("protocolVersion"), + "capabilities": {"tools": {}}, + "serverInfo": {"name": "count", "version": "1"}, + }, + "tools/list": { + "tools": [{"name": "count", "inputSchema": {"type": "object"}, "outputSchema": COUNT_OUTPUT_SCHEMA}] + }, + "tools/call": {"content": [{"type": "text", "text": "ok"}], "structuredContent": structured}, + } + return httpx2.Response( + 200, json={"jsonrpc": "2.0", "id": payload["id"], "result": results.get(payload["method"], {})} + ) + + return respond + + +def methods_received(methods_seen: "queue.SimpleQueue[str]") -> tuple[str, ...]: + return tuple(methods_seen.get_nowait() for _ in range(methods_seen.qsize())) diff --git a/tests/code_coverage_tests/router_code_coverage.py b/tests/code_coverage_tests/router_code_coverage.py index 57ae9f1905f..42ee6178bf0 100644 --- a/tests/code_coverage_tests/router_code_coverage.py +++ b/tests/code_coverage_tests/router_code_coverage.py @@ -176,6 +176,15 @@ ignored_function_names = [ "_deployment_field", # Read by is_batch_capable, tested through the mixed-group tests in test_router.py "_deployment_provider", # Read by without_deployments_that_cannot_batch, tested through the mixed-group tests in test_router.py "_cannot_run_batches", # Tested through the Azure and OpenAI mixed-group acreate_batch tests in test_router.py + "job_cost_of_poll", # Read by job_to_charge_once and charged_video_id, tested through the guardrail-priced poll tests in test_batch_utils.py + "charged_video_id", # Read by job_to_charge_once and get_spend_logs_id, tested through both in test_batch_utils.py and test_spend_tracking_utils.py + "_requested_video_id", # Read by charged_video_id, tested through the polled-id video test in test_batch_utils.py + "_guardrail_cost_of_poll", # Read by job_cost_of_poll and repeat_poll_cost, tested through the guardrail-priced poll tests + "repeat_poll_cost", # Read by job_to_charge_once and _charge_request_cost, tested through every limiter's guardrail poll test + "pays_only_its_guardrail", # Read by get_spend_logs_id, tested through its guardrail-only poll test in test_spend_tracking_utils.py + "charged_poll_cost", # Read by get_spend_logs_id, tested through the finished job guardrail poll tests in test_db_spend_update_writer.py + "_polled_batch", # Read by defers_batch_cost and _charged_batch, tested through the redacted batch retrieve tests + "_charged_batch", # Read by job_to_charge_once, tested through the batch charge identity tests in test_batch_utils.py "_responses_continuation", # Tested through the Responses mid-stream fallback tests in tests/unit/llms/base_llm/responses/test_transformation.py "_responses_continuation_ceilings", # Tested through the Responses mid-stream fallback tests in tests/unit/llms/base_llm/responses/test_transformation.py "_team_deployments_named", # Tested through the team lookups in the concurrent delete tests in test_router.py diff --git a/tests/code_coverage_tests/test_provider_cache.py b/tests/code_coverage_tests/test_provider_cache.py index fc39f2c1e6c..81ad7913559 100644 --- a/tests/code_coverage_tests/test_provider_cache.py +++ b/tests/code_coverage_tests/test_provider_cache.py @@ -377,7 +377,11 @@ def test_stream_completion_controls_publication(store: RedisResponseStore, provi assert len(provider.hits) == (2 if truncated else 1) -def test_store_outage_preserves_provider_success(provider: Provider) -> None: +@pytest.mark.parametrize("cluster_mode", ("0", "1")) +def test_store_outage_preserves_provider_success( + provider: Provider, monkeypatch: pytest.MonkeyPatch, cluster_mode: str, +) -> None: + monkeypatch.setenv("E2E_PROVIDER_CACHE_REDIS_CLUSTER", cluster_mode) with socket.socket() as probe: probe.bind(("127.0.0.1", 0)) port: Final = probe.getsockname()[1] @@ -401,6 +405,20 @@ def test_old_lease_cannot_overwrite_new_owner(store: RedisResponseStore) -> None assert isinstance(hit, CacheHit) and hit.payload == b"new" +def test_separate_namespaces_keep_their_own_response(store: RedisResponseStore) -> None: + other: Final = replace(store, namespace=store.namespace + "-other") + first_lease: Final = store.lookup("shared-key") + assert isinstance(first_lease, CaptureLease) + assert store.publish("shared-key", first_lease, b"first namespace") + other_lease: Final = other.lookup("shared-key") + assert isinstance(other_lease, CaptureLease) + assert other.publish("shared-key", other_lease, b"other namespace") + first_hit: Final = store.lookup("shared-key") + other_hit: Final = other.lookup("shared-key") + assert isinstance(first_hit, CacheHit) and first_hit.payload == b"first namespace" + assert isinstance(other_hit, CacheHit) and other_hit.payload == b"other namespace" + + def test_identity_preserves_values_and_never_contains_credentials() -> None: variants: Final = (b'{}', b'{"a":null}', b'{"a":false}', b'{"a":0}', b'{"a":0.0}', b'{"a":"0"}', b' { }', None, b'') url: Final = "https://example.invalid/v1/chat/completions" diff --git a/tests/e2e/AGENTS.md b/tests/e2e/AGENTS.md index 7843c1a4091..b4ca8bd3d50 100644 --- a/tests/e2e/AGENTS.md +++ b/tests/e2e/AGENTS.md @@ -32,25 +32,25 @@ Each subdirectory under `tests/e2e/` is one suite, scoped to an endpoint family - `migrations/` - isolated Docker startup, concurrent migration, crash recovery, and legacy database compatibility. The CircleCI migration workflow enables `LITELLM_MIGRATION_TESTS=1`. These tests own their proxy containers and databases, so they do not use the shared proxy preflight or shared database cleanup -- `llm_translation/` - LLM endpoint and provider-translation behavior: passthrough, custom pricing, OCR, and the non-chat inference endpoints (`/v1/responses`, `/v1/messages`, `/embeddings`, `/v1/rerank`, `/v1/audio/speech`, `/v1/images/generations`), each against a deployment the test creates via `/model/new` and deletes on teardown +- `llm_translation/` - Azure realtime translation is explicitly skipped pending investigation of intermittent missing transcripts and remains unqualified. OpenAI translation and other Azure realtime cases remain enabled. LLM endpoint and provider-translation behavior: passthrough, custom pricing, OCR, and the non-chat inference endpoints (`/v1/responses`, `/v1/messages`, `/embeddings`, `/v1/rerank`, `/v1/audio/speech`, `/v1/images/generations`), each against a deployment the test creates via `/model/new` and deletes on teardown - `access_control/` - the gateway's authorization and error-shape contract: per-key model allow-lists, route-group permissions (`allowed_routes`), and unknown-model validation - `embeddings/` - the `/embeddings` endpoint across providers -- `batches/` - the `/batches` endpoint (placeholder until the first test lands) +- `batches/` - the `/batches` endpoint. GovCloud batch coverage is explicitly skipped until its infrastructure is provisioned. Other missing credentials and provider errors remain failures - `realtime/` - realtime websocket sessions, including the pipecat audio path - `quota_management/` - quota enforcement and accounting, one subfolder per behavior: `ratelimit/` (rpm/tpm blocks, window reset, pacing headers on live traffic), `budgets/` (budget definition, enforcement, and reset windows: key, team, tag, soft, multi-window), and `spend_tracking/` (spend logging and cost attribution on `/spend/*`) - `management/` - key/team/user/organization management routes: create/update/delete persistence via the info routes, team membership, and llm-only-key route denials (API surface, not Playwright) - `a2a/` - the A2A (agent-to-agent) surface: admin registration via `/v1/agents`, proxy-fronted card discovery at `/.well-known/agent-card.json`, and JSON-RPC `message/send` invocation, driving agents backed by the litellm completion bridge (a real provider) and asserting protocol-version normalization (0.3 vs 1.0) -- `mcp/` - the MCP server surface over api_key auth against the real Datadog remote MCP server (see "MCP suite: real Datadog only" below) +- `mcp/` - the MCP server surface over api_key auth against the real Datadog remote MCP server (see "MCP suite: real Datadog only" below). The semantic tool filter test turns on a proxy-wide setting, so it is marked `proxy_wide_config` and runs on the redeployed managed-files stack after the main run, never next to it - `logging/` - logging-integration delivery (datadog and friends) - `security/` - secret handling and log-leak protection -- `router/` - routing and reliability behavior (fallbacks, cooldowns) plus the memory tests (`test_reliability_memory_e2e.py`: every worker's RSS as read at collection time, before any test traffic, must sit under a fixed idle budget, the release-gate check for a DB-backed boot that idles near the pod limit the way v1.100.x did, and a few hundred failing requests with retries and fallbacks must not grow proxy RSS past a loose ceiling over the measured batches nor store a request snapshot past a fixed size, the release-gate check for the v1.100.0 retry-breadcrumb leak) -- `perf/` - performance-category tests, kept OUT of the main suite: throughput/load SLO tests are a different testing category from functional e2e (variance-driven, historically flaky) and live outside this suite until re-implemented as their own pipeline (LIT-5163). Do not add a live load test that runs in the default collection. What lives here: the weekly session-anomaly test (`test_weekly_session_anomaly_e2e.py`, Claude Code-shaped multi-turn sessions against real providers with ceilings on error rate, cache read/write, turn time, and spend, marked `weekly` and deselected unless `E2E_WEEKLY_ANOMALY` is set, run by hand since its weekly workflow never had working Bedrock credentials), the Redis chaos test (`test_redis_chaos_e2e.py`, locust load against mock deployments split round robin over `/chat/completions` and `/v1/messages`, one endpoint per simulated user, with `CLIENT PAUSE ALL` on the proxy's Redis mid-run to simulate it being down outright, asserting zero failed requests on every endpoint, budgeting RSS and CPU-per-request as ratios against the same run's healthy phase, and holding p50/p90/p99 latency and log-bytes-per-request to flat ceilings (a ratio cannot bound those two: an open breaker skips Redis instead of waiting on it, so the chaos phase can measure cheaper than baseline while still being far slower than a user should see), needs a proxy booted from `gateway/redis_chaos_ci_config.yml` on the same host with `E2E_PROXY_PID` and `E2E_PROXY_LOG` set, marked `redis_chaos`, deselected unless `E2E_PERF_TESTS` is set and excluded from the per-PR selector like the rest of `perf/`, driven by `.github/workflows/test-e2e-redis-chaos.yml` and by the Buildkite `e2e-redis-chaos` step in project-releaser, which runs the proxy, Postgres and Valkey co-located with pytest in one pod and sets the opt-in). New perf tests go here too, marked `perf`, behind the same `E2E_PERF_TESTS` opt-in. The perf cases (`test_perf_cases.py`, one `PerformanceTestCase` per module in `perf/cases/`, no `_e2e` suffix) are the request-performance runs. A case names what the gateway was deployed with (model groups and guardrails, written out in the gateway's config: the `proxy_config` block of project-releaser's perf helm values in the lane, `gateway/perf_ci_config.yml` locally, with each deployment's mock knobs in an `x-mock-knobs` extra header so the model ids are real ones like `gpt-6-luna` and `anthropic.claude-sonnet-5`) and lists what the test creates and sends: teams, Keycloak JWT users, virtual keys, `RequestShape`s (a coding-agent session from `perf/setup/corpus/` whose input-token size is drawn per request from the shape's range, with or without tools, and the output cap), a `Traffic` plan (seed, rps, request count, endpoint, shape and stream shares, or a `Ramp` that steps the rate up through the run, built with `ramped()`, whose plan carries each request's send second and whose run reports `sustained_rps`, the last step the pod held, scored in `perf/setup/ramp.py`, with every step's latency, completed rate, non-200s, CPU per request, Redis latency and Postgres connections cut from the samplers' time-stamped `Series` (`perf/setup/series.py`) to the step's window and emitted as `perf_ramp_step__` JUnit properties for the dashboard) and `Limit`s (one metric at one percentile under one ceiling, or over one floor for `sustained_rps`). The infra side lives in `perf/setup/`: `provision.py` checks the deployed config matches the case and builds the callers, `plan.py` draws the request list from the seed, `run.py` replays it from a separate Locust process (`locustfile.py`) that builds each body right before sending and records status, header, first-byte, first-token and total time per request, and `observe.py` joins all of that with what the mocks received and the workers' RSS to score every `Limit`. Limits are about what the caller sees and what the gateway costs: every non-blocked request is a 200 (`unexpected_status_count`), the seconds the gateway added per request at p50 and p99 (`gateway_added_seconds`, to the first token on streams), the client's time to the first token of a stream at p50 and p99 (`time_to_first_token_seconds`, mock delay included), worker memory growth (`memory_growth_megabytes`), and the Postgres statements and Redis commands a request waits on before it is forwarded (`postgres_calls_per_request`, `redis_calls_per_request`: auth and routing reads, the rate-limiter script), with the work the gateway does after the response or on a timer (spend writes, daily aggregates, config reloads) reported separately as `postgres_background_calls_per_request` and `redis_background_calls_per_request`; the same overhead as the gateway itself reports it, read from the `x-litellm-overhead-duration-ms` header per request (`litellm_overhead_seconds`, gated next to `gateway_added_seconds`, the outside reading of the same thing, so the two can be charted together) and the gateway's own end-to-end time from `x-litellm-response-duration-ms` (`litellm_response_seconds`), the run mean of the gateway's `litellm_redis_latency` histogram from a `/metrics` scrape before and after the run (`telemetry.py`, `prometheus_redis_latency_mean_seconds`), and the database's own view of the run from the same `pg_stat_statements` rows and a once-a-second `pg_stat_activity` sample: execution seconds per wall second (`postgres_active_sessions`, the average-active-sessions number RDS reports as DB load), the slowest statement's mean (`postgres_slowest_statement_seconds`, named in the detail) and the peak client connection count (`postgres_peak_connections`), each under a ceiling set from the first local runs and due a retune once the lane has its ten-run baseline; `datastore.py` reads `pg_stat_statements` before and after the run and holds a Redis `MONITOR` connection during it, sorts each statement by verb and table and each command by key, and the run output lists the top statements and commands per bucket so a red run names what grew. Whether a body, a guardrail call or a spend row is right is printed as a diagnostic, never a limit, that is the functional suites' job. The providers and the guardrail are out-of-process servers in `perf/setup/mocks/` (`server.py openai|anthropic|bedrock|guardrail`, each answering with a reply generated for the request in that provider's wire format, sized from the prompt and output cap, a tool call when tools were offered, waiting the case's `mock_latency` first, request logs at `/mock/requests` and `/mock/calls`), reached through `E2E_PERF_MOCK_*_URL` by both the gateway config and the test. Adding a perf test is adding a module with a `CASE` to `perf/cases/`; a new deployment or guardrail is a config change; a new metric is a name in `Metric` plus a branch in `observe.observe` +- `router/` - routing and reliability behavior (fallbacks, cooldowns) +- `perf/` - performance-category tests, kept OUT of the main suite: throughput/load SLO tests are a different testing category from functional e2e (variance-driven, historically flaky) and live outside this suite until re-implemented as their own pipeline (LIT-5163). Do not add a live load test that runs in the default collection. What lives here: the weekly session-anomaly test (`test_weekly_session_anomaly_e2e.py`, Claude Code-shaped multi-turn sessions against real providers with ceilings on error rate, cache read/write, turn time, and spend, marked `weekly` and deselected unless `E2E_WEEKLY_ANOMALY` is set, run by hand since its weekly workflow never had working Bedrock credentials), the Redis chaos test (`test_redis_chaos_e2e.py`, locust load against mock deployments split round robin over `/chat/completions` and `/v1/messages`, one endpoint per simulated user, with `CLIENT PAUSE ALL` on the proxy's Redis mid-run to simulate it being down outright, asserting zero failed requests on every endpoint, budgeting RSS and CPU-per-request as ratios against the same run's healthy phase, and holding p50/p90/p99 latency and log-bytes-per-request to flat ceilings (a ratio cannot bound those two: an open breaker skips Redis instead of waiting on it, so the chaos phase can measure cheaper than baseline while still being far slower than a user should see), needs a proxy booted from `gateway/redis_chaos_ci_config.yml` on the same host with `E2E_PROXY_PID` and `E2E_PROXY_LOG` set, marked `redis_chaos`, deselected unless `E2E_PERF_TESTS` is set and excluded from the per-PR selector like the rest of `perf/`, driven by `.github/workflows/test-e2e-redis-chaos.yml` and by the Buildkite `e2e-redis-chaos` step in project-releaser, which runs the proxy, Postgres and Valkey co-located with pytest in one pod and sets the opt-in). The memory tests (`test_reliability_memory_e2e.py`, run by the perf lane on its own fresh stack: every worker's RSS as read at collection time, before any test traffic, must sit under a fixed idle budget, the check for a DB-backed boot that idles near the pod limit the way v1.100.x did, and a few hundred failing requests with retries and fallbacks must not grow proxy RSS past a loose ceiling over the measured batches, the check for the v1.100.0 retry-breadcrumb leak). New perf tests go here too, marked `perf`, behind the same `E2E_PERF_TESTS` opt-in. The perf cases (`test_perf_cases.py`, one `PerformanceTestCase` per module in `perf/cases/`, no `_e2e` suffix) are the request-performance runs. A case names what the gateway was deployed with (model groups and guardrails, written out in the gateway's config: the `proxy_config` block of project-releaser's perf helm values in the lane, `gateway/perf_ci_config.yml` locally, with each deployment's mock knobs in an `x-mock-knobs` extra header so the model ids are real ones like `gpt-6-luna` and `anthropic.claude-sonnet-5`) and lists what the test creates and sends: teams, Keycloak JWT users, virtual keys, `RequestShape`s (a coding-agent session from `perf/setup/corpus/` whose input-token size is drawn per request from the shape's range, with or without tools, and the output cap), a `Traffic` plan (seed, rps, request count, endpoint, shape and stream shares, or a `Ramp` that steps the rate up through the run, built with `ramped()`, whose plan carries each request's send second and whose run reports `sustained_rps`, the last step the pod held, scored in `perf/setup/ramp.py`, with every step's latency, completed rate, non-200s, CPU per request, Redis latency and Postgres connections cut from the samplers' time-stamped `Series` (`perf/setup/series.py`) to the step's window and emitted as `perf_ramp_step__` JUnit properties for the dashboard) and `Limit`s (one metric at one percentile under one ceiling, or over one floor for `sustained_rps`). The infra side lives in `perf/setup/`: `provision.py` checks the deployed config matches the case and builds the callers, `plan.py` draws the request list from the seed, `run.py` replays it from a separate Locust process (`locustfile.py`) that builds each body right before sending and records status, header, first-byte, first-token and total time per request, and `observe.py` joins all of that with what the mocks received and the workers' RSS to score every `Limit`. Limits are about what the caller sees and what the gateway costs: every non-blocked request is a 200 (`unexpected_status_count`), the seconds the gateway added per request at p50 and p99 (`gateway_added_seconds`, to the first token on streams), the client's time to the first token of a stream at p50 and p99 (`time_to_first_token_seconds`, mock delay included), worker memory growth (`memory_growth_megabytes`), and the Postgres statements and Redis commands a request waits on before it is forwarded (`postgres_calls_per_request`, `redis_calls_per_request`: auth and routing reads, the rate-limiter script), with the work the gateway does after the response or on a timer (spend writes, daily aggregates, config reloads) reported separately as `postgres_background_calls_per_request` and `redis_background_calls_per_request`; the same overhead as the gateway itself reports it, read from the `x-litellm-overhead-duration-ms` header per request (`litellm_overhead_seconds`, gated next to `gateway_added_seconds`, the outside reading of the same thing, so the two can be charted together) and the gateway's own end-to-end time from `x-litellm-response-duration-ms` (`litellm_response_seconds`), the run mean of the gateway's `litellm_redis_latency` histogram from a `/metrics` scrape before and after the run (`telemetry.py`, `prometheus_redis_latency_mean_seconds`), and the database's own view of the run from the same `pg_stat_statements` rows and a once-a-second `pg_stat_activity` sample: execution seconds per wall second (`postgres_active_sessions`, the average-active-sessions number RDS reports as DB load), the slowest statement's mean (`postgres_slowest_statement_seconds`, named in the detail) and the peak client connection count (`postgres_peak_connections`), each under a ceiling set from the first local runs and due a retune once the lane has its ten-run baseline; `datastore.py` reads `pg_stat_statements` before and after the run and holds a Redis `MONITOR` connection during it, sorts each statement by verb and table and each command by key, and the run output lists the top statements and commands per bucket so a red run names what grew. Whether a body, a guardrail call or a spend row is right is printed as a diagnostic, never a limit, that is the functional suites' job. The providers and the guardrail are out-of-process servers in `perf/setup/mocks/` (`server.py openai|anthropic|bedrock|guardrail`, each answering with a reply generated for the request in that provider's wire format, sized from the prompt and output cap, a tool call when tools were offered, waiting the case's `mock_latency` first, request logs at `/mock/requests` and `/mock/calls`), reached through `E2E_PERF_MOCK_*_URL` by both the gateway config and the test. Adding a perf test is adding a module with a `CASE` to `perf/cases/`; a new deployment or guardrail is a config change; a new metric is a name in `Metric` plus a branch in `observe.observe` - `other/` - the holding-pen suite for the `other.*` registry cluster with no home of its own yet: the master-key auth gate, JWT auth (access tokens issued by a real Keycloak realm, `idp.py` plus `idp_realm.json`, whose JWKS the proxy's `JWT_PUBLIC_KEY_URL` points at, see CONTRIBUTING.md for the start command and config block), and the process-lifecycle health probes (liveness, public readiness, authenticated readiness diagnostics). Promote a cluster out once it is large/stable enough for its own suite - `secret_manager/` - the gateway's `key_management_system` against a real secret manager: deployment keys resolved from it (`os.environ/` where the name exists only in the manager) and virtual keys written to and deleted from it. The tests are backend-agnostic and each backend is its own lane, because the setting is global to the proxy: `E2E_SECRET_MANAGER=` opts in and picks the backend from `secret_backends.BACKENDS`, the proxy is booted from `gateway/secret_manager__ci_config.yml` against the live manager, and the tests reach that manager through the backend's `SecretStore` (`secret_store_.py`). A test needing something not every backend does carries `requires_capability(...)` and is deselected on lanes that lack it. `secret_manager/backend.sh up ` runs a backend in Docker and writes the proxy's and the tests' env. Marked `secret_manager`, deselected unless `E2E_SECRET_MANAGER` is set, and kept out of the per-PR selector. Backends today: `hashicorp_vault` and `cyberark` (CyberArk Conjur, which cannot delete, so the delete test is Vault-only) - `gateway/` - proxy configuration only (`litellm-config.yml`). No tests - `claude_code/` - the Claude Code compatibility matrix: drives the real `claude` CLI (and HTTP probes) against a proxy for each feature x provider cell, reporting tagged-union outcomes via the `compat_result` fixture. Ships its own driver/builder/publisher, covered by `tests/e2e_harness/claude_code/`. The HTTP probes ride the shared transport (`ProxyClient.count_tokens` / `ProxyClient.messages`). The CLI-driving path stays bespoke. `pr_gate_version_resolver.py` only forwards to `cli_harness/npm_version_resolver.py`, because the Buildkite runner images still run that path and install @latest without it - `cli_harness/` - no tests, only the plumbing every suite that drives a real coding-agent CLI shares: `npm_version_resolver.py` picks the CLI release to install (the newest npm release at least three days old, run by CI as `python tests/e2e/cli_harness/npm_version_resolver.py `, stdlib only), `lockdown.py` gives the CLI subprocess an allowlisted env and a fresh empty HOME so it never sees the runner's credentials or dotfiles, and `rate_limiter.py` is the cross-process token bucket per upstream that keeps parallel CLI runs under one request rate. Covered by `tests/e2e_harness/cli_harness/`. Code only one CLI suite needs stays in that suite -- `codex/` - the Codex compatibility suite: drives the real `codex exec --json` CLI through the gateway, one class per row (the command round trip, a file edit with the patch tool, a long session that follows a chain of files one command at a time, and auto-compaction, where Codex reads a random value, compacts through the gateway and is asked for the value after the file is deleted), each row parametrized over `SETUPS` in `setups.py`, one `pytest.param` per setup (GPT on Bedrock Mantle, Bedrock Runtime, Azure OpenAI and OpenAI, plus Claude on Bedrock). A row with one setup broken by a known bug parametrizes over `with_expected_failure(, reason=...)`, which is `SETUPS` with a strict xfail on that one setup. The `setup` fixture registers that setup's deployment under a fresh name via `/model/new` and the `codex` fixture makes a key scoped to it, and both are deleted on teardown. `harness.py` gives each test a throwaway HOME and CODEX_HOME whose `config.toml` points a custom `responses` provider at the gateway and has Codex read its model list from the gateway's `/v1/models` (`model_catalog_url` on the provider), with the sandbox on (`workspace-write`) and approvals off, and runs the CLI under `cli_harness`'s env lockdown and rate limiter. `run.py` reads the JSONL events into a `CodexRun`: the commands with their output, the patched files, the last agent message as `answer`, the usage and the errors, where an error's `status_code` is what lets the suite's 5xx rerun rule catch a turn Codex gave up on. `codex exec --json` prints nothing when Codex compacts, so `rollout.py` reads the compactions from the thread's rollout file under CODEX_HOME/sessions, and a compaction is remote when its replacement history kept the provider's `compaction` item. `exec` and `resume` take `compact_after_tokens`, which forces Codex's remote compaction (`compaction_trigger` items over `/v1/responses`, the path Codex otherwise only takes for OpenAI and Azure) on the gateway provider and compacts once the context grows past that many tokens. A test passes on a random value the model can only learn through the gateway, never on the model's wording or the exact command it typed. It needs `codex` on PATH and fails without it. Covered by `tests/e2e_harness/codex/` +- `codex/` - the Codex compatibility suite: drives the real `codex exec --json` CLI through the gateway, one class per row (the command round trip, a file edit with the patch tool, a long session that follows a chain of files one command at a time, and auto-compaction, where Codex reads a random value, compacts through the gateway and is asked for the value after the file is deleted), each row parametrized over `SETUPS` in `setups.py`, one `pytest.param` per setup (GPT on Bedrock Mantle, Bedrock Runtime, Azure OpenAI and OpenAI, plus Claude on Bedrock). A row with one setup broken by a known bug parametrizes over `with_expected_failure(, reason=...)`, which is `SETUPS` with a strict xfail on that one setup. The `setup` fixture registers that setup's deployment under a fresh name via `/model/new` and the `codex` fixture makes a key scoped to it, and both are deleted on teardown. `harness.py` gives each test a throwaway HOME and CODEX_HOME whose `config.toml` points a custom `responses` provider at the gateway and has Codex read its model list from the gateway's `/v1/models` (`model_catalog_url` on the provider), with the sandbox on (`workspace-write`) and approvals off, and runs the CLI under `cli_harness`'s env lockdown and rate limiter. `run.py` reads the JSONL events into a `CodexRun`: the commands with their output, the patched files, the last agent message as `answer`, the usage and the errors, where an error's `status_code` is what lets the suite's 5xx rerun rule catch a turn Codex gave up on. `codex exec --json` prints nothing when Codex compacts, so `rollout.py` reads the compactions from the thread's rollout file under CODEX_HOME/sessions, and a compaction is remote when its replacement history kept the provider's `compaction` item. `exec` and `resume` take `compact_after_tokens`, which forces Codex's remote compaction (`compaction_trigger` items over `/v1/responses`, the path Codex otherwise only takes for OpenAI and Azure) on the gateway provider and compacts once the context grows past that many tokens. A test passes on a random value the model can only learn through the gateway, never on the model's wording or the exact command it typed. It needs `codex` on PATH and fails without it. The Bedrock Runtime setup is explicitly skipped until stage IAM permits `bedrock:InvokeModel` on its default project. The other setups still run, and skipped Runtime cases do not qualify that route. Covered by `tests/e2e_harness/codex/` - `ui/` - the Admin UI browser suite: Playwright in TypeScript, driving the dashboard served by a live proxy on port 4000 (seeded postgres + mock LLM upstream, see its `run_e2e.sh`). It is a self-contained npm package with its own lockfile and does not use the Python harness, pytest markers, or the shared transport. The Python rules in this file (typed models, `Result` unions, basedpyright zero-error gate) do not apply inside it. Its only Python file, `fixtures/mock_llm_server/server.py`, is excluded from the e2e basedpyright gate via the root `pyrightconfig.json`. Its specs follow the dashboard's rule against testing what shadcn and Base UI already guarantee, in `ui/litellm-dashboard/AGENTS.md` ## MCP suite: real Datadog only @@ -100,7 +100,7 @@ Each suite provides its own `client` fixture (see `llm_translation/passthrough_c Request and response bodies are typed pydantic models in `models.py`. Only the fields a test reads are modelled, and nothing passes raw dicts. Outcomes come back as a `Result[R]` tagged union (`Success`, `NetworkError`, `UnauthorizedError`, `RateLimitedError`, `ValidationError`, `UnknownApiError`). Handle them with `match`, or call `unwrap(...)` when a non-success should fail the test. The harness hard-fails and never skips: a test marked `e2e` fails when no proxy answers its liveness probe, and once a request reaches the proxy any wrong behavior is likewise a hard failure, so a missing proxy turns the run red instead of being mistaken for a pass -Mark live tests with `@pytest.mark.e2e` (on the class or the module). Coverage of the harness itself lives outside the suite in `tests/e2e_harness/` (see its `AGENTS.md`) and runs without a proxy, so nothing under `tests/e2e/` is a markerless test. Add `@pytest.mark.quiet_stack` to a test that measures the proxy itself (RSS, latency): the shared stack lock in `stack_lock.py` then runs it while no other test on the host is hitting the stack, marked or not, so the reading depends only on the test's own traffic. Use `scoped_key` for a fresh all-models key that auto-deletes, `resources` when you need to create and tear down more than a key, and `unique_marker()` from `e2e_config` to keep prompts, tags, and customer ids from colliding across concurrent runs and the shared response cache +Mark live tests with `@pytest.mark.e2e` (on the class or the module). Coverage of the harness itself lives outside the suite in `tests/e2e_harness/` (see its `AGENTS.md`) and runs without a proxy, so nothing under `tests/e2e/` is a markerless test. A test that measures the proxy itself (RSS, latency) cannot share the stack with the rest of the suite, so it goes in `perf/`, marked `perf`. Use `scoped_key` for a fresh all-models key that auto-deletes, `resources` when you need to create and tear down more than a key, and `unique_marker()` from `e2e_config` to keep prompts, tags, and customer ids from colliding across concurrent runs and the shared response cache ## Record and replay fixtures @@ -329,3 +329,7 @@ other... - the `codex/` suite is the one exception: a single setup that a LiteLLM or provider bug breaks may carry `pytest.mark.xfail(strict=True, reason=...)` with a plain one-sentence reason on that setup's `pytest.param`, so it keeps running and goes red once the bug is fixed. Strict only, and never on a flaky setup (one that passes some runs and fails others) - a cell addresses the stack through its front door (`PROXY_BASE_URL`) the way a customer does, never a gateway pod by address, and proves a cross-replica property with N independent calls through that door, naming the miss odds (2^-N at two pods) in its docstring. `PROXY_REPLICA_URLS` is for read-backs that are per pod by nature (polling until a management write has converged on every replica, `/metrics`, RSS), never for steering the scenario a cell asserts on at a chosen pod + +## Provider response cache + +The provider response cache can share the stage Valkey instance through a `rediss://` URL. Set `E2E_PROVIDER_CACHE_REDIS_CLUSTER=1` for a cluster-aware client, including ElastiCache Serverless. Keep the dedicated HMAC key and namespace, and restrict test cleanup to keys in the test's own namespace diff --git a/tests/e2e/batches/COVERAGE.md b/tests/e2e/batches/COVERAGE.md index 1ede9c89b97..ac6f9cd08e4 100644 --- a/tests/e2e/batches/COVERAGE.md +++ b/tests/e2e/batches/COVERAGE.md @@ -11,8 +11,9 @@ cost write-back via a cross-run marker baton (design below). Only supported cells are tested. The capability table in `capabilities.py` holds one row per supported (provider, scenario) pair, so there are no skipped cells in the -parametrized run. The batches suite never skips: missing provider creds or upstream -failures are hard test failures (see `tests/e2e/AGENTS.md`). +parametrized run. Missing provider creds or upstream failures are hard test failures +(see `tests/e2e/AGENTS.md`). GovCloud is explicitly skipped until its infrastructure +is provisioned and does not count as qualified coverage. | Provider | create | retrieve | cancel | list | content download | file backing | |-----------|--------|----------|--------|------|------------------|--------------| diff --git a/tests/e2e/batches/test_batches_e2e.py b/tests/e2e/batches/test_batches_e2e.py index b7a52da5902..eb83db73199 100644 --- a/tests/e2e/batches/test_batches_e2e.py +++ b/tests/e2e/batches/test_batches_e2e.py @@ -1210,6 +1210,7 @@ def _govcloud_params() -> LiteLLMParamsBody: ) +@pytest.mark.skip(reason="GovCloud E2E infrastructure is not provisioned") class TestBedrockBatchGovCloud: """Bedrock batch lifecycle in the AWS GovCloud partition (us-gov-west-1). diff --git a/tests/e2e/claude_code/conftest.py b/tests/e2e/claude_code/conftest.py index b2442d492cf..14201ef6846 100644 --- a/tests/e2e/claude_code/conftest.py +++ b/tests/e2e/claude_code/conftest.py @@ -597,14 +597,21 @@ def _build_control_plane_client(proxy_config: ProxyConfig): ) -def _register_deployment(proxy, deployment: CompatDeployment) -> str: - """Register one deployment and return its proxy-assigned model_id - once it is servable on the data plane. The aliases are shared by every - cell and, under xdist, by every worker, so no call to them belongs to - one test and none is cached: the matrix exists to reach real providers.""" - return proxy.create_model( - deployment.model_name, - deployment.litellm_params, +def _register_deployments(proxy, deployments: tuple[CompatDeployment, ...]): + """The aliases are shared by every cell and, under xdist, by every worker, + so no call to them belongs to one test and none is cached: the matrix + exists to reach real providers.""" + from models import ModelInfoBody, ModelNewBody + + return proxy.register_models( + tuple( + ModelNewBody( + model_name=deployment.model_name, + litellm_params=deployment.litellm_params, + model_info=ModelInfoBody(), + ) + for deployment in deployments + ), provider_live=True, ) @@ -632,18 +639,14 @@ def _compat_models_registered() -> Any: yield return + from proxy_client import ModelNotRegistered, ModelRegistered from requests import RequestException proxy = _build_control_plane_client(proxy_config) - registered_ids: list[str] = [] - failures: list[tuple[str, str]] = [] + registrations = _register_deployments(proxy, load_all_deployments()) + registered_ids = tuple(r.model_id for r in registrations if isinstance(r, ModelRegistered)) + failures = tuple((r.model_name, r.reason) for r in registrations if isinstance(r, ModelNotRegistered)) try: - for deployment in load_all_deployments(): - try: - model_id = _register_deployment(proxy, deployment) - registered_ids.append(model_id) - except (AssertionError, RequestException) as exc: - failures.append((deployment.model_name, str(exc))) if failures: summary = "\n".join( f" - {name}: {reason}" for name, reason in failures diff --git a/tests/e2e/codex/setups.py b/tests/e2e/codex/setups.py index 4716706c51c..f86397686f3 100644 --- a/tests/e2e/codex/setups.py +++ b/tests/e2e/codex/setups.py @@ -13,7 +13,7 @@ from e2e_metadata import Domain, Mode, Provider, Route, Subject, meta from models import LiteLLMParamsBody GPT_ON_BEDROCK_MANTLE: Final = "bedrock_mantle/openai.gpt-6.1-sol" -GPT_ON_BEDROCK_RUNTIME: Final = "bedrock/us.openai.gpt-6.1-sol" +GPT_ON_BEDROCK_RUNTIME: Final = "bedrock/global.openai.gpt-6.1-sol" GPT_ON_AZURE_OPENAI: Final = "azure/gpt-6.1-sol" GPT_ON_OPENAI: Final = "openai/gpt-6.1-sol" CLAUDE_ON_BEDROCK: Final = "bedrock/us.anthropic.claude-sonnet-5-5" @@ -60,7 +60,10 @@ SETUPS: Final = ( RateBucket.BEDROCK_CONVERSE, ), id="bedrock_runtime", - marks=_drives(Provider.BEDROCK, GPT_ON_BEDROCK_RUNTIME), + marks=( + _drives(Provider.BEDROCK, GPT_ON_BEDROCK_RUNTIME), + pytest.mark.skip(reason="Stage IAM lacks bedrock:InvokeModel on the default Bedrock Runtime project"), + ), ), pytest.param( Deployment( diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index 4c7c57ca18f..8fb96bef2f9 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -35,6 +35,7 @@ from e2e_config import ( PROMPT_CACHING_OPT_IN_ENV, PROVIDER_EDGE_HOST_OPT_IN_ENV, PROXY_BASE_URL, + PROXY_WIDE_CONFIG_OPT_IN_ENV, SAGEMAKER_NOVA2_LITE_OPT_IN_ENV, SAGEMAKER_NOVA_OPT_IN_ENV, SECRET_MANAGER_OPT_IN_ENV, @@ -54,7 +55,6 @@ from models import TeamNewBody, UserNewBody, UserNewResponse from provider_cache_routing import LIVE_PROVIDER_REQUIRED from provider_edge import replay_leftover_error from proxy_client import ProxyClient, build_proxy_client -from stack_lock import stack_lock _E2E_TEST_RAN = pytest.StashKey[bool]() _CALL_PASSED = pytest.StashKey[bool]() @@ -77,6 +77,7 @@ OPT_IN_MARKERS: Final = MappingProxyType( "secret_manager": SECRET_MANAGER_OPT_IN_ENV, "sagemaker_nova": SAGEMAKER_NOVA_OPT_IN_ENV, "sagemaker_nova2_lite": SAGEMAKER_NOVA2_LITE_OPT_IN_ENV, + "proxy_wide_config": PROXY_WIDE_CONFIG_OPT_IN_ENV, } ) @@ -162,11 +163,6 @@ def pytest_configure(config: pytest.Config) -> None: "redis_chaos: load test that pauses the proxy's Redis outright mid-run; needs a proxy booted from " "gateway/redis_chaos_ci_config.yml on the same host, and is deselected unless E2E_PERF_TESTS is set", ) - config.addinivalue_line( - "markers", - "quiet_stack: measures the proxy itself, so it runs while no other test on this host is hitting the stack; " - "every other test waits for it to finish", - ) config.addinivalue_line( "markers", "provider_edge_host: routes provider traffic through the pytest host's edge in every fixture mode, so the " @@ -200,6 +196,11 @@ def pytest_configure(config: pytest.Config) -> None: "sagemaker_nova2_lite: needs a deployed SageMaker Nova 2 Lite endpoint the proxy's AWS credentials can invoke; " "deselected unless SAGEMAKER_NOVA2_LITE_ENDPOINT names it", ) + config.addinivalue_line( + "markers", + "proxy_wide_config: turns on a proxy-wide setting through /config/update, so it runs on a stack no other suite " + "is driving; deselected unless E2E_PROXY_WIDE_CONFIG_STACK is set", + ) def pytest_sessionstart(session: pytest.Session) -> None: @@ -269,7 +270,7 @@ def pytest_collection_finish(session: pytest.Session) -> None: ahead of xdist's own collection-finish report, and the controller schedules no test until every worker has reported, so this is the idle footprint of a stack that just passed its readiness gate. The fixture hands the capture to the - idle-budget test in router/test_reliability_memory_e2e.py.""" + idle-budget test in perf/test_reliability_memory_e2e.py.""" if session.config.getoption("collectonly") or not any(_uses_idle_rss(item) for item in session.items): return session.config.stash[_IDLE_RSS] = read_rss_everywhere(build_proxy_client(), timeout=IDLE_RSS_READ_TIMEOUT_SECONDS) @@ -299,12 +300,6 @@ def _proxy_fail_reason() -> str | None: return None -@pytest.hookimpl(wrapper=True) -def pytest_runtest_protocol(item: pytest.Item, nextitem: pytest.Item | None) -> Generator[None, object, object]: - with stack_lock(exclusive=item.get_closest_marker("quiet_stack") is not None): - return (yield) - - @pytest.hookimpl(tryfirst=True) def pytest_runtest_setup(item: pytest.Item) -> None: """Hard-fail `e2e`-marked tests unless a proxy answers its liveness probe. diff --git a/tests/e2e/coverage_registry/guardrail.yaml b/tests/e2e/coverage_registry/guardrail.yaml index 854958fc39e..457f03406f1 100644 --- a/tests/e2e/coverage_registry/guardrail.yaml +++ b/tests/e2e/coverage_registry/guardrail.yaml @@ -20,8 +20,8 @@ - {id: guardrail.ibm_guardrails.post_call.blocks, module: guardrail, tier: P1, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/ibm_guardrails/ibm_detector.py", rationale: "Output policy validation"} - {id: guardrail.semantic_guard.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/semantic_guard", rationale: "Semantic policy compliance"} - {id: guardrail.block_code_execution.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/block_code_execution", rationale: "Code-injection prevention"} -- {id: guardrail.tool_permission.pre_call.allows, module: guardrail, tier: P1, hook_point: pre_call, assertions: [allows], exercised_on: [chat_completions], source: "guardrail_hooks/tool_permission.py", rationale: "Grant allowed tools"} -- {id: guardrail.tool_permission.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/tool_permission.py", rationale: "Block unauthorized tools"} +- {id: guardrail.tool_permission.pre_call.allows, module: guardrail, tier: P1, hook_point: pre_call, assertions: [allows], exercised_on: [chat_completions, messages, responses], source: "guardrail_hooks/tool_permission.py", rationale: "Grant allowed tools"} +- {id: guardrail.tool_permission.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions, messages, responses], source: "guardrail_hooks/tool_permission.py", rationale: "Block unauthorized tools"} - {id: guardrail.microsoft_purview.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/microsoft_purview/purview_dlp.py", rationale: "DLP sensitive-data disclosure"} - {id: guardrail.headroom.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/headroom/headroom.py", rationale: "Anomaly detection threshold"} - {id: guardrail.generic_guardrail_api.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py", rationale: "Vendor-agnostic custom API"} diff --git a/tests/e2e/coverage_registry/quota_management.yaml b/tests/e2e/coverage_registry/quota_management.yaml index cbaf46130d6..49997aef162 100644 --- a/tests/e2e/coverage_registry/quota_management.yaml +++ b/tests/e2e/coverage_registry/quota_management.yaml @@ -44,8 +44,6 @@ - {id: quota_management.spend_tracking.messages_bridge.logs_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: messages_bridge, assertions: [logs_cost], exercised_on: [messages], source: "llms/anthropic/messages/responses_adapters/handler.py", rationale: "A streaming /v1/messages request served by an openai-provider model is bridged through the anthropic-messages -> Responses adapter and must aggregate the consumed SSE stream into one spend row with nonzero cost and token counts, attributed to custom_llm_provider openai under call_type anthropic_messages"} - {id: quota_management.spend_tracking.embeddings.logs_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: embeddings, assertions: [logs_cost], exercised_on: [embeddings], source: "proxy/spend_tracking/spend_tracking_utils.py", rationale: "Embedding calls write nonzero spend rows"} - {id: quota_management.spend_tracking.cache_hit.zero_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: cache_hit, assertions: [zero_cost], exercised_on: [chat_completions], source: "proxy/spend_tracking/spend_tracking_utils.py", rationale: "A response-cache hit logs at zero cost with the cache-hit marker"} -- {id: quota_management.spend_tracking.discount_config.logs_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: discount_config, assertions: [logs_cost], exercised_on: [chat_completions], source: "proxy/management_endpoints/cost_tracking_settings.py + cost_calculator.py", rationale: "A persisted provider discount changes the real request's spend row"} -- {id: quota_management.spend_tracking.margin_config.logs_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: margin_config, assertions: [logs_cost], exercised_on: [chat_completions], source: "proxy/management_endpoints/cost_tracking_settings.py + cost_calculator.py", rationale: "A persisted provider margin is added to the real request's spend row"} - {id: quota_management.spend_tracking.guardrail_cost.logs_cost, module: quota_management, tier: P1, behavior: spend_tracking, variant: guardrail_cost, assertions: [logs_cost], exercised_on: [chat_completions], source: "litellm_core_utils/llm_cost_calc/guardrail_cost.py + proxy/spend_tracking/spend_tracking_utils.py", rationale: "A priced Bedrock guardrail charge reaches the persisted spend row"} - {id: quota_management.spend_tracking.key_rollup.matches_sum_of_logs, module: quota_management, tier: P1, behavior: spend_tracking, variant: key_rollup, assertions: [matches_sum_of_logs], exercised_on: [chat_completions], source: "proxy/db/db_spend_update_writer.py", rationale: "A key's rolled-up spend equals the sum of its log rows"} - {id: quota_management.spend_tracking.concurrent_burst.loses_no_spend, module: quota_management, tier: P1, behavior: spend_tracking, variant: concurrent_burst, assertions: [loses_no_spend], exercised_on: [chat_completions], source: "proxy/db/db_spend_update_writer.py", rationale: "Concurrent calls all land as spend; no row lost to write contention"} diff --git a/tests/e2e/e2e_config.py b/tests/e2e/e2e_config.py index 724aa2362e7..6316d22d3fa 100644 --- a/tests/e2e/e2e_config.py +++ b/tests/e2e/e2e_config.py @@ -210,6 +210,7 @@ OTEL_TLS_OPT_IN_ENV: Final = "E2E_OTEL_EXPORTER_ENDPOINT" SECRET_MANAGER_OPT_IN_ENV: Final = "E2E_SECRET_MANAGER" SAGEMAKER_NOVA_OPT_IN_ENV: Final = "SAGEMAKER_NOVA_ENDPOINT" SAGEMAKER_NOVA2_LITE_OPT_IN_ENV: Final = "SAGEMAKER_NOVA2_LITE_ENDPOINT" +PROXY_WIDE_CONFIG_OPT_IN_ENV: Final = "E2E_PROXY_WIDE_CONFIG_STACK" ANOMALY_SESSIONS = int(os.environ.get("E2E_ANOMALY_SESSIONS", "6")) ANOMALY_TURNS_PER_SESSION = int(os.environ.get("E2E_ANOMALY_TURNS_PER_SESSION", "6")) ANOMALY_TURN_ATTEMPTS = int(os.environ.get("E2E_ANOMALY_TURN_ATTEMPTS", "3")) @@ -234,7 +235,6 @@ MEMORY_RSS_SETTLE_SAMPLES = int(os.environ.get("E2E_MEMORY_RSS_SETTLE_SAMPLES", MEMORY_RSS_SAMPLE_INTERVAL_SECONDS = float(os.environ.get("E2E_MEMORY_RSS_SAMPLE_INTERVAL_SECONDS", "1")) MEMORY_RSS_CEILING_MB = float(os.environ.get("E2E_MEMORY_RSS_CEILING_MB", "200")) MEMORY_IDLE_RSS_BUDGET_MB = float(os.environ.get("E2E_MEMORY_IDLE_RSS_BUDGET_MB", "768")) -MEMORY_STORED_REQUEST_BUDGET_KB = float(os.environ.get("E2E_MEMORY_STORED_REQUEST_BUDGET_KB", "64")) def ws_base_url() -> str: diff --git a/tests/e2e/guardrails/guardrails_client.py b/tests/e2e/guardrails/guardrails_client.py index cf517c7a4fe..9db95822734 100644 --- a/tests/e2e/guardrails/guardrails_client.py +++ b/tests/e2e/guardrails/guardrails_client.py @@ -38,7 +38,7 @@ from models import ( from proxy_client import ProxyClient from pydantic import BaseModel, Field -GUARDRAIL_BACKEND: Final = "gemini/gemini-2.5-flash" +GUARDRAIL_BACKEND: Final = "gemini/gemini-3.8-flash" GuardrailMode = Literal["pre_call", "post_call", "during_call", "logging_only"] BlockedWordAction = Literal["BLOCK", "MASK"] diff --git a/tests/e2e/guardrails/test_openai_moderation_guardrail_e2e.py b/tests/e2e/guardrails/test_openai_moderation_guardrail_e2e.py index b9593feb2ed..0eb1bf79daa 100644 --- a/tests/e2e/guardrails/test_openai_moderation_guardrail_e2e.py +++ b/tests/e2e/guardrails/test_openai_moderation_guardrail_e2e.py @@ -128,7 +128,7 @@ class TestOpenAIModerationGuardrail: ) allowed = unwrap( - client.messages(scoped_key, model, BENIGN_PROMPT, guardrails=[name], max_tokens=64) + client.messages(scoped_key, model, BENIGN_PROMPT, guardrails=[name], max_tokens=512) ) assert allowed.content or allowed.choices, ( "the same moderation guardrail must let a benign /v1/messages prompt through, but " diff --git a/tests/e2e/guardrails/test_tool_permission_guardrail_e2e.py b/tests/e2e/guardrails/test_tool_permission_guardrail_e2e.py index d5a6a9d9866..87545299169 100644 --- a/tests/e2e/guardrails/test_tool_permission_guardrail_e2e.py +++ b/tests/e2e/guardrails/test_tool_permission_guardrail_e2e.py @@ -19,10 +19,9 @@ verdict comes from the proxy itself. from __future__ import annotations -from typing import Final +from typing import Final, Literal import pytest - from e2e_config import unique_marker from e2e_http import StreamingResponse, UnknownApiError from e2e_metadata import Capability, Domain, Mode, Provider, Subject, meta @@ -31,14 +30,29 @@ from guardrails_client import ( ToolPermissionParamsBody, ToolPermissionRuleBody, poll_until_blocked, + poll_until_blocked_stream, poll_until_guardrail_applied, ) from lifecycle import ResourceManager -from models import ChatResponse, ChatTool, ChatToolFunction +from models import ( + AnthropicCustomTool, + AnthropicMessagesBody, + AnthropicMessagesResponse, + AnthropicToolChoice, + ChatMessage, + ChatResponse, + ChatTool, + ChatToolFunction, + JsonSchemaProperty, + ResponsesFunctionTool, + ResponsesToolBody, + ResponsesToolOutput, + ToolInputSchema, +) -pytestmark = pytest.mark.e2e +pytestmark: Final = pytest.mark.e2e -MODEL = "gemini-2.5-flash" +MODEL: Final = "gemini-2.5-flash" #: The one tool the guardrail permits, and one it does not. Both are declared by #: the caller in the request body; the guardrail reads them there. @@ -63,11 +77,15 @@ DENIED_TOOL: Final = ChatTool( TOOL_PROMPT: Final = "What is the weather in Paris right now?" +type FlatToolEndpoint = Literal["messages", "responses"] +ALLOWED_TOOL_SCHEMA: Final = ToolInputSchema(properties={"city": JsonSchemaProperty(type="string")}, required=["city"]) +DENIED_TOOL_SCHEMA: Final = ToolInputSchema() + def _register_tool_permission(client: GuardrailsClient, resources: ResourceManager, *, name: str) -> None: """Allow-list exactly one tool: everything else falls to `default_action=deny` and, with `on_disallowed_action=block`, is rejected outright.""" - guardrail_id = client.register( + guardrail_id: Final = client.register( name, ToolPermissionParamsBody( mode="pre_call", @@ -100,6 +118,57 @@ def _tool_call_names(response: ChatResponse) -> tuple[str, ...]: ) +def _send_flat_tool( + client: GuardrailsClient, + key: str, + endpoint: FlatToolEndpoint, + *, + guardrail: str, + tool: ChatTool, + schema: ToolInputSchema, + require_tool: bool = False, +) -> StreamingResponse: + name: Final = tool.function.name + match endpoint: + case "messages": + return client.proxy.transport.send( + "/v1/messages", + headers=client.proxy.transport.bearer(key), + json=AnthropicMessagesBody( + model=MODEL, + messages=[ChatMessage(role="user", content=TOOL_PROMPT)], + max_tokens=128, + tools=[ + AnthropicCustomTool(name=name, description=tool.function.description or "", input_schema=schema) + ], + tool_choice=AnthropicToolChoice(type="any") if require_tool else None, + guardrails=[guardrail], + ), + ) + case "responses": + return client.proxy.transport.send( + "/v1/responses", + headers=client.proxy.transport.bearer(key), + json=ResponsesToolBody( + model=MODEL, + input=TOOL_PROMPT, + tools=[ResponsesFunctionTool(name=name, description=tool.function.description, parameters=schema)], + tool_choice="required" if require_tool else None, + guardrails=[guardrail], + ), + ) + + +def _flat_tool_call_names(endpoint: FlatToolEndpoint, body: str) -> tuple[str, ...]: + match endpoint: + case "messages": + content: Final = AnthropicMessagesResponse.model_validate_json(body).content or [] + return tuple(block.name for block in content if block.type == "tool_use" and block.name) + case "responses": + output: Final = ResponsesToolOutput.model_validate_json(body).output + return tuple(item.name for item in output if item.type == "function_call" and item.name) + + class TestToolPermissionPreCall: @pytest.mark.covers("guardrail.tool_permission.pre_call.blocks", exercised_on=["chat_completions"]) @meta( @@ -118,10 +187,10 @@ class TestToolPermissionPreCall: rejected with a 400 that names the denied tool. An unauthorized tool that merely reaches the model is the whole failure mode this guardrail exists to prevent, so a 200 here is a hard failure.""" - name = f"e2e-toolperm-block-{unique_marker()}" + name: Final = f"e2e-toolperm-block-{unique_marker()}" _register_tool_permission(client, resources, name=name) - result = poll_until_blocked( + result: Final = poll_until_blocked( lambda: client.chat( scoped_key, MODEL, @@ -161,10 +230,10 @@ class TestToolPermissionPreCall: and the model calls it. Without the header check a guardrail that never attached would pass this test for the wrong reason, so the 200 alone is not the contract.""" - name = f"e2e-toolperm-allow-{unique_marker()}" + name: Final = f"e2e-toolperm-allow-{unique_marker()}" _register_tool_permission(client, resources, name=name) - outcome = poll_until_guardrail_applied( + outcome: Final = poll_until_guardrail_applied( lambda: client.chat_raw( scoped_key, MODEL, @@ -178,13 +247,117 @@ class TestToolPermissionPreCall: ) assert outcome.ok, f"the permitted tool must be served, got {outcome.status_code}: {outcome.body[:400]}" - applied = _applied_guardrails(outcome) + applied: Final = _applied_guardrails(outcome) assert name in applied, ( "the allowed call must carry x-litellm-applied-guardrails naming the guardrail; " f"without it the 200 only proves the guardrail never ran. Got {applied!r}" ) - called = _tool_call_names(ChatResponse.model_validate_json(outcome.body)) + called: Final = _tool_call_names(ChatResponse.model_validate_json(outcome.body)) + assert called == (ALLOWED_TOOL.function.name,), ( + f"the served call must carry one tool call for the permitted tool, got {called!r}: {outcome.body[:400]}" + ) + + @pytest.mark.parametrize( + "endpoint", + [ + pytest.param( + "messages", + marks=pytest.mark.covers("guardrail.tool_permission.pre_call.blocks", exercised_on=["messages"]), + ), + pytest.param( + "responses", + marks=pytest.mark.covers("guardrail.tool_permission.pre_call.blocks", exercised_on=["responses"]), + ), + ], + ) + @meta( + Subject( + domain=Domain.GUARDRAILS, + providers=(Provider.GEMINI,), + models=(MODEL,), + capabilities=(Capability.FUNCTION_CALLING,), + mode=Mode.NONSTREAM, + ) + ) + def test_pre_call_blocks_flat_tool_outside_the_allow_list( + self, + client: GuardrailsClient, + resources: ResourceManager, + scoped_key: str, + endpoint: FlatToolEndpoint, + ) -> None: + name: Final = f"e2e-toolperm-block-{endpoint}-{unique_marker()}" + _register_tool_permission(client, resources, name=name) + + outcome: Final = poll_until_blocked_stream( + lambda: _send_flat_tool( + client, scoped_key, endpoint, guardrail=name, tool=DENIED_TOOL, schema=DENIED_TOOL_SCHEMA + ) + ) + + assert outcome.status_code == 400, ( + f"tool_permission let a tool outside the allow-list through /{endpoint}; " + f"got {outcome.status_code}: {outcome.body[:400]}" + ) + assert DENIED_TOOL.function.name in outcome.body, ( + f"the block must name the denied tool so the caller can fix the request; got: {outcome.body[:400]}" + ) + assert "Violated guardrail policy" in outcome.body, ( + f"the block must be a guardrail verdict, got: {outcome.body[:400]}" + ) + + @pytest.mark.parametrize( + "endpoint", + [ + pytest.param( + "messages", + marks=pytest.mark.covers("guardrail.tool_permission.pre_call.allows", exercised_on=["messages"]), + ), + pytest.param( + "responses", + marks=pytest.mark.covers("guardrail.tool_permission.pre_call.allows", exercised_on=["responses"]), + ), + ], + ) + @meta( + Subject( + domain=Domain.GUARDRAILS, + providers=(Provider.GEMINI,), + models=(MODEL,), + capabilities=(Capability.FUNCTION_CALLING,), + mode=Mode.NONSTREAM, + ) + ) + def test_pre_call_allows_permitted_flat_tool( + self, + client: GuardrailsClient, + resources: ResourceManager, + scoped_key: str, + endpoint: FlatToolEndpoint, + ) -> None: + name: Final = f"e2e-toolperm-allow-{endpoint}-{unique_marker()}" + _register_tool_permission(client, resources, name=name) + + outcome: Final = poll_until_guardrail_applied( + lambda: _send_flat_tool( + client, + scoped_key, + endpoint, + guardrail=name, + tool=ALLOWED_TOOL, + schema=ALLOWED_TOOL_SCHEMA, + require_tool=True, + ), + name, + ) + + assert outcome.ok, f"the permitted tool must be served, got {outcome.status_code}: {outcome.body[:400]}" + applied: Final = _applied_guardrails(outcome) + assert name in applied, ( + f"the allowed call must carry x-litellm-applied-guardrails naming the guardrail; got {applied!r}" + ) + called: Final = _flat_tool_call_names(endpoint, outcome.body) assert called == (ALLOWED_TOOL.function.name,), ( f"the served call must carry one tool call for the permitted tool, got {called!r}: {outcome.body[:400]}" ) diff --git a/tests/e2e/llm_translation/realtime/REALTIME_COVERAGE_MATRIX.md b/tests/e2e/llm_translation/realtime/REALTIME_COVERAGE_MATRIX.md index 49b543bf666..1694024f390 100644 --- a/tests/e2e/llm_translation/realtime/REALTIME_COVERAGE_MATRIX.md +++ b/tests/e2e/llm_translation/realtime/REALTIME_COVERAGE_MATRIX.md @@ -57,8 +57,9 @@ Bedrock and xai (`xai/grok-4-1-fast-non-reasoning`) are supported by the proxy b kept commented out in `PROVIDERS` until they pass end-to-end here; re-enable them by uncommenting their entry. -Every provider is provisioned and asserted; the suite never skips a provider. Per -`tests/e2e/AGENTS.md` there is no sanctioned skip: the whole-suite proxy-liveness +Azure realtime translation is explicitly skipped pending investigation of intermittent +missing transcripts and does not count as qualified coverage. OpenAI translation and +other Azure realtime cases remain enabled. Per `tests/e2e/AGENTS.md`, the whole-suite proxy-liveness probe hard-fails when no proxy answers, and a provider whose credentials or upstream realtime model are missing on the gateway is likewise a hard failure, not a skip. Give the gateway each provider's credentials to turn its tests green. diff --git a/tests/e2e/llm_translation/realtime/test_realtime_e2e.py b/tests/e2e/llm_translation/realtime/test_realtime_e2e.py index 9645e31f50f..7dfb8671202 100644 --- a/tests/e2e/llm_translation/realtime/test_realtime_e2e.py +++ b/tests/e2e/llm_translation/realtime/test_realtime_e2e.py @@ -360,7 +360,18 @@ LIVE_TRANSCRIPTION_PROVIDERS: Final = ( ) TRANSLATION_PARAMS: Final = tuple( - pytest.param(p, id=p.id, marks=audio_marks(p, "translation")) for p in TRANSLATION_PROVIDERS + pytest.param( + p, + id=p.id, + marks=( + *audio_marks(p, "translation"), + pytest.mark.skipif( + p.id == "azure", + reason="Azure realtime translation intermittently returns no transcript; qualification deferred", + ), + ), + ) + for p in TRANSLATION_PROVIDERS ) LIVE_TRANSCRIPTION_PARAMS: Final = tuple( pytest.param(p, id=p.id, marks=audio_marks(p, "transcription")) for p in LIVE_TRANSCRIPTION_PROVIDERS diff --git a/tests/e2e/llm_translation/test_chat_completions_regression_e2e.py b/tests/e2e/llm_translation/test_chat_completions_regression_e2e.py index 1e0a8cbb412..4967f18818a 100644 --- a/tests/e2e/llm_translation/test_chat_completions_regression_e2e.py +++ b/tests/e2e/llm_translation/test_chat_completions_regression_e2e.py @@ -45,7 +45,7 @@ from passthrough_client import PassthroughClient pytestmark = pytest.mark.e2e COHERE_BACKEND = "cohere/command-r-08-2024" -GEMINI_BACKEND = "gemini/gemini-2.5-flash" +GEMINI_BACKEND = "gemini/gemini-3.8-flash" VERTEX_BACKEND: Final = "vertex_ai/gemini-2.5-flash" AZURE_OPENAI_BACKEND: Final = "azure/gpt-5.4-nano" AZURE_OPENAI_API_VERSION: Final = "v1" @@ -243,7 +243,7 @@ _PERSON_SCHEMA: dict[str, object] = { OPENAI_CHAT_MODEL: Final = "gpt-5.5" ANTHROPIC_CHAT_MODEL: Final = "claude-haiku-4-5" -GEMINI_FLASH_MODEL: Final = "gemini-2.5-flash" +GEMINI_FLASH_MODEL: Final = "gemini-3.8-flash" CHAT_MODELS: Final = ( pytest.param( diff --git a/tests/e2e/llm_translation/test_custom_pricing_e2e.py b/tests/e2e/llm_translation/test_custom_pricing_e2e.py index 6d8cd24648e..63b8b60867a 100644 --- a/tests/e2e/llm_translation/test_custom_pricing_e2e.py +++ b/tests/e2e/llm_translation/test_custom_pricing_e2e.py @@ -5,7 +5,7 @@ teardown) instead of relying on a statically configured model, so the check is self-contained and never inherits pricing another suite or a stale config left on the shared proxy. custom-priced-flash sets input/output rates deliberately far above the canonical gemini price; the isolation sibling shares the same -gemini/gemini-2.5-flash backend but sets no override. Three behaviors are checked +gemini/gemini-3.8-flash backend but sets no override. Three behaviors are checked independently: - billing: a real call's logged cost breakdown charges input and output tokens at @@ -37,10 +37,8 @@ from models import ( pytestmark = pytest.mark.e2e -BACKEND_MODEL = "gemini/gemini-2.5-flash" +BACKEND_MODEL = "gemini/gemini-3.8-flash" GEMINI_API_KEY = "os.environ/GEMINI_API_KEY" -# Deliberately ~100x above canonical gemini-2.5-flash (input 3e-7 / output 2.5e-6) -# so an override that is ignored or under-applied bills at the base rate and fails. CUSTOM_INPUT_RATE = 5e-05 CUSTOM_OUTPUT_RATE = 1e-04 @@ -78,7 +76,7 @@ def _provision( input_cost_per_token: float | None, output_cost_per_token: float | None, ) -> str: - """Register a fresh gemini/gemini-2.5-flash deployment (deleted on teardown) and + """Register a fresh gemini/gemini-3.8-flash deployment (deleted on teardown) and return its model name. With the cost fields set the deployment carries a custom pricing override; with them None it is a plain sibling on the same backend. The marker keeps the name unique so concurrent runs on the shared proxy never @@ -251,7 +249,7 @@ class TestCustomPricing: assert sibling_entry is not None, f"{sibling} absent from /model/info" # custom-priced-flash overrides pricing; the sibling shares the same - # gemini/gemini-2.5-flash backend but sets no override, so it must keep its + # gemini/gemini-3.8-flash backend but sets no override, so it must keep its # own price. Equal rates mean the override leaked into the shared cost map. assert ( sibling_entry.model_info.input_cost_per_token diff --git a/tests/e2e/llm_translation/test_google_native_e2e.py b/tests/e2e/llm_translation/test_google_native_e2e.py index b18683bd4ad..65a341dbc2a 100644 --- a/tests/e2e/llm_translation/test_google_native_e2e.py +++ b/tests/e2e/llm_translation/test_google_native_e2e.py @@ -21,7 +21,7 @@ from pydantic import BaseModel pytestmark = pytest.mark.e2e -UPSTREAM_MODEL = "gemini/gemini-2.5-flash" +UPSTREAM_MODEL = "gemini/gemini-3.8-flash" class _GenerateContentPart(BaseModel): diff --git a/tests/e2e/llm_translation/test_image_edits_e2e.py b/tests/e2e/llm_translation/test_image_edits_e2e.py index 66b0ebfc538..917662b9e52 100644 --- a/tests/e2e/llm_translation/test_image_edits_e2e.py +++ b/tests/e2e/llm_translation/test_image_edits_e2e.py @@ -24,7 +24,7 @@ from sdk_clients import SdkClients pytestmark = pytest.mark.e2e -IMAGE_EDIT_BACKEND: Final = "openai/gpt-image-1" +IMAGE_EDIT_BACKEND: Final = "openai/gpt-image-2.5-sunburst" _TEST_PNG = base64.b64decode( "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAIAAAAlC+aJAAAAS0lEQVR42u3PMQ0AAAwDoPo3" diff --git a/tests/e2e/llm_translation/test_messages_mid_conversation_system_e2e.py b/tests/e2e/llm_translation/test_messages_mid_conversation_system_e2e.py index 84e0674d959..59b1224a479 100644 --- a/tests/e2e/llm_translation/test_messages_mid_conversation_system_e2e.py +++ b/tests/e2e/llm_translation/test_messages_mid_conversation_system_e2e.py @@ -41,7 +41,7 @@ from proxy_client import ProxyClient from pydantic import BaseModel from sdk_clients import NO_PROXY_CACHE, SdkClients -pytestmark = pytest.mark.e2e +pytestmark = [pytest.mark.e2e, pytest.mark.provider_live] FLAGGED_INVOKE_MODEL = "bedrock/invoke/us.anthropic.claude-sonnet-5" UNFLAGGED_INVOKE_MODEL = "bedrock/invoke/us.anthropic.claude-haiku-4-5-20251001-v1:0" diff --git a/tests/e2e/llm_translation/test_passthrough_e2e.py b/tests/e2e/llm_translation/test_passthrough_e2e.py index 52d1c10d829..3eaeef6bddd 100644 --- a/tests/e2e/llm_translation/test_passthrough_e2e.py +++ b/tests/e2e/llm_translation/test_passthrough_e2e.py @@ -5,7 +5,7 @@ and verifies the proxy still logged a costed SpendLogs row (call_type="pass_through_endpoint"), correlated by the id the caller was served: the x-litellm-call-id header on gemini, the `msg_...` message id on anthropic. -Covered: gemini ("gemini-2.5-flash") + anthropic ("claude-haiku-4-5"), streaming + +Covered: gemini ("gemini-3.8-flash") + anthropic ("claude-haiku-4-5"), streaming + non-streaming, plus native tool calls. See LLM_TRANSLATION_COVERAGE_MATRIX.md. A passthrough call returning non-2xx fails hard (never a skip); once it returns @@ -32,7 +32,7 @@ from passthrough_client import ( completed_responses_object, ) -GEMINI_MODEL: Final = "gemini-2.5-flash" +GEMINI_MODEL: Final = "gemini-3.8-flash" ANTHROPIC_PASSTHROUGH_MODEL: Final = "claude-haiku-4-5" EMBEDDING_MODEL = "text-embedding-3-small" REALTIME_MODEL = "gpt-realtime-2" @@ -291,7 +291,7 @@ class TestPassthroughModelAllowlist: result = client.anthropic_message(key, ANTHROPIC_PASSTHROUGH_MODEL, f"say hi {unique_marker()}") assert result.status_code == 403, ( - "a key restricted to gemini-2.5-flash must be denied a claude passthrough call, " + "a key restricted to gemini-3.8-flash must be denied a claude passthrough call, " f"got {result.status_code}: {result.body[:300]}" ) diff --git a/tests/e2e/llm_translation/test_responses_e2e.py b/tests/e2e/llm_translation/test_responses_e2e.py index a0f185c4077..6b299fad18c 100644 --- a/tests/e2e/llm_translation/test_responses_e2e.py +++ b/tests/e2e/llm_translation/test_responses_e2e.py @@ -57,7 +57,7 @@ OPENAI_VISION_BACKEND: Final = "openai/gpt-4o" ANTHROPIC_BACKEND: Final = "anthropic/claude-haiku-4-5" BEDROCK_CONVERSE_BACKEND: Final = "bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0" VERTEX_BACKEND: Final = "vertex_ai/gemini-2.5-flash" -GEMINI_BACKEND: Final = "gemini/gemini-2.5-flash" +GEMINI_BACKEND: Final = "gemini/gemini-3.8-flash" OPENAI_RESPONSES_BACKEND: Final = "openai/gpt-5.5" INSTRUCTIONS = "You are a helpful assistant" CAT_IMAGE_URL = "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg" diff --git a/tests/e2e/logging/logging_client.py b/tests/e2e/logging/logging_client.py index 4dd681f59e8..3903d1a8363 100644 --- a/tests/e2e/logging/logging_client.py +++ b/tests/e2e/logging/logging_client.py @@ -18,10 +18,11 @@ import json import os import time from dataclasses import dataclass -from typing import Callable, Literal +from datetime import datetime, timedelta, timezone +from typing import Callable, Final, Literal import pytest -from pydantic import BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, ValidationError +from pydantic import AliasChoices, BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, ValidationError, field_validator from e2e_config import POLL_INTERVAL, POLL_TIMEOUT, settle_propagation from e2e_metadata import step @@ -124,23 +125,39 @@ class CreateGuardrailResponse(BaseModel): guardrail_name: str | None = None +_LANGFUSE_JSON: Final[TypeAdapter[JsonValue]] = TypeAdapter(JsonValue) + + class LangfuseObservation(BaseModel): - model_config = ConfigDict(extra="ignore", populate_by_name=True) + model_config = ConfigDict(extra="ignore", populate_by_name=True, frozen=True) id: str trace_id: str | None = Field(default=None, alias="traceId") name: str | None = None type: str | None = None - calculated_total_cost: float | None = Field(default=None, alias="calculatedTotalCost") + calculated_total_cost: float | None = Field( + default=None, validation_alias=AliasChoices("totalCost", "calculatedTotalCost") + ) level: str | None = None - input: object | None = None - output: object | None = None - metadata: object | None = None + input: JsonValue = None + output: JsonValue = None + metadata: JsonValue = None usage: object | None = None usage_details: object | None = Field(default=None, alias="usageDetails") model: str | None = None + @field_validator("input", "output", "metadata", mode="before") + @classmethod + def decode_json_fields(cls, value: object) -> JsonValue: + if not isinstance(value, str): + return _LANGFUSE_JSON.validate_python(value) + try: + return _LANGFUSE_JSON.validate_json(value) + except ValidationError: + return value + + class LangfuseObservationList(BaseModel): model_config = ConfigDict(extra="ignore") @@ -171,12 +188,15 @@ def is_otel_v2_generation(obs: LangfuseObservation, *, key_alias: str) -> bool: class LangfuseListParams(BaseModel): - model_config = ConfigDict(populate_by_name=True) + model_config = ConfigDict(populate_by_name=True, frozen=True) limit: int = 100 trace_id: str | None = Field(default=None, alias="traceId") name: str | None = None - from_start_time: str | None = Field(default=None, alias="fromStartTime") + from_start_time: str = Field(alias="fromStartTime") + to_start_time: str = Field(alias="toStartTime") + fields: str = "core,basic,io,metadata,model,usage" + expand_metadata: str = Field(default="attributes", alias="expandMetadata") @dataclass(frozen=True, slots=True) @@ -580,14 +600,16 @@ class LoggingClient: name: str | None = None, from_start_time: str | None = None, ) -> list[LangfuseObservation]: - result = get( - URL(f"{creds.host}/api/public/observations"), + now: Final = datetime.now(timezone.utc) + result: Final = get( + URL(f"{creds.host}/api/public/v2/observations"), headers=creds.auth_headers, params=LangfuseListParams( limit=100, traceId=trace_id, name=name, - fromStartTime=from_start_time, + fromStartTime=from_start_time or (now - timedelta(minutes=15)).isoformat(), + toStartTime=now.isoformat(), ), response_type=LangfuseObservationList, timeout=30.0, diff --git a/tests/e2e/logging/test_langsmith_batch_serialization_e2e.py b/tests/e2e/logging/test_langsmith_batch_serialization_e2e.py index e2dadf9e696..37f363772ae 100644 --- a/tests/e2e/logging/test_langsmith_batch_serialization_e2e.py +++ b/tests/e2e/logging/test_langsmith_batch_serialization_e2e.py @@ -32,6 +32,7 @@ pytestmark = pytest.mark.e2e class LangsmithHeaders(Headers): x_api_key: str = Field(serialization_alias="x-api-key") + x_tenant_id: str | None = Field(default=None, serialization_alias="x-tenant-id") class LangsmithRunExtra(BaseModel): @@ -50,6 +51,7 @@ class LangsmithCreds: api_key: str base_url: str project: str + tenant_id: str | None def load_langsmith_creds() -> LangsmithCreds: @@ -62,6 +64,7 @@ def load_langsmith_creds() -> LangsmithCreds: api_key=api_key, base_url=(os.getenv("LANGSMITH_BASE_URL") or "https://api.smith.langchain.com").rstrip("/"), project=os.getenv("LANGSMITH_PROJECT") or "litellm-e2e", + tenant_id=os.getenv("LANGSMITH_TENANT_ID"), ) @@ -69,7 +72,7 @@ def _fetch_run(creds: LangsmithCreds, run_id: uuid.UUID) -> LangsmithRun | None: result = get_external( f"{creds.base_url}/runs/{run_id}", response_type=LangsmithRun, - headers=LangsmithHeaders(x_api_key=creds.api_key), + headers=LangsmithHeaders(x_api_key=creds.api_key, x_tenant_id=creds.tenant_id), ) match result: case Success(data=run): @@ -102,7 +105,10 @@ class TestLangsmithBatchSerialization: async def test_non_json_native_metadata_reaches_langsmith(self) -> None: creds: Final = load_langsmith_creds() logger: Final = LangsmithLogger( - langsmith_api_key=creds.api_key, langsmith_project=creds.project, langsmith_base_url=creds.base_url + langsmith_api_key=creds.api_key, + langsmith_project=creds.project, + langsmith_base_url=creds.base_url, + langsmith_tenant_id=creds.tenant_id, ) assert not logger.is_mock_mode, "LangsmithLogger initialised in mock mode; this e2e needs the real API" marker: Final = unique_marker() diff --git a/tests/e2e/logging/test_otel_trace_e2e.py b/tests/e2e/logging/test_otel_trace_e2e.py index 81a6407f642..fb6e481dc33 100644 --- a/tests/e2e/logging/test_otel_trace_e2e.py +++ b/tests/e2e/logging/test_otel_trace_e2e.py @@ -79,7 +79,7 @@ def _chain_reaches(span_id: str, root_id: str, trace: JaegerTrace) -> bool: return False -def _assert_complete_trace(traces: CallTraces, *, route: str, genai_span: str) -> None: +def assert_complete_trace(traces: CallTraces, *, route: str, genai_span: str) -> None: """The enforced behavior: the destination holds exactly one call-id-tagged trace for the call, rooted at the SERVER span, with auth/db children and the gen-AI span all connected into that one tree - no dangling parent @@ -112,10 +112,9 @@ def _assert_complete_trace(traces: CallTraces, *, route: str, genai_span: str) - roots = [span for span in trace.spans if not span.references] assert len(roots) == 1, f"expected exactly one root span, got {[s.operation_name for s in roots]}; spans: {names}" root = roots[0] - assert root.operation_name == f"POST {route}", ( - f"the root must be the SERVER span 'POST {route}', got {root.operation_name!r}" - ) assert root.kind == "server", f"the root span must have kind=server, got {root.kind!r}" + request_route: Final = root.tag("http.route") or root.tag("url.path") or root.tag("http.target") + assert request_route == route, f"the root must identify route {route!r}, got {request_route!r}" assert f"auth {route}" in names, f"auth phase span 'auth {route}' missing; spans: {names}" assert any(name.startswith(DB_SPAN_PREFIX) for name in names), ( @@ -133,12 +132,12 @@ def _assert_complete_trace(traces: CallTraces, *, route: str, genai_span: str) - def _poll(otel_reader: OtelReader, *, call_id: str, route: str, genai_span: str) -> CallTraces: return otel_reader.poll_traces_for_call( call_id=call_id, - settled_names={f"POST {route}", f"auth {route}", genai_span}, + settled_names={f"auth {route}", genai_span}, settled_prefixes={DB_SPAN_PREFIX}, ) -def _assert_real_ttft(hits: tuple[JaegerTrace, ...], *, genai_span: str) -> None: +def assert_real_ttft(hits: tuple[JaegerTrace, ...], *, genai_span: str) -> None: """The enforced behavior: the gen-AI span for the attempt that served the stream records a TTFT that is a real measurement - present, numeric, positive, and strictly less than that span's own total duration. A TTFT of @@ -282,7 +281,7 @@ class TestOtelTraceCompleteness: assert outcome.call_id is not None, "success response must carry x-litellm-call-id" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=f"chat {MODEL}") - _assert_complete_trace(traces, route=route, genai_span=f"chat {MODEL}") + assert_complete_trace(traces, route=route, genai_span=f"chat {MODEL}") @pytest.mark.covers("logging.otel.success.exports_metric", exercised_on=["chat_completions"]) @meta( @@ -315,7 +314,7 @@ class TestOtelTraceCompleteness: assert outcome.call_id is not None, "success response must carry x-litellm-call-id" hits: Final = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=f"chat {MODEL}") - _assert_complete_trace(hits, route=route, genai_span=f"chat {MODEL}") + assert_complete_trace(hits, route=route, genai_span=f"chat {MODEL}") @pytest.mark.covers("logging.otel.success.exports_metric", exercised_on=["messages"]) @meta( @@ -333,7 +332,7 @@ class TestOtelTraceCompleteness: """This test verifies that one successful non-streaming /v1/messages request produces exactly one complete OTEL trace. - The trace must have a single root span named "POST /v1/messages". The + The trace must have a single root SERVER span identifying /v1/messages. The authentication, database, and model-call spans must all belong to the same trace and have valid parent relationships leading back to that root. @@ -353,7 +352,7 @@ class TestOtelTraceCompleteness: assert outcome.call_id is not None, "success response must carry x-litellm-call-id" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=f"chat {MODEL}") - _assert_complete_trace(traces, route=route, genai_span=f"chat {MODEL}") + assert_complete_trace(traces, route=route, genai_span=f"chat {MODEL}") @pytest.mark.covers("logging.otel.success.exports_metric", exercised_on=["responses"]) @meta( @@ -392,7 +391,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {CHEAP_OPENAI_MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) @pytest.mark.covers("logging.otel.stream.exports_metric", exercised_on=["chat_completions"]) @meta( @@ -443,7 +442,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) served = one_served_genai_span(traces.hits[0], genai_span) assert served.tag("litellm.request.streaming") is True, ( @@ -500,7 +499,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) served = one_served_genai_span(traces.hits[0], genai_span) assert served.tag("litellm.request.streaming") is True, ( @@ -557,7 +556,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {CHEAP_OPENAI_MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) one_served_genai_span(traces.hits[0], genai_span) @@ -614,7 +613,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_real_ttft(traces.hits, genai_span=genai_span) + assert_real_ttft(traces.hits, genai_span=genai_span) @pytest.mark.covers("logging.otel.stream.records_ttft", exercised_on=["messages"]) @meta( @@ -661,7 +660,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_real_ttft(traces.hits, genai_span=genai_span) + assert_real_ttft(traces.hits, genai_span=genai_span) @pytest.mark.covers("logging.otel.stream.records_ttft", exercised_on=["responses"]) @meta( @@ -708,7 +707,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {CHEAP_OPENAI_MODEL}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_real_ttft(traces.hits, genai_span=genai_span) + assert_real_ttft(traces.hits, genai_span=genai_span) @pytest.mark.covers("logging.otel.failure.exports_metric", exercised_on=["chat_completions"]) @meta( @@ -763,7 +762,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {model_name}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) root = next(span for span in traces.hits[0].spans if not span.references) assert str(root.tag("http.status_code")) == "401", ( @@ -825,7 +824,7 @@ class TestOtelTraceCompleteness: genai_span = f"chat {model_name}" traces = _poll(otel_reader, call_id=outcome.call_id, route=route, genai_span=genai_span) - _assert_complete_trace(traces, route=route, genai_span=genai_span) + assert_complete_trace(traces, route=route, genai_span=genai_span) root = next(span for span in traces.hits[0].spans if not span.references) assert str(root.tag("http.status_code")) == "401", ( diff --git a/tests/e2e/management/test_budget_customer_user_org_e2e.py b/tests/e2e/management/test_budget_customer_user_org_e2e.py index ae4e6db4e15..ea4afee78ca 100644 --- a/tests/e2e/management/test_budget_customer_user_org_e2e.py +++ b/tests/e2e/management/test_budget_customer_user_org_e2e.py @@ -418,7 +418,7 @@ class TestBudgetListV1: @pytest.mark.covers("mgmt.budget.list_v1.happy_path") @meta(Subject(domain=Domain.SPEND_BUDGETS, route=Route.BUDGET_MANAGEMENT)) def test_refuses_a_sort_field_and_a_parameter_it_does_not_support(self, client: ManagementClient) -> None: - assert _list_status(client, BudgetPageParams(sort="budget_duration")) == 400 + assert _list_status(client, BudgetPageParams(sort="not_a_sort_field")) == 400 assert _list_status(client, BudgetPageParams(not_a_parameter="b-1")) == 400 @pytest.mark.covers("mgmt.budget.list_v1.admin_only") diff --git a/tests/e2e/mcp/test_mcp_semantic_tool_filter_e2e.py b/tests/e2e/mcp/test_mcp_semantic_tool_filter_e2e.py index 6e564f4625a..1ec2a6822e3 100644 --- a/tests/e2e/mcp/test_mcp_semantic_tool_filter_e2e.py +++ b/tests/e2e/mcp/test_mcp_semantic_tool_filter_e2e.py @@ -5,7 +5,8 @@ embedding deployment, and turns on `litellm_settings.mcp_semantic_tool_filter` t /config/update. A chat completion that attaches the server and asks to search logs must report, in the x-litellm-semantic-filter headers, that the catalog was cut to at most top_k tools, all of them tools the key lists. Requires DD_API_KEY + DD_APP_KEY and the proxy's -OPENAI_API_KEY for the embedding deployment. +OPENAI_API_KEY for the embedding deployment. The filter is proxy-wide, so the test is +marked `proxy_wide_config` and runs on a stack the main suite is not driving. """ from __future__ import annotations @@ -23,7 +24,7 @@ from mcp_client import McpClient from models import ChatBody, ChatMessage, LiteLLMParamsBody, McpChatTool from pydantic import BaseModel -pytestmark = [pytest.mark.e2e, pytest.mark.quiet_stack] +pytestmark = [pytest.mark.e2e, pytest.mark.proxy_wide_config] TOP_K: Final = 3 diff --git a/tests/e2e/models.py b/tests/e2e/models.py index 9c12e2146c9..86af30f413a 100644 --- a/tests/e2e/models.py +++ b/tests/e2e/models.py @@ -653,6 +653,35 @@ class ResponsesStreamBody(BaseModel): cache: dict[str, bool] | None = {"no-cache": True} +class ResponsesFunctionTool(BaseModel): + model_config = ConfigDict(frozen=True) + type: Literal["function"] = "function" + name: str + description: str | None = None + parameters: ToolInputSchema + + +class ResponsesToolBody(BaseModel): + model_config = ConfigDict(frozen=True) + model: str + input: str + tools: list[ResponsesFunctionTool] + tool_choice: Literal["required"] | None = None + guardrails: list[str] + max_output_tokens: int = 128 + + +class ResponsesOutputItem(BaseModel): + model_config = ConfigDict(frozen=True) + type: str + name: str | None = None + + +class ResponsesToolOutput(BaseModel): + model_config = ConfigDict(frozen=True) + output: list[ResponsesOutputItem] + + class CountTokensBody(BaseModel): """POST /v1/messages/count_tokens body: the /v1/messages shape minus max_tokens (the endpoint only counts the prompt).""" @@ -1262,27 +1291,6 @@ class CostMap(RootModel[dict[str, CostMapEntry]]): pass -class ConfigPatchResponse(BaseModel): - status: str - values: dict[str, float | dict[str, float]] - - -class CostDiscountConfig(RootModel[dict[str, float]]): - pass - - -class CostMarginConfig(RootModel[dict[str, float | dict[str, float]]]): - pass - - -class CostDiscountConfigResponse(BaseModel): - values: dict[str, float] - - -class CostMarginConfigResponse(BaseModel): - values: dict[str, float | dict[str, float]] - - class BedrockGuardrailParams(BaseModel): guardrail: Literal["bedrock"] = "bedrock" mode: Literal["pre_call"] = "pre_call" diff --git a/tests/e2e/otel_client.py b/tests/e2e/otel_client.py index 5f93c677d89..b42235484dc 100644 --- a/tests/e2e/otel_client.py +++ b/tests/e2e/otel_client.py @@ -28,11 +28,10 @@ from dataclasses import dataclass from typing import Final import pytest -from pydantic import BaseModel, ConfigDict, Field - from e2e_config import OTEL_QUERY_URL, POLL_INTERVAL, POLL_TIMEOUT from e2e_http import URL, NetworkError, NoBody, Result, Success, get from e2e_metadata import step +from pydantic import BaseModel, ConfigDict, Field #: OTEL resource service.name the proxy exports under (OTEL_SERVICE_NAME default). JAEGER_SERVICE = "litellm" @@ -118,9 +117,15 @@ def _ticks() -> Iterator[None]: time.sleep(POLL_INTERVAL) -def _settled(trace: JaegerTrace, names: set[str], prefixes: set[str]) -> bool: - present = set(trace.span_names()) - return names.issubset(present) and all(any(name.startswith(prefix) for name in present) for prefix in prefixes) +def trace_settled(trace: JaegerTrace, names: set[str], prefixes: set[str]) -> bool: + root: Final = root_span(trace) + present: Final = frozenset(trace.span_names()) + return ( + root is not None + and root.kind == "server" + and names.issubset(present) + and all(any(name.startswith(prefix) for name in present) for prefix in prefixes) + ) def root_span(trace: JaegerTrace) -> JaegerSpan | None: @@ -172,7 +177,7 @@ class _Observation: def settled(self, names: set[str], prefixes: set[str]) -> bool: hits: Final = self.traces.hits - return self.unreachable is None and len(hits) == 1 and not self.missing and _settled(hits[0], names, prefixes) + return self.unreachable is None and len(hits) == 1 and not self.missing and trace_settled(hits[0], names, prefixes) @dataclass(frozen=True, slots=True) diff --git a/tests/e2e/perf/setup/upgrade.py b/tests/e2e/perf/setup/upgrade.py deleted file mode 100644 index c9114ccbff2..00000000000 --- a/tests/e2e/perf/setup/upgrade.py +++ /dev/null @@ -1,223 +0,0 @@ -from __future__ import annotations - -import math -import time -from collections.abc import Callable -from dataclasses import replace -from typing import Final - -import psycopg -from cases.small_bodies_5rps import CASE -from e2e_config import MASTER_KEY -from e2e_http import send_text_external -from prometheus_client.parser import text_string_to_metric_families -from pydantic import BaseModel, ConfigDict, Field, TypeAdapter - -from setup.performance_test_case import PerformanceTestCase -from setup.upgrade_worker import UpgradeState, fingerprints, missing_indexes, read_state, release_indexes -from setup.wire import WireRecord - -MAX_TRAFFIC_SECONDS: Final = 6 * 3600 -_ROWS: Final = TypeAdapter(tuple[tuple[str, str | None], ...]) -_ACTIVITY: Final = TypeAdapter(tuple[list[int], int]) -_COUNT: Final = TypeAdapter(tuple[int]) -_SERVICES: Final = ("postgres", "batch_write_to_db") - - -def upgrade_case() -> PerformanceTestCase: - return replace( - CASE, - name="database_upgrade_5rps", - traffic=replace(CASE.traffic, total_requests=int(CASE.traffic.rps * MAX_TRAFFIC_SECONDS)), - limits=tuple( - limit - for limit in CASE.limits - if limit.metric - in { - "unexpected_status_count", - "gateway_added_seconds", - "time_to_first_token_seconds", - "memory_growth_megabytes", - } - ), - ) - - -class MetricsHeaders(BaseModel): - model_config = ConfigDict(frozen=True) - authorization: str = Field(default=f"Bearer {MASTER_KEY}", alias="Authorization") - - -def postgres_failures(text: str) -> float: - families: Final = tuple(text_string_to_metric_families(text)) - names: Final = frozenset(family.name for family in families) - required: Final = frozenset(f"litellm_{service}_failed_requests" for service in _SERVICES) - samples: Final = tuple(sample for family in families for sample in family.samples) - has_database_activity: Final = any( - sample.name == "litellm_postgres_total_requests_total" and math.isfinite(sample.value) and sample.value > 0 - for sample in samples - ) - assert required <= names or has_database_activity, f"Missing database service counters: {required - names}" - failures: Final = tuple(sample.value for sample in samples if sample.name in {f"{name}_total" for name in required}) - assert all(math.isfinite(value) and value >= 0 for value in failures), "Invalid database failure counter" - return sum(failures) - - -def assert_database_metrics(urls: tuple[str, ...]) -> None: - assert urls, "E2E_DATABASE_METRICS_URLS must name every gateway/backend /metrics endpoint" - for url in urls: - assert_database_metrics_url(url) - - -def assert_database_metrics_url(url: str) -> None: - result: Final = send_text_external("GET", url, headers=MetricsHeaders(), timeout=10) - assert result.status_code == 200, f"Database metrics unavailable at {url}: {result.status_code}" - assert postgres_failures(result.body) == 0, f"Failed PostgreSQL operations reported at {url}" - - -def persisted(connection: psycopg.Connection[tuple[object, ...]], records: tuple[WireRecord, ...]) -> frozenset[str]: - ids: Final = tuple(record.call_id for record in records if record.call_id) - if not ids: - return frozenset() - rows: Final = _ROWS.validate_python( - connection.execute( - 'SELECT request_id, litellm_call_id FROM "LiteLLM_SpendLogs" WHERE litellm_call_id=ANY(%s)', (list(ids),) - ).fetchall() - ) - return matched_response_ids(records, rows) - - -def matched_response_ids(records: tuple[WireRecord, ...], rows: tuple[tuple[str, str | None], ...]) -> frozenset[str]: - call_ids: Final = frozenset(call_id for _, call_id in rows if call_id) - return frozenset(record.response_id for record in records if record.response_id and record.call_id in call_ids) - - -def activity(connection: psycopg.Connection[tuple[object, ...]]) -> tuple[list[int], int]: - return _ACTIVITY.validate_python( - connection.execute("""SELECT - ARRAY(SELECT pid FROM pg_stat_progress_create_index WHERE datname=current_database()), - (SELECT count(*) FROM pg_stat_activity WHERE datname=current_database() AND wait_event_type='Lock') - """).fetchone() - ) - - -def assert_finished(connection: psycopg.Connection[tuple[object, ...]], state: UpgradeState) -> None: - assert state.phase == "complete", f"Upgrade did not finish: {state.phase}" - assert not missing_indexes(connection, state.required_indexes), "Required target indexes are missing or invalid" - (missing,) = _COUNT.validate_python( - connection.execute( - """ - SELECT count(*) FROM unnest(%s::text[]) name WHERE NOT EXISTS ( - SELECT 1 FROM "_prisma_migrations" WHERE migration_name=name - AND finished_at IS NOT NULL AND rolled_back_at IS NULL)""", - (list(state.required_migrations),), - ).fetchone() - ) - assert missing == 0, f"{missing} target migrations were not applied" - (unfinished,) = _COUNT.validate_python( - connection.execute(""" - SELECT count(*) FROM "_prisma_migrations" WHERE finished_at IS NULL AND rolled_back_at IS NULL - """).fetchone() - ) - assert unfinished == 0, f"{unfinished} unfinished migrations" - assert fingerprints(connection, tuple(state.baseline_rows)) == state.baseline_rows, ( - "Baseline rows changed or disappeared" - ) - - -class UpgradeMonitor: - def __init__( - self, - *, - state: Callable[[], UpgradeState], - check_metrics: Callable[[], None], - read_activity: Callable[[], tuple[list[int], int]], - read_persisted: Callable[[tuple[WireRecord, ...]], frozenset[str]], - start_indexes: Callable[[], None], - clock: Callable[[], float] = time.monotonic, - wall_clock: Callable[[], float] = time.time, - ) -> None: - self.state: Final = state - self.check_metrics: Final = check_metrics - self.read_activity: Final = read_activity - self.read_persisted: Final = read_persisted - self.start_indexes: Final = start_indexes - self.clock: Final = clock - self.wall_clock: Final = wall_clock - self.started: Final = clock() - self.status_limit: Final = next( - limit.max for limit in CASE.limits if limit.metric == "unexpected_status_count" and limit.max is not None - ) - self.pending: tuple[WireRecord, ...] = () - self.processed = 0 - self.successes = 0 - self.persisted_count = 0 - self.overlap_writes = 0 - self.max_lock_waiters = 0 - self.previous_builds: frozenset[int] = frozenset() - self.build_seen_at: float | None = None - self.released = False - self.completed_at: float | None = None - - def poll(self, records: tuple[WireRecord, ...]) -> bool: - self.check_metrics() - current: Final = self.state() - assert current.phase != "failed", "Target migration/index worker failed" - assert self.clock() - self.started < MAX_TRAFFIC_SECONDS, "Upgrade exceeded its six-hour operational timeout" - successful: Final = tuple(record for record in records if record.status == 200) - self.processed += len(records) - self.successes += len(successful) - assert self.processed - self.successes <= self.status_limit, "Too many unexpected non-200 responses" - assert all(record.ok and record.text for record in successful), "Failed or empty inference response" - assert all(record.response_id and record.call_id for record in successful), ( - "Response missing spend-log identifiers" - ) - builds, lock_waiters = self.read_activity() - if not self.previous_builds.intersection(builds): - self.build_seen_at = self.wall_clock() if builds else None - self.max_lock_waiters = max(self.max_lock_waiters, lock_waiters) - self.pending += successful - found: Final = self.read_persisted(self.pending) - self.persisted_count += len(found) - still_building, _ = self.read_activity() - since: Final = self.build_seen_at - if since is not None and frozenset(builds).intersection(still_building): - self.overlap_writes += sum( - 1 for record in self.pending if record.response_id in found and record.started_at >= since - ) - self.previous_builds = frozenset(builds) - self.pending = tuple(record for record in self.pending if record.response_id not in found) - if self.successes and not self.released: - self.start_indexes() - self.released = True - if current.phase == "complete" and self.completed_at is None: - self.completed_at = self.clock() - return self.completed_at is not None and self.clock() - self.completed_at >= 60 and self.successes >= 300 - - def assert_complete(self, state: UpgradeState) -> None: - assert self.successes >= 300, f"Only {self.successes} requests completed" - assert not self.pending, f"{len(self.pending)} successful requests have no matching spend log" - assert self.persisted_count == self.successes, "Spend-log count does not match successful requests" - assert not state.pending_indexes or self.overlap_writes > 0, "No persisted requests observed during index work" - - -def upgrade_properties(state: UpgradeState, monitor: UpgradeMonitor) -> tuple[tuple[str, float], ...]: - """The upgrade's own numbers under the `perf_` prefix the dashboard ingests, next to the perf rows.""" - return ( - ("perf_upgrade_schema_seconds", state.schema_seconds), - ("perf_upgrade_index_seconds", state.index_seconds), - ("perf_upgrade_overlap_writes", float(monitor.overlap_writes)), - ("perf_upgrade_max_lock_waiters", float(monitor.max_lock_waiters)), - ("perf_upgrade_pending_indexes", float(len(state.pending_indexes))), - ("perf_upgrade_unmatched_requests", float(len(monitor.pending))), - ) - - -def monitor_for(connection: psycopg.Connection[tuple[object, ...]], urls: tuple[str, ...]) -> UpgradeMonitor: - return UpgradeMonitor( - state=lambda: read_state(connection), - check_metrics=lambda: assert_database_metrics(urls), - read_activity=lambda: activity(connection), - read_persisted=lambda records: persisted(connection, records), - start_indexes=lambda: release_indexes(connection), - ) diff --git a/tests/e2e/perf/test_database_upgrade.py b/tests/e2e/perf/test_database_upgrade.py deleted file mode 100644 index fba6af30014..00000000000 --- a/tests/e2e/perf/test_database_upgrade.py +++ /dev/null @@ -1,102 +0,0 @@ -from __future__ import annotations - -import os -import sys -import time -from pathlib import Path -from typing import Final - -import pytest -from idp import Keycloak -from lifecycle import ResourceManager -from memory_readings import read_rss_everywhere -from proxy_client import ProxyClient -from setup.datastore import read_db_counters, read_redis_counters -from setup.observe import ( - Observation, - evaluate, - junit_properties, - read_mock_requests, - render, - reset_mock, - set_mock_latency, -) -from setup.plan import build_plan -from setup.podcpu import gateway_cpu_sampler -from setup.provision import MOCK_PROVIDERS, build_setup, mock_url -from setup.run import run_plan -from setup.upgrade import assert_finished, monitor_for, upgrade_case, upgrade_properties -from setup.upgrade_worker import connect, read_state - -pytestmark = [pytest.mark.perf, pytest.mark.quiet_stack] - - -@pytest.mark.skipif(os.environ.get("E2E_DATABASE_UPGRADE") != "1", reason="Dedicated disposable snapshot lane only") -def test_database_upgrade( - proxy: ProxyClient, idp: Keycloak, resources: ResourceManager, request: pytest.FixtureRequest -) -> None: - case: Final = upgrade_case() - urls: Final = tuple(mock_url(provider) for provider in MOCK_PROVIDERS) - for url in urls: - _ = reset_mock(url) - _ = set_mock_latency(url, case.mock_latency) - setup: Final = build_setup(case, proxy=proxy, idp=idp, resources=resources) - metrics: Final = tuple(url.strip() for url in os.environ["E2E_DATABASE_METRICS_URLS"].split(",") if url.strip()) - with connect() as connection: - assert read_state(connection).phase == "schema_ready", "Expected the target schema ready, index work waiting" - rss_before: Final = read_rss_everywhere(proxy) - db_before: Final = read_db_counters() - redis_before: Final = read_redis_counters() - monitor: Final = monitor_for(connection, metrics) - with gateway_cpu_sampler() as cpu: - outcome: Final = run_plan(case, build_plan(case), setup, stop_when=monitor.poll, request_timeout=30) - assert outcome.locust_exit == 0, outcome.locust_output - remaining: Final = outcome.records[monitor.processed :] - _ = monitor.poll(remaining) - deadline: Final = time.monotonic() + 120 - while monitor.pending and time.monotonic() < deadline: - time.sleep(1) - _ = monitor.poll(()) - state: Final = read_state(connection) - _ = sys.stdout.write( - f"successful_requests={monitor.successes} persisted_requests={monitor.persisted_count} " - f"unmatched_requests={len(monitor.pending)}\n" - ) - rss_after: Final = read_rss_everywhere(proxy) - observed: Final = Observation( - records=outcome.records, - mock_requests=tuple(seen for url in urls for seen in read_mock_requests(url)), - guardrail_calls=(), - rss_before=rss_before, - rss_after=rss_after, - wall_seconds=outcome.wall_seconds, - spend_logged_share=1, - db_before=db_before, - db_after=read_db_counters(), - redis_before=redis_before, - redis_after=read_redis_counters(), - gateway_cpu_seconds=cpu.seconds, - ) - rows, exceeded = evaluate(case, observed) - item: Final = request.node # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType] # pytest: untyped - assert isinstance(item, pytest.Item) - item.user_properties[:] = [ - entry for entry in item.user_properties if not entry[0].startswith("perf_") - ] # rebind-ok: a --reruns retry replaces the first attempt's numbers instead of appending a second set - item.user_properties.extend(junit_properties(rows)) - item.user_properties.extend(upgrade_properties(state, monitor)) - _ = sys.stdout.write(render(case, observed, rows) + "\n") - _ = sys.stdout.write( - f"schema_seconds={state.schema_seconds:.3f} index_seconds={state.index_seconds:.3f} " - f"overlap_writes={monitor.overlap_writes} max_lock_waiters={monitor.max_lock_waiters} " - f"pending_indexes={state.pending_indexes}\n" - ) - artifact: Final = os.environ.get("E2E_ARTIFACT_DIR") - if artifact: - _ = (Path(artifact) / "database-upgrade.json").write_text(state.model_dump_json(), encoding="utf-8") - monitor.assert_complete(state) - assert_finished(connection, state) - assert {(r.replica, r.hostname) for r in rss_before.readings} == { - (r.replica, r.hostname) for r in rss_after.readings - }, "Gateway pods replaced during the upgrade" - assert not exceeded, "\n".join(exceeded) diff --git a/tests/e2e/perf/test_perf_cases.py b/tests/e2e/perf/test_perf_cases.py index db657a04b00..0a137e84572 100644 --- a/tests/e2e/perf/test_perf_cases.py +++ b/tests/e2e/perf/test_perf_cases.py @@ -48,7 +48,7 @@ from setup.ramp import step_properties from setup.run import run_plan from setup.telemetry import read_gateway_telemetry, telemetry_sampler -pytestmark = [pytest.mark.perf, pytest.mark.quiet_stack] +pytestmark = pytest.mark.perf _SPEND_SAMPLE: Final = 40 _WARMUP_SECONDS: Final = 60 diff --git a/tests/e2e/router/test_reliability_memory_e2e.py b/tests/e2e/perf/test_reliability_memory_e2e.py similarity index 77% rename from tests/e2e/router/test_reliability_memory_e2e.py rename to tests/e2e/perf/test_reliability_memory_e2e.py index fb114092303..2066137bb72 100644 --- a/tests/e2e/router/test_reliability_memory_e2e.py +++ b/tests/e2e/perf/test_reliability_memory_e2e.py @@ -1,17 +1,19 @@ """Live e2e: a few hundred requests that fail before any provider answers must not -grow the proxy's resident memory past a loose ceiling once the proxy is warm. +grow the proxy's resident memory past a loose ceiling once the proxy is warm. It reads +the proxy's own RSS, so it runs in the perf lane, where nothing else is hitting the stack. The regression this guards shipped in v1.100.0: every retry breadcrumb copied the whole request and the copies nested into one router-global list, so a proxy under retry-heavy failing traffic grew until it was OOM-killed. The traffic here has that shape: a model group whose deployments refuse at the socket (an unreachable base -URL) with cooldown_time 0 so the router keeps retrying them, per-request retries, -and a fallback group that refuses the same way, each request carrying a long chat +URL, with a literal key since the perf stack carries no provider keys) with +cooldown_time 0 so the router keeps retrying them, per-request retries, and a +fallback group that refuses the same way, each request carrying a long chat transcript so every whole-request copy costs hundreds of containers instead of a -handful. Under the stack's cooldown policy a -deployment that fails a handful of times in a row is benched (a bad-credential 401 -included), the router answers "No deployments available" without retrying, and the -retry loop that leaks stops running; cooldown_time 0 keeps it running. +handful. Under the stack's cooldown policy a deployment that fails a handful of +times in a row is benched (a bad-credential 401 included), the router answers "No +deployments available" without retrying, and the retry loop that leaks stops running; +cooldown_time 0 keeps it running. Four identical batches run back to back. The first two are the warmup that grows the proxy's caches and allocator arenas, the last two are the measured window, and @@ -35,19 +37,11 @@ single load-balanced address reaches the workers behind it one answer at a time, a worker that answered only one checkpoint would otherwise drop out of the comparison, which is where a leaking worker could hide. -RSS alone is a coarse gauge: on the release stack (spend logs storing prompts, -json logs, prometheus and otel callbacks) the same v1.100.0 breadcrumbs grew RSS -by only about 15 MB per 300 failing requests, while every failing request's stored -request snapshot carried a copy of the request per failed attempt, over 100 KB on -the first call and a couple of MB once the copies nested, against tens of KB with -the fix. So the first check sends one failing request before the phases, reads its -spend log back through /spend/logs, and holds the stored request body to a fixed -size budget: the deterministic catch for a breadcrumb that copies the whole -request. It runs before the phases because the leaking writer drops its own rows -under the phases' traffic (a queue budget hit, a recursion limit on the nested -copies), which would turn the size check into a missing-row check. +The stored-request half of that regression (every failing request's spend log carrying a +copy of the request per failed attempt) is a deterministic size check, so it lives in +tests/integration/spend/test_failing_request_stored_snapshot.py. -A second, cheaper check holds the idle footprint: every worker's RSS as the harness +A cheaper check holds the idle footprint: every worker's RSS as the harness read it at collection time, before this pytest process sent any traffic (see conftest.pytest_collection_finish), must sit under a fixed budget. On the release gate that is a fresh stack right after its readiness gate, one worker per @@ -74,7 +68,6 @@ from typing import Final import pytest -from complexity_router_client import ComplexityRouterClient from e2e_config import ( MEMORY_CONCURRENCY, MEMORY_IDLE_RSS_BUDGET_MB, @@ -83,7 +76,6 @@ from e2e_config import ( MEMORY_RSS_CEILING_MB, MEMORY_RSS_SAMPLE_INTERVAL_SECONDS, MEMORY_RSS_SETTLE_SAMPLES, - MEMORY_STORED_REQUEST_BUDGET_KB, MEMORY_TRANSCRIPT_TURNS, PROXY_REPLICA_URLS, unique_marker, @@ -91,11 +83,12 @@ from e2e_config import ( from e2e_metadata import Domain, Mode, Provider, Subject, meta from lifecycle import ResourceManager from memory_readings import RssCapture, RssReading, WorkerKey, read_rss_everywhere -from models import ChatMessage, RouterSettingsOverride, SpendLogRow +from models import ChatMessage, LiteLLMParamsBody, ReliabilityChatBody, RouterSettingsOverride from proxy_client import ProxyClient -from reliability_support import REAL_MODEL, chat_override, create_never_benched_refusing_deployment -pytestmark = [pytest.mark.e2e, pytest.mark.quiet_stack] +pytestmark = [pytest.mark.e2e, pytest.mark.perf] + +REFUSING_MODEL: Final = "openai/gpt-5.5" DEPLOYMENTS_PER_GROUP: Final = 2 RSS_SAMPLE_CAP: Final = 4 * MEMORY_RSS_SETTLE_SAMPLES @@ -110,7 +103,6 @@ class FailedCall: status_code: int seconds: float body_head: str - call_id: str | None @dataclass(frozen=True, slots=True) @@ -123,8 +115,17 @@ class WorkerGrowth: return self.after.ram_usage_mb - self.warm.ram_usage_mb +def _create_never_benched_refusing_deployment(proxy: ProxyClient, name: str) -> str: + return proxy.create_model( + name, + LiteLLMParamsBody( + model=REFUSING_MODEL, api_key="sk-unreachable", api_base="http://127.0.0.1:9/v1", cooldown_time=0 + ), + ) + + def _register_refusing_group(proxy: ProxyClient, resources: ResourceManager, name: str) -> None: - for model_id in tuple(create_never_benched_refusing_deployment(proxy, name) for _ in range(DEPLOYMENTS_PER_GROUP)): + for model_id in tuple(_create_never_benched_refusing_deployment(proxy, name) for _ in range(DEPLOYMENTS_PER_GROUP)): resources.defer(lambda model_id=model_id: proxy.delete_model(model_id)) @@ -141,10 +142,18 @@ TRANSCRIPT: Final = _transcript(MEMORY_TRANSCRIPT_TURNS) def _fail_once(proxy: ProxyClient, key: str, model: str, override: RouterSettingsOverride) -> FailedCall: started: Final = time.perf_counter() - resp: Final = chat_override( - proxy, key, model, f"memory regression {unique_marker()}", override=override, history=TRANSCRIPT + resp: Final = proxy.transport.send( + "/chat/completions", + headers=proxy.transport.bearer(key), + json=ReliabilityChatBody( + model=model, + messages=[*TRANSCRIPT, ChatMessage(role="user", content=f"memory regression {unique_marker()}")], + max_tokens=512, + router_settings_override=override, + cache={"no-cache": True}, + ), ) - return FailedCall(resp.status_code, time.perf_counter() - started, resp.body[:300], resp.call_id) + return FailedCall(resp.status_code, time.perf_counter() - started, resp.body[:300]) def _fail_many(proxy: ProxyClient, key: str, model: str, override: RouterSettingsOverride) -> tuple[FailedCall, ...]: @@ -259,24 +268,6 @@ def _assert_every_call_failed_through_fallback(calls: Sequence[FailedCall], fall ) -def _stored_request_kb(proxy: ProxyClient, call: FailedCall) -> float: - assert call.call_id, ( - f"the failing call carried no x-litellm-call-id header, so its spend log cannot be read back: {call.body_head}" - ) - rows: Final[Sequence[SpendLogRow]] = proxy.poll_logs_for_request_id(call.call_id) - assert rows, ( - f"no spend log row appeared for failing call {call.call_id} within the poll window: either the stack " - "writes no spend logs or its writer dropped the row, which the v1.100.0 one did once the stored " - "request outgrew the writer's queue budget" - ) - snapshot: Final = rows[0].proxy_server_request - assert snapshot, ( - f"spend log {call.call_id} stored no request body, so the stack is not running with " - "general_settings.store_prompts_in_spend_logs and the stored-request check would pass vacuously" - ) - return len(json.dumps(snapshot).encode()) / 1024 - - class TestReliabilityMemory: @pytest.mark.covers("reliability.perf.idle_memory.under_slo") @meta( @@ -309,13 +300,13 @@ class TestReliabilityMemory: Subject( domain=Domain.ROUTING, providers=(Provider.OPENAI,), - models=(REAL_MODEL,), + models=(REFUSING_MODEL,), mode=Mode.NONSTREAM, ) ) - def test_failing_requests_do_not_grow_rss_or_stored_request( + def test_failing_requests_do_not_grow_rss( self, - client: ComplexityRouterClient, + proxy: ProxyClient, resources: ResourceManager, scoped_key: str, record_property: Callable[[str, object], None], @@ -323,35 +314,25 @@ class TestReliabilityMemory: marker: Final = unique_marker() primary: Final = f"reliability-memory-{marker}" fallback: Final = f"reliability-memory-fb-{marker}" - _register_refusing_group(client.proxy, resources, primary) - _register_refusing_group(client.proxy, resources, fallback) + _register_refusing_group(proxy, resources, primary) + _register_refusing_group(proxy, resources, fallback) override: Final = RouterSettingsOverride( num_retries=MEMORY_RETRIES_PER_REQUEST, fallbacks=[{primary: [fallback]}] ) - probe: Final = _fail_once(client.proxy, scoped_key, primary, override) - _assert_every_call_failed_through_fallback((probe,), fallback) - stored_kb: Final = _stored_request_kb(client.proxy, probe) - assert stored_kb <= MEMORY_STORED_REQUEST_BUDGET_KB, ( - f"the spend log of one failing request stored a {stored_kb:.0f} KB request body, past the " - f"{MEMORY_STORED_REQUEST_BUDGET_KB:.0f} KB budget for a {len(TRANSCRIPT)}-message transcript with " - f"{MEMORY_RETRIES_PER_REQUEST} retries and a fallback; the retry breadcrumbs are copying the whole " - f"request into the stored snapshot the way the v1.100.0 ones did" - ) - warm: Final = _settled_rss_after_batches( - client.proxy, + proxy, scoped_key, primary, override, fallback, record_property, - _settled_rss_per_worker(client.proxy), + _settled_rss_per_worker(proxy), 1, WARMUP_BATCHES, ) after: Final = _settled_rss_after_batches( - client.proxy, + proxy, scoped_key, primary, override, diff --git a/tests/e2e/perf/test_ui_journeys.py b/tests/e2e/perf/test_ui_journeys.py index 63eefedec68..11b2cd1051d 100644 --- a/tests/e2e/perf/test_ui_journeys.py +++ b/tests/e2e/perf/test_ui_journeys.py @@ -33,7 +33,6 @@ from transport import HttpTransport pytestmark = [ pytest.mark.perf, - pytest.mark.quiet_stack, pytest.mark.skipif(os.environ.get("E2E_UI_PERF_DISPOSABLE") != "1", reason="Dedicated disposable database only"), ] diff --git a/tests/e2e/provider_cache_redis.py b/tests/e2e/provider_cache_redis.py index 2c7419cfc0f..c1a3eada4a9 100644 --- a/tests/e2e/provider_cache_redis.py +++ b/tests/e2e/provider_cache_redis.py @@ -14,8 +14,8 @@ from typing import Final, Protocol, cast from provider_cache import LIFETIME_SECONDS, CacheBusy, CacheEdge, CacheHit, CacheLookup, CacheUnavailable, CaptureLease from pydantic import TypeAdapter, ValidationError -from redis import Redis -from redis.exceptions import RedisError +from redis import Redis, RedisCluster +from redis.exceptions import RedisClusterException, RedisError REDIS_ARRAY: Final[TypeAdapter[list[bytes]]] = TypeAdapter(list[bytes]) @@ -83,7 +83,7 @@ class RedisResponseStore: started: Final = time.monotonic() try: result: Final = self.client.eval(LOOKUP, 2, *self.keys(key), token, self.lifetime_ms, self.lease_ms) - except (RedisError, OSError): + except (RedisError, RedisClusterException, OSError): return CacheUnavailable() try: parts: Final = tuple(REDIS_ARRAY.validate_python(result, strict=True)) @@ -102,28 +102,46 @@ class RedisResponseStore: result: Final = self.client.eval( PUBLISH, 2, *self.keys(key), lease.token, lease.captured_at_ms, lease.expires_at_ms, payload, self.lifetime_ms, ) - except (RedisError, OSError): + except (RedisError, RedisClusterException, OSError): return False return result == 1 def release(self, key: str, lease: CaptureLease) -> bool: try: result: Final = self.client.eval(RELEASE, 1, self.keys(key)[1], lease.token) - except (RedisError, OSError): + except (RedisError, RedisClusterException, OSError): return False return result == 1 def discard(self, key: str, payload: bytes) -> bool: try: result: Final = self.client.eval(DISCARD, 1, self.keys(key)[0], payload) - except (RedisError, OSError): + except (RedisError, RedisClusterException, OSError): return False return result == 1 +@functools.lru_cache(maxsize=16) +def _redis_commands(url: str, cluster: bool) -> RedisCommands: + if cluster: + return cast( + RedisCommands, RedisCluster(url=url, socket_timeout=0.25, socket_connect_timeout=0.25, decode_responses=False) + ) + return cast(RedisCommands, Redis.from_url(url, socket_timeout=0.25, socket_connect_timeout=0.25, decode_responses=False)) + + +@dataclass(frozen=True, slots=True) +class LazyRedisCommands: + url: str + cluster: bool + + def eval(self, script: str, numkeys: int, *args: str | bytes | int) -> object: + return _redis_commands(self.url, self.cluster).eval(script, numkeys, *args) + + def redis_store(url: str, namespace: str) -> RedisResponseStore: - client: Final = Redis.from_url(url, socket_timeout=0.25, socket_connect_timeout=0.25, decode_responses=False) - return RedisResponseStore(cast(RedisCommands, client), namespace) + client: Final = LazyRedisCommands(url, os.environ.get("E2E_PROVIDER_CACHE_REDIS_CLUSTER") == "1") + return RedisResponseStore(client, namespace) def write_metrics(cache: CacheEdge) -> None: diff --git a/tests/e2e/proxy_client.py b/tests/e2e/proxy_client.py index 8cc7bf0c2b4..9b842a723d8 100644 --- a/tests/e2e/proxy_client.py +++ b/tests/e2e/proxy_client.py @@ -11,7 +11,7 @@ from __future__ import annotations import os import time import warnings -from collections.abc import Callable, Mapping +from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass, field, replace from datetime import datetime from functools import reduce @@ -155,6 +155,21 @@ class NotServableOn: last_result: Result[ModelsListResponse] | None +@dataclass(frozen=True, slots=True) +class ModelRegistered: + model_name: str + model_id: str + + +@dataclass(frozen=True, slots=True) +class ModelNotRegistered: + model_name: str + reason: str + + +ModelRegistration = ModelRegistered | ModelNotRegistered + + def await_servable( list_models: Callable[[float], Result[ModelsListResponse]], *, @@ -461,6 +476,7 @@ class ProxyClient: model_servable_db_sync_seconds: float = MODEL_SERVABLE_DB_SYNC_SECONDS model_servable_interval: float = MODEL_SERVABLE_INTERVAL model_servable_request_timeout: float = MODEL_SERVABLE_REQUEST_TIMEOUT + settle: Callable[[float], None] = settle_propagation def with_caller(self, caller: Caller) -> ProxyClient: return replace(self, caller=caller) @@ -694,31 +710,59 @@ class ProxyClient: balancer address is configured (every request opens a fresh connection, so the caller's next request re-rolls), so waiting out PROPAGATION_TIMEOUT is what makes the model safe to use anywhere.""" - model_id = unwrap( - self.transport.post( - "/model/new", - headers=self.management_headers(), - json=body.model_copy(update={"litellm_params": route_cache_model( - body.litellm_params, provider_edge_base, - enabled=os.environ.get("E2E_PROVIDER_CACHE", "0") == "1" and not provider_live, - mode=body.model_info.mode, - )}), - response_type=ModelNewResponse, - ) - ).model_id + model_id = unwrap(self._post_model(body, provider_live=provider_live)).model_id written_at = time.monotonic() try: self._await_model_servable(body.model_name, listed_for) except BaseException: self.delete_model(model_id) raise - settle_propagation(written_at) + self.settle(written_at) return model_id + @step("Add a batch of deployments, then wait once for all of them to propagate") + def register_models( + self, bodies: Sequence[ModelNewBody], *, provider_live: bool = False + ) -> tuple[ModelRegistration, ...]: + posted: Final = tuple((body.model_name, self._post_model(body, provider_live=provider_live)) for body in bodies) + written_at: Final = time.monotonic() + registrations: Final = tuple(self._servable_registration(name, result) for name, result in posted) + self.settle(written_at) + return registrations + + def _post_model(self, body: ModelNewBody, *, provider_live: bool) -> Result[ModelNewResponse]: + return self.transport.post( + "/model/new", + headers=self.management_headers(), + json=body.model_copy(update={"litellm_params": route_cache_model( + body.litellm_params, provider_edge_base, + enabled=os.environ.get("E2E_PROVIDER_CACHE", "0") == "1" and not provider_live, + mode=body.model_info.mode, + )}), + response_type=ModelNewResponse, + ) + + def _servable_registration(self, model_name: str, posted: Result[ModelNewResponse]) -> ModelRegistration: + if not isinstance(posted, Success): + return ModelNotRegistered(model_name=model_name, reason=f"/model/new failed: {posted!r}") + match self._servable_outcome(model_name, None): + case Servable(): + return ModelRegistered(model_name=model_name, model_id=posted.data.model_id) + case NotServableOn() as missing: + self.delete_model(posted.data.model_id) + return ModelNotRegistered(model_name=model_name, reason=self._not_servable_message(model_name, missing)) + def _await_model_servable(self, model_name: str, listed_for: str | None = None) -> None: """Block until every replica lists `model_name`, or fail at model_servable_timeout.""" + match self._servable_outcome(model_name, listed_for): + case Servable(): + return + case NotServableOn() as missing: + raise AssertionError(self._not_servable_message(model_name, missing)) + + def _servable_outcome(self, model_name: str, listed_for: str | None) -> Servable | NotServableOn: headers: Final = self.management_headers(listed_for) - outcome: Final = await_servable_everywhere( + return await_servable_everywhere( {url: self._models_poller(transport, headers) for url, transport in self.replicas.items()}, model_name=model_name, timeout=self.model_servable_timeout, @@ -728,19 +772,15 @@ class ProxyClient: now=time.monotonic, sleep=time.sleep, ) - match outcome: - case Servable(): - return - case NotServableOn(replica=replica, last_result=last_result): - raise AssertionError( - servable_timeout_message( - model_name=model_name, - replica=replica, - timeout=self.model_servable_timeout, - db_sync_seconds=self.model_servable_db_sync_seconds, - last_result=last_result, - ) - ) + + def _not_servable_message(self, model_name: str, missing: NotServableOn) -> str: + return servable_timeout_message( + model_name=model_name, + replica=missing.replica, + timeout=self.model_servable_timeout, + db_sync_seconds=self.model_servable_db_sync_seconds, + last_result=missing.last_result, + ) @staticmethod def _models_poller(transport: Transport, headers: AuthHeaders) -> ModelsPoller: diff --git a/tests/e2e/pytest.ini b/tests/e2e/pytest.ini index f15cfd4e98b..891c5fc282c 100644 --- a/tests/e2e/pytest.ini +++ b/tests/e2e/pytest.ini @@ -13,7 +13,6 @@ markers = prompt_caching_stack: needs a proxy running with router_settings.optional_pre_call_checks including prompt_caching; deselected unless E2E_PROMPT_CACHING_STACK is set cli_determinism: drives the real claude CLI for several seconds; deselected unless E2E_CLI_DETERMINISM is set redis_chaos: load test that pauses the proxy's Redis outright mid-run; needs a proxy booted from gateway/redis_chaos_ci_config.yml on the same host, and is deselected unless E2E_PERF_TESTS is set - quiet_stack: measures the proxy itself, so it runs while no other test on this host is hitting the stack; every other test waits for it to finish provider_edge_host: routes provider traffic through the pytest host's edge in every fixture mode, so the gateway must reach the pytest host; deselected unless E2E_PROVIDER_EDGE_HOST_REACHABLE is set owned_gateway: boots its own proxy from source against the stack's Postgres, so it needs DATABASE_URL on the pytest host; deselected unless E2E_OWNED_GATEWAY is set otel_v2: needs a proxy running with LITELLM_OTEL_V2=true; deselected unless E2E_OTEL_V2 is set @@ -21,3 +20,4 @@ markers = secret_manager: needs a proxy booted from gateway/secret_manager__ci_config.yml against that live secret manager; deselected unless E2E_SECRET_MANAGER names the backend (see secret_manager/secret_backends.py) sagemaker_nova: needs a deployed SageMaker Nova endpoint the proxy's AWS credentials can invoke; deselected unless SAGEMAKER_NOVA_ENDPOINT names it sagemaker_nova2_lite: needs a deployed SageMaker Nova 2 Lite endpoint the proxy's AWS credentials can invoke; deselected unless SAGEMAKER_NOVA2_LITE_ENDPOINT names it + proxy_wide_config: turns on a proxy-wide setting through /config/update, so it runs on a stack no other suite is driving; deselected unless E2E_PROXY_WIDE_CONFIG_STACK is set diff --git a/tests/e2e/quota_management/spend_tracking/test_pricing_config_spend_e2e.py b/tests/e2e/quota_management/spend_tracking/test_guardrail_cost_spend_e2e.py similarity index 52% rename from tests/e2e/quota_management/spend_tracking/test_pricing_config_spend_e2e.py rename to tests/e2e/quota_management/spend_tracking/test_guardrail_cost_spend_e2e.py index 8f3bed3f89b..a77ca91ad3a 100644 --- a/tests/e2e/quota_management/spend_tracking/test_pricing_config_spend_e2e.py +++ b/tests/e2e/quota_management/spend_tracking/test_guardrail_cost_spend_e2e.py @@ -17,11 +17,6 @@ from models import ( ChatBody, ChatMessage, ChatResponse, - ConfigPatchResponse, - CostDiscountConfig, - CostDiscountConfigResponse, - CostMarginConfig, - CostMarginConfigResponse, GuardrailCreateBody, GuardrailCreateResponse, KeyDeleteBody, @@ -31,14 +26,11 @@ from models import ( ) from spend_e2e_client import SpendClient -pytestmark = [pytest.mark.e2e, pytest.mark.quiet_stack] +pytestmark = pytest.mark.e2e BACKEND_MODEL: Final = "openai/gpt-6.1-sol" INPUT_RATE: Final = 0.00005 OUTPUT_RATE: Final = 0.0001 -DISCOUNT: Final = 0.25 -MARGIN_PERCENT: Final = 0.1 -MARGIN_FIXED: Final = 0.0005 def _register_bedrock_guardrail( @@ -92,52 +84,6 @@ def _guarded_chat(client: SpendClient, key: str, model: str, name: str) -> Strea ) -def _set_discount(client: SpendClient, values: dict[str, float]) -> float: - unwrap( - client.proxy.transport.patch( - "/config/cost_discount_config", - headers=client.proxy.transport.master, - json=CostDiscountConfig(values), - response_type=ConfigPatchResponse, - ) - ) - return time.monotonic() - - -def _set_margin(client: SpendClient, values: dict[str, float | dict[str, float]]) -> float: - unwrap( - client.proxy.transport.patch( - "/config/cost_margin_config", - headers=client.proxy.transport.master, - json=CostMarginConfig(values), - response_type=ConfigPatchResponse, - ) - ) - return time.monotonic() - - -def _get_discount(client: SpendClient) -> dict[str, float]: - return unwrap( - client.proxy.transport.get( - "/config/cost_discount_config", - headers=client.proxy.transport.master, - params=NoBody(), - response_type=CostDiscountConfigResponse, - ) - ).values - - -def _get_margin(client: SpendClient) -> dict[str, float | dict[str, float]]: - return unwrap( - client.proxy.transport.get( - "/config/cost_margin_config", - headers=client.proxy.transport.master, - params=NoBody(), - response_type=CostMarginConfigResponse, - ) - ).values - - def _register_model(client: SpendClient, resources: ResourceManager, prefix: str) -> str: model: Final = f"{prefix}-{unique_marker()}" model_id: Final = client.proxy.create_model( @@ -199,100 +145,7 @@ def scoped_key(client: SpendClient, strict_resources: ResourceManager) -> str: return key -@pytest.fixture -def restored_pricing_config(client: SpendClient) -> Iterator[None]: - discount: Final = _get_discount(client) - margin: Final = _get_margin(client) - try: - _set_discount(client, {}) - reset_at: Final = _set_margin(client, {}) - settle_propagation(reset_at) - yield - finally: - try: - _set_discount(client, discount) - finally: - restored_at: Final = _set_margin(client, margin) - settle_propagation(restored_at) - - -class TestPricingConfigSpend: - @pytest.mark.covers( - "quota_management.spend_tracking.discount_config.logs_cost", - exercised_on=["chat_completions"], - ) - @meta( - Subject( - domain=Domain.SPEND_BUDGETS, - route=Route.SPEND_REPORTING, - providers=(Provider.OPENAI,), - models=(BACKEND_MODEL,), - mode=Mode.NONSTREAM, - ) - ) - def test_configured_discount_reaches_persisted_spend_row( - self, - client: SpendClient, - strict_resources: ResourceManager, - scoped_key: str, - restored_pricing_config: None, - ) -> None: - discount_set_at: Final = _set_discount(client, {"openai": DISCOUNT}) - settle_propagation(discount_set_at) - model: Final = _register_model(client, strict_resources, "discount-priced") - chat: Final = unwrap(client.chat(scoped_key, model, f"reply with one word {unique_marker()}", max_tokens=16)) - assert chat.id and chat.usage - assert chat.usage.prompt_tokens is not None and chat.usage.prompt_tokens > 0 - assert chat.usage.completion_tokens is not None and chat.usage.completion_tokens >= 0 - - row: Final = poll_cost_row(client.proxy, chat.id) - assert row is not None - base_cost: Final = _base_cost(row, chat.usage.prompt_tokens, chat.usage.completion_tokens) - breakdown: Final = row.breakdown - assert breakdown.discount_percent is not None and approx_equal(breakdown.discount_percent, DISCOUNT) - assert breakdown.discount_amount is not None and approx_equal(breakdown.discount_amount, base_cost * DISCOUNT) - assert row.spend is not None and approx_equal(row.spend, base_cost * (1 - DISCOUNT)) - - @pytest.mark.covers( - "quota_management.spend_tracking.margin_config.logs_cost", - exercised_on=["chat_completions"], - ) - @meta( - Subject( - domain=Domain.SPEND_BUDGETS, - route=Route.SPEND_REPORTING, - providers=(Provider.OPENAI,), - models=(BACKEND_MODEL,), - mode=Mode.NONSTREAM, - ) - ) - def test_configured_margin_reaches_persisted_spend_row( - self, - client: SpendClient, - strict_resources: ResourceManager, - scoped_key: str, - restored_pricing_config: None, - ) -> None: - margin_set_at: Final = _set_margin( - client, {"openai": {"percentage": MARGIN_PERCENT, "fixed_amount": MARGIN_FIXED}} - ) - settle_propagation(margin_set_at) - model: Final = _register_model(client, strict_resources, "margin-priced") - chat: Final = unwrap(client.chat(scoped_key, model, f"reply with one word {unique_marker()}", max_tokens=16)) - assert chat.id and chat.usage - assert chat.usage.prompt_tokens is not None and chat.usage.prompt_tokens > 0 - assert chat.usage.completion_tokens is not None and chat.usage.completion_tokens >= 0 - - row: Final = poll_cost_row(client.proxy, chat.id) - assert row is not None - base_cost: Final = _base_cost(row, chat.usage.prompt_tokens, chat.usage.completion_tokens) - breakdown: Final = row.breakdown - expected_margin: Final = base_cost * MARGIN_PERCENT + MARGIN_FIXED - assert breakdown.margin_percent is not None and approx_equal(breakdown.margin_percent, MARGIN_PERCENT) - assert breakdown.margin_fixed_amount is not None and approx_equal(breakdown.margin_fixed_amount, MARGIN_FIXED) - assert breakdown.margin_total_amount is not None and approx_equal(breakdown.margin_total_amount, expected_margin) - assert row.spend is not None and approx_equal(row.spend, base_cost + expected_margin) - +class TestGuardrailCostSpend: @pytest.mark.covers( "quota_management.spend_tracking.guardrail_cost.logs_cost", exercised_on=["chat_completions"], @@ -311,7 +164,6 @@ class TestPricingConfigSpend: client: SpendClient, strict_resources: ResourceManager, scoped_key: str, - restored_pricing_config: None, ) -> None: identifier: Final = os.environ["BEDROCK_GUARDRAIL_IDENTIFIER"] version: Final = os.environ["BEDROCK_GUARDRAIL_VERSION"] diff --git a/tests/e2e/quota_management/spend_tracking/test_key_attribution_e2e.py b/tests/e2e/quota_management/spend_tracking/test_key_attribution_e2e.py index e0c19ea1b6b..1d0878d073a 100644 --- a/tests/e2e/quota_management/spend_tracking/test_key_attribution_e2e.py +++ b/tests/e2e/quota_management/spend_tracking/test_key_attribution_e2e.py @@ -55,7 +55,7 @@ from spend_e2e_client import ( pytestmark = pytest.mark.e2e -CHAT_MODEL: Final = "gemini-2.5-flash" +CHAT_MODEL: Final = "gemini-3.8-flash" MESSAGES_MODEL: Final = "claude-haiku-4-5" RESPONSES_MODEL: Final = "openai-responses-codex" EMBED_MODEL: Final = "openai-text-embedding-3-small" diff --git a/tests/e2e/quota_management/spend_tracking/test_spend_tracking_e2e.py b/tests/e2e/quota_management/spend_tracking/test_spend_tracking_e2e.py index c7ef81f826a..d452525ec91 100644 --- a/tests/e2e/quota_management/spend_tracking/test_spend_tracking_e2e.py +++ b/tests/e2e/quota_management/spend_tracking/test_spend_tracking_e2e.py @@ -3,7 +3,7 @@ Run against a proxy started with the gateway config. Coverage rationale: SPEND_TRACKING_COVERAGE_MATRIX.md. -Model names are literals from that config: chat tests hit "gemini-2.5-flash", +Model names are literals from that config: chat tests hit "gemini-3.8-flash", embedding tests hit "openai-text-embedding-3-small". Every test: fresh scoped key (isolation) -> real provider call -> unwrap (hard @@ -37,7 +37,7 @@ from spend_reconciliation import BACKEND as TRAFFIC_BACKEND pytestmark = pytest.mark.e2e -GEMINI_MODEL = "gemini-2.5-flash" +GEMINI_MODEL = "gemini-3.8-flash" CLAUDE_MODEL = "claude-haiku-4-5" CODEX_MODEL = "openai-responses-codex" EMBEDDING_MODEL = "openai-text-embedding-3-small" @@ -780,7 +780,7 @@ def test_spend_calculate_returns_nonzero_cost(client: SpendClient) -> None: GEMINI_MODEL, "estimate the cost of this request" ) assert cost > 0, ( - "/spend/calculate returned 0 for gemini-2.5-flash; " + "/spend/calculate returned 0 for gemini-3.8-flash; " "cost map may be missing this model" ) diff --git a/tests/e2e/quota_management/spend_tracking/test_websearch_interception_session_e2e.py b/tests/e2e/quota_management/spend_tracking/test_websearch_interception_session_e2e.py index ab7c2370365..84a598a2bbb 100644 --- a/tests/e2e/quota_management/spend_tracking/test_websearch_interception_session_e2e.py +++ b/tests/e2e/quota_management/spend_tracking/test_websearch_interception_session_e2e.py @@ -30,7 +30,7 @@ from models import ( from proxy_client import ProxyClient from pydantic import BaseModel, ValidationError -pytestmark = pytest.mark.e2e +pytestmark = [pytest.mark.e2e, pytest.mark.provider_live] BEDROCK_INVOKE_BACKEND: Final = "bedrock/invoke/us.anthropic.claude-haiku-4-5-20251001-v1:0" SEARCH_CALL_TYPE: Final = "asearch" diff --git a/tests/e2e/router/reliability_support.py b/tests/e2e/router/reliability_support.py index 03e585273c0..193a94cb581 100644 --- a/tests/e2e/router/reliability_support.py +++ b/tests/e2e/router/reliability_support.py @@ -57,11 +57,9 @@ CONTENT_POLICY_PROMPT = ( COOLDOWN_SECONDS = 30.0 REPLICA_PROPAGATION_SECONDS = 15.0 -# The smallest-context chat model OpenAI still serves (16385 tokens). A prompt -# past that limit comes back as a real `context_length_exceeded` 400, which is -# what litellm maps to ContextWindowExceededError. -SMALL_CONTEXT_MODEL = "openai/gpt-3.5-turbo" -SMALL_CONTEXT_LIMIT_TOKENS = 16385 +SMALL_CONTEXT_MODEL: Final = "openai/gpt-5.4-nano" +# model_prices_and_context_window.json, gpt-5.4-nano, verified 2026-11-02 +SMALL_CONTEXT_LIMIT_TOKENS: Final = 272000 def oversized_prompt(marker: str) -> str: @@ -107,7 +105,7 @@ def create_timeout_deployment(proxy: ProxyClient, name: str) -> str: @step(f"Add a deployment named {{name}} that calls the small-context model {SMALL_CONTEXT_MODEL}") def create_small_context_deployment(proxy: ProxyClient, name: str) -> str: - """Register a deployment on the smallest-context model OpenAI still serves, so an + """Register a deployment with a smaller context window than the backup, so an oversized prompt earns a real context-window refusal from the provider.""" return proxy.create_model(name, LiteLLMParamsBody(model=SMALL_CONTEXT_MODEL, api_key=REAL_KEY)) @@ -263,8 +261,7 @@ def spend_only_request_of(proxy: ProxyClient, spent_key: str) -> None: @step(f"Add a deployment named {{name}} that calls {SMALL_CONTEXT_MODEL} and takes all of its group's traffic") def create_always_picked_small_context_deployment(proxy: ProxyClient, name: str) -> str: - """The always-picked half of a retry pair on the smallest-context model OpenAI - still serves: it holds all of the model group's shuffle weight, so an oversized + """The always-picked half of a retry pair on the smaller-context model: it holds all of the model group's shuffle weight, so an oversized prompt opens on it and earns a real context-window refusal, which never benches a deployment, so only the retry itself can steer the request off it.""" return proxy.register_model( diff --git a/tests/e2e/stack_lock.py b/tests/e2e/stack_lock.py deleted file mode 100644 index 06df7a20b6a..00000000000 --- a/tests/e2e/stack_lock.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Cross-process reader/writer lock over the proxy stack every xdist worker shares. -Every collected test holds it shared, marker or not, since the Claude Code cells and -other unmarked suites drive the same stack; a `quiet_stack` test holds it exclusive, -and the `gate` file makes a waiting exclusive holder win over readers that arrive -after it.""" - -from __future__ import annotations - -import fcntl -import hashlib -import tempfile -from collections.abc import Generator -from contextlib import ExitStack, contextmanager -from pathlib import Path -from typing import Final - -from e2e_config import PROXY_BASE_URL - -STACK_DIGEST: Final = hashlib.sha256(PROXY_BASE_URL.encode()).hexdigest()[:12] -LOCK_DIR: Final = Path(tempfile.gettempdir()) / f"litellm-e2e-stack-{STACK_DIGEST}" -GATE_FILE: Final = LOCK_DIR / "gate" -STACK_FILE: Final = LOCK_DIR / "stack" - - -@contextmanager -def _flock(path: Path, operation: int) -> Generator[None]: - with path.open("a") as handle: - fcntl.flock(handle, operation) - try: - yield - finally: - fcntl.flock(handle, fcntl.LOCK_UN) - - -@contextmanager -def stack_lock(exclusive: bool) -> Generator[None]: - LOCK_DIR.mkdir(parents=True, exist_ok=True) - if exclusive: - with _flock(GATE_FILE, fcntl.LOCK_EX), _flock(STACK_FILE, fcntl.LOCK_EX): - yield - return - with ExitStack() as held: - with _flock(GATE_FILE, fcntl.LOCK_SH): - held.enter_context(_flock(STACK_FILE, fcntl.LOCK_SH)) - yield diff --git a/tests/e2e/ui/helpers/navigation.ts b/tests/e2e/ui/helpers/navigation.ts index be46fcf0395..3943526aebf 100644 --- a/tests/e2e/ui/helpers/navigation.ts +++ b/tests/e2e/ui/helpers/navigation.ts @@ -37,7 +37,7 @@ export async function navigateToPage(page: PlaywrightPage, pageEnum: Page): Prom // assert against the default page. for (let attempt = 0; attempt < 3; attempt++) { await page.goto(`/ui?page=${pageEnum}`); - await page.waitForLoadState("networkidle"); + await expect(page.getByRole("complementary")).toBeVisible(); const url = new URL(page.url()); const onLegacyRoot = url.pathname.replace(/\/+$/, "").endsWith("/ui"); if (!onLegacyRoot || url.searchParams.get("page") === pageEnum) { diff --git a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts index 04b02d9ccc4..f8ce4faf45e 100644 --- a/tests/e2e/ui/tests/modelHub/modelHub.spec.ts +++ b/tests/e2e/ui/tests/modelHub/modelHub.spec.ts @@ -64,7 +64,7 @@ test.describe("AI Hub (internal admin view)", () => { test.describe("Public model hub (/ui/model_hub_table)", () => { // No storageState — the public page is reached anonymously with a `key` query param. - test("Public model_hub_table loads and renders the Model Hub tab", async ({ page }) => { + test("Public model_hub_table loads and renders the Models tab", async ({ page }) => { // The page expects the proxy key as the `key` query param. Use the master // key the e2e runner already exports — this matches what the AI Hub copy // button hands out. @@ -74,13 +74,10 @@ test.describe("Public model hub (/ui/model_hub_table)", () => { // race can't briefly mask the tab while we're evaluating visibility. await dismissFeedbackPopup(page); - // Page loads (no auth redirect) and the Model Hub tab is always present. - // Agent Hub and MCP Hub tabs are conditionally rendered only when public - // agents/MCP servers exist, so we don't assert on them in a fresh CI run. - await expect(page.getByRole("tab", { name: "Model Hub" })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("tab", { name: /^Models/ })).toBeVisible({ timeout: 10_000 }); }); - test("Agent Hub and MCP Hub tabs render their public entries", async ({ page, request }) => { + test("Agents and MCP servers tabs render their public entries", async ({ page, request }) => { const suffix = `${Date.now()}`; const agentName = `e2e-public-agent-${suffix}`; const mcpServerName = `e2e_public_mcp_${suffix}`; @@ -145,18 +142,18 @@ test.describe("Public model hub (/ui/model_hub_table)", () => { await page.goto(`/ui/model_hub_table?key=${masterKey()}`); await dismissFeedbackPopup(page); - const agentHubTab = page.getByRole("tab", { name: "Agent Hub" }); - await expect(agentHubTab).toBeVisible({ timeout: 15_000 }); - await agentHubTab.click(); - await expect(page.getByText("Available Agents")).toBeVisible({ timeout: 10_000 }); - await expect(page.getByRole("row").filter({ hasText: agentName })).toHaveCount(1, { timeout: 10_000 }); + const agentsTab = page.getByRole("tab", { name: /^Agents/ }); + await expect(agentsTab).toBeVisible({ timeout: 15_000 }); + await agentsTab.click(); + await expect(page.getByRole("textbox", { name: "Search agents" })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("link").filter({ hasText: agentName })).toHaveCount(1, { timeout: 10_000 }); await expect(page.getByText("E2E public agent").first()).toBeVisible(); - const mcpHubTab = page.getByRole("tab", { name: "MCP Hub" }); - await expect(mcpHubTab).toBeVisible(); - await mcpHubTab.click(); - await expect(page.getByText("Available MCP Servers")).toBeVisible({ timeout: 10_000 }); - await expect(page.getByRole("row").filter({ hasText: mcpServerName })).toHaveCount(1, { timeout: 10_000 }); + const mcpTab = page.getByRole("tab", { name: /^MCP/ }); + await expect(mcpTab).toBeVisible(); + await mcpTab.click(); + await expect(page.getByRole("textbox", { name: "Search MCP servers" })).toBeVisible({ timeout: 10_000 }); + await expect(page.getByRole("link").filter({ hasText: mcpServerName })).toHaveCount(1, { timeout: 10_000 }); await expect(page.getByText("E2E public MCP server").first()).toBeVisible(); } finally { await request.post("/v1/mcp/make_public", { headers: auth, data: { mcp_server_ids: priorMcpIds } }); diff --git a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts index a99fb937b83..b5e876b36d2 100644 --- a/tests/e2e/ui/tests/modelsPage/addModel.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/addModel.spec.ts @@ -391,7 +391,7 @@ test.describe("Add Model", () => { // Navigate to Deployed Models tab await page.getByRole("tab", { name: "Deployed Models" }).click(); - await page.waitForLoadState("networkidle"); + await expect(page.getByPlaceholder("Search model names")).toBeVisible(); // Search for the model we just added await page.getByPlaceholder("Search model names").fill("claude-haiku-4-5"); @@ -470,7 +470,7 @@ test.describe("Add Model", () => { // The Models table renders team-scoped models with the team id in the row. await page.getByRole("tab", { name: "Deployed Models" }).click(); - await page.waitForLoadState("networkidle"); + await expect(page.getByPlaceholder("Search model names")).toBeVisible(); await page.getByPlaceholder("Search model names").fill("cohere"); @@ -515,7 +515,7 @@ test.describe("Add Model", () => { // Navigate to Deployed Models tab await page.getByRole("tab", { name: "Deployed Models" }).click(); - await page.waitForLoadState("networkidle"); + await expect(page.getByPlaceholder("Search model names")).toBeVisible(); // Search for the wildcard model await page.getByPlaceholder("Search model names").fill("cohere"); diff --git a/tests/e2e/ui/tests/modelsPage/editLitellmParams.spec.ts b/tests/e2e/ui/tests/modelsPage/editLitellmParams.spec.ts index 4480515ae59..0831c003891 100644 --- a/tests/e2e/ui/tests/modelsPage/editLitellmParams.spec.ts +++ b/tests/e2e/ui/tests/modelsPage/editLitellmParams.spec.ts @@ -27,7 +27,7 @@ async function readStoredParams( } function paramsEditor(page: PlaywrightPage) { - return page.getByPlaceholder('"rpm": 100'); + return page.getByRole("textbox", { name: "LiteLLM Params", exact: true }); } async function editParams( diff --git a/tests/e2e/ui/tests/navigation/commandPalette.spec.ts b/tests/e2e/ui/tests/navigation/commandPalette.spec.ts new file mode 100644 index 00000000000..5e80afb8b18 --- /dev/null +++ b/tests/e2e/ui/tests/navigation/commandPalette.spec.ts @@ -0,0 +1,54 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { dismissFeedbackPopup, navigateToPage } from "../../helpers/navigation"; +import { createVirtualKey, deleteVirtualKey, uniqueSuffix } from "../../helpers/traffic"; + +const paletteSearch = (page: PlaywrightPage) => page.getByRole("combobox", { name: "Search" }); +const headerTrigger = (page: PlaywrightPage) => page.getByRole("button", { name: "Search keys…", exact: true }); + +test.describe("Command palette", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test.beforeEach(async ({ page }) => { + await navigateToPage(page, Page.ApiKeys); + await dismissFeedbackPopup(page); + await expect(headerTrigger(page)).toBeVisible(); + }); + + test("opens a key found by its alias", async ({ page }) => { + const alias = `e2e-palette-key-${uniqueSuffix()}`; + const created = await createVirtualKey(page.request, { key_alias: alias }); + try { + await page.keyboard.press("ControlOrMeta+K"); + await paletteSearch(page).fill(alias); + await expect(page.getByRole("option", { name: new RegExp(`^${alias}`) })).toBeVisible(); + await page.keyboard.press("Enter"); + + await expect(page).toHaveURL(new RegExp(`[?&]key=${created.token}`)); + await expect(paletteSearch(page)).toBeHidden(); + } finally { + await deleteVirtualKey(page.request, created.token); + } + }); + + test("does not open over the Create New Key dialog", async ({ page }) => { + await page.getByRole("button", { name: /Create New Key/i }).click(); + const keyName = page.getByLabel(/Key Name/); + await keyName.click(); + + await page.keyboard.press("ControlOrMeta+K"); + await page.keyboard.type("still-in-the-dialog"); + + await expect(keyName).toHaveValue("still-in-the-dialog"); + await expect(paletteSearch(page)).toBeHidden(); + }); + + test("opens from the header trigger and closes with Escape", async ({ page }) => { + await headerTrigger(page).click(); + await expect(paletteSearch(page)).toBeFocused(); + + await page.keyboard.press("Escape"); + await expect(paletteSearch(page)).toBeHidden(); + }); +}); diff --git a/tests/e2e_harness/AGENTS.md b/tests/e2e_harness/AGENTS.md index 83e573ae249..4f507c2fe7b 100644 --- a/tests/e2e_harness/AGENTS.md +++ b/tests/e2e_harness/AGENTS.md @@ -1,6 +1,6 @@ # e2e harness tests -Tests of the harness under `tests/e2e/` (the transport, the clients, the fixture bundle and replay edge, the stack lock, the IdP launcher, the coverage collector, the JUnit properties and the Claude Code driver), not of the product. They live outside `tests/e2e/` because the Buildkite e2e run copies that folder into the runner image and runs every test in it, so a harness test in there counts as a product test in the nightly numbers. Nothing here needs a proxy, provider keys or the network +Tests of the harness under `tests/e2e/` (the transport, the clients, the fixture bundle and replay edge, the IdP launcher, the coverage collector, the JUnit properties and the Claude Code driver), not of the product. They live outside `tests/e2e/` because the Buildkite e2e run copies that folder into the runner image and runs every test in it, so a harness test in there counts as a product test in the nightly numbers. Nothing here needs a proxy, provider keys or the network The layout mirrors `tests/e2e/`: `test_e2e_http.py` covers `tests/e2e/e2e_http.py`, `logging/test_datadog_reader.py` covers `tests/e2e/logging/datadog_reader.py`, `claude_code/` covers the driver, builder and probe, `cli_harness/` covers the version resolver, the CLI env lockdown and the rate limiter, and `codex/` covers how the Codex driver reads the CLI's JSONL events and the compactions in a thread's rollout file. Put a new harness test under the folder that mirrors the suite folder whose module it covers diff --git a/tests/e2e_harness/logging/test_logging_client.py b/tests/e2e_harness/logging/test_logging_client.py new file mode 100644 index 00000000000..c396a6c68e5 --- /dev/null +++ b/tests/e2e_harness/logging/test_logging_client.py @@ -0,0 +1,49 @@ +import json +from typing import Final + +import pytest +from pydantic import JsonValue + +from logging_client import LangfuseObservation, is_otel_v2_generation, observation_spend + + +@pytest.mark.parametrize("encoded", [False, True]) +@pytest.mark.parametrize("cost", [0.0, 0.000023]) +def test_observation_preserves_v2_content_usage_and_cost(encoded: bool, cost: float) -> None: + content: Final[JsonValue] = [{"role": "user", "content": "unique-request-marker"}] + output: Final[JsonValue] = [{"role": "assistant", "content": "response"}] + metadata: Final[JsonValue] = { + "attributes": { + "langfuse.observation.type": "generation", + "litellm.metadata.user_api_key_alias": "test-key-alias", + } + } + usage: Final[JsonValue] = {"input": 17, "output": 3} + observation: Final = LangfuseObservation.model_validate( + { + "id": "observation", + "input": json.dumps(content) if encoded else content, + "output": json.dumps(output) if encoded else output, + "metadata": json.dumps(metadata) if encoded else metadata, + "usageDetails": usage, + "totalCost": cost, + } + ) + + assert observation.input == content + assert observation.output == output + assert observation.usage_details == usage + assert observation_spend(observation) == cost + assert is_otel_v2_generation(observation, key_alias="test-key-alias") + assert not is_otel_v2_generation(observation, key_alias="another-key") + + +def test_observation_preserves_plain_text_and_legacy_cost() -> None: + cost: Final = 0.000031 + observation: Final = LangfuseObservation.model_validate( + {"id": "observation", "input": "plain prompt", "output": "plain reply", "calculatedTotalCost": cost} + ) + + assert observation.input == "plain prompt" + assert observation.output == "plain reply" + assert observation_spend(observation) == cost diff --git a/tests/e2e_harness/logging/test_span_selection.py b/tests/e2e_harness/logging/test_span_selection.py index e7487562e43..76f70e4d8bb 100644 --- a/tests/e2e_harness/logging/test_span_selection.py +++ b/tests/e2e_harness/logging/test_span_selection.py @@ -8,8 +8,21 @@ helper is worth pinning here. from __future__ import annotations +from typing import Final + import pytest -from otel_client import TTFT_TAG, JaegerTrace, one_served_genai_span, served_genai_spans +from otel_client import ( + TTFT_TAG, + CallTraces, + JaegerReference, + JaegerSpan, + JaegerTag, + JaegerTrace, + one_served_genai_span, + served_genai_spans, + trace_settled, +) +from test_otel_trace_e2e import assert_complete_trace, assert_real_ttft GENAI_SPAN = "chat claude-haiku-4-5" @@ -79,3 +92,73 @@ def test_other_operations_are_not_counted() -> None: trace = _trace(_span("chat gpt-5.5", ttft=0.3), _span(GENAI_SPAN, ttft=0.3)) assert len(served_genai_spans(trace, GENAI_SPAN)) == 1 + + +def _request_trace( + *, + root_name: str = "POST", + root_kind: str = "server", + route: str = "/v1/messages", + genai_parent: str = "root", + ttft: float = 0.2, +) -> JaegerTrace: + parent: Final = JaegerReference(refType="CHILD_OF", traceID="request", spanID="root") + return JaegerTrace( + traceID="request", + spans=[ + JaegerSpan( + spanID="root", + operationName=root_name, + tags=[JaegerTag(key="span.kind", value=root_kind), JaegerTag(key="http.target", value=route)], + ), + JaegerSpan(spanID="auth", operationName="auth /v1/messages", references=[parent]), + JaegerSpan(spanID="db", operationName="postgres.select tokens", references=[parent]), + JaegerSpan( + spanID="genai", + operationName=GENAI_SPAN, + duration=1_000_000, + references=[JaegerReference(refType="CHILD_OF", traceID="request", spanID=genai_parent)], + tags=[JaegerTag(key="span.kind", value="client"), JaegerTag(key=TTFT_TAG, value=ttft)], + ), + ], + ) + + +@pytest.mark.parametrize("root_name", ("POST", "POST /v1/messages")) +def test_connected_request_trace_is_independent_of_server_display_name(root_name: str) -> None: + trace: Final = _request_trace(root_name=root_name) + assert_complete_trace(CallTraces((trace,), ()), route="/v1/messages", genai_span=GENAI_SPAN) + assert_real_ttft((trace,), genai_span=GENAI_SPAN) + assert trace_settled(trace, {"auth /v1/messages", GENAI_SPAN}, {"postgres."}) + + +def test_incomplete_trace_cannot_settle_before_the_server_span_arrives() -> None: + trace: Final = _request_trace() + incomplete: Final = trace.model_copy(update={"spans": trace.spans[1:]}) + assert not trace_settled(incomplete, {"auth /v1/messages", GENAI_SPAN}, {"postgres."}) + + +@pytest.mark.parametrize("route", ("/v1/responses", "")) +def test_request_trace_must_still_identify_the_requested_route(route: str) -> None: + trace: Final = _request_trace(root_name="POST /v1/messages", route=route) + with pytest.raises(AssertionError, match="route"): + assert_complete_trace(CallTraces((trace,), ()), route="/v1/messages", genai_span=GENAI_SPAN) + + +def test_request_trace_must_still_be_rooted_at_a_server_span() -> None: + trace: Final = _request_trace(root_name="POST /v1/messages", root_kind="client") + with pytest.raises(AssertionError, match="kind=server"): + assert_complete_trace(CallTraces((trace,), ()), route="/v1/messages", genai_span=GENAI_SPAN) + + +def test_request_trace_must_still_connect_the_model_call_to_the_request() -> None: + trace: Final = _request_trace(genai_parent="absent") + with pytest.raises(AssertionError, match="orphaned trace"): + assert_complete_trace(CallTraces((trace,), ()), route="/v1/messages", genai_span=GENAI_SPAN) + + +@pytest.mark.parametrize("ttft", (0.0, 1.0)) +def test_server_name_tolerance_does_not_accept_invalid_first_token_timing(ttft: float) -> None: + trace: Final = _request_trace(ttft=ttft) + with pytest.raises(AssertionError, match="TTFT"): + assert_real_ttft((trace,), genai_span=GENAI_SPAN) diff --git a/tests/e2e_harness/perf/test_run.py b/tests/e2e_harness/perf/test_run.py index 23f62c9c560..480af96a12a 100644 --- a/tests/e2e_harness/perf/test_run.py +++ b/tests/e2e_harness/perf/test_run.py @@ -8,7 +8,29 @@ from typing import Final import pytest from setup.run import run_until from setup.wire import WireRecord -from test_upgrade import record + + +def record(seq: int = 0, *, started_at: float = 100) -> WireRecord: + return WireRecord( + seq=seq, + marker=f"request-{seq}", + caller="key", + endpoint="chat_completions", + stream=False, + blocked=False, + started_at=started_at, + status=200, + header_s=1, + first_byte_s=1, + first_token_s=None, + total_s=1, + text="answer", + ok=True, + mock_delay_s=1, + call_id=f"call-{seq}", + error=None, + response_id=f"response-{seq}", + ) class StopAfterRecord: diff --git a/tests/e2e_harness/perf/test_upgrade.py b/tests/e2e_harness/perf/test_upgrade.py deleted file mode 100644 index 3414476ba4c..00000000000 --- a/tests/e2e_harness/perf/test_upgrade.py +++ /dev/null @@ -1,375 +0,0 @@ -from __future__ import annotations - -from collections.abc import Mapping -from typing import Final - -import pytest -from cases.small_bodies_5rps import CASE -from memory_readings import RssCapture, RssReading -from setup.observe import Observation, evaluate -from setup.upgrade import ( - MAX_TRAFFIC_SECONDS, - UpgradeMonitor, - matched_response_ids, - postgres_failures, - upgrade_case, - upgrade_properties, -) -from setup.upgrade_worker import UpgradeState -from setup.wire import PlanProgress, WireRecord, response_id - - -def record(seq: int = 0, *, started_at: float = 100) -> WireRecord: - return WireRecord( - seq=seq, - marker=f"request-{seq}", - caller="key", - endpoint="chat_completions", - stream=False, - blocked=False, - started_at=started_at, - status=200, - header_s=1, - first_byte_s=1, - first_token_s=None, - total_s=1, - text="answer", - ok=True, - mock_delay_s=1, - call_id=f"call-{seq}", - error=None, - response_id=f"response-{seq}", - ) - - -def test_spend_logs_match_call_ids_when_response_ids_are_transformed() -> None: - records: Final = (record(0), record(1), record(2)) - rows: Final = (("provider-response-id", "call-0"), ("response-1", "unrelated-call")) - assert matched_response_ids(records, rows) == frozenset({"response-0"}) - - -def state(phase: str = "schema_ready", *, pending: bool = True) -> UpgradeState: - return UpgradeState.model_validate( - { - "phase": phase, - "started_at": 0, - "required_indexes": ["index"], - "required_migrations": ["migration"], - "pending_indexes": ["index"] if pending else [], - "baseline_rows": {"seed-row": "hash"}, - } - ) - - -class Probe: - def __init__(self) -> None: - self.now = 0.0 - self.current = state() - self.builds: list[int] = [] - self.found: frozenset[str] = frozenset() - self.releases = 0 - self.metric_checks = 0 - self.errors = False - - def check_metrics(self) -> None: - self.metric_checks += 1 - assert not self.errors, "Failed PostgreSQL operation" - - def persisted(self, records: tuple[WireRecord, ...]) -> frozenset[str]: - return frozenset(row.response_id for row in records if row.response_id in self.found and row.response_id) - - def release(self) -> None: - self.releases += 1 - - def monitor(self) -> UpgradeMonitor: - return UpgradeMonitor( - state=lambda: self.current, - check_metrics=self.check_metrics, - read_activity=lambda: (self.builds, 2), - read_persisted=self.persisted, - start_indexes=self.release, - clock=lambda: self.now, - wall_clock=lambda: self.now, - ) - - -def test_stop_drains_issued_requests_without_issuing_the_rest() -> None: - progress: Final = PlanProgress(100) - assert progress.take(stop=False) - assert progress.take(stop=False) - progress.completed = 1 - assert not progress.take(stop=True) - assert not progress.drained - assert not progress.take(stop=False) - progress.completed = 2 - assert progress.drained - assert progress.issued == 2 - - -def test_finite_plan_still_waits_for_its_last_response() -> None: - progress: Final = PlanProgress(1) - assert progress.take(stop=False) - assert not progress.take(stop=False) - assert not progress.drained - progress.completed = 1 - assert progress.drained - - -@pytest.mark.parametrize( - "payload,expected", - [ - ({"id": "chat-id"}, "chat-id"), - ({"type": "response.created", "response": {"id": "response-id"}}, "response-id"), - ({"type": "message_start", "message": {"id": "message-id"}}, "message-id"), - ({"type": "response.output_item.added", "item": {"id": "item-id"}}, None), - ({"type": "content_block_delta", "delta": {"text": "hello"}}, None), - ], -) -def test_response_id_uses_response_not_output_item(payload: Mapping[str, object], expected: str | None) -> None: - assert response_id(payload) == expected - - -def metrics(*, failure: str = "0", batch: str = "0") -> str: - return f"""# TYPE litellm_postgres_failed_requests_total counter -litellm_postgres_failed_requests_total{{function_name="query",error_class="Timeout"}} {failure} -# TYPE litellm_batch_write_to_db_failed_requests_total counter -litellm_batch_write_to_db_failed_requests_total{{function_name="flush",error_class="DatabaseError"}} {batch} -""" - - -def test_failed_sql_and_async_batch_writes_are_both_counted() -> None: - assert postgres_failures(metrics(failure="2", batch="3")) == 5 - assert postgres_failures(metrics()) == 0 - - -def test_no_error_label_series_is_a_valid_zero() -> None: - assert ( - postgres_failures("""# TYPE litellm_postgres_failed_requests_total counter -# TYPE litellm_batch_write_to_db_failed_requests_total counter -""") - == 0 - ) - - -@pytest.mark.parametrize("service", [None, "postgres", "batch_write_to_db"]) -def test_multiprocess_metrics_omit_failure_families_until_a_failure(service: str | None) -> None: - total: Final = """# TYPE litellm_postgres_total_requests_total counter -litellm_postgres_total_requests_total{postgres="postgres"} 1 -""" - failure: Final = ( - f"# TYPE litellm_{service}_failed_requests_total counter\n" - f'litellm_{service}_failed_requests_total{{function_name="query",error_class="Timeout"}} 1\n' - if service is not None - else "" - ) - assert postgres_failures(total + failure) == (1 if service is not None else 0) - - -@pytest.mark.parametrize("total", ["0", "-1", "NaN", "+Inf"]) -def test_missing_failure_families_require_observed_database_activity(total: str) -> None: - with pytest.raises(AssertionError): - _ = postgres_failures( - "# TYPE litellm_postgres_total_requests_total counter\n" - f'litellm_postgres_total_requests_total{{postgres="postgres"}} {total}\n' - ) - - -@pytest.mark.parametrize("body", ["", "# TYPE unrelated counter\n", metrics(failure="NaN"), metrics(batch="-1")]) -def test_missing_or_invalid_counters_do_not_pass_as_zero(body: str) -> None: - with pytest.raises(AssertionError): - _ = postgres_failures(body) - - -def test_index_completion_stops_after_the_post_upgrade_window() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - assert not monitor.poll((record(),)) - assert probe.releases == 1 - probe.builds = [17] - probe.now = 101 - assert not monitor.poll(()) - probe.found = frozenset({"response-0", "response-1"}) - probe.now = 103 - assert not monitor.poll((record(1, started_at=102),)) - assert monitor.overlap_writes == 1 - assert monitor.persisted_count == 2 - assert not monitor.pending - assert monitor.max_lock_waiters == 2 - probe.current = state("complete") - assert not monitor.poll(()) - probe.now = 162 - assert not monitor.poll(()) - probe.now = 163 - assert not monitor.poll(()) - probe.found = frozenset(f"response-{i}" for i in range(300)) - assert monitor.poll(tuple(record(i) for i in range(2, 300))) - assert probe.releases == 1 - assert probe.metric_checks == 7 - - -def test_http_200_without_spend_row_cannot_complete() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - records: Final = tuple(record(i) for i in range(300)) - _ = monitor.poll(records) - with pytest.raises(AssertionError, match="no matching spend log"): - monitor.assert_complete(state("complete", pending=False)) - - -def test_no_pending_index_work_still_requires_300_persisted_requests() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - records: Final = tuple(record(i) for i in range(300)) - probe.found = frozenset(f"response-{i}" for i in range(300)) - _ = monitor.poll(records) - monitor.assert_complete(state("complete", pending=False)) - assert monitor.successes == monitor.persisted_count == 300 - with pytest.raises(AssertionError, match="during index work"): - monitor.assert_complete(state("complete", pending=True)) - - -def test_postgres_failure_fails_even_when_inference_succeeded() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - probe.errors = True - with pytest.raises(AssertionError, match="PostgreSQL"): - _ = monitor.poll((record(),)) - - -def test_failed_migration_and_operational_timeout_are_not_success() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - probe.current = state("failed") - with pytest.raises(AssertionError, match="worker failed"): - _ = monitor.poll(()) - probe.current = state() - probe.now = MAX_TRAFFIC_SECONDS - with pytest.raises(AssertionError, match="operational timeout"): - _ = monitor.poll(()) - - -def test_upgrade_case_uses_5rps_with_the_same_request_and_memory_limits() -> None: - case: Final = upgrade_case() - assert case.traffic.rps == 5 - assert not {"cpu_seconds_per_request", "postgres_calls_per_request", "redis_calls_per_request"}.intersection( - limit.metric for limit in case.limits - ) - assert case.limits == tuple( - limit - for limit in CASE.limits - if limit.metric - in { - "unexpected_status_count", - "gateway_added_seconds", - "time_to_first_token_seconds", - "memory_growth_megabytes", - } - ) - - -def failed_record(seq: int) -> WireRecord: - return record(seq).model_copy(update={"status": 503, "ok": False, "text": "", "response_id": None, "call_id": None}) - - -def test_three_non_200s_do_not_start_indexes_or_require_spend_rows() -> None: - probe: Final = Probe() - monitor: Final = probe.monitor() - assert not monitor.poll(tuple(failed_record(i) for i in range(3))) - assert probe.releases == 0 - assert monitor.processed == 3 - assert monitor.successes == 0 - assert not monitor.pending - probe.found = frozenset(f"response-{i}" for i in range(3, 303)) - assert not monitor.poll(tuple(record(i) for i in range(3, 303))) - monitor.assert_complete(state("complete", pending=False)) - assert probe.releases == 1 - assert monitor.processed == 303 - assert monitor.successes == monitor.persisted_count == 300 - - -def test_fourth_non_200_fails_across_poll_batches() -> None: - monitor: Final = Probe().monitor() - assert not monitor.poll(tuple(failed_record(i) for i in range(3))) - with pytest.raises(AssertionError, match="Too many unexpected non-200"): - _ = monitor.poll((failed_record(3),)) - - -@pytest.mark.parametrize( - "failed", [record().model_copy(update={"text": ""}), record().model_copy(update={"ok": False})] -) -def test_invalid_200_is_not_an_allowed_non_200(failed: WireRecord) -> None: - with pytest.raises(AssertionError, match="Failed or empty inference"): - _ = Probe().monitor().poll((failed,)) - - -def _ceiling(metric: str, percentile: int | None = None) -> float: - return next( - limit.max - for limit in upgrade_case().limits - if limit.metric == metric and limit.percentile == percentile and limit.max is not None - ) - - -@pytest.mark.parametrize( - "over,expected", - [ - ("", ()), - ("unexpected_status_count", ("unexpected_status_count",)), - ("gateway_added_seconds", ("gateway_added_seconds percentile 50",)), - ("time_to_first_token_seconds", ("time_to_first_token_seconds percentile 99",)), - ("memory_growth_megabytes", ("memory_growth_megabytes",)), - ], -) -def test_upgrade_verdict_uses_regular_limits_without_cpu_or_datastore_samples( - over: str, expected: tuple[str, ...] -) -> None: - """Each sample sits exactly on the case's ceiling, so only the one nudged past it goes over.""" - failures: Final = int(_ceiling("unexpected_status_count")) + (over == "unexpected_status_count") - overhead: Final = _ceiling("gateway_added_seconds", 50) * (1.01 if over == "gateway_added_seconds" else 1) - ttft: Final = _ceiling("time_to_first_token_seconds", 99) * (1.01 if over == "time_to_first_token_seconds" else 1) - growth: Final = _ceiling("memory_growth_megabytes") + (over == "memory_growth_megabytes") - nonstream: Final = tuple(record(i).model_copy(update={"total_s": overhead, "mock_delay_s": 0}) for i in range(100)) - stream: Final = record(100).model_copy(update={"stream": True, "first_token_s": ttft}) - observed: Final = Observation( - records=(*nonstream, stream, *(failed_record(i) for i in range(101, 101 + failures))), - mock_requests=(), - guardrail_calls=(), - rss_before=RssCapture((RssReading("gateway", "host", 1, 1000),), ()), - rss_after=RssCapture((RssReading("gateway", "host", 1, 1000 + growth),), ()), - wall_seconds=60, - ) - rows, exceeded = evaluate(upgrade_case(), observed) - assert tuple(row.label for row in rows if row.over or row.missing) == expected - assert len(exceeded) == len(expected) - - -def test_upgrade_properties_carry_the_upgrade_timings_and_what_the_monitor_saw() -> None: - state: Final = UpgradeState( - phase="complete", - started_at=0, - schema_seconds=12.5, - index_seconds=240.25, - required_indexes=("idx_a", "idx_b"), - required_migrations=("20260101_a",), - pending_indexes=("idx_b",), - baseline_rows={}, - ) - monitor: Final = UpgradeMonitor( - state=lambda: state, - check_metrics=lambda: None, - read_activity=lambda: ([], 0), - read_persisted=lambda records: frozenset(), - start_indexes=lambda: None, - ) - monitor.overlap_writes = 37 - monitor.max_lock_waiters = 3 - monitor.pending = (record(0), record(1)) - - assert dict(upgrade_properties(state, monitor)) == { - "perf_upgrade_schema_seconds": 12.5, - "perf_upgrade_index_seconds": 240.25, - "perf_upgrade_overlap_writes": 37, - "perf_upgrade_max_lock_waiters": 3, - "perf_upgrade_pending_indexes": 1, - "perf_upgrade_unmatched_requests": 2, - } diff --git a/tests/e2e_harness/pytest.ini b/tests/e2e_harness/pytest.ini index a29166d31bd..9ccf8ba83a0 100644 --- a/tests/e2e_harness/pytest.ini +++ b/tests/e2e_harness/pytest.ini @@ -5,5 +5,8 @@ addopts = --strict-markers --strict-config -p no:cacheprovider pythonpath = ../e2e ../e2e/batches ../e2e/guardrails ../e2e/logging ../e2e/perf markers = + e2e: live assertion modules imported by harness regression tests + meta(subject): metadata on live assertion modules imported by harness regression tests + otel_tls: TLS coverage in live assertion modules imported by harness regression tests covers(cell_id, *, exercised_on=()): coverage-registry cell(s) a test covers; exercised here only to prove the collector and the JUnit properties read it cli_determinism: drives the real claude CLI for several seconds diff --git a/tests/e2e_harness/test_proxy_client.py b/tests/e2e_harness/test_proxy_client.py index e1615ec5f65..d352e5c8d36 100644 --- a/tests/e2e_harness/test_proxy_client.py +++ b/tests/e2e_harness/test_proxy_client.py @@ -12,10 +12,11 @@ injected clock, so nothing here monkeypatches anything. from __future__ import annotations import json +import time from builtins import ExceptionGroup from collections.abc import Callable, Generator, Iterable, Mapping from contextlib import contextmanager -from dataclasses import dataclass +from dataclasses import dataclass, replace from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from itertools import chain, repeat from queue import SimpleQueue @@ -40,7 +41,10 @@ from models import ( LiteLLMParamsBody, McpServerCreateBody, McpServerUpdateBody, + ModelDeleteBody, + ModelInfoBody, ModelListEntry, + ModelNewBody, ModelsListResponse, OrgNewBody, OrgUpdateBody, @@ -59,6 +63,8 @@ from proxy_client import ( ConvergeOutcome, CredentialKind, EverywhereConverged, + ModelNotRegistered, + ModelRegistered, ModelsPoller, NeverConvergedOn, NotConverged, @@ -691,3 +697,85 @@ def test_partial_updates_preserve_explicit_null_at_the_http_boundary(operation: ) assert json.loads(bodies.get_nowait()) == expected assert bodies.empty() + + +@contextmanager +def model_registry( + listed: frozenset[str], refused: frozenset[str] +) -> Generator[tuple[ProxyClient, SimpleQueue[float], SimpleQueue[str]]]: + writes: Final[SimpleQueue[float]] = SimpleQueue() + deleted: Final[SimpleQueue[str]] = SimpleQueue() + + class Handler(BaseHTTPRequestHandler): + def log_message(self, format: str, *args: object) -> None: + pass + + def reply(self, status: int, body: object) -> None: + self.send_response(status) + self.end_headers() + self.wfile.write(json.dumps(body).encode()) + + def do_GET(self) -> None: + self.reply(200, {"data": [{"id": name} for name in sorted(listed)]}) + + def do_POST(self) -> None: + raw: Final = self.rfile.read(int(self.headers.get("Content-Length", "0"))) + if self.path == "/model/delete": + deleted.put(ModelDeleteBody.model_validate_json(raw).id) + self.reply(200, {}) + return + writes.put(time.monotonic()) + name: Final = ModelNewBody.model_validate_json(raw).model_name + if name in refused: + self.reply(500, {"error": "refused"}) + return + self.reply(200, {"model_id": f"id-{name}"}) + + server: Final = ThreadingHTTPServer(("127.0.0.1", 0), Handler) + thread: Final = Thread(target=lambda: server.serve_forever(poll_interval=0.01), daemon=True) + thread.start() + url: Final = f"http://127.0.0.1:{server.server_port}" + proxy: Final = build_proxy_client( + base_url=url, control_plane_base_url=url, replica_urls=(url,), control_replica_urls=(url,), master_key="bootstrap" + ) + try: + yield replace(proxy, model_servable_timeout=0.3, model_servable_interval=0.01), writes, deleted + finally: + server.shutdown() + server.server_close() + thread.join(timeout=5) + + +class TestRegisterModels: + def test_a_batch_waits_once_after_its_last_write_and_reports_each_model(self) -> None: + names: Final = ("first", "refused", "ghost", "last") + settled: Final[SimpleQueue[float]] = SimpleQueue() + with ( + model_registry(listed=frozenset({"first", "last"}), refused=frozenset({"refused"})) as ( + proxy, + writes, + deleted, + ), + without_retries(), + ): + registrations: Final = replace(proxy, settle=settled.put).register_models( + tuple( + ModelNewBody( + model_name=name, litellm_params=LiteLLMParamsBody(model="synthetic"), model_info=ModelInfoBody() + ) + for name in names + ) + ) + last_write: Final = max(writes.get_nowait() for _ in names) + assert writes.empty() + assert settled.get_nowait() >= last_write + assert settled.empty(), "a batch must wait out propagation once, not once per model" + assert tuple((type(r), r.model_name) for r in registrations) == ( + (ModelRegistered, "first"), + (ModelNotRegistered, "refused"), + (ModelNotRegistered, "ghost"), + (ModelRegistered, "last"), + ) + assert tuple(r.model_id for r in registrations if isinstance(r, ModelRegistered)) == ("id-first", "id-last") + assert deleted.get_nowait() == "id-ghost" + assert deleted.empty() diff --git a/tests/e2e_harness/test_stack_lock.py b/tests/e2e_harness/test_stack_lock.py deleted file mode 100644 index b275ca8c755..00000000000 --- a/tests/e2e_harness/test_stack_lock.py +++ /dev/null @@ -1,117 +0,0 @@ -"""Cross-process behavior of the stack lock: readers share it, an exclusive holder waits for -every reader and keeps them out, and a reader arriving behind a waiting exclusive holder -queues behind it instead of starving it.""" - -from __future__ import annotations - -import fcntl -import inspect -import os -import subprocess -import sys -import time -from contextlib import ExitStack -from pathlib import Path -from typing import Final - -import pytest -import stack_lock - -HARNESS_DIR: Final = Path(inspect.getfile(stack_lock)).resolve().parent -DEADLINE_SECONDS: Final = 30.0 -SETTLE_SECONDS: Final = 0.5 -HOLDER_SCRIPT: Final = """ -import sys, time -from pathlib import Path -from stack_lock import stack_lock -name, mode, release_path, log_path = sys.argv[1:] - - -def record(event): - with Path(log_path).open("a") as log: - log.write(f"{name} {event}\\n") - - -record("waiting") -with stack_lock(exclusive=mode == "exclusive"): - record("enter") - while not Path(release_path).exists(): - time.sleep(0.02) - record("exit") -""" - - -def _events(log_path: Path) -> tuple[str, ...]: - return tuple(log_path.read_text().splitlines()) if log_path.exists() else () - - -def _wait_for_event(log_path: Path, event: str) -> None: - deadline: Final = time.monotonic() + DEADLINE_SECONDS - while event not in _events(log_path): - if time.monotonic() > deadline: - pytest.fail(f"{event!r} never appeared; events so far: {_events(log_path)}") - time.sleep(0.02) - - -def _wait_until_gate_is_held_exclusively(gate_path: Path) -> None: - deadline: Final = time.monotonic() + DEADLINE_SECONDS - with gate_path.open("a") as handle: - while True: - try: - fcntl.flock(handle, fcntl.LOCK_SH | fcntl.LOCK_NB) - except BlockingIOError: - return - fcntl.flock(handle, fcntl.LOCK_UN) - if time.monotonic() > deadline: - pytest.fail("no exclusive holder ever took the gate") - time.sleep(0.02) - - -def _start_holder(held: ExitStack, tmp_path: Path, name: str, mode: str) -> subprocess.Popen[bytes]: - holder: Final = held.enter_context( - subprocess.Popen( - ( - sys.executable, - "-P", - "-c", - HOLDER_SCRIPT, - name, - mode, - str(tmp_path / f"release-{name}"), - str(tmp_path / "events"), - ), - cwd=HARNESS_DIR, - env={**os.environ, "TMPDIR": str(tmp_path), "PYTHONPATH": str(HARNESS_DIR)}, - ) - ) - held.callback(holder.kill) - return holder - - -def test_readers_share_exclusive_waits_and_a_waiting_exclusive_beats_later_readers(tmp_path: Path) -> None: - lock_dir: Final = tmp_path / f"litellm-e2e-stack-{stack_lock.STACK_DIGEST}" - lock_dir.mkdir() - log_path: Final = tmp_path / "events" - with ExitStack() as held: - first_reader: Final = _start_holder(held, tmp_path, "A", "shared") - _wait_for_event(log_path, "A enter") - second_reader: Final = _start_holder(held, tmp_path, "R", "shared") - _wait_for_event(log_path, "R enter") - (tmp_path / "release-R").touch() - _wait_for_event(log_path, "R exit") - writer: Final = _start_holder(held, tmp_path, "W", "exclusive") - _wait_until_gate_is_held_exclusively(lock_dir / "gate") - late_reader: Final = _start_holder(held, tmp_path, "B", "shared") - _wait_for_event(log_path, "B waiting") - time.sleep(SETTLE_SECONDS) - (tmp_path / "release-A").touch() - _wait_for_event(log_path, "W enter") - (tmp_path / "release-W").touch() - _wait_for_event(log_path, "B enter") - (tmp_path / "release-B").touch() - for holder in (first_reader, second_reader, writer, late_reader): - assert holder.wait(timeout=DEADLINE_SECONDS) == 0 - events: Final = _events(log_path) - assert events.index("R enter") < events.index("A exit") - assert events.index("W enter") > events.index("A exit") - assert events.index("B enter") > events.index("W exit") diff --git a/tests/integration/authorization/test_jwt_auto_register_map_existing_key.py b/tests/integration/authorization/test_jwt_auto_register_map_existing_key.py index 62eda977438..c8fbcbb1c97 100644 --- a/tests/integration/authorization/test_jwt_auto_register_map_existing_key.py +++ b/tests/integration/authorization/test_jwt_auto_register_map_existing_key.py @@ -7,13 +7,15 @@ from concurrent.futures import ThreadPoolExecutor from contextlib import contextmanager from hashlib import sha256 from pathlib import Path -from typing import Final +from typing import Final, Literal import httpx import jwt import pytest import yaml from cryptography.hazmat.primitives.asymmetric import rsa +from pydantic import JsonValue +from redis import Redis from tests.integration._support.client import Gateway, eventually, string_value from tests.integration._support.database import read_rows @@ -31,7 +33,15 @@ def _hash(key: str) -> str: return sha256(key.encode()).hexdigest() -def _config(directory: Path, claim_field: str) -> Path: +def _config( + directory: Path, + claim_field: str, + *, + behavior: Literal["auto_register", "link_only", "reject"] = "auto_register", + map_existing_key: bool = True, + user_id_upsert: bool = False, + issuers: tuple[dict[str, JsonValue], ...] = (), +) -> Path: config: Final = yaml.safe_load(Path("tests/integration/proxy_config.yaml").read_text()) config["general_settings"] = { **config["general_settings"], @@ -40,8 +50,10 @@ def _config(directory: Path, claim_field: str) -> Path: "user_id_jwt_field": "sub", "user_email_jwt_field": "email", "virtual_key_claim_field": claim_field, - "unregistered_jwt_client_behavior": "auto_register", - "auto_register_map_existing_key": True, + "unregistered_jwt_client_behavior": behavior, + "auto_register_map_existing_key": map_existing_key, + "user_id_upsert": user_id_upsert, + "issuers": list(issuers), }, } path: Final = directory / f"jwt_map_existing_key_{claim_field}.yaml" @@ -107,7 +119,7 @@ def _billed_key(response: httpx.Response) -> str: return string_value(rows[0]["api_key"]) -def test_first_jwt_call_reuses_the_newest_durable_llm_key_and_skips_every_ineligible_newer_key( +def test_first_jwt_call_reuses_the_newest_durable_key_and_skips_every_ineligible_newer_key( gateway: Gateway, tmp_path: Path ) -> None: with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: @@ -118,7 +130,6 @@ def test_first_jwt_call_reuses_the_newest_durable_llm_key_and_skips_every_inelig skipped: Final = { "older_durable": older_durable, "expiring": scenario.key(user_id=user, duration="1h"), - "management_only": scenario.key(user_id=user, allowed_routes=["management_routes"]), "auto_registered_look_alike": scenario.key(user_id=user, metadata={"auto_registered": True}), "other_team": scenario.key(user_id=user, team_id=scenario.team()), "blocked": scenario.key(user_id=user), @@ -140,6 +151,84 @@ def test_first_jwt_call_reuses_the_newest_durable_llm_key_and_skips_every_inelig assert _billed_key(response) == _hash(durable) +@pytest.mark.parametrize( + ("behavior", "map_existing_key"), [("auto_register", True), ("link_only", False), ("link_only", True)] +) +def test_route_restricted_keys_are_linked_without_bypassing_other_key_checks( + gateway: Gateway, + tmp_path: Path, + behavior: Literal["auto_register", "link_only"], + map_existing_key: bool, +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + scenario.key(user_id=user) + restricted: Final = scenario.key(user_id=user, allowed_routes=["/v1/chat/completions"]) + scenario.key(user_id=user, duration="1h") + scenario.key(user_id=user, metadata={"auto_registered": True}) + scenario.key(user_id=user, team_id=scenario.team()) + blocked: Final = scenario.key(user_id=user) + gateway.post("/key/block", {"key": blocked}) + keys_before: Final = _user_key_hashes(user) + token: Final = _token(private_key, user) + + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config( + tmp_path, + "sub", + behavior=behavior, + map_existing_key=map_existing_key, + ), + ) as candidate: + response: Final = _chat(candidate, model, token) + repeated: Final = _chat(candidate, model, token) + restricted_route: Final = candidate.request("GET", "/v1/models", key=token) + + assert response.status_code == 200, response.text + assert repeated.status_code == 200, repeated.text + assert restricted_route.status_code == 403, restricted_route.text + assert _mapped_token("sub", user) == _hash(restricted) + assert _user_key_hashes(user) == keys_before, "a new key was minted instead of linking an existing key" + assert _billed_key(response) == _hash(restricted) + assert _billed_key(repeated) == _hash(restricted) + + +@pytest.mark.parametrize("behavior", ["auto_register", "link_only"]) +def test_management_route_keys_are_linked_by_default_and_keep_their_route_permissions( + gateway: Gateway, tmp_path: Path, behavior: Literal["auto_register", "link_only"] +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + target: Final = f"integration-{uuid.uuid4().hex}" + scenario.key(user_id=user) + management_key: Final = scenario.key(user_id=user, allowed_routes=["/user/new"]) + keys_before: Final = _user_key_hashes(user) + token: Final = _token(private_key, user) + with owned_proxy( + gateway, tmp_path, {"JWT_PUBLIC_KEY_URL": jwks_url}, config=_config(tmp_path, "sub", behavior=behavior) + ) as candidate: + denied: Final = _chat(candidate, model, token) + allowed: Final = candidate.request( + "POST", + "/user/new", + {"user_id": target, "auto_create_key": False, "user_role": "internal_user"}, + key=token, + ) + assert allowed.status_code == 200, allowed.text + scenario.cleanups.callback(scenario.delete_user, target) + repeated: Final = _chat(candidate, model, token) + assert denied.status_code == 403, denied.text + assert repeated.status_code == 403, repeated.text + assert allowed.json()["user_id"] == target + assert _mapped_token("sub", user) == _hash(management_key) + assert _user_key_hashes(user) == keys_before + + def test_user_matched_by_email_instead_of_sub_still_reuses_their_existing_key(gateway: Gateway, tmp_path: Path) -> None: with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: model: Final = scenario.model() @@ -183,8 +272,9 @@ def test_shared_client_claim_never_maps_a_second_user_onto_the_first_users_perso assert read_rows('SELECT request_id FROM "LiteLLM_SpendLogs" WHERE api_key = %s', (_hash(personal),)) == [] -def test_concurrent_first_jwt_calls_of_a_keyless_user_both_succeed_on_one_surviving_mapped_key( - gateway: Gateway, tmp_path: Path +@pytest.mark.parametrize("behavior", ["auto_register", "link_only"]) +def test_concurrent_first_jwt_calls_both_succeed_on_one_surviving_mapped_key( + gateway: Gateway, tmp_path: Path, behavior: Literal["auto_register", "link_only"] ) -> None: writer_url: Final = os.environ.get("INTEGRATION_PROXY_DATABASE_URL") or os.environ["DATABASE_URL"] with ( @@ -194,6 +284,7 @@ def test_concurrent_first_jwt_calls_of_a_keyless_user_both_succeed_on_one_surviv ): model: Final = scenario.model() user: Final = scenario.user(user_role="internal_user") + existing: Final = scenario.key(user_id=user) if behavior == "link_only" else None token: Final = _token(private_key, user) overrides: Final = { "JWT_PUBLIC_KEY_URL": jwks_url, @@ -202,7 +293,12 @@ def test_concurrent_first_jwt_calls_of_a_keyless_user_both_succeed_on_one_surviv } with ( - owned_proxy(gateway, tmp_path, overrides, config=_config(tmp_path, "sub")) as candidate, + owned_proxy( + gateway, + tmp_path, + overrides, + config=_config(tmp_path, "sub", behavior=behavior, map_existing_key=False), + ) as candidate, ThreadPoolExecutor(max_workers=1) as pool, ): held_call: Final = pool.submit(_chat, candidate, model, token) @@ -215,5 +311,242 @@ def test_concurrent_first_jwt_calls_of_a_keyless_user_both_succeed_on_one_surviv assert held.status_code == 200, held.text keys: Final = _user_key_hashes(user) assert len(keys) == 1, keys + if existing is not None: + assert keys == frozenset({_hash(existing)}) assert _mapped_token("sub", user) in keys assert (_billed_key(held), _billed_key(racing)) == (_mapped_token("sub", user),) * 2 + + +@pytest.mark.parametrize("ineligible_keys", [False, True]) +def test_link_only_rejects_without_writes_then_links_a_newly_available_key( + gateway: Gateway, tmp_path: Path, ineligible_keys: bool +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + if ineligible_keys: + scenario.key(user_id=user, duration="1h") + scenario.key(user_id=user, metadata={"auto_registered": True}) + scenario.key(user_id=user, team_id=scenario.team()) + blocked: Final = scenario.key(user_id=user) + gateway.post("/key/block", {"key": blocked}) + keys_before: Final = _user_key_hashes(user) + token: Final = _token(private_key, user) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="link_only", map_existing_key=False), + ) as candidate: + first: Final = _chat(candidate, model, token) + repeated: Final = _chat(candidate, model, token) + assert first.status_code == 403, first.text + assert repeated.status_code == 403, repeated.text + assert _user_key_hashes(user) == keys_before + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE jwt_claim_value=%s', (user,)) == [] + available: Final = scenario.key(user_id=user) + accepted: Final = _chat(candidate, model, token) + assert accepted.status_code == 200, accepted.text + assert _mapped_token("sub", user) == _hash(available) + assert _user_key_hashes(user) == keys_before | {_hash(available)} + assert _billed_key(accepted) == _hash(available) + + +@pytest.mark.parametrize("claim_field", ["client_id", "email"]) +def test_link_only_rejects_shared_or_missing_claims_without_linking_a_personal_key( + gateway: Gateway, tmp_path: Path, claim_field: str +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + key: Final = scenario.key(user_id=user) + shared_claim: Final = f"integration-client-{uuid.uuid4().hex}" + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, claim_field, behavior="link_only", map_existing_key=False), + ) as candidate: + response: Final = _chat(candidate, model, _token(private_key, user, client_id=shared_claim)) + assert response.status_code == 403, response.text + assert _user_key_hashes(user) == frozenset({_hash(key)}) + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE token=%s', (_hash(key),)) == [] + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE jwt_claim_value=%s', (shared_claim,)) == [] + + +@pytest.mark.parametrize("restriction", ["models", "allowed_routes"]) +def test_link_only_enforces_key_permissions_on_the_first_and_cached_requests( + gateway: Gateway, tmp_path: Path, restriction: str +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + allowed: Final = scenario.model() + denied: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + key: Final = scenario.key( + user_id=user, + **({"models": [allowed]} if restriction == "models" else {"allowed_routes": ["/v1/models"]}), + ) + token: Final = _token(private_key, user) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="link_only", map_existing_key=False), + ) as candidate: + first: Final = _chat(candidate, denied, token) + repeated: Final = _chat(candidate, denied, token) + control: Final = ( + _chat(candidate, allowed, token) + if restriction == "models" + else candidate.request("GET", "/v1/models", key=token) + ) + assert first.status_code == 403, first.text + assert repeated.status_code == 403, repeated.text + assert control.status_code == 200, control.text + assert _mapped_token("sub", user) == _hash(key) + assert _user_key_hashes(user) == frozenset({_hash(key)}) + + +def test_link_only_does_not_replace_an_existing_mapping_to_a_blocked_key(gateway: Gateway, tmp_path: Path) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + blocked: Final = scenario.key(user_id=user) + available: Final = scenario.key(user_id=user) + gateway.post("/jwt/key/mapping/new", {"jwt_claim_name": "sub", "jwt_claim_value": user, "key": blocked}) + gateway.post("/key/block", {"key": blocked}) + token: Final = _token(private_key, user) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="link_only", map_existing_key=False), + ) as candidate: + first: Final = _chat(candidate, model, token) + repeated: Final = _chat(candidate, model, token) + assert first.status_code == 401, first.text + assert repeated.status_code == 401, repeated.text + assert _mapped_token("sub", user) == _hash(blocked) + assert _user_key_hashes(user) == frozenset({_hash(blocked), _hash(available)}) + + +def test_link_only_exempts_proxy_admins_but_rechecks_the_role_after_a_cached_exemption( + gateway: Gateway, tmp_path: Path +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + admin: Final = _token(private_key, user, scope="litellm_proxy_admin") + ordinary: Final = _token(private_key, user) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="link_only", map_existing_key=False), + ) as candidate: + first: Final = candidate.request("GET", "/v1/models", key=admin) + repeated: Final = candidate.request("GET", "/v1/models", key=admin) + denied: Final = candidate.request("GET", "/v1/models", key=ordinary) + forbidden_route: Final = _chat(candidate, model, admin) + assert first.status_code == 200, first.text + assert repeated.status_code == 200, repeated.text + assert denied.status_code == 403, denied.text + assert forbidden_route.status_code == 401, forbidden_route.text + assert _user_key_hashes(user) == frozenset() + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE jwt_claim_value=%s', (user,)) == [] + + +@pytest.mark.parametrize("user_id_upsert", [False, True]) +def test_link_only_keeps_user_provisioning_independent_of_key_creation( + gateway: Gateway, tmp_path: Path, user_id_upsert: bool +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + subject: Final = f"integration-link-only-{uuid.uuid4().hex}" + try: + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config( + tmp_path, "sub", behavior="link_only", map_existing_key=False, user_id_upsert=user_id_upsert + ), + ) as candidate: + response: Final = _chat(candidate, model, _token(private_key, subject)) + assert response.status_code == (403 if user_id_upsert else 401), response.text + users: Final = read_rows('SELECT user_id FROM "LiteLLM_UserTable" WHERE user_id=%s', (subject,)) + assert users == ([{"user_id": subject}] if user_id_upsert else []) + assert _user_key_hashes(subject) == frozenset() + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE jwt_claim_value=%s', (subject,)) == [] + finally: + if read_rows('SELECT user_id FROM "LiteLLM_UserTable" WHERE user_id=%s', (subject,)): + scenario.delete_user(subject) + + +@pytest.mark.parametrize("issuer_behavior", ["auto_register", "link_only"]) +def test_issuer_specific_registration_mode_overrides_the_global_mode( + gateway: Gateway, tmp_path: Path, issuer_behavior: Literal["auto_register", "link_only"] +) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + issuer: Final = f"https://integration.example.com/{uuid.uuid4().hex}" + token: Final = _token(private_key, user, iss=issuer, aud="integration") + path: Final = _config( + tmp_path, + "sub", + behavior="auto_register" if issuer_behavior == "link_only" else "link_only", + map_existing_key=False, + issuers=( + { + "issuer": issuer, + "jwks_url": jwks_url, + "audience": "integration", + "user_id_jwt_field": "sub", + "virtual_key_claim_field": "sub", + "unregistered_jwt_client_behavior": issuer_behavior, + }, + ), + ) + with owned_proxy(gateway, tmp_path, {"JWT_PUBLIC_KEY_URL": jwks_url}, config=path) as candidate: + response: Final = _chat(candidate, model, token) + assert response.status_code == (403 if issuer_behavior == "link_only" else 200), response.text + if issuer_behavior == "link_only": + assert _user_key_hashes(user) == frozenset() + assert read_rows('SELECT token FROM "LiteLLM_JWTKeyMapping" WHERE jwt_claim_value=%s', (user,)) == [] + else: + assert len(_user_key_hashes(user)) == 1 + assert _mapped_token("sub", user) in _user_key_hashes(user) + assert _billed_key(response) == _mapped_token("sub", user) + + +def test_link_only_links_after_a_cached_rejection_from_the_previous_mode(gateway: Gateway, tmp_path: Path) -> None: + with _issuer() as (private_key, jwks_url), gateway.scenario() as scenario: + model: Final = scenario.model() + user: Final = scenario.user(user_role="internal_user") + key: Final = scenario.key(user_id=user) + token: Final = _token(private_key, user) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="reject", map_existing_key=False), + ) as rejecting: + denied: Final = _chat(rejecting, model, token) + assert denied.status_code == 403, denied.text + with Redis(host=os.environ["REDIS_HOST"], port=int(os.environ["REDIS_PORT"])) as cache: + eventually( + lambda: cache.get(f"jwt_key_mapping:sub:{user}"), + lambda value: value is not None and b"__NO_MAPPING__" in value, + ) + with owned_proxy( + gateway, + tmp_path, + {"JWT_PUBLIC_KEY_URL": jwks_url}, + config=_config(tmp_path, "sub", behavior="link_only", map_existing_key=False), + ) as linking: + accepted: Final = _chat(linking, model, token) + assert accepted.status_code == 200, accepted.text + assert _mapped_token("sub", user) == _hash(key) + assert _user_key_hashes(user) == frozenset({_hash(key)}) + assert _billed_key(accepted) == _hash(key) diff --git a/tests/integration/configuration/test_fips_mode_boot.py b/tests/integration/configuration/test_fips_mode_boot.py new file mode 100644 index 00000000000..18fb692da6e --- /dev/null +++ b/tests/integration/configuration/test_fips_mode_boot.py @@ -0,0 +1,39 @@ +import _hashlib +import json +from pathlib import Path +from typing import Final + +import pytest + +from tests.integration._support.client import Gateway +from tests.integration._support.process import refused_boot_log + + +def _config(directory: Path, upstream_url: str, fips_mode: object) -> Path: + config: Final = directory / "fips_boot.yaml" + config.write_text( + json.dumps( + { + "model_list": [ + { + "model_name": "fips-model", + "litellm_params": {"model": "openai/fips-model", "api_base": upstream_url, "api_key": "sk-x"}, + } + ], + "general_settings": {"fips_mode": fips_mode}, + } + ) + ) + return config + + +def test_fips_mode_refuses_to_start_when_openssl_does_not_enforce_fips(gateway: Gateway, tmp_path: Path) -> None: + if _hashlib.get_fips_mode() == 1: + pytest.skip("this runner's OpenSSL is in FIPS mode") + log: Final = refused_boot_log(gateway, tmp_path, {}, config=_config(tmp_path, gateway.upstream_url, True)) + assert "general_settings.fips_mode is on but OpenSSL does not enforce FIPS" in log, log + + +def test_fips_mode_refuses_to_start_on_a_value_that_is_not_a_boolean(gateway: Gateway, tmp_path: Path) -> None: + log: Final = refused_boot_log(gateway, tmp_path, {}, config=_config(tmp_path, gateway.upstream_url, "enforced")) + assert "general_settings.fips_mode='enforced' is not a boolean" in log, log diff --git a/tests/integration/database/test_accounting_ledger.py b/tests/integration/database/test_accounting_ledger.py deleted file mode 100644 index 15cd5413149..00000000000 --- a/tests/integration/database/test_accounting_ledger.py +++ /dev/null @@ -1,610 +0,0 @@ -from __future__ import annotations - -import asyncio -import json -import os -import subprocess -import sys -from collections.abc import AsyncGenerator, AsyncIterator, Iterator, Mapping, Sequence -from contextlib import asynccontextmanager -from datetime import datetime, timezone -from decimal import Decimal -from itertools import chain -from pathlib import Path -from typing import TYPE_CHECKING, Final, TypeAlias - -import psycopg -import pytest -import pytest_asyncio -from psycopg import sql -from pydantic import JsonValue - -from litellm.accounting import ( - CostEvent, - DeliveryState, - DeliveryTarget, - EventKind, - InboundUsageEvent, - Metric, - MetricName, - OutboundUsageEvent, - Outcome, - PricingSnapshot, -) -from litellm.proxy.db import accounting_ledger -from litellm.proxy.db.accounting_ledger import AccountingConflictError, AccountingEvent, AccountingLedger, UsageWrite -from litellm.types.utils import CallTypes -from tests.integration._support.database import scratch_database -from tests.integration._support.prisma_clients import connected_proxy_prisma - -if TYPE_CHECKING: - from prisma import Prisma - - from litellm.proxy.utils import PrismaClient - -_Writer: TypeAlias = tuple["PrismaClient", AccountingLedger] - -_SCHEMA: Final = Path(__file__).resolve().parents[3] / "litellm-proxy-extras/litellm_proxy_extras/schema.prisma" -_EVENT_INSERT: Final = ( - 'INSERT INTO "LiteLLM_AccountingEvent" ' - "(event_id, kind, schema_version, payload_hash, occurred_at, payload) " - "VALUES ('event', %s, %s, 'hash', '2026-01-01', '{}') RETURNING kind::text, schema_version" -) -_DELIVERY_INSERT: Final = ( - 'INSERT INTO "LiteLLM_AccountingDelivery" (event_id, target, version, state, attempts) ' - "VALUES ('event', %s, %s, %s, %s) RETURNING target::text, version, state::text, attempts" -) - - -@pytest.fixture(scope="module") -def ledger_database() -> Iterator[str]: - with scratch_database("accounting") as database_url: - result: Final = subprocess.run( - [sys.executable, "-I", "-m", "prisma", "migrate", "deploy", "--schema", str(_SCHEMA)], - env={**os.environ, "DATABASE_URL": database_url}, - capture_output=True, - text=True, - timeout=300, - ) - assert result.returncode == 0, result.stdout + result.stderr - yield database_url - - -@pytest.fixture -def ledger(ledger_database: str) -> Iterator[psycopg.Connection[tuple[object, ...]]]: - with psycopg.connect(ledger_database) as connection: - try: - yield connection - finally: - connection.rollback() - - -@pytest.mark.parametrize("kind", tuple(EventKind)) -def test_ledger_accepts_every_public_event_kind( - ledger: psycopg.Connection[tuple[object, ...]], kind: EventKind -) -> None: - row: Final = ledger.execute(_EVENT_INSERT, (kind.value, 1)).fetchone() - assert row == (kind.value, 1) - - -@pytest.mark.parametrize("target", tuple(DeliveryTarget)) -@pytest.mark.parametrize("state", tuple(DeliveryState)) -def test_delivery_accepts_every_public_target_and_state( - ledger: psycopg.Connection[tuple[object, ...]], target: DeliveryTarget, state: DeliveryState -) -> None: - row: Final = ledger.execute(_DELIVERY_INSERT, (target.value, 1, state.value, 0)).fetchone() - assert row == (target.value, 1, state.value, 0) - - -@pytest.mark.parametrize( - ("enum_name", "values"), - [ - ("AccountingEventKind", tuple(value.value for value in EventKind)), - ("AccountingDeliveryState", tuple(value.value for value in DeliveryState)), - ("AccountingDeliveryTarget", tuple(value.value for value in DeliveryTarget)), - ], -) -def test_migrated_enums_match_the_public_contract( - ledger: psycopg.Connection[tuple[object, ...]], enum_name: str, values: tuple[str, ...] -) -> None: - rows: Final = ledger.execute( - "SELECT e.enumlabel FROM pg_enum e JOIN pg_type t ON t.oid = e.enumtypid WHERE t.typname = %s", - (enum_name,), - ).fetchall() - assert frozenset(row[0] for row in rows) == frozenset(values) - - -@pytest.mark.parametrize("version", [0, -1]) -def test_event_rejects_nonpositive_schema_versions( - ledger: psycopg.Connection[tuple[object, ...]], version: int -) -> None: - with pytest.raises(psycopg.errors.CheckViolation, match="AccountingEvent_schema_version_check"): - ledger.execute(_EVENT_INSERT, (EventKind.OUTBOUND_USAGE.value, version)) - - -@pytest.mark.parametrize( - ("version", "attempts", "constraint"), - [ - (0, 0, "AccountingDelivery_version_check"), - (-1, 0, "AccountingDelivery_version_check"), - (1, -1, "AccountingDelivery_attempts_check"), - ], -) -def test_delivery_rejects_invalid_versions_and_attempts( - ledger: psycopg.Connection[tuple[object, ...]], version: int, attempts: int, constraint: str -) -> None: - with pytest.raises(psycopg.errors.CheckViolation, match=constraint): - ledger.execute(_DELIVERY_INSERT, (DeliveryTarget.KEY_SPEND.value, version, "pending", attempts)) - - -@pytest.mark.parametrize("column", ["state", "target"]) -def test_delivery_rejects_unknown_enum_values(ledger: psycopg.Connection[tuple[object, ...]], column: str) -> None: - ledger.execute(_DELIVERY_INSERT, (DeliveryTarget.KEY_SPEND.value, 1, "pending", 0)) - with pytest.raises(psycopg.errors.InvalidTextRepresentation, match="invalid input value for enum"): - ledger.execute( - sql.SQL('UPDATE "LiteLLM_AccountingDelivery" SET {} = %s').format(sql.Identifier(column)), ("invalid",) - ) - - -def test_event_rejects_unknown_kind(ledger: psycopg.Connection[tuple[object, ...]]) -> None: - with pytest.raises(psycopg.errors.InvalidTextRepresentation, match="invalid input value for enum"): - ledger.execute(_EVENT_INSERT, ("invalid", 1)) - - -def test_new_delivery_defaults_to_pending_with_no_attempts(ledger: psycopg.Connection[tuple[object, ...]]) -> None: - row: Final = ledger.execute( - 'INSERT INTO "LiteLLM_AccountingDelivery" (event_id, target, version) ' - "VALUES ('event', %s, 1) RETURNING state::text, attempts", - (DeliveryTarget.KEY_SPEND.value,), - ).fetchone() - assert row == ("pending", 0) - - -_AT: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) -_CUTOFF: Final = datetime(2026, 2, 1, tzinfo=timezone.utc) -_TARGETS: Final = (DeliveryTarget.USER_SPEND, DeliveryTarget.TEAM_SPEND) - - -def _usage(event_id: str = "usage", *, inbound: bool = False) -> InboundUsageEvent | OutboundUsageEvent: - fields: Final = dict( - event_id=event_id, - request_id="request", - call_type=CallTypes.completion, - started_at=_AT, - ended_at=_AT, - outcome=Outcome.SUCCEEDED, - dimensions=(), - ) - if inbound: - return InboundUsageEvent(**fields, metrics=(Metric(name=MetricName.REQUESTS, value=Decimal(1)),)) - return OutboundUsageEvent(**fields, attempt_id=event_id, retry_of_attempt_id=None, metrics=None) - - -def _cost(source: str = "usage", event_id: str = "cost", amount: str = "0.125") -> CostEvent: - return CostEvent( - event_id=event_id, - usage_event_id=source, - occurred_at=_AT, - dimensions=(), - charges=(Metric(name=MetricName.SPEND, value=Decimal(amount), unit="USD"),), - snapshot=PricingSnapshot( - calculator_version="fixture", - model="fixture", - provider=None, - pricing_source="cost_map", - priced_model_key="fixture", - total_cost=Decimal(amount), - ), - ) - - -def _envelope(event: AccountingEvent, *, context: Mapping[str, JsonValue] | None = None) -> str: - return json.dumps({"event": event.model_dump(mode="json"), "context": context or {}}) - - -async def _insert( - store: AccountingLedger, - event: AccountingEvent, - targets: tuple[DeliveryTarget, ...] = _TARGETS, - *, - context: Mapping[str, JsonValue] | None = None, -) -> str: - return await store.insert_event(event, targets, envelope=_envelope(event, context=context)) - - -def _rows(database_url: str, statement: str, parameters: tuple[object, ...] = ()) -> Sequence[tuple[object, ...]]: - with psycopg.connect(database_url) as connection: - cursor: Final = connection.execute(statement, parameters) - return cursor.fetchall() if cursor.description is not None else [] - - -def _complete_and_age(database_url: str) -> None: - with psycopg.connect(database_url) as connection: - connection.execute('UPDATE "LiteLLM_AccountingEvent" SET recorded_at = %s', (_AT,)) - connection.execute("UPDATE \"LiteLLM_AccountingDelivery\" SET state = 'applied', applied_at = %s", (_AT,)) - - -@pytest_asyncio.fixture(loop_scope="function") -async def writer(ledger_database: str, monkeypatch: pytest.MonkeyPatch) -> AsyncIterator[_Writer]: - monkeypatch.setenv("DATABASE_URL", ledger_database) - monkeypatch.delenv("DATABASE_URL_READ_REPLICA", raising=False) - _rows(ledger_database, 'TRUNCATE "LiteLLM_AccountingDelivery", "LiteLLM_AccountingEvent"') - async with connected_proxy_prisma(ledger_database) as client: - yield client, AccountingLedger.for_client(client) - - -@pytest.mark.asyncio -async def test_event_and_complete_delivery_set_commit_together(writer: _Writer, ledger_database: str) -> None: - _, store = writer - event: Final = _usage() - assert await _insert(store, event, context={"requested_region": "eu", "quantity": "1.25"}) == "inserted" - rows: Final = _rows(ledger_database, 'SELECT kind::text, source_event_id, payload FROM "LiteLLM_AccountingEvent"') - assert rows == [ - ("outbound_usage", None, json.loads(_envelope(event, context={"requested_region": "eu", "quantity": "1.25"}))) - ] - deliveries: Final = _rows( - ledger_database, 'SELECT target::text, state::text, attempts FROM "LiteLLM_AccountingDelivery"' - ) - assert set(deliveries) == {(target.value, "pending", 0) for target in _TARGETS} - - -@pytest.mark.asyncio -async def test_concurrent_duplicates_preserve_completed_deliveries(writer: _Writer, ledger_database: str) -> None: - _, store = writer - event: Final = _usage(inbound=True) - results: Final = await asyncio.gather(*(_insert(store, event) for _ in range(8))) - assert results.count("inserted") == 1 and results.count("duplicate") == 7 - _complete_and_age(ledger_database) - before: Final = _rows(ledger_database, 'SELECT * FROM "LiteLLM_AccountingDelivery" ORDER BY target') - reordered: Final = json.dumps(json.loads(_envelope(event)), sort_keys=True, indent=2) - assert await store.insert_event(event, tuple(reversed(_TARGETS)) + _TARGETS, envelope=reordered) == "duplicate" - assert _rows(ledger_database, 'SELECT * FROM "LiteLLM_AccountingDelivery" ORDER BY target') == before - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(1,)] - - -@pytest.mark.asyncio -@pytest.mark.parametrize("change", ["payload", "context", "targets", "schema_version", "version"]) -async def test_conflicting_acceptance_cannot_overwrite_or_add_work( - writer: _Writer, ledger_database: str, change: str -) -> None: - _, store = writer - event: Final = _usage() - await _insert(store, event) - altered: Final = event.model_copy(update={"outcome": Outcome.FAILED}) if change == "payload" else event - targets: Final = (DeliveryTarget.KEY_SPEND,) if change == "targets" else _TARGETS - context: Final = {"override": "0.5"} if change == "context" else {} - options: Final = {change: 2} if change in ("schema_version", "version") else {} - with pytest.raises(AccountingConflictError, match="conflicts"): - await store.insert_event(altered, targets, envelope=_envelope(altered, context=context), **options) - assert _rows(ledger_database, 'SELECT payload FROM "LiteLLM_AccountingEvent"') == [(json.loads(_envelope(event)),)] - assert set(_rows(ledger_database, 'SELECT target::text FROM "LiteLLM_AccountingDelivery"')) == { - (t.value,) for t in _TARGETS - } - - -@pytest.mark.asyncio -async def test_competing_cost_identities_accept_only_one_authoritative_charge( - writer: _Writer, ledger_database: str -) -> None: - _, store = writer - await _insert(store, _usage()) - results: Final = await asyncio.gather( - _insert(store, _cost()), _insert(store, _cost(event_id="other-cost")), return_exceptions=True - ) - assert sum(result == "inserted" for result in results) == 1 - assert sum(isinstance(result, AccountingConflictError) for result in results) == 1 - accepted: Final = _rows( - ledger_database, "SELECT event_id, payload FROM \"LiteLLM_AccountingEvent\" WHERE kind = 'cost'" - ) - assert len(accepted) == 1 - event: Final = CostEvent.model_validate_json(json.dumps(accepted[0][1]["event"])) - assert await _insert(store, event) == "duplicate" - assert event.snapshot.total_cost == Decimal("0.125") - with pytest.raises(AccountingConflictError): - await _insert(store, _cost(event_id=event.event_id, amount="0.5")) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("source", ["missing", "inbound", "cost"]) -async def test_cost_requires_an_existing_outbound_source(writer: _Writer, ledger_database: str, source: str) -> None: - _, store = writer - if source == "inbound": - await _insert(store, _usage("source", inbound=True)) - if source == "cost": - await _insert(store, _usage()) - await _insert(store, _cost(event_id="source")) - with pytest.raises(AccountingConflictError, match="outbound usage"): - await _insert(store, _cost(source="source", event_id="invalid")) - assert _rows(ledger_database, "SELECT count(*) FROM \"LiteLLM_AccountingEvent\" WHERE event_id = 'invalid'") == [ - (0,) - ] - - -@pytest.mark.asyncio -@pytest.mark.parametrize("failure", ["before_commit", "after_commit", "cancellation"]) -async def test_interruption_rolls_back_or_retry_recognizes_commit( - writer: _Writer, ledger_database: str, failure: str -) -> None: - client, store = writer - - @asynccontextmanager - async def interrupted() -> AsyncGenerator[Prisma]: - async with client.writer_db.tx() as tx: - yield tx - if failure == "before_commit": - raise RuntimeError("injected pre-commit failure") - if failure == "cancellation": - raise asyncio.CancelledError - raise RuntimeError("injected lost acknowledgement") - - error: Final = asyncio.CancelledError if failure == "cancellation" else RuntimeError - with pytest.raises(error): - await _insert(AccountingLedger(interrupted), _usage()) - expected: Final = 1 if failure == "after_commit" else 0 - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(expected,)] - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(expected * len(_TARGETS),)] - assert await _insert(store, _usage()) == ("duplicate" if expected else "inserted") - - -@pytest.mark.asyncio -async def test_delivery_insert_failure_rolls_back_event(writer: _Writer, ledger_database: str) -> None: - from prisma.errors import RawQueryError - - _, store = writer - with psycopg.connect(ledger_database) as connection: - connection.execute( - 'ALTER TABLE "LiteLLM_AccountingDelivery" ADD CONSTRAINT reject_delivery CHECK (version < 1)' - ) - try: - with pytest.raises(RawQueryError, match="reject_delivery"): - await _insert(store, _usage()) - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(0,)] - finally: - with psycopg.connect(ledger_database) as connection: - connection.execute('ALTER TABLE "LiteLLM_AccountingDelivery" DROP CONSTRAINT reject_delivery') - - -@pytest.mark.asyncio -@pytest.mark.parametrize("blocked", ["pending", "parked", "unpriced", "recent_cost", "recovery"]) -async def test_retention_preserves_incomplete_or_recovery_needed_families( - writer: _Writer, ledger_database: str, blocked: str -) -> None: - _, store = writer - await _insert(store, _usage()) - if blocked != "unpriced": - await _insert(store, _cost()) - _complete_and_age(ledger_database) - with psycopg.connect(ledger_database) as connection: - if blocked in ("pending", "parked"): - connection.execute( - "UPDATE \"LiteLLM_AccountingDelivery\" SET state = %s WHERE event_id = 'cost'", (blocked,) - ) - if blocked == "recent_cost": - connection.execute( - "UPDATE \"LiteLLM_AccountingEvent\" SET recorded_at = %s WHERE event_id = 'cost'", (_CUTOFF,) - ) - before: Final = _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent" ORDER BY event_id') - assert ( - await store.purge_completed( - redelivery_before=_CUTOFF, recovery_before=_AT if blocked == "recovery" else _CUTOFF - ) - == 0 - ) - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent" ORDER BY event_id') == before - - -@pytest.mark.asyncio -async def test_retention_removes_completed_family_and_receipts_together(writer: _Writer, ledger_database: str) -> None: - _, store = writer - await _insert(store, _usage()) - await _insert(store, _cost()) - _complete_and_age(ledger_database) - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 2 - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(0,)] - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(0,)] - - -@pytest.mark.asyncio -async def test_retention_respects_batch_bound_and_skips_locked_families(writer: _Writer, ledger_database: str) -> None: - client, store = writer - for name in ("a", "b", "c"): - await _insert(store, _usage(name, inbound=True)) - _complete_and_age(ledger_database) - async with client.writer_db.tx() as tx: - await tx.query_raw("SELECT event_id FROM \"LiteLLM_AccountingEvent\" WHERE event_id = 'a' FOR UPDATE") - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF, limit=1) == 1 - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent" ORDER BY event_id') == [ - ("a",), - ("c",), - ] - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 2 - - -@pytest.mark.asyncio -async def test_retention_makes_progress_past_pending_and_unpriced_events(writer: _Writer, ledger_database: str) -> None: - _, store = writer - await _insert(store, _usage("a", inbound=True)) - await _insert(store, _usage("b")) - await _insert(store, _usage("c", inbound=True)) - _complete_and_age(ledger_database) - _rows(ledger_database, "UPDATE \"LiteLLM_AccountingDelivery\" SET state = 'pending' WHERE event_id = 'a'") - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF, limit=1) == 1 - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent" ORDER BY event_id') == [ - ("a",), - ("b",), - ] - - -@pytest.mark.asyncio -async def test_zero_cost_and_empty_delivery_sets_are_durable(writer: _Writer, ledger_database: str) -> None: - _, store = writer - await _insert(store, _usage(), targets=()) - event: Final = _cost(amount="0") - assert await _insert(store, event, targets=()) == "inserted" - assert await _insert(store, event, targets=()) == "duplicate" - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(0,)] - _complete_and_age(ledger_database) - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 2 - - -@pytest.mark.asyncio -async def test_retention_cannot_delete_source_while_cost_acceptance_is_uncommitted( - writer: _Writer, ledger_database: str -) -> None: - client, store = writer - await _insert(store, _usage()) - _complete_and_age(ledger_database) - inserted: Final = asyncio.Event() - release: Final = asyncio.Event() - - @asynccontextmanager - async def paused_commit() -> AsyncGenerator[Prisma]: - async with client.writer_db.tx() as tx: - yield tx - inserted.set() - await release.wait() - - task: Final = asyncio.create_task(_insert(AccountingLedger(paused_commit), _cost())) - try: - await asyncio.wait_for(inserted.wait(), timeout=5) - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 0 - finally: - release.set() - await task - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent" ORDER BY event_id') == [ - ("cost",), - ("usage",), - ] - - -@pytest.mark.asyncio -async def test_retention_failure_rolls_back_event_and_receipt_deletion(writer: _Writer, ledger_database: str) -> None: - client, store = writer - await _insert(store, _usage(inbound=True)) - _complete_and_age(ledger_database) - - @asynccontextmanager - async def interrupted() -> AsyncGenerator[Prisma]: - async with client.writer_db.tx() as tx: - yield tx - raise RuntimeError("injected cleanup failure") - - with pytest.raises(RuntimeError, match="cleanup failure"): - await AccountingLedger(interrupted).purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(1,)] - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(len(_TARGETS),)] - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 1 - - -@pytest.mark.asyncio -async def test_ledger_reads_and_writes_use_primary_when_replica_is_configured( - writer: _Writer, ledger_database: str, monkeypatch: pytest.MonkeyPatch -) -> None: - with scratch_database("replica") as replica_url: - monkeypatch.setenv("DATABASE_URL_READ_REPLICA", replica_url) - async with connected_proxy_prisma(ledger_database) as client: - store: Final = AccountingLedger.for_client(client) - await _insert(store, _usage()) - assert await _insert(store, _usage()) == "duplicate" - assert await _insert(store, _cost()) == "inserted" - _complete_and_age(ledger_database) - assert await store.purge_completed(redelivery_before=_CUTOFF, recovery_before=_CUTOFF) == 2 - assert _rows(replica_url, "SELECT to_regclass('public.\"LiteLLM_AccountingEvent\"')") == [(None,)] - - -def _index_conditions(plan: object) -> tuple[tuple[object, object], ...]: - if isinstance(plan, dict): - own: Final = ((plan.get("Index Name"), plan.get("Index Cond")),) if "Index Name" in plan else () - children: Final = (plan.get("Plan"), *plan.get("Plans", ())) - return own + tuple(chain.from_iterable(_index_conditions(child) for child in children if child is not None)) - return tuple(chain.from_iterable(_index_conditions(item) for item in plan)) if isinstance(plan, list) else () - - -@pytest.mark.parametrize("statement", [accounting_ledger._DELETE_EVENTS, accounting_ledger._DELETE_DELIVERIES]) -def test_family_cleanup_lookups_use_the_kind_source_index(ledger_database: str, statement: str) -> None: - explained: Final = "EXPLAIN (FORMAT JSON) " + statement.replace("$1", "%s") - with psycopg.connect(ledger_database) as connection: - connection.execute("SET enable_seqscan = off") - plan: Final = connection.execute(explained, ("usage",) * statement.count("$1")).fetchone() - assert plan is not None - conditions: Final = _index_conditions(plan[0]) - assert any( - name == "LiteLLM_AccountingEvent_kind_source_event_id_idx" and "source_event_id" in str(condition) - for name, condition in conditions - ), conditions - - -@pytest.mark.asyncio -@pytest.mark.parametrize("failure", ["before_commit", "after_commit", "conflict"]) -async def test_usage_batch_is_atomic_and_replay_preserves_receipts( - writer: _Writer, ledger_database: str, failure: str -) -> None: - client, store = writer - events: Final = ( - _usage("caller", inbound=True), - _usage("attempt").model_copy( - update={"metrics": (Metric(name=MetricName.PROMPT_TOKENS, value=Decimal("120000")),)} - ), - ) - writes: Final = tuple(UsageWrite(event, _envelope(event), _TARGETS) for event in events) - - @asynccontextmanager - async def interrupted() -> AsyncGenerator[Prisma]: - async with client.writer_db.tx() as tx: - yield tx - if failure == "before_commit": - raise RuntimeError("injected pre-commit failure") - if failure == "after_commit": - raise RuntimeError("injected lost acknowledgement") - - if failure == "conflict": - await store.insert_usage_batch((writes[1],)) - _complete_and_age(ledger_database) - rejected: Final = (writes[0], UsageWrite(events[1], _envelope(events[1], context={"changed": True}), _TARGETS)) - with pytest.raises(AccountingConflictError): - await store.insert_usage_batch(rejected) - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent"') == [("attempt",)] - else: - with pytest.raises(RuntimeError, match="injected"): - await AccountingLedger(interrupted).insert_usage_batch(writes) - count: Final = 2 if failure == "after_commit" else 0 - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(count,)] - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(count * len(_TARGETS),)] - await store.insert_usage_batch(writes) - recovered: Final = await store.load_usage_batch(tuple(event.event_id for event in events)) - assert len(recovered) == 1 - assert recovered[0].event == events[1] - assert json.loads(recovered[0].envelope) == json.loads(writes[1].envelope) - assert set(recovered[0].targets) == set(_TARGETS) - _complete_and_age(ledger_database) - before: Final = _rows(ledger_database, 'SELECT * FROM "LiteLLM_AccountingDelivery" ORDER BY event_id, target') - await store.insert_usage_batch((*writes, *writes)) - assert _rows(ledger_database, 'SELECT * FROM "LiteLLM_AccountingDelivery" ORDER BY event_id, target') == before - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingEvent"') == [(2,)] - - -@pytest.mark.asyncio -async def test_invalid_jsonb_submission_cannot_reject_another_producers_commit( - writer: _Writer, ledger_database: str -) -> None: - from prisma.errors import DataError - - from litellm.proxy.spend_tracking.usage_meter import DurableUsageMeter, UsageCaptureSettings - - _, store = writer - invalid: Final = _usage("invalid") - valid: Final = _usage("valid") - meter: Final = DurableUsageMeter(store.insert_usage_batch, UsageCaptureSettings(batch_size=2)) - rejected: Final = meter.submit( - (UsageWrite(invalid, _envelope(invalid, context={"parent": "bad\x00text"}), _TARGETS),) - ) - accepted: Final = meter.submit((UsageWrite(valid, _envelope(valid), _TARGETS),)) - try: - with pytest.raises(DataError): - await rejected - await accepted - finally: - await meter.close() - assert _rows(ledger_database, 'SELECT event_id FROM "LiteLLM_AccountingEvent"') == [("valid",)] - assert _rows(ledger_database, 'SELECT count(*) FROM "LiteLLM_AccountingDelivery"') == [(len(_TARGETS),)] - assert (meter.pending_bytes, meter.pending_records) == (0, 0) diff --git a/tests/integration/management/test_credential_display_name.py b/tests/integration/management/test_credential_display_name.py index 0e72dcf8b2f..f605be1d059 100644 --- a/tests/integration/management/test_credential_display_name.py +++ b/tests/integration/management/test_credential_display_name.py @@ -122,6 +122,9 @@ def test_patch_rename_is_rejected_and_model_keeps_working(gateway: Gateway) -> N "PATCH", f"/credentials/{name}", {"credential_name": name, "credential_info": {"description": "y"}} ) assert same_name.status_code == 200, same_name.text + assert read_rows( + 'SELECT credential_info FROM "LiteLLM_CredentialsTable" WHERE credential_name = %s', (name,) + ) == [{"credential_info": {"custom_llm_provider": "openai", "description": "y"}}] assert _served_credential(gateway, name)["credential_info"] == { "custom_llm_provider": "openai", "description": "y", diff --git a/tests/integration/mcp/test_mcp_transports.py b/tests/integration/mcp/test_mcp_transports.py index 92fcf37994e..4c3648a3381 100644 --- a/tests/integration/mcp/test_mcp_transports.py +++ b/tests/integration/mcp/test_mcp_transports.py @@ -669,7 +669,7 @@ def test_sdk_progress_and_cancellation_reach_upstream_without_losing_session( import httpx2 import yaml from integration._support.asgi import asgi_server - from integration._support.mcp import _drain_sse_streams + from integration._support.mcp import _acknowledge_notifications_after_dispatch, _drain_sse_streams from integration._support.process import owned_proxy from mcp import ClientSession, MCPError from mcp.client.streamable_http import streamable_http_client @@ -769,7 +769,9 @@ def test_sdk_progress_and_cancellation_reach_upstream_without_losing_session( watchers = tuple(task for task in asyncio.all_tasks() if "_shutdown_watcher" in repr(task.get_coro())) await asyncio.wait_for(asyncio.gather(*watchers), 2) - hosted: Final = Starlette(routes=[Mount("/", app=app)], lifespan=lifespan) + hosted: Final = Starlette( + routes=[Mount("/", app=_acknowledge_notifications_after_dispatch(app))], lifespan=lifespan + ) with asgi_server(hosted) as upstream_url, httpx.Client() as client: config: Final = tmp_path / "proxy.yaml" config.write_text( diff --git a/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_overloaded_wire.py b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_overloaded_wire.py new file mode 100644 index 00000000000..bae775164dd --- /dev/null +++ b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_overloaded_wire.py @@ -0,0 +1,724 @@ +import json +import os +import threading +import time +import uuid +from collections.abc import Callable, Generator, Mapping +from contextlib import contextmanager +from typing import Final, Literal +from urllib.parse import unquote + +import anthropic +import httpx +import openai +import pytest +from integration._support.anthropic_sse import ( + LIFECYCLE, + error_frame, + event_type, + event_types, + message_id, + message_json, + message_stream, + parse_sse, + status_reply, + stream_reply, + user_prompt, +) +from integration._support.client import ( + JSON_OBJECT, + Gateway, + JsonValue, + Scenario, + eventually, + object_value, + string_value, +) +from integration._support.database import read_rows +from integration._support.openai_wire import openai_error +from integration._support.upstream import aws_event_stream_frame +from integration._support.wire import Reply, Request, Wire, wire_server +from redis import Redis + +_MODEL: Final = "claude-sonnet-4-5-20250929" +_API_KEY: Final = "synthetic-anthropic-key" +_BEDROCK_MODEL: Final = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" +_BEDROCK_TOKEN: Final = "synthetic-bedrock-bearer" +_BEDROCK_503: Final = b'{"message": "Bedrock is unable to process your request."}' +_CONVERSE_MODEL: Final = f"bedrock/converse/{_BEDROCK_MODEL}" +_CONVERSE_STREAM_TARGET: Final = f"/model/{_BEDROCK_MODEL}/converse-stream" +_OPENAI_BACKEND: Final = "gpt-4o-mini" +_CHAT_ONLY_BACKEND: Final = "audit-chat-only" +_CODEX_BACKEND: Final = "gpt-5.3-codex" +_TEXT: Final = "Hello" +_KEEPALIVE_SECONDS: Final = 0.2 +_LATE_ANSWER_SECONDS: Final = 1.0 +_OVERLOAD_CLASS: Final = "503_PROVIDER_OVERLOADED" + +Shape = Literal["stream", "non-stream"] + + +def _prompt() -> str: + return "overloaded-" + uuid.uuid4().hex + + +def _always(reply: Reply) -> Callable[[Request], Reply]: + def respond(request: Request) -> Reply: + assert request.method == "POST", request + return reply + + return respond + + +def _late(reply: Reply, seconds: float) -> Callable[[Request], Reply]: + def respond(request: Request) -> Reply: + assert request.method == "POST", request + time.sleep(seconds) + return reply + + return respond + + +def _anthropic_sdk(gateway: Gateway) -> anthropic.Anthropic: + return anthropic.Anthropic(base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0, timeout=60) + + +def _async_anthropic_sdk(gateway: Gateway) -> anthropic.AsyncAnthropic: + return anthropic.AsyncAnthropic( + base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0, timeout=60 + ) + + +def _openai_sdk(gateway: Gateway) -> openai.OpenAI: + return openai.OpenAI(base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0, timeout=60) + + +def _async_openai_sdk(gateway: Gateway) -> openai.AsyncOpenAI: + return openai.AsyncOpenAI(base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0, timeout=60) + + +def _messages_body(model: str, prompt: str, *, stream: bool) -> dict[str, JsonValue]: + return { + "model": model, + "max_tokens": 16, + "stream": stream, + "messages": [{"role": "user", "content": prompt}], + "num_retries": 0, + } + + +def _anthropic_error(response: httpx.Response) -> dict[str, JsonValue]: + assert response.headers["content-type"].startswith("application/json"), dict(response.headers) + payload: Final = object_value(json.loads(response.content)) + assert payload["type"] == "error", response.text + return object_value(payload["error"]) + + +def _openai_error(response: httpx.Response) -> dict[str, JsonValue]: + return object_value(object_value(json.loads(response.content))["error"]) + + +def _assert_overloaded(response: httpx.Response, *, message: str) -> None: + assert response.status_code == 529, response.text + error: Final = _anthropic_error(response) + assert error["type"] == "overloaded_error", response.text + assert message in string_value(error["message"]), response.text + assert "content_block_delta" not in response.text, response.text + + +def _assert_api_error(response: httpx.Response, *, status: int, message: str) -> None: + assert response.status_code == status, response.text + error: Final = _anthropic_error(response) + assert error["type"] == "api_error", response.text + assert message in string_value(error["message"]), response.text + + +def _assert_internal_server_error(response: httpx.Response) -> None: + assert response.status_code == 500, response.text + error: Final = _openai_error(response) + assert error["code"] == "500", response.text + assert "InternalServerError" in string_value(error["message"]), response.text + + +def _one_post(wire: Wire, target: str) -> None: + assert [(request.method, unquote(request.target)) for request in wire.drain()] == [("POST", target)] + + +def _spend_rows(model: str, expected: int) -> list[dict[str, JsonValue]]: + return eventually( + lambda: read_rows( + "SELECT status, metadata->'error_information'->>'error_code' AS code, " + "metadata->'error_information'->>'error_class' AS error_class, " + "metadata->'error_information'->>'normalized_error' AS normalized_error " + 'FROM "LiteLLM_SpendLogs" WHERE model_group=%s', + (model,), + ), + lambda rows: len(rows) >= expected, + seconds=70, + ) + + +def _assert_logged_failure(model: str, *, rows: int, error_class: str, code: str = "529") -> None: + assert ( + _spend_rows(model, rows) + == [{"status": "failure", "code": code, "error_class": error_class, "normalized_error": _OVERLOAD_CLASS}] * rows + ) + + +def test_messages_529_answers_overloaded_error_to_the_sync_sdk_and_logs_each_call_once(gateway: Gateway) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + sdk: Final = _anthropic_sdk(gateway) + prompt: Final = _prompt() + for _ in range(2): + with pytest.raises(anthropic.APIStatusError) as caught: + sdk.messages.create( + model=model, + max_tokens=16, + messages=[{"role": "user", "content": prompt}], + extra_body={"num_retries": 0}, + ) + assert caught.value.status_code == 529, caught.value.response.text + _assert_overloaded(caught.value.response, message="scripted 529") + assert [unquote(request.target) for request in wire.drain()] == ["/v1/messages"] * 2 + _assert_logged_failure(model, rows=2, error_class="InternalServerError") + + +async def test_messages_529_answers_overloaded_error_to_the_async_sdk(gateway: Gateway) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + with pytest.raises(anthropic.APIStatusError) as caught: + await _async_anthropic_sdk(gateway).messages.create( + model=model, + max_tokens=16, + messages=[{"role": "user", "content": _prompt()}], + extra_body={"num_retries": 0}, + ) + assert caught.value.status_code == 529, caught.value.response.text + _assert_overloaded(caught.value.response, message="scripted 529") + _one_post(wire, "/v1/messages") + + +@pytest.mark.parametrize( + ("upstream_status", "error_class", "error_type"), + ((529, "InternalServerError", "overloaded_error"), (503, "ServiceUnavailableError", "api_error")), +) +def test_messages_stream_rejected_before_its_first_byte_answers_the_provider_status_as_json( + gateway: Gateway, upstream_status: int, error_class: str, error_type: str +) -> None: + with wire_server(_always(status_reply(upstream_status))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + assert response.status_code == upstream_status, response.text + error: Final = _anthropic_error(response) + assert error["type"] == error_type, response.text + assert f"scripted {upstream_status}" in string_value(error["message"]), response.text + _one_post(wire, "/v1/messages") + _assert_logged_failure(model, rows=1, error_class=error_class, code=str(upstream_status)) + + +def test_messages_stream_529_raises_529_in_the_sync_sdk(gateway: Gateway) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + with pytest.raises(anthropic.APIStatusError) as caught: + with _anthropic_sdk(gateway).messages.stream( + model=model, + max_tokens=16, + messages=[{"role": "user", "content": _prompt()}], + extra_body={"num_retries": 0}, + ) as stream: + stream.get_final_message() + assert caught.value.status_code == 529, caught.value.response.text + _assert_overloaded(caught.value.response, message="scripted 529") + _one_post(wire, "/v1/messages") + + +def _error_frames(text: str) -> tuple[dict[str, JsonValue], ...]: + return tuple( + object_value(json.loads(line.removeprefix("data:").strip())) + for line in text.splitlines() + if line.startswith("data:") and '"error"' in line + ) + + +def test_messages_529_after_the_keepalive_opened_the_response_ends_with_an_overloaded_error_event( + gateway: Gateway, +) -> None: + with ( + wire_server(_late(status_reply(529), _LATE_ANSWER_SECONDS)) as wire, + gateway.scenario() as scenario, + ): + model: Final = scenario.model( + model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY, keepalive_seconds=_KEEPALIVE_SECONDS + ) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + assert response.status_code == 200, response.text + assert response.headers["content-type"].startswith("text/event-stream"), dict(response.headers) + assert response.text.startswith(":"), response.text + (failure,) = (event for event in parse_sse(response.text) if event.data and event.event != "ping") + assert failure.event == "error", response.text + assert object_value(failure.data["error"])["type"] == "overloaded_error", response.text + assert "[DONE]" not in response.text, response.text + _one_post(wire, "/v1/messages") + + +def _late_bedrock_deployment(scenario: Scenario, wire: Wire) -> str: + return scenario.model( + model=f"bedrock/{_BEDROCK_MODEL}", + api_key=_BEDROCK_TOKEN, + aws_region_name="us-east-1", + api_base=wire.url, + keepalive_seconds=_KEEPALIVE_SECONDS, + ) + + +def _one_bedrock_post(wire: Wire) -> None: + (request,) = wire.drain() + assert unquote(request.target).startswith(f"/model/{_BEDROCK_MODEL}/"), request + + +def test_messages_bedrock_503_after_the_keepalive_opened_the_response_ends_with_an_api_error_event( + gateway: Gateway, +) -> None: + with ( + wire_server(_late(Reply(status=503, body=_BEDROCK_503), _LATE_ANSWER_SECONDS)) as wire, + gateway.scenario() as scenario, + ): + model: Final = _late_bedrock_deployment(scenario, wire) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + assert response.status_code == 200, response.text + assert response.text.startswith(":"), response.text + assert "[DONE]" not in response.text, response.text + (failure,) = (event for event in parse_sse(response.text) if event.data and event.event != "ping") + assert failure.event == "error", response.text + error: Final = object_value(failure.data["error"]) + assert error["type"] == "api_error", response.text + assert "Bedrock is unable to process your request." in string_value(error["message"]), response.text + _one_bedrock_post(wire) + _assert_logged_failure(model, rows=1, error_class="ServiceUnavailableError", code="503") + + +def test_chat_completions_bedrock_503_after_the_keepalive_opened_the_response_keeps_the_openai_error_frame( + gateway: Gateway, +) -> None: + with ( + wire_server(_late(Reply(status=503, body=_BEDROCK_503), _LATE_ANSWER_SECONDS)) as wire, + gateway.scenario() as scenario, + ): + model: Final = _late_bedrock_deployment(scenario, wire) + response: Final = gateway.request( + "POST", + "/v1/chat/completions", + {"model": model, "messages": [{"role": "user", "content": _prompt()}], "stream": True, "num_retries": 0}, + ) + assert response.status_code == 200, response.text + assert response.text.startswith(":"), response.text + assert "event: error" not in response.text, response.text + (frame,) = _error_frames(response.text) + assert object_value(frame["error"])["code"] == "503", response.text + assert response.text.rstrip().endswith("data: [DONE]"), response.text + _one_bedrock_post(wire) + _assert_logged_failure(model, rows=1, error_class="ServiceUnavailableError", code="503") + + +def test_chat_completions_overloaded_frame_after_content_ends_with_a_500_error_frame(gateway: Gateway) -> None: + prompt: Final = _prompt() + upstream: Final = (*message_stream(prompt, _MODEL, _TEXT)[:3], error_frame(529, "Overloaded")) + with wire_server(_always(stream_reply(upstream))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request( + "POST", + "/v1/chat/completions", + {"model": model, "messages": [{"role": "user", "content": prompt}], "stream": True, "num_retries": 0}, + ) + assert response.status_code == 200, response.text + assert _TEXT in response.text, response.text + (frame,) = _error_frames(response.text) + error: Final = object_value(frame["error"]) + assert error["code"] == "500", response.text + _one_post(wire, "/v1/messages") + + +def _overloaded_before_content(prompt: str) -> Reply: + return stream_reply((*message_stream(prompt, _MODEL, _TEXT)[:1], error_frame(529, "Overloaded"))) + + +def test_messages_overloaded_frame_before_content_without_a_retry_budget_is_relayed_as_an_overloaded_event( + gateway: Gateway, +) -> None: + prompt: Final = _prompt() + with wire_server(_always(_overloaded_before_content(prompt))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, prompt, stream=True)) + assert response.status_code == 200, response.text + events: Final = parse_sse(response.text) + assert event_types(events) == ("message_start", "error"), response.text + assert message_id(events) == prompt, response.text + assert events[1].data == {"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}} + _one_post(wire, "/v1/messages") + assert _spend_rows(model, 1) == [ + {"status": "success", "code": None, "error_class": None, "normalized_error": None} + ] + + +def test_chat_completions_overloaded_frame_before_content_answers_500(gateway: Gateway) -> None: + prompt: Final = _prompt() + with wire_server(_always(_overloaded_before_content(prompt))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request( + "POST", + "/v1/chat/completions", + {"model": model, "messages": [{"role": "user", "content": prompt}], "stream": True, "num_retries": 0}, + ) + _assert_internal_server_error(response) + _one_post(wire, "/v1/messages") + + +def test_chat_completions_529_answers_500_to_the_sync_sdk(gateway: Gateway) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + with pytest.raises(openai.APIStatusError) as caught: + _openai_sdk(gateway).chat.completions.create( + model=model, messages=[{"role": "user", "content": _prompt()}], extra_body={"num_retries": 0} + ) + assert caught.value.status_code == 500, caught.value.response.text + _assert_internal_server_error(caught.value.response) + _one_post(wire, "/v1/messages") + _assert_logged_failure(model, rows=1, error_class="InternalServerError", code="500") + + +async def test_chat_completions_stream_529_answers_500_to_the_async_sdk(gateway: Gateway) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + with pytest.raises(openai.APIStatusError) as caught: + await _async_openai_sdk(gateway).chat.completions.create( + model=model, + messages=[{"role": "user", "content": _prompt()}], + stream=True, + extra_body={"num_retries": 0}, + ) + assert caught.value.status_code == 500, caught.value.response.text + _assert_internal_server_error(caught.value.response) + _one_post(wire, "/v1/messages") + + +@pytest.mark.parametrize("shape", ("non-stream", "stream")) +def test_responses_529_answers_500(gateway: Gateway, shape: Shape) -> None: + with wire_server(_always(status_reply(529))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request( + "POST", "/v1/responses", {"model": model, "input": _prompt(), "stream": shape == "stream", "num_retries": 0} + ) + _assert_internal_server_error(response) + _one_post(wire, "/v1/messages") + + +@pytest.mark.parametrize("shape", ("non-stream", "stream")) +def test_messages_bedrock_503_answers_503_api_error(gateway: Gateway, shape: Shape) -> None: + with wire_server(_always(Reply(status=503, body=_BEDROCK_503))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model( + model=f"bedrock/{_BEDROCK_MODEL}", api_key=_BEDROCK_TOKEN, aws_region_name="us-east-1", api_base=wire.url + ) + response: Final = gateway.request( + "POST", "/v1/messages", _messages_body(model, _prompt(), stream=shape == "stream") + ) + _assert_api_error(response, status=503, message="Bedrock is unable to process your request.") + (request,) = wire.drain() + assert unquote(request.target).startswith(f"/model/{_BEDROCK_MODEL}/"), request + _assert_logged_failure(model, rows=1, error_class="ServiceUnavailableError", code="503") + + +def _converse_event(event: str, payload: Mapping[str, JsonValue]) -> bytes: + return aws_event_stream_frame( + {":event-type": event, ":content-type": "application/json", ":message-type": "event"}, + json.dumps(payload).encode(), + ) + + +def _converse_503_after_text() -> Reply: + unavailable: Final = aws_event_stream_frame( + { + ":exception-type": "serviceUnavailableException", + ":content-type": "application/json", + ":message-type": "exception", + }, + _BEDROCK_503, + ) + frames: Final = ( + _converse_event("messageStart", {"role": "assistant"}), + _converse_event("contentBlockDelta", {"delta": {"text": _TEXT}, "contentBlockIndex": 0}), + unavailable, + ) + return Reply(body=b"".join(frames), content_type="application/vnd.amazon.eventstream") + + +def _converse_deployment(scenario: Scenario, wire: Wire) -> str: + return scenario.model(model=_CONVERSE_MODEL, api_key=_BEDROCK_TOKEN, aws_region_name="us-east-1", api_base=wire.url) + + +def test_messages_bedrock_stream_503_after_text_ends_with_an_api_error_event(gateway: Gateway) -> None: + with wire_server(_always(_converse_503_after_text())) as wire, gateway.scenario() as scenario: + model: Final = _converse_deployment(scenario, wire) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + assert response.status_code == 200, response.text + events: Final = parse_sse(response.text) + types: Final = event_types(events) + assert "message_stop" not in types, response.text + assert types.index("content_block_delta") < types.index("error"), response.text + assert _TEXT in response.text, response.text + (failure,) = (event for event in events if event_type(event) == "error") + error: Final = object_value(failure.data["error"]) + assert error["type"] == "api_error", response.text + assert "Bedrock is unable to process your request." in string_value(error["message"]), response.text + _one_post(wire, _CONVERSE_STREAM_TARGET) + _assert_logged_failure(model, rows=1, error_class="ServiceUnavailableError", code="503") + + +def test_chat_completions_bedrock_stream_503_after_text_ends_with_a_503_error_frame(gateway: Gateway) -> None: + with wire_server(_always(_converse_503_after_text())) as wire, gateway.scenario() as scenario: + model: Final = _converse_deployment(scenario, wire) + response: Final = gateway.request( + "POST", + "/v1/chat/completions", + {"model": model, "messages": [{"role": "user", "content": _prompt()}], "stream": True, "num_retries": 0}, + ) + assert response.status_code == 200, response.text + assert _TEXT in response.text, response.text + (frame,) = _error_frames(response.text) + error: Final = object_value(frame["error"]) + assert error["code"] == "503", response.text + _one_post(wire, _CONVERSE_STREAM_TARGET) + _assert_logged_failure(model, rows=1, error_class="ServiceUnavailableError", code="503") + + +@pytest.mark.parametrize("shape", ("non-stream", "stream")) +def test_chat_bridge_503_answers_503_api_error_on_messages(gateway: Gateway, shape: Shape) -> None: + with wire_server(_always(openai_error(503))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model( + model=f"hosted_vllm/{_CHAT_ONLY_BACKEND}", api_base=wire.url + "/v1", api_key="synthetic-vllm-key" + ) + response: Final = gateway.request( + "POST", "/v1/messages", _messages_body(model, _prompt(), stream=shape == "stream") + ) + _assert_api_error(response, status=503, message="scripted 503") + _one_post(wire, "/v1/chat/completions") + + +@pytest.mark.parametrize("shape", ("non-stream", "stream")) +def test_responses_bridge_503_answers_503_api_error_on_messages(gateway: Gateway, shape: Shape) -> None: + with wire_server(_always(openai_error(503))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model( + model=f"openai/{_CODEX_BACKEND}", api_base=wire.url + "/v1", api_key="synthetic-openai-key" + ) + response: Final = gateway.request( + "POST", "/v1/messages", _messages_body(model, _prompt(), stream=shape == "stream") + ) + _assert_api_error(response, status=503, message="scripted 503") + _one_post(wire, "/v1/responses") + + +_SETTLED_ANSWERS: Final = 4 + + +def _messages_to(gateway: Gateway, model: str) -> tuple[tuple[str, httpx.Response], ...]: + prompts: Final = tuple(_prompt() for _ in range(_SETTLED_ANSWERS)) + return tuple( + (prompt, gateway.request("POST", "/v1/messages", _messages_body(model, prompt, stream=False))) + for prompt in prompts + ) + + +def _failure_rows_of(models: tuple[str, str], expected: int) -> list[dict[str, JsonValue]]: + return eventually( + lambda: read_rows( + "SELECT metadata->'error_information'->>'error_code' AS code, " + "metadata->'error_information'->>'error_class' AS error_class, " + "metadata->'error_information'->>'normalized_error' AS normalized_error " + 'FROM "LiteLLM_SpendLogs" WHERE status = %s AND model_group IN (%s, %s)', + ("failure", *models), + ), + lambda rows: len(rows) >= expected, + seconds=70, + ) + + +@contextmanager +def _paused_deployment(gateway: Gateway) -> Generator[tuple[Wire, str, str]]: + unpaused: Final = Reply(body=message_json("msg_unpaused", _MODEL, _TEXT)) + with wire_server(_always(unpaused)) as wire, gateway.scenario() as scenario: + identity: Final = f"audit-paused-{uuid.uuid4().hex}" + name: Final = scenario.model( + model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY, model_info={"id": identity} + ) + paused: Final = gateway.request("PATCH", f"/model/{identity}/update", {"blocked": True}) + assert paused.status_code == 200, paused.text + yield wire, identity, name + + +def _assert_refused_without_an_upstream_call( + gateway: Gateway, wire: Wire, target: str, *, status: int, error_type: str, message: str +) -> None: + answers: Final = eventually( + lambda: _messages_to(gateway, target), + lambda settled: all(response.status_code != 200 for _, response in settled), + seconds=90, + ) + texts: Final = [response.text for _, response in answers] + assert [response.status_code for _, response in answers] == [status] * _SETTLED_ANSWERS, texts + errors: Final = [_anthropic_error(response) for _, response in answers] + assert [error["type"] for error in errors] == [error_type] * _SETTLED_ANSWERS, texts + assert all(message in string_value(error["message"]) for error in errors), texts + reached: Final = {user_prompt(JSON_OBJECT.validate_json(request.body)) for request in wire.drain()} + assert reached.isdisjoint(prompt for prompt, _ in answers), reached + + +def test_messages_to_a_paused_deployment_named_by_its_id_answer_503_api_error(gateway: Gateway) -> None: + with _paused_deployment(gateway) as (wire, identity, name): + _assert_refused_without_an_upstream_call( + gateway, wire, identity, status=503, error_type="api_error", message="is currently paused" + ) + rows: Final = _failure_rows_of((identity, name), _SETTLED_ANSWERS) + assert {(row["code"], row["error_class"], row["normalized_error"]) for row in rows} == { + ("503", "ServiceUnavailableError", _OVERLOAD_CLASS) + }, rows + + +def test_messages_to_a_paused_model_group_named_by_its_name_answer_403_permission_error(gateway: Gateway) -> None: + with _paused_deployment(gateway) as (wire, _, name): + _assert_refused_without_an_upstream_call( + gateway, wire, name, status=403, error_type="permission_error", message="Model is blocked" + ) + + +_MALFORMED_529_BODIES: Final = { + "empty": b"", + "text-5k": b"x" * 5000, + "html": b"

529

", + "api_error-type": b'{"type": "error", "error": {"type": "api_error", "message": "capacity"}}', +} + + +def _overloaded_for_sad_prompts(body: bytes) -> Callable[[Request], Reply]: + def respond(request: Request) -> Reply: + prompt: Final = user_prompt(object_value(json.loads(request.body))) + if prompt.startswith("sad:"): + return Reply(status=529, body=body, content_type="text/plain" if body[:1] != b"{" else "application/json") + return Reply(body=message_json(f"msg_{prompt}", _MODEL, _TEXT)) + + return respond + + +@pytest.mark.parametrize("body", _MALFORMED_529_BODIES.keys()) +def test_messages_529_with_a_malformed_body_still_answers_overloaded_error_and_unrelated_calls_keep_working( + gateway: Gateway, body: str +) -> None: + with wire_server(_overloaded_for_sad_prompts(_MALFORMED_529_BODIES[body])) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + sad: Final = gateway.request("POST", "/v1/messages", _messages_body(model, "sad:" + _prompt(), stream=False)) + assert sad.status_code == 529, sad.text + assert _anthropic_error(sad)["type"] == "overloaded_error", sad.text + prompt: Final = _prompt() + healthy: Final = gateway.request("POST", "/v1/messages", _messages_body(model, prompt, stream=False)) + assert healthy.status_code == 200, healthy.text + assert object_value(json.loads(healthy.content))["id"] == f"msg_{prompt}", healthy.text + assert [unquote(request.target) for request in wire.drain()] == ["/v1/messages"] * 2 + assert sorted(_spend_rows(model, 2), key=lambda row: str(row["status"])) == [ + { + "status": "failure", + "code": "529", + "error_class": "InternalServerError", + "normalized_error": _OVERLOAD_CLASS, + }, + {"status": "success", "code": None, "error_class": None, "normalized_error": None}, + ] + + +@pytest.mark.parametrize(("upstream_status", "error_type"), ((500, "api_error"), (429, "rate_limit_error"))) +def test_messages_other_upstream_statuses_keep_their_status_and_type( + gateway: Gateway, upstream_status: int, error_type: str +) -> None: + with wire_server(_always(status_reply(upstream_status))) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + assert response.status_code == upstream_status, response.text + assert _anthropic_error(response)["type"] == error_type, response.text + _one_post(wire, "/v1/messages") + + +def _anthropic_rejection(status: int, error_type: str, message: str) -> Reply: + return Reply( + status=status, body=json.dumps({"type": "error", "error": {"type": error_type, "message": message}}).encode() + ) + + +_REJECTIONS_SAYING_OVERLOADED: Final = { + "400-tool-name": _anthropic_rejection( + 400, "invalid_request_error", "tools.0.name: 'Overloaded' is not a valid tool name" + ), + "404-model": _anthropic_rejection(404, "not_found_error", "model: claude-Overloaded"), +} + + +@pytest.mark.parametrize("shape", ("non-stream", "stream")) +@pytest.mark.parametrize("rejection", _REJECTIONS_SAYING_OVERLOADED.keys()) +def test_messages_4xx_whose_text_says_overloaded_is_not_answered_as_an_overload( + gateway: Gateway, rejection: str, shape: Shape +) -> None: + with wire_server(_always(_REJECTIONS_SAYING_OVERLOADED[rejection])) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + response: Final = gateway.request( + "POST", "/v1/messages", _messages_body(model, _prompt(), stream=shape == "stream") + ) + assert response.status_code == 500, response.text + error: Final = _anthropic_error(response) + assert error["type"] == "api_error", response.text + assert "Overloaded" in string_value(error["message"]), response.text + _one_post(wire, "/v1/messages") + assert _spend_rows(model, 1) == [ + { + "status": "failure", + "code": "500", + "error_class": "InternalServerError", + "normalized_error": _OVERLOAD_CLASS, + } + ] + + +def _string_values(cache: Redis) -> tuple[bytes, ...]: + keys: Final = tuple(key for key in cache.scan_iter(count=1000) if cache.type(key) == b"string") + return tuple(value for value in cache.mget(keys) if value is not None) if keys else () + + +def _cached_somewhere(served_id: str) -> bool: + with Redis(host=os.environ["REDIS_HOST"], port=int(os.environ["REDIS_PORT"])) as cache: + return any(served_id.encode() in value for value in _string_values(cache)) + + +def _healthy_until(overloaded: threading.Event) -> Callable[[Request], Reply]: + def respond(request: Request) -> Reply: + if overloaded.is_set(): + return status_reply(529) + prompt: Final = user_prompt(object_value(json.loads(request.body))) + return stream_reply(message_stream(f"msg_{prompt}", _MODEL, _TEXT)) + + return respond + + +def test_messages_cache_hit_is_served_while_the_upstream_is_overloaded(gateway: Gateway) -> None: + overloaded: Final = threading.Event() + with wire_server(_healthy_until(overloaded)) as wire, gateway.scenario() as scenario: + model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY) + prompt: Final = _prompt() + body: Final = _messages_body(model, prompt, stream=True) + first: Final = gateway.request("POST", "/v1/messages", body) + assert first.status_code == 200, first.text + assert event_types(parse_sse(first.text)) == LIFECYCLE, first.text + assert message_id(parse_sse(first.text)) == f"msg_{prompt}", first.text + eventually(lambda: _cached_somewhere(f"msg_{prompt}"), bool, seconds=30) + overloaded.set() + replay: Final = gateway.request("POST", "/v1/messages", body) + assert replay.status_code == 200, replay.text + assert message_id(parse_sse(replay.text)) == f"msg_{prompt}", replay.text + fresh: Final = gateway.request("POST", "/v1/messages", _messages_body(model, _prompt(), stream=True)) + _assert_overloaded(fresh, message="scripted 529") + assert [unquote(request.target) for request in wire.drain()] == ["/v1/messages"] * 2 diff --git a/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_chaos.py b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_chaos.py index 9dd1a2d2c18..99239efe5ee 100644 --- a/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_chaos.py +++ b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_chaos.py @@ -135,6 +135,13 @@ def _drop_or_500(request: Request, marker: str) -> Reply: return openai_error(500) +def _overloaded_while(outage: threading.Event) -> Callable[[Request, str], Reply]: + def first_attempt(request: Request, marker: str) -> Reply: + return status_reply(529) if outage.is_set() else _served(request, marker, 1) + + return first_attempt + + def _outage(statuses: Mapping[str, int]) -> Callable[[Request, str], Reply]: def first_attempt(request: Request, marker: str) -> Reply: if request.target == "/v1/messages": @@ -165,7 +172,7 @@ class _Upstream: return self.first_attempt(request, marker) -def _config(wire: Wire, directory: Path, models: _Models) -> Path: +def _config(wire: Wire, directory: Path, models: _Models, *, retries: int = 1) -> Path: config: Final = yaml.safe_load(Path("tests/integration/proxy_config.yaml").read_text()) config["model_list"] = [ { @@ -174,7 +181,7 @@ def _config(wire: Wire, directory: Path, models: _Models) -> Path: "model": f"anthropic/{_ANTHROPIC_BACKEND}", "api_base": wire.url, "api_key": _ANTHROPIC_KEY, - "num_retries": 1, + "num_retries": retries, }, }, { @@ -183,7 +190,7 @@ def _config(wire: Wire, directory: Path, models: _Models) -> Path: "model": f"openai/{_OPENAI_BACKEND}", "api_base": wire.url + "/v1", "api_key": _OPENAI_KEY, - "num_retries": 1, + "num_retries": retries, }, }, ] @@ -408,3 +415,68 @@ async def test_outage_on_every_first_attempt_is_absorbed_by_the_deployment_budge _assert_completed_on_the_second_attempt(item) _assert_each_served_id_landed_exactly_once(models, served) assert all(upstream.attempts.count(call.marker) == 2 for call in calls) + + +def _messages_rows(model: str, expected: int) -> list[dict[str, JsonValue]]: + return eventually( + lambda: read_rows( + "SELECT request_id, status, metadata->'error_information'->>'error_code' AS code " + 'FROM "LiteLLM_SpendLogs" WHERE model_group=%s', + (model,), + ), + lambda found: len(found) >= expected, + seconds=90, + ) + + +@pytest.mark.timeout(300) +async def test_overloaded_outage_during_a_burst_with_a_worker_sigkill_answers_529_and_lands_every_call_once( + gateway: Gateway, tmp_path: Path +) -> None: + models: Final = _models() + calls: Final = _calls((("messages", True, 12), ("messages", False, 6))) + release: Final = threading.Event() + held: Final[SimpleQueue[str]] = SimpleQueue() + outage: Final = threading.Event() + outage.set() + upstream: Final = _Upstream(Attempts(), _overloaded_while(outage), held, release) + with wire_server(upstream) as wire: + config: Final = _config(wire, tmp_path, models, retries=0) + with owned_proxy_process(gateway, tmp_path, {}, config=config, workers=2) as owned: + candidate: Final = owned.gateway + base_url: Final = str(candidate.client.base_url) + workers: Final = eventually( + lambda: tuple(int(pid) for pid in _STARTED_WORKER.findall(owned.log.read_text())), + lambda pids: len(pids) == 2, + seconds=30, + ) + burst: Final = asyncio.create_task( + _burst(base_url, candidate.key, models, calls, tolerate_transport_errors=True) + ) + await asyncio.to_thread(eventually, held.qsize, lambda size: size == len(calls), 90) + held_by: Final = MappingProxyType({pid: _open_upstream_connections(pid, wire.url) for pid in workers}) + assert sum(held_by.values()) == len(calls), held_by + victim_pid, survivor_pid = sorted(workers, key=held_by.__getitem__) + with owned.replacing_worker(victim_pid) as kill: + kill.freeze() + await asyncio.to_thread(kill) + release.set() + served: Final = await burst + assert held_by[survivor_pid] >= 5, held_by + assert len(served) == held_by[survivor_pid], (held_by, len(served)) + for item in served: + assert item.status == 529, (item.call, item.text) + assert "overloaded_error" in item.text, (item.call, item.text) + assert "content_block_delta" not in item.text, (item.call, item.text) + outage.clear() + follow_up: Final = _Call(endpoint="messages", stream=True, marker=uuid.uuid4().hex) + (answered,) = await _burst(base_url, candidate.key, models, (follow_up,)) + assert answered.status == 200, answered.text + assert message_id(parse_sse(answered.text)) == f"msg_{follow_up.marker}_a1", answered.text + rows: Final = _messages_rows(models.messages, len(served) + 1) + assert sorted(string_value(row["status"]) for row in rows) == ["failure"] * len(served) + ["success"], rows + assert {row["code"] for row in rows if row["status"] == "failure"} == {"529"}, rows + assert [row["request_id"] for row in rows if row["status"] == "success"] == [ + f"msg_{follow_up.marker}_a1" + ], rows + assert all(upstream.attempts.count(item.call.marker) == 1 for item in (*served, answered)) diff --git a/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_wire.py b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_wire.py index 041a0a72fdd..83a9ba3d14c 100644 --- a/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_wire.py +++ b/tests/integration/messages_endpoint/providers/anthropic/test_anthropic_messages_pre_content_retry_wire.py @@ -397,14 +397,14 @@ def test_retry_rejected_before_it_opens_counts_against_the_same_budget(gateway: with wire_server(respond) as wire, gateway.scenario() as scenario: model: Final = scenario.model(model=f"anthropic/{_MODEL}", api_base=wire.url, api_key=_API_KEY, num_retries=1) response: Final = gateway.request("POST", "/v1/messages", _body(model, prompt, "deployment")) - _assert_rejected_before_content(response, wire, status=500, attempts=2, error="error") + _assert_rejected_before_content(response, wire, status=529, attempts=2, error="overloaded_error") rows: Final = eventually( lambda: _request_rows(gateway, response.headers["x-litellm-call-id"]), lambda values: len(values) >= 1, seconds=70, ) rejected: Final = gateway.request("POST", "/v1/messages", _body(model, prompt, "request", budget=0)) - _assert_rejected_before_content(rejected, wire, status=500, attempts=1, error="error") + _assert_rejected_before_content(rejected, wire, status=529, attempts=1, error="overloaded_error") rejection_rows: Final = eventually( lambda: _request_rows(gateway, rejected.headers["x-litellm-call-id"]), lambda values: len(values) >= 1, diff --git a/tests/integration/observability/test_anthropic_overloaded_failure_signals.py b/tests/integration/observability/test_anthropic_overloaded_failure_signals.py new file mode 100644 index 00000000000..044c58e44cc --- /dev/null +++ b/tests/integration/observability/test_anthropic_overloaded_failure_signals.py @@ -0,0 +1,404 @@ +import gzip +import json +import time +import uuid +from collections.abc import Callable, Iterator, Mapping, Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Final +from urllib.parse import unquote + +import httpx +import pytest +import yaml +from integration._support.anthropic_sse import error_body +from integration._support.client import ( + JSON_OBJECT, + Gateway, + JsonValue, + eventually, + gateway_from_environment, + object_value, + string_value, +) +from integration._support.otlp_sink import Span, SpanSinks, recorded_spans, spans_for_trace +from integration._support.process import graceful_stop_seconds, owned_proxy +from integration._support.prometheus_series import Sample, scrape +from integration._support.wire import Reply, Request, Wire, wire_server + +pytestmark: Final = pytest.mark.timeout(2 * graceful_stop_seconds() + 120) + +_MODEL: Final = "claude-sonnet-4-5-20250929" +_BEDROCK_MODEL: Final = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" +_BEDROCK_503: Final = b'{"message": "Bedrock is unable to process your request."}' +_MESSAGES_ANTHROPIC: Final = "audit-signals-messages-anthropic" +_MESSAGES_BEDROCK: Final = "audit-signals-messages-bedrock" +_CHAT_ANTHROPIC: Final = "audit-signals-chat-anthropic" +_TRACED_MESSAGES: Final = "audit-signals-traced-messages" +_TRACED_CHAT: Final = "audit-signals-traced-chat" +_DATADOG_MESSAGES_ANTHROPIC: Final = "audit-signals-datadog-messages-anthropic" +_DATADOG_MESSAGES_BEDROCK: Final = "audit-signals-datadog-messages-bedrock" +_DATADOG_CHAT_ANTHROPIC: Final = "audit-signals-datadog-chat-anthropic" +_GUARDED_MESSAGES: Final = "audit-signals-guarded-messages" +_GUARDED_CHAT: Final = "audit-signals-guarded-chat" +_AKTO_DOWN: Final = "audit-signals-akto-down" +_AKTO_UNREACHABLE: Final = "Akto guardrail service unreachable" +_LATE_PASS_THROUGH_BACKEND: Final = "claude-late-pass-through" +_KEEPALIVE_SECONDS: Final = 0.2 +_LATE_ANSWER_SECONDS: Final = 1.0 +_OVERLOADED: Final = Reply(status=529, body=error_body(529, "Overloaded")) +_SERVER: Final = 2 + +AuditConfigWriter = Callable[[Path, Mapping[str, JsonValue]], Path] + + +@dataclass(frozen=True, slots=True) +class _Rig: + proxy: Gateway + wire: Wire + spans: str + datadog: Wire + + +def _respond(request: Request) -> Reply: + assert request.method == "POST", request + if unquote(request.target).startswith("/model/"): + return Reply(status=503, body=_BEDROCK_503) + if json.loads(request.body).get("model") == _LATE_PASS_THROUGH_BACKEND: + time.sleep(_LATE_ANSWER_SECONDS) + return _OVERLOADED + + +def _datadog_intake(request: Request) -> Reply: + assert request.target == "/api/v2/logs", request.target + return Reply(status=202, body=b"{}") + + +def _config(directory: Path, tracing: Path, wire: Wire) -> Path: + configuration: Final = JSON_OBJECT.validate_python(yaml.safe_load(tracing.read_text())) + anthropic: Final = {"model": f"anthropic/{_MODEL}", "api_base": wire.url, "api_key": "synthetic-anthropic-key"} + bedrock: Final = { + "model": f"bedrock/{_BEDROCK_MODEL}", + "api_base": wire.url, + "api_key": "synthetic-bedrock-bearer", + "aws_region_name": "us-east-1", + } + deployments: Final = ( + (_MESSAGES_ANTHROPIC, anthropic), + (_MESSAGES_BEDROCK, bedrock), + (_CHAT_ANTHROPIC, anthropic), + (_TRACED_MESSAGES, anthropic), + (_TRACED_CHAT, anthropic), + (_DATADOG_MESSAGES_ANTHROPIC, anthropic), + (_DATADOG_MESSAGES_BEDROCK, bedrock), + (_DATADOG_CHAT_ANTHROPIC, anthropic), + (_GUARDED_MESSAGES, anthropic), + (_GUARDED_CHAT, anthropic), + ) + akto_down: Final = { + "guardrail_name": _AKTO_DOWN, + "litellm_params": { + "guardrail": "akto", + "mode": "pre_call", + "akto_base_url": "http://127.0.0.1:9", + "akto_api_key": "synthetic-akto-key", + "unreachable_fallback": "fail_closed", + }, + } + path: Final = directory / "anthropic-overloaded-failure-signals.yaml" + path.write_text( + yaml.safe_dump( + { + **configuration, + "model_list": [{"model_name": name, "litellm_params": params} for name, params in deployments], + "router_settings": {**object_value(configuration.get("router_settings", {})), "num_retries": 0}, + "litellm_settings": { + **object_value(configuration["litellm_settings"]), + "sse_keepalive_ping_interval_seconds": _KEEPALIVE_SECONDS, + }, + "guardrails": [akto_down], + } + ) + ) + return path + + +@pytest.fixture(scope="module") +def rig( + tmp_path_factory: pytest.TempPathFactory, audit_sinks: SpanSinks, otel_audit_config: AuditConfigWriter +) -> Iterator[_Rig]: + directory: Final = tmp_path_factory.mktemp("anthropic-overloaded-failure-signals") + multiprocess: Final = directory / "prometheus" + multiprocess.mkdir() + tracing: Final = otel_audit_config(directory, {"callbacks": ["otel", "prometheus", "datadog"]}) + with ( + wire_server(_respond) as wire, + wire_server(_datadog_intake) as datadog, + gateway_from_environment() as shared, + owned_proxy( + shared, + directory, + { + "OTEL_EXPORTER": "http/json", + "OTEL_ENDPOINT": audit_sinks.operator, + "PROMETHEUS_MULTIPROC_DIR": str(multiprocess), + "DD_API_KEY": "synthetic-datadog-key", + "DD_SITE": "datadog.invalid", + "DD_BASE_URL": datadog.url, + "DD_BATCH_SIZE": "1", + "ANTHROPIC_API_BASE": wire.url, + "ANTHROPIC_API_KEY": "synthetic-anthropic-key", + }, + config=_config(directory, tracing, wire), + workers=2, + ) as owned, + ): + yield _Rig(owned, wire, audit_sinks.operator, datadog) + + +@pytest.fixture(scope="module") +def v2_rig( + tmp_path_factory: pytest.TempPathFactory, audit_sinks: SpanSinks, otel_audit_config: AuditConfigWriter +) -> Iterator[_Rig]: + directory: Final = tmp_path_factory.mktemp("anthropic-overloaded-failure-signals-v2") + tracing: Final = otel_audit_config(directory, {"callbacks": ["otel"]}) + with ( + wire_server(_respond) as wire, + wire_server(_datadog_intake) as datadog, + gateway_from_environment() as shared, + owned_proxy( + shared, + directory, + {"OTEL_EXPORTER": "http/json", "OTEL_ENDPOINT": audit_sinks.operator, "LITELLM_OTEL_V2": "1"}, + config=_config(directory, tracing, wire), + workers=2, + ) as owned, + ): + yield _Rig(owned, wire, audit_sinks.operator, datadog) + + +def _send( + rig: _Rig, + route: str, + model: str, + trace_id: str | None = None, + *, + key: str | None = None, + extra: Mapping[str, JsonValue] | None = None, +) -> httpx.Response: + traceparent: Final = {} if trace_id is None else {"traceparent": f"00-{trace_id}-{uuid.uuid4().hex[:16]}-01"} + return rig.proxy.request( + "POST", + route, + { + "model": model, + "max_tokens": 16, + "messages": [{"role": "user", "content": f"signals-{uuid.uuid4().hex}"}], + "num_retries": 0, + **(extra or {}), + }, + key=key, + headers={"anthropic-version": "2023-06-01", **traceparent}, + ) + + +def _series(samples: Sequence[Sample], name: str, model: str) -> tuple[Sample, ...]: + return tuple(sample for sample in samples if sample.name == name and sample.labels.get("requested_model") == model) + + +def _until(rig: _Rig, name: str, model: str) -> tuple[Sample, ...]: + return eventually(lambda: _series(scrape(rig.proxy), name, model), bool, seconds=30) + + +@pytest.mark.parametrize( + ("route", "model", "answered", "deployment_status", "deployment_class"), + ( + pytest.param("/v1/messages", _MESSAGES_ANTHROPIC, 529, "500", "InternalServerError", id="messages-anthropic"), + pytest.param("/v1/messages", _MESSAGES_BEDROCK, 503, "503", "ServiceUnavailableError", id="messages-bedrock"), + pytest.param("/v1/chat/completions", _CHAT_ANTHROPIC, 500, "500", "InternalServerError", id="chat-anthropic"), + ), +) +def test_the_failed_request_metric_counts_the_answered_status_and_the_deployment_metric_the_deployment_status( + rig: _Rig, route: str, model: str, answered: int, deployment_status: str, deployment_class: str +) -> None: + rig.wire.drain() + response: Final = _send(rig, route, model) + assert response.status_code == answered, response.text + assert len(rig.wire.drain()) == 1 + failed: Final = _until(rig, "litellm_proxy_failed_requests_metric_total", model) + assert [(sample.labels["exception_status"], sample.labels["route"], sample.value) for sample in failed] == [ + (str(answered), route, 1.0) + ], failed + deployment: Final = _until(rig, "litellm_deployment_failure_responses_total", model) + assert [(sample.labels["exception_status"], sample.value) for sample in deployment] == [(deployment_status, 1.0)], ( + deployment + ) + assert all(deployment_class in sample.labels["exception_class"] for sample in deployment), deployment + + +def _server_spans(rig: _Rig, since: int, trace_id: str) -> tuple[Span, ...]: + traced: Final = spans_for_trace(recorded_spans(rig.spans, since)[1], trace_id) + return tuple(span for span in traced if span["kind"] == _SERVER) + + +@pytest.mark.parametrize( + ("route", "model", "answered"), + ( + pytest.param("/v1/messages", _TRACED_MESSAGES, 529, id="messages"), + pytest.param("/v1/chat/completions", _TRACED_CHAT, 500, id="chat"), + ), +) +def test_the_server_span_carries_the_status_the_route_answered( + rig: _Rig, route: str, model: str, answered: int +) -> None: + rig.wire.drain() + since, _ = recorded_spans(rig.spans) + trace_id: Final = uuid.uuid4().hex + response: Final = _send(rig, route, model, trace_id) + assert response.status_code == answered, response.text + assert len(rig.wire.drain()) == 1 + server: Final = eventually( + lambda: _server_spans(rig, since, trace_id), + lambda spans: any("error.code" in span["attributes"] for span in spans), + seconds=40, + return_last_on_timeout=True, + ) + assert [ + (str(span["attributes"].get("http.response.status_code")), str(span["attributes"].get("error.code"))) + for span in server + ] == [(str(answered), str(answered))], server + + +def _inflated(request: Request) -> bytes: + return gzip.decompress(request.body) if request.headers.get("content-encoding") == "gzip" else request.body + + +def _datadog_events(batches: Sequence[Request]) -> Iterator[JsonValue]: + for batch in batches: + yield from json.loads(_inflated(batch)) + + +def _datadog_messages(batches: Sequence[Request]) -> tuple[dict[str, JsonValue], ...]: + return tuple( + JSON_OBJECT.validate_json(string_value(object_value(event)["message"])) for event in _datadog_events(batches) + ) + + +def _keyed_by(message: Mapping[str, JsonValue], alias: str) -> bool: + proxy_side: Final = message.get("user_api_key_dict") + llm_side: Final = message.get("metadata") + return any(isinstance(side, dict) and side.get("user_api_key_alias") == alias for side in (proxy_side, llm_side)) + + +def _datadog_status_codes(rig: _Rig, alias: str, expected: int) -> tuple[tuple[str, str], ...]: + batches: Final[list[Request]] = [] # mutable-ok: drain() consumes the queue, later polls must keep earlier batches + + def delivered() -> tuple[tuple[str, str], ...]: + batches.extend(rig.datadog.drain()) + return tuple( + sorted( + ("proxy", str(message["status_code"])) + if "user_api_key_dict" in message + else ("llm", str(object_value(message["error_information"])["error_code"])) + for message in _datadog_messages(batches) + if _keyed_by(message, alias) + ) + ) + + return eventually(delivered, lambda codes: len(codes) >= expected, seconds=40, return_last_on_timeout=True) + + +@pytest.mark.parametrize( + ("route", "model", "answered", "deployment_status"), + ( + pytest.param("/v1/messages", _DATADOG_MESSAGES_ANTHROPIC, 529, "500", id="messages-anthropic"), + pytest.param("/v1/messages", _DATADOG_MESSAGES_BEDROCK, 503, "503", id="messages-bedrock"), + pytest.param("/v1/chat/completions", _DATADOG_CHAT_ANTHROPIC, 500, "500", id="chat-anthropic"), + ), +) +def test_the_datadog_proxy_failure_log_carries_the_answered_status_and_the_llm_failure_log_the_deployment_status( + rig: _Rig, route: str, model: str, answered: int, deployment_status: str +) -> None: + rig.wire.drain() + rig.datadog.drain() + alias: Final = f"audit-signals-datadog-{uuid.uuid4().hex}" + with rig.proxy.scenario() as scenario: + response: Final = _send(rig, route, model, key=scenario.key(key_alias=alias, models=[model])) + assert response.status_code == answered, response.text + assert len(rig.wire.drain()) == 1 + assert _datadog_status_codes(rig, alias, 2) == (("llm", deployment_status), ("proxy", str(answered))) + + +@pytest.mark.parametrize( + ("route", "model", "answered"), + ( + pytest.param("/v1/messages", _GUARDED_MESSAGES, 503, id="messages"), + pytest.param("/v1/chat/completions", _GUARDED_CHAT, 503, id="chat"), + ), +) +def test_a_fail_closed_guardrail_outage_is_answered_and_recorded_with_the_routes_status( + rig: _Rig, route: str, model: str, answered: int +) -> None: + rig.wire.drain() + rig.datadog.drain() + alias: Final = f"audit-signals-guarded-{uuid.uuid4().hex}" + with rig.proxy.scenario() as scenario: + response: Final = _send( + rig, route, model, key=scenario.key(key_alias=alias, models=[model]), extra={"guardrails": [_AKTO_DOWN]} + ) + assert response.status_code == answered, response.text + assert _AKTO_UNREACHABLE in response.text, response.text + assert rig.wire.drain() == () + failed: Final = _until(rig, "litellm_proxy_failed_requests_metric_total", model) + assert [(sample.labels["exception_status"], sample.labels["route"], sample.value) for sample in failed] == [ + (str(answered), route, 1.0) + ], failed + assert _datadog_status_codes(rig, alias, 2) == (("llm", str(answered)), ("proxy", str(answered))) + + +@pytest.mark.parametrize( + ("route", "model", "answered"), + ( + pytest.param("/v1/messages", _TRACED_MESSAGES, 529, id="messages-anthropic"), + pytest.param("/v1/messages", _MESSAGES_BEDROCK, 503, id="messages-bedrock"), + pytest.param("/v1/chat/completions", _TRACED_CHAT, 500, id="chat-anthropic"), + ), +) +def test_the_otel_v2_server_span_carries_the_status_the_route_answered( + v2_rig: _Rig, route: str, model: str, answered: int +) -> None: + v2_rig.wire.drain() + since, _ = recorded_spans(v2_rig.spans) + trace_id: Final = uuid.uuid4().hex + response: Final = _send(v2_rig, route, model, trace_id) + assert response.status_code == answered, response.text + assert len(v2_rig.wire.drain()) == 1 + server: Final = eventually( + lambda: _server_spans(v2_rig, since, trace_id), + lambda spans: any("litellm.provider.error.code" in span["attributes"] for span in spans), + seconds=40, + return_last_on_timeout=True, + ) + assert [str(span["attributes"].get("litellm.provider.error.code")) for span in server] == [str(answered)], server + + +def test_an_anthropic_pass_through_stream_that_fails_after_the_keepalive_opened_relays_the_upstream_body( + rig: _Rig, +) -> None: + rig.wire.drain() + response: Final = rig.proxy.request( + "POST", + "/anthropic/v1/messages", + { + "model": _LATE_PASS_THROUGH_BACKEND, + "max_tokens": 16, + "stream": True, + "messages": [{"role": "user", "content": f"signals-{uuid.uuid4().hex}"}], + }, + headers={"anthropic-version": "2023-06-01"}, + ) + assert response.status_code == 200, response.text + pings, _, relayed = response.text.rpartition("\n\n") + assert pings and set(pings.split("\n")) <= {"", ": ping"}, response.text + assert relayed == _OVERLOADED.body.decode(), response.text + assert [unquote(request.target) for request in rig.wire.drain()] == ["/v1/messages"] diff --git a/tests/integration/observability/test_arize_otel_v2_openinference_chaos.py b/tests/integration/observability/test_arize_otel_v2_openinference_chaos.py index 79b0d9084aa..0095080d944 100644 --- a/tests/integration/observability/test_arize_otel_v2_openinference_chaos.py +++ b/tests/integration/observability/test_arize_otel_v2_openinference_chaos.py @@ -1,12 +1,13 @@ from __future__ import annotations +import socket import threading import uuid from concurrent.futures import ThreadPoolExecutor from contextlib import ExitStack from pathlib import Path +from queue import SimpleQueue from typing import Final -from urllib.parse import urlsplit import httpx import pytest @@ -44,7 +45,7 @@ from _openinference_support import ( ) from integration._support.client import Gateway, eventually from integration._support.process import graceful_stop_seconds -from integration._support.wire import Reply, Request, wire_server +from integration._support.wire import Reply, Request, Wire, wire_server def _call( @@ -249,11 +250,12 @@ def test_arize_otel_v2_f1_sink_outage_and_recovery(gateway: Gateway, tmp_path: P _assert_response(response, marker, surface, stream, model_name) with ExitStack() as servers: - initial_stack: Final = servers.enter_context(ExitStack()) - stopped_destination: Final = initial_stack.enter_context(wire_server(_owned_sink_handler(sink))) - sink_port: Final = urlsplit(stopped_destination.url).port - assert sink_port is not None, stopped_destination.url - initial_stack.close() + outage_socket: Final = servers.enter_context(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) + outage_socket.bind(("127.0.0.1", 0)) # test-quality-ok: socket stays bound throughout the outage + sink_port: Final = outage_socket.getsockname()[1] + stopped_destination: Final = Wire( + f"http://127.0.0.1:{sink_port}", SimpleQueue(), SimpleQueue(), SimpleQueue(), SimpleQueue() + ) with _rig( gateway, tmp_path, @@ -272,6 +274,7 @@ def test_arize_otel_v2_f1_sink_outage_and_recovery(gateway: Gateway, tmp_path: P with httpx.Client(trust_env=False) as client, pytest.raises(httpx.ConnectError): client.get(stopped_destination.url + "/health", timeout=2) recovered_stack: Final = servers.enter_context(ExitStack()) + outage_socket.close() recovered_destination: Final = recovered_stack.enter_context( wire_server(_owned_sink_handler(sink), port=sink_port) ) diff --git a/tests/integration/pricing/test_cost_margin_discount.py b/tests/integration/pricing/test_cost_margin_discount.py index b6cda8f125f..613fe72ed70 100644 --- a/tests/integration/pricing/test_cost_margin_discount.py +++ b/tests/integration/pricing/test_cost_margin_discount.py @@ -7,7 +7,7 @@ import httpx import pytest from integration._support.client import Gateway, eventually, object_value, string_value from integration._support.database import read_rows -from pydantic import BaseModel, JsonValue +from pydantic import BaseModel, ConfigDict, JsonValue class CostEstimate(BaseModel): @@ -110,6 +110,25 @@ def _assert_logged_spends(expectations: tuple[tuple[str, float], ...]) -> None: assert actual == pytest.approx(dict(expectations)), rows +class LoggedBreakdown(BaseModel): + model_config = ConfigDict(frozen=True) + + original_cost: float + discount_percent: float | None = None + discount_amount: float | None = None + margin_percent: float | None = None + margin_fixed_amount: float | None = None + margin_total_amount: float | None = None + + +def _logged_breakdown(request_id: str) -> LoggedBreakdown: + rows: Final = read_rows('SELECT metadata FROM "LiteLLM_SpendLogs" WHERE request_id = %s', (request_id,)) + assert len(rows) == 1, rows + metadata: Final = rows[0]["metadata"] + parsed: Final = object_value(json.loads(metadata) if isinstance(metadata, str) else metadata) + return LoggedBreakdown.model_validate(parsed["cost_breakdown"]) + + def _estimated(gateway: Gateway, model: str) -> CostEstimate: response: Final = gateway.request( "POST", "/cost/estimate", {"model": model, "input_tokens": 20, "output_tokens": 20} @@ -236,6 +255,17 @@ def test_discount_and_margin_reprice_a_request_and_survive_rejection(gateway: Ga (streamed_global_request_id, 0.12), ) ) + discounted: Final = _logged_breakdown(discounted_request_id) + assert discounted.original_cost == pytest.approx(0.06), discounted + assert discounted.discount_percent == pytest.approx(0.05), discounted + assert discounted.discount_amount == pytest.approx(0.06 * 0.05), discounted + margined: Final = _logged_breakdown(provider_margin_request_id) + assert margined.original_cost == pytest.approx(0.06), margined + assert margined.discount_percent == pytest.approx(0.05), margined + assert margined.discount_amount == pytest.approx(0.06 * 0.05), margined + assert margined.margin_percent == pytest.approx(0.1), margined + assert margined.margin_fixed_amount == pytest.approx(0.001), margined + assert margined.margin_total_amount == pytest.approx(0.057 * 0.1 + 0.001), margined rejections: Final = ( ( diff --git a/tests/integration/pricing/test_model_listing_token_limits.py b/tests/integration/pricing/test_model_listing_token_limits.py index 5f75b5bb38a..5473d802f7f 100644 --- a/tests/integration/pricing/test_model_listing_token_limits.py +++ b/tests/integration/pricing/test_model_listing_token_limits.py @@ -5,6 +5,7 @@ from pathlib import Path from typing import Final import pytest +from litellm import get_model_info from integration._support.client import Gateway, object_value from integration._support.process import owned_proxy from pydantic import JsonValue @@ -104,6 +105,21 @@ def test_v1_models_carries_cost_map_context_window_for_a_known_model(gateway: Ga assert listed["max_output_tokens"] == 16384, listed +def test_v1_models_keeps_a_sibling_override_off_a_plain_deployment_of_the_same_model(gateway: Gateway) -> None: + catalog_info: Final = get_model_info("openai/gpt-4o-mini") + with gateway.scenario() as scenario: + overridden: Final = scenario.model( + model="openai/gpt-4o-mini", + model_info={"max_input_tokens": 4321, "max_output_tokens": 987}, + ) + plain: Final = scenario.model(model="openai/gpt-4o-mini") + listed_override: Final = _listed_model(gateway, overridden) + listed_plain: Final = _listed_model(gateway, plain) + + assert _limits(listed_plain) == (catalog_info["max_input_tokens"], catalog_info["max_output_tokens"]) + assert _limits(listed_override) == (4321, 987) + + def test_v1_models_carries_deployment_model_info_limits_for_an_unknown_model(gateway: Gateway) -> None: unknown: Final = f"openai/custom-{uuid.uuid4().hex}" with gateway.scenario() as scenario: diff --git a/tests/integration/pricing/test_price_data_reload_prices.py b/tests/integration/pricing/test_price_data_reload_prices.py index a91f5cba1ad..aa0addb7673 100644 --- a/tests/integration/pricing/test_price_data_reload_prices.py +++ b/tests/integration/pricing/test_price_data_reload_prices.py @@ -25,9 +25,16 @@ _GEO: Final = f"au.{_ROW}" _FINETUNE: Final = "ft:gpt-audit-reload-2025-01-01" _CLAUDE: Final = "claude-audit-reload-sonnet" _MINI: Final = "gpt-audit-reload-mini" +_FREE: Final = "gpt-audit-reload-free" +_RULE_FREE: Final = "gpt-audit-rule-free" +_FILL_FREE: Final = "gpt-audit-fill-free" +_RULE_UNPRICED: Final = "gpt-audit-rule-unpriced" +_REPORTED_FREE: Final = "gpt-audit-reported-free" _USAGE: Final = re.compile(r"bill (\d+) (\d+) (\d+)") _CUSTOM: Final = MappingProxyType({"input_cost_per_token": 1.1e-06, "output_cost_per_token": 2.3e-06}) -_COST_MAP_LIMITS: Final = MappingProxyType({"MODEL_COST_MAP_MIN_MODEL_COUNT": "1", "MODEL_COST_MAP_MAX_SHRINK_RATIO": "0"}) +_COST_MAP_LIMITS: Final = MappingProxyType( + {"MODEL_COST_MAP_MIN_MODEL_COUNT": "1", "MODEL_COST_MAP_MAX_SHRINK_RATIO": "0"} +) _PUBLISHED_SCALE: Final = 2.0 _CATALOG: Final[Mapping[str, Mapping[str, JsonValue]]] = MappingProxyType( { @@ -60,6 +67,13 @@ _CATALOG: Final[Mapping[str, Mapping[str, JsonValue]]] = MappingProxyType( "output_cost_per_token": 1.5e-05, "cache_read_input_token_cost": 3e-07, }, + _FILL_FREE: {"litellm_provider": "openai", "mode": "chat"}, + _FREE: { + "litellm_provider": "openai", + "mode": "chat", + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + }, _MINI: { "litellm_provider": "openai", "mode": "chat", @@ -99,6 +113,8 @@ class _Deployment: prices: Mapping[str, float] usage: _Usage rates: Callable[[float], _Rates | None] + reported_cost: float | None = None + listed_rates: Callable[[float], _Rates | None] | None = None @dataclass(frozen=True, slots=True) @@ -107,10 +123,23 @@ class _Charge: expected: float -def _catalog(scale: float) -> dict[str, dict[str, JsonValue]]: +def _catalog(scale: float) -> dict[str, JsonValue]: return { - model: {name: value * scale if isinstance(value, float) else value for name, value in row.items()} - for model, row in _CATALOG.items() + **{ + model: {name: value * scale if isinstance(value, float) else value for name, value in row.items()} + for model, row in _CATALOG.items() + }, + "fallback_generalizations": { + "rules": [ + { + "name": "free-pricing", + "pattern": f"({_RULE_FREE}|{_FILL_FREE})", + "fill_missing_for_providers": ["openai"], + "model_info": {"mode": "chat", "input_cost_per_token": 0.0, "output_cost_per_token": 0.0}, + }, + {"name": "unpriced-capabilities", "pattern": _RULE_UNPRICED, "model_info": {"mode": "chat"}}, + ] + }, } @@ -240,6 +269,7 @@ def _reply(request: Request) -> Reply: "completion_tokens": usage.completion, "total_tokens": usage.prompt + usage.completion, "prompt_tokens_details": {"cached_tokens": usage.cached}, + **({"cost": 0.0} if object_value(json.loads(request.body))["model"] == _REPORTED_FREE else {}), }, } ).encode() @@ -281,10 +311,16 @@ def _billed(client: httpx.Client, key: str, deployment: _Deployment) -> tuple[st def _charged_as_listed(proxy: Gateway, deployment: _Deployment, scale: float) -> _Charge: rates: Final = deployment.rates(scale) - assert _listed_rates(proxy, deployment.name) == _expected_listing(rates), deployment.name + assert _listed_rates(proxy, deployment.name) == _expected_listing( + deployment.listed_rates(scale) if deployment.listed_rates is not None else rates + ), deployment.name request_id, charged = _billed(proxy.client, proxy.key, deployment) - expected: Final = _expected_cost(rates, deployment.usage) - assert charged == pytest.approx(expected, rel=1e-6), deployment.name + expected: Final = ( + deployment.reported_cost if deployment.reported_cost is not None else _expected_cost(rates, deployment.usage) + ) + assert charged == ( + None if rates is None and deployment.reported_cost is None else pytest.approx(expected, rel=1e-6) + ), deployment.name return _Charge(request_id=request_id, expected=expected) @@ -352,8 +388,31 @@ def test_price_data_reload_reprices_catalog_deployments_and_keeps_custom_prices( _Usage(2000, 1000, 1000), _catalog_rates(_FINETUNE), ), - _Deployment("finetune-custom", f"openai/{_FINETUNE}:org:suffix:z", _CUSTOM, _Usage(2000, 0, 1000), _custom_rates), + _Deployment( + "finetune-custom", f"openai/{_FINETUNE}:org:suffix:z", _CUSTOM, _Usage(2000, 0, 1000), _custom_rates + ), _Deployment("cross-provider-unpriced", f"azure/{_CLAUDE}", {}, _Usage(2000, 1000, 1000), _unpriced), + _Deployment("catalog-free", f"openai/{_FREE}", {}, _Usage(2000, 1000, 1000), _catalog_rates(_FREE)), + _Deployment("rule-free", f"openai/{_RULE_FREE}", {}, _Usage(2000, 1000, 1000), _catalog_rates(_FREE)), + _Deployment( + "fill-rule-free", + f"openai/{_FILL_FREE}", + {}, + _Usage(2000, 1000, 1000), + _catalog_rates(_FREE), + listed_rates=_unpriced, + ), + _Deployment("rule-unpriced", f"openai/{_RULE_UNPRICED}", {}, _Usage(2000, 1000, 1000), _unpriced), + _Deployment( + "provider-reported-free", f"openrouter/{_REPORTED_FREE}", {}, _Usage(2000, 1000, 1000), _unpriced, 0.0 + ), + _Deployment( + "cross-provider-free", + f"azure/{_CLAUDE}", + {"input_cost_per_token": 0.0, "output_cost_per_token": 0.0}, + _Usage(2000, 1000, 1000), + _catalog_rates(_FREE), + ), _Deployment( "region-custom-above-threshold", f"bedrock/ap-northeast-1.{_ROW}", diff --git a/tests/integration/providers/test_decisions_wire.py b/tests/integration/providers/test_decisions_wire.py index 4e091460667..bb4973d6f2f 100644 --- a/tests/integration/providers/test_decisions_wire.py +++ b/tests/integration/providers/test_decisions_wire.py @@ -139,6 +139,7 @@ _PROVIDERS: Final = ( False, "databricks/databricks-openjev-qwen35-4b", ), + _Provider("empiriolabs", "empiriolabs/aplomb-test", "/v1/decisions", "aplomb-test", _API_KEY, False, None), ) _LOCAL_PROVIDERS: Final = ( pytest.param( diff --git a/tests/integration/providers/test_responses_lifecycle_wire.py b/tests/integration/providers/test_responses_lifecycle_wire.py index ac5b4dc6d5c..35a555fc776 100644 --- a/tests/integration/providers/test_responses_lifecycle_wire.py +++ b/tests/integration/providers/test_responses_lifecycle_wire.py @@ -4,7 +4,7 @@ import json import os import threading import uuid -from collections.abc import Callable +from collections.abc import Callable, Iterator from types import MappingProxyType from typing import Final, Literal from urllib.parse import parse_qsl, urlsplit @@ -12,7 +12,15 @@ from urllib.parse import parse_qsl, urlsplit import httpx import openai import pytest -from integration._support.client import Gateway, Scenario, eventually, object_value, string_value +from integration._support.client import ( + Gateway, + Scenario, + eventually, + gateway_from_environment, + object_value, + string_value, +) +from integration._support.process import owned_proxy from integration._support.responses_vendor import same_response from integration._support.wire import Reply, Request, Wire, wire_server from integration._support.worker_readiness import wait_for_worker_readiness @@ -26,6 +34,16 @@ _PROXY_WORKERS: Final = max(1, int(os.environ.get("INTEGRATION_PROXY_WORKERS", " _ALIASES: Final = ("/v1", "", "/openai/v1") +@pytest.fixture(scope="module") +def cancel_gateway(tmp_path_factory: pytest.TempPathFactory) -> Iterator[Gateway]: + directory: Final = tmp_path_factory.mktemp("responses-cancel") + with ( + gateway_from_environment() as shared, + owned_proxy(shared, directory, {"PROXY_BATCH_POLLING_ENABLED": "false"}, workers=_PROXY_WORKERS) as owned, + ): + yield owned + + class _DeleteResponse(BaseModel): model_config = ConfigDict(extra="forbid") @@ -737,7 +755,7 @@ def test_delete_returns_the_client_held_id_not_the_provider_id( @pytest.mark.parametrize("prefix", _ALIASES) def test_cancel_forwards_provider_id_and_returns_cancelled_response( - gateway: Gateway, + cancel_gateway: Gateway, prefix: str, ) -> None: raw_id: Final = f"resp_cancel_{uuid.uuid4().hex}" @@ -756,9 +774,9 @@ def test_cancel_forwards_provider_id_and_returns_cancelled_response( assert request.body in (b"", b"{}") return Reply(body=_response(raw_id, "cancelled")) - with wire_server(respond) as wire, gateway.scenario() as scenario: + with wire_server(respond) as wire, cancel_gateway.scenario() as scenario: model: Final = _deployment(scenario, wire) - client: Final = _client(gateway, gateway.key, prefix) + client: Final = _client(cancel_gateway, cancel_gateway.key, prefix) created: Final = client.responses.create(model=model, input="cancel lifecycle", background=True) cancelled: Final = client.responses.cancel(created.id) assert cancelled.id != raw_id and same_response(cancelled.id, created.id), cancelled.model_dump_json() diff --git a/tests/integration/providers/test_responses_minted_reasoning_replay_chaos.py b/tests/integration/providers/test_responses_minted_reasoning_replay_chaos.py index f32764f3840..826ed6f6840 100644 --- a/tests/integration/providers/test_responses_minted_reasoning_replay_chaos.py +++ b/tests/integration/providers/test_responses_minted_reasoning_replay_chaos.py @@ -416,7 +416,7 @@ class _LoginDeployment: _LOGIN_DEPLOYMENTS: Final = ( _LoginDeployment("github_copilot", f"github_copilot/{_CODEX}", None), _LoginDeployment("chatgpt", f"chatgpt/{_CODEX}", None), - _LoginDeployment("azure_ai-foundry-host", "azure_ai/deepseek-v3", "synthetic-azure-key"), + _LoginDeployment("azure_ai-foundry-host", "azure_ai/deepseek-v3.2", "synthetic-azure-key"), ) diff --git a/tests/integration/providers/test_vertex_passthrough_stream_wire.py b/tests/integration/providers/test_vertex_passthrough_stream_wire.py new file mode 100644 index 00000000000..1ec2f740480 --- /dev/null +++ b/tests/integration/providers/test_vertex_passthrough_stream_wire.py @@ -0,0 +1,121 @@ +import json +import os +import uuid +from collections.abc import Iterator +from dataclasses import dataclass +from typing import Final + +import httpx +import pytest +from integration._support.client import Gateway, gateway_from_environment +from integration._support.forward_proxy import ForwardProxy, tunnelling_forward_proxy +from integration._support.process import owned_proxy +from integration._support.tls import server_context, write_self_signed_cert +from integration._support.wire import Reply, Request, Wire, wire_server + +_LOCATION: Final = "us-central1" +_HOST: Final = f"{_LOCATION}-aiplatform.googleapis.com" +_PROJECT: Final = "audit-vertex-pass-through" +_STREAMING_MODEL: Final = "gemini-3.8-flash" +_UNAVAILABLE_MODEL: Final = "gemini-3.8-flash-unavailable" +_VENDOR_TOKEN: Final = "Bearer synthetic-vertex-token" +_CHUNKS: Final = ( + b'data: {"candidates": [{"content": {"role": "model", "parts": [{"text": "Hel"}]}}]}\r\n\r\n', + b'data: {"candidates": [{"content": {"role": "model", "parts": [{"text": "lo"}]}, "finishReason": "STOP"}],' + b' "usageMetadata": {"promptTokenCount": 3, "candidatesTokenCount": 2, "totalTokenCount": 5}}\r\n\r\n', +) +_UNAVAILABLE_MESSAGE: Final = "The service is currently unavailable." +_UNAVAILABLE_BODY: Final = json.dumps( + {"error": {"code": 503, "message": _UNAVAILABLE_MESSAGE, "status": "UNAVAILABLE"}} +).encode() +_SCRUBBED: Final = frozenset( + { + "HTTPS_PROXY", + "HTTP_PROXY", + "ALL_PROXY", + "NO_PROXY", + "SSL_CERT_FILE", + "DEFAULT_VERTEXAI_PROJECT", + "DEFAULT_VERTEXAI_LOCATION", + "DEFAULT_GOOGLE_APPLICATION_CREDENTIALS", + "GOOGLE_APPLICATION_CREDENTIALS", + } +) + + +@dataclass(frozen=True, slots=True) +class _Vertex: + wire: Wire + tunnel: ForwardProxy + proxy: Gateway + + def stream(self, model: str, marker: str) -> httpx.Response: + return self.proxy.request( + "POST", + f"/vertex_ai/v1/{_model_path(model)}:streamGenerateContent", + {"contents": [{"role": "user", "parts": [{"text": marker}]}]}, + headers={"Authorization": _VENDOR_TOKEN, "x-litellm-api-key": f"Bearer {self.proxy.key}"}, + ) + + +def _model_path(model: str) -> str: + return f"projects/{_PROJECT}/locations/{_LOCATION}/publishers/google/models/{model}" + + +def _respond(request: Request) -> Reply: + if request.target.startswith(f"/v1/{_model_path(_UNAVAILABLE_MODEL)}:"): + return Reply(status=503, body=_UNAVAILABLE_BODY) + if request.target.startswith(f"/v1/{_model_path(_STREAMING_MODEL)}:"): + return Reply(content_type="text/event-stream", chunks=_CHUNKS) + return Reply(status=404, body=b'{"error": "no scripted Vertex model"}') + + +@pytest.fixture(scope="module") +def vertex(tmp_path_factory: pytest.TempPathFactory) -> Iterator[_Vertex]: + directory: Final = tmp_path_factory.mktemp("vertex-pass-through-stream") + cert, key = write_self_signed_cert(directory, (_HOST,)) + with ( + wire_server(_respond, tls=server_context(cert, key)) as wire, + tunnelling_forward_proxy(int(wire.url.rsplit(":", 1)[1])) as tunnel, + gateway_from_environment() as rig, + owned_proxy( + rig, + directory, + {"HTTPS_PROXY": tunnel.url, "NO_PROXY": "127.0.0.1,localhost", "SSL_CERT_FILE": str(cert)}, + remove_environment=tuple(name for name in os.environ if name.upper() in _SCRUBBED), + workers=2, + ) as proxy, + ): + yield _Vertex(wire, tunnel, proxy) + + +@pytest.fixture(autouse=True) +def fresh_edges(vertex: _Vertex) -> None: + vertex.wire.drain() + vertex.tunnel.drain() + + +def test_vertex_pass_through_stream_relays_every_sse_chunk_byte_for_byte(vertex: _Vertex) -> None: + marker: Final = uuid.uuid4().hex + response: Final = vertex.stream(_STREAMING_MODEL, marker) + sent: Final = vertex.wire.drain() + + assert response.status_code == 200, response.text + assert response.headers["content-type"].startswith("text/event-stream"), response.headers + assert response.content == b"".join(_CHUNKS), response.text + assert [request.target for request in sent] == [ + f"/v1/{_model_path(_STREAMING_MODEL)}:streamGenerateContent?alt=sse" + ], sent + assert sent[0].headers["authorization"] == _VENDOR_TOKEN + assert json.loads(sent[0].body) == {"contents": [{"role": "user", "parts": [{"text": marker}]}]} + assert set(vertex.tunnel.targets()) == {f"{_HOST}:443"} + + +def test_vertex_pass_through_stream_relays_an_upstream_503_as_503(vertex: _Vertex) -> None: + response: Final = vertex.stream(_UNAVAILABLE_MODEL, uuid.uuid4().hex) + + assert response.status_code == 503, response.text + assert response.content == _UNAVAILABLE_BODY, response.text + assert [request.target for request in vertex.wire.drain()] == [ + f"/v1/{_model_path(_UNAVAILABLE_MODEL)}:streamGenerateContent?alt=sse" + ] diff --git a/tests/integration/routing/test_anthropic_overloaded_cooldown_owned_proxy.py b/tests/integration/routing/test_anthropic_overloaded_cooldown_owned_proxy.py new file mode 100644 index 00000000000..e6df49a73bd --- /dev/null +++ b/tests/integration/routing/test_anthropic_overloaded_cooldown_owned_proxy.py @@ -0,0 +1,247 @@ +import json +import os +import uuid +from collections.abc import Iterator +from dataclasses import dataclass +from pathlib import Path +from typing import Final +from urllib.parse import unquote + +import httpx +import pytest +import yaml +from integration._support.anthropic_sse import ( + LIFECYCLE, + error_body, + event_types, + message_id, + message_stream, + parse_sse, + status_reply, + stream_reply, + user_prompt, +) +from integration._support.client import ( + Gateway, + JsonValue, + gateway_from_environment, + object_value, + string_value, +) +from integration._support.process import graceful_stop_seconds, owned_proxy +from integration._support.wire import Reply, Request, Wire, wire_server + +_OVERLOADED: Final = "claude-overloaded" +_HEALTHY: Final = "claude-healthy" +_COUNTED: Final = "claude-counted" +_COUNTED_OVERLOADED: Final = "claude-counted-overloaded" +_COUNTED_RATE_LIMITED: Final = "claude-counted-rate-limited" +_CHAT_ONLY_BACKEND: Final = "audit-chat-only" +_API_KEY: Final = "synthetic-anthropic-key" +_TEXT: Final = "Hello" +_COOLDOWN_GROUP: Final = "audit-overloaded-cooldown" +_COUNT_DISABLED_GROUP: Final = "audit-count-tokens-disabled" +_COUNT_UPSTREAM_GROUP: Final = "audit-count-tokens-upstream-503" +_COUNT_OVERLOADED_GROUP: Final = "audit-count-tokens-upstream-529" +_COUNT_RATE_LIMITED_GROUP: Final = "audit-count-tokens-upstream-429" +_GATEWAY: Final = "/claude_code_gateway" +_GATEWAY_GROUPS: Final = { + "non-stream": "audit-gateway-overloaded-non-stream", + "stream": "audit-gateway-overloaded-stream", +} +_COUNT_STATUSES: Final = {_COUNTED: 503, _COUNTED_OVERLOADED: 529, _COUNTED_RATE_LIMITED: 429} +_COUNT_TOKENS_TARGET: Final = "/v1/messages/count_tokens" +_UPSTREAM_URL_PLACEHOLDER: Final = "upstream-url" + +pytestmark: Final = pytest.mark.timeout(2 * graceful_stop_seconds() + 120) + + +def _served_id(marker: str) -> str: + return f"msg_{_HEALTHY}_{marker}" + + +def _respond(request: Request) -> Reply: + body: Final = object_value(json.loads(request.body)) + backend: Final = str(body["model"]) + if unquote(request.target) == _COUNT_TOKENS_TARGET: + status: Final = _COUNT_STATUSES[backend] + return Reply(status=status, body=error_body(status, f"scripted {status}")) + if backend == _OVERLOADED: + return status_reply(529) + if backend == _HEALTHY: + return stream_reply(message_stream(_served_id(user_prompt(body)), backend, _TEXT)) + return Reply(status=404, body=b'{"error": "no scripted backend"}') + + +def _deployment(name: str, model: str, api_base: str, api_key: str, **params: JsonValue) -> dict[str, JsonValue]: + return {"model_name": name, "litellm_params": {"model": model, "api_base": api_base, "api_key": api_key, **params}} + + +def _config(wire: Wire, directory: Path) -> Path: + config: Final = yaml.safe_load(Path("tests/integration/proxy_config.yaml").read_text()) + config["model_list"] = [ + _deployment(_COOLDOWN_GROUP, f"anthropic/{_OVERLOADED}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY, order=1), + _deployment(_COOLDOWN_GROUP, f"anthropic/{_HEALTHY}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY, order=2), + _deployment( + _COUNT_DISABLED_GROUP, + f"hosted_vllm/{_CHAT_ONLY_BACKEND}", + _UPSTREAM_URL_PLACEHOLDER + "/v1", + "synthetic-vllm-key", + ), + _deployment(_COUNT_UPSTREAM_GROUP, f"anthropic/{_COUNTED}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY), + _deployment(_COUNT_OVERLOADED_GROUP, f"anthropic/{_COUNTED_OVERLOADED}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY), + _deployment( + _COUNT_RATE_LIMITED_GROUP, f"anthropic/{_COUNTED_RATE_LIMITED}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY + ), + *( + _deployment(group, f"anthropic/{_OVERLOADED}", _UPSTREAM_URL_PLACEHOLDER, _API_KEY) + for group in _GATEWAY_GROUPS.values() + ), + ] + config["router_settings"] = { + "num_retries": 0, + "allowed_fails_policy": {"InternalServerErrorAllowedFails": 0}, + "cooldown_time": 120, + "redis_host": os.environ["REDIS_HOST"], + "redis_port": int(os.environ["REDIS_PORT"]), + } + config["litellm_settings"]["disable_token_counter"] = True + config["general_settings"]["enable_claude_code_gateway"] = True + path: Final = directory / "anthropic-overloaded-cooldown.yaml" + path.write_text(yaml.safe_dump(config).replace(_UPSTREAM_URL_PLACEHOLDER, wire.url)) + return path + + +@dataclass(frozen=True, slots=True) +class _Rig: + proxy: Gateway + wire: Wire + + def messages(self, path: str, model: str, marker: str, **extra: JsonValue) -> httpx.Response: + return self.proxy.request( + "POST", + path, + { + "model": model, + "max_tokens": 16, + "stream": True, + "messages": [{"role": "user", "content": marker}], + **extra, + }, + headers={"anthropic-version": "2023-06-01"}, + ) + + def count_tokens(self, model: str, prefix: str = "") -> httpx.Response: + return self.proxy.request( + "POST", + prefix + _COUNT_TOKENS_TARGET, + {"model": model, "messages": [{"role": "user", "content": "count me"}]}, + headers={"anthropic-version": "2023-06-01"}, + ) + + def targets_called(self) -> tuple[str, ...]: + return tuple(unquote(request.target) for request in self.wire.drain()) + + def backends_called(self) -> tuple[str, ...]: + return tuple(str(object_value(json.loads(request.body))["model"]) for request in self.wire.drain()) + + +@pytest.fixture(scope="module") +def rig(tmp_path_factory: pytest.TempPathFactory) -> Iterator[_Rig]: + directory: Final = tmp_path_factory.mktemp("anthropic-overloaded-cooldown") + with gateway_from_environment() as gateway, wire_server(_respond) as wire: + overrides: Final = {"ANTHROPIC_API_BASE": wire.url, "ANTHROPIC_API_KEY": _API_KEY} + with owned_proxy(gateway, directory, overrides, config=_config(wire, directory), workers=2) as proxy: + yield _Rig(proxy, wire) + + +@pytest.fixture(autouse=True) +def _fresh_wire(rig: _Rig) -> None: + rig.wire.drain() + + +def _assert_overloaded_json(response: httpx.Response) -> None: + assert response.status_code == 529, response.text + payload: Final = object_value(json.loads(response.content)) + assert object_value(payload["error"])["type"] == "overloaded_error", response.text + + +def _count_tokens_error(response: httpx.Response, status: int) -> dict[str, JsonValue]: + assert response.status_code == status, response.text + envelope: Final = object_value(json.loads(response.content)) + assert envelope["type"] == "error", response.text + return object_value(envelope["error"]) + + +def test_an_overloaded_deployment_is_cooled_down_by_the_internal_server_error_allowed_fails_policy(rig: _Rig) -> None: + marker: Final = uuid.uuid4().hex + first: Final = rig.messages("/v1/messages", _COOLDOWN_GROUP, marker, num_retries=0, disable_fallbacks=True) + _assert_overloaded_json(first) + assert rig.backends_called() == (_OVERLOADED,) + second: Final = rig.messages("/v1/messages", _COOLDOWN_GROUP, marker, num_retries=0, disable_fallbacks=True) + assert second.status_code == 200, second.text + events: Final = parse_sse(second.text) + assert event_types(events) == LIFECYCLE, events + assert message_id(events) == _served_id(marker), events + assert rig.backends_called() == (_HEALTHY,) + + +def test_count_tokens_disabled_without_a_provider_counter_answers_503_api_error(rig: _Rig) -> None: + response: Final = rig.count_tokens(_COUNT_DISABLED_GROUP) + error: Final = _count_tokens_error(response, 503) + assert error["type"] == "api_error", response.text + assert "Token counting is disabled" in string_value(error["message"]), response.text + assert rig.targets_called() == () + + +def test_count_tokens_whose_upstream_answers_503_stays_503_api_error(rig: _Rig) -> None: + response: Final = rig.count_tokens(_COUNT_UPSTREAM_GROUP) + error: Final = _count_tokens_error(response, 503) + assert error["type"] == "api_error", response.text + assert "scripted 503" in string_value(error["message"]), response.text + assert rig.targets_called() == (_COUNT_TOKENS_TARGET,) +@pytest.mark.parametrize( + ("group", "status", "error_type"), + ( + pytest.param(_COUNT_OVERLOADED_GROUP, 529, "overloaded_error", id="529"), + pytest.param(_COUNT_RATE_LIMITED_GROUP, 429, "rate_limit_error", id="429"), + ), +) +def test_count_tokens_keeps_other_upstream_statuses_and_their_error_body( + rig: _Rig, group: str, status: int, error_type: str +) -> None: + response: Final = rig.count_tokens(group) + error: Final = _count_tokens_error(response, status) + assert error["type"] == error_type, response.text + assert f"scripted {status}" in string_value(error["message"]), response.text + assert rig.targets_called() == (_COUNT_TOKENS_TARGET,) + + +def test_pass_through_messages_stream_forwards_an_upstream_529_verbatim(rig: _Rig) -> None: + response: Final = rig.messages("/anthropic/v1/messages", _OVERLOADED, uuid.uuid4().hex) + assert response.status_code == 529, response.text + assert response.content == error_body(529, "scripted 529"), response.text + assert rig.backends_called() == (_OVERLOADED,) + + +@pytest.mark.parametrize("shape", _GATEWAY_GROUPS.keys()) +def test_the_claude_code_gateway_messages_route_answers_an_upstream_529_as_overloaded_error( + rig: _Rig, shape: str +) -> None: + response: Final = rig.messages( + f"{_GATEWAY}/v1/messages", + _GATEWAY_GROUPS[shape], + uuid.uuid4().hex, + stream=shape == "stream", + num_retries=0, + ) + _assert_overloaded_json(response) + assert rig.backends_called() == (_OVERLOADED,) + + +def test_the_claude_code_gateway_count_tokens_route_answers_an_upstream_503_as_503(rig: _Rig) -> None: + response: Final = rig.count_tokens(_COUNT_UPSTREAM_GROUP, prefix=_GATEWAY) + error: Final = _count_tokens_error(response, 503) + assert error["type"] == "api_error", response.text + assert "scripted 503" in string_value(error["message"]), response.text + assert rig.targets_called() == (_COUNT_TOKENS_TARGET,) diff --git a/tests/integration/routing/test_messages_stream_retry_budget_sources_owned_proxy.py b/tests/integration/routing/test_messages_stream_retry_budget_sources_owned_proxy.py index 0257b8372b0..e0900bc0926 100644 --- a/tests/integration/routing/test_messages_stream_retry_budget_sources_owned_proxy.py +++ b/tests/integration/routing/test_messages_stream_retry_budget_sources_owned_proxy.py @@ -45,10 +45,19 @@ _FALLBACK_GROUP: Final = "audit-fallback" _CW_FALLBACK_GROUP: Final = "audit-cw-fallback" _LONELY: Final = "audit-lonely" _POLICY: Final = "audit-policy" +_SERVICE_UNAVAILABLE_RETRY_POLICY: Final = "audit-service-unavailable-retry-policy" +_INTERNAL_RETRY_POLICY: Final = "audit-internal-retry-policy" _UPSTREAM_URL_PLACEHOLDER: Final = "upstream-url" Behavior = Literal[ - "drop-once", "drop-always", "hold-ping", "drop-then-too-long", "drop-then-401", "overloaded-frames-fallback-503" + "drop-once", + "drop-always", + "hold-ping", + "drop-then-too-long", + "drop-then-401", + "overloaded-frames-fallback-529", + "overloaded-frames", + "overloaded-once", ] pytestmark = pytest.mark.timeout(240) @@ -81,13 +90,15 @@ def _primary_reply( return Reply(status=400, body=error_body(400, "prompt is too long: 250000 tokens > 200000 maximum")) case "drop-then-401": return dropped if attempt == 1 else status_reply(401) - case "overloaded-frames-fallback-503": + case "overloaded-frames-fallback-529" | "overloaded-frames": return stream_reply((error_frame(529, "scripted overloaded"),)) + case "overloaded-once": + return status_reply(529) if attempt == 1 else stream_reply(full) raise AssertionError(behavior) def _fallback_reply(behavior: str, full: tuple[bytes, bytes, bytes, bytes]) -> Reply: - if behavior == "overloaded-frames-fallback-503": + if behavior == "overloaded-frames-fallback-529": return status_reply(503) return stream_reply(full) @@ -141,13 +152,19 @@ def _config(wire: Wire, directory: Path) -> Path: _deployment(_CW_FALLBACK_GROUP, _CONTEXT_WINDOW), _deployment(_LONELY, _PRIMARY, num_retries=1), _deployment(_POLICY, _PRIMARY), + _deployment(_SERVICE_UNAVAILABLE_RETRY_POLICY, _PRIMARY), + _deployment(_INTERNAL_RETRY_POLICY, _PRIMARY), ] config["router_settings"] = { "num_retries": 1, "disable_cooldowns": True, "fallbacks": [{_WITH_FALLBACKS: [_FALLBACK_GROUP]}], "context_window_fallbacks": [{_WITH_FALLBACKS: [_CW_FALLBACK_GROUP]}], - "model_group_retry_policy": {_POLICY: {"DefaultRetries": 2}}, + "model_group_retry_policy": { + _POLICY: {"DefaultRetries": 2}, + _SERVICE_UNAVAILABLE_RETRY_POLICY: {"ServiceUnavailableErrorRetries": 1, "InternalServerErrorRetries": 0}, + _INTERNAL_RETRY_POLICY: {"InternalServerErrorRetries": 1, "ServiceUnavailableErrorRetries": 0}, + }, } path: Final = directory / "messages-retry-budget-sources.yaml" path.write_text(yaml.safe_dump(config).replace(_UPSTREAM_URL_PLACEHOLDER, wire.url)) @@ -290,10 +307,34 @@ def test_a_retry_rejected_with_401_ends_the_retries_and_reaches_the_client_uncha assert rig.attempts_on(_PRIMARY, marker) == 2 -def test_overloaded_frames_whose_fallback_fails_answer_the_mapped_internal_server_error(rig: _Rig) -> None: +def test_overloaded_frames_whose_fallback_fails_answer_529_overloaded_error(rig: _Rig) -> None: marker: Final = uuid.uuid4().hex - response: Final = rig.stream(_WITH_FALLBACKS, "overloaded-frames-fallback-503", marker) - assert response.status_code == 500, response.text + response: Final = rig.stream(_WITH_FALLBACKS, "overloaded-frames-fallback-529", marker) + assert response.status_code == 529, response.text + assert "overloaded_error" in response.text, response.text assert "content_block_delta" not in response.text, response.text assert rig.attempts_on(_PRIMARY, marker) == 2 assert rig.attempts_on(_FALLBACK, marker) == 2 + + +def test_overloaded_frames_that_spend_the_budget_without_a_fallback_relay_the_last_attempts_frame(rig: _Rig) -> None: + marker: Final = uuid.uuid4().hex + response: Final = rig.stream(_LONELY, "overloaded-frames", marker) + assert response.status_code == 200, response.text + assert response.text == error_frame(529, "scripted overloaded").decode(), response.text + assert rig.attempts_on(_PRIMARY, marker) == 2 + + +def test_a_529_is_retried_under_the_internal_server_error_retry_policy(rig: _Rig) -> None: + marker: Final = uuid.uuid4().hex + _assert_completed(rig.stream(_INTERNAL_RETRY_POLICY, "overloaded-once", marker), _served_id(_PRIMARY, marker, 2)) + assert rig.attempts_on(_PRIMARY, marker) == 2 + + +def test_a_529_is_not_retried_under_the_service_unavailable_error_retry_policy(rig: _Rig) -> None: + marker: Final = uuid.uuid4().hex + response: Final = rig.stream(_SERVICE_UNAVAILABLE_RETRY_POLICY, "overloaded-once", marker) + assert response.status_code == 529, response.text + assert "overloaded_error" in response.text, response.text + assert "content_block_delta" not in response.text, response.text + assert rig.attempts_on(_PRIMARY, marker) == 1 diff --git a/tests/integration/spend/test_baggage_tag_mappings.py b/tests/integration/spend/test_baggage_tag_mappings.py index e08348a074c..af68d7b242b 100644 --- a/tests/integration/spend/test_baggage_tag_mappings.py +++ b/tests/integration/spend/test_baggage_tag_mappings.py @@ -61,10 +61,10 @@ class Rig: @property def exact_tag(self) -> str: - return f"usecase-a-{self.marker}" + return f"app-a-{self.marker}" - def usecase_tag(self, value: str) -> str: - return f"usecase-{self.marker}:{value}" + def app_tag(self, value: str) -> str: + return f"app-{self.marker}:{value}" def tenant_tag(self, value: str) -> str: return f"tenant-{self.marker}:{value}" @@ -195,7 +195,7 @@ def _routed_deployment(marker: str, provider: Wire, name: str, tag: str) -> dict def _mapped_models(marker: str, provider: Wire, upstream_url: str) -> list[JsonValue]: routed: Final = ( - _routed_deployment(marker, provider, "tagged", f"usecase-a-{marker}"), + _routed_deployment(marker, provider, "tagged", f"app-a-{marker}"), _routed_deployment(marker, provider, "default", "default"), ) realtime: Final[dict[str, JsonValue]] = { @@ -221,8 +221,8 @@ def _mapped_models(marker: str, provider: Wire, upstream_url: str) -> list[JsonV def _mapped_rules(marker: str, *, tenant_required: bool) -> list[JsonValue]: return [ - {"key": "entrypoint", "value": "a", "tag": f"usecase-a-{marker}"}, - {"key": "entrypoint", "tag_prefix": f"usecase-{marker}:"}, + {"key": "service", "value": "a", "tag": f"app-a-{marker}"}, + {"key": "service", "tag_prefix": f"app-{marker}:"}, {"key": "tenant", "tag_prefix": f"tenant-{marker}:", "required": tenant_required}, {"key": "budgeted", "tag_prefix": f"budgeted-{marker}:", "require_budgeted_tag": True}, {"key": "team+id", "tag_prefix": f"team-{marker}:"}, @@ -403,7 +403,7 @@ def test_mapped_baggage_tags_reach_spend_and_callbacks_but_not_requester_metadat ROUTE_PATHS[route], _inference_body(route, mapped.chat_model, f"baggage {tenant}", caller_tag), key=key, - headers={"baggage": f"entrypoint=a, tenant={tenant}"}, + headers={"baggage": f"service=a, tenant={tenant}"}, ) assert response.status_code == 200, response.text assert len(mapped.provider.drain()) == 1 @@ -423,7 +423,7 @@ def test_json_string_metadata_gains_exactly_the_mapped_tags(optional: Rig) -> No key: Final = scenario.key() optional.provider.drain() plain: Final = _chat(optional, plain_key, None, fields=fields) - response: Final = _chat(optional, key, f"entrypoint=a,tenant={tenant}", fields=fields) + response: Final = _chat(optional, key, f"service=a,tenant={tenant}", fields=fields) assert (plain.status_code, response.status_code) == (200, 200), (plain.text, response.text) assert len(optional.provider.drain()) == 2 assert _spend_tags(optional.proxy, key) == _spend_tags(optional.proxy, plain_key) | mapped_tags @@ -494,7 +494,7 @@ def test_a2a_spend_row_carries_the_mapped_tags(mapped: Rig) -> None: }, }, key=key, - headers={"baggage": f"entrypoint=a,tenant={tenant}"}, + headers={"baggage": f"service=a,tenant={tenant}"}, ) assert sent.status_code == 200, sent.text assert {mapped.exact_tag, mapped.tenant_tag(tenant)} <= _spend_tags(mapped.proxy, key) @@ -514,8 +514,8 @@ def test_spent_mapped_tag_budget_rejects_the_next_request_before_the_provider( mapped: Rig, key_fields: Mapping[str, JsonValue], fields: Mapping[str, JsonValue], headers: Mapping[str, str] ) -> None: entry: Final = uuid.uuid4().hex - tag: Final = mapped.usecase_tag(entry) - baggage: Final = f"entrypoint={entry},tenant={entry}" + tag: Final = mapped.app_tag(entry) + baggage: Final = f"service={entry},tenant={entry}" with mapped.proxy.scenario() as scenario: key: Final = scenario.key(**key_fields) mapped.proxy.post("/tag/new", {"name": tag, "max_budget": 0.0001}) @@ -528,7 +528,7 @@ def test_spent_mapped_tag_budget_rejects_the_next_request_before_the_provider( assert blocked.status_code == 422, blocked.text assert "budget" in blocked.text.lower(), blocked.text assert mapped.provider.drain() == () - other: Final = _chat(mapped, key, f"entrypoint=other-{entry},tenant={entry}") + other: Final = _chat(mapped, key, f"service=other-{entry},tenant={entry}") assert other.status_code == 200, other.text @@ -536,10 +536,10 @@ def test_required_mapping_rejects_a_request_without_its_baggage_value_before_the with mapped.proxy.scenario() as scenario: key: Final = scenario.key() mapped.provider.drain() - rejected: Final = _chat(mapped, key, "entrypoint=a") + rejected: Final = _chat(mapped, key, "service=a") assert rejected.status_code == 400, rejected.text assert mapped.provider.drain() == () - admitted: Final = _chat(mapped, key, f"entrypoint=a,tenant={uuid.uuid4().hex}") + admitted: Final = _chat(mapped, key, f"service=a,tenant={uuid.uuid4().hex}") assert admitted.status_code == 200, admitted.text @@ -591,7 +591,7 @@ def test_mapped_tags_never_select_a_tag_routed_deployment(mapped: Rig, caller_ta _chat( mapped, key, - f"entrypoint=a,tenant={tenant}", + f"service=a,tenant={tenant}", model=f"routed-{mapped.marker}", fields=fields, **headers, @@ -604,8 +604,8 @@ def test_mapped_tags_never_select_a_tag_routed_deployment(mapped: Rig, caller_ta def test_mapped_tags_never_trigger_a_tag_scoped_policy_attachment(mapped: Rig) -> None: entry: Final = uuid.uuid4().hex - tag: Final = mapped.usecase_tag(entry) - baggage: Final = f"entrypoint={entry},tenant={entry}" + tag: Final = mapped.app_tag(entry) + baggage: Final = f"service={entry},tenant={entry}" proxy: Final = mapped.proxy with wire_server(lambda _request: Reply(body=GUARDRAIL_VERDICT)) as guardrail, proxy.scenario() as scenario: key: Final = scenario.key() @@ -662,7 +662,7 @@ def test_required_mapping_refuses_websocket_inference_before_the_upstream(mapped upstream.get("/__observations").raise_for_status() status: Final = _handshake_status( f"ws{proxy_url.removeprefix('http')}/v1/realtime?model=realtime-{mapped.marker}", - {"Authorization": f"Bearer {key}", "baggage": f"entrypoint=a,tenant={uuid.uuid4().hex}"}, + {"Authorization": f"Bearer {key}", "baggage": f"service=a,tenant={uuid.uuid4().hex}"}, ) assert status == 403, status observed: Final = list_value(JSON_OBJECT.validate_json(upstream.get("/__observations").content)["requests"]) @@ -706,7 +706,7 @@ def test_custom_passthrough_spend_row_carries_the_mapped_tags(mapped: Rig) -> No path, {"model": mapped.chat_model, "messages": [{"role": "user", "content": tenant}]}, key=key, - headers={"baggage": f"entrypoint=a,tenant={tenant}"}, + headers={"baggage": f"service=a,tenant={tenant}"}, ) assert response.status_code == 200, response.text assert {mapped.exact_tag, mapped.tenant_tag(tenant)} <= _spend_tags(mapped.proxy, key) @@ -777,7 +777,7 @@ def test_passthrough_forwards_the_client_body_unchanged_while_its_spend_row_carr plain: Final = optional.proxy.request("POST", path, body, key=plain_key) plain_forwarded: Final = _forwarded_bodies(optional) response: Final = optional.proxy.request( - "POST", path, body, key=key, headers={"baggage": f"entrypoint=a,tenant={tenant}"} + "POST", path, body, key=key, headers={"baggage": f"service=a,tenant={tenant}"} ) assert (plain.status_code, response.status_code) == (200, 200), (plain.text, response.text) assert plain_forwarded == (_upstream_body(entrypoint, body),) @@ -929,7 +929,7 @@ def test_reject_clientside_metadata_tags_with_baggage( ) -> None: tenant: Final = uuid.uuid4().hex fields, headers = _rejected_client_tag(client_tag, f"caller-{tenant}") - baggage: Final = f"entrypoint=a,tenant={tenant}" if baggage_maps else f"region={tenant}" + baggage: Final = f"service=a,tenant={tenant}" if baggage_maps else f"region={tenant}" mapped_tags: Final = frozenset({rejecting.exact_tag, rejecting.tenant_tag(tenant)} if baggage_maps else ()) with rejecting.proxy.scenario() as scenario: plain_key: Final = scenario.key() @@ -952,7 +952,7 @@ def test_plus_sign_rule_key_matches_either_wire_spelling_but_not_both( optional: Rig, baggage_keys: tuple[str, ...], maps_team: bool ) -> None: value: Final = uuid.uuid4().hex - baggage: Final = ",".join(("entrypoint=a", *(f"{baggage_key}={value}" for baggage_key in baggage_keys))) + baggage: Final = ",".join(("service=a", *(f"{baggage_key}={value}" for baggage_key in baggage_keys))) with optional.proxy.scenario() as scenario: key: Final = scenario.key() response: Final = _chat(optional, key, baggage) @@ -991,7 +991,7 @@ def test_baggage_mapping_a_deployment_tag_keeps_the_team_tag_guardrail_mode_firs @pytest.mark.parametrize("metadata", ["run-123", {"tags": "run-123"}], ids=["non_object", "string_tags"]) @pytest.mark.parametrize( ("header_tag", "baggage"), - [(True, "region=sg"), (True, "entrypoint=a"), (False, "region=sg"), (False, "entrypoint=a")], + [(True, "region=sg"), (True, "service=a"), (False, "region=sg"), (False, "service=a")], ids=["header_tag_unmapped_baggage", "header_tag_mapped_baggage", "unmapped_baggage", "mapped_baggage"], ) def test_malformed_metadata_keeps_its_status_under_baggage( diff --git a/tests/integration/spend/test_failing_request_stored_snapshot.py b/tests/integration/spend/test_failing_request_stored_snapshot.py new file mode 100644 index 00000000000..4f016c33f37 --- /dev/null +++ b/tests/integration/spend/test_failing_request_stored_snapshot.py @@ -0,0 +1,84 @@ +import json +import uuid +from collections.abc import Iterator +from pathlib import Path +from typing import Final + +import pytest +import yaml +from integration._support.client import Gateway, eventually, gateway_from_environment, object_value +from integration._support.process import owned_proxy +from pydantic import JsonValue + +UNREACHABLE_BASE: Final = "http://127.0.0.1:9/v1" +TRANSCRIPT_TURNS: Final = 40 +RETRIES: Final = 2 +STORED_REQUEST_BUDGET_KB: Final = 64 + + +def _config_storing_prompts(directory: Path) -> Path: + config: Final = object_value(yaml.safe_load(Path("tests/integration/proxy_config.yaml").read_text())) + general_settings: Final = object_value(config["general_settings"]) + path: Final = directory / "store-prompts.yaml" + path.write_text( + yaml.safe_dump({**config, "general_settings": {**general_settings, "store_prompts_in_spend_logs": True}}) + ) + return path + + +@pytest.fixture(scope="module") +def gateway(tmp_path_factory: pytest.TempPathFactory) -> Iterator[Gateway]: + with gateway_from_environment() as parent: + directory: Final = tmp_path_factory.mktemp("failing_request_stored_snapshot") + with owned_proxy(parent, directory, {}, config=_config_storing_prompts(directory)) as owned: + yield owned + + +def _transcript(marker: str) -> list[JsonValue]: + turns: Final[list[JsonValue]] = [ + {"role": role, "content": f"turn {turn // 2} {role}"} + for turn, role in enumerate(("user", "assistant") * TRANSCRIPT_TURNS) + ] + return [*turns, {"role": "user", "content": marker}] + + +def _stored_request(gateway: Gateway, call_id: str) -> JsonValue: + rows: Final = eventually( + lambda: gateway.rows('SELECT proxy_server_request FROM "LiteLLM_SpendLogs" WHERE request_id = %s', (call_id,)), + lambda values: len(values) == 1, + seconds=40, + ) + stored: Final = rows[0]["proxy_server_request"] + return json.loads(stored) if isinstance(stored, str) else stored + + +def test_failing_request_with_retries_and_fallback_stores_one_copy_of_the_request(gateway: Gateway) -> None: + with gateway.scenario() as scenario: + primary: Final = f"stored-snapshot-{uuid.uuid4().hex}" + fallback: Final = f"stored-snapshot-fb-{uuid.uuid4().hex}" + for name in (primary, primary, fallback, fallback): + scenario.model(name=name, api_base=UNREACHABLE_BASE, cooldown_time=0) + key: Final = scenario.key(models=[primary, fallback]) + marker: Final = f"stored snapshot {uuid.uuid4().hex}" + + response: Final = gateway.request( + "POST", + "/v1/chat/completions", + { + "model": primary, + "max_tokens": 16, + "messages": _transcript(marker), + "router_settings_override": {"num_retries": RETRIES, "fallbacks": [{primary: [fallback]}]}, + }, + key=key, + ) + assert response.status_code != 200, response.text + assert fallback in response.text, response.text + + serialized: Final = json.dumps(_stored_request(gateway, response.headers["x-litellm-call-id"])) + stored_kb: Final = len(serialized.encode()) / 1024 + assert marker in serialized, serialized[:400] + assert stored_kb <= STORED_REQUEST_BUDGET_KB, ( + f"one failing request stored a {stored_kb:.0f} KB request snapshot for a " + f"{2 * TRANSCRIPT_TURNS + 1}-message transcript with {RETRIES} retries and a fallback" + ) diff --git a/tests/integration/spend/test_session_grouped_logs_pages.py b/tests/integration/spend/test_session_grouped_logs_pages.py new file mode 100644 index 00000000000..7989cb6a7bc --- /dev/null +++ b/tests/integration/spend/test_session_grouped_logs_pages.py @@ -0,0 +1,467 @@ +import itertools +import json +import os +import uuid +from collections.abc import AsyncIterator, Iterator, Mapping, Sequence +from contextlib import asynccontextmanager +from dataclasses import dataclass +from datetime import UTC, datetime, timedelta +from typing import Final +from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit + +import psycopg +import pytest +from prisma import Prisma +from psycopg import sql +from pydantic import BaseModel, ConfigDict, Field + +from litellm.proxy.spend_tracking.session_page import read_session_page +from litellm.proxy.spend_tracking.spend_management_endpoints import _fetch_session_representatives +from tests.integration._support.client import Gateway +from tests.integration._support.database import write_rows + +_URL: Final = "/spend/logs/ui" +_WINDOW_SECONDS: Final = 3600 + + +@dataclass(frozen=True, slots=True) +class _Row: + request: str + session: str | None + api_key: str + second: int + status: str = "success" + model: str = "m-a" + + +_ROWS: Final = ( + _Row("z1", "z", "key-1", -1), + _Row("a1", "a", "key-1", 10), + _Row("a2", "a", "key-1", 50), + _Row("b1", "b", "key-1", 20, model="m-b"), + _Row("b2", "b", "key-1", 60), + _Row("b3", "b", "key-1", 30), + _Row("c1", "c", "key-2", 60), + _Row("d1", "b", "key-2", 40, model="m-b"), + _Row("e1", None, "key-1", 55, model="m-b"), + _Row("f1", "", "key-1", 45), + _Row("g1", "g", "key-3", 5, status="failure"), + _Row("g2", "g", "key-3", 15, status="failure"), + *(_Row(f"h{second - 69}", "h", "key-1", second) for second in range(70, 76)), + _Row("i1", "i", "key-3", 35, status="failure"), + _Row("i2", "i", "key-3", _WINDOW_SECONDS + 1), +) +_NEWEST_FIRST: Final = ( + ("h", "key-1"), + ("c", "key-2"), + ("b", "key-1"), + ("e1", "key-1"), + ("a", "key-1"), + ("f1", "key-1"), + ("b", "key-2"), + ("i", "key-3"), + ("g", "key-3"), +) + + +class _LogRow(BaseModel): + model_config = ConfigDict(frozen=True) + + request_id: str + session_id: str | None + api_key: str + session_total_count: int + + +class _Page(BaseModel): + model_config = ConfigDict(frozen=True) + + data: tuple[_LogRow, ...] + total: int + total_is_capped: bool + has_more: bool + next_session_cursor: str | None + + +@dataclass(frozen=True, slots=True) +class _Seeded: + prefix: str + window: Mapping[str, str] + + def session(self, row: _LogRow) -> tuple[str, str]: + key: Final = row.session_id or row.request_id + assert key.startswith(self.prefix), key + return key.removeprefix(self.prefix), row.api_key + + +@pytest.fixture +def seeded(gateway: Gateway) -> Iterator[_Seeded]: + prefix: Final = f"sess{uuid.uuid4().hex[:12]}-" + start: Final = datetime(1900, 1, 1) + timedelta(days=int(prefix[4:16], 16) % 200000) + rows: Final = [ + { + "request_id": f"{prefix}{row.request}", + "session_id": None if row.session is None else f"{prefix}{row.session}" if row.session else "", + "api_key": row.api_key, + "start_time": (start + timedelta(seconds=row.second)).isoformat(), + "status": row.status, + "model": row.model, + } + for row in _ROWS + ] + with gateway.scenario() as scenario: + scenario.cleanups.callback( + write_rows, 'DELETE FROM "LiteLLM_SpendLogs" WHERE request_id LIKE %s', (f"{prefix}%",) + ) + write_rows( + 'INSERT INTO "LiteLLM_SpendLogs" (request_id, call_type, api_key, "startTime", "endTime", model, ' + "session_id, status) SELECT request_id, 'acompletion', api_key, start_time, start_time, model, session_id, " + "status FROM jsonb_to_recordset(%s::jsonb) AS seeded(request_id text, session_id text, api_key text, " + "start_time timestamp, status text, model text)", + (json.dumps(rows),), + ) + yield _Seeded( + prefix, + { + "start_date": f"{start:%Y-%m-%d %H:%M:%S}", + "end_date": f"{start + timedelta(seconds=_WINDOW_SECONDS):%Y-%m-%d %H:%M:%S}", + "group_by_session": "true", + }, + ) + + +def _page(gateway: Gateway, seeded: _Seeded, params: Mapping[str, str]) -> _Page: + return _Page.model_validate(gateway.get(_URL, {**seeded.window, **params})) + + +def _cursor_walk(gateway: Gateway, seeded: _Seeded, params: Mapping[str, str], number: int = 1) -> tuple[_Page, ...]: + page: Final = _page(gateway, seeded, {**params, "page_size": "2", "page": str(number)}) + if page.next_session_cursor is None or number > len(_NEWEST_FIRST): + return (page,) + following: Final = {**params, "session_cursor": page.next_session_cursor} + return (page, *_cursor_walk(gateway, seeded, following, number + 1)) + + +def _rows(pages: tuple[_Page, ...]) -> Iterator[_LogRow]: + return itertools.chain.from_iterable(page.data for page in pages) + + +def _sessions(seeded: _Seeded, pages: tuple[_Page, ...]) -> tuple[tuple[str, str], ...]: + return tuple(seeded.session(row) for row in _rows(pages)) + + +def test_cursor_pages_show_every_session_once_newest_first_with_its_newest_row( + gateway: Gateway, seeded: _Seeded +) -> None: + pages: Final = _cursor_walk(gateway, seeded, {}) + assert _sessions(seeded, pages) == _NEWEST_FIRST + assert [(page.total, page.total_is_capped) for page in pages] == [(9, False)] * 5 + assert [page.has_more for page in pages] == [True, True, True, True, False] + representatives: Final = tuple( + (row.request_id.removeprefix(seeded.prefix), row.session_total_count) for row in _rows(pages) + ) + assert representatives == ( + ("h6", 6), + ("c1", 1), + ("b2", 3), + ("e1", 1), + ("a2", 2), + ("f1", 1), + ("d1", 1), + ("i1", 2), + ("g2", 2), + ) + + +@pytest.mark.parametrize( + "sort_order, expected", + [("desc", _NEWEST_FIRST), ("asc", tuple(reversed(_NEWEST_FIRST)))], + ids=["newest-first", "oldest-first"], +) +def test_numbered_pages_show_the_same_sessions_as_the_cursor_walk( + gateway: Gateway, seeded: _Seeded, sort_order: str, expected: tuple[tuple[str, str], ...] +) -> None: + pages: Final = tuple( + _page(gateway, seeded, {"page_size": "2", "page": str(number), "sort_order": sort_order}) + for number in range(1, 7) + ) + assert _sessions(seeded, pages) == expected + assert [len(page.data) for page in pages] == [2, 2, 2, 2, 1, 0] + assert [page.has_more for page in pages] == [True, True, True, True, False, False] + assert {page.total for page in pages} == {len(expected)} + + +def test_failure_filter_keeps_only_sessions_whose_outcome_failed(gateway: Gateway, seeded: _Seeded) -> None: + pages: Final = _cursor_walk(gateway, seeded, {"status_filter": "failure"}) + assert _sessions(seeded, pages) == (("i", "key-3"), ("g", "key-3")) + assert pages[0].total == 2 + + +def test_model_filter_ranks_each_session_by_its_matching_rows_only(gateway: Gateway, seeded: _Seeded) -> None: + pages: Final = _cursor_walk(gateway, seeded, {"model": "m-b"}) + assert _sessions(seeded, pages) == (("e1", "key-1"), ("b", "key-2"), ("b", "key-1")) + assert tuple(row.request_id.removeprefix(seeded.prefix) for row in _rows(pages)) == ("e1", "d1", "b1") + assert pages[0].total == 3 + + +_COPIED_ROWS: Final = 20_000 +_COPY_START: Final = datetime(2000, 1, 1, tzinfo=UTC) +_COPY_WINDOW: Final = ( + "\"startTime\" >= ($1::timestamptz AT TIME ZONE 'UTC') AND \"startTime\" <= ($2::timestamptz AT TIME ZONE 'UTC')" +) +_COPY_WINDOW_PARAMS: Final = (_COPY_START, _COPY_START + timedelta(days=1)) + + +def _generated_sessions(rows_per_session: int) -> tuple[sql.SQL, tuple[object, ...]]: + """`_COPIED_ROWS` rows a second apart in sessions of `rows_per_session` rows, a one-row session keyed by request_id.""" + return sql.SQL( + 'INSERT INTO {table} (request_id, call_type, api_key, "startTime", "endTime", session_id) ' + "SELECT 'r' || n, 'acompletion', 'key-1', moment, moment, " + "CASE WHEN %s > 1 THEN 's' || (n - 1) / %s END FROM generate_series(1, %s) n, " + "LATERAL (SELECT %s::timestamp + n * INTERVAL '1 second' AS moment) seconds" + ), (rows_per_session, rows_per_session, _COPIED_ROWS, _COPY_START.replace(tzinfo=None)) + + +def _sessions_tied_at_the_newest_moment() -> tuple[sql.SQL, tuple[object, ...]]: + """`_generated_sessions(1)` plus thirty one-row sessions sharing a newer moment, inserted and keyed against session + order so a read cut inside the tie misses the sessions that rank first.""" + start: Final = _COPY_START.replace(tzinfo=None) + return sql.SQL( + 'INSERT INTO {table} (request_id, call_type, api_key, "startTime", "endTime", session_id) ' + "SELECT request_id, 'acompletion', 'key-1', moment, moment, session_id FROM (" + "SELECT n AS ordinal, 'r' || n AS request_id, %s::timestamp + n * INTERVAL '1 second' AS moment, " + "NULL AS session_id FROM generate_series(1, %s) n UNION ALL " + "SELECT %s + 30 - n, 't' || lpad((29 - n)::text, 2, '0'), %s::timestamp + %s * INTERVAL '1 second', " + "'s' || lpad(n::text, 2, '0') FROM generate_series(0, 29) n) seeded ORDER BY ordinal" + ), (start, _COPIED_ROWS, _COPIED_ROWS, start, _COPIED_ROWS + 1) + + +@dataclass(frozen=True, slots=True) +class _Call: + session: str + call_type: str + status: str | None + second: int + + +def _calls(calls: tuple[_Call, ...]) -> tuple[sql.SQL, tuple[object, ...]]: + """One row per call, `second` seconds into the window, ending a second later.""" + seeded: Final = tuple( + { + "request_id": f"{call.session}-{call.second}", + "call_type": call.call_type, + "session_id": call.session, + "status": call.status, + "start_time": (_COPY_START + timedelta(seconds=call.second)).replace(tzinfo=None).isoformat(), + } + for call in calls + ) + return sql.SQL( + 'INSERT INTO {table} (request_id, call_type, api_key, "startTime", "endTime", session_id, status) ' + "SELECT request_id, call_type, 'key-1', start_time, start_time + INTERVAL '1 second', session_id, status " + "FROM jsonb_to_recordset(%s::jsonb) AS calls(request_id text, call_type text, session_id text, status text, " + "start_time timestamp)" + ), (json.dumps(seeded),) + + +@asynccontextmanager +async def _spend_logs_copy(insert: sql.SQL, args: tuple[object, ...]) -> AsyncIterator[Prisma]: + """A private schema holding a copy of the migrated `LiteLLM_SpendLogs` with its indexes, seeded by `insert`.""" + schema: Final = f"integration_{uuid.uuid4().hex}" + url: Final = os.environ["DATABASE_URL"] + table: Final = sql.Identifier(schema, "LiteLLM_SpendLogs") + with psycopg.connect(url, autocommit=True) as setup: + setup.execute(sql.SQL("CREATE SCHEMA {}").format(sql.Identifier(schema))) + try: + setup.execute(sql.SQL('CREATE TABLE {} (LIKE "LiteLLM_SpendLogs" INCLUDING ALL)').format(table)) + setup.execute(insert.format(table=table), args) + setup.execute(sql.SQL("ANALYZE {}").format(table)) + parsed: Final = urlsplit(url) + query: Final = urlencode({**dict(parse_qsl(parsed.query)), "schema": schema}) + database: Final = Prisma(datasource={"url": urlunsplit(parsed._replace(query=query))}) + await database.connect() + try: + yield database + finally: + await database.disconnect() + finally: + setup.execute(sql.SQL("DROP SCHEMA {} CASCADE").format(sql.Identifier(schema))) + + +class _PlanNode(BaseModel): + model_config = ConfigDict(frozen=True) + + relation_name: str | None = Field(default=None, alias="Relation Name") + actual_rows: float = Field(alias="Actual Rows") + rows_removed_by_filter: float = Field(default=0, alias="Rows Removed by Filter") + actual_loops: int = Field(alias="Actual Loops") + plans: tuple["_PlanNode", ...] = Field(default=(), alias="Plans") + + def spend_log_rows_scanned(self) -> int: + own: Final = ( + (self.actual_rows + self.rows_removed_by_filter) * self.actual_loops + if self.relation_name == "LiteLLM_SpendLogs" + else 0 + ) + return round(own) + sum(child.spend_log_rows_scanned() for child in self.plans) + + +class _Explained(BaseModel): + model_config = ConfigDict(frozen=True) + + plan: _PlanNode = Field(alias="Plan") + + +class _ExplainedReads: + """Runs each read through Prisma, first under EXPLAIN ANALYZE to count the `LiteLLM_SpendLogs` rows it scans.""" + + def __init__(self, database: Prisma) -> None: + self.database: Final = database + self.rows_scanned: tuple[int, ...] = () + + @asynccontextmanager + async def tx(self, *, timeout: timedelta) -> AsyncIterator["_ExplainedReads"]: + async with self.database.tx(timeout=timeout) as transaction: + reads: Final = _ExplainedReads(transaction) + try: + yield reads + finally: + self.rows_scanned = (*self.rows_scanned, *reads.rows_scanned) + + async def execute_raw(self, query: str, *args: object) -> int: + return await self.database.execute_raw(query, *args) + + async def query_raw(self, query: str, *args: object) -> Sequence[Mapping[str, object]]: + explained: Final = await self.database.query_raw(f"EXPLAIN (ANALYZE, FORMAT JSON) {query}", *args) + plan: Final = _Explained.model_validate(explained[0]["QUERY PLAN"][0]).plan + self.rows_scanned = (*self.rows_scanned, plan.spend_log_rows_scanned()) + return await self.database.query_raw(query, *args) + + +@pytest.mark.parametrize( + "rows_per_session, page, newest_session, rows_scanned_per_read, total", + [ + pytest.param(1, 1, ("r", 20_000), (102,), (100, True), id="first-page"), + pytest.param(1, 3, ("r", 19_980), (102,), (100, True), id="third-page"), + pytest.param(10, 1, ("s", 1_999), (102, 1_171), (100, True), id="second-read-sized-by-the-first-reads-density"), + pytest.param(100, 1, ("s", 199), (102, 6_439, 12_878), (100, True), id="third-read-at-least-doubled"), + pytest.param(1_000, 1, ("s", 19), (102, 12_878, 20_000), (20, False), id="last-read-runs-out-of-rows"), + ], +) +async def test_a_newest_first_page_scans_only_the_rows_its_reads_ask_for( + rows_per_session: int, + page: int, + newest_session: tuple[str, int], + rows_scanned_per_read: tuple[int, ...], + total: tuple[int, bool], +) -> None: + async with _spend_logs_copy(*_generated_sessions(rows_per_session)) as database: + reads: Final = _ExplainedReads(database) + session_page: Final = await read_session_page( + reads, + where_clause=_COPY_WINDOW, + params=_COPY_WINDOW_PARAMS, + page=page, + page_size=10, + status=None, + sort_desc=True, + session_cursor=None, + cap=100, + ) + prefix, newest = newest_session + assert tuple(session.key for session in session_page.sessions) == tuple( + f"{prefix}{newest - n}" for n in range(10) + ) + assert (session_page.total, session_page.total_is_capped) == total + assert reads.rows_scanned == rows_scanned_per_read + + +async def test_a_page_cut_inside_a_tie_reads_on_until_the_tie_is_whole() -> None: + async with _spend_logs_copy(*_sessions_tied_at_the_newest_moment()) as database: + reads: Final = _ExplainedReads(database) + session_page: Final = await read_session_page( + reads, + where_clause=_COPY_WINDOW, + params=_COPY_WINDOW_PARAMS, + page=1, + page_size=5, + status=None, + sort_desc=True, + session_cursor=None, + cap=10, + ) + assert tuple(session.key for session in session_page.sessions) == ("s29", "s28", "s27", "s26", "s25") + assert reads.rows_scanned == (12, 24, 48) + + +@dataclass(frozen=True, slots=True) +class _ExplainedPrisma: + read_db: _ExplainedReads + + +async def test_a_pages_representatives_are_found_by_session_not_by_scanning_the_window() -> None: + async with _spend_logs_copy(*_generated_sessions(rows_per_session=10)) as database: + reads: Final = _ExplainedReads(database) + representatives: Final = await _fetch_session_representatives( + prisma_client=_ExplainedPrisma(reads), # pyright: ignore[reportArgumentType] # only .read_db is used + where_clause=_COPY_WINDOW, + sql_params=_COPY_WINDOW_PARAMS, + next_param_index=len(_COPY_WINDOW_PARAMS) + 1, + session_keys=(("s1999", "key-1"), ("s1998", "key-1")), + ) + assert tuple(row["request_id"] for row in representatives) == ("r20000", "r19990") + assert reads.rows_scanned == (20,), "only the two sessions' rows, not the window's 20,000" + + +async def test_a_page_number_far_past_the_cap_shows_no_session() -> None: + async with _spend_logs_copy(*_generated_sessions(rows_per_session=1)) as database: + session_page: Final = await read_session_page( + _ExplainedReads(database), + where_clause=_COPY_WINDOW, + params=_COPY_WINDOW_PARAMS, + page=9_300_000_000_000_000, + page_size=1000, + status=None, + sort_desc=True, + session_cursor=None, + cap=100, + ) + assert (session_page.sessions, session_page.total, session_page.total_is_capped) == ((), 100, True) + + +_OUTCOME_CALLS: Final = ( + _Call("agent", "asend_message", "failure", 10), + _Call("agent", "acompletion", "success", 20), + _Call("mcp", "acompletion", "failure", 5), + _Call("mcp", "call_mcp_tool", "success", 30), + _Call("unknown", "acompletion", None, 40), + _Call("failed", "acompletion", "success", 1), + _Call("failed", "acompletion", "failure", 50), + _Call("recovered", "acompletion", "failure", 2), + _Call("recovered", "acompletion", "success", 45), +) + + +@pytest.mark.parametrize( + "status, expected", + [("failure", ("failed", "mcp", "agent")), ("success", ("recovered", "unknown"))], +) +async def test_a_status_filter_keeps_sessions_whose_representative_call_has_that_status( + status: str, expected: tuple[str, ...] +) -> None: + """An agent message stands for its session, then the newest call that is not an MCP tool call, and a missing status + counts as success. The page stays ordered by each session's newest call.""" + async with _spend_logs_copy(*_calls(_OUTCOME_CALLS)) as database: + session_page: Final = await read_session_page( + _ExplainedReads(database), + where_clause=_COPY_WINDOW, + params=_COPY_WINDOW_PARAMS, + status=status, + page=1, + page_size=10, + sort_desc=True, + session_cursor=None, + cap=100, + ) + assert tuple(session.key for session in session_page.sessions) == expected + assert (session_page.total, session_page.total_is_capped) == (len(expected), False) diff --git a/tests/integration/streaming/test_interception_converted_stream_options.py b/tests/integration/streaming/test_interception_converted_stream_options.py index 5a7855102c0..6fc38dfb440 100644 --- a/tests/integration/streaming/test_interception_converted_stream_options.py +++ b/tests/integration/streaming/test_interception_converted_stream_options.py @@ -848,60 +848,36 @@ def test_wrong_azure_key_surfaces_azures_401(rig: _Rig, scenario: Scenario) -> N assert len(_upstream(rig, deployment).model_calls) == 1 -def _tool_call(call: Mapping[str, JsonValue]) -> tuple[str, Mapping[str, JsonValue]]: - function: Final = object_value(call["function"]) - return string_value(function["name"]), iv.JSON_OBJECT.validate_json(string_value(function["arguments"])) +def _assert_failed_once(call_id: str, *, spend: float) -> None: + rows: Final = eventually( + lambda: read_rows('SELECT status, spend FROM "LiteLLM_SpendLogs" WHERE litellm_call_id = %s', (call_id,)), + lambda found: len(found) >= 1, + seconds=70, + ) + assert [row["status"] for row in rows] == ["failure"], rows + assert float(rows[0]["spend"]) == pytest.approx(spend), rows -def _stream_choices(stream: _Stream) -> Iterator[Mapping[str, JsonValue]]: - for frame in stream.frames: - yield from rv.ITEMS.validate_python(frame.get("choices") or []) - - -def _streamed_tool_calls(stream: _Stream) -> Iterator[tuple[str, Mapping[str, JsonValue]]]: - for choice in _stream_choices(stream): - yield from map( - _tool_call, rv.ITEMS.validate_python(object_value(choice.get("delta") or {}).get("tool_calls") or []) - ) - - -def _handed_back(deployment: _Deployment) -> list[tuple[str, Mapping[str, JsonValue]]]: - return [(iv.SEARCH_TOOL, {"query": iv.query(deployment.name)})] - - -def _assert_succeeded_once(model: str, identity: str) -> None: - rows: Final = _spend_rows(model, 1) - assert [string_value(row["request_id"]) for row in rows if row["status"] == "success"] == [identity], rows - - -def test_failed_follow_up_call_hands_the_search_tool_call_back_with_its_usage(rig: _Rig, scenario: Scenario) -> None: - deployment: Final = _deploy(rig, scenario, "followup-fails") - stream: Final = _chat(rig, _chat_body(deployment, extra={**_NO_CACHE, "stream_options": dict(_USAGE)})) - assert stream.status == 200 and stream.content == "", stream.text - assert list(_streamed_tool_calls(stream)) == _handed_back(deployment), stream.text - finished: Final = [choice["finish_reason"] for choice in _stream_choices(stream) if choice.get("finish_reason")] - assert finished == ["tool_calls"], stream.text - assert [_totals(usage) for usage in stream.usages] == [iv.FIRST_USAGE["total_tokens"]], stream.text - (identity,) = stream.ids +@pytest.mark.parametrize("stream", [True, False], ids=["stream", "json"]) +def test_failed_follow_up_call_answers_with_the_providers_error_instead_of_the_search_tool_call( + rig: _Rig, scenario: Scenario, stream: bool +) -> None: + deployment: Final = _deploy( + rig, scenario, "followup-fails", priced=True + ) + extra: Final = {**_NO_CACHE, "stream_options": dict(_USAGE)} if stream else _NO_CACHE + body: Final = _chat_body(deployment, stream=stream, extra=extra) + response: Final = rig.gateway.request("POST", "/v1/chat/completions", body) + assert response.status_code == 500 and iv.FOLLOWUP_FAILURE in response.text, response.text + assert iv.SEARCH_TOOL not in response.text, response.text _assert_upstream(_upstream(rig, deployment), model_calls=2, searches=1) - _assert_succeeded_once(deployment.model, identity) + _assert_failed_once( + response.headers["x-litellm-call-id"], + spend=iv.FIRST_USAGE["prompt_tokens"] * 0.001 + iv.FIRST_USAGE["completion_tokens"] * 0.002, + ) assert rig.gateway.chat(_deploy(rig, scenario, "plain").model)["object"] == "chat.completion" -def test_failed_follow_up_call_on_a_json_request_hands_the_search_tool_call_back(rig: _Rig, scenario: Scenario) -> None: - deployment: Final = _deploy(rig, scenario, "followup-fails") - response: Final = rig.gateway.request("POST", "/v1/chat/completions", _chat_body(deployment, stream=False)) - assert response.status_code == 200, response.text - body: Final = iv.JSON_OBJECT.validate_json(response.content) - (choice,) = rv.ITEMS.validate_python(body["choices"]) - calls: Final = rv.ITEMS.validate_python(object_value(choice["message"])["tool_calls"]) - assert [_tool_call(call) for call in calls] == _handed_back(deployment), body - assert choice["finish_reason"] == "tool_calls", body - assert _totals(object_value(body["usage"])) == iv.FIRST_USAGE["total_tokens"], body - _assert_upstream(_upstream(rig, deployment), model_calls=2, searches=1) - _assert_succeeded_once(deployment.model, string_value(body["id"])) - - def test_search_outage_still_restreams_the_follow_up_answer(rig: _Rig, scenario: Scenario) -> None: deployment: Final = _deploy(rig, scenario, "search-down") stream: Final = _chat(rig, _chat_body(deployment, extra={**_NO_CACHE, "stream_options": dict(_USAGE)})) diff --git a/tests/test_litellm_rust/cache/test_python_cache.py b/tests/test_litellm_rust/cache/test_python_cache.py index beb818c7162..c9ac7af1a78 100644 --- a/tests/test_litellm_rust/cache/test_python_cache.py +++ b/tests/test_litellm_rust/cache/test_python_cache.py @@ -5,13 +5,12 @@ import pytest from pydantic import TypeAdapter import litellm -from litellm import _v2 from litellm.caching.caching import Cache, CacheMode from litellm.caching.caching_handler import ( _PENDING_CACHE_WRITES, # pyright: ignore[reportPrivateUsage] # await the existing background cache writer before the next request ) from litellm.litellm_core_utils.add_retry_fallback_headers import get_hidden_params_dict -from litellm.proxy._types import Litellm_EntityType, UserAPIKeyAuth +from litellm.proxy._types import Litellm_EntityType from litellm.proxy.hooks.model_max_budget_limiter import ( PROXY_VirtualKeyModelMaxBudgetLimiter, model_budget_spend_cache_key, @@ -19,10 +18,8 @@ from litellm.proxy.hooks.model_max_budget_limiter import ( from litellm.proxy.hooks.parallel_request_limiter_v3 import PROXY_MaxParallelRequestsHandler_v3 from litellm.proxy.utils import InternalUsageCache from litellm.rust_bridge.catalog import Route -from litellm.types.caching import CachingSupportedCallTypes from tests.test_litellm_rust.cache.support import cache_key, collect, invoke, payload from tests.test_litellm_rust.messages.data import MESSAGES, MESSAGES_EVENTS, MESSAGES_MODEL, MESSAGES_RESPONSE -from tests.test_litellm_rust.responses.data import RESPONSES_RESPONSE from tests.test_litellm_rust.support.callback_recorder import RecordingLogger, drain_logging, request_body from tests.test_litellm_rust.support.recording_server import RecordingServer, ResponseSpec from tests.test_litellm_rust.support.routing import native_route @@ -32,27 +29,18 @@ pytestmark = pytest.mark.requires_rust_extension @pytest.mark.asyncio @pytest.mark.parametrize( - ("route", "stream", "legacy"), - ( - ("chat", False, False), - ("messages", False, False), - ("responses", False, False), - ("messages", True, False), - ("messages", False, True), - ("messages", True, True), - ), + "stream", + (False, True), ) @pytest.mark.parametrize("native", (False, True), ids=("python", "rust")) -async def test_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( +async def test_legacy_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( recording_server: RecordingServer, - route: Literal["chat", "messages", "responses"], stream: bool, native: bool, monkeypatch: pytest.MonkeyPatch, - legacy: bool, ) -> None: monkeypatch.setenv("LITELLM_RUST", "1" if native else "0") - litellm.cache = Cache() if legacy else _v2.Cache.memory() + litellm.cache = Cache() counters: Final = litellm.DualCache() budget: Final = PROXY_VirtualKeyModelMaxBudgetLimiter(counters) limiter: Final = PROXY_MaxParallelRequestsHandler_v3( @@ -72,7 +60,7 @@ async def test_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( } spend_key: Final = model_budget_spend_cache_key(Litellm_EntityType.KEY, key_hash, "cached-model", "1h") token_key: Final = limiter.create_rate_limit_keys("api_key", key_hash, "tokens") - first: Final = await invoke(route, recording_server, options, native=native) + first: Final = await invoke("messages", recording_server, options, native=native) if stream: await collect(first) await asyncio.gather(*tuple(_PENDING_CACHE_WRITES)) @@ -81,13 +69,13 @@ async def test_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( first_log: Final = TypeAdapter(dict[str, object]).validate_python(first_events[0].kwargs) first_payload: Final = TypeAdapter(dict[str, object]).validate_python(first_log["standard_logging_object"]) expected_cost: Final = TypeAdapter(float).validate_python(first_log["response_cost"]) - usage: Final = RESPONSES_RESPONSE["usage"] if route == "responses" else MESSAGES_RESPONSE["usage"] + usage: Final = MESSAGES_RESPONSE["usage"] expected_tokens: Final = usage["input_tokens"] + usage["output_tokens"] assert expected_cost > 0 assert counters.get_cache(spend_key) == pytest.approx(expected_cost) assert counters.get_cache(token_key) == first_payload["total_tokens"] == expected_tokens - second: Final = await invoke(route, recording_server, options, native=native) + second: Final = await invoke("messages", recording_server, options, native=native) if stream: await collect(second) await drain_logging() @@ -101,7 +89,7 @@ async def test_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( assert cached_payload["cache_hit"] is True assert cached_payload["id"] != first_payload["id"] assert cached_payload["custom_llm_provider"] == first_payload["custom_llm_provider"] - assert cached_payload["custom_llm_provider"] == ("openai" if route == "responses" else "anthropic"), { + assert cached_payload["custom_llm_provider"] == "anthropic", { "miss_provider": first_log.get("custom_llm_provider"), "hit_provider": cached_log.get("custom_llm_provider"), } @@ -111,110 +99,22 @@ async def test_cache_hit_keeps_model_budget_spend_but_accounts_for_usage( @pytest.mark.asyncio -@pytest.mark.parametrize("native", (False, True), ids=("python", "rust")) -@pytest.mark.parametrize("backend", ("disabled", "memory", "redis")) -async def test_response_cache_backend_does_not_control_coordination( - recording_server: RecordingServer, - native: bool, - monkeypatch: pytest.MonkeyPatch, - backend: Literal["disabled", "memory", "redis"], - redis_url: str, -) -> None: - monkeypatch.setenv("LITELLM_RUST", "1" if native else "0") - litellm.cache = ( - None - if backend == "disabled" - else _v2.Cache.memory() - if backend == "memory" - else _v2.Cache.redis(redis_url, namespace="independent-coordination") - ) - recording_server.expected_requests = 2 if backend == "disabled" else 1 - counters: Final = litellm.DualCache() - budget: Final = PROXY_VirtualKeyModelMaxBudgetLimiter(counters) - limiter: Final = PROXY_MaxParallelRequestsHandler_v3( - InternalUsageCache(counters), model_group_resolver=lambda model: model - ) - key_hash: Final = "b" * 64 - identity: Final = UserAPIKeyAuth(api_key=key_hash, rpm_limit=2, tpm_limit=1000, max_parallel_requests=1) - spend_key: Final = model_budget_spend_cache_key(Litellm_EntityType.KEY, key_hash, "cached-model", "1h") - request_key: Final = limiter.create_rate_limit_keys("api_key", key_hash, "requests") - token_key: Final = limiter.create_rate_limit_keys("api_key", key_hash, "tokens") - parallel_key: Final = limiter.create_rate_limit_keys("api_key", key_hash, "max_parallel_requests") - expected_tokens: Final = MESSAGES_RESPONSE["usage"]["input_tokens"] + MESSAGES_RESPONSE["usage"]["output_tokens"] - recorder: Final = RecordingLogger() - - async def request(call_id: str, successes: int) -> object: - data: Final = { - "model": MESSAGES_MODEL, - "messages": list(MESSAGES), - "litellm_call_id": call_id, - "max_tokens": 32, - "metadata": { - "user_api_key": key_hash, - "model_group": "cached-model", - "user_api_key_model_max_budget": {"cached-model": {"max_budget": 1, "budget_duration": "1h"}}, - }, - } - await limiter.async_pre_call_hook(identity, counters, data, "acompletion") - assert len(TypeAdapter(dict[str, float]).validate_python(counters.get_cache(parallel_key))) == 1 - response: Final = await invoke( - "chat", recording_server, {**data, "callbacks": [budget, limiter, recorder]}, native=native - ) - await asyncio.gather(*tuple(_PENDING_CACHE_WRITES)) - await recorder.wait_for_async("async_log_success_event", count=successes) - return response - - await asyncio.create_task(request("cache-miss", 1)) - assert counters.get_cache(parallel_key) == {} - assert counters.get_cache(request_key) == 1 - assert counters.get_cache(token_key) == expected_tokens - first_events: Final = await recorder.wait_for_async("async_log_success_event") - first_cost: Final = TypeAdapter(float).validate_python(first_events[0].kwargs["response_cost"]) - assert first_cost > 0 - assert counters.get_cache(spend_key) == pytest.approx(first_cost) - await asyncio.create_task(request("cache-hit", 2)) - expected_spend: Final = first_cost * recording_server.expected_requests - assert counters.get_cache(spend_key) == pytest.approx(expected_spend) - assert len(recording_server.requests) == recording_server.expected_requests - assert counters.get_cache(parallel_key) == {} - assert counters.get_cache(request_key) == 2 - assert counters.get_cache(token_key) == 2 * expected_tokens - with pytest.raises(litellm.RateLimitError): - await asyncio.create_task(request("over-rpm-limit", 3)) - assert len(recording_server.requests) == recording_server.expected_requests - assert counters.get_cache(parallel_key) == {} - assert counters.get_cache(token_key) == 2 * expected_tokens - - assert counters.get_cache(spend_key) == pytest.approx(expected_spend) - if litellm.cache is not None: - await litellm.cache.disconnect() - - -@pytest.mark.asyncio -@pytest.mark.parametrize( - ("route", "legacy"), (("chat", False), ("messages", False), ("responses", False), ("messages", True)) -) -async def test_cache_controls_and_backend_credential_key_semantics( - recording_server: RecordingServer, - route: Literal["chat", "messages", "responses"], - legacy: bool, -) -> None: - recording_server.expected_requests = 3 if legacy else 4 - litellm.cache = Cache() if legacy else _v2.Cache.memory() - await invoke(route, recording_server, {"cache": {"no-store": True}}) - await invoke(route, recording_server, {}) - await invoke(route, recording_server, {}) +async def test_legacy_cache_controls_and_backend_credential_key_semantics(recording_server: RecordingServer) -> None: + recording_server.expected_requests = 3 + litellm.cache = Cache() + await invoke("messages", recording_server, {"cache": {"no-store": True}}) + await invoke("messages", recording_server, {}) + await invoke("messages", recording_server, {}) assert len(recording_server.requests) == 2 - await invoke(route, recording_server, {"cache": {"no-cache": True}}) - await invoke(route, recording_server, {"api_key": "another-key"}) + await invoke("messages", recording_server, {"cache": {"no-cache": True}}) + await invoke("messages", recording_server, {"api_key": "another-key"}) assert len(recording_server.requests) == recording_server.expected_requests @pytest.mark.asyncio -@pytest.mark.parametrize("legacy", (False, True)) -async def test_v2_messages_replays_a_completed_stream(recording_server: RecordingServer, legacy: bool) -> None: +async def test_legacy_messages_replays_a_completed_stream(recording_server: RecordingServer) -> None: recording_server.default_response = ResponseSpec(body=None, events=MESSAGES_EVENTS) - litellm.cache = Cache() if legacy else _v2.Cache.memory() + litellm.cache = Cache() recorder: Final = RecordingLogger() parameters: Final = { "model": MESSAGES_MODEL, @@ -280,74 +180,30 @@ async def test_rust_messages_uses_a_legacy_cache_without_python_inference( @pytest.mark.asyncio -@pytest.mark.parametrize("route", ("chat", "messages", "responses")) @pytest.mark.parametrize("native", (False, True)) -@pytest.mark.parametrize("excluded", (None, [], ["embedding"])) -async def test_v2_cache_honors_supported_call_types_for_reads_and_writes( +async def test_legacy_cache_default_off_requires_opt_in_even_for_existing_entries( recording_server: RecordingServer, monkeypatch: pytest.MonkeyPatch, - route: Literal["chat", "messages", "responses"], native: bool, - excluded: list[CachingSupportedCallTypes] | None, ) -> None: monkeypatch.setenv("LITELLM_RUST", "1" if native else "0") - litellm.cache = _v2.Cache.memory() - call_type: Final[CachingSupportedCallTypes] = ( - "acompletion" if route == "chat" else "anthropic_messages" if route == "messages" else "aresponses" - ) - recording_server.expected_requests = 4 - litellm.cache.supported_call_types = excluded - await invoke(route, recording_server, {}, native=native) - await invoke(route, recording_server, {}, native=native) - assert len(recording_server.requests) == 2 - litellm.cache.supported_call_types = [call_type] - await invoke(route, recording_server, {}, native=native) - assert len(recording_server.requests) == 3 - await asyncio.gather(*tuple(_PENDING_CACHE_WRITES)) - await invoke(route, recording_server, {}, native=native) - assert len(recording_server.requests) == 3 - litellm.cache.supported_call_types = excluded - await invoke(route, recording_server, {}, native=native) - assert len(recording_server.requests) == 4 - - -@pytest.mark.asyncio -@pytest.mark.parametrize("native", (False, True)) -@pytest.mark.parametrize( - ("route", "legacy"), (("chat", False), ("messages", False), ("responses", False), ("messages", True)) -) -async def test_v2_default_off_requires_opt_in_even_for_existing_entries( - recording_server: RecordingServer, - monkeypatch: pytest.MonkeyPatch, - route: Literal["chat", "messages", "responses"], - native: bool, - legacy: bool, -) -> None: - monkeypatch.setenv("LITELLM_RUST", "1" if native else "0") - litellm.cache = Cache() if legacy else _v2.Cache.memory() + litellm.cache = Cache() litellm.cache.mode = CacheMode.default_off recording_server.expected_requests = 4 - await invoke(route, recording_server, {}, native=native) - await invoke(route, recording_server, {}, native=native) + await invoke("messages", recording_server, {}, native=native) + await invoke("messages", recording_server, {}, native=native) assert len(recording_server.requests) == 2 - await invoke(route, recording_server, {"cache": {"use-cache": True}}, native=native) + await invoke("messages", recording_server, {"cache": {"use-cache": True}}, native=native) assert len(recording_server.requests) == 3 await asyncio.gather(*tuple(_PENDING_CACHE_WRITES)) - await invoke(route, recording_server, {"cache": {"use-cache": True}}, native=native) + await invoke("messages", recording_server, {"cache": {"use-cache": True}}, native=native) assert len(recording_server.requests) == 3 - await invoke(route, recording_server, {}, native=native) + await invoke("messages", recording_server, {}, native=native) assert len(recording_server.requests) == 4 @pytest.mark.asyncio -@pytest.mark.parametrize( - ("route", "legacy"), (("chat", False), ("messages", False), ("responses", False), ("messages", True)) -) -async def test_cache_lookup_uses_backend_request_callback_semantics( - recording_server: RecordingServer, - route: Literal["chat", "messages", "responses"], - legacy: bool, -) -> None: +async def test_legacy_cache_lookup_uses_backend_request_callback_semantics(recording_server: RecordingServer) -> None: class Rewrite(RecordingLogger): temperature = 0.1 @@ -356,22 +212,40 @@ async def test_cache_lookup_uses_backend_request_callback_semantics( super().log_pre_api_call(model, messages, kwargs) logger: Final = Rewrite() - litellm.cache = Cache() if legacy else _v2.Cache.memory() - recording_server.expected_requests = 1 if legacy else 2 - await invoke(route, recording_server, {"callbacks": [logger]}) - first_hit: Final = await invoke(route, recording_server, {"callbacks": [logger]}) + litellm.cache = Cache() + recording_server.expected_requests = 1 + await invoke("messages", recording_server, {"callbacks": [logger]}) + first_hit: Final = await invoke("messages", recording_server, {"callbacks": [logger]}) logger.temperature = 0.8 - await invoke(route, recording_server, {"callbacks": [logger]}) - second_hit: Final = await invoke(route, recording_server, {"callbacks": [logger]}) + await invoke("messages", recording_server, {"callbacks": [logger]}) + second_hit: Final = await invoke("messages", recording_server, {"callbacks": [logger]}) assert logger.names.count("log_pre_api_call") == 4 assert len(recording_server.requests) == recording_server.expected_requests assert recording_server.requests[0].body["temperature"] == 0.1 - if not legacy: - assert recording_server.requests[1].body["temperature"] == 0.8 assert isinstance(cache_key(first_hit), str) assert isinstance(cache_key(second_hit), str) - if not legacy: - assert cache_key(first_hit) != cache_key(second_hit) + + +@pytest.mark.asyncio +async def test_rust_chat_completions_rejects_a_configured_python_cache( + recording_server: RecordingServer, monkeypatch: pytest.MonkeyPatch +) -> None: + recording_server.expected_requests = 0 + monkeypatch.setattr(litellm, "cache", Cache()) + with pytest.raises(RuntimeError, match="this inference route does not support configured caches"): + await invoke("chat", recording_server, {}) + assert recording_server.requests == [] + + +@pytest.mark.asyncio +async def test_rust_responses_rejects_a_configured_python_cache( + recording_server: RecordingServer, monkeypatch: pytest.MonkeyPatch +) -> None: + recording_server.expected_requests = 0 + monkeypatch.setattr(litellm, "cache", Cache()) + with pytest.raises(RuntimeError, match="this inference route does not support configured caches"): + await invoke("responses", recording_server, {}) + assert recording_server.requests == [] @pytest.mark.asyncio diff --git a/tests/test_litellm_rust/cache/v2/test_cache.py b/tests/test_litellm_rust/cache/v2/test_cache.py deleted file mode 100644 index 1461e6eac61..00000000000 --- a/tests/test_litellm_rust/cache/v2/test_cache.py +++ /dev/null @@ -1,207 +0,0 @@ -import asyncio -from collections.abc import AsyncIterator -from typing import Final, Literal - -import pytest -from pydantic import TypeAdapter - -import litellm -from litellm import _v2 -from litellm._v2.cache import NativeBackend -from litellm.caching.caching_handler import ( - _PENDING_CACHE_WRITES, # pyright: ignore[reportPrivateUsage] # await the existing background cache writer before the next request -) -from litellm.litellm_core_utils.add_retry_fallback_headers import get_hidden_params_dict -from litellm.types.utils import ModelResponse -from tests.test_litellm_rust.messages.data import MESSAGES, MESSAGES_MODEL, MESSAGES_RESPONSE -from tests.test_litellm_rust.responses.data import RESPONSES_MODEL, RESPONSES_RESPONSE, RESPONSES_TEXT -from tests.test_litellm_rust.cache.support import cache_key, invoke, payload -from tests.test_litellm_rust.support.callback_recorder import RecordingLogger, drain_logging -from tests.test_litellm_rust.support.recording_server import RecordingServer, ResponseSpec - -pytestmark = pytest.mark.requires_rust_extension - - -@pytest.mark.asyncio -@pytest.mark.parametrize("route", ("chat", "messages", "responses")) -@pytest.mark.parametrize("backend", ("memory", "redis")) -async def test_v2_cache_skips_provider_and_reports_one_success_per_call( - recording_server: RecordingServer, - route: Literal["chat", "messages", "responses"], - backend: Literal["memory", "redis"], - redis_url: str, -) -> None: - recording_server.expected_requests = 2 - litellm.cache = _v2.Cache.memory() if backend == "memory" else _v2.Cache.redis(redis_url, namespace="headers") - recorder: Final = RecordingLogger() - first: Final = await invoke(route, recording_server, {"callbacks": [recorder]}) - await recorder.wait_for_async("async_log_success_event") - second: Final = await invoke(route, recording_server, {"callbacks": [recorder]}) - assert payload(first) == payload(second) - assert cache_key(first) is None - key: Final = cache_key(second) - assert isinstance(key, str) - assert key == get_hidden_params_dict(second)["cache_key"] - assert len(recording_server.requests) == 1 - await drain_logging() - successes: Final = await recorder.wait_for_async("async_log_success_event", count=2) - assert len(successes) == 2 - cached_log: Final = TypeAdapter(dict[str, object]).validate_python(successes[-1].kwargs) - assert cached_log["cache_hit"] is True - assert cached_log["response_cost"] == 0 - await litellm.cache.delete_cache_keys([key]) - refreshed: Final = await invoke(route, recording_server, {"callbacks": [recorder]}) - assert cache_key(refreshed) is None - assert len(recording_server.requests) == 2 - assert len(await recorder.wait_for_async("async_log_success_event", count=3)) == 3 - await litellm.cache.disconnect() - - -@pytest.mark.asyncio -async def test_v2_global_cache_leaves_legacy_only_calls_usable() -> None: - litellm.cache = _v2.Cache.memory() - response: Final = await litellm.aembedding( - model="openai/cache-test-embedding", - input=["hello"], - api_key="test-key", - mock_response=[0.25, 0.75], - ) - assert response.model_dump(include={"data"}) == { - "data": [{"embedding": [0.25, 0.75], "index": 0, "object": "embedding"}] - } - - -@pytest.mark.parametrize("route", ("chat", "responses")) -def test_v2_cache_works_through_python_inference( - recording_server: RecordingServer, route: Literal["chat", "messages", "responses"], monkeypatch: pytest.MonkeyPatch -) -> None: - monkeypatch.setenv("LITELLM_RUST", "0") - litellm.cache = _v2.Cache.memory() - common: Final = {"api_key": "test-key", "api_base": recording_server.base_url} - if route == "responses": - recording_server.default_response = ResponseSpec(body=RESPONSES_RESPONSE) - parameters: Final = {"model": RESPONSES_MODEL, "input": "hello", **common} - first: Final = litellm.responses(**parameters) - second: Final = litellm.responses(**parameters) - assert payload(first) == payload(second) - else: - recording_server.default_response = ResponseSpec(body=MESSAGES_RESPONSE) - arguments: Final = {"model": MESSAGES_MODEL, "messages": list(MESSAGES), "max_tokens": 32, **common} - initial: Final = litellm.completion(**arguments) - cached: Final = litellm.completion(**arguments) - assert isinstance(initial, ModelResponse) and isinstance(cached, ModelResponse) - assert ( - initial.choices[0].message.content - == cached.choices[0].message.content - == MESSAGES_RESPONSE["content"][0]["text"] - ) - assert len(recording_server.requests) == 1 - - -@pytest.mark.asyncio -async def test_v2_facade_and_backend_share_storage_and_management() -> None: - cache: Final = _v2.Cache.memory() - await cache.async_add_cache({"answer": 7}, cache_key="shared") - assert cache.get_cache(cache_key="shared") == {"answer": 7} - assert await cache.ping() is True - await cache.delete_cache_keys(["shared"]) - assert await cache.async_get_cache(cache_key="shared") is None - cache.add_cache({"answer": 8}, cache_key="flush") - backend: Final = cache.cache - assert isinstance(backend, NativeBackend) - backend.flush_cache() - assert cache.get_cache(cache_key="flush") is None - await cache.disconnect() - - -@pytest.mark.asyncio -@pytest.mark.parametrize("control", ("s-maxage", "s-max-age")) -async def test_v2_native_cache_accepts_existing_freshness_aliases( - recording_server: RecordingServer, control: str -) -> None: - litellm.cache = _v2.Cache.memory() - first: Final = await invoke("responses", recording_server, {}) - second: Final = await invoke("responses", recording_server, {"cache": {control: 600}}) - assert payload(first) == payload(second) - assert len(recording_server.requests) == 1 - - -@pytest.mark.asyncio -async def test_v2_cache_does_not_force_native_responses_streaming( - recording_server: RecordingServer, monkeypatch: pytest.MonkeyPatch -) -> None: - monkeypatch.setenv("LITELLM_RUST", "0") - litellm.cache = _v2.Cache.memory() - recording_server.default_response = ResponseSpec( - body=None, - events=( - ("response.created", {"type": "response.created", "sequence_number": 0, "response": RESPONSES_RESPONSE}), - ( - "response.completed", - {"type": "response.completed", "sequence_number": 1, "response": RESPONSES_RESPONSE}, - ), - ), - ) - response: Final = await litellm.aresponses( - model=RESPONSES_MODEL, - input="hello", - stream=True, - caching=False, - api_key="test-key", - api_base=recording_server.base_url, - ) - assert isinstance(response, AsyncIterator) - chunks: Final = [chunk async for chunk in response] - assert chunks[-1].type == "response.completed" - assert chunks[-1].response.output[0].content[0].text == RESPONSES_TEXT - - -@pytest.mark.asyncio -async def test_v2_cache_works_through_python_messages( - recording_server: RecordingServer, monkeypatch: pytest.MonkeyPatch -) -> None: - monkeypatch.setenv("LITELLM_RUST", "0") - litellm.cache = _v2.Cache.memory() - recording_server.default_response = ResponseSpec(body=MESSAGES_RESPONSE) - parameters: Final = { - "model": MESSAGES_MODEL, - "messages": list(MESSAGES), - "max_tokens": 32, - "api_key": "test-key", - "api_base": recording_server.base_url, - } - first: Final = await litellm.anthropic_messages(**parameters) - await asyncio.gather(*tuple(_PENDING_CACHE_WRITES)) - second: Final = await litellm.anthropic_messages(**parameters) - assert payload(first) == payload(second) - assert len(recording_server.requests) == 1 - - -@pytest.mark.asyncio -@pytest.mark.parametrize("asynchronous", (False, True)) -async def test_v2_redis_flush_only_removes_its_namespace( - redis_url: str, recording_server: RecordingServer, asynchronous: bool -) -> None: - own: Final = _v2.Cache.redis(redis_url, namespace="flush-own") - other: Final = _v2.Cache.redis(redis_url, namespace="flush-other") - litellm.cache = own - recording_server.expected_requests = 2 - await own.async_add_cache({"answer": "own"}, cache_key="shared") - await other.async_add_cache({"answer": "other"}, cache_key="shared") - await invoke("responses", recording_server, {}) - hit: Final = await invoke("responses", recording_server, {}) - assert isinstance(cache_key(hit), str) - assert await own.async_get_cache(cache_key="shared") == {"answer": "own"} - backend: Final = own.cache - assert isinstance(backend, NativeBackend) - if asynchronous: - await backend.async_flush_cache() - else: - backend.flush_cache() - assert await own.async_get_cache(cache_key="shared") is None - assert await other.async_get_cache(cache_key="shared") == {"answer": "other"} - refreshed: Final = await invoke("responses", recording_server, {}) - assert cache_key(refreshed) is None - assert len(recording_server.requests) == 2 - await own.disconnect() - await other.disconnect() diff --git a/tests/unit/accounting/test_projection.py b/tests/unit/accounting/test_projection.py new file mode 100644 index 00000000000..ec0ab44e505 --- /dev/null +++ b/tests/unit/accounting/test_projection.py @@ -0,0 +1,431 @@ +import json +from collections.abc import Mapping +from datetime import datetime, timezone +from typing import Final + +import pytest +from pydantic import TypeAdapter + +from litellm.accounting.projection import project +from litellm.accounting.snapshot import build_accounting_payload +from litellm.constants import BILLED_RETRY_ATTEMPT_METADATA_KEY, INTERNAL_CALL_ORIGIN_METADATA_KEY +from litellm.proxy._types import SpendLogsPayload +from litellm.proxy.db.db_spend_update_writer import DBSpendUpdateWriter +from litellm.proxy.proxy_server import proxy_logging_obj +from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload +from litellm.proxy.utils import PrismaClient +from litellm.types.utils import ModelResponse, Usage + +_NOW: Final = datetime(2026, 11, 2, tzinfo=timezone.utc) +_FIELDS: Final = TypeAdapter(dict[str, object]) + + +@pytest.mark.parametrize("status", ["success", "failure"]) +@pytest.mark.parametrize("origin", [None, "auto_router", "billed_retry"]) +@pytest.mark.parametrize("spend", [0.0, 0.25]) +@pytest.mark.asyncio +async def test_daily_projection_matches_legacy_writer(status: str, origin: str | None, spend: float) -> None: + payload: Final = get_logging_payload( + kwargs={ + "model": "projection-test", + "custom_llm_provider": "openai", + "call_type": "acompletion", + "litellm_call_id": "projection-request", + "litellm_params": {"metadata": {"user_api_key": "key", "user_api_key_user_id": "user", "status": status}}, + }, + response_obj=ModelResponse( + id="projection-request", model="projection-test", usage=Usage(prompt_tokens=10, completion_tokens=5) + ), + start_time=_NOW, + end_time=_NOW, + ) + metadata: Final = { + **_FIELDS.validate_json(payload["metadata"]), + **({BILLED_RETRY_ATTEMPT_METADATA_KEY: True} if origin == "billed_retry" else {}), + **({INTERNAL_CALL_ORIGIN_METADATA_KEY: origin} if origin not in (None, "billed_retry") else {}), + } + row: Final = {**payload, "spend": spend, "metadata": json.dumps(metadata), "request_duration_ms": 25} + legacy: Final = await DBSpendUpdateWriter()._common_add_spend_log_transaction_to_daily_transaction( + row, PrismaClient(database_url="postgresql://unused", proxy_logging_obj=proxy_logging_obj), "user" + ) + assert legacy is not None + accounting_input: Final[SpendLogsPayload] = {**row} + accounting: Final = build_accounting_payload( + accounting_input, + entities={"api_key": payload["api_key"], "user": payload["user"]}, + model_access_groups=(), + windows=(), + router=lambda: None, + ) + restored: Final = _FIELDS.validate_json(json.dumps(accounting, default=str)) + stored_daily: Final = next(item for item in project(restored) if item.table == "LiteLLM_DailyUserSpend") + assert dict(stored_daily.increments) == {name: legacy[name] for name, _ in stored_daily.increments} + assert dict(stored_daily.key)["date"] == legacy["date"] + assert dict(stored_daily.key)["api_key"] == legacy["api_key"] + assert not {"messages", "response", "proxy_server_request", "requester_ip_address"}.intersection(accounting) + + +def test_projection_uses_frozen_savings_and_keeps_duplicate_entity_tags() -> None: + row: Final[Mapping[str, object]] = { + "request_id": "saved", + "startTime": _NOW, + "api_key": "key", + "user": "user", + "model": "unpriced-projection-test", + "model_group": "group", + "custom_llm_provider": "openai", + "request_tags": '["tag", "tag"]', + "spend": 0.5, + "metadata": json.dumps( + { + "accounting_savings": { + "compression": 1.0, + "prompt_caching": -2.0, + "gateway_injected_caching": 3.0, + "autorouter": 4.0, + }, + "autorouter_savings": 9000, + } + ), + } + contributions: Final = project(row) + tags: Final = tuple(item for item in contributions if item.table == "LiteLLM_TagTable") + daily_tags: Final = tuple(item for item in contributions if item.table == "LiteLLM_DailyTagSpend") + assert sum(dict(item.increments)["spend"] for item in tags) == 1.0 + assert len(daily_tags) == 1 + assert {key: value for key, value in daily_tags[0].increments if key.endswith("savings_spend")} == { + "compression_savings_spend": 1.0, + "prompt_caching_savings_spend": -2.0, + "gateway_injected_caching_savings_spend": 3.0, + "autorouter_savings_spend": 4.0, + } + + +def test_projection_carries_windows_and_does_not_reprice_guardrails() -> None: + row: Final = { + "request_id": "guarded", + "startTime": _NOW, + "api_key": "key", + "spend": 0.75, + "metadata": json.dumps( + { + "accounting_windows": [ + { + "entity_type": "key", + "entity_id": "key", + "window_duration": "1d", + "window_start": _NOW.isoformat(), + } + ], + "guardrail_information": [ + { + "guardrail_id": "guard", + "guardrail_usage": {"text": 3, "images": 2}, + "guardrail_cost_by_unit": {"text": 0.3, "images": None}, + } + ], + } + ), + } + contributions: Final = project(row) + window: Final = next(item for item in contributions if item.table == "LiteLLM_BudgetWindowSpend") + units: Final = { + dict(item.key)["usage_unit"]: dict(item.increments) + for item in contributions + if item.table == "LiteLLM_DailyGuardrailUsageUnits" + } + assert dict(window.increments) == {"spend": 0.75} + assert dict(window.values)["window_start"] == _NOW.isoformat() + assert units == { + "text": {"units": 3, "cost": 0.3, "untracked_units": 0}, + "images": {"units": 2, "cost": 0.0, "untracked_units": 2}, + } + + +@pytest.mark.asyncio +async def test_entity_projection_matches_real_request_queues(monkeypatch: pytest.MonkeyPatch) -> None: + from litellm.proxy import proxy_server + from litellm.responses.streaming_iterator import drain_logging_tasks + + client: Final = PrismaClient(database_url="postgresql://unused", proxy_logging_obj=proxy_logging_obj) + monkeypatch.setattr(proxy_server, "prisma_client", client) + monkeypatch.setattr(proxy_server, "disable_spend_logs", False) + monkeypatch.setattr(client, "spend_log_transactions", []) + monkeypatch.setattr(client, "model_usage_transactions", []) + writer: Final = DBSpendUpdateWriter() + charged: Final = await writer.update_database( + token="sk-projection", + user_id="user", + end_user_id="end-user", + team_id="team", + org_id="org", + project_id="project", + kwargs={ + "model": "projection-test", + "custom_llm_provider": "openai", + "call_type": "acompletion", + "litellm_call_id": "entity-request", + "agent_id": "agent", + "user": "end-user", + "litellm_params": { + "metadata": { + "user_api_key": "sk-projection", + "user_api_key_user_id": "user", + "user_api_key_team_id": "team", + "user_api_key_org_id": "org", + "user_api_key_project_id": "project", + "user_api_key_end_user_id": "end-user", + "tags": ["tag", "tag"], + }, + "user": "end-user", + }, + }, + completion_response=ModelResponse( + id="entity-request", model="projection-test", usage=Usage(prompt_tokens=10, completion_tokens=5) + ), + start_time=_NOW, + end_time=_NOW, + response_cost=0.25, + ) + await drain_logging_tasks() + assert charged + assert len(client.spend_log_transactions) == 1 + queued: Final = await writer.spend_update_queue.flush_all_updates_from_in_memory_queue() + logged: Final[SpendLogsPayload] = client.spend_log_transactions[0] + projected: Final = project( + _FIELDS.validate_json( + json.dumps( + build_accounting_payload( + logged, + entities={ + "api_key": logged["api_key"], + "user": "user", + "end_user": "end-user", + "team_id": "team", + "organization_id": "org", + "project_id": "project", + "agent_id": "agent", + }, + model_access_groups=(), + windows=(), + router=lambda: None, + ), + default=str, + ) + ) + ) + legacy: Final = {(item["entity_type"].value, item["entity_id"]): item["response_cost"] for item in queued} + assert legacy[("key", proxy_server.hash_token("sk-projection"))] == next( + dict(item.increments)["spend"] for item in projected if item.table == "LiteLLM_VerificationToken" + ) + for table, entity, identity in ( + ("LiteLLM_UserTable", "user", "user"), + ("LiteLLM_TeamTable", "team", "team"), + ("LiteLLM_OrganizationTable", "organization", "org"), + ("LiteLLM_EndUserTable", "end_user", "end-user"), + ("LiteLLM_AgentsTable", "agent", "agent"), + ("LiteLLM_ProjectTable", "project", "project"), + ): + assert legacy[(entity, identity)] == next( + dict(item.increments)["spend"] for item in projected if item.table == table + ) + assert sum(dict(item.increments)["spend"] for item in projected if item.table == "LiteLLM_TagTable") == sum( + item["response_cost"] or 0 for item in queued if item["entity_type"].value == "tag" + ) + + daily_queues: Final = ( + ("LiteLLM_DailyUserSpend", writer.daily_spend_update_queue), + ("LiteLLM_DailyTeamSpend", writer.daily_team_spend_update_queue), + ("LiteLLM_DailyOrganizationSpend", writer.daily_org_spend_update_queue), + ("LiteLLM_DailyEndUserSpend", writer.daily_end_user_spend_update_queue), + ("LiteLLM_DailyAgentSpend", writer.daily_agent_spend_update_queue), + ("LiteLLM_DailyTagSpend", writer.daily_tag_spend_update_queue), + ) + for table, queue in daily_queues: + transactions = await queue.flush_and_get_aggregated_daily_spend_update_transactions() + matching = tuple(item for item in projected if item.table == table) + assert matching, table + contribution = matching[0] + assert len(transactions) == 1 + transaction = next(iter(transactions.values())) + assert dict(contribution.increments) == {name: transaction[name] for name, _ in contribution.increments} + assert len(client.model_usage_transactions) == 1 + model: Final = client.model_usage_transactions[0] + model_projection: Final = next(item for item in projected if item.table == "LiteLLM_DailyModelUsage") + assert dict(model_projection.increments) == { + "spend": model.spend, + "prompt_tokens": model.prompt_tokens, + "completion_tokens": model.completion_tokens, + "request_count": int(model.counts_as_request), + "successful_requests": int(model.successful), + "failed_requests": int(not model.successful), + } + + +@pytest.mark.parametrize("served_id", ["requested", "fallback", "missing"]) +@pytest.mark.asyncio +async def test_projection_preserves_fallback_narrowed_model_access_groups(served_id: str) -> None: + from litellm import Router + from litellm.proxy.db.db_spend_update_writer import debitable_model_access_groups + + router: Final = Router( + model_list=[ + { + "model_name": "first", + "litellm_params": {"model": "openai/projection-test", "api_key": "unused"}, + "model_info": {"id": "requested", "access_groups": ["first-group"]}, + }, + { + "model_name": "second", + "litellm_params": {"model": "openai/projection-test", "api_key": "unused"}, + "model_info": {"id": "fallback", "access_groups": ["second-group", "unattributed"]}, + }, + ] + ) + attributed: Final = ("first-group", "second-group", "first-group") + writer: Final = DBSpendUpdateWriter() + client: Final = PrismaClient(database_url="postgresql://unused", proxy_logging_obj=proxy_logging_obj) + await writer._update_model_access_group_db(0.5, attributed, served_id, client, router) + queued: Final = await writer.spend_update_queue.flush_all_updates_from_in_memory_queue() + frozen_groups: Final = debitable_model_access_groups(attributed, served_id, router) + projected: Final = project( + {"spend": 0.5, "metadata": json.dumps({"accounting_model_access_groups": frozen_groups})} + ) + assert {dict(item.key)["access_group_name"]: dict(item.increments)["spend"] for item in projected} == { + item["entity_id"]: item["response_cost"] for item in queued + } + assert "unattributed" not in frozen_groups + + +def test_guardrail_projection_skips_unmeasured_counters() -> None: + row: Final = { + "request_id": "guarded", + "startTime": _NOW, + "metadata": json.dumps( + { + "guardrail_information": [ + {"guardrail_usage": {"text": 3}}, + { + "guardrail_id": "guard", + "guardrail_usage": {"zero": 0, "negative": -1, "bool": True, "unknown": None, "text": 2}, + }, + ] + } + ), + } + units: Final = tuple(item for item in project(row) if item.table == "LiteLLM_DailyGuardrailUsageUnits") + assert len(units) == 1 + assert dict(units[0].key)["usage_unit"] == "text" + assert dict(units[0].increments) == {"units": 2, "cost": 0.0, "untracked_units": 2} + + +def test_lean_row_without_model_does_not_fabricate_daily_entity_usage() -> None: + projected: Final = project( + { + "request_id": "lean", + "api_key": "key", + "startTime": _NOW, + "spend": 0.5, + "metadata": {"accounting_entities": {"api_key": "key"}}, + } + ) + assert tuple(item.table for item in projected) == ("LiteLLM_VerificationToken", "LiteLLM_DailyModelUsage") + + +@pytest.mark.parametrize( + "cost_fields", + [ + {"guardrail_cost_in_spend": False, "guardrail_cost_by_unit": {"text": 1.0}}, + {"guardrail_cost_by_unit": {"text": -1.0}}, + ], +) +def test_report_only_or_invalid_guardrail_prices_remain_untracked(cost_fields: Mapping[str, object]) -> None: + row: Final = { + "request_id": "guarded", + "startTime": _NOW, + "request_tags": '["task:debugging"]', + "metadata": json.dumps( + { + "guardrail_information": [ + {"guardrail_id": "guard", "guardrail_usage": {"text": 2}, **cost_fields}, + ] + } + ), + } + contributions: Final = project(row) + guardrail: Final = next(item for item in contributions if item.table == "LiteLLM_DailyGuardrailUsageUnits") + model: Final = next(item for item in contributions if item.table == "LiteLLM_DailyModelUsage") + assert dict(guardrail.increments) == {"units": 2, "cost": 0.0, "untracked_units": 2} + assert dict(model.key)["task_type"] == "debugging" + + +@pytest.mark.parametrize("organization_id", [None, "", "charged-org"]) +def test_daily_organization_uses_frozen_charged_identity(organization_id: str | None) -> None: + row: Final[Mapping[str, object]] = { + "startTime": _NOW, + "api_key": "key", + "model": "projection-test", + "model_group": "group", + "custom_llm_provider": "openai", + "organization_id": "stored-org", + "spend": 0.25, + "metadata": {"accounting_entities": {"organization_id": organization_id}}, + } + daily: Final = tuple(item for item in project(row) if item.table == "LiteLLM_DailyOrganizationSpend") + if organization_id is None: + assert daily == () + else: + assert len(daily) == 1 + assert dict(daily[0].key)["organization_id"] == organization_id + assert dict(daily[0].increments)["spend"] == 0.25 + + +@pytest.mark.parametrize("start", [_NOW, "2026-11-02T12:00:00+00:00", "2026-11-02 12:00:00+00:00"]) +def test_every_daily_row_uses_the_calendar_date_of_the_request(start: object) -> None: + row: Final[Mapping[str, object]] = { + "request_id": "dated", + "startTime": start, + "api_key": "key", + "user": "user", + "team_id": "team", + "model": "unpriced-projection-test", + "model_group": "group", + "custom_llm_provider": "openai", + "spend": 0.5, + "metadata": "{}", + } + dates: Final = { + item.table: dict(item.key)["date"] for item in project(row) if item.table.startswith("LiteLLM_Daily") + } + assert {"LiteLLM_DailyUserSpend", "LiteLLM_DailyModelUsage"} <= dates.keys() + assert set(dates.values()) == {"2026-11-02"} + + +def test_unparseable_start_time_skips_only_guardrail_usage() -> None: + projected: Final = project( + { + "request_id": "bad-time", + "startTime": "yesterday at noon", + "spend": 0.5, + "metadata": { + "accounting_entities": {"api_key": "key"}, + "guardrail_information": [{"guardrail_id": "guard", "guardrail_usage": {"text": 2}}], + }, + } + ) + tables: Final = {item.table for item in projected} + assert "LiteLLM_VerificationToken" in tables + assert "LiteLLM_DailyGuardrailUsageUnits" not in tables + + +def test_guardrail_usage_is_dated_in_utc() -> None: + projected: Final = project( + { + "request_id": "offset", + "startTime": "2026-11-02T22:30:00-05:00", + "metadata": {"guardrail_information": [{"guardrail_id": "guard", "guardrail_usage": {"text": 1}}]}, + } + ) + assert [dict(item.key)["date"] for item in projected if item.table == "LiteLLM_DailyGuardrailUsageUnits"] == [ + "2026-11-03" + ] diff --git a/tests/unit/accounting/test_types.py b/tests/unit/accounting/test_types.py index f645ee6c2cc..c1be39ab111 100644 --- a/tests/unit/accounting/test_types.py +++ b/tests/unit/accounting/test_types.py @@ -19,7 +19,6 @@ from litellm.accounting import ( Dimension, DimensionFilter, DimensionName, - EventKind, Granularity, InboundUsageEvent, Metric, @@ -442,7 +441,6 @@ def test_subset_token_counts_round_trip_without_changing_totals() -> None: @pytest.mark.parametrize( ("enum_name", "python_enum"), [ - ("AccountingEventKind", EventKind), ("AccountingDeliveryState", DeliveryState), ("AccountingDeliveryTarget", DeliveryTarget), ], diff --git a/tests/unit/batches/test_batch_utils.py b/tests/unit/batches/test_batch_utils.py index b32afd51fd3..0f85f1e3993 100644 --- a/tests/unit/batches/test_batch_utils.py +++ b/tests/unit/batches/test_batch_utils.py @@ -22,7 +22,6 @@ from typing import Final, Literal import httpx import pytest import respx -from openai.types.batch import BatchRequestCounts import litellm @@ -2028,61 +2027,6 @@ def test_extract_credentials_keeps_openai_workload_identity_params(): } -# --------------------------------------------------------------------------- # -# batch_cost_is_final -# --------------------------------------------------------------------------- # - - -def _retrieved_batch( - status: str, output_file_id: str | None = None, counts: BatchRequestCounts | None = None -) -> LiteLLMBatch: - return LiteLLMBatch( - id="batch_abc", - completion_window="24h", - created_at=1, - endpoint="/v1/chat/completions", - input_file_id="file-in", - object="batch", - status="validating", - output_file_id=output_file_id, - request_counts=counts, - ).model_copy(update={"status": status}) - - -class TestBatchCostIsFinal: - """Every retrieve of one batch writes the same spend row, so the first retrieve - that prices it decides the row for good. A poll before the output exists must - therefore not count as final: pricing it recorded $0 and pinned it (LIT-7048).""" - - @pytest.mark.parametrize("status", ["validating", "in_progress", "finalizing", "cancelling"]) - def test_in_flight_batch_is_not_final(self, status): - assert bu.batch_cost_is_final(_retrieved_batch(status)) is False - - @pytest.mark.parametrize("status", ["completed", "complete"]) - def test_completed_with_output_is_final(self, status): - assert bu.batch_cost_is_final(_retrieved_batch(status, output_file_id="file-out")) is True - - def test_completed_without_output_and_unknown_counts_is_not_final(self): - assert bu.batch_cost_is_final(_retrieved_batch("completed")) is False - - def test_completed_without_output_and_zero_counts_is_not_final(self): - counts = BatchRequestCounts(total=0, completed=0, failed=0) - assert bu.batch_cost_is_final(_retrieved_batch("completed", counts=counts)) is False - - def test_completed_without_output_but_successful_lines_is_not_final(self): - counts = BatchRequestCounts(total=2, completed=2, failed=0) - assert bu.batch_cost_is_final(_retrieved_batch("completed", counts=counts)) is False - - @pytest.mark.parametrize("status", ["completed", "complete"]) - def test_completed_without_output_and_every_line_failed_is_final(self, status): - counts = BatchRequestCounts(total=2, completed=0, failed=2) - assert bu.batch_cost_is_final(_retrieved_batch(status, counts=counts)) is True - - @pytest.mark.parametrize("status", ["failed", "expired", "cancelled"]) - def test_other_terminal_statuses_are_final(self, status): - assert bu.batch_cost_is_final(_retrieved_batch(status)) is True - - def _ocr_row(pages_processed, annotation_pages=None, model="mistral-ocr-latest"): usage_info = {"pages_processed": pages_processed, "doc_size_bytes": 4096} if annotation_pages is not None: @@ -2766,9 +2710,9 @@ def test_aggregate_batch_cost_uses_custom_model_info(): assert result.cost == pytest.approx(expected), f"Expected total cost {expected}, got {result.cost}" @pytest.mark.usefixtures("_vcr_outcome_gate", "setup_and_teardown") -@pytest.mark.parametrize("data_residency", ["eu", "us"]) +@pytest.mark.parametrize("data_residency", ["eu", "us", "fedramp"]) def test_batch_cost_calculator_applies_data_residency_uplift(data_residency, monkeypatch): - """batch_cost_calculator should apply the regional uplift multiplier when + """batch_cost_calculator should apply the regional or FedRAMP uplift multiplier when data_residency is set and the model carries a configured multiplier.""" monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") prev_model_cost = litellm.model_cost @@ -2965,7 +2909,7 @@ async def test_batch_retrieve_cost_tracking_with_completed_batch_no_explicit_cos from unittest.mock import AsyncMock, patch from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes, LiteLLMBatch + from litellm.types.utils import CallTypes # Mock batch result with completed status mock_batch = LiteLLMBatch( @@ -3060,7 +3004,6 @@ async def test_handle_completed_batch_computes_real_cost_from_output_file( wrong token sum, or mispriced model fails this test. """ from litellm.batches.batch_utils import handle_completed_batch - from litellm.types.utils import LiteLLMBatch batch = LiteLLMBatch( id="batch-real-cost-123", @@ -3105,7 +3048,7 @@ async def test_batch_retrieve_cost_tracking_with_explicit_cost_data(): from unittest.mock import AsyncMock, patch from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes, LiteLLMBatch + from litellm.types.utils import CallTypes # Mock batch result with completed status mock_batch = LiteLLMBatch( @@ -3187,7 +3130,7 @@ async def test_batch_retrieve_explicit_cost_split_sets_cost_breakdown(): cost_breakdown carries real input/output costs; without it the UI's Cost Breakdown card renders blank for every batch. Regression for the split being dropped.""" from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes, LiteLLMBatch + from litellm.types.utils import CallTypes mock_batch = LiteLLMBatch( id="batch-breakdown-1", @@ -3243,7 +3186,7 @@ async def test_batch_retrieve_cost_tracking_with_unified_file_id_incomplete_batc from unittest.mock import AsyncMock, patch from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes, LiteLLMBatch, SpecialEnums + from litellm.types.utils import CallTypes, SpecialEnums # Create a proper unified file ID by encoding the correct prefix unified_id_str = f"{SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value}:test_file_789;unified_id:batch-789" @@ -3322,7 +3265,7 @@ async def test_batch_retrieve_cost_tracking_with_partial_explicit_data(): from unittest.mock import AsyncMock, patch from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes, LiteLLMBatch + from litellm.types.utils import CallTypes # Mock batch result with completed status mock_batch = LiteLLMBatch( diff --git a/tests/unit/batches/test_main.py b/tests/unit/batches/test_main.py index 8b22395f462..5ffe6558dc3 100644 --- a/tests/unit/batches/test_main.py +++ b/tests/unit/batches/test_main.py @@ -858,7 +858,7 @@ async def test_batch_logging_azure_credentials_regression(): "timeout": 600, } - batch_output = b'{"id": "batch_req_1", "custom_id": "request-1", "response": {"status_code": 200, "body": {"id": "chatcmpl-azure", "object": "chat.completion", "model": "gpt-4", "usage": {"prompt_tokens": 15, "completion_tokens": 25, "total_tokens": 40}}}}\n' + batch_output = b'{"id": "batch_req_1", "custom_id": "request-1", "response": {"status_code": 200, "body": {"id": "chatcmpl-azure", "object": "chat.completion", "model": "gpt-4.1", "usage": {"prompt_tokens": 15, "completion_tokens": 25, "total_tokens": 40}}}}\n' print("\n1. Testing credential extraction...") diff --git a/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py b/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py index 2d02468fd8c..19aefb430c7 100644 --- a/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py +++ b/tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py @@ -484,55 +484,6 @@ async def test_bridged_text_completion_is_logged_as_the_text_completion_it_serve assert logged.payload["response"]["choices"][0]["text"] == "pong" -@pytest.mark.asyncio -async def test_a_bridged_chat_response_waits_on_the_usage_ack_like_any_chat_response( - monkeypatch: pytest.MonkeyPatch, -) -> None: - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - from tests.unit.proxy.spend_tracking.test_usage_meter import CommitBarrier - - monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": True}) - logging_obj: Final = Logging( - model="openai/gpt-5.3-codex", - messages=[], - stream=False, - call_type="acompletion", - start_time=datetime.datetime.now(), - litellm_call_id="bridged-chat", - function_id="bridged-chat", - ) - with respx.mock(assert_all_called=True) as router: - router.post(url__startswith="https://api.openai.com/v1/responses").mock( - return_value=httpx.Response(200, json=_searched_responses_body("openai/gpt-5.3-codex")) - ) - served: Final = await litellm.acompletion( - model="openai/gpt-5.3-codex", - messages=[{"role": "user", "content": "ping"}], - api_key="test-key", - litellm_logging_obj=logging_obj, - ) - response: Final = asyncio.create_task(capture_proxy_response(logging_obj, served)) - try: - await database.entered.wait() - assert not response.done() - assert database.committed == () - database.release.set() - await response - assert "bridged-chat:inbound" in database.committed - finally: - database.release.set() - await runtime.stop(1) - await response - - def _served_message_start_id(events: Sequence[bytes]) -> object: lines: Final = b"".join(events).decode().splitlines() starts: Final = tuple( @@ -1905,6 +1856,51 @@ def test_transform_request_system_only_message_maps_to_system_input_item(): assert not result.get("instructions") +_JSON_SYSTEM_PROMPT: Final = "You output JSON objects." +_PLAIN_USER_ITEM: Final = { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": "List three colors."}], +} +_COLORS_SCHEMA: Final = { + "type": "json_schema", + "json_schema": {"name": "colors", "schema": {"type": "object", "properties": {}}}, +} + + +@pytest.mark.parametrize( + ("response_format", "expected_input", "expected_instructions"), + [ + ( + {"type": "json_object"}, + [ + {"type": "message", "role": "system", "content": [{"type": "input_text", "text": _JSON_SYSTEM_PROMPT}]}, + _PLAIN_USER_ITEM, + ], + None, + ), + (_COLORS_SCHEMA, [_PLAIN_USER_ITEM], _JSON_SYSTEM_PROMPT), + ], + ids=["json_object_keeps_system_in_input", "json_schema_keeps_system_in_instructions"], +) +def test_transform_request_json_object_keeps_system_prompt_where_the_json_word_check_reads_it( + response_format, expected_input, expected_instructions +): + result = LiteLLMResponsesTransformationHandler().transform_request( + model="gpt-5.4-mini", + messages=[ + {"role": "system", "content": _JSON_SYSTEM_PROMPT}, + {"role": "user", "content": "List three colors."}, + ], + optional_params={"response_format": response_format}, + litellm_params={}, + headers={}, + litellm_logging_obj=Mock(), + ) + + assert (result["input"], result.get("instructions")) == (expected_input, expected_instructions), result + + def test_transform_request_single_char_keys_not_matched(): """Test that single-character keys are not incorrectly matched to 'metadata' or 'previous_response_id' diff --git a/tests/unit/decisions/test_main.py b/tests/unit/decisions/test_main.py index 789d59bd613..1e42e74806d 100644 --- a/tests/unit/decisions/test_main.py +++ b/tests/unit/decisions/test_main.py @@ -132,6 +132,7 @@ _PROVIDERS: Final[tuple[tuple[str, str, str, str], ...]] = ( "https://openrouter.ai/api/alpha/decisions", "typesafe/jev-1.13", ), + ("empiriolabs", "empiriolabs/aplomb-1", "https://api.empiriolabs.ai/v1/decisions", "aplomb-1"), ) _OPENAI_RESPONSE: Final[Mapping[str, object]] = { @@ -1549,6 +1550,7 @@ _PROVIDERS_WHOSE_CHAT_MODELS_ALSO_DECIDE: Final = frozenset( LlmProviders.LM_STUDIO.value, LlmProviders.OLLAMA.value, LlmProviders.OLLAMA_CHAT.value, + LlmProviders.EMPIRIOLABS.value, } ) _DEDICATED_DECIDER_PROVIDERS: Final[tuple[str, ...]] = tuple( @@ -3449,6 +3451,147 @@ async def test_fireworks_ai_bills_cached_input_tokens_at_the_cache_read_rate( assert recording_logger.standard_logging_object["response_cost"] < 300 * float(prices["input_cost_per_token"]) +_EMPIRIOLABS_MODEL: Final = "empiriolabs/aplomb-1" +_EMPIRIOLABS_DEFAULT_URL: Final = "https://api.empiriolabs.ai/v1/decisions" +_EMPIRIOLABS_STATE: Final = "I was charged twice for order 4411 and want a refund today." +_EMPIRIOLABS_QUESTIONS: Final[Mapping[str, object]] = MappingProxyType( + {"is_refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}} +) +_EMPIRIOLABS_RESPONSE: Final[Mapping[str, object]] = { + "model": "aplomb-1", + "answers": {"is_refund": {"type": "noul", "noul": 0.97, "confidence": 0.94}}, + "usage": {"input_tokens": 24, "output_tokens": 0}, +} + + +@pytest.mark.parametrize( + ("api_base", "environment_api_base", "url"), + ( + (None, None, _EMPIRIOLABS_DEFAULT_URL), + (None, "https://environment-gateway.example/v1", "https://environment-gateway.example/v1/decisions"), + ( + "https://gateway.example/v1/", + "https://environment-gateway.example/v1", + "https://gateway.example/v1/decisions", + ), + ("https://gateway.example", None, "https://gateway.example/v1/decisions"), + ), + ids=("default_base", "environment_base", "explicit_base_over_environment", "explicit_base_without_v1"), +) +def test_empiriolabs_posts_the_systemone_body_to_v1_decisions_on_the_resolved_base( + api_base: str | None, + environment_api_base: str | None, + url: str, + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, +) -> None: + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + if environment_api_base is not None: + monkeypatch.setenv("EMPIRIOLABS_API_BASE", environment_api_base) + route: Final = respx_mock.post(url).respond(json=_EMPIRIOLABS_RESPONSE) + + response: Final = litellm.systemone( + model=_EMPIRIOLABS_MODEL, + state=_EMPIRIOLABS_STATE, + questions=_EMPIRIOLABS_QUESTIONS, + api_base=api_base, + api_key="caller-key", + ) + + assert route.call_count == 1 + assert json.loads(route.calls[0].request.content) == { + "model": "aplomb-1", + "state": _EMPIRIOLABS_STATE, + "questions": {"is_refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}}, + } + assert response.answers["is_refund"] == NoulAnswer(type="noul", noul=0.97, confidence=0.94) + assert response._hidden_params["custom_llm_provider"] == "empiriolabs" + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("api_key", "authorization"), + ((None, "Bearer environment-key"), ("caller-key", "Bearer caller-key")), + ids=("environment_key", "caller_key_over_environment"), +) +async def test_empiriolabs_sends_the_caller_key_or_else_the_environment_key( + api_key: str | None, + authorization: str, + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, +) -> None: + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + monkeypatch.setenv("EMPIRIOLABS_API_KEY", "environment-key") + route: Final = respx_mock.post(_EMPIRIOLABS_DEFAULT_URL).respond(json=_EMPIRIOLABS_RESPONSE) + + response: Final = await litellm.adecisions( + model=_EMPIRIOLABS_MODEL, + input=_EMPIRIOLABS_STATE, + questions=[{"type": "predicate", "name": "is_refund", "instructions": "Is the customer asking for a refund?"}], + api_key=api_key, + ) + + assert route.call_count == 1 + assert route.calls[0].request.headers["authorization"] == authorization + assert isinstance(response, OpenAIDecisionResponse) + assert [answer.model_dump(mode="json") for answer in response.answers] == [ + {"type": "predicate", "name": "is_refund", "probability": 0.97} + ] + + +def test_empiriolabs_without_a_key_is_refused_before_http( + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, +) -> None: + monkeypatch.delenv("EMPIRIOLABS_API_KEY", raising=False) + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + + with pytest.raises(litellm.AuthenticationError, match="Missing API key for Decisions provider 'empiriolabs'"): + litellm.systemone(model=_EMPIRIOLABS_MODEL, state=_EMPIRIOLABS_STATE, questions=_EMPIRIOLABS_QUESTIONS) + + assert len(respx_mock.calls) == 0 + + +@pytest.mark.parametrize( + ("status", "error_type", "code", "exception"), + ( + (401, "authentication_error", "invalid_api_key", litellm.AuthenticationError), + (402, "insufficient_credits", "insufficient_credits", litellm.PaymentRequiredError), + (404, "not_found_error", "model_not_found", litellm.NotFoundError), + (422, "invalid_request_error", "invalid_request", litellm.BadRequestError), + (429, "rate_limit_error", "rate_limit_exceeded", litellm.RateLimitError), + (503, "service_unavailable", "model_unavailable", litellm.ServiceUnavailableError), + ), +) +def test_empiriolabs_upstream_errors_keep_their_status_and_message( + status: int, + error_type: str, + code: str, + exception: type[Exception], + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, +) -> None: + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + message: Final = f"upstream refused the decision with {code}" + route: Final = respx_mock.post(_EMPIRIOLABS_DEFAULT_URL).respond( + status, + json={"error": {"message": message, "type": error_type, "code": code}}, + headers={"retry-after": "1"}, + ) + + with pytest.raises(exception, match=message) as caught: + litellm.systemone( + model=_EMPIRIOLABS_MODEL, + state=_EMPIRIOLABS_STATE, + questions=_EMPIRIOLABS_QUESTIONS, + api_key="caller-key", + num_retries=0, + ) + + assert getattr(caught.value, "status_code") == status + assert route.call_count == 1 + + _BOX_QUESTION_REQUESTS: Final[tuple[tuple[str, Mapping[str, object]], ...]] = ( ( "decisions", diff --git a/tests/unit/integrations/code_interpreter_interception/test_handler.py b/tests/unit/integrations/code_interpreter_interception/test_handler.py index c037b6be80d..f4f6a127fc7 100644 --- a/tests/unit/integrations/code_interpreter_interception/test_handler.py +++ b/tests/unit/integrations/code_interpreter_interception/test_handler.py @@ -5,16 +5,24 @@ All sandbox dependencies are injected: a FakeSandbox stands in for the real e2b config and records how it is called. """ +import asyncio import time +from collections.abc import Mapping from typing import Final +import httpx import pytest +import respx + +import litellm from litellm.integrations.code_interpreter_interception.handler import ( _INTERCEPTION_ACTIVE_KEY as _ACTIVE_KEY, ) from litellm.integrations.code_interpreter_interception.handler import ( _SANDBOX_KEY, + _SANDBOX_LOST_RESULT_OUTPUT, + _SANDBOX_UNAVAILABLE_OUTPUT, _SESSION_SCOPED_KEY, LITELLM_CODE_EXECUTION_TOOL_NAME, CodeInterpreterInterceptionLogger, @@ -23,6 +31,7 @@ from litellm.llms.base_llm.sandbox.transformation import CodeExecutionResult from litellm.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, CODE_INTERPRETER_STREAM_OPTIONS_KEY, + AgenticLoopPlan, NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, is_interception_internal_key, ) @@ -831,11 +840,7 @@ async def test_post_hook_delete_is_idempotent_across_loop_levels(): @pytest.mark.asyncio -async def test_build_plan_deletes_sandbox_when_execution_raises(): - """If sandbox execution raises before a plan is built (e.g. E2B aborts - output over its cap), the cached sandbox must be deleted before re-raising, - otherwise a caller can leak paid containers until the prune TTL.""" - +async def test_a_failed_code_run_still_deletes_its_sandbox_once_the_loop_ends(): class RaisingSandbox(FakeSandbox): async def arun_code(self, *, container, code, **kwargs): raise ValueError("output exceeded cap") @@ -843,13 +848,11 @@ async def test_build_plan_deletes_sandbox_when_execution_raises(): sandbox = RaisingSandbox() logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) - with pytest.raises(ValueError, match="exceeded cap"): - await _build_plan(logger, sandbox, call_id="k1") + plan = await _build_plan(logger, sandbox, call_id="k1") + await logger.async_agentic_loop_cleanup_hook(plan=plan, kwargs={}) - assert len(sandbox.create_calls) == 1, "the sandbox must have been created" - assert len(sandbox.delete_calls) == 1, ( - "a build failure must delete the cached sandbox so it does not keep running and billing" - ) + assert len(sandbox.create_calls) == 1 + assert len(sandbox.delete_calls) == 1 assert "sbxkey1" not in logger._container_cache @@ -1448,3 +1451,215 @@ async def test_per_identity_cap_evicts_lru_session(): "the entry with the oldest last_accessed timestamp must be evicted first (LRU)" ) assert len(sandbox.delete_calls) == 1, "evicted sandbox must be deleted, not just removed from cache" + + +class _SandboxThatCannotStart(FakeSandbox): + async def acreate_sandbox(self, **kwargs): + raise ConnectionError("sandbox provider unreachable") + + +class _SandboxThatCannotRunCode(FakeSandbox): + async def arun_code(self, *, container, code, **kwargs): + raise TimeoutError("sandbox run timed out") + + +_SANDBOX_FAILURES: Final = ( + pytest.param(None, _SANDBOX_UNAVAILABLE_OUTPUT, id="no_sandbox"), + pytest.param(_SandboxThatCannotStart(), _SANDBOX_UNAVAILABLE_OUTPUT, id="create_fails"), + pytest.param(_SandboxThatCannotRunCode(), _SANDBOX_LOST_RESULT_OUTPUT, id="run_fails"), +) + + +@pytest.mark.asyncio +@pytest.mark.parametrize(("sandbox", "expected_output"), _SANDBOX_FAILURES) +async def test_a_sandbox_failure_answers_the_responses_tool_call_with_a_fixed_failure(sandbox, expected_output): + plan: Final = await _build_plan(CodeInterpreterInterceptionLogger(sandbox_config=sandbox), sandbox) + + outputs: Final = [message for message in _iter_messages(plan) if message.get("type") == "function_call_output"] + assert plan.run_agentic_loop is True + assert [(output["call_id"], output["output"]) for output in outputs] == [("c1", expected_output)] + assert [call["status"] for call in plan.metadata["code_interpreter_calls"]] == ["failed"] + + +@pytest.mark.asyncio +@pytest.mark.parametrize(("sandbox", "expected_output"), _SANDBOX_FAILURES) +async def test_a_sandbox_failure_answers_the_chat_tool_call_with_a_fixed_failure(sandbox, expected_output): + logger: Final = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + + plan: Final = await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + {"id": "call_1", "name": LITELLM_CODE_EXECUTION_TOOL_NAME, "arguments": '{"code":"print(40 + 2)"}'} + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response={"choices": [{"message": {"tool_calls": [_chat_function_call_item()]}}]}, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id="k1"), + stream=False, + kwargs={ + "litellm_call_id": "k1", + _ACTIVE_KEY: True, + _SANDBOX_KEY: "sbxkey1", + "_agentic_loop_api_surface": CHAT_COMPLETION_AGENTIC_SURFACE, + }, + ) + + tool_messages: Final = [message for message in _iter_messages(plan) if message.get("role") == "tool"] + assert plan.run_agentic_loop is True + assert [(message["tool_call_id"], message["content"]) for message in tool_messages] == [ + ("call_1", expected_output) + ] + assert [call["status"] for call in plan.metadata["code_interpreter_calls"]] == ["failed"] + + +_SESSION_PLAN_SURFACES: Final = ( + pytest.param({}, FakeResponse(output=[_function_call_item()]), id="responses"), + pytest.param( + {_ACTIVE_KEY: True, "_agentic_loop_api_surface": CHAT_COMPLETION_AGENTIC_SURFACE}, + {"choices": [{"message": {"tool_calls": [_chat_function_call_item()]}}]}, + id="chat", + ), +) + + +async def _build_session_plan( + logger: CodeInterpreterInterceptionLogger, + request_id: str, + surface_kwargs: Mapping[str, object] | None = None, + response: object | None = None, +) -> AgenticLoopPlan: + return await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "id": "call_1", + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(1)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=response or FakeResponse(output=[_function_call_item()]), + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id=request_id), + stream=False, + kwargs={ + "litellm_call_id": request_id, + _SANDBOX_KEY: "conv-1", + _SESSION_SCOPED_KEY: True, + **(surface_kwargs or {}), + }, + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize(("surface_kwargs", "response"), _SESSION_PLAN_SURFACES) +async def test_a_session_sandbox_whose_code_run_fails_is_not_reused_by_the_next_request( + surface_kwargs: Mapping[str, object], response: object +) -> None: + sandbox: Final = _SandboxThatCannotRunCode() + logger: Final = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + + await _build_session_plan(logger, "req1", surface_kwargs, response) + await _build_session_plan(logger, "req2", surface_kwargs, response) + + assert len(sandbox.create_calls) == 2 + assert len(sandbox.delete_calls) == 2 + + +class _SandboxWhoseFirstContainerDies(FakeSandbox): + def __init__(self) -> None: + super().__init__() + self.handles: list[FakeHandle] = [] + self.first_run_started = asyncio.Event() + self.first_run_may_finish = asyncio.Event() + + async def acreate_sandbox(self, **kwargs: object) -> FakeHandle: + self.create_calls.append(kwargs) + handle: Final = FakeHandle(sandbox_id=f"sbx_{len(self.handles)}") + self.handles.append(handle) + return handle + + async def arun_code(self, *, container: FakeHandle, code: str, **kwargs: object) -> CodeExecutionResult: + self.run_calls.append({"container": container, "code": code}) + if len(self.run_calls) == 1: + self.first_run_started.set() + await self.first_run_may_finish.wait() + if container is self.handles[0]: + raise TimeoutError("sandbox expired") + return CodeExecutionResult(stdout=self.stdout) + + +@pytest.mark.asyncio +async def test_a_late_failure_on_a_dead_session_sandbox_keeps_its_healthy_replacement() -> None: + sandbox: Final = _SandboxWhoseFirstContainerDies() + logger: Final = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + + slow_request: Final = asyncio.create_task(_build_session_plan(logger, "req-slow")) + await sandbox.first_run_started.wait() + await _build_session_plan(logger, "req-fails-fast") + await _build_session_plan(logger, "req-gets-replacement") + sandbox.first_run_may_finish.set() + await slow_request + await _build_session_plan(logger, "req-after") + + assert [call["container"] for call in sandbox.delete_calls] == [sandbox.handles[0]] + assert [call["container"] for call in sandbox.run_calls[-2:]] == [sandbox.handles[1], sandbox.handles[1]] + + +def _anthropic_message(content: list[dict[str, object]], stop_reason: str) -> dict[str, object]: + return { + "id": "msg_1", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-5-5", + "content": content, + "stop_reason": stop_reason, + "stop_sequence": None, + "usage": {"input_tokens": 10, "output_tokens": 5}, + } + + +@pytest.mark.asyncio +async def test_a_responses_request_bridged_to_claude_runs_the_code_and_ends_without_the_internal_call( + monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter +): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + sandbox: Final = FakeSandbox(stdout="42") + monkeypatch.setattr(litellm, "callbacks", [CodeInterpreterInterceptionLogger(sandbox_config=sandbox)]) + provider: Final = respx_mock.post("https://api.anthropic.com/v1/messages").mock( + side_effect=[ + httpx.Response( + 200, + json=_anthropic_message( + [ + { + "type": "tool_use", + "id": "toolu_1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "input": {"code": "print(40 + 2)"}, + } + ], + "tool_use", + ), + ), + httpx.Response(200, json=_anthropic_message([{"type": "text", "text": "The answer is 42."}], "end_turn")), + ] + ) + + response: Final = await litellm.aresponses( + model="anthropic/claude-sonnet-5-5", + input="compute 40 + 2", + api_key="sk-test", + tools=[{"type": "code_interpreter", "container": {"type": "auto"}}], + ) + + assert provider.call_count == 2 + assert [run["code"] for run in sandbox.run_calls] == ["print(40 + 2)"] + assert not [item for item in response.output if getattr(item, "type", None) == "function_call"] diff --git a/tests/unit/integrations/test_prometheus_invalid_key_filtering.py b/tests/unit/integrations/test_prometheus_invalid_key_filtering.py index 8c5f22c5fd0..e6a12d8667a 100644 --- a/tests/unit/integrations/test_prometheus_invalid_key_filtering.py +++ b/tests/unit/integrations/test_prometheus_invalid_key_filtering.py @@ -513,6 +513,20 @@ def _provider_auth_failure_kwargs() -> dict[str, object]: } +def _deployment_entra_rejection_kwargs() -> dict[str, object]: + from litellm.llms.azure.common_utils import EntraRejectionError + + reason: Final = "Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided." + try: + raise litellm.AuthenticationError(message=reason, llm_provider="azure", model="gpt-4.1") from ( + EntraRejectionError(status_code=401, message=reason) + ) + except litellm.AuthenticationError as raised: + return { + key: value for key, value in _provider_auth_failure_kwargs().items() if key not in CALLED_THE_PROVIDER + } | {"exception": raised} + + def _metric_values(name: str, model_id: str) -> list[float]: return [ sample.value @@ -542,6 +556,20 @@ class TestProviderAuthFailures: assert _metric_values("litellm_deployment_failure_responses_total", "deployment-1") == [1.0] assert _metric_values("litellm_deployment_state", "deployment-1") == [1.0] + @pytest.mark.asyncio + async def test_log_failure_event_records_entra_rejecting_the_deployment_credential_before_the_call( + self, prometheus_logger + ): + await prometheus_logger.async_log_failure_event( + kwargs=_deployment_entra_rejection_kwargs(), + response_obj=None, + start_time=None, + end_time=None, + ) + + assert _metric_values("litellm_llm_api_failed_requests_metric_total", "deployment-1") == [1.0] + assert _metric_values("litellm_deployment_failure_responses_total", "deployment-1") == [1.0] + def test_deployment_failure_metrics_record_a_provider_401(self, prometheus_logger): prometheus_logger.set_llm_deployment_failure_metrics(_provider_auth_failure_kwargs()) diff --git a/tests/unit/integrations/websearch_interception/test_websearch_agentic_loop_cap.py b/tests/unit/integrations/websearch_interception/test_websearch_agentic_loop_cap.py index 351ca9beb4d..e5ef356b0f3 100644 --- a/tests/unit/integrations/websearch_interception/test_websearch_agentic_loop_cap.py +++ b/tests/unit/integrations/websearch_interception/test_websearch_agentic_loop_cap.py @@ -12,6 +12,7 @@ config.yaml through to the settings the loop actually reads. """ import json +import time from unittest.mock import MagicMock, patch import pytest @@ -24,6 +25,8 @@ from litellm.integrations.websearch_interception.handler import ( WebSearchInterceptionLogger, ) from litellm.integrations.websearch_interception.tools import get_litellm_web_search_tool +from litellm.litellm_core_utils.agentic_followup_failure import is_agentic_followup_failure +from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.agentic_loop_settings import DEFAULT_MAX_AGENTIC_LOOPS from litellm.llms.anthropic.messages.native.fake_stream_iterator import ( FakeAnthropicMessagesStreamIterator, @@ -31,6 +34,7 @@ from litellm.llms.anthropic.messages.native.fake_stream_iterator import ( from litellm.llms.base_llm.search.transformation import SearchResponse, SearchResult from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.secret_managers.main import get_secret +from litellm.types.llms.openai import ResponsesAPIResponse from litellm.types.integrations.custom_logger import ( AgenticLoopPlan, AgenticLoopRequestPatch, @@ -38,6 +42,7 @@ from litellm.types.integrations.custom_logger import ( ) INTERNAL_TOOL_NAME = "litellm_web_search" +CODE_EXECUTION_TOOL_NAME = "litellm_code_execution" @pytest.fixture(autouse=True) @@ -99,6 +104,29 @@ def _tool_use_names(response: dict) -> list[str]: return [block.get("name") for block in response["content"] if block.get("type") == "tool_use"] +def _responses_turn(*function_calls: tuple[str, str]) -> ResponsesAPIResponse: + return ResponsesAPIResponse( + id="resp_1", + created_at=0, + status="completed", + model="claude-sonnet-4-5", + object="response", + output=[ + { + "type": "message", + "id": "msg_1", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "Let me check one more source.", "annotations": []}], + }, + *( + {"type": "function_call", "id": f"fc_{call_id}", "call_id": call_id, "name": name, "arguments": "{}"} + for call_id, name in function_calls + ), + ], + ) + + class _InterceptingCallback(CustomLogger): """ Stands in for the websearch interceptor: asks for another loop whenever the @@ -152,14 +180,46 @@ class _InterceptingCallback(CustomLogger): return response -def _logging_obj(callback: CustomLogger, converted_stream: bool = False) -> MagicMock: +class _ResponsesInterceptingCallback(_InterceptingCallback): + async def async_should_run_agentic_loop( + self, response, model, messages, tools, stream, custom_llm_provider, kwargs + ): + calls = [ + {"id": item.call_id, "call_id": item.call_id, "name": item.name} + for item in response.output + if item.type == "function_call" and item.name == INTERNAL_TOOL_NAME + ] + return bool(calls), {"tool_calls": calls} + + +def _logging_obj(*callbacks: CustomLogger, converted_stream: bool = False) -> MagicMock: logging_obj = MagicMock() logging_obj.model_call_details = {"websearch_interception_converted_stream": converted_stream} - logging_obj.dynamic_success_callbacks = [callback] + logging_obj.dynamic_success_callbacks = list(callbacks) logging_obj.litellm_call_id = "call-abc" return logging_obj +class _CodeExecutionGate(CustomLogger): + async def async_should_run_agentic_loop( + self, response, model, messages, tools, stream, custom_llm_provider, kwargs + ): + calls = ( + [ + {"id": block["id"], "name": block["name"]} + for block in response["content"] + if block.get("type") == "tool_use" and block.get("name") == CODE_EXECUTION_TOOL_NAME + ] + if isinstance(response, dict) + else [ + {"call_id": item.call_id, "name": item.name} + for item in response.output + if item.type == "function_call" and item.name == CODE_EXECUTION_TOOL_NAME + ] + ) + return bool(calls), {"tool_calls": calls} + + async def _run_hooks( handler: BaseLLMHTTPHandler, callback: CustomLogger, @@ -314,6 +374,48 @@ class TestCappedLoopReturnsTerminalResponse: assert _tool_use_names(result) == ["get_weather"] assert result["stop_reason"] == "tool_use" + @pytest.mark.asyncio + async def test_every_hooks_internal_call_is_dropped_and_the_clients_survives(self): + response = _response_asking_for_another_search() + response["content"] += [ + {"id": "toolu_code_1", "type": "tool_use", "name": CODE_EXECUTION_TOOL_NAME, "input": {}}, + {"id": "toolu_client_1", "type": "tool_use", "name": "get_weather", "input": {}}, + ] + + result = await self.handler._call_agentic_completion_hooks( + response=response, + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={}, + logging_obj=_logging_obj(self.callback, _CodeExecutionGate()), + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 3, "max_agentic_loops": 3}, + ) + + assert _tool_use_names(result) == ["get_weather"] + assert self.callback.plan_calls == 0 + + @pytest.mark.asyncio + async def test_responses_surface_drops_every_hooks_internal_call_and_keeps_the_clients(self): + result = await self.handler._call_agentic_completion_hooks( + response=_responses_turn( + ("call_search", INTERNAL_TOOL_NAME), ("call_code", CODE_EXECUTION_TOOL_NAME), ("call_client", "lookup") + ), + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={}, + logging_obj=_logging_obj(_ResponsesInterceptingCallback(), _CodeExecutionGate()), + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 3, "max_agentic_loops": 3}, + api_surface="responses", + ) + + assert [getattr(item, "call_id", None) for item in result.output] == [None, "call_client"] + def test_only_the_refused_tool_calls_are_dropped(self): """ A block is matched on the id the rail refused, not on the tool name, so a @@ -347,6 +449,18 @@ class TestCappedLoopReturnsTerminalResponse: assert _tool_use_names(result) == [] assert result["stop_reason"] == "end_turn" + def test_a_name_only_refusal_keeps_the_native_search_blocks_sharing_that_name(self): + response = _response_asking_for_another_search() + response["content"][-1] = {"type": "tool_use", "name": "web_search", "input": {}} + + result = BaseLLMHTTPHandler._finalize_refused_agentic_response( + response=response, + tool_calls={"tool_calls": [{"name": "web_search", "input": {}}]}, + ) + + assert _block_types(result) == ["server_tool_use", "web_search_tool_result", "text"] + assert result["stop_reason"] == "end_turn" + @pytest.mark.asyncio async def test_streaming_caller_is_left_to_its_existing_behavior(self): """ @@ -365,19 +479,34 @@ class TestCappedLoopReturnsTerminalResponse: assert self.callback.plan_calls == 0 @pytest.mark.asyncio - async def test_responses_surface_is_left_to_its_existing_behavior(self): - """ - The responses surface carries a pydantic model rather than the anthropic - dict this finalizer rewrites, so it keeps raising instead of being handed - a response that was never actually finalized. - """ - with pytest.raises(AgenticLoopSafetyError): - await _run_hooks( - self.handler, - self.callback, - kwargs={"_agentic_loop_depth": 3, "max_agentic_loops": 3}, - api_surface="responses", - ) + async def test_responses_surface_drops_the_refused_function_call_and_keeps_the_answer(self): + response = _responses_turn(("call_search", INTERNAL_TOOL_NAME), ("call_client", "lookup")) + callback = _ResponsesInterceptingCallback() + + result = await _run_hooks( + self.handler, + callback, + kwargs={"_agentic_loop_depth": 3, "max_agentic_loops": 3}, + response=response, + api_surface="responses", + ) + + assert isinstance(result, ResponsesAPIResponse) + assert [(item.type, getattr(item, "name", None)) for item in result.output] == [ + ("message", None), + ("function_call", "lookup"), + ] + assert callback.plan_calls == 0 + + def test_responses_refusal_by_call_id_alone_keeps_the_calls_it_did_not_list(self): + response = _responses_turn(("call_search", INTERNAL_TOOL_NAME), ("call_unlisted", INTERNAL_TOOL_NAME)) + + result = BaseLLMHTTPHandler._finalize_refused_agentic_response( + response=response, + tool_calls={"tool_calls": [{"call_id": "call_search", "name": INTERNAL_TOOL_NAME}]}, + ) + + assert [getattr(item, "call_id", None) for item in result.output] == [None, "call_unlisted"] @pytest.mark.asyncio async def test_non_dict_response_is_returned_untouched(self): @@ -509,6 +638,156 @@ def _response_asking_for_searches(*queries: str) -> dict: } +def _request_logging_obj(call_type: str, *callbacks: CustomLogger) -> Logging: + logging_obj = Logging( + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + stream=False, + call_type=call_type, + start_time=time.time(), + litellm_call_id="call-abc", + function_id="f", + dynamic_success_callbacks=list(callbacks), + ) + logging_obj.update_environment_variables( + model="claude-sonnet-4-5", user="", optional_params={}, litellm_params={}, custom_llm_provider="anthropic" + ) + return logging_obj + + +def _assert_first_call_billed(logging_obj: Logging, *, input_tokens: int, output_tokens: int) -> None: + price = litellm.model_cost["claude-sonnet-4-5"] + assert logging_obj.model_call_details["combined_usage_object"].total_tokens == input_tokens + output_tokens + assert logging_obj.model_call_details["response_cost"] == pytest.approx( + input_tokens * price["input_cost_per_token"] + output_tokens * price["output_cost_per_token"] + ) + + +class TestFailedFollowUpCallReachesTheClient: + def setup_method(self): + self.handler = BaseLLMHTTPHandler() + self.logger = WebSearchInterceptionLogger(enabled_providers=["anthropic"]) + + @staticmethod + async def _rate_limited_follow_up(**call_kwargs): + raise RateLimitError( + "UserByModelByMinuteUncachedInputTokens", llm_provider="anthropic", model="claude-sonnet-4-5" + ) + + @staticmethod + async def _search(query, kwargs=None, rich=None): + found = SearchResult(title="Result", url="https://example.com", snippet="A result.", date=None) + return ("Title: Result\nURL: https://example.com", SearchResponse(results=[found])) + + @pytest.mark.asyncio + @pytest.mark.parametrize("converted_stream", (False, True), ids=("non_streaming", "streaming_client")) + async def test_follow_up_rate_limit_is_raised_to_the_caller(self, monkeypatch, converted_stream): + monkeypatch.setattr("litellm.anthropic_interface.messages.acreate", self._rate_limited_follow_up) + + with ( + patch.object(self.logger, "_execute_search", side_effect=self._search), + pytest.raises(RateLimitError, match="UserByModelByMinuteUncachedInputTokens"), + ): + await self.handler._call_agentic_completion_hooks( + response=_response_asking_for_searches("who won the world cup"), + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={"tools": [get_litellm_web_search_tool()]}, + logging_obj=_logging_obj(self.logger, converted_stream=converted_stream), + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 0, "max_agentic_loops": 3, WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: True}, + ) + + @pytest.mark.asyncio + async def test_follow_up_rate_limit_bills_the_first_call_on_the_failure_log(self, monkeypatch): + monkeypatch.setattr("litellm.anthropic_interface.messages.acreate", self._rate_limited_follow_up) + logging_obj = _request_logging_obj("anthropic_messages", self.logger) + + with ( + patch.object(self.logger, "_execute_search", side_effect=self._search), + pytest.raises(RateLimitError), + ): + await self.handler._call_agentic_completion_hooks( + response=_response_asking_for_searches("who won the world cup"), + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={"tools": [get_litellm_web_search_tool()]}, + logging_obj=logging_obj, + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 0, "max_agentic_loops": 3, WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: True}, + ) + + _assert_first_call_billed(logging_obj, input_tokens=10, output_tokens=5) + + @pytest.mark.asyncio + async def test_a_crashed_plan_build_is_not_retried_and_bills_the_first_call(self): + logging_obj = _request_logging_obj("anthropic_messages", self.logger) + + with ( + patch.object(self.logger, "async_build_agentic_loop_plan", side_effect=ValueError("the plan crashed")), + pytest.raises(ValueError, match="the plan crashed") as raised, + ): + await self.handler._call_agentic_completion_hooks( + response=_response_asking_for_searches("who won the world cup"), + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={"tools": [get_litellm_web_search_tool()]}, + logging_obj=logging_obj, + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 0, "max_agentic_loops": 3, WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: True}, + ) + + assert is_agentic_followup_failure(raised.value) + _assert_first_call_billed(logging_obj, input_tokens=10, output_tokens=5) + + @pytest.mark.asyncio + async def test_responses_follow_up_rate_limit_bills_the_first_call_on_the_failure_log(self, monkeypatch): + monkeypatch.setattr(litellm, "aresponses", self._rate_limited_follow_up) + callback = _ResponsesInterceptingCallback() + logging_obj = _request_logging_obj("aresponses", callback) + first_call = _responses_turn(("call_search", INTERNAL_TOOL_NAME)).model_copy( + update={"usage": {"input_tokens": 13, "output_tokens": 6, "total_tokens": 19}} + ) + + with pytest.raises(RateLimitError): + await self.handler._call_agentic_completion_hooks( + response=first_call, + model="claude-sonnet-4-5", + messages=[{"role": "user", "content": "who won the world cup"}], + anthropic_messages_provider_config=MagicMock(), + anthropic_messages_optional_request_params={}, + logging_obj=logging_obj, + stream=False, + custom_llm_provider="anthropic", + kwargs={"_agentic_loop_depth": 0, "max_agentic_loops": 3}, + api_surface="responses", + ) + + _assert_first_call_billed(logging_obj, input_tokens=13, output_tokens=6) + + @pytest.mark.asyncio + async def test_responses_follow_up_rate_limit_is_raised_to_the_caller(self, monkeypatch): + monkeypatch.setattr(litellm, "aresponses", self._rate_limited_follow_up) + callback = _ResponsesInterceptingCallback() + + with pytest.raises(RateLimitError, match="UserByModelByMinuteUncachedInputTokens"): + await _run_hooks( + self.handler, + callback, + kwargs={"_agentic_loop_depth": 0, "max_agentic_loops": 3}, + response=_responses_turn(("call_search", INTERNAL_TOOL_NAME)), + api_surface="responses", + ) + + assert callback.plan_calls == 1 + + class TestFailedSearchEndsTheTurn: """ A search that failed used to come back to the client as an empty successful diff --git a/tests/unit/integrations/websearch_interception/test_websearch_thinking_constraint.py b/tests/unit/integrations/websearch_interception/test_websearch_thinking_constraint.py index b2d5225070c..ee4c4d32478 100644 --- a/tests/unit/integrations/websearch_interception/test_websearch_thinking_constraint.py +++ b/tests/unit/integrations/websearch_interception/test_websearch_thinking_constraint.py @@ -371,8 +371,7 @@ class TestLoggingObjExcludedFromFollowUp: class TestFollowUpErrorScenarios: """Regression tests: the agentic loop must surface errors properly and - not silently swallow them (except at the _call_agentic_completion_hooks - level which intentionally catches to return the initial response).""" + not silently swallow them.""" @pytest.mark.asyncio async def test_followup_400_raises(self): diff --git a/tests/unit/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/unit/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 0a08142c66f..3964f472bd9 100644 --- a/tests/unit/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/unit/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -33,6 +33,7 @@ from litellm.types.llms.base import CachedTokensDetails from litellm.types.utils import ( CacheCreationTokenDetails, CompletionTokensDetailsWrapper, + DataResidency, ImageObject, ImageResponse, ImageUsage, @@ -2579,6 +2580,51 @@ def test_data_residency_applies_uplift(data_residency, model, _local_model_cost_ assert regional[1] == pytest.approx(base[1] * 1.10, rel=1e-9) +def test_fedramp_data_residency_applies_configured_uplift() -> None: + usage: Final = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) + model_info: Final[ModelInfo] = { + "key": "fedramp-fixture", + "max_tokens": None, + "max_input_tokens": None, + "max_output_tokens": None, + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "litellm_provider": "openai", + "mode": "chat", + "supported_openai_params": None, + "regional_processing_uplift_multiplier_us": 1.5, + "fedramp_uplift_multiplier": 1.1, + } + + base: Final = generic_cost_per_token( + model="fedramp-fixture", + usage=usage, + custom_llm_provider="openai", + model_info=model_info, + ) + fedramp: Final = generic_cost_per_token( + model="fedramp-fixture", + usage=usage, + custom_llm_provider="openai", + data_residency=DataResidency.FEDRAMP.value, + model_info=model_info, + ) + + us: Final = generic_cost_per_token( + model="fedramp-fixture", + usage=usage, + custom_llm_provider="openai", + data_residency=DataResidency.US.value, + model_info=model_info, + ) + + assert sum(base) > 0 + assert fedramp[0] == pytest.approx(base[0] * 1.1) + assert fedramp[1] == pytest.approx(base[1] * 1.1) + assert us[0] == pytest.approx(base[0] * 1.5) + assert us[1] == pytest.approx(base[1] * 1.5) + + @pytest.mark.parametrize("model", ["gpt-5", "gpt-5-mini", "gpt-5-nano", "gpt-5-pro", "gpt-4o", "gpt-4.1"]) def test_data_residency_no_uplift_for_pre_march_2026_models(model, _local_model_cost_map): """Models released before 2026-03-05 must not have the regional uplift.""" @@ -2719,6 +2765,22 @@ def test_vertex_uplift_invalid_multiplier_defaults_to_one(): ) +@pytest.mark.parametrize( + "model_info, data_residency", + [ + ({"fedramp_uplift_multiplier": "not-a-number"}, "fedramp"), + ({}, "fedramp"), + ({"fedramp_uplift_multiplier": 1.1}, "us"), + ({"fedramp_uplift_multiplier": 1.1}, None), + ], +) +def test_fedramp_uplift_defaults_to_one(model_info: ModelInfo, data_residency: str | None): + """A malformed or missing FedRAMP multiplier, or a non-FedRAMP host, bills base pricing and never raises.""" + from litellm.litellm_core_utils.llm_cost_calc.utils import get_fedramp_uplift_multiplier + + assert get_fedramp_uplift_multiplier(model_info, data_residency) == 1.0 + + def test_service_tier_suffixes_constant_in_sync_with_enum(): from litellm.litellm_core_utils.llm_cost_calc.utils import _SERVICE_TIER_SUFFIXES from litellm.types.utils import ServiceTier @@ -4248,6 +4310,37 @@ def test_azure_gpt_6_foundry_price_sheet(_local_model_cost_map, model_base): assert azure_eu_info[field] == pytest.approx(1.2 * base) +@pytest.mark.parametrize("model", ["azure/gpt-6-luna", "azure/gpt-6-luna-2026-09-22"]) +@pytest.mark.parametrize( + ("prompt_tokens", "input_rate", "cache_read_rate", "output_rate"), + [ + (100_000, 5e-08, 5e-09, 2.5e-07), + (300_000, 1e-07, 1e-08, 3.75e-07), + ], +) +def test_generic_cost_per_token_azure_gpt_6_luna_flex_tiers( + _local_model_cost_map, model, prompt_tokens, input_rate, cache_read_rate, output_rate +): + """azure/gpt-6-luna bills Azure's Flex Global rates, half of Standard, with the long context step past 272K.""" + cached_tokens = 20_000 + completion_tokens = 1_000 + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), + ) + prompt_cost, completion_cost = generic_cost_per_token( + model=model, + usage=usage, + custom_llm_provider="azure", + service_tier="flex", + ) + expected_prompt = (prompt_tokens - cached_tokens) * input_rate + cached_tokens * cache_read_rate + assert prompt_cost == pytest.approx(expected_prompt) + assert completion_cost == pytest.approx(completion_tokens * output_rate) + + @pytest.mark.parametrize("region_prefix", ["azure/", "azure/us/", "azure/eu/"]) def test_azure_gpt_5_6_alias_matches_sol_pricing(_local_model_cost_map, region_prefix): """The bare gpt-5.6 alias routes to GPT-5.6 Sol, so every Azure region must bill the diff --git a/tests/unit/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py b/tests/unit/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py index 9da2632943c..06e673c3e68 100644 --- a/tests/unit/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py +++ b/tests/unit/litellm_core_utils/llm_cost_calc/test_tool_call_cost_tracking.py @@ -1230,3 +1230,27 @@ def test_vertex_gemini_deployment_priced_web_search_stays_per_prompt( def test_built_in_tool_lookups_ignore_malformed_request_values(kwargs: dict[str, object]) -> None: assert StandardBuiltInToolCostTracking.get_web_search_options(kwargs) is None assert StandardBuiltInToolCostTracking.get_file_search_tool_call(kwargs) is None + + +def test_per_call_web_search_path_bills_the_count_the_usage_reports(local_model_cost_map): + """ + A provider with no web search calculator of its own (an OpenAI-compatible JSON provider) that + reports server_tool_use.web_search_requests is billed that many per-call searches, not one. + """ + from litellm.types.utils import ServerToolUse, Usage + + model = "empiriolabs/qwen3-7-plus" + per_search = litellm.model_cost[model]["search_context_cost_per_query"]["search_context_size_medium"] + response = ModelResponse(model="qwen3-7-plus") + usage = Usage( + prompt_tokens=4, completion_tokens=5, total_tokens=9, server_tool_use=ServerToolUse(web_search_requests=3) + ) + + cost = StandardBuiltInToolCostTracking.get_cost_for_built_in_tools( + model=model, + response_object=response, + usage=usage, + custom_llm_provider="empiriolabs", + standard_built_in_tools_params=None, + ) + assert cost == pytest.approx(3 * per_search) diff --git a/tests/unit/litellm_core_utils/llm_cost_calc/test_utils.py b/tests/unit/litellm_core_utils/llm_cost_calc/test_utils.py index b6c18e0da86..1d2a5e4cc9b 100644 --- a/tests/unit/litellm_core_utils/llm_cost_calc/test_utils.py +++ b/tests/unit/litellm_core_utils/llm_cost_calc/test_utils.py @@ -12,7 +12,7 @@ import litellm from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER from litellm.types.llms.base import CachedTokensDetails -from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage +from litellm.types.utils import CompletionTokensDetailsWrapper, ModelResponse, PromptTokensDetailsWrapper, Usage TIER_MODEL: Final = "tier-priced-test-model" TIER_ROW: Final[Mapping[str, float]] = MappingProxyType( @@ -48,6 +48,190 @@ AUDIO_TIER_ROW: Final[Mapping[str, float]] = MappingProxyType( ) +@pytest.mark.parametrize("image_tokens", (60, 100)) +@pytest.mark.parametrize( + ("service_tier", "priority_rate", "image_rate"), + ((None, 11e-06, 7e-06), ("priority", 11e-06, 11e-06), ("flex", 11e-06, 7e-06), ("priority", 0.0, 0.0)), +) +def test_completion_cost_uses_priority_image_output_rate( + local_model_cost_map: None, service_tier: str | None, priority_rate: float, image_rate: float, image_tokens: int +) -> None: + model: Final = "gemini-synthetic-image-priority" + litellm.register_model( + { + model: { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 3e-06, + "output_cost_per_token": 5e-06, + "output_cost_per_image_token": 7e-06, + "output_cost_per_image_token_priority": priority_rate, + } + } + ) + response: Final = ModelResponse( + model=model, + usage=Usage( + prompt_tokens=20, + completion_tokens=100, + total_tokens=120, + completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=image_tokens), + ), + ) + cost: Final = litellm.completion_cost( + completion_response=response, model=model, custom_llm_provider="vertex_ai", service_tier=service_tier + ) + assert cost == pytest.approx(20 * 3e-06 + (100 - image_tokens) * 5e-06 + image_tokens * image_rate) + + +@pytest.mark.parametrize("prompt_tokens", (199_999, 200_000, 200_001)) +@pytest.mark.parametrize( + ("service_tier", "flat_rate", "context_rate"), + ((None, 7e-06, 11e-06), ("priority", 13e-06, 17e-06), ("flex", 19e-06, 23e-06)), +) +def test_completion_cost_uses_total_prompt_for_audio_context_tiers( + local_model_cost_map: None, prompt_tokens: int, service_tier: str | None, flat_rate: float, context_rate: float +) -> None: + model: Final = "gemini-synthetic-audio-context" + litellm.register_model( + { + model: { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 3e-06, + "output_cost_per_token": 5e-06, + "input_cost_per_audio_token": 7e-06, + "input_cost_per_audio_token_above_200k_tokens": 11e-06, + "input_cost_per_audio_token_priority": 13e-06, + "input_cost_per_audio_token_above_200k_tokens_priority": 17e-06, + "input_cost_per_audio_token_flex": 19e-06, + "input_cost_per_audio_token_above_200k_tokens_flex": 23e-06, + } + } + ) + response: Final = ModelResponse( + model=model, + usage=Usage( + prompt_tokens=prompt_tokens, + completion_tokens=10, + total_tokens=prompt_tokens + 10, + prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=100), + ), + ) + cost: Final = litellm.completion_cost( + completion_response=response, model=model, custom_llm_provider="vertex_ai", service_tier=service_tier + ) + audio_rate: Final = context_rate if prompt_tokens > 200_000 else flat_rate + assert cost == pytest.approx((prompt_tokens - 100) * 3e-06 + 100 * audio_rate + 10 * 5e-06) + + +@pytest.mark.parametrize("prompt_tokens", (200_000, 200_001)) +def test_batch_cost_uses_audio_context_and_image_output_tiers(local_model_cost_map: None, prompt_tokens: int) -> None: + from litellm.cost_calculator import batch_cost_calculator + + model: Final = "gemini-synthetic-batch-audio-context" + litellm.register_model( + { + model: { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 6e-06, + "output_cost_per_token": 10e-06, + "input_cost_per_token_batches": 3e-06, + "output_cost_per_token_batches": 5e-06, + "input_cost_per_token_above_200k_tokens_batches": 13e-06, + "output_cost_per_token_above_200k_tokens_batches": 17e-06, + "output_cost_per_image_token_batches": 19e-06, + "input_cost_per_audio_token_batches": 7e-06, + "input_cost_per_audio_token_above_200k_tokens_batches": 11e-06, + } + } + ) + usage: Final = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=20, + total_tokens=prompt_tokens + 20, + prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=100), + completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=10), + ) + cost: Final = batch_cost_calculator(model=model, custom_llm_provider="vertex_ai", usage=usage) + audio_rate: Final = 11e-06 if prompt_tokens > 200_000 else 7e-06 + input_rate: Final = 13e-06 if prompt_tokens > 200_000 else 3e-06 + output_rate: Final = 17e-06 if prompt_tokens > 200_000 else 5e-06 + assert sum(cost) == pytest.approx( + (prompt_tokens - 100) * input_rate + 100 * audio_rate + 10 * output_rate + 10 * 19e-06 + ) + + +@pytest.mark.parametrize("prompt_tokens", (200_000, 200_001)) +def test_completion_cost_uses_flex_context_input_and_output_rates( + local_model_cost_map: None, prompt_tokens: int +) -> None: + model: Final = "gemini-synthetic-flex-context" + litellm.register_model( + { + model: { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 6e-06, + "output_cost_per_token": 10e-06, + "input_cost_per_token_flex": 3e-06, + "output_cost_per_token_flex": 5e-06, + "input_cost_per_token_above_200k_tokens_flex": 7e-06, + "output_cost_per_token_above_200k_tokens_flex": 11e-06, + } + } + ) + response: Final = ModelResponse( + model=model, usage=Usage(prompt_tokens=prompt_tokens, completion_tokens=10, total_tokens=prompt_tokens + 10) + ) + cost: Final = litellm.completion_cost( + completion_response=response, model=model, custom_llm_provider="vertex_ai", service_tier="flex" + ) + input_rate: Final = 7e-06 if prompt_tokens > 200_000 else 3e-06 + output_rate: Final = 11e-06 if prompt_tokens > 200_000 else 5e-06 + assert cost == pytest.approx(prompt_tokens * input_rate + 10 * output_rate) + + +@pytest.mark.parametrize( + ("audio_fields", "audio_rate"), + ( + ({}, 3e-06), + ({"input_cost_per_audio_token": 7e-06}, 7e-06), + ({"input_cost_per_audio_token_above_200k_tokens": 11e-06}, 11e-06), + ({"input_cost_per_audio_token_above_200k_tokens_priority": 0.0}, 0.0), + ), +) +def test_audio_context_rates_preserve_fallbacks_and_explicit_zero( + local_model_cost_map: None, audio_fields: Mapping[str, float], audio_rate: float +) -> None: + model: Final = "gemini-synthetic-audio-fallback" + litellm.register_model( + { + model: { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 3e-06, + "output_cost_per_token": 5e-06, + **audio_fields, + } + } + ) + response: Final = ModelResponse( + model=model, + usage=Usage( + prompt_tokens=200_001, + completion_tokens=10, + total_tokens=200_011, + prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=100), + ), + ) + cost: Final = litellm.completion_cost( + completion_response=response, model=model, custom_llm_provider="vertex_ai", service_tier="priority" + ) + assert cost == pytest.approx(199_901 * 3e-06 + 100 * audio_rate + 10 * 5e-06) + + def _cost_at(prices: Mapping[str, float], column_suffix: str) -> float: return ( (PROMPT_TOKENS - CACHED_TOKENS) * prices[f"input_cost_per_token{column_suffix}"] diff --git a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py index 146bb08a4fd..a17deedf7a9 100644 --- a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py +++ b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py @@ -39,6 +39,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( schema_has_unresolvable_ref, schema_property_count, split_concatenated_json_objects, + split_embedding_media_for_tpm, split_media_for_tpm, strip_encrypted_reasoning_from_messages, strip_litellm_response_only_fields, @@ -2709,3 +2710,22 @@ def test_content_list_flattening_returns_new_messages_and_leaves_the_callers_unt assert flattened == [{"role": "user", "content": "Hello world"}, {"role": "assistant", "content": "Hi"}] assert messages == messages_before + + +@pytest.mark.parametrize("mime", ["application/pdf", "video/mp4", "audio/wav", "image/png"]) +def test_embedding_media_split_holds_at_most_one_copy_of_a_large_payload(mime: str) -> None: + import gc + import tracemalloc + + uri: Final = f"data:{mime};base64," + base64.b64encode(bytes(3 * 1024 * 1024)).decode("ascii") + split_embedding_media_for_tpm(f"data:{mime};base64,AAAA") + gc.collect() + tracemalloc.start() + try: + _, media = split_embedding_media_for_tpm(uri) + _, peak = tracemalloc.get_traced_memory() + finally: + tracemalloc.stop() + + assert media + assert peak < 1.5 * len(uri) diff --git a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_mid_conversation_system.py b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_mid_conversation_system.py index aab57b87a90..d7696c00dee 100644 --- a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_mid_conversation_system.py +++ b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_mid_conversation_system.py @@ -327,7 +327,7 @@ EMPTY_ASSISTANT = pytest.mark.parametrize( ) -def test_flagged_placement_handles_a_long_run_of_unanswered_tool_search_uses_in_linear_time(): +def test_flagged_placement_turns_reminders_before_a_long_run_of_unanswered_tool_search_uses_into_user_turns(): search_uses: Final = tuple( { "role": "assistant", @@ -349,12 +349,9 @@ def test_flagged_placement_handles_a_long_run_of_unanswered_tool_search_uses_in_ {"role": "user", "content": "end"}, ] - started: Final = time.perf_counter() placed: Final = place_mid_conversation_system(messages, supports_mid_conversation_system=True) - elapsed: Final = time.perf_counter() - started assert set(_roles(placed)[:21]) == {"user"} - assert elapsed < 1.0 def test_flagged_placement_merges_every_system_run_that_shares_a_user_turn(): diff --git a/tests/unit/litellm_core_utils/test_batch_utils.py b/tests/unit/litellm_core_utils/test_batch_utils.py index cd37d635c43..45461b50761 100644 --- a/tests/unit/litellm_core_utils/test_batch_utils.py +++ b/tests/unit/litellm_core_utils/test_batch_utils.py @@ -5,14 +5,22 @@ from io import BytesIO from typing import Dict, Final, List import pytest +from openai.types.batch import BatchRequestCounts import litellm from litellm.litellm_core_utils.batch_utils import ( + POLLED_BATCH_COST_IS_FINAL_KWARG, + POLLED_BATCH_ID_KWARG, + ChargeOnceJob, InMemoryFile, + batch_cost_is_final, + defers_batch_cost, get_router_metadata_variable_name, parse_jsonl_with_embedded_newlines, replace_model_in_jsonl, ) +from litellm.types.utils import LiteLLMBatch +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG, encode_video_id_with_provider from tests._vcr_conftest_common import install_live_call_probe, record_vcr_outcome @@ -382,30 +390,241 @@ def setup_and_teardown(): asyncio.set_event_loop(None) -@pytest.mark.parametrize( - ("call_type", "cost", "eligible"), - (("aretrieve_batch", 0.25, True), ("retrieve_batch", 0.25, True), ("acompletion", 0.25, False), - ("aretrieve_batch", 0, False), ("aretrieve_batch", -0.25, False)), -) -def test_batch_charge_identity_requires_a_cost_bearing_retrieval(call_type: str, cost: float, eligible: bool) -> None: +def _batch(status: str, output_file_id: str | None = None, counts: BatchRequestCounts | None = None): from openai.types import Batch - from litellm.litellm_core_utils.batch_utils import batch_id_to_charge_once - - batch: Final = Batch( - id="batch-budget", completion_window="24h", created_at=1, endpoint="/v1/chat/completions", - input_file_id="file-budget", object="batch", status="completed", + return Batch( + id="job-budget", completion_window="24h", created_at=1, endpoint="/v1/chat/completions", + input_file_id="file-budget", object="batch", status=status, output_file_id=output_file_id, + request_counts=counts, ) - assert batch_id_to_charge_once(call_type, batch, cost) == (batch.id if eligible else None) - assert batch_id_to_charge_once(call_type, {"id": batch.id}, cost) is None -def test_batch_charge_markers_keep_counters_and_batches_independent() -> None: +def _completed_batch(): + return _batch("completed", output_file_id="file-out") + + +def _completed_video(): + from litellm.types.videos.main import VideoObject + + return VideoObject(id="job-budget", object="video", status="completed") + + +@pytest.mark.parametrize( + ("call_type", "polled", "cost", "kind"), + ( + ("aretrieve_batch", _completed_batch, 0.25, "batch"), + ("retrieve_batch", _completed_batch, 0.25, "batch"), + ("avideo_status", _completed_video, 0.25, "video"), + ("video_status", _completed_video, 0.25, "video"), + ("acompletion", _completed_batch, 0.25, None), + ("avideo_status", _completed_batch, 0.25, None), + ("aretrieve_batch", _completed_video, 0.25, None), + ("avideo_generation", _completed_video, 0.25, None), + ("aretrieve_batch", _completed_batch, 0, None), + ("avideo_status", _completed_video, 0, None), + ("avideo_status", _completed_video, -0.25, None), + ), +) +def test_job_charge_identity_requires_a_cost_bearing_poll(call_type: str, polled, cost: float, kind) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + + expected: Final = None if kind is None else ChargeOnceJob(kind=kind, job_id="job-budget") + kwargs: Final = { + "call_type": call_type, + POLLED_VIDEO_STATUS_KWARG: "completed", + "standard_logging_object": {"response_cost": cost}, + } + assert job_to_charge_once(kwargs, polled(), cost) == expected + assert job_to_charge_once(kwargs, {"id": "job-budget"}, cost) is None + + +@pytest.mark.parametrize( + ("call_type", "polled", "guardrail_only_poll", "kind", "repeat_cost"), + ( + ("aretrieve_batch", _completed_batch, lambda: _batch("in_progress"), "batch", 0.0), + ("avideo_status", _completed_video, _completed_video, "video", 0.01), + ), +) +def test_only_the_jobs_own_cost_is_charged_once_since_a_guardrail_charges_every_poll( + call_type: str, polled, guardrail_only_poll, kind, repeat_cost +) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + + kwargs: Final = { + "call_type": call_type, + POLLED_VIDEO_STATUS_KWARG: "completed", + "standard_logging_object": {"guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}]}, + } + assert job_to_charge_once(kwargs, guardrail_only_poll(), 0.01) is None + assert job_to_charge_once(kwargs, polled(), 0.43) == ChargeOnceJob( + kind=kind, job_id="job-budget", repeat_poll_cost=repeat_cost + ) + + +@pytest.mark.parametrize("status", ("completed", "failed", "cancelled", "expired")) +def test_a_finished_batch_that_cost_nothing_itself_is_still_charged_once_for_its_retrieves_guardrail( + status: str, +) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + + kwargs: Final = { + "call_type": "aretrieve_batch", + "standard_logging_object": {"guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}]}, + } + finished_batch: Final = _batch(status, output_file_id="file-out") + assert job_to_charge_once(kwargs, finished_batch, 0.01) == ChargeOnceJob(kind="batch", job_id="job-budget") + + +def test_a_completed_batch_still_waiting_on_its_output_does_not_take_the_charge_its_real_cost_needs() -> None: + """Its first retrieve can report completed before the output file exists, and settling on that guardrail-only + retrieve would leave the batch's real cost nothing to charge once the output arrives.""" + from litellm.litellm_core_utils.batch_utils import job_to_charge_once + + kwargs: Final = { + "call_type": "aretrieve_batch", + "standard_logging_object": {"guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}]}, + } + assert job_to_charge_once(kwargs, _batch("completed"), 0.01) is None + assert defers_batch_cost(kwargs, _batch("completed")) + + +def test_a_video_is_charged_once_it_completes_under_the_id_the_client_polled() -> None: + """A provider may price a running video too, and a custom provider's own id can repeat across deployments.""" + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + from litellm.types.videos.main import VideoObject + + def poll(status: str, polled_id: str) -> ChargeOnceJob | None: + return job_to_charge_once( + {"call_type": "avideo_status", POLLED_VIDEO_STATUS_KWARG: status, "litellm_params": {"video_id": polled_id}}, + VideoObject(id="job-1", object="video", status=status), + 0.42, + ) + + assert poll("in_progress", "video_deployment_a") is None + assert poll("completed", "video_deployment_a") == ChargeOnceJob(kind="video", job_id="video_deployment_a") + assert poll("completed", "video_deployment_b") == ChargeOnceJob(kind="video", job_id="video_deployment_b") + + +@pytest.mark.parametrize("status", ["in_progress", "completed"]) +def test_a_video_poll_is_judged_by_its_logged_status_when_redaction_replaced_the_video(status: str) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + + kwargs: Final = { + "call_type": "avideo_status", + POLLED_VIDEO_STATUS_KWARG: status, + "litellm_params": {"video_id": "video_deployment_a"}, + } + expected: Final = ChargeOnceJob(kind="video", job_id="video_deployment_a") if status == "completed" else None + + assert job_to_charge_once(kwargs, {"text": "redacted-by-litellm"}, 0.42) == expected + + +@pytest.mark.parametrize( + ("cost_is_final", "deferred", "expected"), + [(False, True, None), (True, False, ChargeOnceJob(kind="batch", job_id="batch_abc"))], +) +def test_a_batch_retrieve_is_judged_by_its_logged_batch_when_redaction_replaced_it( + cost_is_final: bool, deferred: bool, expected: ChargeOnceJob | None +) -> None: + from litellm.litellm_core_utils.batch_utils import job_to_charge_once + + kwargs: Final = { + "call_type": "aretrieve_batch", + POLLED_BATCH_ID_KWARG: "batch_abc", + POLLED_BATCH_COST_IS_FINAL_KWARG: cost_is_final, + "standard_logging_object": {"guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}]}, + } + redacted: Final = {"text": "redacted-by-litellm"} + + assert defers_batch_cost(kwargs, redacted) is deferred + assert job_to_charge_once(kwargs, redacted, 0.01) == expected + + +def test_a_video_polled_with_or_without_its_base64_padding_is_charged_as_one_job() -> None: + from litellm.litellm_core_utils.batch_utils import job_to_charge_once + + padded: Final = encode_video_id_with_provider("job-1", "custom_video", "deployment-a") + assert padded.endswith("=") + + def charged_job(polled_id: str): + return job_to_charge_once( + {"call_type": "avideo_status", POLLED_VIDEO_STATUS_KWARG: "completed", "litellm_params": {"video_id": polled_id}}, + None, + 0.42, + ) + + assert charged_job(padded.rstrip("=")) == charged_job(padded) + assert charged_job(padded) is not None + + +def test_job_charge_markers_keep_counters_jobs_and_kinds_independent() -> None: from litellm.caching.in_memory_cache import InMemoryCache - from litellm.litellm_core_utils.batch_utils import batch_charged_once_marker_key + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_charged_once_marker_key + first_batch: Final = ChargeOnceJob(kind="batch", job_id="first-job") cache: Final = InMemoryCache() - cache.set_cache(batch_charged_once_marker_key("provider-spend", "first-batch"), 1) - assert cache.get_cache(batch_charged_once_marker_key("provider-spend", "first-batch")) == 1 - assert cache.get_cache(batch_charged_once_marker_key("deployment-spend", "first-batch")) is None - assert cache.get_cache(batch_charged_once_marker_key("provider-spend", "second-batch")) is None + cache.set_cache(job_charged_once_marker_key("provider-spend", first_batch), 1) + assert cache.get_cache(job_charged_once_marker_key("provider-spend", first_batch)) == 1 + assert cache.get_cache(job_charged_once_marker_key("deployment-spend", first_batch)) is None + assert cache.get_cache(job_charged_once_marker_key("provider-spend", ChargeOnceJob("batch", "second-job"))) is None + assert cache.get_cache(job_charged_once_marker_key("provider-spend", ChargeOnceJob("video", "first-job"))) is None + + +def test_batch_charge_marker_keeps_the_key_markers_already_written_use() -> None: + """Markers written before the video kind existed must still block a re-charge after an upgrade.""" + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_charged_once_marker_key + + assert job_charged_once_marker_key("provider-spend", ChargeOnceJob("batch", "first-job")) == ( + "provider-spend:batch:first-job" + ) + + +def _retrieved_batch( + status: str, output_file_id: str | None = None, counts: BatchRequestCounts | None = None +) -> LiteLLMBatch: + return LiteLLMBatch( + id="batch_abc", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="file-in", + object="batch", + status="validating", + output_file_id=output_file_id, + request_counts=counts, + ).model_copy(update={"status": status}) + + +class TestBatchCostIsFinal: + """Every retrieve of one batch writes the same spend row, so the first retrieve + that prices it decides the row for good. A poll before the output exists must + therefore not count as final: pricing it recorded $0 and pinned it (LIT-7048).""" + + @pytest.mark.parametrize("status", ["validating", "in_progress", "finalizing", "cancelling"]) + def test_in_flight_batch_is_not_final(self, status): + assert batch_cost_is_final(_retrieved_batch(status)) is False + + @pytest.mark.parametrize("status", ["completed", "complete"]) + def test_completed_with_output_is_final(self, status): + assert batch_cost_is_final(_retrieved_batch(status, output_file_id="file-out")) is True + + def test_completed_without_output_and_unknown_counts_is_not_final(self): + assert batch_cost_is_final(_retrieved_batch("completed")) is False + + def test_completed_without_output_and_zero_counts_is_not_final(self): + counts = BatchRequestCounts(total=0, completed=0, failed=0) + assert batch_cost_is_final(_retrieved_batch("completed", counts=counts)) is False + + def test_completed_without_output_but_successful_lines_is_not_final(self): + counts = BatchRequestCounts(total=2, completed=2, failed=0) + assert batch_cost_is_final(_retrieved_batch("completed", counts=counts)) is False + + @pytest.mark.parametrize("status", ["completed", "complete"]) + def test_completed_without_output_and_every_line_failed_is_final(self, status): + counts = BatchRequestCounts(total=2, completed=0, failed=2) + assert batch_cost_is_final(_retrieved_batch(status, counts=counts)) is True + + @pytest.mark.parametrize("status", ["failed", "expired", "cancelled"]) + def test_other_terminal_statuses_are_final(self, status): + assert batch_cost_is_final(_retrieved_batch(status)) is True diff --git a/tests/unit/litellm_core_utils/test_chat_completion_agentic_loop.py b/tests/unit/litellm_core_utils/test_chat_completion_agentic_loop.py index 6167e91c37f..d26da679246 100644 --- a/tests/unit/litellm_core_utils/test_chat_completion_agentic_loop.py +++ b/tests/unit/litellm_core_utils/test_chat_completion_agentic_loop.py @@ -21,6 +21,8 @@ they stay out of the body even without it. """ import datetime +import json +from collections.abc import Mapping, Sequence from typing import Any, Dict, Final, List, Optional, Tuple from unittest.mock import AsyncMock, MagicMock, patch @@ -28,11 +30,13 @@ import pytest from openai._legacy_response import LegacyAPIResponse import litellm +from litellm.caching.caching import Cache from litellm.integrations.code_interpreter_interception.handler import ( CodeInterpreterInterceptionLogger, ) from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.chat_completion_agentic_loop import ( + _execute_chat_completion_agentic_plan, maybe_run_chat_completion_agentic_loop, ) from litellm.litellm_core_utils.litellm_logging import Logging @@ -43,6 +47,7 @@ from litellm.types.integrations.custom_logger import ( WEBSEARCH_INTERCEPTION_PREFIX, AgenticLoopPlan, AgenticLoopRequestPatch, + AgenticLoopSafetyError, ) from litellm.types.utils import ( ChatCompletionMessageToolCall, @@ -50,6 +55,7 @@ from litellm.types.utils import ( Function, Message, ModelResponse, + Usage, ) # The internal control fields that must never reach a provider request body. @@ -260,8 +266,6 @@ class _GateOnlyLogger(CustomLogger): class _RecordingGateLogger(_GateOnlyLogger): - """Also keeps every follow-up response the loop hands to the post hook.""" - def __init__(self, plan: AgenticLoopPlan, tool_calls: Dict[str, Any]) -> None: super().__init__(plan=plan, tool_calls=tool_calls) self.followups: tuple[ModelResponse, ...] = () @@ -441,72 +445,544 @@ async def test_dispatcher_followup_does_not_repeat_a_request_param_found_in_requ assert call_kwargs["api_base"] == "https://a" +_REFUSED_GATE_TOOL_CALLS: Final = {"tool_calls": [{"id": "call_abc", "name": "litellm_code_execution"}]} +_REFUSING_RAILS: Final = ( + {"_agentic_loop_depth": 3, "max_agentic_loops": 3}, + { + "_agentic_loop_depth": 0, + "max_agentic_loops": 3, + "_agentic_loop_fingerprints": [json.dumps(_REFUSED_GATE_TOOL_CALLS, sort_keys=True, default=str)], + }, +) + + +def _tool_call(call_id: str, name: str) -> ChatCompletionMessageToolCall: + return ChatCompletionMessageToolCall(id=call_id, type="function", function=Function(name=name, arguments="{}")) + + +async def _run_refused_rerun( + response: ModelResponse, + rail_kwargs: Dict[str, object], + stream: bool = False, + gate_tool_calls: Optional[Dict[str, object]] = _REFUSED_GATE_TOOL_CALLS, + other_hooks_tool_calls: tuple[Dict[str, object], ...] = (), +) -> Any: + litellm.callbacks = [ + _GateOnlyLogger(plan=AgenticLoopPlan(run_agentic_loop=True), tool_calls=tool_calls) + for tool_calls in (gate_tool_calls, *other_hooks_tool_calls) + ] + acompletion_mock = AsyncMock() + with patch.object(litellm, "acompletion", acompletion_mock): + try: + return await maybe_run_chat_completion_agentic_loop( + response=response, + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + kwargs=dict(rail_kwargs), + logging_obj=_LoggingStub(), + custom_llm_provider="openai", + stream=stream, + ) + finally: + acompletion_mock.assert_not_awaited() + + @pytest.mark.asyncio -async def test_dispatcher_raises_when_depth_reaches_max_agentic_loops( - restore_callbacks, +@pytest.mark.parametrize("rail_kwargs", _REFUSING_RAILS, ids=["max_agentic_loops", "repeated_fingerprint"]) +async def test_refused_rerun_ends_the_turn_without_the_internal_tool_call( + restore_callbacks, rail_kwargs: Dict[str, object] ): - """depth >= max_agentic_loops -> ValueError mentioning max_agentic_loops, - before any follow-up call is attempted.""" - logger = _GateOnlyLogger( - plan=AgenticLoopPlan(run_agentic_loop=True), + result = await _run_refused_rerun(_tool_call_model_response(), rail_kwargs) + + assert isinstance(result, ModelResponse) + assert not result.choices[0].message.tool_calls + assert result.choices[0].finish_reason == "stop" + + +@pytest.mark.asyncio +async def test_refused_rerun_keeps_the_clients_own_tool_call(restore_callbacks): + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message=Message( + role="assistant", + content="Running it.", + tool_calls=[_tool_call("call_abc", "litellm_code_execution"), _tool_call("call_client", "lookup")], + ), + ) + ] + ) + + result = await _run_refused_rerun(response, _REFUSING_RAILS[0]) + + assert [call.id for call in result.choices[0].message.tool_calls] == ["call_client"] + assert result.choices[0].finish_reason == "tool_calls" + assert result.choices[0].message.content == "Running it." + + +def _indexed_tool_call(call_id: str, name: str, index: int) -> ChatCompletionMessageToolCall: + return ChatCompletionMessageToolCall( + id=call_id, type="function", function=Function(name=name, arguments="{}"), index=index + ) + + +def _turn_with_the_internal_call_between_the_clients() -> ModelResponse: + return ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message=Message( + role="assistant", + content=None, + tool_calls=[ + _indexed_tool_call("call_weather", "get_weather", 0), + _indexed_tool_call("call_abc", "litellm_code_execution", 1), + _indexed_tool_call("call_time", "get_time", 2), + ], + ), + ) + ] + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("rail_kwargs", _REFUSING_RAILS, ids=["max_agentic_loops", "repeated_fingerprint"]) +async def test_refused_rerun_renumbers_the_kept_tool_calls_from_zero(restore_callbacks, rail_kwargs: Dict[str, object]): + result = await _run_refused_rerun(_turn_with_the_internal_call_between_the_clients(), rail_kwargs) + + assert [(call.id, call.index) for call in result.choices[0].message.tool_calls] == [ + ("call_weather", 0), + ("call_time", 1), + ] + + +@pytest.mark.asyncio +async def test_refused_rerun_on_a_converted_stream_replays_the_kept_tool_calls_from_index_zero( + restore_callbacks, monkeypatch: pytest.MonkeyPatch +): + monkeypatch.setattr( + litellm, + "callbacks", + [_GateOnlyLogger(plan=AgenticLoopPlan(run_agentic_loop=True), tool_calls=_REFUSED_GATE_TOOL_CALLS)], + ) + logging_obj: Final = Logging( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hi"}], + stream=False, + call_type="acompletion", + start_time=datetime.datetime(2026, 1, 1, tzinfo=datetime.timezone.utc), + litellm_call_id="call-refused-stream", + function_id="fn-refused-stream", + ) + + result: Final = await maybe_run_chat_completion_agentic_loop( + response=_turn_with_the_internal_call_between_the_clients(), + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hi"}], + optional_params={}, + kwargs={**_REFUSING_RAILS[1], f"{CODE_INTERPRETER_INTERCEPTION_PREFIX}_converted_stream": True}, + logging_obj=logging_obj, + custom_llm_provider="openai", + stream=False, + ) + + assert isinstance(result, CustomStreamWrapper) + deltas: Final = [chunk.choices[0].delta async for chunk in result if chunk.choices] + streamed_calls: Final = [call for delta in deltas for call in delta.tool_calls or []] + assert [(call.id, call.index) for call in streamed_calls] == [("call_weather", 0), ("call_time", 1)] + + +@pytest.mark.asyncio +async def test_refused_rerun_drops_every_hooks_internal_call_and_keeps_the_clients(restore_callbacks): + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message=Message( + role="assistant", + content=None, + tool_calls=[ + _tool_call("call_abc", "litellm_code_execution"), + _tool_call("call_search", "litellm_web_search"), + _tool_call("call_client", "lookup"), + ], + ), + ) + ] + ) + + result = await _run_refused_rerun( + response, + _REFUSING_RAILS[0], + other_hooks_tool_calls=({"tool_calls": [{"id": "call_search", "name": "litellm_web_search"}]},), + ) + + assert [call.id for call in result.choices[0].message.tool_calls] == ["call_client"] + + +@pytest.mark.asyncio +async def test_refused_rerun_drops_a_call_claimed_only_by_name_next_to_one_claimed_by_id(restore_callbacks): + response: Final = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message=Message( + role="assistant", + content=None, + tool_calls=[ + _tool_call("call_abc", "litellm_code_execution"), + _tool_call("call_search", "litellm_web_search"), + _tool_call("call_client", "lookup"), + ], + ), + ) + ] + ) + + result: Final = await _run_refused_rerun( + response, + _REFUSING_RAILS[0], + gate_tool_calls={"tool_calls": [{"id": "call_abc"}]}, + other_hooks_tool_calls=({"tool_calls": [{"name": "litellm_web_search"}]},), + ) + + assert [call.id for call in result.choices[0].message.tool_calls] == ["call_client"] + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "response, gate_tool_calls", + [(_plain_model_response(), _REFUSED_GATE_TOOL_CALLS), (_tool_call_model_response(), None)], + ids=["turn_has_no_tool_calls", "gate_names_no_tool_call"], +) +async def test_refused_rerun_returns_a_turn_it_cannot_match_unchanged( + restore_callbacks, response: ModelResponse, gate_tool_calls: Optional[Dict[str, object]] +): + result = await _run_refused_rerun(response, _REFUSING_RAILS[0], gate_tool_calls=gate_tool_calls) + + assert result.choices[0].model_dump() == response.choices[0].model_dump() + + +@pytest.mark.asyncio +async def test_refused_rerun_on_a_live_stream_still_raises(restore_callbacks): + with pytest.raises(AgenticLoopSafetyError, match="max_agentic_loops"): + await _run_refused_rerun(_tool_call_model_response(), _REFUSING_RAILS[0], stream=True) + + +@pytest.mark.asyncio +async def test_dispatcher_surfaces_a_failed_follow_up_instead_of_the_tool_call_turn(): + logger: Final = _GateOnlyLogger( + plan=AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=_patched_messages(), kwargs={"mock_response": "litellm.RateLimitError"} + ), + ), + tool_calls={"tool_calls": [{"id": "call_abc"}]}, + ) + + with pytest.raises(litellm.RateLimitError): + await _dispatch_tool_call_turn(_tool_call_model_response(), logger) + + assert logger.cleanup_calls == 1 + + +class _PlanRecordingLogger(_GateOnlyLogger): + def __init__(self, plan: AgenticLoopPlan, tool_calls: Dict[str, Any]) -> None: + super().__init__(plan=plan, tool_calls=tool_calls) + self.logging_objs: List[Any] = [] + + async def async_build_agentic_loop_plan( + self, + tools: Dict[str, Any], + model: str, + messages: List[Dict[str, Any]], + response: Any, + anthropic_messages_provider_config: Any, + anthropic_messages_optional_request_params: Dict[str, Any], + logging_obj: Any, + stream: bool, + kwargs: Dict[str, Any], + ) -> AgenticLoopPlan: + self.logging_objs.append(logging_obj) + return self._plan + + +@pytest.mark.asyncio +async def test_acompletion_bills_the_first_call_on_the_failure_log_when_the_follow_up_fails(restore_callbacks): + logger: Final = _PlanRecordingLogger( + plan=AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=_patched_messages(), kwargs={"mock_response": "litellm.RateLimitError"} + ), + ), tool_calls={"tool_calls": [{"id": "call_abc"}]}, ) litellm.callbacks = [logger] + first_call: Final = ModelResponse( + choices=_tool_call_model_response().choices, usage=Usage(prompt_tokens=5, completion_tokens=3, total_tokens=8) + ) + mock_client: Final = MagicMock() + mock_client.post = AsyncMock(return_value=_raw_response_for(first_call)) - acompletion_mock = AsyncMock() - with patch.object(litellm, "acompletion", acompletion_mock): - with pytest.raises(ValueError, match="max_agentic_loops"): - await maybe_run_chat_completion_agentic_loop( - response=_tool_call_model_response(), - model="gpt-4o-mini", - messages=[{"role": "user", "content": "hi"}], - optional_params={}, - kwargs={"_agentic_loop_depth": 3, "max_agentic_loops": 3}, - logging_obj=_LoggingStub(), - custom_llm_provider="openai", - stream=False, - ) + with pytest.raises(litellm.RateLimitError): + await litellm.acompletion( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "what is 6*7?"}], + api_key="sk-test", + client=mock_client, + num_retries=0, + ) - acompletion_mock.assert_not_awaited() + (logging_obj,) = logger.logging_objs + price: Final = litellm.model_cost["gpt-4o-mini"] + assert logging_obj.model_call_details["combined_usage_object"].total_tokens == 8 + assert logging_obj.model_call_details["response_cost"] == pytest.approx( + 5 * price["input_cost_per_token"] + 3 * price["output_cost_per_token"] + ) + + +async def _agentic_turn_with_follow_up( + logger: _PlanRecordingLogger, monkeypatch: pytest.MonkeyPatch +) -> tuple[ModelResponse, Logging]: + monkeypatch.setattr(litellm, "callbacks", [logger]) + mock_client: Final = MagicMock() + mock_client.post = AsyncMock(return_value=_raw_response_for(_tool_call_model_response())) + response: Final = await litellm.acompletion( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "what is 6*7?"}], + api_key="sk-test", + client=mock_client, + num_retries=0, + ) + assert isinstance(response, ModelResponse) + (logging_obj,) = logger.logging_objs + return response, logging_obj + + +def _follow_up_logger() -> _PlanRecordingLogger: + return _PlanRecordingLogger( + plan=AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch(messages=_patched_messages(), kwargs={"mock_response": "42"}), + ), + tool_calls={"tool_calls": [{"id": "call_abc"}]}, + ) @pytest.mark.asyncio -async def test_dispatcher_raises_on_repeated_tool_call_fingerprint(restore_callbacks): - """A tool_calls fingerprint already present in _agentic_loop_fingerprints -> - ValueError about the repeated fingerprint (cycle guard), with no follow-up - call.""" - import json +async def test_acompletion_records_the_cost_breakdown_of_a_successful_follow_up(monkeypatch): + response, logging_obj = await _agentic_turn_with_follow_up(_follow_up_logger(), monkeypatch) - # The dispatcher fingerprints the whole value the gate returns as its second - # tuple element, so the seeded fingerprint must mirror that dict exactly. - gate_tool_calls = {"tool_calls": [{"id": "call_abc", "name": "litellm_code_execution"}]} - fingerprint = json.dumps(gate_tool_calls, sort_keys=True, default=str) + response_cost: Final = response._hidden_params["response_cost"] + assert response_cost > 0 + assert logging_obj.cost_breakdown is not None + assert logging_obj.cost_breakdown["total_cost"] == pytest.approx(response_cost) - logger = _GateOnlyLogger( - plan=AgenticLoopPlan(run_agentic_loop=True), - tool_calls=gate_tool_calls, + +@pytest.mark.asyncio +async def test_acompletion_keeps_a_cached_follow_up_free(monkeypatch): + monkeypatch.setattr(litellm, "cache", Cache(type="local")) + await litellm.acompletion(model="openai/gpt-4o-mini", messages=_patched_messages(), mock_response="42") + + response, _ = await _agentic_turn_with_follow_up(_follow_up_logger(), monkeypatch) + + assert response._hidden_params["cache_hit"] is True + assert response._hidden_params["response_cost"] == 0 + + +def _tool_call_turn(call_id: str, usage: Usage) -> ModelResponse: + return ModelResponse( + model="gpt-4o-mini", + choices=[ + Choices( + finish_reason="tool_calls", + message=Message(role="assistant", content=None, tool_calls=[_tool_call(call_id, "litellm_web_search")]), + ) + ], + usage=usage, ) - litellm.callbacks = [logger] - acompletion_mock = AsyncMock() - with patch.object(litellm, "acompletion", acompletion_mock): - with pytest.raises(ValueError, match="fingerprint"): - await maybe_run_chat_completion_agentic_loop( - response=_tool_call_model_response(), + +class _TwoRoundLogger(CustomLogger): + def __init__(self, middle_round: ModelResponse) -> None: + super().__init__() + self._follow_ups: Final = (middle_round, "litellm.RateLimitError") + self.logging_objs: tuple[object, ...] = () + + async def async_should_run_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + assert isinstance(response, ModelResponse) + calls: Final = response.choices[0].message.tool_calls or [] + return bool(calls), {"tool_calls": [{"id": call.id} for call in calls]} + + async def async_build_agentic_loop_plan( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + anthropic_messages_provider_config: object, + anthropic_messages_optional_request_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> AgenticLoopPlan: + follow_up: Final = self._follow_ups[len(self.logging_objs)] + self.logging_objs = (*self.logging_objs, logging_obj) + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch(messages=_patched_messages(), kwargs={"mock_response": follow_up}), + ) + + +@pytest.mark.asyncio +async def test_acompletion_bills_every_served_round_on_the_failure_log_when_a_later_follow_up_fails( + restore_callbacks, monkeypatch: pytest.MonkeyPatch +): + first_usage: Final = Usage(prompt_tokens=5, completion_tokens=3, total_tokens=8) + middle_usage: Final = Usage(prompt_tokens=11, completion_tokens=4, total_tokens=15) + logger: Final = _TwoRoundLogger(middle_round=_tool_call_turn("call_next", middle_usage)) + monkeypatch.setattr(litellm, "callbacks", [logger]) + mock_client: Final = MagicMock() + mock_client.post = AsyncMock(return_value=_raw_response_for(_tool_call_turn("call_abc", first_usage))) + + with pytest.raises(litellm.RateLimitError): + await litellm.acompletion( + model="openai/gpt-4o-mini", + messages=[{"role": "user", "content": "what is 6*7?"}], + api_key="sk-test", + client=mock_client, + num_retries=0, + ) + + first_logging_obj, _ = logger.logging_objs + assert isinstance(first_logging_obj, Logging) + price: Final = litellm.model_cost["gpt-4o-mini"] + billed_usage: Final = first_logging_obj.model_call_details["combined_usage_object"] + assert (billed_usage.prompt_tokens, billed_usage.completion_tokens) == (5 + 11, 3 + 4) + assert first_logging_obj.model_call_details["response_cost"] == pytest.approx( + (5 + 11) * price["input_cost_per_token"] + (3 + 4) * price["output_cost_per_token"] + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("stream", [False, True], ids=["non_stream", "stream"]) +async def test_a_follow_up_failing_with_a_builtin_error_reaches_the_caller_unchanged(stream: bool): + from litellm.litellm_core_utils.chat_completion_agentic_loop import bill_first_round_on_failure + + sandbox_error: Final = RuntimeError("sandbox crashed") + + async def failing_follow_up() -> ModelResponse: + raise sandbox_error + + with pytest.raises(RuntimeError) as raised: + await bill_first_round_on_failure( + failing_follow_up(), + logging_obj=Logging( model="gpt-4o-mini", messages=[{"role": "user", "content": "hi"}], - optional_params={}, - kwargs={ - "_agentic_loop_depth": 0, - "max_agentic_loops": 3, - "_agentic_loop_fingerprints": [fingerprint], - }, - logging_obj=_LoggingStub(), - custom_llm_provider="openai", - stream=False, - ) + stream=stream, + call_type="acompletion", + start_time=datetime.datetime(2026, 1, 1, tzinfo=datetime.timezone.utc), + litellm_call_id="call-builtin-error", + function_id="fn-builtin-error", + ), + response=_tool_call_turn("call_abc", Usage(prompt_tokens=5, completion_tokens=3, total_tokens=8)), + stream=stream, + ) - acompletion_mock.assert_not_awaited() + assert raised.value is sandbox_error + + +class _RunOnlyLoopLogger(CustomLogger): + def __init__(self, result: ModelResponse) -> None: + super().__init__() + self._result = result + + async def async_should_run_agentic_loop( + self, + response: Any, + model: str, + messages: List[Dict[str, Any]], + tools: Optional[List[Dict[str, Any]]], + stream: bool, + custom_llm_provider: str, + kwargs: Dict[str, Any], + ) -> Tuple[bool, Dict[str, Any]]: + return True, {"tool_calls": [{"id": "call_abc"}]} + + async def async_run_agentic_loop( + self, + tools: Dict[str, Any], + model: str, + messages: List[Dict[str, Any]], + response: Any, + anthropic_messages_provider_config: Any, + anthropic_messages_optional_request_params: Dict[str, Any], + logging_obj: Any, + stream: bool, + kwargs: Dict[str, Any], + ) -> Any: + return self._result + + +class _LoggingWithCallbacks(_LoggingStub): + def __init__(self, callbacks: Tuple[CustomLogger, ...]) -> None: + self.dynamic_success_callbacks = list(callbacks) + + +async def _dispatch_tool_call_turn( + response: ModelResponse, *callbacks: CustomLogger +) -> ModelResponse | CustomStreamWrapper | None: + return await maybe_run_chat_completion_agentic_loop( + response=response, + model="gpt-4o-mini", + messages=[{"role": "user", "content": "what is 6*7?"}], + optional_params={}, + kwargs={}, + logging_obj=_LoggingWithCallbacks(callbacks), + custom_llm_provider="openai", + stream=False, + ) + + +@pytest.mark.asyncio +async def test_dispatcher_returns_the_tool_call_turn_when_the_plan_terminates(): + response: Final = _tool_call_model_response() + terminating: Final = _GateOnlyLogger( + plan=AgenticLoopPlan(terminate=True), tool_calls={"tool_calls": [{"id": "call_abc"}]} + ) + + assert await _dispatch_tool_call_turn(response, terminating) is response + + +@pytest.mark.asyncio +async def test_dispatcher_moves_past_a_plan_that_declines_the_loop(): + override: Final = _plain_model_response("from the next callback") + tool_calls: Final = {"tool_calls": [{"id": "call_abc"}]} + declined: Final = _GateOnlyLogger(plan=AgenticLoopPlan(run_agentic_loop=False), tool_calls=tool_calls) + overriding: Final = _GateOnlyLogger(plan=AgenticLoopPlan(response_override=override), tool_calls=tool_calls) + + assert await _dispatch_tool_call_turn(_tool_call_model_response(), declined, overriding) is override + + +@pytest.mark.asyncio +async def test_dispatcher_returns_a_run_only_loops_result(): + loop_result: Final = _plain_model_response() + + assert await _dispatch_tool_call_turn(_tool_call_model_response(), _RunOnlyLoopLogger(loop_result)) is loop_result @pytest.mark.asyncio @@ -562,3 +1038,55 @@ async def test_converted_stream_replays_the_final_answer_with_the_requested_usag if include_usage else [] ) + + +class TestAgenticFollowUpKeepsTheProviderPrefix: + @staticmethod + async def _run_followup( + model: str, custom_llm_provider: str, patched_model: str | None = None + ) -> ModelResponse: + response: Final = await _execute_chat_completion_agentic_plan( + plan=AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[{"role": "user", "content": "hi"}], model=patched_model + ), + ), + callback=CustomLogger(), + model=model, + optional_params={"mock_response": "ok from followup"}, + kwargs={}, + logging_obj=None, + custom_llm_provider=custom_llm_provider, + depth=0, + max_loops=2, + fingerprints=[], + fingerprint="fp", + ) + assert isinstance(response, ModelResponse) + return response + + @pytest.mark.asyncio + async def test_a_sub_path_model_still_resolves_a_provider(self) -> None: + response: Final = await self._run_followup("mantle/anthropic.claude-sonnet-5", "bedrock") + + assert response.choices[0].message.content == "ok from followup" + + @pytest.mark.asyncio + async def test_an_ordinary_model_still_resolves_a_provider(self) -> None: + response: Final = await self._run_followup("gpt-4o-mini", "openai") + + assert response.choices[0].message.content == "ok from followup" + + @pytest.mark.asyncio + async def test_a_hook_that_patches_another_providers_model_reaches_that_provider(self) -> None: + response: Final = await self._run_followup("gpt-4o", "openai", patched_model="anthropic/claude-sonnet-4-5") + + assert response.model == "claude-sonnet-4-5" + assert response._hidden_params["custom_llm_provider"] == "anthropic" + + @pytest.mark.asyncio + async def test_a_hook_echoing_an_org_style_model_stays_on_the_request_provider(self) -> None: + response: Final = await self._run_followup("openai/gpt-4o", "openrouter", patched_model="openai/gpt-4o") + + assert response._hidden_params["custom_llm_provider"] == "openrouter" diff --git a/tests/unit/litellm_core_utils/test_error_normalization.py b/tests/unit/litellm_core_utils/test_error_normalization.py index 87b9463cb01..524dda41218 100644 --- a/tests/unit/litellm_core_utils/test_error_normalization.py +++ b/tests/unit/litellm_core_utils/test_error_normalization.py @@ -217,6 +217,22 @@ def test_unknown_exception_falls_back_to_status_then_unclassified() -> None: assert normalize_error(Exception("x"), "", "x") == "UNCLASSIFIED" +def test_a_529_the_anthropic_routes_answered_is_an_overload() -> None: + paused = _proxy_exc("Model is paused", "service_unavailable_error", 529) + assert StandardLoggingPayloadSetup.get_error_information(paused)["normalized_error"] == "503_PROVIDER_OVERLOADED" + + +@pytest.mark.parametrize("body", ["", "bad gateway", '{"type":"error","error":{"type":"api_error"}}']) +def test_an_internal_server_error_answered_as_529_is_an_overload_whatever_its_body_says(body: str) -> None: + overloaded = litellm.InternalServerError(body, llm_provider="anthropic", model="claude", provider_status_code=529) + overloaded.status_code = 529 + internal = litellm.InternalServerError(body, llm_provider="anthropic", model="claude") + assert ( + StandardLoggingPayloadSetup.get_error_information(overloaded)["normalized_error"], + StandardLoggingPayloadSetup.get_error_information(internal)["normalized_error"], + ) == ("503_PROVIDER_OVERLOADED", "500_PROVIDER_INTERNAL_ERROR") + + def test_budget_exceeded_error_with_custom_wording_is_still_a_budget_error() -> None: exc = litellm.BudgetExceededError(current_cost=2.0, max_budget=1.0, message="Spending cap reached for key") assert StandardLoggingPayloadSetup.get_error_information(exc)["normalized_error"] == "429_BUDGET_EXCEEDED" diff --git a/tests/unit/litellm_core_utils/test_exception_mapping_utils.py b/tests/unit/litellm_core_utils/test_exception_mapping_utils.py index 1b00a32d31d..32a99a37870 100644 --- a/tests/unit/litellm_core_utils/test_exception_mapping_utils.py +++ b/tests/unit/litellm_core_utils/test_exception_mapping_utils.py @@ -1230,12 +1230,12 @@ def test_openai_incorrect_api_key_keeps_the_proxy_authentication_error_type() -> class _UpstreamHTTPError(Exception): - def __init__(self, status_code: int) -> None: - super().__init__("upstream failure") - self.message = "upstream failure" + def __init__(self, status_code: int, message: str = "upstream failure") -> None: + super().__init__(message) + self.message = message self.status_code = status_code self.request = httpx.Request("POST", "https://api.example.com/v1/chat/completions") - self.response = httpx.Response(status_code=status_code, request=self.request, text="upstream failure") + self.response = httpx.Response(status_code=status_code, request=self.request, text=message) UPSTREAM_STATUS_CODES = (400, 401, 402, 403, 404, 408, 422, 429, 500, 503) @@ -1433,6 +1433,48 @@ def test_an_already_mapped_litellm_exception_passes_through_untouched(provider, assert returned is already_mapped +@pytest.mark.parametrize( + "message", + ['{"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}}', "upstream failure"], +) +def test_an_anthropic_529_stays_an_internal_server_error_that_carries_the_529(message, quiet_exception_mapping): + with pytest.raises(litellm.InternalServerError) as raised: + exception_type( + model="claude-opus-5-5", + original_exception=_UpstreamHTTPError(status_code=529, message=message), + custom_llm_provider="anthropic", + ) + + assert (type(raised.value), raised.value.status_code, raised.value.provider_status_code) == ( + litellm.InternalServerError, + 500, + 529, + ) + + +@pytest.mark.parametrize("status_code", [400, 404]) +def test_an_anthropic_client_error_naming_overloaded_carries_no_overload_status(status_code, quiet_exception_mapping): + with pytest.raises(litellm.InternalServerError) as raised: + exception_type( + model="claude-opus-5-5", + original_exception=_UpstreamHTTPError(status_code=status_code, message="model: claude-Overloaded"), + custom_llm_provider="anthropic", + ) + + assert raised.value.provider_status_code is None + + +def test_an_anthropic_500_carries_its_own_status_as_the_provider_status(quiet_exception_mapping): + with pytest.raises(litellm.InternalServerError) as raised: + exception_type( + model="claude-opus-5-5", + original_exception=_UpstreamHTTPError(status_code=500), + custom_llm_provider="anthropic", + ) + + assert (raised.value.status_code, raised.value.provider_status_code) == (500, 500) + + @pytest.mark.parametrize("status_code", UPSTREAM_STATUS_CODES) @pytest.mark.parametrize("provider", PROVIDERS_WITHOUT_A_HANDLER) def test_a_provider_without_a_handler_maps_by_the_upstream_status(provider, status_code, quiet_exception_mapping): diff --git a/tests/unit/litellm_core_utils/test_get_litellm_params.py b/tests/unit/litellm_core_utils/test_get_litellm_params.py index 738ab774b2f..a1184039102 100644 --- a/tests/unit/litellm_core_utils/test_get_litellm_params.py +++ b/tests/unit/litellm_core_utils/test_get_litellm_params.py @@ -340,6 +340,13 @@ class TestGetLitellmParamsDataResidency: ) assert result["data_residency"] == "us" + def test_fedramp_host_resolves_to_fedramp(self): + result = get_litellm_params( + custom_llm_provider="openai", + api_base="https://gov.api.openai.com/v1", + ) + assert result["data_residency"] == "fedramp" + def test_global_host_resolves_to_none(self): result = get_litellm_params( custom_llm_provider="openai", diff --git a/tests/unit/litellm_core_utils/test_litellm_logging.py b/tests/unit/litellm_core_utils/test_litellm_logging.py index 13e181b2b92..a014d1d352d 100644 --- a/tests/unit/litellm_core_utils/test_litellm_logging.py +++ b/tests/unit/litellm_core_utils/test_litellm_logging.py @@ -14451,3 +14451,146 @@ def test_record_native_bedrock_guardrail_bills_each_call_its_own_trace_when_call (first_cost, second_cost) = costs assert second_cost == pytest.approx(2 * first_cost) assert shared_metadata == {"tag": "x"} + + +@pytest.mark.asyncio +async def test_a_redacted_video_poll_still_tells_the_spend_callbacks_the_video_finished( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, job_to_charge_once + from litellm.types.videos.main import VideoObject + + class Recorder(CustomLogger): + def __init__(self) -> None: + super().__init__() + self.events: tuple[tuple[dict[str, object], object], ...] = () + + async def async_log_success_event( + self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object + ) -> None: + self.events = (*self.events, (dict(kwargs), response_obj)) + + recorder: Final = Recorder() + monkeypatch.setattr(litellm, "_async_success_callback", []) + monkeypatch.setattr(litellm, "success_callback", []) + monkeypatch.setattr(litellm, "turn_off_message_logging", True) + logging_obj: Final = LitellmLogging( + model="custom-video", + messages=[], + stream=False, + call_type="avideo_status", + start_time=datetime.datetime.now(), + litellm_call_id="poll-1", + function_id="poll-1", + dynamic_async_success_callbacks=[recorder], + ) + logging_obj.update_environment_variables( + litellm_params={"metadata": {}, "video_id": "video_abc"}, optional_params={} + ) + + await logging_obj.async_success_handler(result=VideoObject(id="video_abc", object="video", status="completed")) + + ((kwargs, response_obj),) = recorder.events + assert not isinstance(response_obj, VideoObject) + assert job_to_charge_once(kwargs, response_obj, 0.42) == ChargeOnceJob(kind="video", job_id="video_abc") + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("status", "output_file_id", "deferred", "charged"), + [("in_progress", None, True, False), ("completed", "file-out", False, True)], +) +async def test_a_redacted_batch_retrieve_still_tells_the_spend_callbacks_whether_the_batch_cost_is_final( + monkeypatch: pytest.MonkeyPatch, status: str, output_file_id: str | None, deferred: bool, charged: bool +) -> None: + from litellm.litellm_core_utils.batch_utils import ChargeOnceJob, defers_batch_cost, job_to_charge_once + from litellm.types.utils import LiteLLMBatch, Usage + + class Recorder(CustomLogger): + def __init__(self) -> None: + super().__init__() + self.events: tuple[tuple[dict[str, object], object], ...] = () + + async def async_log_success_event( + self, kwargs: dict[str, object], response_obj: object, start_time: object, end_time: object + ) -> None: + self.events = (*self.events, (dict(kwargs), response_obj)) + + recorder: Final = Recorder() + monkeypatch.setattr(litellm, "_async_success_callback", []) + monkeypatch.setattr(litellm, "success_callback", []) + monkeypatch.setattr(litellm, "turn_off_message_logging", True) + logging_obj: Final = LitellmLogging( + model="gpt-4o-mini", + messages=[], + stream=False, + call_type="aretrieve_batch", + start_time=datetime.datetime.now(), + litellm_call_id="retrieve-1", + function_id="retrieve-1", + dynamic_async_success_callbacks=[recorder], + ) + logging_obj.update_environment_variables(litellm_params={"metadata": {}}, optional_params={}) + + await logging_obj.async_success_handler( + result=LiteLLMBatch( + id="batch_abc", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="file-in", + object="batch", + status=status, + output_file_id=output_file_id, + ), + batch_cost=0.42, + batch_usage=Usage(prompt_tokens=20, completion_tokens=18, total_tokens=38), + batch_models=["gpt-4o-mini"], + ) + + ((kwargs, response_obj),) = recorder.events + assert not isinstance(response_obj, LiteLLMBatch) + assert defers_batch_cost(kwargs, response_obj) is deferred + assert (job_to_charge_once(kwargs, response_obj, 0.42) == ChargeOnceJob(kind="batch", job_id="batch_abc")) is charged + + +_FAILED_ATTEMPT: Final = (Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500), 0.5) + + +async def _success_row_cost_after_a_failed_attempt(stash: Callable[[LitellmLogging], None]) -> float: + logging_obj: Final = LitellmLogging( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hi"}], + stream=False, + call_type="acompletion", + start_time=time.time(), + litellm_call_id="recovered-attempt", + function_id="recovered-attempt", + ) + logging_obj.update_environment_variables( + model="gpt-4o-mini", user="", optional_params={}, litellm_params={"metadata": {}}, custom_llm_provider="openai" + ) + stash(logging_obj) + logging_obj.carry_failed_attempt_usage() + now: Final = datetime.datetime.now() + await logging_obj.async_success_handler( + ModelResponse(model="gpt-4o-mini", usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)), + now, + now, + ) + return logging_obj.model_call_details["standard_logging_object"]["response_cost"] + + +@pytest.mark.asyncio +async def test_a_recovered_attempt_bills_a_failed_agentic_follow_up_but_not_a_failure_that_logged_its_own_row() -> None: + own_cost: Final = await _success_row_cost_after_a_failed_attempt(lambda logging_obj: None) + after_a_dropped_stream: Final = await _success_row_cost_after_a_failed_attempt( + lambda logging_obj: logging_obj.record_partial_usage_for_failure(*_FAILED_ATTEMPT) + ) + after_a_failed_follow_up: Final = await _success_row_cost_after_a_failed_attempt( + lambda logging_obj: logging_obj.record_served_rounds_for_failure((_FAILED_ATTEMPT,)) + ) + + assert own_cost > 0 + assert after_a_dropped_stream == pytest.approx(own_cost) + assert after_a_failed_follow_up == pytest.approx(own_cost + _FAILED_ATTEMPT[1]) diff --git a/tests/unit/litellm_core_utils/test_redact_messages.py b/tests/unit/litellm_core_utils/test_redact_messages.py index 2fd3f16ab17..491ea5315ff 100644 --- a/tests/unit/litellm_core_utils/test_redact_messages.py +++ b/tests/unit/litellm_core_utils/test_redact_messages.py @@ -54,9 +54,25 @@ SIGNATURE: Final = "opaque-thought-signature" def _provider_fields() -> dict[str, object]: return { "reasoning_content": SECRET, + "reasoning": SECRET, + "reasoning_text": SECRET, + "reasoning_details": [{"type": "reasoning.text", "text": SECRET, "index": 0, "format": "unknown"}], "thinking_blocks": [{"type": "thinking", "thinking": SECRET}], "reasoningContentBlocks": [{"reasoningText": {"text": SECRET}}], + "reasoningContent": {"reasoningText": {"text": SECRET}}, + "annotations": [{"type": "file", "file": {"name": "notes.pdf", "content": [{"type": "text", "text": SECRET}]}}], "citations": [{"cited_text": SECRET}], + "citationsContent": [{"citations": [{"location": {}}], "content": [{"text": SECRET}]}], + "citation": {"type": "char_location", "cited_text": SECRET}, + "web_search_calls": [{"type": "web_search_call", "action": {"type": "search", "query": SECRET}}], + "web_search_results": [{"type": "web_search_tool_result", "content": [{"title": SECRET}]}], + "tool_results": [{"type": "web_fetch_tool_result", "content": {"text": SECRET}}], + "code_interpreter_results": [{"stdout": SECRET}], + "search_results": [{"object": "vector_store.search_results.page", "data": [{"content": [{"text": SECRET}]}]}], + "rerank_results": {"results": [{"document": {"text": SECRET}}]}, + "executed_tools": [{"type": "search", "search_results": {"results": [{"content": SECRET}]}}], + "custom_outputs": {"answer": SECRET}, + "tool_search_items": [{"type": "tool_search_output", "tools": [{"description": SECRET}]}], "compaction_blocks": [{"type": "compaction", "content": SECRET, "signature": "compaction-signature"}], "compaction_start": {"type": "compaction", "content": SECRET}, "compaction_delta": {"type": "compaction_delta", "content": SECRET}, diff --git a/tests/unit/litellm_core_utils/test_token_counter.py b/tests/unit/litellm_core_utils/test_token_counter.py index 8ef118154d1..d0b837646b4 100644 --- a/tests/unit/litellm_core_utils/test_token_counter.py +++ b/tests/unit/litellm_core_utils/test_token_counter.py @@ -1246,7 +1246,7 @@ def test_token_counter_counts_deepseek_v3_and_v4_with_the_packaged_deepseek_toke @pytest.mark.parametrize( "model", - ["together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "fireworks_ai/accounts/fireworks/models/deepseek-v2p5"], + ["deepinfra/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "fireworks_ai/accounts/fireworks/models/deepseek-v2p5"], ) def test_token_counter_skips_the_deepseek_tokenizer_for_deepseek_models_built_on_other_vocabularies(model: str): assert litellm.token_counter(model=model, text=_CODE_SAMPLE) != _deepseek_token_count(_CODE_SAMPLE) @@ -2875,7 +2875,7 @@ def test_prefixed_openai_model_names_count_text_like_the_model_they_name(deploym @pytest.mark.parametrize( - "model", ["azure/gpt-chat-latest", "azure/codex-mini", "azure/gpt-5.3-chat", "azure/gpt-4.5-preview"] + "model", ["azure/gpt-chat-latest", "azure/codex-mini", "azure/gpt-5.3-chat"] ) def test_azure_openai_models_only_azure_lists_get_openai_pricing_and_o200k(model: str): assert _pdf_cost(model, _pdf_base64((_REVENUE_LINE,))) == _text_cost(model, _REVENUE_LINE) + 13 @@ -3594,7 +3594,6 @@ def test_token_counter_counts_unlisted_names_of_newer_openai_releases_with_o200k "azure/my-deployment", "azure/ada", "azure/mistral-large-latest", - "azure/command-r-plus", "azure_ai/mistral-large-latest", ], ) diff --git a/tests/unit/litellm_core_utils/test_url_utils.py b/tests/unit/litellm_core_utils/test_url_utils.py index 9b29eac208e..958960af27a 100644 --- a/tests/unit/litellm_core_utils/test_url_utils.py +++ b/tests/unit/litellm_core_utils/test_url_utils.py @@ -16,11 +16,25 @@ from litellm.litellm_core_utils.url_utils import ( encode_url_path_segment, encode_url_path_segments, ensure_api_route, + is_https_url_with_host, join_api_base, validate_url, ) +@pytest.mark.parametrize( + ("url", "expected"), + ( + ("https://example.com/path", True), + ("http://example.com/path", False), + ("https:///path", False), + ("https://[", False), + ), +) +def test_is_https_url_with_host(url: str, expected: bool) -> None: + assert is_https_url_with_host(url) is expected + + @pytest.fixture def mock_dns_public(monkeypatch): """Resolve any hostname to 93.184.216.34 (public).""" diff --git a/tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py index fc873832956..f1c38ef4e64 100644 --- a/tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/unit/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -2631,6 +2631,46 @@ def test_get_config_with_model_uses_dynamic_max_tokens(): assert config_claude37["max_tokens"] == 64000 +def test_transform_request_uses_deployment_max_output_tokens_before_catalog() -> None: + model: Final = "claude-sonnet-4-6" + catalog_max_output_tokens: Final = litellm.get_model_info(model)["max_output_tokens"] + config: Final = AnthropicConfig() + + configured: Final = config.transform_request( + model=model, + messages=[{"role": "user", "content": "hello"}], + optional_params={}, + litellm_params={"model_info": {"max_output_tokens": 987}}, + headers={}, + ) + catalog_default: Final = config.transform_request( + model=model, + messages=[{"role": "user", "content": "hello"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + non_mapping_model_info: Final = config.transform_request( + model=model, + messages=[{"role": "user", "content": "hello"}], + optional_params={}, + litellm_params={"model_info": "invalid"}, + headers={}, + ) + requested: Final = config.transform_request( + model=model, + messages=[{"role": "user", "content": "hello"}], + optional_params={"max_tokens": 654}, + litellm_params={"model_info": {"max_output_tokens": 987}}, + headers={}, + ) + + assert configured["max_tokens"] == 987 + assert catalog_default["max_tokens"] == catalog_max_output_tokens + assert non_mapping_model_info["max_tokens"] == catalog_max_output_tokens + assert requested["max_tokens"] == 654 + + def test_get_config_without_model_uses_fallback(): """ Test that get_config without model parameter uses 128000 fallback. @@ -4477,7 +4517,9 @@ def test_transform_request_carries_prompt_cache_breakpoint_to_cache_control(): ], ) def test_transform_request_prompt_cache_breakpoint_without_claude_equivalent(marker, expected_block): - messages: Final = [{"role": "user", "content": [{"type": "text", "text": "Hello", "prompt_cache_breakpoint": marker}]}] + messages: Final = [ + {"role": "user", "content": [{"type": "text", "text": "Hello", "prompt_cache_breakpoint": marker}]} + ] with pytest.raises(litellm.utils.UnsupportedParamsError, match="drop_params"): _anthropic_body(copy.deepcopy(messages)) @@ -4488,7 +4530,12 @@ def test_transform_request_prompt_cache_breakpoint_without_claude_equivalent(mar def test_transform_request_names_the_marker_ttl_rule_for_a_ttl_only_claude_takes(): messages: Final = [ - {"role": "user", "content": [{"type": "text", "text": "Hello", "prompt_cache_breakpoint": {"mode": "explicit", "ttl": "1h"}}]} + { + "role": "user", + "content": [ + {"type": "text", "text": "Hello", "prompt_cache_breakpoint": {"mode": "explicit", "ttl": "1h"}} + ], + } ] with pytest.raises(litellm.utils.UnsupportedParamsError, match="optional ttl of '30m' or '12h'"): @@ -10249,7 +10296,9 @@ def test_chat_response_without_input_transformations_adds_no_key() -> None: def test_json_mode_chat_response_keeps_input_transformations() -> None: completion_response: Final = { **_input_transformations_response(), - "content": [{"type": "tool_use", "id": "toolu_json", "name": RESPONSE_FORMAT_TOOL_NAME, "input": {"answer": 42}}], + "content": [ + {"type": "tool_use", "id": "toolu_json", "name": RESPONSE_FORMAT_TOOL_NAME, "input": {"answer": 42}} + ], "stop_reason": "tool_use", } result: Final = AnthropicConfig().transform_response( @@ -10689,7 +10738,9 @@ def test_chat_transform_request_never_blames_a_numeric_assistant_tool_call_id_fo { "role": "assistant", "content": None, - "tool_calls": [{"id": 5, "type": "function", "function": {"name": "get_weather", "arguments": "{}"}}], + "tool_calls": [ + {"id": 5, "type": "function", "function": {"name": "get_weather", "arguments": "{}"}} + ], }, {"role": "tool", "tool_call_id": "5", "content": "rain"}, ], @@ -10726,9 +10777,15 @@ def test_chat_transform_request_names_no_field_for_a_failure_outside_roles_and_t def _five_cache_hint_conversation() -> list[dict[str, object]]: return [ - {"role": "system", "content": [{"type": "text", "text": "system prompt", "cache_control": {"type": "ephemeral"}}]}, + { + "role": "system", + "content": [{"type": "text", "text": "system prompt", "cache_control": {"type": "ephemeral"}}], + }, {"role": "user", "content": [{"type": "text", "text": "turn 1", "cache_control": {"type": "ephemeral"}}]}, - {"role": "assistant", "content": [{"type": "text", "text": "answer 1", "cache_control": {"type": "ephemeral"}}]}, + { + "role": "assistant", + "content": [{"type": "text", "text": "answer 1", "cache_control": {"type": "ephemeral"}}], + }, {"role": "user", "content": [{"type": "text", "text": "turn 2", "cache_control": {"type": "ephemeral"}}]}, {"role": "assistant", "content": "answer 2"}, {"role": "user", "content": [{"type": "text", "text": "turn 3", "cache_control": {"type": "ephemeral"}}]}, diff --git a/tests/unit/llms/anthropic/messages/native/test_agentic_streaming_iterator.py b/tests/unit/llms/anthropic/messages/native/test_agentic_streaming_iterator.py index 90a6ae3a574..7db81b7841e 100644 --- a/tests/unit/llms/anthropic/messages/native/test_agentic_streaming_iterator.py +++ b/tests/unit/llms/anthropic/messages/native/test_agentic_streaming_iterator.py @@ -9,7 +9,7 @@ from unittest.mock import AsyncMock, MagicMock import pytest - +import litellm from litellm.constants import STREAM_SSE_KEEPALIVE_PING_BYTES from litellm.llms.anthropic.messages.native.agentic_streaming_iterator import ( SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES, @@ -21,6 +21,10 @@ from litellm.llms.anthropic.messages.native.agentic_streaming_iterator import ( _handle_message_start, _parse_sse_events, ) +from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing +from litellm.proxy.utils import ProxyLogging # --------------------------------------------------------------------------- @@ -88,6 +92,14 @@ def _build_simple_text_stream() -> List[bytes]: return chunks +def _sse_frames(wire: str) -> list[tuple[str, str]]: + return [ + (frame.split("\n")[0].removeprefix("event: "), frame.split("data: ", 1)[1]) + for frame in wire.split("\n\n") + if frame.startswith("event: ") and "data: " in frame + ] + + def _build_tool_use_stream() -> List[bytes]: """Produce SSE bytes for a response with a tool_use block.""" chunks = [] @@ -727,8 +739,7 @@ class TestAgenticStreamingIteratorPhase2: class TestAgenticStreamingIteratorErrorHandling: @pytest.mark.asyncio - async def test_should_swallow_hook_errors(self): - """Errors in hook processing should be swallowed; Phase 1 chunks are still yielded.""" + async def test_a_hook_failure_reaches_the_client_after_the_streamed_message(self): chunks = _build_simple_text_stream() mock_stream = MockAsyncStream(chunks) @@ -751,11 +762,15 @@ class TestAgenticStreamingIteratorErrorHandling: ) collected = [] - async for chunk in iterator: - collected.append(chunk) - # All Phase 1 chunks should still have been yielded - assert len(collected) == len(chunks) + async def _drain(): + async for chunk in iterator: + collected.append(chunk) + + with pytest.raises(RuntimeError, match="hook exploded"): + await _drain() + + assert collected == chunks @pytest.mark.asyncio async def test_should_handle_empty_stream(self): @@ -924,21 +939,54 @@ class TestAgenticStreamingIteratorHoldBack: assert [c for c in collected if c != STREAM_SSE_KEEPALIVE_PING_BYTES] == phase2_chunks @pytest.mark.asyncio - async def test_should_error_instead_of_replaying_server_fulfilled_tool_use_when_hook_crashes(self): - """A hook crash must not replay the buffered retrieval tool_use: that is the unknown-tool bug.""" + async def test_a_rate_limited_follow_up_reaches_the_client_as_its_anthropic_error_event(self): chunks = _build_tool_use_stream() mock_handler = MagicMock() - mock_handler._call_agentic_completion_hooks = AsyncMock(side_effect=RuntimeError("hook exploded")) + mock_handler._call_agentic_completion_hooks = AsyncMock( + side_effect=litellm.RateLimitError( + message="follow-up rate limited", llm_provider="anthropic", model="claude-sonnet-4-20250514" + ) + ) - iterator = _build_hold_back_iterator(MockAsyncStream(chunks), mock_handler) + wire = "".join( + [ + frame if isinstance(frame, str) else frame.decode() + async for frame in ProxyBaseLLMRequestProcessing.async_sse_data_generator( + response=_build_hold_back_iterator(MockAsyncStream(chunks), mock_handler), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), + request_data={"model": "claude-sonnet-4-20250514"}, + proxy_logging_obj=ProxyLogging(user_api_key_cache=MagicMock()), + ) + ] + ) - collected = [] - async for chunk in iterator: - collected.append(chunk) + error_events = [json.loads(data) for event, data in _sse_frames(wire) if event == "error"] + assert [event["error"]["type"] for event in error_events] == ["rate_limit_error"] + assert "follow-up rate limited" in error_events[0]["error"]["message"] + assert "litellm_content_retrieve" not in wire - assert [c for c in collected if c != STREAM_SSE_KEEPALIVE_PING_BYTES] == [SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES] - assert b"litellm_content_retrieve" not in b"".join(collected) + @pytest.mark.asyncio + async def test_a_capped_loop_tells_the_client_to_raise_max_agentic_loops_instead_of_retrying(self): + mock_handler = MagicMock() + mock_handler._call_agentic_completion_hooks = AsyncMock( + side_effect=lambda **_: BaseLLMHTTPHandler._check_agentic_loop_safety( + tool_calls=[{"name": "litellm_content_retrieve"}], + fingerprints=[], + depth=1, + max_loops=1, + model="claude-sonnet-4-20250514", + ) + ) + + iterator = _build_hold_back_iterator(MockAsyncStream(_build_tool_use_stream()), mock_handler) + wire = b"".join([chunk async for chunk in iterator if chunk != STREAM_SSE_KEEPALIVE_PING_BYTES]).decode() + + error_messages = [json.loads(data)["error"]["message"] for event, data in _sse_frames(wire) if event == "error"] + assert len(error_messages) == 1 + assert "max_agentic_loops" in error_messages[0] + assert "retry" not in error_messages[0].lower() + assert "litellm_content_retrieve" not in wire @pytest.mark.asyncio async def test_should_error_instead_of_replaying_when_no_hook_fires_on_tool_use(self): diff --git a/tests/unit/llms/anthropic/test_anthropic_common_utils.py b/tests/unit/llms/anthropic/test_anthropic_common_utils.py index 0ee5d52d50d..99e6438c723 100644 --- a/tests/unit/llms/anthropic/test_anthropic_common_utils.py +++ b/tests/unit/llms/anthropic/test_anthropic_common_utils.py @@ -4605,6 +4605,7 @@ def test_anthropic_error_frame_exception_classes_an_overloaded_frame_as_internal error: Final = anthropic_error_frame_exception("overloaded_error", "Overloaded", 503, "claude-sonnet-4-5") assert type(error) is litellm.InternalServerError + assert error.provider_status_code == 529 _NON_NUMERIC_BUDGETS: Final = (True, False, None, "", "lots", [4096]) diff --git a/tests/unit/llms/azure/decisions/test_transformation.py b/tests/unit/llms/azure/decisions/test_transformation.py index b6de6084b9c..3f5e97ee000 100644 --- a/tests/unit/llms/azure/decisions/test_transformation.py +++ b/tests/unit/llms/azure/decisions/test_transformation.py @@ -125,6 +125,29 @@ async def test_default_credential_refreshes_without_opt_in( ) +@pytest.mark.parametrize("is_async", (False, True)) +@pytest.mark.parametrize("systemone", (False, True)) +async def test_entra_rejecting_the_default_credential_answers_401_with_only_the_aadsts_reason( + monkeypatch: pytest.MonkeyPatch, is_async: bool, systemone: bool +) -> None: + from azure.core.exceptions import ClientAuthenticationError + + app_client_id: Final = "0d08e61f-6184-4980-9cef-a2e9128f4cb2" + credential = Mock(spec=["get_token"]) + credential.get_token.side_effect = ClientAuthenticationError( + message="Authentication failed: AADSTS7000215: Invalid client secret provided. Ensure the secret being " + f"sent in the request is the client secret value, for a secret added to app '{app_client_id}'." + ) + monkeypatch.setattr("azure.identity.DefaultAzureCredential", Mock(return_value=credential)) + + with pytest.raises(litellm.AuthenticationError) as excinfo: + await call_decisions(is_async, systemone, {}) + + assert excinfo.value.status_code == 401 + assert "AADSTS7000215" in str(excinfo.value) + assert app_client_id not in str(excinfo.value) + + @pytest.mark.parametrize("kind", ("service-principal", "oidc", "provider")) async def test_azure_decisions_reuses_existing_azure_credential_resolution( monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter, kind: str diff --git a/tests/unit/llms/azure/search/test_bing_grounding_search_transformation.py b/tests/unit/llms/azure/search/test_bing_grounding_search_transformation.py index dfd1474211f..53f426d8505 100644 --- a/tests/unit/llms/azure/search/test_bing_grounding_search_transformation.py +++ b/tests/unit/llms/azure/search/test_bing_grounding_search_transformation.py @@ -466,6 +466,12 @@ def _mock_response(): response.content = json.dumps(MOCK_BING_GROUNDING_RESPONSE).encode() return response +async def _search_what_is_litellm(is_async: bool) -> object: + if is_async: + return await litellm.asearch(query="what is litellm", search_provider="bing_grounding") + return litellm.search(query="what is litellm", search_provider="bing_grounding") + + @pytest.mark.usefixtures("_vcr_outcome_gate") class TestBingGroundingSearchTransformation: """ @@ -546,6 +552,36 @@ class TestBingGroundingSearchTransformation: } ] + @pytest.mark.parametrize("is_async", (False, True)) + async def test_entra_rejecting_the_default_credential_answers_401_with_only_the_aadsts_reason( + self, monkeypatch: pytest.MonkeyPatch, is_async: bool + ): + from azure.core.exceptions import ClientAuthenticationError + + from litellm.llms.azure import common_utils + + app_client_id: Final = "0d08e61f-6184-4980-9cef-a2e9128f4cb2" + credential = Mock(spec=["get_token"]) + credential.get_token.side_effect = ClientAuthenticationError( + message="Authentication failed: AADSTS7000215: Invalid client secret provided. Ensure the secret being " + f"sent in the request is the client secret value, for a secret added to app '{app_client_id}'." + ) + monkeypatch.delenv("BING_GROUNDING_TOKEN") + for name in ("AZURE_CLIENT_ID", "AZURE_TENANT_ID", "AZURE_CLIENT_SECRET", "AZURE_USERNAME", "AZURE_PASSWORD"): + monkeypatch.delenv(name, raising=False) + monkeypatch.setattr("azure.identity.DefaultAzureCredential", Mock(return_value=credential)) + common_utils._cached_azure_ad_token_refresh_provider.cache_clear() + + try: + with pytest.raises(litellm.AuthenticationError) as excinfo: + await _search_what_is_litellm(is_async) + finally: + common_utils._cached_azure_ad_token_refresh_provider.cache_clear() + + assert excinfo.value.status_code == 401 + assert "AADSTS7000215" in str(excinfo.value) + assert app_client_id not in str(excinfo.value) + @pytest.mark.asyncio async def test_bing_grounding_asearch(self): with patch( # test-quality-ok: litellm.asearch has no client injection seam diff --git a/tests/unit/llms/azure/test_azure_common_utils.py b/tests/unit/llms/azure/test_azure_common_utils.py index 0a41861e088..d66ba3f324e 100644 --- a/tests/unit/llms/azure/test_azure_common_utils.py +++ b/tests/unit/llms/azure/test_azure_common_utils.py @@ -1,16 +1,23 @@ +import asyncio +import io import json import os +import sys import threading import traceback import uuid +from collections.abc import Mapping from concurrent.futures import ThreadPoolExecutor from queue import SimpleQueue -from typing import Callable, Final, Optional +from typing import Awaitable, Callable, Final, Optional from urllib.parse import parse_qs from unittest.mock import ANY, MagicMock, patch import httpx +import openai import pytest +import requests +from azure.core.pipeline.transport import RequestsTransport import litellm from litellm.llms.azure.common_utils import ( @@ -2500,3 +2507,487 @@ def test_an_azure_client_litellm_built_its_own_http_client_for_is_still_closed(m closer.reap() assert wrapper.is_closed() is True + + +_ENTRA_APP_CLIENT_ID: Final = "0d08e61f-6184-4980-9cef-a2e9128f4cb2" +_ENTRA_TRACE_ID: Final = "f0c149d3-ef20-4194-8b81-c81a5e002500" +_ENTRA_REJECTIONS: Final = { + "wrong_secret": ( + "AADSTS7000215", + "Authentication failed: AADSTS7000215: Invalid client secret provided. Ensure the secret being sent in the " + "request is the client secret value, not the client secret ID, for a secret added to app " + f"'{_ENTRA_APP_CLIENT_ID}'. Trace ID: {_ENTRA_TRACE_ID} Correlation ID: ecfd2c73-ec0a-474c-bba7-205be903159d " + "Timestamp: 2026-10-29 07:19:40Z", + ), + "expired_secret": ( + "AADSTS7000222", + f"Authentication failed: AADSTS7000222: The provided client secret keys for app '{_ENTRA_APP_CLIENT_ID}' " + f"are expired. Visit the Azure portal to create new keys for your app. Trace ID: {_ENTRA_TRACE_ID} " + "Correlation ID: 354f9b57-5fed-47e4-a67d-89a7c56f6ddc Timestamp: 2026-10-29 07:19:50Z", + ), +} + + +def _raising_token_provider(error: Exception) -> Callable[[], str]: + def token_provider() -> str: + raise error + + return token_provider + + +def _azure_chat(token_provider: Callable[[], str]) -> object: + return litellm.completion( + model="azure/gpt-4.1", + api_base="https://litellm-qa.openai.azure.com", + api_version="2025-04-01-preview", + messages=[{"role": "user", "content": "hi"}], + azure_ad_token_provider=token_provider, + max_retries=0, + ) + + +def _azure_async_chat(token_provider: Callable[[], str]) -> object: + return asyncio.run( + litellm.acompletion( + model="azure/gpt-4.1", + api_base="https://litellm-qa.openai.azure.com", + api_version="2025-04-01-preview", + messages=[{"role": "user", "content": "hi"}], + azure_ad_token_provider=token_provider, + ) + ) + + +def _azure_responses(token_provider: Callable[[], str]) -> object: + return litellm.responses( + model="azure/gpt-4.1", + api_base="https://litellm-qa.openai.azure.com", + api_version="2025-04-01-preview", + input="hi", + azure_ad_token_provider=token_provider, + ) + + +def _azure_ai_decisions(token_provider: Callable[[], str]) -> object: + return litellm.systemone( + model="azure_ai/decision-1-qa", + api_base="https://litellm-qa.services.ai.azure.com", + state={"ticket": "The login page returns a 500 error after deploy"}, + questions={"is_bug": {"type": "noul", "instructions": "Is this a software bug report?"}}, + azure_ad_token_provider=token_provider, + ) + + +_AZURE_CALLS: Final = { + "chat": _azure_chat, + "async_chat": _azure_async_chat, + "responses": _azure_responses, + "decisions": _azure_ai_decisions, +} + + +@pytest.fixture +def no_azure_api_key(monkeypatch): + for env_var in ( + "AZURE_API_KEY", + "AZURE_OPENAI_API_KEY", + "AZURE_AI_API_KEY", + "AZURE_AD_TOKEN", + "AZURE_TENANT_ID", + "AZURE_CLIENT_ID", + "AZURE_CLIENT_SECRET", + "AZURE_USERNAME", + "AZURE_PASSWORD", + ): + monkeypatch.delenv(env_var, raising=False) + monkeypatch.setattr(litellm, "api_key", None) + monkeypatch.setattr(litellm, "azure_key", None) + + +@pytest.mark.parametrize("call", _AZURE_CALLS.values(), ids=_AZURE_CALLS.keys()) +@pytest.mark.parametrize(("aadsts_code", "entra_message"), _ENTRA_REJECTIONS.values(), ids=_ENTRA_REJECTIONS.keys()) +def test_entra_rejecting_the_credential_answers_401_with_only_the_aadsts_reason( + no_azure_api_key, call, aadsts_code, entra_message +): + from azure.core.exceptions import ClientAuthenticationError + + with pytest.raises(litellm.AuthenticationError) as excinfo: + call(_raising_token_provider(ClientAuthenticationError(message=entra_message))) + + assert excinfo.value.status_code == 401 + assert aadsts_code in str(excinfo.value) + assert _ENTRA_APP_CLIENT_ID not in str(excinfo.value) + assert _ENTRA_TRACE_ID not in str(excinfo.value) + + +@pytest.mark.parametrize("api_version", ["2025-04-01-preview", "v1"]) +def test_entra_rejecting_an_async_token_provider_answers_401(no_azure_api_key, api_version): + from azure.core.exceptions import ClientAuthenticationError + + aadsts_code, entra_message = _ENTRA_REJECTIONS["wrong_secret"] + + async def token_provider() -> str: + raise ClientAuthenticationError(message=entra_message) + + async_token_provider: Final[Callable[[], Awaitable[str]]] = token_provider + + with pytest.raises(litellm.AuthenticationError) as excinfo: + asyncio.run( + litellm.acompletion( + model="azure/gpt-4.1", + api_base="https://litellm-qa.openai.azure.com", + api_version=api_version, + messages=[{"role": "user", "content": "hi"}], + azure_ad_token_provider=async_token_provider, + ) + ) + + assert excinfo.value.status_code == 401 + assert aadsts_code in str(excinfo.value) + assert _ENTRA_APP_CLIENT_ID not in str(excinfo.value) + assert _ENTRA_TRACE_ID not in str(excinfo.value) + + +def _azure_identity_wrapped_token_provider(inner_error: Exception) -> Callable[[], str]: + from azure.core.exceptions import ClientAuthenticationError + + def token_provider() -> str: + try: + raise inner_error + except Exception as error: + raise ClientAuthenticationError(message=f"Authentication failed: {error}") from error + + return token_provider + + +@pytest.mark.parametrize("call", _AZURE_CALLS.values(), ids=_AZURE_CALLS.keys()) +@pytest.mark.parametrize("failed_leg", ["request", "response"]) +def test_unreachable_entra_endpoint_stays_a_retryable_server_error(no_azure_api_key, call, failed_leg): + from azure.core.exceptions import ServiceRequestError, ServiceResponseError + + transport_error: Final = {"request": ServiceRequestError, "response": ServiceResponseError}[failed_leg]( + "('Unable to connect to proxy', NewConnectionError(\"Failed to establish a new connection: " + "[Errno 111] Connection refused\"))" + ) + + with pytest.raises(openai.APIError) as excinfo: + call(_azure_identity_wrapped_token_provider(transport_error)) + + assert not isinstance(excinfo.value, litellm.AuthenticationError) + assert excinfo.value.status_code != 401 + + +_ENTRA_TENANT_ID: Final = "5b3e1c7a-2f4d-4e8b-9a61-0c2d7f8e9b14" +_ENTRA_SCOPE: Final = "https://cognitiveservices.azure.com/.default" +_ENTRA_OPENID_CONFIGURATION: Final = { + "token_endpoint": f"https://login.microsoftonline.com/{_ENTRA_TENANT_ID}/oauth2/v2.0/token", + "authorization_endpoint": f"https://login.microsoftonline.com/{_ENTRA_TENANT_ID}/oauth2/v2.0/authorize", + "issuer": f"https://login.microsoftonline.com/{_ENTRA_TENANT_ID}/v2.0", +} + + +class _FakeEntraAdapter(requests.adapters.BaseAdapter): + def __init__(self, status_code: int | None, body: Mapping[str, object] | str) -> None: + super().__init__() + self.status_code: Final = status_code + self.body: Final = body + + def send(self, request: requests.PreparedRequest, *args: object, **kwargs: object) -> requests.Response: + if "openid-configuration" in str(request.url): + return self._answer(request, 200, _ENTRA_OPENID_CONFIGURATION) + if self.status_code is None: + raise requests.exceptions.ConnectionError("Failed to establish a new connection: Connection refused") + return self._answer(request, self.status_code, self.body) + + def close(self) -> None: + pass + + @staticmethod + def _answer( + request: requests.PreparedRequest, status_code: int, body: Mapping[str, object] | str + ) -> requests.Response: + response: Final = requests.Response() + response.status_code = status_code + response.raw = io.BytesIO((body if isinstance(body, str) else json.dumps(body)).encode()) + response.headers = requests.structures.CaseInsensitiveDict( + {"Content-Type": "application/json", "Retry-After": "0"} + ) + response.url = str(request.url) + response.request = request + return response + + +def _entra_answering(status_code: int | None, body: Mapping[str, object] | str = "") -> RequestsTransport: + session: Final = requests.Session() + session.mount("https://", _FakeEntraAdapter(status_code, body)) + return RequestsTransport(session=session, session_owner=False) + + +def _entra_error(aadsts_code: str, description: str) -> Mapping[str, object]: + return { + "error": "invalid_client", + "error_description": f"{aadsts_code}: {description} Trace ID: {_ENTRA_TRACE_ID}", + "error_codes": [int(aadsts_code.removeprefix("AADSTS"))], + } + + +def _real_credential_token_provider(credential_kind: str, entra: RequestsTransport) -> Callable[[], str]: + from azure.identity import ( + ChainedTokenCredential, + ClientAssertionCredential, + ClientSecretCredential, + get_bearer_token_provider, + ) + + client_secret: Final = ClientSecretCredential( + _ENTRA_TENANT_ID, _ENTRA_APP_CLIENT_ID, "wrong-secret", transport=entra, disable_instance_discovery=True + ) + credentials: Final = { + "client_secret": client_secret, + "default_azure_credential_chain": ChainedTokenCredential(client_secret), + "workload_identity": ClientAssertionCredential( + _ENTRA_TENANT_ID, _ENTRA_APP_CLIENT_ID, lambda: "federated-token", transport=entra, retry_total=0 + ), + } + return get_bearer_token_provider(credentials[credential_kind], _ENTRA_SCOPE) + + +_REAL_CREDENTIAL_REJECTIONS: Final = { + "wrong_secret": ("client_secret", "AADSTS7000215", "Invalid client secret provided."), + "wrong_secret_through_a_credential_chain": ( + "default_azure_credential_chain", + "AADSTS7000215", + "Invalid client secret provided.", + ), + "unknown_app_through_workload_identity": ( + "workload_identity", + "AADSTS700016", + f"Application with identifier '{_ENTRA_APP_CLIENT_ID}' was not found in the directory.", + ), + "unknown_federated_subject_through_workload_identity": ( + "workload_identity", + "AADSTS700213", + "No matching federated identity record found for presented assertion subject 'system:serviceaccount:qa:proxy'.", + ), +} + + +@pytest.mark.parametrize( + ("credential_kind", "aadsts_code", "description"), + _REAL_CREDENTIAL_REJECTIONS.values(), + ids=_REAL_CREDENTIAL_REJECTIONS.keys(), +) +def test_entra_rejection_through_a_real_azure_identity_credential_answers_401( + no_azure_api_key, credential_kind, aadsts_code, description +): + entra: Final = _entra_answering(401, _entra_error(aadsts_code, description)) + + with pytest.raises(litellm.AuthenticationError) as excinfo: + _azure_chat(_real_credential_token_provider(credential_kind, entra)) + + assert excinfo.value.status_code == 401 + assert aadsts_code in str(excinfo.value) + assert _ENTRA_APP_CLIENT_ID not in str(excinfo.value) + assert _ENTRA_TRACE_ID not in str(excinfo.value) + + +_REAL_CREDENTIAL_NON_REJECTIONS: Final = { + "unreachable_through_a_credential_chain": ("default_azure_credential_chain", None, ""), + "html_503_through_a_credential_chain": ( + "default_azure_credential_chain", + 503, + "Service Unavailable", + ), + "json_503_through_a_credential_chain": ( + "default_azure_credential_chain", + 503, + _entra_error("AADSTS90033", "A transient error has occurred."), + ), + "throttled_client_secret": ( + "client_secret", + 429, + _entra_error("AADSTS50196", "The server terminated an operation because it encountered a client request loop."), + ), + "tenant_throttled_client_secret": ( + "client_secret", + 429, + _entra_error("AADSTS90055", "There are too many incoming requests."), + ), + "request_budget_exceeded_client_secret": ( + "client_secret", + 400, + _entra_error("AADSTS90024", "A transient error has occurred. Try again."), + ), + "retryable_error_client_secret": ( + "client_secret", + 400, + _entra_error("AADSTS50033", "A transient error has occurred."), + ), + "external_server_retryable_client_secret": ( + "client_secret", + 400, + _entra_error("AADSTS90006", "The service is temporarily unavailable."), + ), + "graph_retryable_client_secret": ( + "client_secret", + 400, + _entra_error("AADSTS90090", "The service is temporarily unavailable."), + ), + "token_issuance_error_client_secret": ( + "client_secret", + 400, + _entra_error("AADSTS50000", "There was an error issuing a token or an issue with our sign-in service."), + ), + "throttled_without_a_code_through_a_credential_chain": ( + "default_azure_credential_chain", + 429, + {"error": "temporarily_unavailable", "error_description": "Too many requests."}, + ), + "json_503_through_workload_identity": ( + "workload_identity", + 503, + _entra_error("AADSTS90033", "A transient error has occurred."), + ), + "throttled_workload_identity_naming_a_rejection_code": ( + "workload_identity", + 429, + _entra_error("AADSTS7000215", "Invalid client secret provided."), + ), +} + + +@pytest.mark.parametrize( + ("credential_kind", "status_code", "body"), + _REAL_CREDENTIAL_NON_REJECTIONS.values(), + ids=_REAL_CREDENTIAL_NON_REJECTIONS.keys(), +) +def test_entra_outage_or_throttle_through_a_real_azure_identity_credential_stays_retryable( + no_azure_api_key, credential_kind, status_code, body +): + with pytest.raises(openai.APIError) as excinfo: + _azure_chat(_real_credential_token_provider(credential_kind, _entra_answering(status_code, body))) + + assert not isinstance(excinfo.value, litellm.AuthenticationError) + assert excinfo.value.status_code != 401 + + +def _azure_chat_with_oidc_token( + respx_mock, + monkeypatch, + entra_status_code: int, + entra_body: Mapping[str, object], + entra_headers: Mapping[str, str] | None = None, +): + monkeypatch.setenv("LITELLM_QA_FEDERATED_TOKEN", "federated-token") + monkeypatch.delenv("AZURE_AUTHORITY_HOST", raising=False) + respx_mock.post(f"https://login.microsoftonline.com/{_ENTRA_TENANT_ID}/oauth2/v2.0/token").mock( + return_value=httpx.Response(entra_status_code, json=entra_body, headers=entra_headers) + ) + return litellm.completion( + model="azure/gpt-4.1", + api_base="https://litellm-qa.openai.azure.com", + api_version="2025-04-01-preview", + messages=[{"role": "user", "content": "hi"}], + azure_ad_token="oidc/env/LITELLM_QA_FEDERATED_TOKEN", + tenant_id=_ENTRA_TENANT_ID, + client_id=_ENTRA_APP_CLIENT_ID, + max_retries=0, + ) + + +def test_entra_rejecting_an_oidc_token_answers_401_with_only_the_aadsts_reason(no_azure_api_key, respx_mock, monkeypatch): + rejection: Final = _entra_error( + "AADSTS700213", f"No matching federated identity record found for app '{_ENTRA_APP_CLIENT_ID}'." + ) + + with pytest.raises(litellm.AuthenticationError) as excinfo: + _azure_chat_with_oidc_token(respx_mock, monkeypatch, 400, rejection) + + assert excinfo.value.status_code == 401 + assert "AADSTS700213" in str(excinfo.value) + assert _ENTRA_APP_CLIENT_ID not in str(excinfo.value) + assert _ENTRA_TRACE_ID not in str(excinfo.value) + + +def test_entra_answering_an_oidc_exchange_with_a_transient_code_keeps_its_status( + no_azure_api_key, respx_mock, monkeypatch +): + with pytest.raises(openai.APIError) as excinfo: + _azure_chat_with_oidc_token( + respx_mock, monkeypatch, 400, _entra_error("AADSTS90024", "A transient error has occurred. Try again.") + ) + + assert excinfo.value.status_code == 400 + assert not isinstance(excinfo.value, litellm.AuthenticationError) + + +def test_entra_throttling_an_oidc_exchange_keeps_its_retry_after(no_azure_api_key, respx_mock, monkeypatch): + with pytest.raises(litellm.RateLimitError) as excinfo: + _azure_chat_with_oidc_token( + respx_mock, + monkeypatch, + 429, + _entra_error("AADSTS90055", "TenantThrottlingError: There are too many pending requests."), + {"Retry-After": "7"}, + ) + + assert excinfo.value.response.headers.get("retry-after") == "7" + + +class _EntraServiceUnavailableAnswer: + status_code: Final = 503 + reason: Final = "Service Unavailable" + + def text(self, encoding: str | None = None) -> str: + return "Service Unavailable" + + +@pytest.mark.parametrize("call", _AZURE_CALLS.values(), ids=_AZURE_CALLS.keys()) +@pytest.mark.parametrize("credential_kind", ["client_secret", "managed_identity"]) +def test_entra_answering_a_server_error_stays_a_retryable_server_error(no_azure_api_key, call, credential_kind): + from azure.core.exceptions import ClientAuthenticationError + from msal.exceptions import MsalServiceError + + token_providers: Final = { + "client_secret": _azure_identity_wrapped_token_provider( + MsalServiceError("HTTP Error: 503", error=None, error_description=None) + ), + "managed_identity": _raising_token_provider( + ClientAuthenticationError(message="No token received.", response=_EntraServiceUnavailableAnswer()) + ), + } + + with pytest.raises(openai.APIError) as excinfo: + call(token_providers[credential_kind]) + + assert not isinstance(excinfo.value, litellm.AuthenticationError) + assert excinfo.value.status_code != 401 + + +def test_token_provider_error_without_azure_core_installed_keeps_the_generic_token_error(monkeypatch): + monkeypatch.setitem(sys.modules, "azure.core.exceptions", None) + + with pytest.raises(RuntimeError, match="Failed to get Azure AD token: token service down"): + get_azure_ad_token( + GenericLiteLLMParams(azure_ad_token_provider=_raising_token_provider(ValueError("token service down"))) + ) + + +def test_image_generation_with_an_api_key_ignores_a_non_callable_azure_ad_token_provider(): + import respx + + with respx.mock: + image_route: Final = respx.post(url__startswith="https://litellm-qa.openai.azure.com").mock( + return_value=httpx.Response(200, json={"created": 1, "data": [{"b64_json": "aGk="}]}) + ) + response: Final = litellm.image_generation( + model="azure/gpt-image-1", + prompt="a red dot", + api_base="https://litellm-qa.openai.azure.com", + api_version="2025-04-01-preview", + api_key="configured-key", + azure_ad_token_provider="not-a-callable", + ) + + assert image_route.calls.last.request.headers["api-key"] == "configured-key" + assert response.data[0].b64_json == "aGk=" diff --git a/tests/unit/llms/azure_ai/anthropic/count_tokens/test_token_counter.py b/tests/unit/llms/azure_ai/anthropic/count_tokens/test_token_counter.py index faa33e559ca..7ce5d8b6a02 100644 --- a/tests/unit/llms/azure_ai/anthropic/count_tokens/test_token_counter.py +++ b/tests/unit/llms/azure_ai/anthropic/count_tokens/test_token_counter.py @@ -171,6 +171,79 @@ async def test_upstream_error_status_becomes_an_error_response_with_that_status_ ) +@pytest.mark.asyncio +async def test_entra_rejecting_the_deployment_oidc_token_becomes_a_401_error_response_without_counting( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setenv("LITELLM_QA_FEDERATED_TOKEN", "federated-token") + monkeypatch.delenv("AZURE_AUTHORITY_HOST", raising=False) + with respx.mock: + respx.post("https://login.microsoftonline.com/qa-tenant/oauth2/v2.0/token").mock( + return_value=httpx.Response( + 401, + json={ + "error": "invalid_client", + "error_description": "AADSTS700211: No matching federated identity record found for presented " + "assertion issuer 'https://oidc.litellm-qa.invalid'. Trace ID: 0f5c2b1e", + "error_codes": [700211], + }, + ) + ) + count_route: Final = respx.post(COUNT_URL).mock(return_value=httpx.Response(200, json={"input_tokens": 6})) + result: Final = await AzureAIAnthropicTokenCounter().count_tokens( + model_to_use="claude-sonnet-4-5", + messages=[{"role": "user", "content": "hi"}], + contents=None, + deployment={ + "litellm_params": { + "api_base": API_BASE, + "tenant_id": "qa-tenant", + "client_id": "qa-client", + "azure_ad_token": "oidc/env/LITELLM_QA_FEDERATED_TOKEN", + } + }, + request_model="azure_ai/claude-sonnet-4-5", + ) + + assert result is not None + assert (result.error, result.status_code) == (True, 401) + assert "AADSTS700211" in (result.error_message or "") + assert not count_route.called + + +@pytest.mark.asyncio +async def test_entra_throttling_the_deployment_oidc_token_exchange_keeps_its_429( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setenv("LITELLM_QA_FEDERATED_TOKEN", "federated-token") + monkeypatch.delenv("AZURE_AUTHORITY_HOST", raising=False) + with respx.mock: + respx.post("https://login.microsoftonline.com/throttled-tenant/oauth2/v2.0/token").mock( + return_value=httpx.Response(429, json={"error": "temporarily_unavailable", "error_codes": [90055]}) + ) + count_route: Final = respx.post(COUNT_URL).mock(return_value=httpx.Response(200, json={"input_tokens": 6})) + result: Final = await AzureAIAnthropicTokenCounter().count_tokens( + model_to_use="claude-sonnet-4-5", + messages=[{"role": "user", "content": "hi"}], + contents=None, + deployment={ + "litellm_params": { + "api_base": API_BASE, + "tenant_id": "throttled-tenant", + "client_id": "qa-client", + "azure_ad_token": "oidc/env/LITELLM_QA_FEDERATED_TOKEN", + } + }, + request_model="azure_ai/claude-sonnet-4-5", + ) + + assert result is not None + assert (result.error, result.status_code) == (True, 429) + assert not count_route.called + + @pytest.mark.asyncio async def test_undecodable_count_body_becomes_a_500_error_response_carrying_the_decoder_message( monkeypatch: pytest.MonkeyPatch, diff --git a/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py b/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py index b1d52c9dd51..6b34589d1ee 100644 --- a/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py +++ b/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py @@ -1360,7 +1360,7 @@ def test_azure_ai_services_handler(api_base, expected_url): with patch.object(client, "post") as mock_client: try: response = litellm.completion( - model="azure_ai/Meta-Llama-3.1-70B-Instruct", + model="azure_ai/Llama-3.3-70B-Instruct", messages=[{"role": "user", "content": "Hello, how are you?"}], api_key="my-fake-api-key", api_base=api_base, @@ -1385,7 +1385,7 @@ def test_azure_ai_services_with_api_version(): with patch.object(client, "post") as mock_client: try: response = litellm.completion( - model="azure_ai/Meta-Llama-3.1-70B-Instruct", + model="azure_ai/Llama-3.3-70B-Instruct", messages=[{"role": "user", "content": "Hello, how are you?"}], api_key="my-fake-api-key", api_version="2024-05-01-preview", diff --git a/tests/unit/llms/azure_ai/rerank/test_azure_ai_rerank_transformation.py b/tests/unit/llms/azure_ai/rerank/test_azure_ai_rerank_transformation.py index 3de27199e2e..80e37655b60 100644 --- a/tests/unit/llms/azure_ai/rerank/test_azure_ai_rerank_transformation.py +++ b/tests/unit/llms/azure_ai/rerank/test_azure_ai_rerank_transformation.py @@ -9,7 +9,7 @@ from litellm.llms.azure_ai.rerank.transformation import AzureAIRerankConfig class TestAzureAIRerankConfigGetCompleteUrl: def setup_method(self): self.config = AzureAIRerankConfig() - self.model = "azure_ai/cohere-rerank-v3-english" + self.model = "azure_ai/cohere-rerank-v4.0-pro" def test_api_base_required(self): with pytest.raises(ValueError, match='Azure AI API Base is required\\. api_base=None\\. Set in') as exc_info: @@ -99,7 +99,7 @@ class TestAzureAIRerankConfigValidateEnvironment: def test_uses_api_key_when_set(self): headers = AzureAIRerankConfig().validate_environment( headers={}, - model="azure_ai/cohere-rerank-v3-english", + model="azure_ai/cohere-rerank-v4.0-pro", api_key="my-key", ) @@ -111,7 +111,7 @@ class TestAzureAIRerankConfigValidateEnvironment: headers = AzureAIRerankConfig().validate_environment( headers={}, - model="azure_ai/cohere-rerank-v3-english", + model="azure_ai/cohere-rerank-v4.0-pro", litellm_params={"azure_ad_token": "entra-token"}, ) diff --git a/tests/unit/llms/azure_ai/test_azure_ai_entra_auth.py b/tests/unit/llms/azure_ai/test_azure_ai_entra_auth.py index d0c1c4a6fb2..a8958b301db 100644 --- a/tests/unit/llms/azure_ai/test_azure_ai_entra_auth.py +++ b/tests/unit/llms/azure_ai/test_azure_ai_entra_auth.py @@ -254,6 +254,57 @@ def test_agent_entra_values_resolve_os_environ_references(monkeypatch): assert get_azure_ai_agent_entra_token({"azure_ad_token": "os.environ/FOUNDRY_AGENT_AD_TOKEN"}) == "token-from-env" +_AGENT_APP_CLIENT_ID: Final = "0d08e61f-6184-4980-9cef-a2e9128f4cb2" +_AGENT_CREDENTIALS: Final = { + "service_principal": ( + "litellm.llms.azure.common_utils.get_azure_ad_token_from_entra_id", + {"tenant_id": "tenant", "client_id": _AGENT_APP_CLIENT_ID, "client_secret": "wrong-secret"}, + ), + "username_password": ( + "litellm.llms.azure.common_utils.get_azure_ad_token_from_username_password", + {"client_id": _AGENT_APP_CLIENT_ID, "azure_username": "agent@example.com", "azure_password": "wrong"}, + ), +} + + +def _raising(error: Exception): + def token() -> str: + raise error + + return token + + +@pytest.mark.parametrize(("token_fetch", "agent_params"), _AGENT_CREDENTIALS.values(), ids=_AGENT_CREDENTIALS.keys()) +def test_agent_entra_rejection_raises_401_with_only_the_aadsts_reason(token_fetch, agent_params): + from azure.core.exceptions import ClientAuthenticationError + + rejection: Final = ClientAuthenticationError( + message="Authentication failed: AADSTS7000215: Invalid client secret provided. Ensure the secret being sent " + f"in the request is the client secret value, for a secret added to app '{_AGENT_APP_CLIENT_ID}'." + ) + + with patch(token_fetch, return_value=_raising(rejection)): # test-quality-ok: stands in for the Entra token endpoint answering a wrong credential, the shape azure-identity raises + with pytest.raises(litellm.AuthenticationError) as excinfo: + get_azure_ai_agent_entra_token(agent_params) + + assert excinfo.value.status_code == 401 + assert "AADSTS7000215" in str(excinfo.value) + assert _AGENT_APP_CLIENT_ID not in str(excinfo.value) + + +def test_agent_entra_endpoint_unreachable_is_not_an_auth_error(): + from azure.core.exceptions import ClientAuthenticationError + + unreachable: Final = ClientAuthenticationError(message="Authentication failed: Connection refused") + token_fetch, agent_params = _AGENT_CREDENTIALS["service_principal"] + + with patch(token_fetch, return_value=_raising(unreachable)): # test-quality-ok: stands in for an unreachable Entra token endpoint, the shape azure-identity raises + with pytest.raises(ClientAuthenticationError) as excinfo: + get_azure_ai_agent_entra_token(agent_params) + + assert not isinstance(excinfo.value, litellm.AuthenticationError) + + def test_agent_entra_token_failure_names_the_credential_fields(): with pytest.raises(ValueError, match="client_secret"): get_azure_ai_agent_entra_token({"azure_scope": "https://ai.azure.com/.default"}) diff --git a/tests/unit/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py b/tests/unit/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py index 9b20192c3f2..a5b66efe7cd 100644 --- a/tests/unit/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py +++ b/tests/unit/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py @@ -22,6 +22,7 @@ TOKEN_PRICED_NAMES: Final = ( "cohere-command-a", "grok-4-20-reasoning", "grok-4-20-non-reasoning", + "claude-mythos-5-1", ) GROK_4_20_NAMES: Final = ("grok-4-20-reasoning", "grok-4-20-non-reasoning") CATALOG_NAMES: Final = TOKEN_PRICED_NAMES + ("whisper",) diff --git a/tests/unit/llms/bedrock/embed/test_bedrock_embedding.py b/tests/unit/llms/bedrock/embed/test_bedrock_embedding.py index 889faf784bf..904ca3bb8ca 100644 --- a/tests/unit/llms/bedrock/embed/test_bedrock_embedding.py +++ b/tests/unit/llms/bedrock/embed/test_bedrock_embedding.py @@ -61,6 +61,7 @@ def test_bedrock_embedding_with_api_key_bearer_token(model, input_type, embed_re with patch.object(client, "post") as mock_post: mock_response = Mock() mock_response.status_code = 200 + mock_response.headers = httpx.Headers() mock_response.text = json.dumps(embed_response) mock_response.json = lambda: json.loads(mock_response.text) mock_post.return_value = mock_response @@ -774,6 +775,7 @@ def test_bedrock_embedding_header_forwarding(model, embed_response): with patch.object(client, "post") as mock_post: mock_response = Mock() mock_response.status_code = 200 + mock_response.headers = httpx.Headers() mock_response.text = json.dumps(embed_response) mock_response.json = lambda: json.loads(mock_response.text) mock_post.return_value = mock_response @@ -909,6 +911,7 @@ def test_bedrock_cohere_v4_embedding_response_parsing(): with patch.object(client, "post") as mock_post: mock_response = Mock() mock_response.status_code = 200 + mock_response.headers = httpx.Headers() mock_response.text = json.dumps(cohere_v4_response) mock_response.json = lambda: json.loads(mock_response.text) mock_post.return_value = mock_response @@ -1317,6 +1320,7 @@ def test_bedrock_cohere_embedding_types_wrapped_as_list( with patch.object(client, "post") as mock_post: mock_response = Mock() mock_response.status_code = 200 + mock_response.headers = httpx.Headers() mock_response.text = json.dumps(cohere_embedding_response) mock_response.json = lambda: json.loads(mock_response.text) mock_post.return_value = mock_response @@ -1848,6 +1852,7 @@ def test_bedrock_embedding_models(model, input_type, embed_response): with patch.object(client, "post") as mock_post: mock_response = Mock() mock_response.status_code = 200 + mock_response.headers = httpx.Headers() mock_response.text = json.dumps(embed_response) mock_response.json = lambda: json.loads(mock_response.text) mock_post.return_value = mock_response diff --git a/tests/unit/llms/bedrock/passthrough/guardrail_translation/test_handler.py b/tests/unit/llms/bedrock/passthrough/guardrail_translation/test_handler.py index aec1672b335..1c0ce5a334a 100644 --- a/tests/unit/llms/bedrock/passthrough/guardrail_translation/test_handler.py +++ b/tests/unit/llms/bedrock/passthrough/guardrail_translation/test_handler.py @@ -1394,6 +1394,7 @@ class _ScriptedGuardrail(CustomGuardrail): self.skip_system_message_in_guardrail = skip_system self.scan_only_tool_results = scan_only_tool_results self.received: tuple[GenericGuardrailAPIInputs, ...] = () + self.request_data: tuple[Mapping[str, object], ...] = () async def apply_guardrail( self, @@ -1403,6 +1404,7 @@ class _ScriptedGuardrail(CustomGuardrail): logging_obj: LiteLLMLoggingObj | None = None, ) -> Mapping[str, object]: self.received = (*self.received, copy.deepcopy(inputs)) + self.request_data = (*self.request_data, request_data) return self.reply(inputs) @@ -1429,16 +1431,30 @@ def _converse_request(body: dict) -> dict: async def _sent_to_bedrock(data: dict) -> dict: - """The Converse body Bedrock receives when the passthrough route sends ``data``.""" + """The body Bedrock receives when the passthrough route sends ``data``.""" received: list[httpx.Request] = [] + action: Final = data["endpoint"].rsplit("/", 1)[-1] + def respond(request: httpx.Request) -> httpx.Response: received.append(request) - reply = { - "output": {"message": {"role": "assistant", "content": [{"text": "ok"}]}}, - "stopReason": "end_turn", - "usage": {"inputTokens": 1, "outputTokens": 1, "totalTokens": 2}, - } + reply: Final = ( + { + "id": "msg_bdrk_1", + "type": "message", + "role": "assistant", + "model": "claude-haiku-4-5", + "content": [{"type": "text", "text": "ok"}], + "stop_reason": "end_turn", + "usage": {"input_tokens": 1, "output_tokens": 1}, + } + if action == "invoke" + else { + "output": {"message": {"role": "assistant", "content": [{"text": "ok"}]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 1, "outputTokens": 1, "totalTokens": 2}, + } + ) return httpx.Response(200, json=reply, request=request) router = litellm.Router( @@ -1457,7 +1473,7 @@ async def _sent_to_bedrock(data: dict) -> dict: await router.allm_passthrough_route( model="bedrock-model", method="POST", - endpoint="/model/bedrock-model/converse", + endpoint=f"/model/bedrock-model/{action}", data=data["data"], client=AsyncHTTPHandler(transport=httpx.MockTransport(respond)), ) @@ -3843,6 +3859,260 @@ class TestInvokeStreamGuardrailRelay: assert rewritten_key == received_key +def _anthropic_invoke_data( + action: str = "invoke", +) -> dict[str, object]: # mutable-ok: tests write into the request the handler gets + return { + "endpoint": f"model/anthropic.claude-sonnet/{action}", + "custom_llm_provider": "bedrock", + "model": "anthropic.claude-sonnet", + "litellm_call_id": "call-1", + "data": { + "anthropic_version": "bedrock-2023-05-31", + "max_tokens": 512, + "system": "You are a travel assistant. Card on file 4111 1111 1111 1111", + "tools": [ + { + "name": "find_trips", + "description": "Search trips", + "input_schema": {"type": "object", "properties": {"port": {"type": "string"}}}, + } + ], + "messages": [ + {"role": "user", "content": "Pay with 4111 1111 1111 1111"}, + { + "role": "assistant", + "content": [ + {"type": "tool_use", "id": "toolu_1", "name": "find_trips", "input": {"port": "Miami"}} + ], + }, + { + "role": "user", + "content": [ + {"type": "tool_result", "tool_use_id": "toolu_1", "content": "Card 4111 1111 1111 1111 charged"} + ], + }, + ], + }, + } + + +def _masked_anthropic_invoke_body() -> Mapping[str, object]: + body: Final = _anthropic_invoke_data()["data"] + return { + **body, + "system": "You are a travel assistant. Card on file [CARD] 1111 1111 1111", + "messages": [ + {"role": "user", "content": "Pay with [CARD] 1111 1111 1111"}, + body["messages"][1], + { + "role": "user", + "content": [ + {"type": "tool_result", "tool_use_id": "toolu_1", "content": "Card [CARD] 1111 1111 1111 charged"} + ], + }, + ], + } + + +def _rows_masked(inputs: GenericGuardrailAPIInputs) -> Mapping[str, object]: + rows = [ + {**row, "content": row["content"].replace("4111", "[CARD]")} if isinstance(row.get("content"), str) else row + for row in inputs["structured_messages"] + ] + return {"structured_messages": rows} + + +@pytest.mark.asyncio +@pytest.mark.parametrize("action", ["invoke", "invoke-with-response-stream"]) +async def test_anthropic_invoke_masks_are_written_back_into_the_body(action: str) -> None: + guardrail = _ScriptedGuardrail(lambda inputs: {"texts": _masked_texts(inputs)}) + + result = await BedrockPassthroughGuardrailHandler().process_input_messages( + _anthropic_invoke_data(action), guardrail + ) + + assert result["data"] == _masked_anthropic_invoke_body() + + +@pytest.mark.asyncio +async def test_anthropic_invoke_masks_reach_bedrock() -> None: + data: Final = _anthropic_invoke_data() + + await BedrockPassthroughGuardrailHandler().process_input_messages( + data, _ScriptedGuardrail(lambda inputs: {"texts": _masked_texts(inputs)}) + ) + + assert await _sent_to_bedrock(data) == _masked_anthropic_invoke_body() + + +@pytest.mark.asyncio +async def test_anthropic_invoke_guardrail_gets_roles_tools_and_the_body_at_the_top_level_of_request_data() -> None: + data: Final = _anthropic_invoke_data() + guardrail = _replying({}) + + await BedrockPassthroughGuardrailHandler().process_input_messages(data, guardrail) + + inputs = guardrail.received[-1] + assert [row["role"] for row in inputs["structured_messages"]] == ["system", "user", "assistant", "tool"] + assert [tool["function"]["name"] for tool in inputs["tools"]] == ["find_trips"] + assert inputs["model"] == data["model"] + request_data: Final = guardrail.request_data[-1] + assert request_data["messages"] is data["data"]["messages"] + assert request_data["endpoint"] == data["endpoint"] + assert request_data["litellm_call_id"] == data["litellm_call_id"] + assert "data" not in request_data + + +@pytest.mark.asyncio +async def test_anthropic_invoke_structured_message_rewrites_are_written_back() -> None: + result = await BedrockPassthroughGuardrailHandler().process_input_messages( + _anthropic_invoke_data(), _ScriptedGuardrail(_rows_masked) + ) + + assert result["data"]["messages"][0] == { + "role": "user", + "content": [{"type": "text", "text": "Pay with [CARD] 1111 1111 1111"}], + } + + +@pytest.mark.asyncio +@pytest.mark.parametrize("model", ["anthropic.claude-sonnet", "prod-model", ""]) +async def test_anthropic_invoke_structured_rewrite_keeps_cache_control_whatever_the_model_is_called( + model: str, +) -> None: + cached: Final = {"type": "ephemeral"} + data: Final = _anthropic_invoke_data() + data["model"] = model + data["data"]["system"] = [{"type": "text", "text": "Card on file 4111 1111 1111 1111", "cache_control": cached}] + data["data"]["tools"] = [ + { + "name": "find_trips", + "description": "Search trips", + "input_schema": {"type": "object", "properties": {}}, + "cache_control": cached, + } + ] + data["data"]["messages"] = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Pay with 4111 1111 1111 1111", "cache_control": {**cached, "ttl": "1h"}} + ], + } + ] + + result = await BedrockPassthroughGuardrailHandler().process_input_messages( + data, + _ScriptedGuardrail( + lambda inputs: { + "structured_messages": json.loads(json.dumps(inputs["structured_messages"]).replace("4111", "[CARD]")), + "tools": json.loads(json.dumps(inputs["tools"])), + } + ), + ) + + assert result["data"]["system"] == [ + {"type": "text", "text": "Card on file [CARD] 1111 1111 1111", "cache_control": cached} + ] + assert result["data"]["messages"][0]["content"] == [ + {"type": "text", "text": "Pay with [CARD] 1111 1111 1111", "cache_control": {**cached, "ttl": "1h"}} + ] + assert result["data"]["tools"][0]["cache_control"] == cached + + +@pytest.mark.asyncio +async def test_invoke_body_without_anthropic_version_is_scanned_whole() -> None: + data: Final = {**_anthropic_invoke_data(), "data": {"messages": [{"role": "user", "content": "hello"}]}} + guardrail = _replying({}) + + await BedrockPassthroughGuardrailHandler().process_input_messages(data, guardrail) + + assert json.loads(guardrail.received[-1]["texts"][0])["data"] == data["data"] + assert "structured_messages" not in guardrail.received[-1] + + +_PLAIN_TEXT_DOCUMENT: Final = { + "type": "document", + "source": {"type": "text", "media_type": "text/plain", "data": "Card 4111 1111 1111 1111"}, +} + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "content", + [ + pytest.param([{"type": "text", "text": "Summarize this"}, _PLAIN_TEXT_DOCUMENT], id="document"), + pytest.param( + [ + { + "type": "search_result", + "source": "https://example.com/cards", + "title": "Cards", + "content": [{"type": "text", "text": "Card 4111 1111 1111 1111"}], + } + ], + id="search_result", + ), + pytest.param( + [{"type": "tool_result", "tool_use_id": "toolu_1", "content": [_PLAIN_TEXT_DOCUMENT]}], + id="document_in_tool_result", + ), + ], +) +async def test_anthropic_invoke_body_with_blocks_the_messages_handler_does_not_scan_is_scanned_whole(content: Sequence[object]) -> None: + invoke: Final = _anthropic_invoke_data() + data: Final = {**invoke, "data": {**invoke["data"], "messages": [{"role": "user", "content": content}]}} + guardrail = _replying({}) + + await BedrockPassthroughGuardrailHandler().process_input_messages(data, guardrail) + + assert json.loads(guardrail.received[-1]["texts"][0])["data"] == data["data"] + assert "structured_messages" not in guardrail.received[-1] + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "messages", + [ + pytest.param([{"content": "hi"}], id="message_without_role"), + pytest.param([{"role": "user", "content": ["hi"]}], id="bare_string_in_content"), + pytest.param([{"role": "user", "content": None}], id="null_content"), + pytest.param(["hi"], id="message_is_a_string"), + ], +) +async def test_anthropic_invoke_malformed_messages_are_scanned_whole_and_left_for_bedrock_to_reject( + messages: Sequence[object], +) -> None: + invoke: Final = _anthropic_invoke_data() + data: Final = {**invoke, "data": {**invoke["data"], "messages": messages}} + guardrail = _replying({}) + + result: Final = await BedrockPassthroughGuardrailHandler().process_input_messages(data, guardrail) + + assert json.loads(guardrail.received[-1]["texts"][0])["data"] == data["data"] + assert result["data"]["messages"] == messages + + +@pytest.mark.asyncio +async def test_anthropic_invoke_response_masks_are_written_back() -> None: + response = { + "id": "msg_bdrk_1", + "type": "message", + "role": "assistant", + "content": [{"type": "text", "text": "Charged 4111 1111 1111 1111"}], + "stop_reason": "end_turn", + } + + result: Final = await BedrockPassthroughGuardrailHandler().process_output_response( + response=response, + guardrail_to_apply=_ScriptedGuardrail(lambda inputs: {"texts": _masked_texts(inputs)}), + request_data=_anthropic_invoke_data(), + ) + + assert result == {**response, "content": [{"type": "text", "text": "Charged [CARD] 1111 1111 1111"}]} + + def test_sse_events_keeps_a_data_line_whose_json_holds_unicode_line_separators() -> None: event: Final = { "type": "content_block_delta", diff --git a/tests/unit/llms/cohere/embed/test_v1_transformation.py b/tests/unit/llms/cohere/embed/test_v1_transformation.py index 31b4450ed88..63e61eaecd1 100644 --- a/tests/unit/llms/cohere/embed/test_v1_transformation.py +++ b/tests/unit/llms/cohere/embed/test_v1_transformation.py @@ -1,5 +1,7 @@ from unittest.mock import MagicMock +import httpx +import pytest from litellm.llms.cohere.embed.v1_transformation import CohereEmbeddingConfig from litellm.types.utils import EmbeddingResponse @@ -183,6 +185,7 @@ class TestCohereEmbeddingV1Transform: "meta": {}, # No billed_units } mock_response.json = MagicMock(return_value=response_json) + mock_response.headers = httpx.Headers() input_data = ["test text"] data = {"texts": input_data, "input_type": "search_query"} @@ -208,3 +211,31 @@ class TestCohereEmbeddingV1Transform: # Verify encoding was called self.encoding.encode.assert_called() + + @pytest.mark.parametrize( + ("body_key", "item", "expected_tokens"), + [("images", "data:image/png;base64," + "iVBORw0KGgo" * 400, 2047), ("texts", "a short sentence", 5)], + ) + def test_transform_response_counts_bedrock_billed_tokens_only_for_images( + self, body_key: str, item: str, expected_tokens: int + ): + response = httpx.Response( + 200, + json={"id": "e1", body_key: [{}], "embeddings": [[0.1, 0.2]], "response_type": "embeddings_floats"}, + headers={"x-amzn-bedrock-input-token-count": "2047"}, + ) + + result = self.config.transform_response( + response=response, + api_key=None, + logging_obj=self.logging_obj, + data={body_key: [item], "input_type": "image" if body_key == "images" else "search_document"}, + model_response=EmbeddingResponse(), + model="cohere.embed-v4:0", + encoding=self.encoding, + input=[item], + ) + + assert result.usage is not None + assert result.usage.prompt_tokens == expected_tokens + assert result.usage.total_tokens == expected_tokens diff --git a/tests/unit/llms/custom_httpx/test_llm_http_handler.py b/tests/unit/llms/custom_httpx/test_llm_http_handler.py index 01760572f33..dd0e5762845 100644 --- a/tests/unit/llms/custom_httpx/test_llm_http_handler.py +++ b/tests/unit/llms/custom_httpx/test_llm_http_handler.py @@ -5,7 +5,7 @@ import json import logging import threading import time -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Mapping, Sequence from types import MappingProxyType from typing import Final from unittest.mock import AsyncMock, Mock, patch @@ -68,6 +68,7 @@ from litellm.responses.streaming_iterator import ( BaseResponsesAPIStreamingIterator, MockResponsesAPIStreamingIterator, ) +from litellm.types.integrations.custom_logger import AgenticLoopPlan, AgenticLoopRequestPatch from litellm.types.llms.openai import HttpxBinaryResponseContent, ResponsesAPIResponse from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ( @@ -3614,20 +3615,40 @@ class TestServerFulfilledToolsInRequest: == frozenset() ) - def test_interception_callbacks_declare_their_retrieval_tools(self): + @pytest.mark.parametrize("hook", ["compression", "headroom", "compresr"]) + def test_a_retrieval_hook_holds_back_a_stream_carrying_its_retrieve_tool(self, hook): from litellm.integrations.compression_interception.handler import ( LITELLM_CONTENT_RETRIEVE_TOOL_NAME, CompressionInterceptionLogger, ) + from litellm.proxy.guardrails.guardrail_hooks.compresr.compresr import ( + COMPRESR_RETRIEVE_TOOL_NAME, + CompresrGuardrail, + ) from litellm.proxy.guardrails.guardrail_hooks.headroom.headroom import ( HEADROOM_RETRIEVE_TOOL_NAME, HeadroomGuardrail, ) - assert HeadroomGuardrail.server_fulfilled_tool_names == frozenset({HEADROOM_RETRIEVE_TOOL_NAME}) - assert CompressionInterceptionLogger.server_fulfilled_tool_names == frozenset( - {LITELLM_CONTENT_RETRIEVE_TOOL_NAME} - ) + callback, retrieve_tool_name = { + "compression": (CompressionInterceptionLogger(), LITELLM_CONTENT_RETRIEVE_TOOL_NAME), + "headroom": ( + HeadroomGuardrail(api_base="https://headroom.test", api_key="hr-test", guardrail_name="headroom"), + HEADROOM_RETRIEVE_TOOL_NAME, + ), + "compresr": ( + CompresrGuardrail(api_base="https://compresr.test", api_key="cr-test", guardrail_name="compresr"), + COMPRESR_RETRIEVE_TOOL_NAME, + ), + }[hook] + tools = [ + {"name": "Bash", "input_schema": {"type": "object"}}, + {"name": retrieve_tool_name, "input_schema": {"type": "object"}}, + ] + + assert BaseLLMHTTPHandler._server_fulfilled_tools_in_request( + logging_obj=self._logging_obj_with([callback]), tools=tools + ) == frozenset({retrieve_tool_name}) def _make_stub_direct_vector_store_config(response): @@ -5341,6 +5362,165 @@ async def test_responses_agentic_followup_sends_the_plans_request_param_over_a_s assert followup_calls[0]["prompt_cache_key"] == "from-plan-params" +class _PlannedChatLoop(CustomLogger): + def __init__(self, plan: AgenticLoopPlan, claimed_call_name: str = "litellm_web_search") -> None: + super().__init__() + self._plan = plan + self._claimed_call_name = claimed_call_name + self.logging_objs: list[object] = [] + + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + claimed: Final = [ + {"id": call.id, "name": call.function.name} + for call in _tool_calls_of(response) + if call.function.name == self._claimed_call_name + ] + return True, {"tool_calls": claimed or [{"id": "call_search", "name": self._claimed_call_name}]} + + async def async_build_chat_completion_agentic_loop_plan( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> AgenticLoopPlan: + self.logging_objs.append(logging_obj) + return self._plan + + +class _RunOnlyChatLoop(CustomLogger): + def __init__(self, result: ModelResponse) -> None: + super().__init__() + self._result = result + + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + return True, {"tool_calls": [{"id": "call_search", "name": "litellm_web_search"}]} + + async def async_run_chat_completion_agentic_loop( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> object: + return self._result + + +def _tool_calls_of(response: object) -> list: + if not isinstance(response, ModelResponse) or not response.choices: + return [] + return response.choices[0].message.tool_calls or [] + + +async def _run_chat_completion_agentic_hooks( + response: ModelResponse, *callbacks: CustomLogger, kwargs: Mapping[str, object] = MappingProxyType({}) +) -> object: + logging_obj: Final = Mock(litellm_call_id="call-1", dynamic_success_callbacks=list(callbacks), model_call_details={}) + return await BaseLLMHTTPHandler()._call_agentic_chat_completion_hooks( + response=response, + model="gpt-4o-mini", + messages=[{"role": "user", "content": "what is new in litellm?"}], + optional_params={}, + logging_obj=logging_obj, + stream=False, + custom_llm_provider="openai", + kwargs=dict(kwargs), + ) + + +@pytest.mark.asyncio +async def test_chat_completion_agentic_hooks_surface_a_failed_follow_up_instead_of_the_tool_call_turn(): + rate_limited_follow_up: Final = AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[{"role": "user", "content": "here are the search results"}], + kwargs={"mock_response": "litellm.RateLimitError"}, + ), + ) + + with pytest.raises(litellm.RateLimitError): + await _run_chat_completion_agentic_hooks(ModelResponse(), _PlannedChatLoop(rate_limited_follow_up)) + + +@pytest.mark.asyncio +async def test_chat_completion_agentic_hooks_return_the_tool_call_turn_when_the_plan_terminates(): + response: Final = ModelResponse() + + assert await _run_chat_completion_agentic_hooks(response, _PlannedChatLoop(AgenticLoopPlan(terminate=True))) is response + + +@pytest.mark.asyncio +async def test_chat_completion_agentic_hooks_move_past_a_plan_that_declines_the_loop(): + override: Final = ModelResponse() + declined: Final = _PlannedChatLoop(AgenticLoopPlan(run_agentic_loop=False)) + overriding: Final = _PlannedChatLoop(AgenticLoopPlan(response_override=override)) + + assert await _run_chat_completion_agentic_hooks(ModelResponse(), declined, overriding) is override + + +@pytest.mark.asyncio +async def test_chat_completion_agentic_hooks_return_a_run_only_loops_result(): + loop_result: Final = ModelResponse() + + assert await _run_chat_completion_agentic_hooks(ModelResponse(), _RunOnlyChatLoop(loop_result)) is loop_result + + +@pytest.mark.asyncio +async def test_chat_completion_agentic_hooks_at_the_cap_drop_every_hooks_internal_call_and_keep_the_clients(): + response: Final = ModelResponse( + choices=[ + { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + {"id": f"call_{name}", "type": "function", "function": {"name": name, "arguments": "{}"}} + for name in ("litellm_web_search", "litellm_code_execution", "lookup") + ], + }, + } + ] + ) + searching: Final = _PlannedChatLoop(AgenticLoopPlan(run_agentic_loop=True)) + running_code: Final = _PlannedChatLoop( + AgenticLoopPlan(run_agentic_loop=True), claimed_call_name="litellm_code_execution" + ) + + result: Final = await _run_chat_completion_agentic_hooks( + response, searching, running_code, kwargs={"_agentic_loop_depth": 1, "max_agentic_loops": 1} + ) + + assert [call.id for call in _tool_calls_of(result)] == ["call_lookup"] + + @pytest.mark.asyncio async def test_chat_completion_agentic_followup_does_not_repeat_request_params_from_plan_kwargs(monkeypatch): """A plan whose kwargs repeat a request param, or the explicitly passed model, must not crash the chat follow-up with a duplicate keyword""" @@ -5364,17 +5544,17 @@ async def test_chat_completion_agentic_followup_does_not_repeat_request_params_f ), ) - response: Final = await BaseLLMHTTPHandler()._execute_chat_completion_agentic_plan( - plan=plan, + response: Final = await BaseLLMHTTPHandler()._call_agentic_chat_completion_hooks( + response=ModelResponse(), model="gpt-5", messages=[{"role": "user", "content": "x"}], optional_params={"temperature": 0.2}, - kwargs=dict(request_kwargs), + logging_obj=Mock( + litellm_call_id="call-1", dynamic_success_callbacks=[_PlannedChatLoop(plan)], model_call_details={} + ), + stream=False, custom_llm_provider="openai", - depth=0, - max_loops=3, - fingerprints=[], - fingerprint="fp", + kwargs=dict(request_kwargs), ) assert response == "followup-response" @@ -5384,6 +5564,138 @@ async def test_chat_completion_agentic_followup_does_not_repeat_request_params_f assert followup_calls[0]["model"] == "openai/gpt-5" +def _a_search_tool_call_turn(call_number: int) -> httpx.Response: + return httpx.Response( + 200, + json={ + "id": f"chatcmpl-{call_number}", + "object": "chat.completion", + "created": 0, + "model": "served-model", + "choices": [ + { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": f"call-{call_number}", + "type": "function", + "function": {"name": "litellm_web_search", "arguments": '{"query": "weather"}'}, + } + ], + }, + } + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8}, + }, + ) + + +class _SearchAgainLoop(CustomLogger): + def __init__(self, client: AsyncHTTPHandler) -> None: + super().__init__() + self._client = client + + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + searches: Final = [ + {"id": call.id, "name": call.function.name} + for call in response.choices[0].message.tool_calls or [] + if call.function.name == "litellm_web_search" + ] + return bool(searches), {"tool_calls": searches} + + async def async_build_chat_completion_agentic_loop_plan( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> AgenticLoopPlan: + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[*messages, {"role": "user", "content": "here are the search results"}], + kwargs={"client": self._client}, + ), + ) + + +@pytest.mark.asyncio +async def test_hosted_vllm_bills_the_first_call_on_the_failure_log_when_the_follow_up_fails(monkeypatch): + loop: Final = _PlannedChatLoop( + AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[{"role": "user", "content": "here are the search results"}], + kwargs={"mock_response": "litellm.RateLimitError"}, + ), + ) + ) + monkeypatch.setattr(litellm, "callbacks", [loop]) + + with pytest.raises(litellm.RateLimitError): + await litellm.acompletion( + model="hosted_vllm/served-model", + api_base="https://fake-vllm.test/v1", + api_key="transport-only", + client=AsyncHTTPHandler(transport=httpx.MockTransport(lambda request: _a_search_tool_call_turn(1))), + messages=[{"role": "user", "content": "search the web for the weather"}], + input_cost_per_token=0.001, + output_cost_per_token=0.002, + num_retries=0, + max_retries=0, + ) + + (logging_obj,) = loop.logging_objs + assert logging_obj.model_call_details["combined_usage_object"].total_tokens == 8 + assert logging_obj.model_call_details["response_cost"] == pytest.approx(5 * 0.001 + 3 * 0.002) + + +@pytest.mark.asyncio +async def test_hosted_vllm_ends_the_turn_at_max_agentic_loops_without_the_internal_search_call(monkeypatch): + upstream_calls: Final[asyncio.Queue[httpx.Request]] = asyncio.Queue() + + def always_search(request: httpx.Request) -> httpx.Response: + upstream_calls.put_nowait(request) + return _a_search_tool_call_turn(upstream_calls.qsize()) + + client: Final = AsyncHTTPHandler(transport=httpx.MockTransport(always_search)) + monkeypatch.setattr(litellm, "callbacks", [_SearchAgainLoop(client)]) + + response: Final = await asyncio.wait_for( + litellm.acompletion( + model="hosted_vllm/served-model", + api_base="https://fake-vllm.test/v1", + api_key="transport-only", + client=client, + messages=[{"role": "user", "content": "search the web for the weather"}], + max_agentic_loops=1, + num_retries=0, + max_retries=0, + ), + timeout=10, + ) + + assert upstream_calls.qsize() == 2 + assert not response.choices[0].message.tool_calls + + _UPSTREAM_HEADERS: Final = {"x-request-id": "req_upstream", "x-ratelimit-remaining-requests": "41"} @@ -6016,63 +6328,6 @@ async def _search_through(transport: httpx.MockTransport, is_async: bool) -> obj return await litellm.vector_stores.asearch(**search_kwargs, client=client) -class TestAgenticFollowUpKeepsTheProviderPrefix: - @staticmethod - async def _run_followup( - model: str, custom_llm_provider: str, patched_model: str | None = None - ) -> ModelResponse: - from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler - from litellm.types.integrations.custom_logger import ( - AgenticLoopPlan, - AgenticLoopRequestPatch, - ) - - response: Final = await BaseLLMHTTPHandler()._execute_chat_completion_agentic_plan( - plan=AgenticLoopPlan( - run_agentic_loop=True, - request_patch=AgenticLoopRequestPatch( - messages=[{"role": "user", "content": "hi"}], model=patched_model - ), - ), - model=model, - messages=[{"role": "user", "content": "hi"}], - optional_params={"mock_response": "ok from followup"}, - kwargs={}, - custom_llm_provider=custom_llm_provider, - depth=0, - max_loops=2, - fingerprints=[], - fingerprint="fp", - ) - assert isinstance(response, ModelResponse) - return response - - @pytest.mark.asyncio - async def test_a_sub_path_model_still_resolves_a_provider(self) -> None: - response: Final = await self._run_followup("mantle/anthropic.claude-sonnet-5", "bedrock") - - assert response.choices[0].message.content == "ok from followup" - - @pytest.mark.asyncio - async def test_an_ordinary_model_still_resolves_a_provider(self) -> None: - response: Final = await self._run_followup("gpt-4o-mini", "openai") - - assert response.choices[0].message.content == "ok from followup" - - @pytest.mark.asyncio - async def test_a_hook_that_patches_another_providers_model_reaches_that_provider(self) -> None: - response: Final = await self._run_followup("gpt-4o", "openai", patched_model="anthropic/claude-sonnet-4-5") - - assert response.model == "claude-sonnet-4-5" - assert response._hidden_params["custom_llm_provider"] == "anthropic" - - @pytest.mark.asyncio - async def test_a_hook_echoing_an_org_style_model_stays_on_the_request_provider(self) -> None: - response: Final = await self._run_followup("openai/gpt-4o", "openrouter", patched_model="openai/gpt-4o") - - assert response._hidden_params["custom_llm_provider"] == "openrouter" - - @pytest.mark.asyncio @pytest.mark.parametrize("stream", [False, True]) @pytest.mark.parametrize("environment_base", [None, "ANTHROPIC_API_BASE", "ANTHROPIC_BASE_URL"]) diff --git a/tests/test_litellm_rust/cache/v2/__init__.py b/tests/unit/llms/empiriolabs/__init__.py similarity index 100% rename from tests/test_litellm_rust/cache/v2/__init__.py rename to tests/unit/llms/empiriolabs/__init__.py diff --git a/tests/unit/llms/empiriolabs/decisions/__init__.py b/tests/unit/llms/empiriolabs/decisions/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/unit/llms/empiriolabs/decisions/test_empiriolabs_decisions_transformation.py b/tests/unit/llms/empiriolabs/decisions/test_empiriolabs_decisions_transformation.py new file mode 100644 index 00000000000..02e9eab00ae --- /dev/null +++ b/tests/unit/llms/empiriolabs/decisions/test_empiriolabs_decisions_transformation.py @@ -0,0 +1,40 @@ +from collections.abc import Mapping + +import pytest + +from litellm.llms.empiriolabs.decisions.transformation import EmpirioLabsDecisionsConfig + + +@pytest.mark.parametrize( + ("environment", "connection"), + ( + ({}, ("https://api.empiriolabs.ai", None)), + ( + {"EMPIRIOLABS_API_BASE": "https://gateway.example/v1", "EMPIRIOLABS_API_KEY": "environment-key"}, + ("https://gateway.example/v1", "environment-key"), + ), + ), + ids=("public_api_without_a_key", "environment"), +) +def test_connection_comes_from_the_environment_before_the_public_api( + environment: Mapping[str, str], + connection: tuple[str, str | None], + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + monkeypatch.delenv("EMPIRIOLABS_API_KEY", raising=False) + for name, value in environment.items(): + monkeypatch.setenv(name, value) + + assert EmpirioLabsDecisionsConfig().resolve_connection(None, None) == connection + + +@pytest.mark.parametrize( + "api_base", + ("https://api.empiriolabs.ai", "https://api.empiriolabs.ai/v1", "https://api.empiriolabs.ai/v1/"), +) +def test_requests_go_to_v1_decisions_whether_or_not_the_base_ends_in_v1(api_base: str) -> None: + assert ( + EmpirioLabsDecisionsConfig().get_complete_url(api_base=api_base, model="aplomb-1") + == "https://api.empiriolabs.ai/v1/decisions" + ) diff --git a/tests/unit/llms/gemini/test_gemini_client_setup.py b/tests/unit/llms/gemini/test_gemini_client_setup.py index 48b010aca48..bc3a9aec365 100644 --- a/tests/unit/llms/gemini/test_gemini_client_setup.py +++ b/tests/unit/llms/gemini/test_gemini_client_setup.py @@ -12,7 +12,7 @@ def mock_gemini_api_key(monkeypatch): def test_gemini_completion(): response = completion( - model="gemini/gemini-2.0-flash-exp-image-generation", + model="gemini/gemini-2.5-flash", messages=[{"role": "user", "content": "Test message"}], mock_response="Test Message", ) diff --git a/tests/unit/llms/github_copilot/test_per_user_auth.py b/tests/unit/llms/github_copilot/test_per_user_auth.py index 9f91250d3c0..1aefcf2c534 100644 --- a/tests/unit/llms/github_copilot/test_per_user_auth.py +++ b/tests/unit/llms/github_copilot/test_per_user_auth.py @@ -827,6 +827,25 @@ def test_e2e_embedding_per_user_caller_authorization_cannot_override_session(per _assert_session_token_on_the_wire(requests) +def test_e2e_custom_client_id_sends_the_github_token_to_the_accounts_copilot_host(per_user_credential, monkeypatch): + monkeypatch.setenv("GITHUB_COPILOT_CLIENT_ID", "Ov23liCustomOrgApp") + lookups: Final[list[httpx.Request]] = [] + with ( + github_http( + _copilot_surface_responder(_embedding_response_payload()), answer_plan(PLAN_COPILOT_HOST), lookups + ) as requests, + _no_authenticator(), + ): + litellm.embedding( + model="github_copilot/text-embedding-3-small", + input=["hi"], + litellm_credential_name="copilot-cred", + secret_fields=_secret_fields("user-a", "gho_org_app"), + ) + assert [request.headers["authorization"] for request in lookups] == ["token gho_org_app"] + _assert_direct_model_call(requests, "gho_org_app", PLAN_COPILOT_HOST) + + def test_e2e_responses_per_user_caller_authorization_cannot_override_session(per_user_credential): with ( github_http(_copilot_surface_responder(_responses_payload())) as requests, diff --git a/tests/unit/llms/openai/responses/test_openai_responses_data_residency.py b/tests/unit/llms/openai/responses/test_openai_responses_data_residency.py index cf9f9706cfe..8fce7a73422 100644 --- a/tests/unit/llms/openai/responses/test_openai_responses_data_residency.py +++ b/tests/unit/llms/openai/responses/test_openai_responses_data_residency.py @@ -112,6 +112,28 @@ def test_responses_us_api_base_sets_data_residency(): assert logging_obj.litellm_params.get("data_residency") == "us" +def test_responses_fedramp_api_base_sets_data_residency(): + mock_client = _make_mock_http_client(_make_responses_api_response_body()) + captured, init_spy = _capture_logging_obj() + + with ( + patch( + "litellm.llms.custom_httpx.llm_http_handler.get_httpx_client", + return_value=mock_client, + ), + patch.object(litellm.Logging, "__init__", init_spy), + ): + litellm.responses( + model="gpt-4.1", + input="hi", + api_base="https://gov.api.openai.com/v1", + api_key="test-key", + ) + + logging_obj = captured["logging_obj"] + assert logging_obj.litellm_params.get("data_residency") == "fedramp" + + def test_responses_global_api_base_leaves_data_residency_none(): mock_client = _make_mock_http_client(_make_responses_api_response_body()) captured, init_spy = _capture_logging_obj() diff --git a/tests/unit/llms/openai/test_data_residency.py b/tests/unit/llms/openai/test_data_residency.py index ecb5739133c..443d14d3102 100644 --- a/tests/unit/llms/openai/test_data_residency.py +++ b/tests/unit/llms/openai/test_data_residency.py @@ -2,7 +2,9 @@ import pytest +import litellm from litellm.llms.openai.data_residency import infer_openai_data_residency +from litellm.types.utils import DataResidency @pytest.mark.parametrize( @@ -13,6 +15,8 @@ from litellm.llms.openai.data_residency import infer_openai_data_residency ("https://us.api.openai.com/v1", "us"), ("https://us.api.openai.com", "us"), ("https://EU.api.openai.com/v1", "eu"), + ("https://gov.api.openai.com/v1", DataResidency.FEDRAMP.value), + ("https://GOV.api.openai.com/v1", DataResidency.FEDRAMP.value), ("https://api.openai.com/v1", None), ("https://api.openai.com", None), ("https://example.com/v1", None), @@ -28,7 +32,26 @@ def test_infer_openai_data_residency(api_base, expected): @pytest.mark.parametrize("custom_llm_provider", [None, "anthropic", "azure", "bedrock"]) def test_infer_openai_data_residency_non_openai_provider(custom_llm_provider): - assert ( - infer_openai_data_residency(custom_llm_provider, "https://eu.api.openai.com/v1") - is None - ) + assert infer_openai_data_residency(custom_llm_provider, "https://eu.api.openai.com/v1") is None + + +@pytest.mark.parametrize("env_var", ["OPENAI_BASE_URL", "OPENAI_API_BASE"]) +def test_infer_openai_data_residency_from_env_when_api_base_unset(monkeypatch, env_var): + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_API_BASE", raising=False) + monkeypatch.setenv(env_var, "https://gov.api.openai.com/v1") + assert infer_openai_data_residency("openai", None) == DataResidency.FEDRAMP.value + + +def test_infer_openai_data_residency_explicit_api_base_wins_over_env(monkeypatch): + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.setenv("OPENAI_BASE_URL", "https://gov.api.openai.com/v1") + assert infer_openai_data_residency("openai", "https://eu.api.openai.com/v1") == "eu" + + +def test_infer_openai_data_residency_from_litellm_api_base(monkeypatch): + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_API_BASE", raising=False) + monkeypatch.setattr(litellm, "api_base", "https://us.api.openai.com/v1") + assert infer_openai_data_residency("openai", None) == "us" diff --git a/tests/unit/llms/openai/test_openai.py b/tests/unit/llms/openai/test_openai.py index 77a7c3b83fd..f61cfba9a10 100644 --- a/tests/unit/llms/openai/test_openai.py +++ b/tests/unit/llms/openai/test_openai.py @@ -3,7 +3,7 @@ import gzip import importlib import json import os -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Mapping, Sequence from types import ModuleType from typing import Final, Literal, Protocol from unittest.mock import MagicMock, Mock, patch @@ -19,6 +19,8 @@ from pydantic import BaseModel, ConfigDict, JsonValue import litellm from litellm import create_thread, get_thread from litellm.caching.caching import Cache, LiteLLMCacheType +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.integrations.custom_logger import AgenticLoopPlan, AgenticLoopRequestPatch from litellm.llms.openai.openai import ( AssistantEventHandler, AsyncAssistantEventHandler, @@ -225,6 +227,333 @@ async def test_acompletion_returns_prompt_cache_details_over_injected_transport( assert prompt_tokens_details.cached_tokens == 7 + +class _SearchingChatGate(CustomLogger): + def __init__(self) -> None: + super().__init__() + self.runs = 0 + + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + return True, {"tool_calls": [{"id": "call-1"}]} + + +class _FailingFollowUpChatLoop(_SearchingChatGate): + def __init__(self, error: Exception) -> None: + super().__init__() + self.error = error + self.logging_objs: list[object] = [] + + async def async_run_chat_completion_agentic_loop( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> object: + self.runs += 1 + self.logging_objs.append(logging_obj) + raise self.error + + +class _RaisingAgenticGate(CustomLogger): + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + raise ValueError("gate bug") + + +class _AnsweringChatLoop(_SearchingChatGate): + async def async_run_chat_completion_agentic_loop( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> object: + self.runs += 1 + return litellm.ModelResponse( + choices=[ + {"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "after the search"}} + ] + ) + + +def _respond_with_a_search_tool_call(request: httpx.Request) -> httpx.Response: + return httpx.Response( + 200, + json={ + "id": "chatcmpl-search", + "object": "chat.completion", + "created": 0, + "model": "gpt-5.6", + "choices": [ + { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call-1", + "type": "function", + "function": {"name": "litellm_web_search", "arguments": '{"query": "weather"}'}, + } + ], + }, + } + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8}, + }, + ) + + +async def _acompletion_through(http_client: httpx.AsyncClient) -> object: + return await asyncio.wait_for( + litellm.acompletion( + model="openai/gpt-5.6", + api_key="transport-only", + client=AsyncOpenAI(api_key="transport-only", http_client=http_client), + messages=[{"role": "user", "content": "search the web for the weather"}], + num_retries=0, + max_retries=0, + ), + timeout=10, + ) + + +@pytest.mark.asyncio +async def test_acompletion_surfaces_a_failed_agentic_follow_up_instead_of_the_first_turn( + monkeypatch: pytest.MonkeyPatch, +): + follow_up_error: Final = litellm.RateLimitError( + message="follow-up rate limited", llm_provider="openai", model="gpt-5.6" + ) + loop: Final = _FailingFollowUpChatLoop(follow_up_error) + monkeypatch.setattr(litellm, "callbacks", [loop]) + + async with httpx.AsyncClient(transport=httpx.MockTransport(_respond_with_a_search_tool_call)) as http_client: + with pytest.raises(litellm.RateLimitError) as raised: + await _acompletion_through(http_client) + + assert raised.value.message == follow_up_error.message + assert loop.runs == 1 + + +@pytest.mark.asyncio +async def test_a_failed_agentic_follow_up_bills_the_first_call_on_the_failure_log(monkeypatch: pytest.MonkeyPatch): + loop: Final = _FailingFollowUpChatLoop( + litellm.RateLimitError(message="follow-up rate limited", llm_provider="openai", model="gpt-5.6") + ) + monkeypatch.setattr(litellm, "callbacks", [loop]) + + async with httpx.AsyncClient(transport=httpx.MockTransport(_respond_with_a_search_tool_call)) as http_client: + with pytest.raises(litellm.RateLimitError): + await _acompletion_through(http_client) + + (logging_obj,) = loop.logging_objs + price: Final = litellm.model_cost["gpt-5.6"] + assert logging_obj.model_call_details["combined_usage_object"].total_tokens == 8 + assert logging_obj.model_call_details["response_cost"] == pytest.approx( + 5 * price["input_cost_per_token"] + 3 * price["output_cost_per_token"] + ) + + +@pytest.mark.asyncio +async def test_acompletion_does_not_resend_the_first_call_when_the_agentic_follow_up_answers_422( + monkeypatch: pytest.MonkeyPatch, +): + follow_up_error: Final = litellm.UnprocessableEntityError( + message="follow-up rejected", + model="gpt-5.6", + llm_provider="openai", + response=httpx.Response(422, request=httpx.Request("POST", "https://api.openai.com/v1/chat/completions")), + ) + loop: Final = _FailingFollowUpChatLoop(follow_up_error) + monkeypatch.setattr(litellm, "callbacks", [loop]) + monkeypatch.setattr(litellm, "drop_params", True) + + async with httpx.AsyncClient(transport=httpx.MockTransport(_respond_with_a_search_tool_call)) as http_client: + with pytest.raises(litellm.UnprocessableEntityError) as raised: + await _acompletion_through(http_client) + + assert raised.value.message == follow_up_error.message + assert loop.runs == 1 + + +def _rejecting_the_user_param(outbound: asyncio.Queue[dict[str, object]]) -> httpx.MockTransport: + def respond(request: httpx.Request) -> httpx.Response: + body: Final = json.loads(request.content) + outbound.put_nowait(body) + if "user" in body: + rejection: Final = json.dumps({"detail": [{"loc": ["body", "user"], "msg": "extra fields not permitted"}]}) + return httpx.Response(422, json={"error": {"message": rejection}}) + return httpx.Response( + 200, + json={ + "id": "chatcmpl-resent", + "object": "chat.completion", + "created": 0, + "model": "gpt-5.6", + "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "done"}}], + }, + ) + + return httpx.MockTransport(respond) + + +async def _acompletion_with_a_user_param(http_client: httpx.AsyncClient) -> object: + return await asyncio.wait_for( + litellm.acompletion( + model="openai/gpt-5.6", + api_key="transport-only", + client=AsyncOpenAI(api_key="transport-only", http_client=http_client), + messages=[{"role": "user", "content": "hi"}], + user="end-user-1", + num_retries=0, + max_retries=0, + ), + timeout=10, + ) + + +@pytest.mark.asyncio +async def test_acompletion_resends_without_the_param_a_422_rejects_when_drop_params_is_set( + monkeypatch: pytest.MonkeyPatch, +): + monkeypatch.setattr(litellm, "drop_params", True) + outbound: Final[asyncio.Queue[dict[str, object]]] = asyncio.Queue() + + async with httpx.AsyncClient(transport=_rejecting_the_user_param(outbound)) as http_client: + response: Final = await _acompletion_with_a_user_param(http_client) + + assert response.choices[0].message.content == "done" + assert outbound.get_nowait()["user"] == "end-user-1" + assert "user" not in outbound.get_nowait() + assert outbound.empty() + + +@pytest.mark.asyncio +async def test_acompletion_raises_a_422_without_resending_when_drop_params_is_off(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(litellm, "drop_params", False) + outbound: Final[asyncio.Queue[dict[str, object]]] = asyncio.Queue() + + async with httpx.AsyncClient(transport=_rejecting_the_user_param(outbound)) as http_client: + with pytest.raises(litellm.BadRequestError, match="extra fields not permitted"): + await _acompletion_with_a_user_param(http_client) + + assert outbound.get_nowait()["user"] == "end-user-1" + assert outbound.empty() + + +@pytest.mark.asyncio +async def test_acompletion_skips_a_failing_agentic_gate_and_runs_the_next_hook(monkeypatch: pytest.MonkeyPatch): + loop: Final = _AnsweringChatLoop() + monkeypatch.setattr(litellm, "callbacks", [_RaisingAgenticGate(), loop]) + + async with httpx.AsyncClient(transport=httpx.MockTransport(_respond_with_a_search_tool_call)) as http_client: + response = await _acompletion_through(http_client) + + assert loop.runs == 1 + assert response.choices[0].message.content == "after the search" + + +class _SearchAgainLoop(CustomLogger): + def __init__(self, client: AsyncOpenAI) -> None: + super().__init__() + self._client = client + + async def async_should_run_chat_completion_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + searches: Final = [ + {"id": call.id, "name": call.function.name} + for call in response.choices[0].message.tool_calls or [] + if call.function.name == "litellm_web_search" + ] + return bool(searches), {"tool_calls": searches} + + async def async_build_chat_completion_agentic_loop_plan( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + optional_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> AgenticLoopPlan: + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[*messages, {"role": "user", "content": "here are the search results"}], + kwargs={"client": self._client}, + ), + ) + + +@pytest.mark.asyncio +async def test_acompletion_ends_the_turn_at_max_agentic_loops_without_the_internal_search_call( + monkeypatch: pytest.MonkeyPatch, +): + upstream_calls: Final[asyncio.Queue[httpx.Request]] = asyncio.Queue() + + def always_search(request: httpx.Request) -> httpx.Response: + upstream_calls.put_nowait(request) + return _respond_with_a_search_tool_call(request) + + async with httpx.AsyncClient(transport=httpx.MockTransport(always_search)) as http_client: + client: Final = AsyncOpenAI(api_key="transport-only", http_client=http_client) + monkeypatch.setattr(litellm, "callbacks", [_SearchAgainLoop(client)]) + response: Final = await asyncio.wait_for( + litellm.acompletion( + model="openai/gpt-5.6", + api_key="transport-only", + client=client, + messages=[{"role": "user", "content": "search the web for the weather"}], + max_agentic_loops=1, + num_retries=0, + max_retries=0, + ), + timeout=10, + ) + + assert upstream_calls.qsize() == 2 + assert not response.choices[0].message.tool_calls + + _REASONING_TOOL_CALL_REPLY: Final = { "id": "chatcmpl-fake-stream", "object": "chat.completion", diff --git a/tests/unit/llms/openai_like/test_dynamic_config.py b/tests/unit/llms/openai_like/test_dynamic_config.py index de70f98c3f1..f26f0821c4b 100644 --- a/tests/unit/llms/openai_like/test_dynamic_config.py +++ b/tests/unit/llms/openai_like/test_dynamic_config.py @@ -139,3 +139,24 @@ class TestForceStoreFalse: headers={}, ) assert params["store"] is True + + +class TestWebSearchRequestsFromToolUsage: + def test_counts_only_paid_search_tools(self): + payload = {"usage": {"tool_usage": {"web_search": 2, "image_search": 1, "code_interpreter": 3, "web_extractor": 1}}} + assert dynamic_config.web_search_requests_from_tool_usage(payload) == 3 + + @pytest.mark.parametrize( + "payload", + [ + {"usage": {"prompt_tokens": 1}}, + {"usage": {"tool_usage": {"code_interpreter": 2}}}, + {"usage": {"tool_usage": {"web_search": "2"}}}, + {"usage": {"tool_usage": {"web_search": True}}}, + {"usage": {"tool_usage": []}}, + {"usage": None}, + "not a mapping", + ], + ) + def test_anything_but_a_positive_search_count_is_none(self, payload): + assert dynamic_config.web_search_requests_from_tool_usage(payload) is None diff --git a/tests/unit/llms/openai_like/test_empiriolabs_provider.py b/tests/unit/llms/openai_like/test_empiriolabs_provider.py index a8acce5cc9d..58d8a2371d8 100644 --- a/tests/unit/llms/openai_like/test_empiriolabs_provider.py +++ b/tests/unit/llms/openai_like/test_empiriolabs_provider.py @@ -2,13 +2,19 @@ Unit tests for the EmpirioLabs OpenAI-like provider. """ +import json import os import sys +from typing import Final + +import pytest +import respx sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../..")) ) +import litellm from litellm.llms.openai_like.dynamic_config import create_config_class from litellm.llms.openai_like.json_loader import JSONProviderRegistry @@ -61,3 +67,114 @@ def test_empiriolabs_complete_url_appends_endpoint(): stream=False, ) assert url == f"{EMPIRIOLABS_BASE_URL}/chat/completions" + + +def test_empiriolabs_chat_models_still_route_through_the_json_provider( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setenv("EMPIRIOLABS_API_KEY", "environment-key") + monkeypatch.delenv("EMPIRIOLABS_API_BASE", raising=False) + + with respx.mock() as upstream: + route: Final = upstream.post(f"{EMPIRIOLABS_BASE_URL}/chat/completions").respond( + 200, + json={ + "id": "chatcmpl_empiriolabs", + "object": "chat.completion", + "created": 1_789_550_000, + "model": "qwen3-7-plus", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello from EmpirioLabs AI"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 4, "completion_tokens": 5, "total_tokens": 9}, + }, + ) + response: Final = litellm.completion( + model="empiriolabs/qwen3-7-plus", + messages=[{"role": "user", "content": "Say hello"}], + max_completion_tokens=16, + ) + + request: Final = route.calls.last.request + assert litellm.get_llm_provider("empiriolabs/qwen3-7-plus") == ( + "qwen3-7-plus", + "empiriolabs", + "environment-key", + EMPIRIOLABS_BASE_URL, + ) + assert route.call_count == 1 + assert request.headers["authorization"] == "Bearer environment-key" + assert json.loads(request.content) == { + "model": "qwen3-7-plus", + "messages": [{"role": "user", "content": "Say hello"}], + "max_tokens": 16, + } + assert response.choices[0].message.content == "Hello from EmpirioLabs AI" + + +EMBEDDING_RESPONSE: Final = { + "object": "list", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}], + "model": "text-embedding-v4", + "usage": {"prompt_tokens": 2, "total_tokens": 2}, +} + + +def test_empiriolabs_embeddings_post_to_v1_embeddings_with_the_bearer_key(): + with respx.mock() as upstream: + route: Final = upstream.post(f"{EMPIRIOLABS_BASE_URL}/embeddings").respond(200, json=EMBEDDING_RESPONSE) + response: Final = litellm.embedding( + model="empiriolabs/text-embedding-v4", + input=["Say hello"], + api_key="empiriolabs-test-key", + ) + + request: Final = route.calls.last.request + body: Final = json.loads(request.content) + assert route.call_count == 1 + assert request.headers["authorization"] == "Bearer empiriolabs-test-key" + assert body["model"] == "text-embedding-v4" + assert response.data[0]["embedding"] == [0.1, 0.2, 0.3] + + +SEARCHED_CHAT_RESPONSE: Final = { + "id": "chatcmpl_empiriolabs_search", + "object": "chat.completion", + "created": 1_789_550_000, + "model": "qwen3-7-plus", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Two searched answers"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 4, + "completion_tokens": 5, + "total_tokens": 9, + "tool_usage": {"web_search": 2, "code_interpreter": 1}, + }, +} + + +def test_empiriolabs_web_search_calls_in_tool_usage_are_billed_per_call(local_model_cost_map: None) -> None: + with respx.mock() as upstream: + upstream.post(f"{EMPIRIOLABS_BASE_URL}/chat/completions").respond(200, json=SEARCHED_CHAT_RESPONSE) + response: Final = litellm.completion( + model="empiriolabs/qwen3-7-plus", + messages=[{"role": "user", "content": "Search twice"}], + api_key="empiriolabs-test-key", + ) + + row: Final = litellm.model_cost["empiriolabs/qwen3-7-plus"] + token_cost: Final = 4 * row["input_cost_per_token"] + 5 * row["output_cost_per_token"] + per_search: Final = row["search_context_cost_per_query"]["search_context_size_medium"] + assert response.usage.server_tool_use.web_search_requests == 2 + assert litellm.completion_cost( + completion_response=response, model="empiriolabs/qwen3-7-plus", custom_llm_provider="empiriolabs" + ) == pytest.approx(token_cost + 2 * per_search) diff --git a/tests/unit/llms/openai_like/test_wally_provider.py b/tests/unit/llms/openai_like/test_wally_provider.py new file mode 100644 index 00000000000..ac730b5760f --- /dev/null +++ b/tests/unit/llms/openai_like/test_wally_provider.py @@ -0,0 +1,269 @@ +import json +from pathlib import Path +from typing import Final + +import pytest +import respx + +import litellm +from litellm.caching.llm_caching_handler import LLMClientCache +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + +_DEFAULT_API_BASE: Final = "https://inference.runanywhere.ai/v1" +_CHAT_COMPLETIONS_URL: Final = f"{_DEFAULT_API_BASE}/chat/completions" +_CHAT_COMPLETION: Final = { + "id": "chatcmpl_wally", + "object": "chat.completion", + "created": 1_790_000_000, + "model": "glm-5.3-flash", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello from Wally", "reasoning_content": "Say hi"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 4, "completion_tokens": 3, "total_tokens": 7}, +} +_EXPECTED_ENDPOINTS: Final = { + "chat_completions": True, + "messages": True, + "responses": True, + "embeddings": False, + "image_generations": False, + "audio_transcriptions": False, + "audio_speech": False, + "moderations": False, + "batches": False, + "rerank": False, +} +WALLY_MODELS: Final = tuple(sorted(name for name in litellm.model_cost if name.startswith("wally/"))) + + +def test_wally_is_a_registered_provider(): + assert litellm.LlmProviders.WALLY.value == "wally" + assert "wally" in litellm.provider_list + assert "wally" in litellm.constants.openai_compatible_providers + assert _DEFAULT_API_BASE in litellm.constants.openai_compatible_endpoints + + +def test_wally_provider_resolution(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("WALLY_API_KEY", "wally-test-key") + monkeypatch.delenv("WALLY_API_BASE", raising=False) + + model, provider, api_key, api_base = get_llm_provider( + model="wally/glm-5.3-flash", + custom_llm_provider=None, + api_base=None, + api_key=None, + ) + + assert model == "glm-5.3-flash" + assert provider == "wally" + assert api_key == "wally-test-key" + assert api_base == _DEFAULT_API_BASE + + +def test_wally_provider_keeps_explicit_credentials(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("WALLY_API_KEY", "wally-env-key") + monkeypatch.setenv("WALLY_API_BASE", "https://wally.env.example/v1") + + _, provider, api_key, api_base = get_llm_provider( + model="wally/glm-5.3-flash", + custom_llm_provider=None, + api_base="https://wally.internal.example/v1", + api_key="wally-explicit-key", + ) + + assert provider == "wally" + assert api_key == "wally-explicit-key" + assert api_base == "https://wally.internal.example/v1" + + +def test_wally_api_base_env_overrides_default(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("WALLY_API_KEY", "wally-env-key") + monkeypatch.setenv("WALLY_API_BASE", "https://wally.env.example/v1") + + _, provider, _, api_base = get_llm_provider( + model="wally/glm-5.3-flash", + custom_llm_provider=None, + api_base=None, + api_key=None, + ) + + assert provider == "wally" + assert api_base == "https://wally.env.example/v1" + + +def test_wally_api_base_autodetects_provider(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("WALLY_API_KEY", "wally-env-key") + + model, provider, api_key, api_base = get_llm_provider( + model="glm-5.3-flash", + custom_llm_provider=None, + api_base=_DEFAULT_API_BASE, + api_key=None, + ) + + assert model == "glm-5.3-flash" + assert provider == "wally" + assert api_key == "wally-env-key" + assert api_base == _DEFAULT_API_BASE + + +@pytest.mark.parametrize("model", WALLY_MODELS) +def test_wally_model_cost_and_capabilities(model: str): + from litellm.cost_calculator import cost_per_token + from litellm.utils import ( + supports_function_calling, + supports_prompt_caching, + supports_reasoning, + supports_response_schema, + supports_tool_choice, + supports_vision, + ) + + entry: Final = litellm.model_cost[model] + prompt_cost, completion_cost = cost_per_token( + model=model, + prompt_tokens=1_000_000, + completion_tokens=1_000_000, + custom_llm_provider="wally", + ) + cached_prompt_cost, _ = cost_per_token( + model=model, + prompt_tokens=1_000_000, + completion_tokens=0, + cache_read_input_tokens=1_000_000, + custom_llm_provider="wally", + ) + model_info: Final = litellm.get_model_info(model) + + assert prompt_cost == pytest.approx(entry["input_cost_per_token"] * 1_000_000) + assert completion_cost == pytest.approx(entry["output_cost_per_token"] * 1_000_000) + assert cached_prompt_cost == pytest.approx(entry["cache_read_input_token_cost"] * 1_000_000) + assert model_info["max_tokens"] == entry["max_output_tokens"] + assert model_info["litellm_provider"] == "wally" + assert supports_function_calling(model) is entry["supports_function_calling"] + assert supports_tool_choice(model) is entry["supports_tool_choice"] + assert supports_reasoning(model) is entry["supports_reasoning"] + assert supports_prompt_caching(model) is entry["supports_prompt_caching"] + assert supports_response_schema(model) is entry["supports_response_schema"] + assert supports_vision(model) is ("image" in entry["supported_modalities"]) + + +@pytest.mark.usefixtures("local_model_cost_map") +def test_wally_wildcard_expands_to_every_wally_row(): + from litellm.proxy.auth.model_checks import get_known_models_from_wildcard + + assert set(get_known_models_from_wildcard("wally/*")) == set(WALLY_MODELS) + + +def test_wally_model_missing_from_cost_map_uses_provider_defaults(): + from litellm.utils import supports_function_calling, supports_reasoning + + assert supports_function_calling("wally/a-model-not-in-the-cost-map") is True + assert supports_reasoning("wally/a-model-not-in-the-cost-map") is True + + +def test_wally_backup_registry_mirrors_cost_map(): + package_root: Final = Path(litellm.__file__).parent + cost_map: Final = json.loads((package_root.parent / "model_prices_and_context_window.json").read_text()) + backup: Final = json.loads((package_root / "model_prices_and_context_window_backup.json").read_text()) + wally_entries: Final = {name: entry for name, entry in cost_map.items() if name.startswith("wally/")} + + assert wally_entries + assert tuple(sorted(wally_entries)) == WALLY_MODELS + assert wally_entries == {name: backup[name] for name in wally_entries} + + +def test_wally_is_available_in_add_model_form(): + fields_path: Final = Path(litellm.__file__).parent / "proxy" / "public_endpoints" / "provider_create_fields.json" + providers: Final = json.loads(fields_path.read_text()) + wally: Final = next(provider for provider in providers if provider["litellm_provider"] == "wally") + + assert wally["provider"] == "WALLY" + assert wally["provider_display_name"] == "Wally" + assert wally["default_model_placeholder"] in WALLY_MODELS + assert {field["key"]: field["required"] for field in wally["credential_fields"]} == { + "api_base": False, + "api_key": True, + } + + +def test_wally_supported_endpoints(): + backup_path: Final = Path(litellm.__file__).parent / "provider_endpoints_support_backup.json" + root_path: Final = Path(litellm.__file__).parent.parent / "provider_endpoints_support.json" + + backup: Final = json.loads(backup_path.read_text())["providers"]["wally"]["endpoints"] + root: Final = json.loads(root_path.read_text())["providers"]["wally"]["endpoints"] + + assert backup == root + assert {endpoint: root[endpoint] for endpoint in _EXPECTED_ENDPOINTS} == _EXPECTED_ENDPOINTS + + +@pytest.mark.parametrize("reasoning_effort", ["none", "max"]) +def test_wally_chat_completion_request(reasoning_effort: str): + with respx.mock() as upstream: + route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_CHAT_COMPLETION) + response: Final = litellm.completion( + model="wally/glm-5.3-flash", + messages=[{"role": "user", "content": "Say hello"}], + max_completion_tokens=64, + reasoning_effort=reasoning_effort, + api_key="wally-test-key", + ) + + request: Final = route.calls.last.request + body: Final = json.loads(request.content) + assert route.call_count == 1 + assert str(request.url) == _CHAT_COMPLETIONS_URL + assert request.headers["authorization"] == "Bearer wally-test-key" + assert body["model"] == "glm-5.3-flash" + assert body["messages"] == [{"role": "user", "content": "Say hello"}] + assert body["max_completion_tokens"] == 64 + assert body["reasoning_effort"] == reasoning_effort + assert response.choices[0].message.content == "Hello from Wally" + assert response.choices[0].message.reasoning_content == "Say hi" + + +def test_wally_responses_request_is_bridged_to_chat_completions(): + with respx.mock() as upstream: + route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_CHAT_COMPLETION) + response: Final = litellm.responses( + model="wally/glm-5.3-flash", + input="Say hello", + api_key="wally-test-key", + ) + + request: Final = route.calls.last.request + body: Final = json.loads(request.content) + assert route.call_count == 1 + assert request.headers["authorization"] == "Bearer wally-test-key" + assert body["model"] == "glm-5.3-flash" + assert body["messages"] == [{"role": "user", "content": "Say hello"}] + message_items: Final = [item for item in response.output if item.type == "message"] + assert message_items[0].content[0].text == "Hello from Wally" + + +@pytest.mark.asyncio +async def test_wally_anthropic_messages_request_is_bridged_to_chat_completions(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setattr(litellm, "in_memory_llm_clients_cache", LLMClientCache()) + with respx.mock() as upstream: + route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_CHAT_COMPLETION) + response: Final = await litellm.anthropic.messages.acreate( + model="wally/glm-5.3-flash", + messages=[{"role": "user", "content": "Say hello"}], + max_tokens=32, + api_key="wally-test-key", + ) + + request: Final = route.calls.last.request + body: Final = json.loads(request.content) + assert route.call_count == 1 + assert request.headers["authorization"] == "Bearer wally-test-key" + assert body["model"] == "glm-5.3-flash" + assert body["messages"] == [{"role": "user", "content": "Say hello"}] + assert body["max_tokens"] == 32 + assert any(block.get("text") == "Hello from Wally" for block in response["content"]) diff --git a/tests/unit/llms/opencode/test_transformation.py b/tests/unit/llms/opencode/test_transformation.py index 96203c23122..6f0fcc2f6af 100644 --- a/tests/unit/llms/opencode/test_transformation.py +++ b/tests/unit/llms/opencode/test_transformation.py @@ -4,7 +4,7 @@ import pytest from litellm.constants import SESSION_ID_GENERATED_METADATA_KEY from litellm.litellm_core_utils.provider_affinity import add_provider_affinity_header -from litellm.llms.opencode.transformation import OpenCodeProviderAdapter +from litellm.litellm_core_utils.get_llm_provider_logic import get_provider_affinity_header @pytest.mark.parametrize( @@ -22,7 +22,7 @@ def test_opencode_defaults_affinity_header_to_the_callers_session(api_base: str) headers: Final = {"Authorization": "Bearer test-key"} params: Final = {"metadata": {"session_id": "conversation-123"}} assert add_provider_affinity_header( - headers, params, OpenCodeProviderAdapter().get_provider_affinity_header(api_base) + headers, params, get_provider_affinity_header(api_base) ) == { **headers, "x-opencode-session": "conversation-123", @@ -50,7 +50,7 @@ def test_other_destinations_do_not_receive_an_automatic_session_header(api_base: add_provider_affinity_header( headers, {"litellm_session_id": "conversation-123"}, - OpenCodeProviderAdapter().get_provider_affinity_header(api_base), + get_provider_affinity_header(api_base), ) == headers ) @@ -71,7 +71,7 @@ def test_other_destinations_do_not_receive_an_automatic_session_header(api_base: def test_opencode_does_not_invent_a_conversation_id(params: dict[str, object]) -> None: assert ( add_provider_affinity_header( - {}, params, OpenCodeProviderAdapter().get_provider_affinity_header("https://opencode.ai/zen/go/v1") + {}, params, get_provider_affinity_header("https://opencode.ai/zen/go/v1") ) == {} ) @@ -81,10 +81,10 @@ def test_opencode_preserves_explicit_header_and_affinity_configuration() -> None assert add_provider_affinity_header( {"X-OpenCode-Session": "explicit"}, {"litellm_session_id": "conversation-123"}, - OpenCodeProviderAdapter().get_provider_affinity_header("https://opencode.ai/zen/go/v1"), + get_provider_affinity_header("https://opencode.ai/zen/go/v1"), ) == {"X-OpenCode-Session": "explicit"} assert add_provider_affinity_header( {}, {"litellm_session_id": "conversation-123", "provider_affinity_header": "x-custom-session"}, - OpenCodeProviderAdapter().get_provider_affinity_header("https://opencode.ai/zen/go/v1"), + get_provider_affinity_header("https://opencode.ai/zen/go/v1"), ) == {"x-custom-session": "conversation-123"} diff --git a/tests/unit/llms/sagemaker/test_sagemaker_completion_handler.py b/tests/unit/llms/sagemaker/test_sagemaker_completion_handler.py index 5e40c2ebb60..4c253a10116 100644 --- a/tests/unit/llms/sagemaker/test_sagemaker_completion_handler.py +++ b/tests/unit/llms/sagemaker/test_sagemaker_completion_handler.py @@ -549,7 +549,7 @@ async def test_completion_sagemaker( assert route.called assert response.choices[0].message.content == "SageMaker reply" - assert litellm.completion_cost(completion_response=response) == pytest.approx(rate) + assert litellm.completion_cost(completion_response=response, custom_cost_per_second=rate) == pytest.approx(rate) @pytest.mark.asyncio diff --git a/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 2eecb83e2df..03bc308a89d 100644 --- a/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -1785,6 +1785,289 @@ def test_vertex_ai_map_tools(): assert tools == new_tools +def test_vertex_ai_map_tools_declares_a_namespace_by_its_members() -> None: + namespace = { + "type": "namespace", + "name": "github", + "tools": [ + {"type": "function", "name": "create_issue", "parameters": {"type": "object", "properties": {}}}, + {"type": "function", "function": {"name": "close_issue"}}, + ], + } + + tools = VertexGeminiConfig().map_function(value=[namespace], optional_params={}) + + assert [declaration["name"] for declaration in tools[0]["function_declarations"]] == [ + "create_issue", + "close_issue", + ] + + +def test_vertex_ai_map_tools_declares_a_namespace_member_like_the_same_chat_tool() -> None: + parameters = { + "type": "object", + "properties": {"repo": {"$ref": "#/$defs/Repo"}, "label": {"type": ["string", "null"]}}, + "$defs": {"Repo": {"type": "string"}}, + } + function = {"name": "create_issue", "description": "Open an issue", "parameters": parameters} + namespace = {"type": "namespace", "name": "github", "tools": [{"type": "function", **function}]} + + from_namespace = VertexGeminiConfig().map_function(value=[namespace], optional_params={}) + from_chat_tool = VertexGeminiConfig().map_function( + value=[{"type": "function", "function": function}], optional_params={} + ) + + assert from_namespace == from_chat_tool + + +_GET_WEATHER = {"type": "function", "name": "get_weather", "parameters": {"type": "object", "properties": {}}} + + +_WEATHER_NAMESPACE = {"type": "namespace", "name": "weather", "tools": [_GET_WEATHER]} +_TOP_LEVEL_GET_WEATHER = {"type": "function", "function": {k: v for k, v in _GET_WEATHER.items() if k != "type"}} + + +@pytest.mark.parametrize( + ("tools", "declared"), + [ + ( + [{"type": "namespace", "name": "forecast", "tools": [_GET_WEATHER]}, _WEATHER_NAMESPACE], + ["forecast__get_weather", "weather__get_weather"], + ), + ([_TOP_LEVEL_GET_WEATHER, _WEATHER_NAMESPACE], ["get_weather", "weather__get_weather"]), + ([_WEATHER_NAMESPACE, _TOP_LEVEL_GET_WEATHER], ["weather__get_weather", "get_weather"]), + ], + ids=["namespaces_share_a_member", "top_level_first", "namespace_first"], +) +def test_vertex_ai_map_tools_declares_qualified_member_names_when_another_tool_declares_a_member_name( + tools: list[dict], declared: list[str] +) -> None: + mapped = VertexGeminiConfig().map_function(value=tools, optional_params={}) + + assert [declaration["name"] for declaration in mapped[0]["function_declarations"]] == declared + + +def test_vertex_ai_map_tools_declares_a_member_a_namespace_lists_twice_once_from_its_first_entry() -> None: + first = {**_GET_WEATHER, "description": "Current weather"} + repeat = {**_GET_WEATHER, "description": "Forecast"} + namespace = {"type": "namespace", "name": "weather", "tools": [first, repeat]} + + mapped = VertexGeminiConfig().map_function(value=[namespace], optional_params={}) + + declarations = mapped[0]["function_declarations"] + assert [(declaration["name"], declaration["description"]) for declaration in declarations] == [ + ("get_weather", "Current weather") + ] + + +@pytest.mark.parametrize( + ("tools", "declared"), + [ + ( + [ + _WEATHER_NAMESPACE, + _TOP_LEVEL_GET_WEATHER, + {"type": "function", "function": {"name": "weather__get_weather", "description": "Top level"}}, + ], + [("get_weather", None), ("weather__get_weather", "Top level")], + ), + ( + [ + {"type": "namespace", "name": "mcp__ops", "tools": [{"type": "function", "name": "exec"}]}, + {"type": "namespace", "name": "mcp", "tools": [{"type": "function", "name": "ops__exec"}]}, + {"type": "function", "function": {"name": "exec"}}, + {"type": "function", "function": {"name": "ops__exec"}}, + ], + [("mcp__ops__exec", None), ("exec", None), ("ops__exec", None)], + ), + ( + [_TOP_LEVEL_GET_WEATHER, _TOP_LEVEL_GET_WEATHER, _WEATHER_NAMESPACE], + [("get_weather", None), ("get_weather", None), ("weather__get_weather", None)], + ), + ], + ids=["top_level_function_has_the_qualified_name", "namespaces_build_one_qualified_name", "client_repeats_a_function"], +) +def test_vertex_ai_map_tools_never_repeats_a_generated_name( + tools: list[dict], declared: list[tuple[str, str | None]] +) -> None: + mapped = VertexGeminiConfig().map_function(value=tools, optional_params={}) + + declarations = mapped[0]["function_declarations"] + assert [(declaration["name"], declaration.get("description")) for declaration in declarations] == declared + + +def test_vertex_ai_map_openai_params_qualifies_members_by_the_namespace_top_level_name() -> None: + namespace = { + "type": "namespace", + "name": "safe", + "function": {"name": "lookup"}, + "tools": [{"type": "function", "name": "lookup"}], + } + + mapped = VertexGeminiConfig().map_openai_params( + non_default_params={ + "tools": [namespace], + "tool_choice": {"type": "function", "function": {"name": "lookup"}}, + }, + optional_params={}, + model="gemini-3.8-flash", + drop_params=False, + ) + + declared = [declaration["name"] for tool in mapped["tools"] for declaration in tool["function_declarations"]] + assert declared == ["safe__lookup"] + assert mapped["tool_choice"]["functionCallingConfig"]["allowed_function_names"] == ["safe__lookup"] + + +def test_vertex_ai_map_openai_params_declares_qualified_member_names_when_functions_repeat_a_member() -> None: + mapped = VertexGeminiConfig().map_openai_params( + non_default_params={ + "tools": [{"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]}], + "functions": [{"name": "lookup", "parameters": {"type": "object", "properties": {}}}], + }, + optional_params={}, + model="gemini-3.8-flash", + drop_params=False, + ) + + declared = [declaration["name"] for tool in mapped["tools"] for declaration in tool["function_declarations"]] + assert declared == ["mcp__weather__lookup", "lookup"] + + +def test_vertex_ai_map_tools_keeps_repeated_chat_tools_when_no_namespace_is_sent() -> None: + function = {k: v for k, v in _GET_WEATHER.items() if k != "type"} + + mapped = VertexGeminiConfig().map_function( + value=[{"type": "function", "function": function}, {"type": "function", "function": function}], + optional_params={}, + ) + + assert [declaration["name"] for declaration in mapped[0]["function_declarations"]] == ["get_weather", "get_weather"] + + +@pytest.mark.parametrize( + ("forced_name", "other_tools", "allowed_function_names"), + [ + ("github", [], ["create_issue", "close_issue"]), + ("close_issue", [], ["close_issue"]), + ("github", [{"type": "function", "function": {"name": "github"}}], ["github"]), + ( + "github", + [{"type": "function", "function": {"name": "close_issue"}}], + ["github__create_issue", "github__close_issue"], + ), + ( + "close_issue", + [{"type": "namespace", "name": "gitlab", "tools": [{"type": "function", "name": "close_issue"}]}], + ["github__close_issue", "gitlab__close_issue"], + ), + ( + "github", + [ + {"type": "function", "function": {"name": "close_issue"}}, + {"type": "function", "function": {"name": "github__close_issue"}}, + ], + ["github__create_issue"], + ), + ( + "close_issue", + [ + {"type": "namespace", "name": "gitlab", "tools": [{"type": "function", "name": "close_issue"}]}, + {"type": "function", "function": {"name": "github__close_issue"}}, + ], + ["gitlab__close_issue"], + ), + ( + "mcp", + [ + {"type": "namespace", "name": "mcp__ops", "tools": [{"type": "function", "name": "exec"}]}, + { + "type": "namespace", + "name": "mcp", + "tools": [{"type": "function", "name": "ops__exec"}, {"type": "function", "name": "status"}], + }, + {"type": "function", "function": {"name": "exec"}}, + {"type": "function", "function": {"name": "ops__exec"}}, + ], + ["mcp__status"], + ), + ], + ids=[ + "names_the_namespace", + "names_a_member", + "names_a_function_sharing_the_namespace_name", + "names_a_namespace_whose_member_a_function_repeats", + "names_a_member_two_namespaces_share", + "names_a_namespace_whose_generated_name_a_function_holds", + "names_a_member_whose_generated_name_a_function_holds", + "names_a_namespace_whose_generated_name_another_namespace_holds", + ], +) +def test_vertex_ai_tool_choice_forces_only_functions_gemini_is_sent( + forced_name: str, other_tools: list[dict[str, object]], allowed_function_names: list[str] +) -> None: + namespace = { + "type": "namespace", + "name": "github", + "tools": [{"type": "function", "name": "create_issue"}, {"type": "function", "name": "close_issue"}], + } + + mapped = VertexGeminiConfig().map_openai_params( + non_default_params={ + "tools": [namespace, *other_tools], + "tool_choice": {"type": "function", "function": {"name": forced_name}}, + }, + optional_params={}, + model="gemini-3.8-flash", + drop_params=False, + ) + + declared = [declaration["name"] for declaration in mapped["tools"][0]["function_declarations"]] + assert mapped["tool_choice"]["functionCallingConfig"]["allowed_function_names"] == allowed_function_names + assert set(allowed_function_names) <= set(declared) + + +@pytest.mark.parametrize( + ("forced_name", "tools"), + [ + ( + "weather", + [ + {"type": "namespace", "name": "weather", "tools": [{"type": "function", "name": "lookup"}]}, + {"type": "function", "function": {"name": "lookup"}}, + {"type": "function", "function": {"name": "weather__lookup"}}, + ], + ), + ( + "ops__exec", + [ + {"type": "namespace", "name": "ops", "tools": [{"type": "function", "name": "exec"}]}, + {"type": "namespace", "name": "ops__exec", "tools": [{"type": "function", "name": "status"}]}, + {"type": "function", "function": {"name": "exec"}}, + {"type": "function", "function": {"name": "status"}}, + {"type": "function", "function": {"name": "ops__exec__status"}}, + ], + ), + ], + ids=["its_members_lose_to_functions", "another_namespace_generates_its_name"], +) +def test_vertex_ai_tool_choice_naming_a_namespace_gemini_gets_no_member_of( + forced_name: str, tools: list[dict[str, object]] +) -> None: + params = {"tools": tools, "tool_choice": {"type": "function", "function": {"name": forced_name}}} + + with pytest.raises(litellm.UnsupportedParamsError, match=f"tool_choice '{forced_name}'"): + VertexGeminiConfig().map_openai_params( + non_default_params=params, optional_params={}, model="gemini-3.8-flash", drop_params=False + ) + dropped = VertexGeminiConfig().map_openai_params( + non_default_params=params, optional_params={}, model="gemini-3.8-flash", drop_params=True + ) + + assert "tool_choice" not in dropped + assert dropped["tools"][0]["function_declarations"] + + def test_vertex_ai_map_tool_with_anyof(): """ Related issue: https://github.com/BerriAI/litellm/issues/11164 diff --git a/tests/unit/mcp_client/test_mcp_client.py b/tests/unit/mcp_client/test_mcp_client.py index 43021d248a9..866a29b70e9 100644 --- a/tests/unit/mcp_client/test_mcp_client.py +++ b/tests/unit/mcp_client/test_mcp_client.py @@ -3,6 +3,7 @@ import base64 import importlib import json import os +import queue import sys from collections.abc import AsyncIterator, Callable from contextlib import AbstractAsyncContextManager, asynccontextmanager @@ -74,6 +75,7 @@ from litellm.proxy.mcp_server.outbound_credentials.httpx_auth import StaticHeade from litellm.types.mcp import MCPAuth, MCPStdioConfig, MCPTransport from litellm.types.mcp_server.mcp_server_manager import MCPServer from tests._support.clock_loops import AutojumpClockLoop, ManualClockLoop +from tests._support.mcp_count_tool_upstream import count_tool_upstream, methods_received _JSONRPC_MESSAGE_ADAPTER: Final = TypeAdapter(JSONRPCMessage) @@ -5250,3 +5252,55 @@ async def test_notification_callback_keeps_stream_errors_separate_from_protocol_ await _receive_session_message(failure, stream_error=stream_error, on_notification=receiver) assert stream_error.result() is first receiver.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_a_skipping_client_never_asks_the_upstream_for_its_tools() -> None: + methods_seen: Final[queue.SimpleQueue[str]] = queue.SimpleQueue() + client: Final = _MockTransportClient( + count_tool_upstream({"n": 1}, methods_seen), + server_url="https://upstream.example/mcp", + transport_type=MCPTransport.http, + skip_tool_output_validation=True, + ) + + result: Final = await client.call_tool(CallToolRequestParams(name="count", arguments={})) + + assert isinstance(result, CallToolResult) + assert result.is_error is False + assert "tools/list" not in methods_received(methods_seen) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("skip_validation", "rejected"), [(False, True), (True, False)], ids=["validating", "skipping"] +) +async def test_invalid_structured_output_is_rejected_unless_validation_is_skipped( + skip_validation: bool, rejected: bool +) -> None: + client: Final = _MockTransportClient( + count_tool_upstream({"n": "not-an-integer"}, queue.SimpleQueue()), + server_url="https://upstream.example/mcp", + transport_type=MCPTransport.http, + skip_tool_output_validation=skip_validation, + ) + + result: Final = await client.call_tool(CallToolRequestParams(name="count", arguments={})) + + assert isinstance(result, CallToolResult) + assert result.is_error is rejected + + +@pytest.mark.asyncio +async def test_a_skipped_validation_passes_the_upstreams_result_through_unchanged() -> None: + client: Final = _MockTransportClient( + count_tool_upstream({"n": "not-an-integer"}, queue.SimpleQueue()), + server_url="https://upstream.example/mcp", + transport_type=MCPTransport.http, + skip_tool_output_validation=True, + ) + + result: Final = await client.call_tool(CallToolRequestParams(name="count", arguments={})) + + assert isinstance(result, CallToolResult) + assert result.structured_content == {"n": "not-an-integer"} diff --git a/tests/unit/proxy/agent_endpoints/test_a2a_endpoints.py b/tests/unit/proxy/agent_endpoints/test_a2a_endpoints.py index 88da07c612b..0aacc1f8bda 100644 --- a/tests/unit/proxy/agent_endpoints/test_a2a_endpoints.py +++ b/tests/unit/proxy/agent_endpoints/test_a2a_endpoints.py @@ -1268,6 +1268,153 @@ async def test_task_method_failure_hook_uses_enriched_request_data(): assert failure_data.get("agent_id") == "test-agent" +_ENTRA_APP_CLIENT_ID: Final = "0d08e61f-6184-4980-9cef-a2e9128f4cb2" +_ENTRA_TRACE_ID: Final = "f0c149d3-ef20-4194-8b81-c81a5e002500" + + +class _JsonRpcError(BaseModel): + model_config = ConfigDict(frozen=True) + + code: int + message: str + + +class _JsonRpcErrorBody(BaseModel): + model_config = ConfigDict(frozen=True) + + error: _JsonRpcError + + +async def _send_message_to_entra_agent( + litellm_params: Mapping[str, str], *patches: AbstractContextManager[object] +) -> tuple[int, _JsonRpcError]: + agent = _make_agent_mock() + agent.litellm_params = dict(litellm_params) + mock_request = _make_request_mock( + "message/send", + {"message": {"role": "user", "parts": [{"kind": "text", "text": "Hello"}], "messageId": "msg-1"}}, + ) + mock_proxy_logging = MagicMock() + mock_proxy_logging.pre_call_hook = AsyncMock( + side_effect=lambda user_api_key_dict, data, call_type, skip_guardrails=False: data + ) + mock_proxy_logging.post_call_failure_hook = AsyncMock(return_value=None) + + with ExitStack() as stack: + for p in (*_base_patches(agent), *patches): + stack.enter_context(p) + stack.enter_context( + patch( # test-quality-ok: same proxy_logging_obj injection the sibling failure-hook tests use + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging + ) + ) + + from litellm.proxy.agent_endpoints.a2a_endpoints import invoke_agent_a2a + + response = await invoke_agent_a2a( + agent_id="test-agent", + request=mock_request, + fastapi_response=MagicMock(), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test", user_id="u1", team_id="t1"), + ) + + return response.status_code, _JsonRpcErrorBody.model_validate_json(response.body).error + + +@pytest.mark.asyncio +async def test_entra_rejecting_the_agent_credential_returns_401_with_only_the_aadsts_reason(): + from azure.core.exceptions import ClientAuthenticationError + + def rejected_token() -> str: + raise ClientAuthenticationError( + message="Authentication failed: AADSTS7000215: Invalid client secret provided. Ensure the secret being " + f"sent in the request is the client secret value, for a secret added to app '{_ENTRA_APP_CLIENT_ID}'." + ) + + status_code, error = await _send_message_to_entra_agent( + {"tenant_id": "tenant", "client_id": _ENTRA_APP_CLIENT_ID, "client_secret": "wrong-secret"}, + patch( # test-quality-ok: stands in for the Entra token endpoint answering a wrong secret, the shape azure-identity raises + "litellm.llms.azure.common_utils.get_azure_ad_token_from_entra_id", return_value=rejected_token + ), + ) + + assert status_code == 401 + assert "AADSTS7000215" in error.message + assert _ENTRA_APP_CLIENT_ID not in error.message + + +@pytest.mark.asyncio +async def test_entra_rejecting_the_agent_oidc_token_returns_401_with_only_the_aadsts_reason(monkeypatch, respx_mock): + monkeypatch.setenv("LITELLM_QA_FEDERATED_TOKEN", "federated-token") + monkeypatch.delenv("AZURE_AUTHORITY_HOST", raising=False) + respx_mock.post("https://login.microsoftonline.com/qa-tenant/oauth2/v2.0/token").mock( + return_value=httpx.Response( + 400, + json={ + "error": "invalid_client", + "error_description": "AADSTS700213: No matching federated identity record found for app " + f"'{_ENTRA_APP_CLIENT_ID}'. Trace ID: {_ENTRA_TRACE_ID}", + }, + ) + ) + + status_code, error = await _send_message_to_entra_agent( + { + "tenant_id": "qa-tenant", + "client_id": _ENTRA_APP_CLIENT_ID, + "azure_ad_token": "oidc/env/LITELLM_QA_FEDERATED_TOKEN", + } + ) + + assert status_code == 401 + assert error.code == -32000 + assert "AADSTS700213" in error.message + assert _ENTRA_APP_CLIENT_ID not in error.message + assert _ENTRA_TRACE_ID not in error.message + + +@pytest.mark.asyncio +async def test_upstream_authentication_error_without_an_entra_rejection_stays_an_internal_error(): + agent = _make_agent_mock() + mock_handler = MagicMock() + mock_handler.post = AsyncMock( + side_effect=litellm.AuthenticationError( + message="Incorrect API key provided.", llm_provider="openai", model="gpt-4.1" + ) + ) + mock_proxy_logging = MagicMock() + mock_proxy_logging.pre_call_hook = AsyncMock( + side_effect=lambda user_api_key_dict, data, call_type, skip_guardrails=False: data + ) + mock_proxy_logging.post_call_failure_hook = AsyncMock(return_value=None) + + with ExitStack() as stack: + for p in _base_patches(agent): + stack.enter_context(p) + stack.enter_context( + patch( # test-quality-ok: stands in for the upstream agent rejecting the provider key it was sent + "litellm.llms.custom_httpx.http_handler.get_async_httpx_client", return_value=mock_handler + ) + ) + stack.enter_context( + patch( # test-quality-ok: same proxy_logging_obj injection the sibling failure-hook tests use + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging + ) + ) + + from litellm.proxy.agent_endpoints.a2a_endpoints import invoke_agent_a2a + + response = await invoke_agent_a2a( + agent_id="test-agent", + request=_make_request_mock("tasks/get", {"id": "task-1"}), + fastapi_response=MagicMock(), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test", user_id="u1", team_id="t1"), + ) + + assert response.status_code == 500 + assert json.loads(response.body.decode())["error"]["code"] == -32603 + + @pytest.mark.asyncio async def test_agentcore_invalid_context_id_returns_jsonrpc_invalid_params_400(): from litellm.proxy._types import UserAPIKeyAuth diff --git a/tests/unit/proxy/analytics_endpoints/test_analytics_endpoints.py b/tests/unit/proxy/analytics_endpoints/test_analytics_endpoints.py index 1c706978fed..2d28292a487 100644 --- a/tests/unit/proxy/analytics_endpoints/test_analytics_endpoints.py +++ b/tests/unit/proxy/analytics_endpoints/test_analytics_endpoints.py @@ -26,6 +26,7 @@ from litellm.proxy.analytics_endpoints.cache_activity import ( ADMIN: Final = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin-user") INTERNAL_USER: Final = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER, user_id="user-1") +from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_error, statement_timeout_reads async def _no_teams(auth: UserAPIKeyAuth) -> tuple[str, ...]: @@ -70,10 +71,15 @@ MODEL_ROWS = [{"model": "gpt-5.1"}] def build_prisma(query_raw: AsyncMock) -> MagicMock: prisma = MagicMock() - prisma.db.query_raw = query_raw + statement_timeout_reads(prisma, query_raw) return prisma +def _spend_log_reads(prisma: MagicMock) -> AsyncMock: + """The raw reads the cache dashboard ran under the statement timeout.""" + return prisma.read_db.tx.return_value.query_raw + + def dispatching_query_raw() -> AsyncMock: async def dispatch(sql: str, *params: object) -> list[dict[str, object]]: if "error_code" in sql: @@ -120,7 +126,9 @@ async def test_filters_are_passed_to_sql_as_json_arrays(mock_prisma: MagicMock): await _activity(ADMIN, key_aliases=["my-key"], models=["gpt-5.1", "claude-opus-4-8"]) filtered_calls = [ - call for call in mock_prisma.db.query_raw.call_args_list if call.args[0] in (GROUPS_SQL, ERROR_BREAKDOWN_SQL) + call + for call in _spend_log_reads(mock_prisma).call_args_list + if call.args[0] in (GROUPS_SQL, ERROR_BREAKDOWN_SQL) ] assert len(filtered_calls) == 2 for call in filtered_calls: @@ -135,7 +143,9 @@ async def test_every_query_excludes_the_same_info_routes(mock_prisma: MagicMock) the SQL does with it is covered against Postgres in tests/integration/spend/test_cache_activity.py.""" await _activity(ADMIN) - exclusions_by_query = {call.args[0]: json.loads(call.args[-4]) for call in mock_prisma.db.query_raw.call_args_list} + exclusions_by_query = { + call.args[0]: json.loads(call.args[-4]) for call in _spend_log_reads(mock_prisma).call_args_list + } assert set(exclusions_by_query) == {GROUPS_SQL, ERROR_BREAKDOWN_SQL, KEY_ALIAS_OPTIONS_SQL, MODEL_OPTIONS_SQL} for excluded_call_types in exclusions_by_query.values(): assert excluded_call_types == LiteLLMRoutes.info_routes.value @@ -147,7 +157,7 @@ async def test_every_query_excludes_the_same_info_routes(mock_prisma: MagicMock) async def test_admin_queries_grant_all_rows(mock_prisma: MagicMock): await _activity(ADMIN) - scope_by_query = {call.args[0]: call.args[-3:] for call in mock_prisma.db.query_raw.call_args_list} + scope_by_query = {call.args[0]: call.args[-3:] for call in _spend_log_reads(mock_prisma).call_args_list} assert set(scope_by_query) == {GROUPS_SQL, ERROR_BREAKDOWN_SQL, KEY_ALIAS_OPTIONS_SQL, MODEL_OPTIONS_SQL} for scope_args in scope_by_query.values(): assert scope_args == (True, None, "[]") @@ -157,7 +167,7 @@ async def test_admin_queries_grant_all_rows(mock_prisma: MagicMock): async def test_internal_user_queries_are_scoped_to_own_and_team_rows(mock_prisma: MagicMock): await _activity(INTERNAL_USER, team_lookup=_team_nine) - scope_by_query = {call.args[0]: call.args[-3:] for call in mock_prisma.db.query_raw.call_args_list} + scope_by_query = {call.args[0]: call.args[-3:] for call in _spend_log_reads(mock_prisma).call_args_list} assert set(scope_by_query) == {GROUPS_SQL, ERROR_BREAKDOWN_SQL, KEY_ALIAS_OPTIONS_SQL, MODEL_OPTIONS_SQL} for scope_args in scope_by_query.values(): assert scope_args == (False, "user-1", json.dumps(["team-9"])) @@ -168,7 +178,7 @@ async def test_key_without_user_id_grants_no_rows(mock_prisma: MagicMock): no_user = UserAPIKeyAuth(user_role=LitellmUserRoles.TEAM, user_id=None) await _activity(no_user) - for call in mock_prisma.db.query_raw.call_args_list: + for call in _spend_log_reads(mock_prisma).call_args_list: assert call.args[-3:] == (False, None, "[]") @@ -178,7 +188,27 @@ async def test_rejects_malformed_dates_with_400(mock_prisma: MagicMock): await _activity(ADMIN, start_date="07/01/2026") assert exc_info.value.status_code == 400 - mock_prisma.db.query_raw.assert_not_called() + _spend_log_reads(mock_prisma).assert_not_called() + + +@pytest.mark.asyncio +async def test_every_cache_activity_read_is_cancelled_by_postgres_after_30s(mock_prisma: MagicMock): + await _activity(ADMIN) + + transaction = mock_prisma.read_db.tx.return_value + assert transaction.statements == ("SET LOCAL statement_timeout = 30000",) * 4 + + +@pytest.mark.asyncio +async def test_cancelled_cache_activity_read_is_reported_as_503_with_a_date_range_hint(monkeypatch: pytest.MonkeyPatch): + prisma = build_prisma(AsyncMock(side_effect=statement_timeout_error())) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", prisma) + + with pytest.raises(HTTPException) as exc_info: + await _activity(ADMIN) + + assert exc_info.value.status_code == 503 + assert exc_info.value.detail == {"error": "Cache activity took longer than 30s. Pick a shorter date range"} def test_totals_ratio_is_zero_without_requests(): diff --git a/tests/unit/proxy/anthropic_endpoints/test_answered_status.py b/tests/unit/proxy/anthropic_endpoints/test_answered_status.py new file mode 100644 index 00000000000..ff6c98e2282 --- /dev/null +++ b/tests/unit/proxy/anthropic_endpoints/test_answered_status.py @@ -0,0 +1,116 @@ +from typing import Final + +import openai +import pytest + +import litellm +from litellm.exceptions import MidStreamFallbackError +from litellm.proxy._types import ProxyException +from litellm.proxy.anthropic_endpoints.answered_status import with_answered_status + + +def _anthropic_internal_server_error(provider_status_code: int | None) -> litellm.InternalServerError: + return litellm.InternalServerError( + message="AnthropicError - Overloaded", + llm_provider="anthropic", + model="claude-opus-5-5", + provider_status_code=provider_status_code, + ) + + +@pytest.mark.parametrize("code", [400, 500, 503]) +def test_a_proxy_exception_passes_through_unchanged(code: int) -> None: + exc: Final = ProxyException(message="budget service unavailable", type="None", param=None, code=code) + + answered_exc: Final = with_answered_status(exc) + + assert answered_exc is exc + assert exc.code == str(code) + + +@pytest.mark.parametrize( + ("exc", "answered", "copy"), + [ + pytest.param( + litellm.ServiceUnavailableError(message="BedrockException", llm_provider="bedrock", model="m"), + 503, + False, + id="service-unavailable-503", + ), + pytest.param(_anthropic_internal_server_error(529), 529, True, id="anthropic-overload"), + pytest.param(_anthropic_internal_server_error(500), 500, True, id="anthropic-500"), + pytest.param(_anthropic_internal_server_error(None), 500, False, id="internal-server-error"), + pytest.param( + MidStreamFallbackError( + message="fallbacks failed", + model="m", + llm_provider="anthropic", + original_exception=_anthropic_internal_server_error(529), + ), + 529, + True, + id="fallback-over-an-anthropic-overload", + ), + pytest.param( + MidStreamFallbackError( + message="fallbacks failed", + model="m", + llm_provider="anthropic", + original_exception=MidStreamFallbackError( + message="retry failed before content", + model="m", + llm_provider="anthropic", + original_exception=_anthropic_internal_server_error(529), + ), + ), + 529, + True, + id="nested-fallback-over-an-anthropic-overload", + ), + pytest.param( + litellm.RateLimitError(message="slow down", llm_provider="anthropic", model="m"), + 429, + False, + id="rate-limit", + ), + ], +) +def test_a_litellm_exception_answers_the_status_anthropic_gives_its_failure( + exc: openai.APIStatusError, answered: int, copy: bool +) -> None: + raised_status: Final = exc.status_code + + answered_exc: Final = with_answered_status(exc) + + assert (answered_exc is not exc) is copy + assert (type(answered_exc), answered_exc.status_code, str(answered_exc)) == (type(exc), answered, str(exc)) + assert exc.status_code == raised_status + + +def _raise_a_bedrock_overload_caused_by_a_reset() -> None: + try: + raise ConnectionError("upstream reset") + except ConnectionError as upstream: + raise litellm.InternalServerError( + message="AnthropicError - Overloaded", + llm_provider="anthropic", + model="m", + provider_status_code=529, + ) from upstream + + +def test_the_answered_copy_keeps_the_raised_exceptions_cause_and_traceback() -> None: + with pytest.raises(litellm.InternalServerError) as raised: + _raise_a_bedrock_overload_caused_by_a_reset() + + answered_exc: Final = with_answered_status(raised.value) + + assert isinstance(raised.value.__cause__, ConnectionError) + assert (answered_exc.__cause__, answered_exc.__traceback__) == (raised.value.__cause__, raised.value.__traceback__) + + +def test_an_exception_without_a_status_passes_through_unchanged() -> None: + exc: Final = ValueError("no status") + + assert with_answered_status(exc) is exc + assert not hasattr(exc, "status_code") diff --git a/tests/unit/proxy/anthropic_endpoints/test_claude_code_marketplace.py b/tests/unit/proxy/anthropic_endpoints/test_claude_code_marketplace.py index 9c693ff1c3e..ea2fe42a01f 100644 --- a/tests/unit/proxy/anthropic_endpoints/test_claude_code_marketplace.py +++ b/tests/unit/proxy/anthropic_endpoints/test_claude_code_marketplace.py @@ -4,15 +4,20 @@ Unit tests for claude_code_marketplace.py source validation. Covers the git-subdir and archive source types added alongside the existing github and url types. """ +import hashlib +import io import json -from collections.abc import Mapping +import zipfile +from collections.abc import AsyncIterator, Mapping from types import MappingProxyType -from typing import Final -from unittest.mock import AsyncMock, MagicMock +from typing import Final, TypeAlias +from unittest.mock import AsyncMock, MagicMock, call import httpx import pytest from fastapi import HTTPException +from prisma import Base64, models as prisma_models +from prisma.errors import UniqueViolationError from pydantic import ValidationError import litellm @@ -29,7 +34,9 @@ from litellm.proxy.anthropic_endpoints.claude_code_endpoints.claude_code_marketp register_plugin, update_plugin, ) +from litellm.proxy.deps.repositories import get_plugin_upload_repository from litellm.proxy.proxy_server import LitellmUserRoles, hash_token +from litellm.repositories.plugin_upload_repository import PluginUploadRepository from litellm.types.proxy.claude_code_endpoints import ( MarketplaceResponse, RegisterPluginRequest, @@ -83,6 +90,7 @@ def _make_mock_prisma(): mock_table.create = AsyncMock(side_effect=_create) mock_table.update = AsyncMock(side_effect=_update) mock_client.db.litellm_claudecodeplugintable = mock_table + mock_client.writer_db = mock_client.db return mock_client @@ -196,6 +204,25 @@ async def test_update_plugin_replaces_existing_source(): assert stored["version"] == "2.0.0" +@pytest.mark.asyncio +async def test_metadata_edits_preserve_uploaded_source_and_reject_a_changed_http_destination() -> None: + table: Final = litellm.proxy.proxy_server.prisma_client.writer_db.litellm_claudecodeplugintable + await table.create(data={"name": _UPLOAD_NAME, "manifest_json": json.dumps({"source": _UPLOAD_SOURCE})}) + request: Final = UpdatePluginRequest(source=_UPLOAD_SOURCE, description="Updated description") + response: Final = await update_plugin(plugin_name=_UPLOAD_NAME, request=request, user_api_key_dict=_USER) + expected: Final = {"name": _UPLOAD_NAME, "source": _UPLOAD_SOURCE, "description": "Updated description"} + assert response.plugin.description == "Updated description" + assert await _read_stored_manifest(_UPLOAD_NAME) == expected + with pytest.raises(HTTPException) as error: + await update_plugin( + plugin_name=_UPLOAD_NAME, user_api_key_dict=_USER, + request=UpdatePluginRequest(source={**_UPLOAD_SOURCE, "url": "http://other.example/skill.zip"}), + ) + assert error.value.status_code == 400 + table.update.assert_awaited_once() + assert await _read_stored_manifest(_UPLOAD_NAME) == expected + + @pytest.mark.asyncio async def test_update_plugin_not_found(): """PUT on a name that does not exist raises HTTP 404.""" @@ -1171,3 +1198,164 @@ async def test_marketplace_without_a_database_answers_db_not_connected_for_a_que assert response.status_code == 500 assert CommonProxyErrors.db_not_connected_error.value in response.text + + +_UploadClient: TypeAlias = tuple[httpx.AsyncClient, MagicMock, MagicMock] +_UPLOAD_NAME: Final = "local-skill" +_UPLOAD_DIGEST: Final = hashlib.sha256(b"stored archive").hexdigest() +_UPLOAD_PATH: Final = f"/claude-code/plugins/{_UPLOAD_NAME}/archive/{_UPLOAD_DIGEST}.zip" +_UPLOAD_SOURCE: Final = {"source": "archive", "url": f"http://test{_UPLOAD_PATH}", "sha256": _UPLOAD_DIGEST} + + +@pytest.fixture +async def upload_storage(monkeypatch: pytest.MonkeyPatch) -> AsyncIterator[_UploadClient]: + prisma: Final = litellm.proxy.proxy_server.prisma_client + prisma.writer_db = MagicMock() + plugins: Final = prisma.writer_db.litellm_claudecodeplugintable + archives: Final = prisma.writer_db.litellm_claudecodepluginupload + plugins.create = AsyncMock(return_value=prisma_models.LiteLLM_ClaudeCodePluginTable( + id="uploaded-id", name=_UPLOAD_NAME, enabled=True, + )) + plugins.find_unique = AsyncMock(return_value=prisma_models.LiteLLM_ClaudeCodePluginTable( + id="uploaded-id", name=_UPLOAD_NAME, enabled=True, manifest_json=json.dumps({"source": _UPLOAD_SOURCE}), + )) + archives.find_first = AsyncMock(return_value=prisma_models.LiteLLM_ClaudeCodePluginUpload( + plugin_name=_UPLOAD_NAME, content=Base64.encode(b"stored archive"), + )) + monkeypatch.setattr(litellm.proxy.proxy_server.app, "dependency_overrides", { + get_plugin_upload_repository: lambda: PluginUploadRepository(prisma), + }) + monkeypatch.setattr(litellm.proxy.proxy_server, "general_settings", {}) + monkeypatch.setattr(litellm.proxy.proxy_server, "user_api_key_cache", DualCache()) + monkeypatch.setenv("PROXY_BASE_URL", "http://test") + monkeypatch.delenv("SERVER_ROOT_PATH", raising=False) + for key, skills in ((_LIMITED_KEY, [_UPLOAD_NAME]), ("sk-plain", [])): + litellm.proxy.proxy_server.user_api_key_cache.set_cache( + key=hash_token(key), value=_granted_user(skills).model_copy(update={"token": hash_token(key)}), + ) + async with httpx.AsyncClient( + transport=httpx.ASGITransport(app=litellm.proxy.proxy_server.app), base_url="http://test", + ) as client: + yield client, plugins, archives + + +async def _post_skill_upload( + client: httpx.AsyncClient, content: bytes, metadata: str, key: str = MASTER_KEY, +) -> httpx.Response: + return await client.post( + "/claude-code/plugins/upload", headers={"Authorization": f"Bearer {key}"}, + data={"metadata": metadata}, files={"file": ("local.zip", content, "application/zip")}, + ) + + +@pytest.mark.asyncio +async def test_skill_upload_creates_metadata_and_bytes_atomically_and_rejects_duplicates( + upload_storage: _UploadClient, +) -> None: + client, plugins, _ = upload_storage + buffer: Final = io.BytesIO() + with zipfile.ZipFile(buffer, "w") as archive: + archive.writestr("SKILL.md", "Read the personal instructions") + metadata: Final = {"name": _UPLOAD_NAME, "description": "My notes", "namespace": "personal", "version": "2.0.0"} + response: Final = await _post_skill_upload(client, buffer.getvalue(), json.dumps(metadata)) + assert response.status_code == 200, response.text + plugins.create.assert_awaited_once() + stored: Final = plugins.create.await_args.kwargs["data"] + content: Final = stored["uploaded_archive"]["create"]["content"].decode() + digest: Final = hashlib.sha256(content).hexdigest() + source: Final = { + "source": "archive", + "url": f"http://test/claude-code/plugins/{_UPLOAD_NAME}/archive/{digest}.zip", + "sha256": digest, + } + assert json.loads(stored["manifest_json"]) == {**metadata, "source": source} + assert stored == { + "name": _UPLOAD_NAME, "description": "My notes", "version": "2.0.0", + "manifest_json": stored["manifest_json"], "created_by": litellm.proxy.proxy_server.litellm_proxy_admin_name, + "uploaded_archive": {"create": {"content": Base64.encode(content)}}, + } + assert response.json() == {"status": "success", "action": "created", "plugin": { + "id": "uploaded-id", "name": _UPLOAD_NAME, "version": "2.0.0", "description": "My notes", + "source": source, "installation_preference": None, "enabled": True, + }} + plugins.create.side_effect = UniqueViolationError({"message": "duplicate", "error_code": "P2002"}) + duplicate: Final = await _post_skill_upload(client, buffer.getvalue(), json.dumps(metadata)) + assert duplicate.status_code == 409, duplicate.text + + +@pytest.mark.asyncio +@pytest.mark.parametrize("content, metadata, key, status", [ + (b"invalid", '{"name":"local-skill"}', MASTER_KEY, 400), + (b"x" * (10 * 1024 * 1024 + 1), '{"name":"local-skill"}', MASTER_KEY, 413), + (b"ignored", "not-json", MASTER_KEY, 400), + (b"ignored", '{"name":"../invalid"}', MASTER_KEY, 422), + (b"ignored", '{"name":"local-skill"}', _LIMITED_KEY, 403), +], ids=["invalid-zip", "file-over-cap", "invalid-metadata", "invalid-name", "non-admin"]) +async def test_skill_upload_rejects_invalid_or_unauthorized_input_without_writing( + upload_storage: _UploadClient, content: bytes, metadata: str, key: str, status: int, +) -> None: + client, plugins, _ = upload_storage + response: Final = await _post_skill_upload(client, content, metadata, key) + assert response.status_code == status, response.text + plugins.create.assert_not_awaited() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("enabled, key, source, blob, status, reads", [ + (True, None, _UPLOAD_SOURCE, True, 200, 1), + (False, None, _UPLOAD_SOURCE, True, 404, 0), + (False, "sk-plain", _UPLOAD_SOURCE, True, 404, 0), + (False, _LIMITED_KEY, _UPLOAD_SOURCE, True, 200, 1), + (True, None, None, True, 404, 0), + (True, None, {**_UPLOAD_SOURCE, "sha256": "0" * 64}, True, 404, 0), + (True, None, {**_UPLOAD_SOURCE, "url": "https://external.example/skill.zip"}, True, 404, 0), + (True, None, _UPLOAD_SOURCE, False, 404, 1), +]) +async def test_archive_reads_only_authorized_current_bytes( + upload_storage: _UploadClient, enabled: bool, key: str | None, + source: Mapping[str, str] | None, blob: bool, status: int, reads: int, +) -> None: + client, plugins, archives = upload_storage + manifest: Final = json.dumps({"source": source}) + plugins.find_unique.return_value = None if source is None else prisma_models.LiteLLM_ClaudeCodePluginTable( + id="uploaded-id", name=_UPLOAD_NAME, enabled=enabled, manifest_json=manifest, + ) + if not blob: + archives.find_first.return_value = None + response: Final = await client.get(_UPLOAD_PATH, headers={"Authorization": f"Bearer {key}"} if key else {}) + assert response.status_code == status, response.text + assert (response.content == b"stored archive") is (status == 200) + plugins.find_unique.assert_awaited_once_with(where={"name": _UPLOAD_NAME}) + assert archives.find_first.await_args_list == ([call(where={ + "plugin_name": _UPLOAD_NAME, "plugin": {"is": {"manifest_json": manifest, "enabled": enabled}}, + })] if reads else []) + litellm.proxy.proxy_server.prisma_client.db.litellm_claudecodeplugintable.find_unique.assert_not_awaited() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("required, header, credential, query, status", [ + (True, "Authorization", _LIMITED_KEY, None, 200), + (False, "x-skill-key", _LIMITED_KEY, MASTER_KEY, 200), + (True, "x-skill-key", _LIMITED_KEY, MASTER_KEY, 200), + (False, "x-skill-key", "", MASTER_KEY, 401), + (False, None, None, MASTER_KEY, 404), + (False, None, None, "invalid-query-key", 404), + (True, None, None, MASTER_KEY, 401), + (True, None, None, None, 401), +]) +async def test_archive_requires_header_credentials_for_private_content( + upload_storage: _UploadClient, monkeypatch: pytest.MonkeyPatch, required: bool, + header: str | None, credential: str | None, query: str | None, status: int, +) -> None: + client, plugins, archives = upload_storage + plugins.find_unique.return_value.enabled = False + monkeypatch.setattr(litellm.proxy.proxy_server, "general_settings", { + "litellm_key_header_name": "X-Skill-Key", "claude_code_marketplace_auth_required": required, + }) + response: Final = await client.get( + _UPLOAD_PATH, headers={header: f"Bearer {credential}" if credential else ""} if header else {}, + params={"key": query} if query else {}, + ) + assert response.status_code == status, response.text + assert (response.content == b"stored archive") is (status == 200) + assert archives.find_first.await_count == int(status == 200) diff --git a/tests/unit/proxy/anthropic_endpoints/test_endpoints.py b/tests/unit/proxy/anthropic_endpoints/test_endpoints.py index 553c3d4a9d7..80e7e793f6e 100644 --- a/tests/unit/proxy/anthropic_endpoints/test_endpoints.py +++ b/tests/unit/proxy/anthropic_endpoints/test_endpoints.py @@ -2,6 +2,7 @@ Test for anthropic_endpoints/endpoints.py, focusing on handling dictionary objects in streaming responses """ +import datetime import json import logging import unittest @@ -17,6 +18,7 @@ from fastapi.responses import StreamingResponse from fastapi.testclient import TestClient from starlette.types import Message +import litellm from litellm import Router from litellm._logging import verbose_proxy_logger from litellm.caching.dual_cache import DualCache @@ -248,6 +250,86 @@ class TestBlockedResponseUsage: assert response["content"] == [{"type": "text", "text": _BLOCK_TEXT}] +class TestAnthropicResponseReportsWhatAFailedAgenticFollowUpBills: + async def _error_response_cost(self, raise_the_follow_up_error: bool) -> float: + import litellm + import litellm.proxy.anthropic_endpoints.endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.litellm_core_utils.chat_completion_agentic_loop import bill_first_round_on_failure + from litellm.proxy.proxy_server import ProxyConfig + from litellm.proxy.utils import ProxyLogging + from litellm.types.utils import ModelResponse, Usage + + proxy_logging: Final = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging.alert_types = [] + follow_up_error: Final = litellm.RateLimitError(message="rate limited", llm_provider="openai", model="gpt-4o") + + async def failing_follow_up() -> ModelResponse: + raise follow_up_error + + async def fail_after_the_first_call(self: ProxyBaseLLMRequestProcessing, **kwargs: object) -> None: + _, logging_obj = await self.common_processing_pre_call_logic( + request=kwargs["request"], + general_settings={}, + user_api_key_dict=kwargs["user_api_key_dict"], + proxy_logging_obj=proxy_logging, + proxy_config=MagicMock(spec=ProxyConfig), + route_type="anthropic_messages", + ) + logging_obj.model_call_details.update(first_api_call_start_time=datetime.datetime.now()) + with pytest.raises(litellm.RateLimitError): + await bill_first_round_on_failure( + failing_follow_up(), + logging_obj=logging_obj, + response=ModelResponse( + model="gpt-4o", usage=Usage(prompt_tokens=559, completion_tokens=113, total_tokens=672) + ), + stream=False, + ) + raise ( + follow_up_error + if raise_the_follow_up_error + else litellm.RateLimitError(message="rate limited", llm_provider="openai", model="gpt-4o") + ) + + request: Final = MagicMock(spec=Request) + request.headers = {} + with ( + patch.object( + ep, + "read_request_body", + new=AsyncMock( + return_value={ + "model": "gpt-4o", + "max_tokens": 64, + "messages": [{"role": "user", "content": "what is 6*7?"}], + } + ), + ), + patch.object(ep.ProxyBaseLLMRequestProcessing, "base_process_llm_request", new=fail_after_the_first_call), + patch.object(proxy_server, "proxy_logging_obj", proxy_logging), + ): + response: Final = await ep.anthropic_response( + fastapi_response=Response(), + request=request, + user_api_key_dict=UserAPIKeyAuth(request_route="/v1/messages"), + ) + + assert response.status_code == 429 + return float(response.headers["x-litellm-response-cost"]) + + @pytest.mark.asyncio + async def test_the_error_response_reports_the_served_first_call_its_failure_row_bills(self) -> None: + import litellm + + price: Final = litellm.model_cost["gpt-4o"] + + assert await self._error_response_cost(raise_the_follow_up_error=True) == pytest.approx( + 559 * price["input_cost_per_token"] + 113 * price["output_cost_per_token"] + ) + assert await self._error_response_cost(raise_the_follow_up_error=False) == 0 + + class TestProxyExceptionAnthropicEnvelope: @pytest.mark.asyncio async def test_anthropic_response_maps_proxy_exception_to_anthropic_envelope(self): @@ -302,7 +384,7 @@ class TestProxyExceptionAnthropicEnvelope: import litellm.proxy.anthropic_endpoints.endpoints as ep from litellm.proxy._types import ProxyException - request = MagicMock() + request: Final = MagicMock() request.headers = {} response = ep._anthropic_error_json_response( @@ -319,10 +401,135 @@ class TestProxyExceptionAnthropicEnvelope: assert fallback.status_code == 500 assert json.loads(fallback.body)["error"]["type"] == "api_error" + @pytest.mark.asyncio + @pytest.mark.parametrize( + "unavailable, expected_status, expected_error_type, hook_receives_copy", + [ + pytest.param( + litellm.ServiceUnavailableError( + message='BedrockException - {"message": "Bedrock is unable to process your request."}', + llm_provider="bedrock", + model="global.anthropic.claude-opus-5-5", + ), + 503, + "api_error", + False, + id="bedrock-503", + ), + pytest.param( + litellm.InternalServerError( + message='AnthropicError - {"type":"error","error":{"type":"overloaded_error","message":"Overloaded"}}', + llm_provider="anthropic", + model="claude-opus-5-5", + provider_status_code=529, + ), + 529, + "overloaded_error", + True, + id="anthropic-529", + ), + ], + ) + async def test_anthropic_response_records_the_answered_status( + self, + unavailable: litellm.ServiceUnavailableError | litellm.InternalServerError, + expected_status: int, + expected_error_type: str, + hook_receives_copy: bool, + ) -> None: + import litellm.proxy.anthropic_endpoints.endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.proxy._types import UserAPIKeyAuth + + raised_status: Final = unavailable.status_code + request: Final = MagicMock() + request.headers = {} + + with ( + patch.object(ep, "read_request_body", new=AsyncMock(return_value={})), + patch.object( + ep.ProxyBaseLLMRequestProcessing, + "base_process_llm_request", + new=AsyncMock(side_effect=unavailable), + ), + patch.object(proxy_server, "proxy_logging_obj") as mock_logging, + ): + mock_logging.post_call_failure_hook = AsyncMock() + response = await ep.anthropic_response( + fastapi_response=MagicMock(), + request=request, + user_api_key_dict=UserAPIKeyAuth(), + ) + + recorded: Final = mock_logging.post_call_failure_hook.await_args.kwargs["original_exception"] + assert recorded is not None + assert (recorded is not unavailable) is hook_receives_copy + assert recorded.status_code == expected_status + assert unavailable.status_code == raised_status + assert response.status_code == expected_status + assert json.loads(response.body)["error"]["type"] == expected_error_type + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "unavailable, expected_status, expected_error_type", + [ + pytest.param( + litellm.ServiceUnavailableError( + message="Bedrock is unable to process your request.", + llm_provider="bedrock", + model="global.anthropic.claude-opus-5-5", + ), + 503, + "api_error", + id="bedrock-503", + ), + pytest.param( + litellm.InternalServerError( + message='AnthropicError - {"type":"error","error":{"type":"overloaded_error","message":"Overloaded"}}', + llm_provider="anthropic", + model="claude-opus-5-5", + provider_status_code=529, + ), + 529, + "overloaded_error", + id="anthropic-529", + ), + ], + ) + async def test_count_tokens_answers_provider_errors( + self, + unavailable: litellm.ServiceUnavailableError | litellm.InternalServerError, + expected_status: int, + expected_error_type: str, + ) -> None: + import litellm.proxy.anthropic_endpoints.endpoints as ep + import litellm.proxy.proxy_server as proxy_server + from litellm.proxy._types import UserAPIKeyAuth + + request = MagicMock() + request.headers = {} + + with ( + patch.object( + ep, + "read_request_body", + new=AsyncMock(return_value={"model": "claude-sonnet", "messages": [{"role": "user", "content": "hi"}]}), + ), + patch.object( + proxy_server, "count_request_tokens", new=AsyncMock(side_effect=unavailable) + ), + ): + response: Final = await ep.count_tokens(request=request, user_api_key_dict=UserAPIKeyAuth()) + + body: Final = json.loads(response.body) + assert response.status_code == expected_status + assert body["type"] == "error" + assert body["error"]["type"] == expected_error_type + @pytest.mark.parametrize("status_code", [402, 409, 422, 504]) def test_a_failure_inside_the_handler_keeps_the_api_error_type_for_a_status_outside_the_table( self, status_code: int - ): + ) -> None: import litellm.proxy.anthropic_endpoints.endpoints as ep from litellm.proxy._types import ProxyException @@ -334,7 +541,6 @@ class TestProxyExceptionAnthropicEnvelope: assert response.status_code == status_code assert json.loads(response.body)["error"] == {"type": "api_error", "message": "scripted"} - @staticmethod def _call_id_error_response(general_settings, provider_specific_fields=None): import litellm.proxy.anthropic_endpoints.endpoints as ep @@ -428,7 +634,7 @@ class TestHttpExceptionDictDetail: ) as mock_logging, # test-quality-ok: module global imported at call time; no injection seam ): mock_logging.post_call_failure_hook = AsyncMock() - response = await ep.anthropic_response( + response: Final = await ep.anthropic_response( fastapi_response=MagicMock(), request=request, user_api_key_dict=UserAPIKeyAuth(), diff --git a/tests/unit/proxy/anthropic_endpoints/test_skill_upload_archive.py b/tests/unit/proxy/anthropic_endpoints/test_skill_upload_archive.py new file mode 100644 index 00000000000..04b81605e29 --- /dev/null +++ b/tests/unit/proxy/anthropic_endpoints/test_skill_upload_archive.py @@ -0,0 +1,64 @@ +import io +import json +import stat +import zipfile +from typing import Final + +import pytest + +from litellm.proxy.anthropic_endpoints.claude_code_endpoints.skill_upload_archive import ( + MAX_SKILL_ARCHIVE_COMPRESSION_RATIO, + build_uploaded_skill_archive, +) + +MANIFEST: Final = b"---\nname: skill\ndescription: Notes\n---\nRead guide.md" + + +def _zip(files: tuple[tuple[str | zipfile.ZipInfo, bytes], ...], compression: int = zipfile.ZIP_DEFLATED) -> bytes: + buffer: Final = io.BytesIO() + with zipfile.ZipFile(buffer, "w", compression=compression) as archive: + for name, content in files: + archive.writestr(name, content) + return buffer.getvalue() + + +def _entry(name: str, mode: int, system: int = 3) -> zipfile.ZipInfo: + entry: Final = zipfile.ZipInfo(name) + entry.create_system = system + entry.external_attr = mode << 16 + return entry + + +@pytest.mark.parametrize("prefix, manifest", [ + ("", MANIFEST), ("folder/", MANIFEST), + ("folder/", b"---\nnotes: " + b"[" * 1000 + b"0" + b"]" * 1000 + b"\n---\nRead guide.md"), +]) +@pytest.mark.parametrize("script, compression", [ + (b"#!/bin/sh\necho ready\n", zipfile.ZIP_DEFLATED), (b"echo ready\n" * 10000, zipfile.ZIP_STORED), +]) +def test_installable_archive_preserves_files_and_safe_modes( + prefix: str, manifest: bytes, script: bytes, compression: int, +) -> None: + files: Final = ( + ("SKILL.md", manifest, 0o644, 3, 0o644), + ("guide.md", b"Keep filenames unchanged", 0o666, 3, 0o644), + ("scripts/run.sh", script, stat.S_IFREG | 0o7777, 3, 0o755), + ("scripts/owner.sh", script, stat.S_IFREG | 0o700, 3, 0o744), + ("scripts/no-type.sh", script, 0o755, 3, 0o755), + ("scripts/dos.sh", script, stat.S_IFREG | 0o7777, 0, 0o644), + ("scripts/dos-link.sh", script, stat.S_IFLNK | 0o777, 0, 0o644), + ) + upload: Final = _zip(tuple( + (_entry(prefix + name, mode, system), data) for name, data, mode, system, _ in files + ) + ((prefix + ".DS_Store", b"Finder"), ("__MACOSX/._SKILL.md", b"Finder"))) + result: Final = build_uploaded_skill_archive(upload, "registered-name") + expected: Final = {".claude-plugin/plugin.json": (json.dumps({"name": "registered-name"}).encode(), 0o644)} | { + f"skills/registered-name/{name}": (data, mode) for name, data, _, _, mode in files + } + assert result is not None + with zipfile.ZipFile(io.BytesIO(result)) as archive: + assert {entry.filename: (archive.read(entry), entry.external_attr >> 16) for entry in archive.infolist()} == expected + assert {entry.create_system for entry in archive.infolist()} == {3} + assert {entry.compress_type for entry in archive.infolist()} == {compression} + assert sum(entry.file_size for entry in archive.infolist()) <= len(result) * MAX_SKILL_ARCHIVE_COMPRESSION_RATIO + assert build_uploaded_skill_archive(upload, "registered-name") == result diff --git a/tests/unit/proxy/auth/test_auth_checks_object_access_and_lookup.py b/tests/unit/proxy/auth/test_auth_checks_object_access_and_lookup.py index 7a060a214f7..024d00d5d95 100644 --- a/tests/unit/proxy/auth/test_auth_checks_object_access_and_lookup.py +++ b/tests/unit/proxy/auth/test_auth_checks_object_access_and_lookup.py @@ -4023,11 +4023,11 @@ async def test_common_checks_rejects_a_spent_mapped_tag_whatever_non_list_tags_t async def fake_find_many(**kwargs): if "where" not in kwargs: - return [_tag_registry_row("usecase:spent")] + return [_tag_registry_row("app:spent")] return [_tag_db_row(name, max_budget=1.0) for name in kwargs["where"]["tag_name"]["in"]] async def spent_tag_spend(counter_key, fallback_spend, max_budget=None, **kwargs): - return 1.5 if counter_key == "spend:tag:usecase:spent" else fallback_spend + return 1.5 if counter_key == "spend:tag:app:spent" else fallback_spend async def receive(): return { @@ -4047,7 +4047,7 @@ async def test_common_checks_rejects_a_spent_mapped_tag_whatever_non_list_tags_t "scheme": "http", "server": ("localhost", 4000), "query_string": b"", - "headers": [(b"content-type", b"application/json"), (b"baggage", b"entrypoint=spent")], + "headers": [(b"content-type", b"application/json"), (b"baggage", b"service=spent")], }, receive, ) @@ -4055,7 +4055,7 @@ async def test_common_checks_rejects_a_spent_mapped_tag_whatever_non_list_tags_t await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, request_body, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) @@ -4077,7 +4077,7 @@ async def test_common_checks_rejects_a_spent_mapped_tag_whatever_non_list_tags_t valid_token=UserAPIKeyAuth(token="test-token", metadata=key_metadata), request=request, ) - assert exc_info.value.entity_id == "usecase:spent" + assert exc_info.value.entity_id == "app:spent" @pytest.mark.asyncio @@ -11789,8 +11789,8 @@ def _client_tag_request(client_tags, baggage): { "type": "http", "headers": [ - *([(b"baggage", b"entrypoint=a")] if baggage else []), - *([(b"x-litellm-tags", b"usecase:a")] if client_tags == "header" else []), + *([(b"baggage", b"service=a")] if baggage else []), + *([(b"x-litellm-tags", b"app:a")] if client_tags == "header" else []), ], } ) @@ -11814,7 +11814,7 @@ def _client_tag_rejection(route, data, request): @pytest.mark.asyncio @pytest.mark.parametrize("route", ["/v1/chat/completions", "/v1/messages", "/v1/responses"]) -@pytest.mark.parametrize("client_tags", [None, [], ["usecase:a"], '{"tags":["usecase:a"]}', "header"]) +@pytest.mark.parametrize("client_tags", [None, [], ["app:a"], '{"tags":["app:a"]}', "header"]) @pytest.mark.parametrize("client_slot", ["metadata", "litellm_metadata"]) @pytest.mark.parametrize("baggage", [True, False]) async def test_baggage_mapping_rejects_exactly_the_client_tags_rejected_without_it( @@ -11833,7 +11833,7 @@ async def test_baggage_mapping_rejects_exactly_the_client_tags_rejected_without_ await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( mapped_request, mapped, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) @@ -11843,19 +11843,19 @@ async def test_baggage_mapping_rejects_exactly_the_client_tags_rejected_without_ route, unmapped, unmapped_request ) if client_tags is None and baggage: - assert "usecase:a" in mapped["metadata" if "chat/completions" in route else "litellm_metadata"]["tags"] + assert "app:a" in mapped["metadata" if "chat/completions" in route else "litellm_metadata"]["tags"] @pytest.mark.asyncio async def test_baggage_mapping_still_rejects_client_metadata_tags(): from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup - request = _client_tag_request(["usecase:b"], baggage=True) - data = {"metadata": {"tags": ["usecase:b"]}} + request = _client_tag_request(["app:b"], baggage=True) + data = {"metadata": {"tags": ["app:b"]}} await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) diff --git a/tests/unit/proxy/auth/test_user_api_key_auth.py b/tests/unit/proxy/auth/test_user_api_key_auth.py index 43a096e0f42..19f9d0dcc73 100644 --- a/tests/unit/proxy/auth/test_user_api_key_auth.py +++ b/tests/unit/proxy/auth/test_user_api_key_auth.py @@ -2203,7 +2203,7 @@ async def test_websocket_inference_is_denied_when_a_required_baggage_mapping_is_ { "request_tag_mappings": [ {"key": "region", "tag_prefix": "region:"}, - {"key": "entrypoint", "tag_prefix": "usecase:", "required": True}, + {"key": "service", "tag_prefix": "app:", "required": True}, ] }, ), @@ -2227,7 +2227,7 @@ async def test_websocket_invalid_key_is_rejected_before_the_required_baggage_map with ( patch.dict( litellm.proxy.proxy_server.general_settings, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "required": True}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "required": True}]}, ), patch( "litellm.proxy.auth.user_api_key_auth.user_api_key_auth", @@ -2248,7 +2248,7 @@ async def test_websocket_inference_is_allowed_when_no_baggage_mapping_is_require with ( patch.dict( litellm.proxy.proxy_server.general_settings, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}]}, ), patch( "litellm.proxy.auth.user_api_key_auth.user_api_key_auth", new=AsyncMock(return_value=authorized) @@ -2273,8 +2273,8 @@ async def _auth_then_serve_chat(monkeypatch, general_settings, body, headers): model_list=[ { "model_name": "gpt-4", - "litellm_params": {"model": "gpt-4o", "tags": ["usecase:a"], "mock_response": "hi"}, - "model_info": {"id": "usecase-deployment"}, + "litellm_params": {"model": "gpt-4o", "tags": ["app:a"], "mock_response": "hi"}, + "model_info": {"id": "app-deployment"}, }, { "model_name": "gpt-4", @@ -2321,30 +2321,30 @@ async def test_auth_header_tag_merge_keeps_json_string_metadata_tags_routable(mo body = { "model": "gpt-4", "messages": [{"role": "user", "content": "hi"}], - "metadata": json.dumps({"tags": ["usecase:a"]}), + "metadata": json.dumps({"tags": ["app:a"]}), } cached_body, served_by = await _auth_then_serve_chat( monkeypatch, general_settings, body, [(b"x-litellm-tags", b"other"), (b"baggage", b"region=sg")] ) - assert served_by == "usecase-deployment" - assert cached_body["metadata"] == {"tags": ["usecase:a", "other"]} + assert served_by == "app-deployment" + assert cached_body["metadata"] == {"tags": ["app:a", "other"]} @pytest.mark.asyncio @pytest.mark.parametrize("client_metadata", ["run-123", 42, ["x"]]) @pytest.mark.parametrize( "baggage", - [[], [(b"baggage", b"region=sg")], [(b"baggage", b"entrypoint=a")]], + [[], [(b"baggage", b"region=sg")], [(b"baggage", b"service=a")]], ids=["no_baggage", "unmapped_baggage", "mapped_baggage"], ) async def test_non_object_metadata_is_served_like_unset_mappings_whatever_the_baggage_maps( monkeypatch, client_metadata, baggage ): body = {"model": "gpt-4", "messages": [{"role": "user", "content": "hi"}], "metadata": client_metadata} - headers = [(b"x-litellm-tags", b"usecase:a"), *baggage] + headers = [(b"x-litellm-tags", b"app:a"), *baggage] unset = await _auth_then_serve_chat(monkeypatch, {}, body, headers) mapped = await _auth_then_serve_chat( - monkeypatch, {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, body, headers + monkeypatch, {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, body, headers ) assert mapped == unset - assert mapped[1] == "usecase-deployment" + assert mapped[1] == "app-deployment" diff --git a/tests/unit/proxy/common_utils/test_fips.py b/tests/unit/proxy/common_utils/test_fips.py new file mode 100644 index 00000000000..a72772df492 --- /dev/null +++ b/tests/unit/proxy/common_utils/test_fips.py @@ -0,0 +1,120 @@ +import _hashlib +from collections.abc import Mapping +from contextlib import nullcontext +from typing import Final +from unittest.mock import Mock + +import pytest +from cryptography.exceptions import InternalError +from cryptography.hazmat.bindings._rust import openssl as rust_openssl +from cryptography.hazmat.primitives import hashes + +from litellm.proxy.common_utils.fips import ( + FipsModeError, + cryptography_enforces_fips, + enforce_fips_mode, + openssl_enforces_fips, +) + + +def _fake_openssl(monkeypatch: pytest.MonkeyPatch, *, fips_flag: int, md5_refused: bool) -> None: + real_new: Final = _hashlib.new + + def new(name: str, data: bytes = b"", *, usedforsecurity: bool = True) -> _hashlib.HASH: + if name == "md5" and md5_refused: + raise _hashlib.UnsupportedDigestmodError("unsupported hash type") + return real_new(name, data, usedforsecurity=usedforsecurity) + + monkeypatch.setattr(_hashlib, "get_fips_mode", lambda: fips_flag) + monkeypatch.setattr(_hashlib, "new", new) + + +@pytest.mark.parametrize( + "fips_flag, md5_refused, enforces", + [(1, True, True), (0, True, False), (1, False, False)], + ids=["fips", "fips-flag-off", "md5-allowed"], +) +def test_openssl_enforces_fips_needs_the_fips_flag_and_a_refused_md5( + monkeypatch: pytest.MonkeyPatch, fips_flag: int, md5_refused: bool, enforces: bool +) -> None: + _fake_openssl(monkeypatch, fips_flag=fips_flag, md5_refused=md5_refused) + assert openssl_enforces_fips() is enforces + + +def test_openssl_without_get_fips_mode_does_not_enforce_fips(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delattr(_hashlib, "get_fips_mode") + assert openssl_enforces_fips() is False + + +@pytest.mark.parametrize( + "fips_enabled, sha256_broken, enforces", + [(True, False, True), (False, False, False), (True, True, False)], + ids=["fips", "fips-flag-off", "sha256-broken"], +) +def test_cryptography_enforces_fips_needs_the_fips_flag_and_a_working_sha256( + monkeypatch: pytest.MonkeyPatch, fips_enabled: bool, sha256_broken: bool, enforces: bool +) -> None: + monkeypatch.setattr(rust_openssl, "is_fips_enabled", lambda: fips_enabled) + if sha256_broken: + monkeypatch.setattr(hashes, "Hash", Mock(side_effect=InternalError("Unknown OpenSSL error", []))) + assert cryptography_enforces_fips() is enforces + + +def test_cryptography_without_is_fips_enabled_does_not_enforce_fips(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.delattr(rust_openssl, "is_fips_enabled") + assert cryptography_enforces_fips() is False + + +def test_this_runners_python_openssl_does_not_enforce_fips() -> None: + if _hashlib.get_fips_mode() == 1: + pytest.skip("this runner's Python OpenSSL is in FIPS mode") + assert openssl_enforces_fips() is False + + +def test_this_runners_cryptography_openssl_does_not_enforce_fips() -> None: + if rust_openssl.is_fips_enabled(): + pytest.skip("this runner's cryptography OpenSSL is in FIPS mode") + assert cryptography_enforces_fips() is False + + +@pytest.mark.parametrize( + "general_settings, openssl_fips, cryptography_fips, refusal", + [ + ({}, False, False, None), + ({"fips_mode": False}, False, False, None), + ({"fips_mode": "false"}, False, False, None), + ({"fips_mode": True}, True, True, None), + ({"fips_mode": "true"}, True, True, None), + ({"fips_mode": True}, False, True, "general_settings.fips_mode is on but OpenSSL does not enforce FIPS"), + ({"fips_mode": " TRUE "}, False, True, "general_settings.fips_mode is on but OpenSSL does not enforce FIPS"), + ({"fips_mode": True}, True, False, "cryptography library's OpenSSL has no working FIPS provider"), + ({"fips_mode": "enforced"}, True, True, "general_settings.fips_mode='enforced' is not a boolean"), + ({"fips_mode": 1}, True, True, "general_settings.fips_mode=1 is not a boolean"), + ({"fips_mode": None}, True, True, "general_settings.fips_mode has no value"), + ], + ids=[ + "unset", + "false", + "false-from-a-string", + "fips", + "fips-from-a-string", + "openssl-not-fips", + "openssl-not-fips-from-a-padded-string", + "cryptography-not-fips", + "not-a-boolean", + "a-number", + "no-value", + ], +) +def test_enforce_fips_mode( + monkeypatch: pytest.MonkeyPatch, + general_settings: Mapping[str, object], + openssl_fips: bool, + cryptography_fips: bool, + refusal: str | None, +) -> None: + _fake_openssl(monkeypatch, fips_flag=int(openssl_fips), md5_refused=openssl_fips) + monkeypatch.setattr(rust_openssl, "is_fips_enabled", lambda: cryptography_fips) + + with pytest.raises(FipsModeError, match=refusal) if refusal else nullcontext(): + enforce_fips_mode(general_settings) diff --git a/tests/unit/proxy/common_utils/test_http_parsing_utils.py b/tests/unit/proxy/common_utils/test_http_parsing_utils.py index 84cb62f0426..0fc7bb15a32 100644 --- a/tests/unit/proxy/common_utils/test_http_parsing_utils.py +++ b/tests/unit/proxy/common_utils/test_http_parsing_utils.py @@ -65,6 +65,24 @@ def _starlette_request( return Request(scope, receive) +@pytest.mark.asyncio +@pytest.mark.parametrize("path, path_params", [ + ("/claude-code/plugins/skill/archive/digest.zip", {"plugin_name": "skill"}), + ("/vector_stores/vs-safe/files", {}), +]) +async def test_request_vector_store_values_cannot_forge_debug_log_lines( + path: str, path_params: Mapping[str, str], caplog: pytest.LogCaptureFixture, +) -> None: + untrusted: Final = "private-value\r\nFORGED LOG LINE" + request: Final = _starlette_request(b"{}", "application/json", path) + request.scope["path_params"] = dict(path_params) + with caplog.at_level("DEBUG", logger=http_parsing_utils.verbose_proxy_logger.name): + result: Final = populate_request_with_path_params({"vector_store_ids": [untrusted]}, request) + assert untrusted in result["vector_store_ids"] + assert "private-value" not in caplog.text + assert "FORGED LOG LINE" not in caplog.text + + @pytest.mark.asyncio @pytest.mark.parametrize( "body", @@ -377,7 +395,7 @@ async def test_form_data_with_json_metadata(): @pytest.mark.asyncio -async def test_form_data_with_invalid_json_metadata(): +async def test_form_data_with_invalid_json_metadata() -> None: """ Test that form data with invalid JSON in metadata field raises an exception. @@ -398,9 +416,10 @@ async def test_form_data_with_invalid_json_metadata(): mock_request.scope = {} mock_request.state._cached_headers = None - # Should raise JSONDecodeError when trying to parse invalid JSON metadata - with pytest.raises(json.JSONDecodeError): + with pytest.raises(ProxyException) as exc_info: await read_request_body(mock_request) + assert exc_info.value.code == "400" + assert exc_info.value.param == "metadata" @pytest.mark.asyncio diff --git a/tests/unit/proxy/common_utils/test_request_tag_mappings.py b/tests/unit/proxy/common_utils/test_request_tag_mappings.py index e1644544c46..e8e7d881e1d 100644 --- a/tests/unit/proxy/common_utils/test_request_tag_mappings.py +++ b/tests/unit/proxy/common_utils/test_request_tag_mappings.py @@ -26,7 +26,7 @@ def test_accounting_enrichment_without_request_state_preserves_legacy_metadata() @pytest.mark.asyncio -@pytest.mark.parametrize("header", [b"", b"entrypoint=a,entrypoint=b"]) +@pytest.mark.parametrize("header", [b"", b"service=a,service=b"]) @pytest.mark.parametrize("client_metadata", [None, '{"requester_metadata":{"guardrails":["qa-guard"]}}']) async def test_mapping_rejection_keeps_string_metadata_safe_for_auth_failure_logging(header, client_metadata): from litellm.utils import get_request_guardrails @@ -37,7 +37,7 @@ async def test_mapping_rejection_keeps_string_metadata_safe_for_auth_failure_log await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "required": True}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "required": True}]}, None, MagicMock(), ) @@ -59,7 +59,7 @@ async def test_invalid_mapper_metadata_maps_like_empty_metadata_and_keeps_the_cl return {"type": "http.request", "body": json.dumps(body).encode()} request = Request( - {"type": "http", "method": "POST", "path": "/v1/responses", "headers": [(b"baggage", b"entrypoint=a")]}, + {"type": "http", "method": "POST", "path": "/v1/responses", "headers": [(b"baggage", b"service=a")]}, receive, ) data, parse_error = await _read_request_body_deferring_parse_failure(request) @@ -67,11 +67,11 @@ async def test_invalid_mapper_metadata_maps_like_empty_metadata_and_keeps_the_cl await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) - assert "usecase:a" in data["litellm_metadata"]["tags"] + assert "app:a" in data["litellm_metadata"]["tags"] assert "tags" not in data[slot] or slot == "litellm_metadata" assert get_request_guardrails(data) == [] assert await read_request_body(request) == body @@ -80,60 +80,60 @@ async def test_invalid_mapper_metadata_maps_like_empty_metadata_and_keeps_the_cl @pytest.mark.parametrize( "header,value", [ - ("entrypoint=a", "a"), - ("entrypoint = a", "a"), - ("region=sg, entrypoint=customer%20support;version=1", "customer support"), + ("service=a", "a"), + ("service = a", "a"), + ("region=sg, service=customer%20support;version=1", "customer support"), ], ) def test_variable_mapper_uses_decoded_baggage_value(header, value): - rule = RequestTagMapping(key="entrypoint", tag_prefix="usecase:") - assert resolve_baggage_tag_mappings(header, [rule]) == ((f"usecase:{value}", False),) + rule = RequestTagMapping(key="service", tag_prefix="app:") + assert resolve_baggage_tag_mappings(header, [rule]) == ((f"app:{value}", False),) @pytest.mark.parametrize( "header,expected", - [("entrypoint=a", "department:support"), ("entrypoint=b", "department:sales"), ("entrypoint=x", "usecase:x")], + [("service=a", "department:support"), ("service=b", "department:sales"), ("service=x", "app:x")], ) def test_exact_rules_override_variable_fallback_independent_of_order(header, expected): rules = [ - RequestTagMapping(key="entrypoint", tag_prefix="usecase:"), - RequestTagMapping(key="entrypoint", value="a", tag="department:support"), - RequestTagMapping(key="entrypoint", value="b", tag="department:sales"), + RequestTagMapping(key="service", tag_prefix="app:"), + RequestTagMapping(key="service", value="a", tag="department:support"), + RequestTagMapping(key="service", value="b", tag="department:sales"), ] assert resolve_baggage_tag_mappings(header, rules) == ((expected, False),) assert resolve_baggage_tag_mappings(header, list(reversed(rules))) == ((expected, False),) -@pytest.mark.parametrize("header", ["entrypoint=%00", "entrypoint=a%0Ab", "entrypoint=b,entry%70oint=a"]) +@pytest.mark.parametrize("header", ["service=%00", "service=a%0Ab", "service=b,serv%69ce=a"]) def test_variable_mapper_skips_control_characters_and_percent_encoded_duplicate_keys(header): - rule = RequestTagMapping(key="entrypoint", tag_prefix="usecase:") + rule = RequestTagMapping(key="service", tag_prefix="app:") assert resolve_baggage_tag_mappings(header, [rule]) == () @pytest.mark.parametrize( "header,expected", [ - ("entry+point=a", (("usecase:a", False),)), - ("entry%2Bpoint=a", (("usecase:a", False),)), - ("entry+point=a,entry%2Bpoint=b", ()), + ("serv+ice=a", (("app:a", False),)), + ("serv%2Bice=a", (("app:a", False),)), + ("serv+ice=a,serv%2Bice=b", ()), ], ) def test_key_with_a_plus_matches_both_wire_spellings_and_rejects_both_at_once(header, expected): - rule = RequestTagMapping(key="entry+point", tag_prefix="usecase:") + rule = RequestTagMapping(key="serv+ice", tag_prefix="app:") assert resolve_baggage_tag_mappings(header, [rule]) == expected def test_exact_rules_work_without_a_variable_rule(): - rule = RequestTagMapping(key="entrypoint", value="a", tag="usecase:manual") - assert resolve_baggage_tag_mappings("entrypoint=a", [rule]) == (("usecase:manual", False),) - assert resolve_baggage_tag_mappings("entrypoint=x", [rule]) == () + rule = RequestTagMapping(key="service", value="a", tag="app:manual") + assert resolve_baggage_tag_mappings("service=a", [rule]) == (("app:manual", False),) + assert resolve_baggage_tag_mappings("service=x", [rule]) == () @pytest.mark.parametrize( - "header", ["", "region=sg", "entrypoint=", "entrypoint=a,entrypoint=b", "entrypoint=b,entry%70oint=a", "entrypoint=x"] + "header", ["", "region=sg", "service=", "service=a,service=b", "service=b,serv%69ce=a", "service=x"] ) def test_required_mapping_rejects_missing_ambiguous_or_unmapped_values(header): - rule = RequestTagMapping(key="entrypoint", value="a", tag="usecase:a", required=True) + rule = RequestTagMapping(key="service", value="a", tag="app:a", required=True) with pytest.raises(ProxyException) as error: resolve_baggage_tag_mappings(header, [rule]) assert error.value.code == "400" @@ -142,10 +142,10 @@ def test_required_mapping_rejects_missing_ambiguous_or_unmapped_values(header): @pytest.mark.parametrize( "rule", [ - {"key": "entrypoint", "tag": "usecase:a"}, - {"key": "entrypoint", "value": "a", "tag_prefix": "usecase:"}, - {"key": "entrypoint", "value": "a", "tag": "usecase:a", "tag_prefix": "usecase:"}, - {"key": "", "tag_prefix": "usecase:"}, + {"key": "service", "tag": "app:a"}, + {"key": "service", "value": "a", "tag_prefix": "app:"}, + {"key": "service", "value": "a", "tag": "app:a", "tag_prefix": "app:"}, + {"key": "", "tag_prefix": "app:"}, ], ) def test_invalid_rule_is_rejected_during_config_validation(rule): @@ -158,8 +158,8 @@ def test_duplicate_fallbacks_are_rejected(): ConfigGeneralSettings.model_validate( { "request_tag_mappings": [ - {"key": "entrypoint", "tag_prefix": "one:"}, - {"key": "entrypoint", "tag_prefix": "two:"}, + {"key": "service", "tag_prefix": "one:"}, + {"key": "service", "tag_prefix": "two:"}, ] } ) @@ -171,23 +171,23 @@ def test_duplicate_fallbacks_are_rejected(): [("/chat/completions", "metadata"), ("/v1/messages", "litellm_metadata"), ("/v1/responses", "litellm_metadata")], ) async def test_mapped_tag_merges_with_existing_tags_in_the_budget_metadata_slot(route, slot): - data = {slot: {"tags": ["existing", "usecase:a"]}} - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a")]}) + data = {slot: {"tags": ["existing", "app:a"]}} + request = Request({"type": "http", "headers": [(b"baggage", b"service=a")]}) LiteLLMProxyRequestSetup.pre_seed_litellm_metadata_for_route(data, route) await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) - assert data[slot]["tags"] == ["existing", "usecase:a"] + assert data[slot]["tags"] == ["existing", "app:a"] @pytest.mark.asyncio async def test_budgeted_mapper_requires_a_database(): - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a")]}) - config = {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}]} + request = Request({"type": "http", "headers": [(b"baggage", b"service=a")]}) + config = {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}]} with pytest.raises(ProxyException) as error: await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, {}, config, None, MagicMock() @@ -197,10 +197,10 @@ async def test_budgeted_mapper_requires_a_database(): @pytest.mark.asyncio async def test_budgeted_mapper_answers_503_when_the_tag_budget_lookup_fails(): - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a")]}) + request = Request({"type": "http", "headers": [(b"baggage", b"service=a")]}) prisma_client = MagicMock() prisma_client.db.litellm_tagtable.find_many = AsyncMock(side_effect=Exception("database unreachable")) - config = {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}]} + config = {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}]} with pytest.raises(ProxyException, match="could not read tag budgets") as error: await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, {"metadata": {}}, config, prisma_client, UserApiKeyCache() @@ -210,16 +210,16 @@ async def test_budgeted_mapper_answers_503_when_the_tag_budget_lookup_fails(): @pytest.mark.asyncio async def test_budgeted_mapper_serves_through_a_db_outage_when_requests_are_allowed_on_db_unavailable(): - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a")]}) + request = Request({"type": "http", "headers": [(b"baggage", b"service=a")]}) prisma_client = MagicMock() prisma_client.db.litellm_tagtable.find_many = AsyncMock(side_effect=Exception("database unreachable")) data = {"metadata": {}} - config = {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}]} + config = {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}]} with patch.dict("litellm.proxy.proxy_server.general_settings", {"allow_requests_on_db_unavailable": True}): await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, config, prisma_client, UserApiKeyCache() ) - assert data["metadata"]["tags"] == ["usecase:a"] + assert data["metadata"]["tags"] == ["app:a"] async def _cache_with_tag(tag: LiteLLM_TagTable) -> UserApiKeyCache: @@ -234,11 +234,11 @@ async def _cache_with_tag(tag: LiteLLM_TagTable) -> UserApiKeyCache: [None, LiteLLM_BudgetTable(budget_id="qa-budget", max_budget=None, tpm_limit=10)], ) async def test_budgeted_mapper_rejects_a_tag_without_a_max_budget(budget_table): - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a")]}) + request = Request({"type": "http", "headers": [(b"baggage", b"service=a")]}) data = {"metadata": {}} - cache = await _cache_with_tag(LiteLLM_TagTable(tag_name="usecase:a", litellm_budget_table=budget_table)) - config = {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}]} - with pytest.raises(ProxyException, match="usecase:a requires an existing tag budget") as error: + cache = await _cache_with_tag(LiteLLM_TagTable(tag_name="app:a", litellm_budget_table=budget_table)) + config = {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}]} + with pytest.raises(ProxyException, match="app:a requires an existing tag budget") as error: await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, config, MagicMock(), cache ) @@ -248,23 +248,23 @@ async def test_budgeted_mapper_rejects_a_tag_without_a_max_budget(budget_table): @pytest.mark.asyncio async def test_budgeted_mapper_admits_a_tag_with_a_max_budget_and_unbudgeted_rules_need_none(): - request = Request({"type": "http", "headers": [(b"baggage", b"entrypoint=a,region=sg")]}) + request = Request({"type": "http", "headers": [(b"baggage", b"service=a,region=sg")]}) data = {"metadata": {}} cache = await _cache_with_tag( LiteLLM_TagTable( - tag_name="usecase:a", litellm_budget_table=LiteLLM_BudgetTable(budget_id="qa-budget", max_budget=1.0) + tag_name="app:a", litellm_budget_table=LiteLLM_BudgetTable(budget_id="qa-budget", max_budget=1.0) ) ) config = { "request_tag_mappings": [ - {"key": "entrypoint", "tag_prefix": "usecase:", "require_budgeted_tag": True}, + {"key": "service", "tag_prefix": "app:", "require_budgeted_tag": True}, {"key": "region", "tag_prefix": "region:"}, ] } await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, config, MagicMock(), cache ) - assert data["metadata"]["tags"] == ["usecase:a", "region:sg"] + assert data["metadata"]["tags"] == ["app:a", "region:sg"] @pytest.mark.parametrize( @@ -325,7 +325,7 @@ async def test_mapping_survives_cached_body_read_without_leaking_auth_query_fiel "scheme": "http", "server": ("localhost", 4000), "query_string": query, - "headers": [(b"content-type", b"application/json"), (b"baggage", b"entrypoint=a")], + "headers": [(b"content-type", b"application/json"), (b"baggage", b"service=a")], }, receive, ) @@ -337,7 +337,7 @@ async def test_mapping_survives_cached_body_read_without_leaking_auth_query_fiel await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( request, data, - {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, + {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock(), ) @@ -350,7 +350,7 @@ async def test_mapping_survives_cached_body_read_without_leaking_auth_query_fiel proxy_config=ProxyConfig(), general_settings={}, ) - assert enriched[slot]["tags"] == ["usecase:a"] + assert enriched[slot]["tags"] == ["app:a"] assert "key" not in enriched assert "api-version" not in enriched assert "key" not in enriched["proxy_server_request"]["body"] @@ -360,7 +360,7 @@ async def test_mapping_survives_cached_body_read_without_leaking_auth_query_fiel @pytest.mark.asyncio @pytest.mark.parametrize( "client_metadata", - [{"trace_name": "qa"}, {"trace_name": "qa", "tags": ["client:x"]}, {"trace_name": "qa", "tags": ["usecase:a"]}], + [{"trace_name": "qa"}, {"trace_name": "qa", "tags": ["client:x"]}, {"trace_name": "qa", "tags": ["app:a"]}], ) async def test_mapped_tags_stay_out_of_requester_metadata(client_metadata): from litellm.proxy._types import UserAPIKeyAuth @@ -380,14 +380,14 @@ async def test_mapped_tags_stay_out_of_requester_metadata(client_metadata): "scheme": "http", "server": ("localhost", 4000), "query_string": b"", - "headers": [(b"content-type", b"application/json"), (b"baggage", b"entrypoint=a")], + "headers": [(b"content-type", b"application/json"), (b"baggage", b"service=a")], }, receive, ) data, parse_error = await _read_request_body_deferring_parse_failure(request) assert parse_error is None await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( - request, data, {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, None, MagicMock() + request, data, {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock() ) enriched = await add_litellm_data_to_request( data=await read_request_body(request), @@ -397,7 +397,7 @@ async def test_mapped_tags_stay_out_of_requester_metadata(client_metadata): general_settings={}, ) requester_metadata = enriched["metadata"]["requester_metadata"] - assert "usecase:a" in enriched["metadata"]["tags"] + assert "app:a" in enriched["metadata"]["tags"] assert requester_metadata["trace_name"] == "qa" assert requester_metadata.get("tags") == client_metadata.get("tags") assert BAGGAGE_MAPPED_TAGS_METADATA_KEY not in requester_metadata @@ -407,9 +407,9 @@ async def test_mapped_tags_stay_out_of_requester_metadata(client_metadata): @pytest.mark.parametrize( "headers,body_fields", [ - ([(b"x-litellm-tags", b"usecase:a")], {}), - ([], {"tags": ["usecase:a"]}), - ([], {"litellm_metadata": {"tags": ["usecase:a"]}}), + ([(b"x-litellm-tags", b"app:a")], {}), + ([], {"tags": ["app:a"]}), + ([], {"litellm_metadata": {"tags": ["app:a"]}}), ], ids=["header", "body", "litellm_metadata"], ) @@ -432,14 +432,14 @@ async def test_client_sent_tag_still_routes_when_baggage_maps_to_it(headers, bod "scheme": "http", "server": ("localhost", 4000), "query_string": b"", - "headers": [(b"content-type", b"application/json"), (b"baggage", b"entrypoint=a"), *headers], + "headers": [(b"content-type", b"application/json"), (b"baggage", b"service=a"), *headers], }, receive, ) data, parse_error = await _read_request_body_deferring_parse_failure(request) assert parse_error is None await LiteLLMProxyRequestSetup.apply_baggage_tag_mappings_pre_auth( - request, data, {"request_tag_mappings": [{"key": "entrypoint", "tag_prefix": "usecase:"}]}, None, MagicMock() + request, data, {"request_tag_mappings": [{"key": "service", "tag_prefix": "app:"}]}, None, MagicMock() ) enriched = await add_litellm_data_to_request( data=await read_request_body(request), @@ -452,8 +452,8 @@ async def test_client_sent_tag_still_routes_when_baggage_maps_to_it(headers, bod model_list=[ { "model_name": "gpt-4", - "litellm_params": {"model": "gpt-4o", "tags": ["usecase:a"]}, - "model_info": {"id": "usecase-deployment"}, + "litellm_params": {"model": "gpt-4o", "tags": ["app:a"]}, + "model_info": {"id": "app-deployment"}, }, { "model_name": "gpt-4", @@ -470,4 +470,4 @@ async def test_client_sent_tag_still_routes_when_baggage_maps_to_it(headers, bod metadata=dict(enriched["metadata"]), mock_response="hi", ) - assert response._hidden_params["model_id"] == "usecase-deployment" + assert response._hidden_params["model_id"] == "app-deployment" diff --git a/tests/unit/proxy/credential_endpoints/test_endpoints.py b/tests/unit/proxy/credential_endpoints/test_endpoints.py index b74d833a6df..20278e88339 100644 --- a/tests/unit/proxy/credential_endpoints/test_endpoints.py +++ b/tests/unit/proxy/credential_endpoints/test_endpoints.py @@ -3153,18 +3153,34 @@ class TestCredentialDisplayName: repository.update_by_name.assert_not_awaited() assert litellm.credential_list == [_labeled_credential()] - def test_patch_accepts_the_same_credential_name_echoed_back(self, restore_credential_list, monkeypatch): + @pytest.mark.parametrize( + ("patch_info", "expected_info"), + [ + ({}, {"custom_llm_provider": "openai"}), + ({"description": "y"}, {"custom_llm_provider": "openai", "description": "y"}), + ({"custom_llm_provider": "anthropic"}, {"custom_llm_provider": "anthropic"}), + ], + ) + def test_patch_accepts_the_same_credential_name_echoed_back( + self, restore_credential_list, monkeypatch, patch_info: dict[str, str], expected_info: dict[str, str] + ): monkeypatch.setattr(litellm, "credential_list", [_labeled_credential()]) with _repository_holding(_labeled_credential()) as repository: response = _patch_credential( "openai-prod", - {"credential_name": "openai-prod", "credential_values": {"api_key": "sk-new"}, "credential_info": {}}, + { + "credential_name": "openai-prod", + "credential_values": {"api_key": "sk-new"}, + "credential_info": patch_info, + }, ) assert response.status_code == 200, response.text assert [(c.credential_name, c.credential_values) for c in litellm.credential_list] == [ ("openai-prod", {"api_key": "sk-new"}) ] + assert json.loads(repository.update_by_name.await_args.kwargs["data"]["credential_info"]) == expected_info + assert litellm.credential_list[0].credential_info == expected_info def test_patch_on_a_config_credential_is_rejected_as_config_owned(self, restore_credential_list, monkeypatch): config_credential = _labeled_credential(display_name=None).model_copy(update={"source": "config"}) diff --git a/tests/unit/proxy/db/fake_prisma_engine.py b/tests/unit/proxy/db/fake_prisma_engine.py index 4221eeced5a..082659a3adc 100644 --- a/tests/unit/proxy/db/fake_prisma_engine.py +++ b/tests/unit/proxy/db/fake_prisma_engine.py @@ -2,8 +2,10 @@ marking the DB I/O witness the way ``_TrackedPrismaEngine`` does, so the producer under test emits its service event.""" -from typing import TypeVar -from unittest.mock import AsyncMock +from typing import Final, TypeVar +from unittest.mock import AsyncMock, MagicMock + +from prisma.errors import DataError from litellm.proxy.db.log_db_metrics import record_db_io @@ -16,3 +18,73 @@ def engine_call(return_value: _T | None = None) -> AsyncMock: return return_value return AsyncMock(side_effect=run) + + +class RecordingReadTransaction: + """The transaction ``query_raw_within_statement_timeout`` opens on ``read_db``, keeping its ``SET LOCAL`` statements.""" + + def __init__(self, query_raw: AsyncMock) -> None: + self.statements: tuple[str, ...] = () + self.query_raw: AsyncMock = query_raw + + async def execute_raw(self, sql: str) -> int: + self.statements = (*self.statements, sql) + return 0 + + async def __aenter__(self) -> "RecordingReadTransaction": + return self + + async def __aexit__(self, *exc_info: object) -> None: + pass + + +def statement_timeout_reads(prisma: MagicMock, query_raw: AsyncMock) -> RecordingReadTransaction: + """Make ``prisma.read_db.tx()`` hand out one recording transaction whose raw reads answer with ``query_raw``.""" + transaction: Final = RecordingReadTransaction(query_raw) + prisma.read_db.tx = MagicMock(return_value=transaction) + return transaction + + +def statement_timeout_error() -> DataError: + """The error prisma raises when Postgres cancels a statement under ``statement_timeout`` (SQLSTATE 57014).""" + message: Final = "canceling statement due to statement timeout" + return DataError( + data={ + "user_facing_error": { + "is_panic": False, + "message": f"Error querying the database: {message}", + "meta": {"code": "57014", "message": message}, + } + } + ) + + +class _DelegatingReadTransaction: + """``read_db.tx()`` of a hand-written prisma fake: raw reads go to the fake's ``db.query_raw``.""" + + def __init__(self, db: object) -> None: + self.db: Final = db + self.statements: tuple[str, ...] = () + + async def execute_raw(self, sql: str) -> int: + self.statements = (*self.statements, sql) + return 0 + + async def query_raw(self, sql: str, *args: object) -> object: + return await getattr(self.db, "query_raw")(sql, *args) + + async def __aenter__(self) -> "_DelegatingReadTransaction": + return self + + async def __aexit__(self, *exc_info: object) -> None: + pass + + +class ReadTargetOf: + """What a prisma fake returns from ``read_db``: ``tx()`` opens a transaction over the fake's ``db``.""" + + def __init__(self, db: object) -> None: + self.db: Final = db + + def tx(self, **kwargs: object) -> _DelegatingReadTransaction: + return _DelegatingReadTransaction(self.db) diff --git a/tests/unit/proxy/db/test_accounting_ledger.py b/tests/unit/proxy/db/test_accounting_ledger.py deleted file mode 100644 index 6b301146301..00000000000 --- a/tests/unit/proxy/db/test_accounting_ledger.py +++ /dev/null @@ -1,149 +0,0 @@ -import json -from datetime import datetime, timezone -from decimal import Decimal -from typing import Final, Literal, NoReturn - -import pytest - -from litellm.accounting import InboundUsageEvent, Metric, MetricName, Outcome -from litellm.proxy.db.accounting_ledger import AccountingConflictError, AccountingLedger, UsageWrite -from litellm.types.utils import CallTypes - - -def _no_transaction() -> NoReturn: - raise AssertionError("Invalid accounting input must not open a database transaction") - - -@pytest.fixture -def event() -> InboundUsageEvent: - return InboundUsageEvent( - event_id="caller", - request_id="caller", - call_type=CallTypes.completion, - started_at=datetime(2026, 1, 1, tzinfo=timezone.utc), - ended_at=datetime(2026, 1, 1, tzinfo=timezone.utc), - outcome=Outcome.SUCCEEDED, - dimensions=(), - metrics=(Metric(name=MetricName.REQUESTS, value=Decimal(1)),), - ) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("envelope", ["{}", "[]", '{"event":null}', '{"event":{}}', "not json"]) -async def test_invalid_envelopes_fail_before_database_access(event: InboundUsageEvent, envelope: str) -> None: - with pytest.raises(ValueError, match=r"Accounting envelope|validation error"): - await AccountingLedger(_no_transaction).insert_event(event, (), envelope=envelope) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("version", [0, -1, True, 1.5]) -@pytest.mark.parametrize("field", ["version", "schema_version"]) -async def test_invalid_versions_fail_before_database_access( - event: InboundUsageEvent, version: int | float, field: Literal["version", "schema_version"] -) -> None: - with pytest.raises(ValueError, match="positive integers"): - await AccountingLedger(_no_transaction).insert_event( - event, - (), - envelope="{}", - **{field: version}, # pyright: ignore[reportArgumentType] # Exercise invalid runtime versions - ) - - -@pytest.mark.asyncio -async def test_constructed_invalid_events_are_revalidated(event: InboundUsageEvent) -> None: - invalid: Final = event.model_copy(update={"event_id": ""}) - with pytest.raises(ValueError, match="event_id"): - await AccountingLedger(_no_transaction).insert_event(invalid, (), envelope="{}") - - -@pytest.mark.asyncio -@pytest.mark.parametrize("limit", [0, -1, 1001, True, 1.5]) -async def test_retention_rejects_invalid_batch_bounds(limit: int | float) -> None: - with pytest.raises(ValueError, match="batch size"): - await AccountingLedger(_no_transaction).purge_completed( - limit=limit # pyright: ignore[reportArgumentType] # Exercise invalid runtime batch sizes - ) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("missing", ["both", "redelivery", "recovery"]) -async def test_retention_requires_both_safety_watermarks(missing: Literal["both", "redelivery", "recovery"]) -> None: - cutoff: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) - deleted: Final = await AccountingLedger(_no_transaction).purge_completed( - redelivery_before=cutoff if missing == "recovery" else None, - recovery_before=cutoff if missing == "redelivery" else None, - ) - assert deleted == 0 - - -@pytest.mark.asyncio -async def test_retention_rejects_ambiguous_naive_watermarks() -> None: - with pytest.raises(ValueError, match="timezone"): - await AccountingLedger(_no_transaction).purge_completed( - redelivery_before=datetime(2026, 1, 1), recovery_before=datetime(2026, 1, 1, tzinfo=timezone.utc) - ) - - -@pytest.mark.asyncio -async def test_nonfinite_context_is_rejected_before_database_access(event: InboundUsageEvent) -> None: - envelope: Final = json.dumps({"event": event.model_dump(mode="json"), "context": {"price": float("nan")}}) - with pytest.raises(ValueError, match="Out of range float"): - await AccountingLedger(_no_transaction).insert_event(event, (), envelope=envelope) - - -@pytest.mark.asyncio -async def test_unknown_delivery_target_is_rejected_before_database_access(event: InboundUsageEvent) -> None: - envelope: Final = json.dumps({"event": event.model_dump(mode="json")}) - with pytest.raises(ValueError, match="not a valid DeliveryTarget"): - await AccountingLedger(_no_transaction).insert_event( - event, - ("unknown",), # pyright: ignore[reportArgumentType] # Exercise an unknown runtime target - envelope=envelope, - ) - - -@pytest.mark.asyncio -async def test_empty_usage_batches_do_not_open_a_transaction() -> None: - ledger: Final = AccountingLedger(_no_transaction) - await ledger.insert_usage_batch(()) - assert await ledger.load_usage_batch(()) == () - - -@pytest.mark.asyncio -async def test_conflicting_usage_within_one_batch_is_rejected_before_database_access(event: InboundUsageEvent) -> None: - original: Final = UsageWrite(event, json.dumps({"event": event.model_dump(mode="json")})) - changed: Final = UsageWrite(event, json.dumps({"event": event.model_dump(mode="json"), "pricing": {"rate": 2}})) - with pytest.raises(AccountingConflictError, match="conflicting copies"): - await AccountingLedger(_no_transaction).insert_usage_batch((original, changed)) - - -@pytest.mark.asyncio -async def test_loaded_usage_preserves_decimal_metrics_from_jsonb(event: InboundUsageEvent) -> None: - from collections.abc import AsyncIterator - from contextlib import asynccontextmanager - from typing import cast - from unittest.mock import AsyncMock - - from prisma import Prisma - - from litellm.accounting import OutboundUsageEvent - - outbound: Final = OutboundUsageEvent( - **event.model_dump(exclude={"metrics"}), - attempt_id="attempt", - retry_of_attempt_id=None, - metrics=(Metric(name=MetricName.PROMPT_TOKENS, value=Decimal("120000")),), - ) - payload: Final = {"event": outbound.model_dump(mode="json"), "usage": {"prompt_tokens": 120000}} - database: Final = AsyncMock(spec=Prisma) - database.query_raw.return_value = [{"payload": payload, "targets": []}] - - @asynccontextmanager - async def transaction() -> AsyncIterator[Prisma]: - yield cast(Prisma, database) - - recovered: Final = await AccountingLedger(transaction).load_usage_batch((outbound.event_id,)) - assert len(recovered) == 1 - assert recovered[0].event == outbound - assert json.loads(recovered[0].envelope) == payload diff --git a/tests/unit/proxy/db/test_db_lookup_gate.py b/tests/unit/proxy/db/test_db_lookup_gate.py index 68903170840..3450d3e05b8 100644 --- a/tests/unit/proxy/db/test_db_lookup_gate.py +++ b/tests/unit/proxy/db/test_db_lookup_gate.py @@ -46,21 +46,37 @@ async def test_bounded_db_lookup_returns_a_prompt_answer_without_recording_a_sta assert tracker.stalled_within(30) is False +class _PendingLookups: + def __init__(self) -> None: + self.pending = 0 + self.peak = 0 + + async def never_answers(self) -> None: + self.pending += 1 + self.peak = max(self.peak, self.pending) + try: + await asyncio.Event().wait() + finally: + self.pending -= 1 + + @pytest.mark.asyncio async def test_bounded_db_lookup_fails_a_whole_stalled_burst_within_one_deadline(): tracker: Final = DBLookupStallTracker() burst: Final = 200 - started: Final = time.monotonic() + lookups: Final = _PendingLookups() results: Final = await asyncio.gather( *( - bounded_db_lookup(_never_answers(), name=f"key-{i}", deadline_seconds=0.1, tracker=tracker) + bounded_db_lookup(lookups.never_answers(), name=f"key-{i}", deadline_seconds=0.1, tracker=tracker) for i in range(burst) ), return_exceptions=True, ) - assert time.monotonic() - started < 2 + assert lookups.peak == burst, ( + f"only {lookups.peak} of {burst} stalled lookups were waiting on their deadline together" + ) assert len(results) == burst assert all(isinstance(result, DBLookupDeadlineExceeded) for result in results) assert tracker.stalled_within(30) is True diff --git a/tests/unit/proxy/db/test_db_spend_update_writer.py b/tests/unit/proxy/db/test_db_spend_update_writer.py index 298b153f621..3a79df0370b 100644 --- a/tests/unit/proxy/db/test_db_spend_update_writer.py +++ b/tests/unit/proxy/db/test_db_spend_update_writer.py @@ -1,10 +1,12 @@ import asyncio import copy +import functools +import itertools import json import logging import re -from collections.abc import AsyncIterator, Callable -from contextlib import AbstractAsyncContextManager, asynccontextmanager +from collections.abc import AsyncIterator, Awaitable, Callable, Iterator +from contextlib import AbstractAsyncContextManager, asynccontextmanager, contextmanager from datetime import datetime, timedelta, timezone from pathlib import Path from types import SimpleNamespace @@ -4456,7 +4458,7 @@ async def _update_database_with( end_user_id=None, team_id=None, org_id=None, - kwargs={"model": "gpt-5.6-luna", "call_type": "aretrieve_batch"}, + kwargs={"model": "gpt-5.6-luna", "call_type": payload["call_type"]}, completion_response=None, start_time=datetime.now(timezone.utc), end_time=datetime.now(timezone.utc), @@ -4707,6 +4709,391 @@ async def test_update_database_queues_every_other_spend_row_for_the_next_flush(p assert db_writer._batch_database_updates.await_count == 1 +def _video_status_payload(request_id: str = "video_abc_video_cost") -> dict: + return { + **_minimal_spend_payload(), + "request_id": request_id, + "call_type": "avideo_status", + "status": "success", + } + + +class _SpendLogsTable: + """LiteLLM_SpendLogs keyed by its request_id primary key, as the writer's three queries see it.""" + + def __init__(self) -> None: + self.rows: dict[str, dict] = {} + + async def create_many(self, data: list[dict], skip_duplicates: bool) -> int: + fresh = {row["request_id"]: dict(row) for row in data if row["request_id"] not in self.rows} + self.rows.update(fresh) + return len(fresh) + + async def find_unique(self, where: dict) -> SimpleNamespace | None: + row = self.rows.get(where["request_id"]) + return None if row is None else SimpleNamespace(**row) + + async def update_many(self, data: dict, where: dict) -> int: + row = self.rows.get(where["request_id"]) + if row is None or any(row.get(field) != value for field, value in where.items()): + return 0 + row.update(data) + return 1 + + +def _spend_logs_table_prisma() -> MagicMock: + prisma = _tool_usage_prisma() + prisma.jsonify_object = lambda data: dict(data) + prisma.db.litellm_spendlogs = _SpendLogsTable() + return prisma + + +async def _flush_queued_spend_logs(prisma: MagicMock) -> None: + await prisma.db.litellm_spendlogs.create_many(data=prisma.spend_log_transactions, skip_duplicates=True) + prisma.spend_log_transactions.clear() + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "flushes_before_completion", + [0, 1], + ids=["rendering_poll_still_queued", "rendering_poll_already_written"], +) +async def test_update_database_charges_a_video_once_from_the_poll_that_sees_it_completed( + flushes_before_completion: int, +): + """ + A custom video provider reports the video's cost on the status poll that sees it completed, + and clients poll a finished video again before they download it, so only that first completed + poll may charge, and its spend row has to carry the charge (LIT-10404). The $0 poll written + while the video was still rendering keeps the bare video id, so it lands on the create row. + """ + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + rendering = await _update_database_with(db_writer, prisma, _video_status_payload("video_abc"), response_cost=0.0) + for _ in range(flushes_before_completion): + await _flush_queued_spend_logs(prisma) + completed = await _update_database_with(db_writer, prisma, _video_status_payload(), response_cost=0.42) + repolled = await _update_database_with(db_writer, prisma, _video_status_payload(), response_cost=0.42) + await _flush_queued_spend_logs(prisma) + + assert (rendering, completed, repolled) == (True, True, False) + assert db_writer._batch_database_updates.await_count == 2 + rows = prisma.db.litellm_spendlogs.rows + assert {request_id: (row["call_type"], row["spend"]) for request_id, row in rows.items()} == { + "video_abc": ("avideo_status", 0.0), + "video_abc_video_cost": ("avideo_status", 0.42), + } + + +def _indexed_guardrail_request_ids(prisma: MagicMock, index_table: str) -> tuple[str, ...]: + calls: Final = getattr(prisma.db, index_table).create_many.await_args_list + batches: Final = tuple(call.kwargs["data"] for call in calls) + return tuple(row["request_id"] for row in itertools.chain.from_iterable(batches)) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("disable_spend_logs", [False, True], ids=["spend_logs_on", "spend_logs_off"]) +@pytest.mark.parametrize("left_uncharged", [False, True], ids=["claimed_row", "taken_over_row"]) +async def test_a_job_cost_row_counts_its_polls_guardrail_run_in_guardrail_usage_once( + disable_spend_logs: bool, left_uncharged: bool +): + """The job's cost row is written by its claim, not the spend log queue whose flush counts guardrail runs, so a + finished video's guardrail run must still reach guardrail usage, once, and not when spend logs are off.""" + from litellm.repositories.table_repositories import ( + DailyGuardrailMetricsRepository, + SpendLogGuardrailIndexRepository, + ) + + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + getattr(prisma.db, SpendLogGuardrailIndexRepository.table_name).create_many = AsyncMock(return_value=1) + getattr(prisma.db, DailyGuardrailMetricsRepository.table_name).upsert = AsyncMock() + if left_uncharged: + prisma.db.litellm_spendlogs.rows["video_abc_video_cost"] = {**_video_status_payload(), "spend": 0.0} + guarded_poll: Final = { + **_video_status_payload(), + "metadata": json.dumps( + {"guardrail_information": [{"guardrail_name": "priced", "guardrail_status": "success"}]} + ), + } + + charged = [ + await _update_database_with( + db_writer, prisma, guarded_poll, disable_spend_logs=disable_spend_logs, response_cost=0.43 + ) + for _ in range(2) + ] + + assert charged == [True, False] + assert _indexed_guardrail_request_ids(prisma, SpendLogGuardrailIndexRepository.table_name) == ( + () if disable_spend_logs else ("video_abc_video_cost",) + ) + + +def _job_poll( + litellm_call_id: str, + response_cost: float, + status: str = "completed", + polled_video_id: str = "video_abc", + guardrail_cost: float = 0.0, + call_type: str = "avideo_status", +) -> tuple[dict[str, object], object]: + """The logging kwargs a job poll reaches the spend writer with: litellm logs the provider's own cost as + response_cost and the guardrails' on top of it only in the standard logging payload.""" + from litellm.types.utils import LiteLLMBatch + from litellm.types.videos.main import VideoObject + + polled_job = ( + VideoObject(id="video_abc", object="video", status=status) + if call_type == "avideo_status" + else LiteLLMBatch( + id="batch_abc", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="f", + object="batch", + status=status, + output_file_id="file-out", + ) + ) + kwargs: Final[dict[str, object]] = { + "model": "sora-2", + "call_type": call_type, + "litellm_call_id": litellm_call_id, + "response_cost": response_cost - guardrail_cost, + "polled_video_status": status, + "litellm_params": {"metadata": {"user_api_key": "test-key"}, "video_id": polled_video_id}, + **( + { + "standard_logging_object": { + "response_cost": response_cost, + "guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": guardrail_cost}], + "metadata": {}, + "hidden_params": {}, + "request_tags": [], + "model_map_information": None, + } + } + if guardrail_cost + else {} + ), + } + return kwargs, polled_job + + +def _spend_writer_call(db_writer: DBSpendUpdateWriter, polled_job: object) -> Callable[..., Awaitable[bool]]: + return functools.partial( + db_writer.update_database, + token="test-token", + user_id="test-user", + end_user_id=None, + team_id=None, + org_id=None, + completion_response=polled_job, + start_time=datetime.now(timezone.utc), + end_time=datetime.now(timezone.utc), + ) + + +@contextmanager +def _proxy_globals(prisma: MagicMock) -> Iterator[None]: + with ( + patch( # test-quality-ok: update_database reads this proxy_server global at call time, no seam + "litellm.proxy.proxy_server.prisma_client", prisma + ), + patch( # test-quality-ok: update_database reads this proxy_server global at call time, no seam + "litellm.proxy.proxy_server.litellm_proxy_budget_name", "test-budget" + ), + ): + yield + + +async def _update_database_for_video_poll( + db_writer: DBSpendUpdateWriter, + prisma: MagicMock, + litellm_call_id: str, + response_cost: float, + status: str = "completed", + polled_video_id: str = "video_abc", + guardrail_cost: float = 0.0, + call_type: str = "avideo_status", +) -> bool: + kwargs, polled_job = _job_poll(litellm_call_id, response_cost, status, polled_video_id, guardrail_cost, call_type) + with _proxy_globals(prisma): + charged = await _spend_writer_call(db_writer, polled_job)(kwargs=kwargs, response_cost=response_cost) + await asyncio.sleep(0) + return charged + + +@pytest.mark.asyncio +async def test_update_database_charges_the_completed_polls_cost_and_not_a_running_polls(): + """A provider that also prices a running video must still have the completed poll's cost billed, once.""" + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + running = await _update_database_for_video_poll(db_writer, prisma, "poll-1", 0.1, status="in_progress") + completed = await _update_database_for_video_poll(db_writer, prisma, "poll-2", 0.42) + repolled = await _update_database_for_video_poll(db_writer, prisma, "poll-3", 0.42) + await _flush_queued_spend_logs(prisma) + + assert (running, completed, repolled) == (True, True, False) + assert prisma.db.litellm_spendlogs.rows["video_abc_video_cost"]["spend"] == 0.42 + + +@pytest.mark.asyncio +async def test_a_poll_paying_only_its_guardrail_cannot_take_the_jobs_charge_whatever_call_id_the_client_sent(): + """A client picks its own x-litellm-call-id, so a guardrail-only video poll naming the job's charge row, once or + twice, must still be charged each time and leave the finished job's charge to the poll that sees it done. An + unfinished batch retrieve never reaches the writer, and its row is named by the batch id the provider sent.""" + call_type: Final = "avideo_status" + charge_row_id: Final = "video_abc_video_cost" + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + charged = [ + await _update_database_for_video_poll( + db_writer, prisma, call_id, cost, status=status, guardrail_cost=0.01, call_type=call_type + ) + for call_id, cost, status in ( + (charge_row_id, 0.01, "in_progress"), + (charge_row_id, 0.01, "in_progress"), + ("poll-3", 0.43, "completed"), + ) + ] + await _flush_queued_spend_logs(prisma) + + assert charged == [True, True, True] + assert prisma.db.litellm_spendlogs.rows[charge_row_id]["spend"] == 0.43 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("call_type", "charge_row_id"), + [("avideo_status", "video_abc_video_cost"), ("aretrieve_batch", "batch_abc_batch_cost")], +) +async def test_a_finished_job_cheaper_than_its_polls_guardrail_is_still_charged_once(call_type, charge_row_id): + """A batch of a few cheap requests costs less than a priced guardrail on its retrieve, and the finished job's + cost must still settle under its charge row once instead of being billed again on every poll.""" + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + charged = [ + await _update_database_for_video_poll( + db_writer, prisma, call_id, 0.01002775, guardrail_cost=0.01, call_type=call_type + ) + for call_id in ("poll-1", "poll-2") + ] + await _flush_queued_spend_logs(prisma) + + assert charged == [True, False] + assert {request_id: row["spend"] for request_id, row in prisma.db.litellm_spendlogs.rows.items()} == { + charge_row_id: 0.01002775 + } + + +@pytest.mark.asyncio +async def test_a_finished_batch_that_cost_nothing_itself_settles_its_retrieves_guardrail_once(): + """A batch whose every request failed costs nothing itself, and its finished retrieves must still settle under + the batch's charge row once, as they always have, instead of each paying the retrieve's guardrail again.""" + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + charged = [ + await _update_database_for_video_poll( + db_writer, prisma, call_id, 0.01, status="failed", guardrail_cost=0.01, call_type="aretrieve_batch" + ) + for call_id in ("poll-1", "poll-2", "poll-3") + ] + await _flush_queued_spend_logs(prisma) + + assert charged == [True, False, False] + assert {request_id: row["spend"] for request_id, row in prisma.db.litellm_spendlogs.rows.items()} == { + "batch_abc_batch_cost": 0.01 + } + + +@pytest.mark.asyncio +async def test_a_repeat_poll_of_a_finished_video_charges_its_guardrail_on_a_row_of_its_own(): + from litellm.proxy.hooks.proxy_track_cost_callback import _charge_request_cost + + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + await _update_database_for_video_poll(db_writer, prisma, "poll-1", 0.43, guardrail_cost=0.01) + kwargs, polled_job = _job_poll("poll-2", 0.43, guardrail_cost=0.01) + + async def charge_spend( + *, kwargs: dict[str, object], response_cost: float, budget_reservation: dict[str, object] | None + ) -> bool: + return await _spend_writer_call(db_writer, polled_job)(kwargs=kwargs, response_cost=response_cost) + + with _proxy_globals(prisma): + repeat_charge = await _charge_request_cost(charge_spend, kwargs, 0.43, None) + await asyncio.sleep(0) + await _flush_queued_spend_logs(prisma) + + assert repeat_charge == 0.01 + assert {request_id: row["spend"] for request_id, row in prisma.db.litellm_spendlogs.rows.items()} == { + "video_abc_video_cost": 0.43, + "poll-2_guardrail_cost": 0.01, + } + + +@pytest.mark.asyncio +async def test_update_database_charges_each_deployments_video_though_their_provider_ids_match(): + """A custom provider's own ids can repeat across deployments, so the cost row keys off the id the client polled.""" + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + + charged = [ + await _update_database_for_video_poll(db_writer, prisma, call_id, 0.42, polled_video_id=polled_id) + for call_id, polled_id in (("poll-1", "video_dep_a"), ("poll-2", "video_dep_b"), ("poll-3", "video_dep_a")) + ] + + assert charged == [True, True, False] + assert { + request_id: row["spend"] + for request_id, row in prisma.db.litellm_spendlogs.rows.items() + if request_id.endswith("_video_cost") + } == {"video_dep_a_video_cost": 0.42, "video_dep_b_video_cost": 0.42} + + +@pytest.mark.asyncio +async def test_update_database_writes_no_row_of_its_own_for_a_video_poll_that_reports_no_cost(): + """ + Built-in video providers charge at create and report nothing on the status poll, so those + polls keep the ordinary queued write under the create row's id and still count as requests + in the daily rollups, leaving the video with its one create row. + """ + db_writer = DBSpendUpdateWriter() + db_writer._batch_database_updates = AsyncMock() + prisma = _spend_logs_table_prisma() + await prisma.db.litellm_spendlogs.create_many( + data=[{**_minimal_spend_payload(), "request_id": "video_abc", "call_type": "avideo_generation", "spend": 0.3}], + skip_duplicates=True, + ) + + assert await _update_database_for_video_poll(db_writer, prisma, "poll-1", response_cost=0.0) is True + assert await _update_database_for_video_poll(db_writer, prisma, "poll-2", response_cost=0.0) is True + await _flush_queued_spend_logs(prisma) + + assert db_writer._batch_database_updates.await_count == 2 + rows = prisma.db.litellm_spendlogs.rows + assert {request_id: (row["call_type"], row["spend"]) for request_id, row in rows.items()} == { + "video_abc": ("avideo_generation", 0.3) + } + + @pytest.mark.asyncio @pytest.mark.parametrize( "injected_deployment, attributed", diff --git a/tests/unit/proxy/db/test_exception_handler.py b/tests/unit/proxy/db/test_exception_handler.py index d70d4403962..46704bf52ef 100644 --- a/tests/unit/proxy/db/test_exception_handler.py +++ b/tests/unit/proxy/db/test_exception_handler.py @@ -953,6 +953,21 @@ def test_is_lock_timeout_error_excludes_other_failures(error: Exception) -> None assert PrismaDBExceptionHandler.is_lock_timeout_error(error) is False +@pytest.mark.parametrize( + ("error", "expected"), + [ + (_lock_timeout_error(message="canceling statement due to statement timeout", code="57014"), True), + (_lock_timeout_error(message="canceling statement due to statement timeout", code=None), True), + (_lock_timeout_error(), False), + (_capacity_error("sorry, too many clients already"), False), + (RuntimeError("canceling statement due to statement timeout"), False), + ], + ids=["meta_57014", "message_only", "lock_timeout_55P03", "capacity_53300", "not_prisma"], +) +def test_is_statement_timeout_error(error: Exception, expected: bool) -> None: + assert PrismaDBExceptionHandler.is_statement_timeout_error(error) is expected + + @pytest.mark.parametrize( ("body", "retryable"), [ diff --git a/tests/unit/proxy/db/test_prisma_query_span.py b/tests/unit/proxy/db/test_prisma_query_span.py index 9f2ba28ea7e..55ba03d9f2c 100644 --- a/tests/unit/proxy/db/test_prisma_query_span.py +++ b/tests/unit/proxy/db/test_prisma_query_span.py @@ -19,6 +19,7 @@ from litellm.proxy.db.prisma_query_span import UNKNOWN_PRISMA_QUERY, parse_prism _REPO: Final = Path(__file__).resolve().parents[4] _SOURCE_ROOTS: Final = ("litellm", "enterprise", "litellm-proxy-extras") _RAW_METHODS: Final = frozenset({"query_first", "query_raw", "execute_raw"}) +_CROSS_MODULE_FORWARDERS: Final = frozenset({"query_raw_within_statement_timeout"}) _MODEL_METHODS: Final = frozenset( { "find_unique", @@ -297,7 +298,7 @@ def _parameter_site( """A statement that arrives as a parameter: a ``query_raw`` forwarder adds no round trip of its own, any other helper is named by what its callers in the module hand it.""" function: Final = next(p for p in parents if isinstance(p, (ast.AsyncFunctionDef, ast.FunctionDef))) - if function.name in _RAW_METHODS: + if function.name in _RAW_METHODS or function.name in _CROSS_MODULE_FORWARDERS: return _PrismaCallSite(location, method, "forwarder", f"(callers of {function.name})") callers: Final = tuple( node diff --git a/tests/unit/proxy/discovery_endpoints/test_agent_skills_archive.py b/tests/unit/proxy/discovery_endpoints/test_agent_skills_archive.py index dd5ac230aac..a3d968263e5 100644 --- a/tests/unit/proxy/discovery_endpoints/test_agent_skills_archive.py +++ b/tests/unit/proxy/discovery_endpoints/test_agent_skills_archive.py @@ -1,6 +1,8 @@ import hashlib import io +import stat import zipfile +from typing import Final from litellm.proxy.discovery_endpoints.agent_skills_archive import ( MAX_ARCHIVE_ENTRIES, @@ -16,7 +18,7 @@ Read the PDF, then write the brief. """ -def zip_bytes(files: dict[str, bytes]) -> bytes: +def zip_bytes(files: dict[str | zipfile.ZipInfo, bytes]) -> bytes: buffer = io.BytesIO() with zipfile.ZipFile(buffer, "w", zipfile.ZIP_DEFLATED) as archive: for name, content in files.items(): @@ -104,3 +106,15 @@ def test_an_upload_over_the_entry_cap_is_not_installable(): } assert build_skill_archive(zip_bytes(files)) is None + + +def test_discovery_keeps_its_permission_and_finder_metadata_policy() -> None: + executable: Final = zipfile.ZipInfo("scripts/run.sh") + executable.create_system = 3 + executable.external_attr = (stat.S_IFREG | 0o7777) << 16 + result: Final = build_skill_archive(zip_bytes({"SKILL.md": MANIFEST, executable: b"echo ready\n"})) + assert build_skill_archive(zip_bytes({"folder/SKILL.md": MANIFEST, "__MACOSX/._SKILL.md": b"Finder"})) is None + assert result is not None + with zipfile.ZipFile(io.BytesIO(result.content)) as archive: + assert {entry.create_system for entry in archive.infolist()} == {3} + assert {entry.external_attr >> 16 for entry in archive.infolist()} == {0o644} diff --git a/tests/unit/proxy/enterprise_billing/test_billing_metrics.py b/tests/unit/proxy/enterprise_billing/test_billing_metrics.py index 0446cfeeab0..14a8fea04f7 100644 --- a/tests/unit/proxy/enterprise_billing/test_billing_metrics.py +++ b/tests/unit/proxy/enterprise_billing/test_billing_metrics.py @@ -1,398 +1,379 @@ -""" -Tests for the enterprise billing-metrics recorder and its factory. - -These verify the license gate, the missing-config and missing-cert disable -paths, the OTLP/HTTP exporter wiring (client cert+key authenticate us to the -collector's mTLS-terminating front end; CA override optional for private -collectors), the metric attribute mapping, and that recording produces the -expected OTLP counter via an in-memory reader. -""" - -import os -import socket -import stat -from pathlib import Path -from typing import Dict, List, Optional +from collections.abc import Generator, Mapping +from typing import Final import pytest +import requests +import requests_mock from opentelemetry.sdk.metrics import MeterProvider -from opentelemetry.sdk.metrics.export import InMemoryMetricReader +from opentelemetry.sdk.metrics.export import InMemoryMetricReader, Sum +from pydantic import BaseModel, ConfigDict, TypeAdapter +from litellm.proxy._types import EnterpriseLicenseData from litellm.proxy.enterprise_billing import billing_metrics as bm from litellm.proxy.middleware.billable_request_metrics_middleware import BillableCategory -_ENV_VARS = ( + +class _RecordedRequest(BaseModel): + model_config = ConfigDict(from_attributes=True, frozen=True) + + url: str | None + body: str | bytes | None + headers: dict[str, str] + + +def _request_history(http_mock: requests_mock.Mocker) -> tuple[_RecordedRequest, ...]: + return TypeAdapter(tuple[_RecordedRequest, ...]).validate_python(http_mock.request_history) + + +def _metric_category(attributes: Mapping[str, object] | None) -> str: + if attributes is None: + return "" + return str(attributes.get("litellm.endpoint.category")) + + +_ENV_VARS: Final = ( bm.ENDPOINT_ENV, - bm.CLIENT_CERT_ENV, - bm.CLIENT_KEY_ENV, - bm.CA_CERT_ENV, + bm.CLIENT_ID_ENV, + bm.CLIENT_SECRET_ENV, + bm.TOKEN_URL_ENV, bm.EXPORT_INTERVAL_ENV, ) @pytest.fixture(autouse=True) -def clear_env(monkeypatch): +def clear_env(monkeypatch: pytest.MonkeyPatch) -> Generator[None, None, None]: for name in _ENV_VARS: monkeypatch.delenv(name, raising=False) yield bm.shutdown_billing_metrics_recorder() -def _write_certs(tmp_path: Path) -> Dict[str, str]: - files = { - bm.CA_CERT_ENV: ("ca.pem", b"ca-bytes"), - bm.CLIENT_CERT_ENV: ("client.pem", b"client-cert-bytes"), - bm.CLIENT_KEY_ENV: ("client.key", b"client-key-bytes"), - } - paths = {} - for env_name, (filename, content) in files.items(): - path = tmp_path / filename - path.write_bytes(content) - paths[env_name] = str(path) - return paths +def _set_full_env(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4318") + monkeypatch.setenv(bm.CLIENT_ID_ENV, "client-id") + monkeypatch.setenv(bm.CLIENT_SECRET_ENV, "client-secret") + monkeypatch.setenv(bm.TOKEN_URL_ENV, "https://auth.example/oauth2/token") -def _set_full_env(monkeypatch, tmp_path: Path) -> Dict[str, str]: - paths = _write_certs(tmp_path) - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CA_CERT_ENV, paths[bm.CA_CERT_ENV]) - monkeypatch.setenv(bm.CLIENT_CERT_ENV, paths[bm.CLIENT_CERT_ENV]) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, paths[bm.CLIENT_KEY_ENV]) - return paths - - -def _config(tmp_path: Path, license_id: Optional[str] = "org-1") -> bm.BillingMetricsConfig: - paths = _write_certs(tmp_path) +def _config(license_id: str | None = "org-1") -> bm.BillingMetricsConfig: return bm.BillingMetricsConfig( - endpoint="https://collector.example:4317", - client_cert_path=paths[bm.CLIENT_CERT_ENV], - client_key_path=paths[bm.CLIENT_KEY_ENV], - ca_cert_path=paths[bm.CA_CERT_ENV], + endpoint="https://collector.example:4318", + token_url="https://auth.example/oauth2/token", + client_id="client-id", + client_secret="client-secret", export_interval_ms=60_000, litellm_version="1.2.3", license_id=license_id, ) -# ── Factory gating ──────────────────────────────────────────────────────────── +@pytest.mark.parametrize("missing_env", (bm.ENDPOINT_ENV, bm.CLIENT_ID_ENV, bm.CLIENT_SECRET_ENV, bm.TOKEN_URL_ENV)) +def test_missing_required_env_disables_metrics_without_logging_values( + monkeypatch: pytest.MonkeyPatch, + caplog: pytest.LogCaptureFixture, + missing_env: str, +) -> None: + _set_full_env(monkeypatch) + monkeypatch.setenv(bm.CLIENT_SECRET_ENV, "secret-for-test") + monkeypatch.delenv(missing_env) + + assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None + assert f"licensed deployment missing config ({missing_env})" in caplog.text + assert "secret-for-test" not in caplog.text -def test_not_premium_returns_none(tmp_path, monkeypatch): - _set_full_env(monkeypatch, tmp_path) +def test_empty_required_env_disables_metrics(monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture) -> None: + _set_full_env(monkeypatch) + monkeypatch.setenv(bm.ENDPOINT_ENV, "") + + assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None + assert bm.ENDPOINT_ENV in caplog.text + + +@pytest.mark.parametrize( + ("env_name", "url"), + ( + (bm.ENDPOINT_ENV, "http://collector.example:4318"), + (bm.TOKEN_URL_ENV, "http://auth.example/oauth2/token"), + ), +) +def test_http_urls_disable_metrics( + monkeypatch: pytest.MonkeyPatch, + caplog: pytest.LogCaptureFixture, + env_name: str, + url: str, +) -> None: + _set_full_env(monkeypatch) + monkeypatch.setenv(env_name, url) + + assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None + assert f"{env_name} must use HTTPS" in caplog.text + + +def test_valid_config_contains_bearer_credentials(monkeypatch: pytest.MonkeyPatch) -> None: + _set_full_env(monkeypatch) + + license_data: Final[EnterpriseLicenseData] = {"user_id": "org-1"} + config: Final = bm.load_billing_metrics_config(license_data=license_data, litellm_version="1.0") + assert config is not None + + assert config == bm.BillingMetricsConfig( + endpoint="https://collector.example:4318", + token_url="https://auth.example/oauth2/token", + client_id="client-id", + client_secret="client-secret", + export_interval_ms=bm.DEFAULT_EXPORT_INTERVAL_MS, + litellm_version="1.0", + license_id="org-1", + ) + assert "client-secret" not in repr(config) + assert "license-secret" not in repr(config) + + +def test_not_premium_returns_none_without_warning( + monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture +) -> None: + _set_full_env(monkeypatch) + assert bm.build_billing_metrics_recorder(premium=False, license_data=None, litellm_version="1.0") is None + assert "Enterprise billing metrics disabled" not in caplog.text -def test_premium_without_config_returns_none(monkeypatch): +def test_premium_without_config_returns_none(monkeypatch: pytest.MonkeyPatch) -> None: assert bm.build_billing_metrics_recorder(premium=True, license_data={"user_id": "x"}, litellm_version="1.0") is None -def test_premium_with_missing_cert_files_returns_none(monkeypatch, tmp_path): - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CA_CERT_ENV, str(tmp_path / "missing-ca.pem")) - monkeypatch.setenv(bm.CLIENT_CERT_ENV, str(tmp_path / "missing-cert.pem")) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, str(tmp_path / "missing-key.pem")) - assert bm.build_billing_metrics_recorder(premium=True, license_data=None, litellm_version="1.0") is None +def test_premium_builds_recorder_and_logs_enabled( + monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture +) -> None: + _set_full_env(monkeypatch) + caplog.set_level("INFO", logger=bm.verbose_proxy_logger.name) + token_url: Final = "https://auth.example/oauth2/token" + export_url: Final = "https://collector.example:4318/v1/metrics" + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post(token_url, json={"access_token": "access-token", "expires_in": 3600}) + http_mock.post(export_url, status_code=200) + recorder: Final = bm.build_billing_metrics_recorder( + premium=True, license_data={"user_id": "org-1"}, litellm_version="1.0" + ) + assert recorder is not None + assert "Enterprise billing metrics enabled: exporting to https://collector.example:4318 every 60000 ms" in caplog.text + bm.shutdown_billing_metrics_recorder() -def test_premium_with_full_config_builds_recorder(monkeypatch, tmp_path): - """Builds a real MeterProvider, so the exporter is stubbed: the live one - resolves the collector and opens a TLS connection during the shutdown flush. - The getaddrinfo spy keeps that stub from being quietly dropped later.""" - _set_full_env(monkeypatch, tmp_path) - monkeypatch.setattr(bm, "OTLPMetricExporter", _fake_exporter_class({})) +class _ShutdownCountingMeterProvider(MeterProvider): + def __init__(self) -> None: + super().__init__() + self.shutdown_calls = 0 - resolved: List[str] = [] - real_getaddrinfo = socket.getaddrinfo + def shutdown(self, timeout_millis: float = 30_000) -> None: + self.shutdown_calls += 1 + super().shutdown(timeout_millis=timeout_millis) - def _spy_getaddrinfo(host, port, *args, **kwargs): - resolved.append(str(host)) - return real_getaddrinfo(host, port, *args, **kwargs) - monkeypatch.setattr(socket, "getaddrinfo", _spy_getaddrinfo) +def test_shutdown_flushes_active_recorder_once(monkeypatch: pytest.MonkeyPatch) -> None: + provider: Final = _ShutdownCountingMeterProvider() + _set_full_env(monkeypatch) - recorder = bm.build_billing_metrics_recorder( - premium=True, license_data={"user_id": "org-1"}, litellm_version="1.0" + def create_provider(config: bm.BillingMetricsConfig) -> MeterProvider: + return provider + + monkeypatch.setattr(bm, "build_bearer_meter_provider", create_provider) + recorder: Final = bm.build_billing_metrics_recorder(premium=True, license_data=None, litellm_version="1.0") + assert recorder is not None + + bm.shutdown_billing_metrics_recorder() + bm.shutdown_billing_metrics_recorder() + + assert provider.shutdown_calls == 1 + + +class _FakeClock: + def __init__(self, now: float) -> None: + self._now = now + + def __call__(self) -> float: + return self._now + + def set(self, now: float) -> None: + self._now = now + + +def test_bearer_auth_fetches_and_caches_client_credentials_token() -> None: + token_url: Final = "https://auth.example/oauth2/token" + export_url: Final = "https://collector.example/v1/metrics" + auth: Final = bm.BillingMetricsBearerAuth( + client_id="client-id", + client_secret="client-secret", + token_url=token_url, + clock=lambda: 100.0, ) - assert isinstance(recorder, bm.BillingMetricsRecorder) - recorder.record(category=BillableCategory.LLM, route="/chat/completions", status_code=200, model_id=None) - bm.shutdown_billing_metrics_recorder() + session: Final = requests.Session() + session.auth = auth + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post(token_url, json={"access_token": "access-token", "expires_in": 3600}) + http_mock.post(export_url, status_code=200) + session.post(export_url, data=b"metrics") + session.post(export_url, data=b"metrics") - assert [host for host in resolved if "collector.example" in host] == [] + request_records: Final = _request_history(http_mock) + token_requests: Final = tuple(request for request in request_records if request.url == token_url) + export_requests: Final = tuple(request for request in request_records if request.url == export_url) + assert len(token_requests) == 1 + assert token_requests[0].headers["Authorization"].startswith("Basic ") + assert token_requests[0].body == "grant_type=client_credentials&scope=telemetry%2Fwrite" + assert tuple(request.headers["Authorization"] for request in export_requests) == ( + "Bearer access-token", + "Bearer access-token", + ) -def test_building_the_recorder_logs_an_affirmative_line(monkeypatch, tmp_path): - """ - Every disable path logs; a successful build must log too. Otherwise an - operator cannot tell a metering component from one that silently returned - None, which is how an unlicensed component looks healthy while exporting - nothing. - """ - _set_full_env(monkeypatch, tmp_path) +def test_bearer_auth_refreshes_before_token_expiry() -> None: + token_url: Final = "https://auth.example/oauth2/token" + export_url: Final = "https://collector.example/v1/metrics" + clock: Final = _FakeClock(1000.0) + auth: Final = bm.BillingMetricsBearerAuth( + client_id="client-id", + client_secret="client-secret", + token_url=token_url, + clock=clock, + ) + session: Final = requests.Session() + session.auth = auth + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post( + token_url, + [ + {"json": {"access_token": "first-token", "expires_in": 120}}, + {"json": {"access_token": "second-token", "expires_in": 120}}, + ], + ) + http_mock.post(export_url, status_code=200) + session.post(export_url, data=b"metrics") + clock.set(1059.0) + session.post(export_url, data=b"metrics") + clock.set(1060.0) + session.post(export_url, data=b"metrics") + + request_records: Final = _request_history(http_mock) + token_requests: Final = tuple(request for request in request_records if request.url == token_url) + export_requests: Final = tuple(request for request in request_records if request.url == export_url) + assert len(token_requests) == 2 + assert tuple(request.headers["Authorization"] for request in export_requests) == ( + "Bearer first-token", + "Bearer first-token", + "Bearer second-token", + ) + + +def test_bearer_export_sets_authorization_header() -> None: + config: Final = _config() + token_url: Final = config.token_url + export_url: Final = f"{config.endpoint}/v1/metrics" + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post(token_url, json={"access_token": "export-token", "expires_in": 3600}) + http_mock.post(export_url, status_code=200) + provider: Final = bm.build_bearer_meter_provider(config) + try: + provider.get_meter("test").create_counter("requests").add(1) + assert provider.force_flush(timeout_millis=1000) + finally: + provider.shutdown(timeout_millis=1000) + + export_requests: Final = tuple(request for request in _request_history(http_mock) if request.url == export_url) + assert export_requests + assert all(request.headers["Authorization"] == "Bearer export-token" for request in export_requests) + + +def test_unauthorized_export_refetches_token_before_next_export() -> None: + config: Final = _config() + token_url: Final = config.token_url + export_url: Final = f"{config.endpoint}/v1/metrics" + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post( + token_url, + [ + {"json": {"access_token": "first-token", "expires_in": 3600}}, + {"json": {"access_token": "second-token", "expires_in": 3600}}, + ], + ) + http_mock.post(export_url, [{"status_code": 401}, {"status_code": 200}]) + provider: Final = bm.build_bearer_meter_provider(config) + try: + counter: Final = provider.get_meter("test").create_counter("requests") + counter.add(1) + provider.force_flush(timeout_millis=1000) + counter.add(1) + provider.force_flush(timeout_millis=1000) + finally: + provider.shutdown(timeout_millis=1000) + + requests: Final = _request_history(http_mock) + token_requests: Final = tuple(request for request in requests if request.url == token_url) + export_requests: Final = tuple(request for request in requests if request.url == export_url) + assert len(token_requests) == 2 + assert tuple(request.headers["Authorization"] for request in export_requests[:2]) == ( + "Bearer first-token", + "Bearer second-token", + ) + + +def test_failed_bearer_token_fetch_does_not_raise_from_export() -> None: + config: Final = _config() + token_url: Final = config.token_url + export_url: Final = f"{config.endpoint}/v1/metrics" + http_mock: Final = requests_mock.Mocker() + with http_mock: + http_mock.post(token_url, status_code=400) + provider: Final = bm.build_bearer_meter_provider(config) + try: + provider.get_meter("test").create_counter("requests").add(1) + assert provider.force_flush(timeout_millis=1000) + finally: + provider.shutdown(timeout_millis=1000) + + request_urls: Final = tuple(request.url for request in _request_history(http_mock)) + assert token_url in request_urls + assert export_url not in request_urls + + +def test_export_interval_default_and_override(monkeypatch: pytest.MonkeyPatch) -> None: + _set_full_env(monkeypatch) + default_config: Final = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") + assert default_config is not None + assert default_config.export_interval_ms == bm.DEFAULT_EXPORT_INTERVAL_MS monkeypatch.setenv(bm.EXPORT_INTERVAL_ENV, "5000") - monkeypatch.setattr(bm, "OTLPMetricExporter", _fake_exporter_class({})) - - infos: List[str] = [] - monkeypatch.setattr(bm.verbose_proxy_logger, "info", lambda msg, *args: infos.append(msg % args if args else msg)) - - recorder = bm.build_billing_metrics_recorder(premium=True, license_data={"user_id": "org-1"}, litellm_version="1.0") - - assert recorder is not None - joined = "\n".join(infos) - assert "https://collector.example:4317" in joined - assert "5000" in joined - - -def test_unlicensed_build_does_not_warn(monkeypatch, tmp_path): - """Unlicensed is the common OSS case; warning there would be pure noise.""" - _set_full_env(monkeypatch, tmp_path) - - warnings: List[str] = [] - monkeypatch.setattr(bm.verbose_proxy_logger, "warning", lambda msg, *args: warnings.append(str(msg))) - - assert bm.build_billing_metrics_recorder(premium=False, license_data=None, litellm_version="1.0") is None - assert warnings == [] - - -def test_shutdown_flushes_active_recorder_once(monkeypatch, tmp_path): - """The shutdown hook must flush the recorder the factory built (buffered - counts are lost on restart otherwise) and be idempotent for repeat calls.""" - _set_full_env(monkeypatch, tmp_path) - shutdowns = [] - - class _SpyProvider: - def get_meter(self, name): - return MeterProvider().get_meter(name) - - def shutdown(self, timeout_millis=None): - shutdowns.append(timeout_millis) - - monkeypatch.setattr(bm, "build_mtls_meter_provider", lambda config: _SpyProvider()) - recorder = bm.build_billing_metrics_recorder(premium=True, license_data=None, litellm_version="1.0") - assert recorder is not None - - bm.shutdown_billing_metrics_recorder() - bm.shutdown_billing_metrics_recorder() - assert shutdowns == [bm.SHUTDOWN_FLUSH_TIMEOUT_MS] - - -def test_shutdown_without_active_recorder_is_noop(): - bm.shutdown_billing_metrics_recorder() - - -# ── Config loading ──────────────────────────────────────────────────────────── - - -def test_load_config_carries_license_id(monkeypatch, tmp_path): - _set_full_env(monkeypatch, tmp_path) - config = bm.load_billing_metrics_config(license_data={"user_id": "org-42"}, litellm_version="9.9") - assert config is not None and config.license_id == "org-42" and config.litellm_version == "9.9" - - -def test_load_config_with_empty_string_env_is_disabled(monkeypatch, tmp_path): - """An env var set to the empty string is as unusable as an unset one and - must disable metering rather than produce a config with a blank endpoint.""" - paths = _write_certs(tmp_path) - monkeypatch.setenv(bm.ENDPOINT_ENV, "") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, paths[bm.CLIENT_CERT_ENV]) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, paths[bm.CLIENT_KEY_ENV]) - assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None - - -_CLIENT_CERT_PEM = "-----BEGIN CERTIFICATE-----\nclient-cert-body\n-----END CERTIFICATE-----" -_CLIENT_KEY_PEM = "-----BEGIN PRIVATE KEY-----\nclient-key-body\n-----END PRIVATE KEY-----" -_CA_CERT_PEM = "-----BEGIN CERTIFICATE-----\nca-body\n-----END CERTIFICATE-----" - - -def test_load_config_materializes_inline_pem_content(monkeypatch): - """ - ECS and Cloud Run inject secrets as env content, not as mounted files, so the - cert env vars must accept PEM directly. The exporter takes paths, so the PEM - is written to disk and the config points at those files. - """ - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, _CLIENT_CERT_PEM) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, _CLIENT_KEY_PEM) - monkeypatch.setenv(bm.CA_CERT_ENV, _CA_CERT_PEM) - - config = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") - - assert config is not None - assert config.ca_cert_path is not None - written = { - config.client_cert_path: _CLIENT_CERT_PEM, - config.client_key_path: _CLIENT_KEY_PEM, - config.ca_cert_path: _CA_CERT_PEM, - } - for path, pem in written.items(): - assert path != pem, "config must carry a file path, not the PEM itself" - assert os.path.isfile(path) - assert Path(path).read_text(encoding="utf-8") == f"{pem}\n" - - # The private key must not be world- or group-readable. - assert stat.S_IMODE(os.stat(config.client_key_path).st_mode) == 0o600 - - -def test_load_config_accepts_a_mix_of_pem_content_and_file_paths(monkeypatch, tmp_path): - """A deployment may mount the CA but inject the client credentials inline.""" - paths = _write_certs(tmp_path) - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, _CLIENT_CERT_PEM) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, _CLIENT_KEY_PEM) - monkeypatch.setenv(bm.CA_CERT_ENV, paths[bm.CA_CERT_ENV]) - - config = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") - - assert config is not None - assert config.ca_cert_path == paths[bm.CA_CERT_ENV] - assert Path(config.client_cert_path).read_text(encoding="utf-8") == f"{_CLIENT_CERT_PEM}\n" - - -def test_load_config_leaves_file_paths_untouched(monkeypatch, tmp_path): - """Path-valued env vars keep working; nothing is copied or rewritten.""" - paths = _set_full_env(monkeypatch, tmp_path) - - config = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") - - assert config is not None - assert config.client_cert_path == paths[bm.CLIENT_CERT_ENV] - assert config.client_key_path == paths[bm.CLIENT_KEY_ENV] - assert config.ca_cert_path == paths[bm.CA_CERT_ENV] - - -def test_load_config_with_inline_pem_disabled_when_unwritable(monkeypatch): - """A failure to materialize the PEM disables metering instead of raising.""" - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, _CLIENT_CERT_PEM) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, _CLIENT_KEY_PEM) - - def _explode(prefix=None): - raise OSError("read-only filesystem") - - monkeypatch.setattr(bm.tempfile, "mkdtemp", _explode) - - assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None - - -def test_load_config_never_logs_credential_values(monkeypatch): - """ - A value that is neither a readable path nor `-----BEGIN`-prefixed PEM is - still secret material. The disable warning must name the env vars, never - echo their contents, or a malformed key lands in the proxy logs. - """ - secret_material = "MIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQ-not-pem-prefixed" - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, secret_material) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, secret_material) - - logged: List[str] = [] - - def _capture(msg, *args): - logged.append(msg % args if args else msg) - - monkeypatch.setattr(bm.verbose_proxy_logger, "warning", _capture) - - assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None - - joined = "\n".join(logged) - assert secret_material not in joined - assert bm.CLIENT_CERT_ENV in joined and bm.CLIENT_KEY_ENV in joined - - -def test_load_config_with_empty_pem_env_is_disabled(monkeypatch): - """Empty stays empty: an unset secret must not be mistaken for inline PEM.""" - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://collector.example:4317") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, "") - monkeypatch.setenv(bm.CLIENT_KEY_ENV, "") - - assert bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") is None - - -def test_export_interval_default_and_override(monkeypatch): - assert bm._export_interval_ms() == bm.DEFAULT_EXPORT_INTERVAL_MS - monkeypatch.setenv(bm.EXPORT_INTERVAL_ENV, "5000") - assert bm._export_interval_ms() == 5000 + override_config: Final = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") + assert override_config is not None + assert override_config.export_interval_ms == 5000 monkeypatch.setenv(bm.EXPORT_INTERVAL_ENV, "not-a-number") - assert bm._export_interval_ms() == bm.DEFAULT_EXPORT_INTERVAL_MS + invalid_config: Final = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") + assert invalid_config is not None + assert invalid_config.export_interval_ms == bm.DEFAULT_EXPORT_INTERVAL_MS -# ── OTLP/HTTP exporter wiring ───────────────────────────────────────────────── +def test_resource_attributes_include_license_id() -> None: + attributes: Final = bm.resource_attributes(_config(license_id="org-7")) + assert attributes == { + "service.name": "litellm-proxy", + "litellm.version": "1.2.3", + "litellm.license.id": "org-7", + } -def test_metrics_endpoint_appends_signal_path(): - assert bm._metrics_endpoint("https://telemetry.example.com") == "https://telemetry.example.com/v1/metrics" - assert bm._metrics_endpoint("https://telemetry.example.com/") == "https://telemetry.example.com/v1/metrics" - assert bm._metrics_endpoint("https://telemetry.example.com/v1/metrics") == "https://telemetry.example.com/v1/metrics" +def test_resource_attributes_omit_license_id_when_absent() -> None: + assert "litellm.license.id" not in bm.resource_attributes(_config(license_id=None)) -def _fake_exporter_class(captured: Dict[str, object]) -> type: - """A no-network stand-in for OTLPMetricExporter. Tests that build a real - MeterProvider must install this: the real exporter resolves the collector - host and opens a TLS connection on the reader's first export and on the - shutdown flush.""" - - class _FakeExporter: - # PeriodicExportingMetricReader probes these on the exporter it wraps. - _preferred_temporality: dict = {} - _preferred_aggregation: dict = {} - - def __init__(self, **kwargs): - captured.update(kwargs) - - def export(self, *args, **kwargs): - return None - - def shutdown(self, *args, **kwargs): - return None - - def force_flush(self, *args, **kwargs): - return True - - return _FakeExporter - - -def test_meter_provider_wires_client_cert_into_http_exporter(tmp_path, monkeypatch): - """Client cert+key authenticate us at the collector's mTLS front end; CA override rides certificate_file.""" - captured: Dict[str, object] = {} - - monkeypatch.setattr(bm, "OTLPMetricExporter", _fake_exporter_class(captured)) - config = _config(tmp_path) - provider = bm.build_mtls_meter_provider(config) - provider.shutdown() - - assert captured["endpoint"] == "https://collector.example:4317/v1/metrics" - assert captured["client_certificate_file"] == config.client_cert_path - assert captured["client_key_file"] == config.client_key_path - assert captured["certificate_file"] == config.ca_cert_path - - -def test_load_config_without_ca_is_valid(monkeypatch, tmp_path): - """The production collector presents a public web-PKI cert: no CA override required.""" - paths = _write_certs(tmp_path) - monkeypatch.setenv(bm.ENDPOINT_ENV, "https://telemetry.example.com") - monkeypatch.setenv(bm.CLIENT_CERT_ENV, paths[bm.CLIENT_CERT_ENV]) - monkeypatch.setenv(bm.CLIENT_KEY_ENV, paths[bm.CLIENT_KEY_ENV]) - config = bm.load_billing_metrics_config(license_data=None, litellm_version="1.0") - assert config is not None and config.ca_cert_path is None - - -# ── Resource and metric attributes ──────────────────────────────────────────── - - -def test_resource_attributes_include_license_id(tmp_path): - attrs = bm._resource_attributes(_config(tmp_path, license_id="org-7")) - assert attrs["service.name"] == "litellm-proxy" - assert attrs["litellm.version"] == "1.2.3" - assert attrs["litellm.license.id"] == "org-7" - - -def test_resource_attributes_omit_license_id_when_absent(tmp_path): - attrs = bm._resource_attributes(_config(tmp_path, license_id=None)) - assert "litellm.license.id" not in attrs - - -def test_billable_attributes_with_model_id(): - attrs = bm._billable_attributes(BillableCategory.LLM, "/chat/completions", 200, "deploy-3") - assert attrs == { +def test_billable_attributes_include_model_id() -> None: + assert bm.billable_attributes( + BillableCategory.LLM, "/chat/completions", 200, "deploy-3" + ) == { "litellm.endpoint.category": "llm", "http.route": "/chat/completions", "http.response.status_code": 200, @@ -400,33 +381,35 @@ def test_billable_attributes_with_model_id(): } -def test_billable_attributes_omit_model_id_when_none(): - attrs = bm._billable_attributes(BillableCategory.MCP, "/mcp", 200, None) - assert "litellm.model_id" not in attrs +def test_billable_attributes_omit_model_id_when_none() -> None: + attributes: Final = bm.billable_attributes( + BillableCategory.MCP, "/mcp", 200, None + ) + assert "litellm.model_id" not in attributes -# ── End-to-end recording via in-memory reader ───────────────────────────────── +def _counter_points(reader: InMemoryMetricReader) -> tuple[tuple[str, int], ...]: + data: Final = reader.get_metrics_data() + if data is None: + return () + return tuple( + (_metric_category(point.attributes), int(point.value)) + for resource_metric in data.resource_metrics + for scope_metric in resource_metric.scope_metrics + for metric in scope_metric.metrics + if metric.name == bm.METRIC_NAME and isinstance(metric.data, Sum) + for point in metric.data.data_points + ) -def _counter_points(reader: InMemoryMetricReader): - data = reader.get_metrics_data() - for resource_metric in data.resource_metrics: - for scope_metric in resource_metric.scope_metrics: - for metric in scope_metric.metrics: - if metric.name == bm.METRIC_NAME: - return list(metric.data.data_points) - return [] - - -def test_record_increments_counter_with_attributes(): - reader = InMemoryMetricReader() - recorder = bm.BillingMetricsRecorder(MeterProvider(metric_readers=[reader])) +def test_record_increments_counter_with_attributes() -> None: + reader: Final = InMemoryMetricReader() + recorder: Final = bm.BillingMetricsRecorder(MeterProvider(metric_readers=[reader])) recorder.record(category=BillableCategory.LLM, route="/chat/completions", status_code=200, model_id="m1") recorder.record(category=BillableCategory.LLM, route="/chat/completions", status_code=200, model_id="m1") recorder.record(category=BillableCategory.MCP, route="/mcp", status_code=200, model_id=None) - points = _counter_points(reader) - by_category = {point.attributes["litellm.endpoint.category"]: point.value for point in points} - assert by_category["llm"] == 2 - assert by_category["mcp"] == 1 + points: Final = _counter_points(reader) + by_category: Final = dict(points) + assert by_category == {"llm": 2, "mcp": 1} diff --git a/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py b/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py index 91f00edfb39..46e766f753c 100644 --- a/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py +++ b/tests/unit/proxy/guardrails/guardrail_hooks/test_tool_permission.py @@ -2,11 +2,12 @@ Unit tests for Tool Permission Guardrail (OpenAI tool_calls semantics) """ +import copy import json import logging import re from itertools import chain -from typing import Final +from typing import Final, Literal from unittest.mock import patch import pytest @@ -26,6 +27,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import ( PermissionError, ) from litellm.types.utils import ( + CallTypesLiteral, ChatCompletionCustomToolCallPayload, ChatCompletionDeltaToolCall, ChatCompletionMessageCustomToolCall, @@ -467,6 +469,346 @@ class TestToolPermissionGuardrail: ] assert kept == [[], ["Read"]] + @pytest.mark.asyncio + @pytest.mark.parametrize("stream", [False, True], ids=["non_streaming", "streaming"]) + async def test_post_call_judges_a_returned_namespace_member_by_its_qualified_name(self, stream: bool) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-mcp", + rules=[{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]} + data = {"guardrails": ["post-mcp"], "tools": [namespace]} + function = Function(name="lookup", arguments='{"city": "Paris"}') + + if stream: + + async def _stream(): + yield ModelResponseStream( + choices=[ + StreamingChoices( + index=0, + delta=Delta( + tool_calls=[ + ChatCompletionDeltaToolCall( + id="call_lookup", index=0, type="function", function=function + ) + ] + ), + ) + ] + ) + + chunks = [ + chunk + async for chunk in guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=UserAPIKeyAuth(), response=_stream(), request_data=data + ) + ] + deltas = [choice.delta for choice in chain.from_iterable(chunk.choices for chunk in chunks)] + called = [call.function.name for call in chain.from_iterable(delta.tool_calls or () for delta in deltas)] + content = "".join(delta.content or "" for delta in deltas) + else: + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_lookup", "type": "function", "function": function}]}, + ) + ] + ) + await guardrail.async_post_call_success_hook( + data=data, user_api_key_dict=UserAPIKeyAuth(), response=response + ) + message = response.choices[0].message + called = [call.function.name for call in message.tool_calls or ()] + content = message.content or "" + + assert called == [] + assert "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'" in content + + @pytest.mark.asyncio + async def test_post_call_judges_a_returned_namespace_member_by_the_rule_its_arguments_satisfy(self) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-exec", + rules=[ + { + "id": "allow_ls", + "tool_name": r"^exec$", + "decision": "allow", + "allowed_param_patterns": {"command": r"^ls$"}, + }, + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + {"id": "allow_ops", "tool_name": r"^ops__.*$", "decision": "allow"}, + ], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "ops", "tools": [{"type": "function", "name": "exec"}]} + data = {"guardrails": ["post-exec"], "tools": [namespace]} + function = Function(name="exec", arguments='{"command": "rm -rf /data"}') + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_exec", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook(data=data, user_api_key_dict=UserAPIKeyAuth(), response=response) + + message = response.choices[0].message + assert [call.function.name for call in message.tool_calls or ()] == [] + assert "Tool 'exec'" in (message.content or "") + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rules", "command", "kept"), + [ + ( + [ + { + "id": "allow_ls", + "tool_name": r"^exec$", + "decision": "allow", + "allowed_param_patterns": {"command": r"^ls$"}, + }, + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + ], + "ls", + ["run"], + ), + ( + [ + { + "id": "allow_ls", + "tool_name": r"^exec$", + "decision": "allow", + "allowed_param_patterns": {"command": r"^ls$"}, + }, + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + ], + "pwd", + [], + ), + ( + [ + {"id": "allow_run", "tool_name": r"^run$", "decision": "allow"}, + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + ], + "ls", + [], + ), + ], + ids=["namespace_rule_allows", "namespace_rule_denies", "namespace_deny_beats_member_allow"], + ) + async def test_post_call_judges_a_returned_namespace_member_by_a_rule_on_its_namespace_name( + self, rules: list[dict[str, object]], command: str, kept: list[str] + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-exec", + rules=rules, + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "exec", "tools": [{"type": "function", "name": "run"}]} + function = Function(name="run", arguments=json.dumps({"command": command})) + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_run", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-exec"], "tools": [namespace]}, + user_api_key_dict=UserAPIKeyAuth(), + response=response, + ) + + message = response.choices[0].message + assert [call.function.name for call in message.tool_calls or ()] == kept + assert ("Tool 'exec'" in (message.content or "")) == (not kept) + + @pytest.mark.asyncio + @pytest.mark.parametrize("namespace_first", [True, False], ids=["namespace_first", "top_level_first"]) + async def test_post_call_judges_a_returned_name_a_top_level_function_carries_as_that_function( + self, namespace_first: bool + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-mcp", + rules=[{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]} + top_level = {"type": "function", "function": {"name": "lookup"}} + tools = [namespace, top_level] if namespace_first else [top_level, namespace] + function = Function(name="lookup", arguments='{"country": "France"}') + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_lookup", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-mcp"], "tools": tools}, user_api_key_dict=UserAPIKeyAuth(), response=response + ) + + assert [call.function.name for call in response.choices[0].message.tool_calls or ()] == ["lookup"] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "same_named_tool", + [ + {"type": "mcp", "server_label": "lookup", "server_url": "https://mcp.example.com/sse"}, + {"type": "mcp_toolset", "mcp_server_name": "lookup"}, + {"ignored": {"name": "lookup"}}, + {"type": "custom", "custom": {"name": "lookup"}}, + {"functionDeclarations": [{"name": "lookup"}]}, + ], + ids=["mcp", "mcp_toolset", "unknown_keyed_block", "chat_custom", "native_declaration"], + ) + async def test_post_call_judges_a_returned_namespace_member_as_that_member_beside_a_same_named_non_function( + self, same_named_tool: dict[str, object] + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-mcp", + rules=[{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]} + function = Function(name="lookup", arguments='{"country": "France"}') + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_lookup", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-mcp"], "tools": [namespace, same_named_tool]}, + user_api_key_dict=UserAPIKeyAuth(), + response=response, + ) + + assert [call.function.name for call in response.choices[0].message.tool_calls or ()] == [] + + @pytest.mark.asyncio + async def test_post_call_judges_a_call_tagged_with_a_namespace_as_that_member_beside_a_same_named_function( + self, + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-mcp", + rules=[{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]} + top_level = {"type": "function", "function": {"name": "lookup"}} + tagged_call = { + "id": "call_lookup", + "type": "function", + "function": Function(name="lookup", arguments='{"country": "France"}'), + "provider_specific_fields": {"namespace": "mcp__weather"}, + } + response = ModelResponse( + choices=[Choices(finish_reason="tool_calls", message={"tool_calls": [tagged_call]})] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-mcp"], "tools": [namespace, top_level]}, + user_api_key_dict=UserAPIKeyAuth(), + response=response, + ) + + message = response.choices[0].message + assert [call.function.name for call in message.tool_calls or ()] == [] + assert "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'" in (message.content or "") + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("decision", "default_action", "kept"), + [("allow", "deny", ["weather__get_weather"]), ("deny", "allow", [])], + ids=["bare_name_allowed", "bare_name_denied"], + ) + async def test_post_call_judges_a_returned_qualified_member_name_by_the_member_names_pre_call_judged( + self, decision: Literal["allow", "deny"], default_action: Literal["allow", "deny"], kept: list[str] + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-weather", + rules=[{"id": "get_weather", "tool_name": r"^get_weather$", "decision": decision}], + default_action=default_action, + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "weather", "tools": [{"type": "function", "name": "get_weather"}]} + top_level = {"type": "function", "function": {"name": "get_weather"}} + function = Function(name="weather__get_weather", arguments='{"city": "Istanbul"}') + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_weather", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-weather"], "tools": [namespace, top_level]}, + user_api_key_dict=UserAPIKeyAuth(), + response=response, + ) + + assert [call.function.name for call in response.choices[0].message.tool_calls or ()] == kept + + @pytest.mark.asyncio + async def test_post_call_judges_a_top_level_function_by_its_own_name_beside_a_member_qualified_the_same( + self, + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name="post-exec", + rules=[{"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.post_call, + ) + namespace = {"type": "namespace", "name": "ops", "tools": [{"type": "function", "name": "exec"}]} + top_level = {"type": "function", "function": {"name": "ops__exec"}} + function = Function(name="ops__exec", arguments="{}") + response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + message={"tool_calls": [{"id": "call_ops", "type": "function", "function": function}]}, + ) + ] + ) + + await guardrail.async_post_call_success_hook( + data={"guardrails": ["post-exec"], "tools": [namespace, top_level]}, + user_api_key_dict=UserAPIKeyAuth(), + response=response, + ) + + assert [call.function.name for call in response.choices[0].message.tool_calls or ()] == ["ops__exec"] + @pytest.mark.asyncio async def test_async_post_call_success_hook_missing_arguments_blocks_param_rule( self, @@ -1361,6 +1703,1723 @@ class TestToolPermissionGuardrailAnthropicMessages: def _tool_use(self, name, tool_id="tu_1"): return {"type": "tool_use", "id": tool_id, "name": name, "input": {"command": "ls"}} + def _always_on_pre_call(self, on_disallowed_action: Literal["block", "rewrite"]) -> ToolPermissionGuardrail: + return ToolPermissionGuardrail( + guardrail_name=f"anthropic-pre-call-{on_disallowed_action}", + rules=self.rules, + default_action="deny", + on_disallowed_action=on_disallowed_action, + event_hook=GuardrailEventHooks.pre_call, + default_on=True, + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "denied_tool", + [ + {"name": "Read", "input_schema": {"type": "object", "properties": {}}}, + {"type": "custom", "name": "Read", "input_schema": {"type": "object", "properties": {}}}, + {"type": "function", "name": "Read", "parameters": {"type": "object", "properties": {}}}, + ], + ids=["anthropic", "anthropic_custom_type", "responses_api_flat_function"], + ) + async def test_pre_call_blocks_denied_request_tool_in_flat_format(self, denied_tool: dict[str, object]) -> None: + data = {"model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "hi"}], "tools": [denied_tool]} + + with pytest.raises(HTTPException) as excinfo: + await self._always_on_pre_call("block").async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(), + cache=DualCache(default_in_memory_ttl=1), + data=data, + call_type="anthropic_messages", + ) + + assert excinfo.value.status_code == 400 + assert excinfo.value.detail["detection_message"] == "Tool 'Read' denied by rule 'deny_read'" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("tool_fields", "rule_tool_type", "call_type"), + [ + ({"format": {"type": "text"}}, r"^custom$", "responses"), + ({"input_schema": {"type": "object", "properties": {}}}, r"^function$", "anthropic_messages"), + ({"input_schema": {"type": "object", "properties": {}}}, r"^function$", "pass_through_endpoint"), + ({"custom": {"name": "apply_patch"}, "input_schema": {"type": "object"}}, r"^custom$", "acompletion"), + ], + ids=[ + "responses_custom_stays_custom", + "anthropic_custom_reads_as_function", + "anthropic_custom_reads_as_function_on_a_pass_through_route", + "chat_custom_with_a_stray_input_schema_stays_custom", + ], + ) + async def test_pre_call_tool_type_rules_follow_the_request_format( + self, tool_fields: dict[str, object], rule_tool_type: str, call_type: CallTypesLiteral + ) -> None: + guardrail = ToolPermissionGuardrail( + guardrail_name=f"tool-type-{call_type}", + rules=[{"id": "deny_type", "tool_type": rule_tool_type, "decision": "deny"}], + default_action="allow", + on_disallowed_action="block", + event_hook=GuardrailEventHooks.pre_call, + default_on=True, + ) + data = { + "model": "claude-sonnet-4-5", + "messages": [{"role": "user", "content": "hi"}], + "tools": [{"type": "custom", "name": "apply_patch", **tool_fields}], + } + + with pytest.raises(HTTPException) as excinfo: + await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(), + cache=DualCache(default_in_memory_ttl=1), + data=data, + call_type=call_type, + ) + + assert excinfo.value.status_code == 400 + assert excinfo.value.detail["detection_message"] == "Tool 'apply_patch' denied by rule 'deny_type'" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("tool_shape", "tool_choice", "call_type", "expected_tool_choice"), + [ + ( + {"input_schema": {"type": "object", "properties": {}}}, + {"type": "tool", "name": "Read"}, + "anthropic_messages", + {"type": "none"}, + ), + ( + {"type": "function", "parameters": {"type": "object", "properties": {}}}, + {"type": "function", "name": "Read"}, + "responses", + "none", + ), + ], + ids=["anthropic", "responses_api"], + ) + async def test_pre_call_rewrite_strips_denied_flat_tool_and_forced_choice( + self, + tool_shape: dict[str, object], + tool_choice: dict[str, str], + call_type: CallTypesLiteral, + expected_tool_choice: dict[str, str] | str, + ) -> None: + data = { + "model": "claude-sonnet-4-5", + "messages": [{"role": "user", "content": "hi"}], + "tools": [{"name": "Bash", **tool_shape}, {"name": "Read", **tool_shape}], + "tool_choice": tool_choice, + } + + result = await self._always_on_pre_call("rewrite").async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(), + cache=DualCache(default_in_memory_ttl=1), + data=data, + call_type=call_type, + ) + + assert [tool["name"] for tool in result["tools"]] == ["Bash"] + assert result["tool_choice"] == expected_tool_choice + + def _pre_call( + self, + rules: list[dict[str, str]], + on_disallowed_action: Literal["block", "rewrite"], + default_action: Literal["allow", "deny"] = "allow", + ) -> ToolPermissionGuardrail: + return ToolPermissionGuardrail( + guardrail_name=f"request-shapes-{on_disallowed_action}", + rules=rules, + default_action=default_action, + on_disallowed_action=on_disallowed_action, + event_hook=GuardrailEventHooks.pre_call, + default_on=True, + ) + + async def _run_pre_call( + self, guardrail: ToolPermissionGuardrail, data: dict[str, object], call_type: CallTypesLiteral + ) -> dict[str, object]: + return await guardrail.async_pre_call_hook( + user_api_key_dict=UserAPIKeyAuth(), + cache=DualCache(default_in_memory_ttl=1), + data=data, + call_type=call_type, + ) + + def _namespace(self, *member_names: str) -> dict[str, object]: + return { + "type": "namespace", + "name": "mcp__everything", + "tools": [{"type": "function", "name": name, "parameters": {"type": "object"}} for name in member_names], + } + + @pytest.mark.asyncio + async def test_pre_call_blocks_a_denied_tool_inside_a_responses_namespace(self) -> None: + guardrail = self._pre_call( + [{"id": "deny_sum", "tool_name": r"^mcp__everything__get_sum$", "decision": "deny"}], "block" + ) + data = {"model": "gpt-5", "input": "add 2 and 3", "tools": [self._namespace("echo", "get_sum")]} + + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "responses") + + assert excinfo.value.detail["detection_message"] == "Tool 'mcp__everything__get_sum' denied by rule 'deny_sum'" + + @pytest.mark.asyncio + async def test_pre_call_rewrite_drops_denied_namespace_members_and_empty_namespaces(self) -> None: + guardrail = self._pre_call([{"id": "deny_sum", "tool_name": r"^.*get_sum$", "decision": "deny"}], "rewrite") + partly_denied = {**self._namespace("echo", "get_sum"), "name": "mcp__kept"} + data = {"model": "gpt-5", "input": "add", "tools": [partly_denied, self._namespace("get_sum")]} + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [{**partly_denied, "tools": partly_denied["tools"][:1]}] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("call_type", "malformed"), + [ + ( + "agenerate_content", + {"toolConfig": {"functionCallingConfig": {"mode": "ANY", "allowedFunctionNames": [{"a": 1}]}}}, + ), + ("responses", {"input": [{"type": {"x": 1}, "content": "hi"}]}), + ("acompletion", {"extra_body": {"input": [{"type": [1]}]}}), + ("acompletion", {"extra_body": {"function_call": {"name": 5}}}), + ], + ids=["unhashable_allowed_function_name", "unhashable_input_type", "extra_body_input_type", "int_function_call"], + ) + async def test_pre_call_rewrite_leaves_a_malformed_value_for_the_provider_to_reject( + self, call_type: CallTypesLiteral, malformed: dict[str, object] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], "rewrite", "deny" + ) + tools = [{"type": "function", "function": {"name": name}} for name in ("get_weather", "shell")] + + result = await self._run_pre_call(guardrail, {"model": "m", "tools": tools, **malformed}, call_type) + + assert result["tools"] == tools[:1] + assert {key: result[key] for key in malformed} == malformed + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rule", "tool", "call_type", "detection_message"), + [ + ( + {"id": "deny_mcp", "tool_type": r"^mcp$", "decision": "deny"}, + {"type": "mcp", "server_label": "weather", "server_url": "https://mcp.example.com"}, + "responses", + "Tool 'weather' denied by rule 'deny_mcp'", + ), + ( + {"id": "allow_bash", "tool_name": r"^Bash$", "decision": "allow"}, + {"type": "mcp_toolset", "mcp_server_name": "weather"}, + "anthropic_messages", + "Tool 'weather' denied by default action", + ), + ( + {"id": "deny_weather", "tool_name": r"^weather$", "decision": "deny"}, + {"type": "mcp", "server_label": "weather", "server_url": "https://mcp.example.com"}, + "responses", + "Tool 'weather' denied by rule 'deny_weather'", + ), + ( + {"id": "deny_lookup", "tool_name": r"^mcp__weather__lookup$", "decision": "deny"}, + {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "custom", "name": "lookup"}]}, + "responses", + "Tool 'mcp__weather__lookup' denied by rule 'deny_lookup'", + ), + ( + {"id": "deny_lookup", "tool_name": r"^mcp__weather__lookup$", "decision": "deny"}, + {"functionDeclarations": [{"name": "mcp__weather__lookup"}, {"name": "get_weather"}]}, + "agenerate_content", + "Tool 'mcp__weather__lookup' denied by rule 'deny_lookup'", + ), + ( + {"id": "allow_bash", "tool_name": r"^Bash$", "decision": "allow"}, + {"googleSearch": {}}, + "acompletion", + "Tool 'googleSearch' denied by default action", + ), + ( + {"id": "deny_custom", "tool_type": r"^custom$", "decision": "deny"}, + {"type": "custom", "custom": {"name": "grammar_tool"}}, + "acompletion", + "Tool 'grammar_tool' denied by rule 'deny_custom'", + ), + ( + {"id": "deny_read", "tool_name": r"^Read$", "decision": "deny"}, + {"type": "custom", "custom": {"name": "Read"}}, + "acompletion", + "Tool 'Read' denied by rule 'deny_read'", + ), + ], + ids=[ + "responses_mcp_by_type", + "anthropic_mcp_toolset_by_server_name", + "responses_mcp_by_server_label", + "responses_namespace_custom_member", + "gemini_function_declarations", + "gemini_type_only_tool_by_its_key", + "chat_custom_keeps_its_type", + "chat_custom_nested_name", + ], + ) + async def test_pre_call_checks_tools_that_carry_no_top_level_name( + self, rule: dict[str, str], tool: dict[str, object], call_type: CallTypesLiteral, detection_message: str + ) -> None: + guardrail = self._pre_call([rule], "block", default_action="deny") + data = {"model": "m", "messages": [{"role": "user", "content": "hi"}], "input": "hi", "tools": [tool]} + + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, call_type) + + assert excinfo.value.detail["detection_message"] == detection_message + + @pytest.mark.asyncio + async def test_pre_call_lets_a_type_rule_allow_a_gemini_tool_by_its_key(self) -> None: + guardrail = self._pre_call( + [{"id": "allow_search", "tool_type": r"^googleSearch$", "decision": "allow"}], "block", default_action="deny" + ) + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [{"googleSearch": {}}]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [{"googleSearch": {}}] + + @pytest.mark.asyncio + async def test_pre_call_blocks_a_denied_tool_loaded_by_responses_tool_search(self) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "block") + loaded = {"type": "tool_search_output", "tools": [{"type": "function", "name": "mcp__weather__lookup"}]} + data = {"model": "gpt-5", "input": [loaded], "tools": []} + + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "responses") + + assert excinfo.value.detail["detection_message"] == "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'" + + @pytest.mark.asyncio + async def test_pre_call_rewrite_keeps_the_allowed_members_of_grouped_and_loaded_tools(self) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + get_weather = {"type": "function", "name": "get_weather"} + lookup = {"type": "function", "name": "mcp__weather__lookup"} + data = { + "model": "m", + "input": [{"role": "user", "content": "hi"}, {"type": "tool_search_output", "tools": [get_weather, lookup]}], + "tools": [{"functionDeclarations": [{"name": "get_weather"}, {"name": "mcp__weather__lookup"}]}], + } + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [{"functionDeclarations": [{"name": "get_weather"}]}] + assert result["input"] == [ + {"role": "user", "content": "hi"}, + {"type": "tool_search_output", "tools": [get_weather]}, + ] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("call_type", "message", "rewritten"), + [ + ( + "anthropic_messages", + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_1", + "content": [{"type": "tool_reference", "tool_name": "mcp__weather__lookup"}], + }, + {"type": "text", "text": "Tool loaded."}, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_1", + "content": [ + { + "type": "text", + "text": "Permission denied: tool mcp__weather__lookup is not available", + } + ], + }, + {"type": "text", "text": "Tool loaded."}, + ], + }, + ), + ( + "anthropic_messages", + { + "role": "assistant", + "content": [ + { + "type": "tool_search_tool_result", + "tool_use_id": "srvtoolu_1", + "content": { + "type": "tool_search_tool_search_result", + "tool_references": [ + {"type": "tool_reference", "tool_name": "mcp__weather__lookup"}, + {"type": "tool_reference", "tool_name": "get_weather"}, + ], + }, + } + ], + }, + { + "role": "assistant", + "content": [ + { + "type": "tool_search_tool_result", + "tool_use_id": "srvtoolu_1", + "content": { + "type": "tool_search_tool_search_result", + "tool_references": [{"type": "tool_reference", "tool_name": "get_weather"}], + }, + } + ], + }, + ), + ( + "acompletion", + { + "role": "tool", + "tool_call_id": "call_1", + "content": [ + {"type": "tool_reference", "tool_name": "mcp__weather__lookup"}, + {"type": "tool_reference", "tool_name": "get_weather"}, + ], + }, + { + "role": "tool", + "tool_call_id": "call_1", + "content": [{"type": "tool_reference", "tool_name": "get_weather"}], + }, + ), + ], + ) + async def test_pre_call_rewrite_drops_references_to_a_removed_tool(self, call_type, message, rewritten) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + data = { + "model": "m", + "messages": [{"role": "user", "content": "weather in Istanbul"}, message], + "tools": [ + {"name": "get_weather", "input_schema": {"type": "object"}, "defer_loading": True}, + {"name": "mcp__weather__lookup", "input_schema": {"type": "object"}, "defer_loading": True}, + ], + } + + result = await self._run_pre_call(guardrail, data, call_type) + + assert [tool["name"] for tool in result["tools"]] == ["get_weather"] + assert result["messages"] == [{"role": "user", "content": "weather in Istanbul"}, rewritten] + + @pytest.mark.asyncio + @pytest.mark.parametrize("tool_choice", ["required", {"type": "function", "name": "get_weather"}]) + async def test_pre_call_rewrite_keeps_tool_choice_when_only_loaded_tools_remain(self, tool_choice) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + get_weather = {"type": "function", "name": "get_weather"} + lookup = {"type": "function", "name": "mcp__weather__lookup"} + data = { + "model": "m", + "input": [{"role": "user", "content": "hi"}, {"type": "tool_search_output", "tools": [get_weather, lookup]}], + "tools": [lookup], + "tool_choice": tool_choice, + } + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [] + assert result["input"][1] == {"type": "tool_search_output", "tools": [get_weather]} + assert result["tool_choice"] == tool_choice + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rule", "tools", "tool_choice"), + [ + ( + {"id": "deny_search", "tool_type": r"^web_search$", "decision": "deny"}, + [{"type": "web_search"}, {"type": "function", "name": "get_weather"}], + {"type": "web_search"}, + ), + ( + {"id": "deny_weather", "tool_name": r"^weather$", "decision": "deny"}, + [ + {"type": "mcp", "server_label": "weather", "server_url": "https://a.example.com"}, + {"type": "mcp", "server_label": "files", "server_url": "https://b.example.com"}, + ], + {"type": "mcp", "server_label": "weather"}, + ), + ], + ids=["hosted_tool_type", "hosted_mcp_server"], + ) + async def test_pre_call_rewrite_relaxes_a_hosted_tool_choice_it_removed( + self, rule: dict[str, str], tools: list[dict[str, str]], tool_choice: dict[str, str] + ) -> None: + guardrail = self._pre_call([rule], "rewrite") + data = {"model": "gpt-5", "input": "hi", "tools": tools, "tool_choice": tool_choice} + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == tools[1:] + assert result["tool_choice"] == "none" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("tools", "tool_choice", "call_type", "expected_tool_choice"), + [ + ( + [{"type": "custom", "name": "Bash"}, {"type": "custom", "name": "Read"}], + {"type": "custom", "name": "Read"}, + "responses", + "none", + ), + ( + [{"type": "function", "name": "Bash"}, {"type": "function", "name": "Read"}], + { + "type": "allowed_tools", + "mode": "required", + "tools": [{"type": "function", "name": "Read"}, {"type": "function", "name": "Bash"}], + }, + "responses", + {"type": "allowed_tools", "mode": "required", "tools": [{"type": "function", "name": "Bash"}]}, + ), + ( + [{"type": "function", "name": "Bash"}, {"type": "function", "name": "Read"}], + {"type": "allowed_tools", "mode": "required", "tools": [{"type": "function", "name": "Read"}]}, + "responses", + "none", + ), + ( + [ + {"type": "function", "function": {"name": "Bash"}}, + {"type": "function", "function": {"name": "Read"}}, + ], + { + "type": "allowed_tools", + "allowed_tools": {"mode": "auto", "tools": [{"type": "function", "function": {"name": "Read"}}]}, + }, + "acompletion", + "none", + ), + ( + [{"name": "Read", "input_schema": {"type": "object"}}], + {"type": "any"}, + "anthropic_messages", + {"type": "none"}, + ), + ( + [ + {"name": "Bash", "input_schema": {"type": "object"}}, + {"name": "Read", "input_schema": {"type": "object"}}, + ], + {"type": "any"}, + "anthropic_messages", + {"type": "any"}, + ), + ( + [{"type": "custom", "custom": {"name": "Bash"}}, {"type": "custom", "custom": {"name": "Read"}}], + {"type": "custom", "custom": {"name": "Read"}}, + "acompletion", + "none", + ), + ], + ids=[ + "responses_custom_choice", + "responses_allowed_tools_keeps_the_rest", + "responses_allowed_tools_all_denied", + "chat_allowed_tools_all_denied", + "anthropic_any_with_no_tools_left", + "anthropic_any_with_a_tool_left", + "chat_custom_choice", + ], + ) + async def test_pre_call_rewrite_leaves_no_tool_choice_that_needs_a_removed_tool( + self, + tools: list[dict[str, object]], + tool_choice: dict[str, object], + call_type: CallTypesLiteral, + expected_tool_choice: dict[str, object] | str, + ) -> None: + guardrail = self._pre_call(self.rules, "rewrite") + data = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "tools": tools, + "tool_choice": tool_choice, + } + + result = await self._run_pre_call(guardrail, data, call_type) + + assert result["tool_choice"] == expected_tool_choice + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("tool", "tool_choice", "call_type", "allowed_tool_type"), + [ + ({"type": "custom", "name": "apply_patch"}, {"type": "custom", "name": "apply_patch"}, "responses", r"^custom$"), + ( + {"type": "web_search_20250305", "name": "web_search"}, + {"type": "tool", "name": "web_search"}, + "anthropic_messages", + r"^web_search_\d+$", + ), + ], + ids=["responses_custom_tool", "anthropic_server_tool"], + ) + async def test_pre_call_checks_a_forced_choice_with_its_tool_type( + self, + tool: dict[str, str], + tool_choice: dict[str, str], + call_type: CallTypesLiteral, + allowed_tool_type: str, + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_type", "tool_type": allowed_tool_type, "decision": "allow"}], "block", default_action="deny" + ) + data = {"model": "m", "input": "hi", "tools": [tool], "tool_choice": tool_choice} + + result = await self._run_pre_call(guardrail, data, call_type) + + assert result["tools"] == [tool] + assert result["tool_choice"] == tool_choice + + @pytest.mark.asyncio + async def test_pre_call_rewrite_relaxes_a_choice_for_a_tool_a_type_rule_removed(self) -> None: + guardrail = self._pre_call([{"id": "deny_custom", "tool_type": r"^custom$", "decision": "deny"}], "rewrite") + data = { + "model": "gpt-5", + "input": "patch it", + "tools": [{"type": "function", "name": "Bash"}, {"type": "custom", "name": "apply_patch"}], + "tool_choice": {"type": "custom", "name": "apply_patch"}, + } + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [{"type": "function", "name": "Bash"}] + assert result["tool_choice"] == "none" + + async def _blocked_and_rewritten( + self, rules: list[dict[str, str]], request: dict[str, object], call_type: CallTypesLiteral + ) -> tuple[str, dict[str, object]]: + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(self._pre_call(rules, "block"), copy.deepcopy(request), call_type) + rewritten = await self._run_pre_call(self._pre_call(rules, "rewrite"), request, call_type) + return excinfo.value.detail["detection_message"], rewritten + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("denied_tool", "detection_message"), + [ + ( + {"type": "function", "name": "mcp__weather__lookup"}, + "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'", + ), + ( + {"type": "namespace", "name": "mcp__weather", "tools": [{"type": "function", "name": "lookup"}]}, + "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'", + ), + ({"type": "web_search"}, "Tool 'web_search' denied by rule 'deny_search'"), + ], + ids=["flat", "namespace_member", "hosted_type"], + ) + async def test_pre_call_checks_and_strips_tools_in_responses_additional_tools_items( + self, denied_tool: dict[str, object], detection_message: str + ) -> None: + rules = [ + {"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}, + {"id": "deny_search", "tool_type": r"^web_search$", "decision": "deny"}, + ] + get_weather = {"type": "function", "name": "get_weather"} + request = { + "model": "gpt-5", + "input": [ + {"role": "user", "content": "hi"}, + {"type": "additional_tools", "tools": [get_weather, denied_tool]}, + {"type": "additional_tools", "tools": [denied_tool]}, + ], + "tools": [], + "tool_choice": "required", + } + + message, rewritten = await self._blocked_and_rewritten(rules, request, "responses") + + assert message == detection_message + assert rewritten["input"] == [ + {"role": "user", "content": "hi"}, + {"type": "additional_tools", "tools": [get_weather]}, + ] + assert rewritten["tool_choice"] == "required" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rules", "default_action", "kept"), + [ + ([{"id": "allow_exec", "tool_name": r"^exec$", "decision": "allow"}], "deny", ["run"]), + ([{"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}], "allow", []), + ( + [ + {"id": "allow_run", "tool_name": r"^run$", "decision": "allow"}, + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + ], + "allow", + [], + ), + ( + [ + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + {"id": "allow_all", "tool_name": r"^.*$", "decision": "allow"}, + ], + "allow", + [], + ), + ( + [ + {"id": "allow_exec", "tool_name": r"^exec$", "decision": "allow"}, + {"id": "deny_all", "tool_name": r"^.*$", "decision": "deny"}, + ], + "deny", + ["run"], + ), + ], + ids=[ + "namespace_allowed", + "namespace_denied", + "namespace_deny_beats_member_allow", + "namespace_deny_beats_a_catch_all_allow", + "namespace_allow_beats_a_catch_all_deny", + ], + ) + async def test_pre_call_judges_a_namespace_member_by_a_rule_on_its_namespace_name( + self, rules: list[dict[str, str]], default_action: Literal["allow", "deny"], kept: list[str] + ) -> None: + namespace = {"type": "namespace", "name": "exec", "tools": [{"type": "function", "name": "run"}]} + request = {"model": "gpt-5", "input": "Run pwd", "tools": [namespace]} + + rewritten = await self._run_pre_call(self._pre_call(rules, "rewrite", default_action), request, "responses") + + tools = rewritten.get("tools") or [] + assert [member["name"] for member in chain.from_iterable(tool["tools"] for tool in tools)] == kept + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("call_type", "bash", "read"), + [ + ( + "acompletion", + {"type": "function", "function": {"name": "Bash"}}, + {"type": "function", "function": {"name": "Read"}}, + ), + ("responses", {"type": "function", "name": "Bash"}, {"type": "function", "name": "Read"}), + ( + "anthropic_messages", + {"name": "Bash", "input_schema": {"type": "object"}}, + {"name": "Read", "input_schema": {"type": "object"}}, + ), + ], + ids=["chat", "responses", "anthropic"], + ) + async def test_pre_call_checks_and_strips_tools_in_extra_body( + self, call_type: CallTypesLiteral, bash: dict[str, object], read: dict[str, object] + ) -> None: + request = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "input": "hi", + "extra_body": {"tools": [bash, read], "seed": 7}, + } + + message, rewritten = await self._blocked_and_rewritten(self.rules, request, call_type) + + assert message == "Tool 'Read' denied by rule 'deny_read'" + assert rewritten["extra_body"] == {"tools": [bash], "seed": 7} + + @pytest.mark.asyncio + async def test_pre_call_checks_and_strips_tools_loaded_in_extra_body_input(self) -> None: + rules = [{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}] + get_weather = {"type": "function", "name": "get_weather"} + lookup = {"type": "function", "name": "mcp__weather__lookup"} + user = {"role": "user", "content": "hi"} + request = { + "model": "gpt-5", + "input": "hi", + "tools": [], + "extra_body": {"input": [user, {"type": "tool_search_output", "tools": [get_weather, lookup]}], "seed": 7}, + } + + message, rewritten = await self._blocked_and_rewritten(rules, request, "responses") + + assert message == "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'" + assert rewritten["extra_body"] == { + "input": [user, {"type": "tool_search_output", "tools": [get_weather]}], + "seed": 7, + } + + @pytest.mark.asyncio + async def test_rewrite_relaxes_an_extra_body_tool_choice_that_names_a_dropped_tool(self) -> None: + guardrail = self._pre_call([{"id": "deny_delete", "tool_name": r"^delete_db$", "decision": "deny"}], "rewrite") + weather = {"type": "function", "function": {"name": "get_weather"}} + delete_db = {"type": "function", "function": {"name": "delete_db"}} + data = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "extra_body": { + "tools": [weather, delete_db], + "tool_choice": {"type": "function", "function": {"name": "delete_db"}}, + "seed": 7, + }, + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["extra_body"] == {"tools": [weather], "tool_choice": "none", "seed": 7} + + @pytest.mark.asyncio + async def test_rewrite_relaxes_a_required_tool_choice_when_the_extra_body_tools_sent_lose_every_tool( + self, + ) -> None: + guardrail = self._pre_call([{"id": "deny_delete", "tool_name": r"^delete_db$", "decision": "deny"}], "rewrite") + weather = {"type": "function", "function": {"name": "get_weather"}} + delete_db = {"type": "function", "function": {"name": "delete_db"}} + data = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "tools": [weather], + "extra_body": {"tools": [delete_db], "tool_choice": "required", "seed": 7}, + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + assert result["extra_body"] == {"tools": [], "tool_choice": "none", "seed": 7} + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("call_type", "tool_choice"), + [ + ("acompletion", {"type": "function", "function": {"name": "Read"}}), + ("responses", {"type": "function", "name": "Read"}), + ], + ids=["chat", "responses"], + ) + async def test_pre_call_checks_an_extra_body_tool_choice_that_names_an_unlisted_tool( + self, call_type: CallTypesLiteral, tool_choice: dict[str, object] + ) -> None: + request = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "input": "hi", + "tools": [{"type": "function", "name": "Bash"}], + "extra_body": {"tool_choice": tool_choice, "seed": 7}, + } + + message, rewritten = await self._blocked_and_rewritten(self.rules, request, call_type) + + assert message == "Tool 'Read' denied by rule 'deny_read'" + assert rewritten["extra_body"] == {"tool_choice": "none", "seed": 7} + + @pytest.mark.asyncio + async def test_pre_call_checks_and_strips_legacy_functions_in_extra_body(self) -> None: + rules = [{"id": "deny_delete", "tool_name": r"^delete_db$", "decision": "deny"}] + weather = {"name": "get_weather", "parameters": {"type": "object"}} + delete_db = {"name": "delete_db", "parameters": {"type": "object"}} + request = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "extra_body": {"functions": [weather, delete_db], "function_call": {"name": "delete_db"}, "seed": 7}, + } + + message, rewritten = await self._blocked_and_rewritten(rules, request, "acompletion") + + assert message == "Tool 'delete_db' denied by rule 'deny_delete'" + assert rewritten["extra_body"] == {"functions": [weather], "function_call": "none", "seed": 7} + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "fields", + [ + {"functions": [{"name": "get_weather"}, {"name": "delete_db"}], "function_call": "delete_db"}, + { + "tools": [{"type": "function", "function": {"name": "get_weather"}}], + "mcp_servers": [{"type": "url", "url": "https://mcp.example.com/mcp", "name": "delete_db"}], + "tool_choice": "required", + }, + { + "tools": [{"functionDeclarations": [{"name": "get_weather"}, {"name": "delete_db"}]}], + "toolConfig": {"functionCallingConfig": {"mode": "ANY", "allowedFunctionNames": ["delete_db"]}}, + }, + ], + ids=["legacy_functions", "mcp_servers", "gemini_tool_config"], + ) + async def test_extra_body_fields_are_judged_and_rewritten_like_the_same_top_level_fields( + self, fields: dict[str, object] + ) -> None: + rules = [{"id": "deny_delete", "tool_name": r"^delete_db$", "decision": "deny"}] + base = {"model": "m", "messages": [{"role": "user", "content": "hi"}]} + top_request = {**base, **copy.deepcopy(fields)} + extra_request = {**base, "extra_body": {**copy.deepcopy(fields), "seed": 7}} + + top_message, top = await self._blocked_and_rewritten(rules, top_request, "acompletion") + extra_message, extra = await self._blocked_and_rewritten(rules, extra_request, "acompletion") + + top_fields = {key: top[key] for key in fields} + assert top_fields != fields + assert extra_message == top_message + assert extra["extra_body"] == {**top_fields, "seed": 7} + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "rule", + [ + {"id": "deny_deepwiki", "tool_name": r"^deepwiki$", "decision": "deny"}, + {"id": "deny_deepwiki", "tool_name": r"^deepwiki$", "tool_type": r"^mcp_toolset$", "decision": "deny"}, + ], + ids=["by_server_name", "by_server_name_and_mcp_toolset_type"], + ) + async def test_pre_call_checks_and_strips_anthropic_mcp_servers_like_an_mcp_toolset( + self, rule: dict[str, str] + ) -> None: + deepwiki = {"type": "url", "url": "https://mcp.deepwiki.com/mcp", "name": "deepwiki"} + files = {"type": "url", "url": "https://files.example.com/mcp", "name": "files"} + request = { + "model": "claude-sonnet-4-5", + "messages": [{"role": "user", "content": "hi"}], + "mcp_servers": [deepwiki, files], + "tool_choice": {"type": "any"}, + } + + message, rewritten = await self._blocked_and_rewritten([rule], request, "anthropic_messages") + + assert message == "Tool 'deepwiki' denied by rule 'deny_deepwiki'" + assert rewritten["mcp_servers"] == [files] + assert rewritten["tool_choice"] == {"type": "any"} + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rule", "tool", "detection_message", "kept_tools"), + [ + ( + {"id": "deny_code", "tool_type": r"^codeExecution$", "decision": "deny"}, + {"googleSearch": {}, "codeExecution": {}}, + "Tool 'codeExecution' denied by rule 'deny_code'", + [{"googleSearch": {}}], + ), + ( + {"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}, + {"functionDeclarations": [{"name": "mcp__weather__lookup"}], "googleSearch": {}}, + "Tool 'mcp__weather__lookup' denied by rule 'deny_mcp'", + [{"googleSearch": {}}], + ), + ( + {"id": "deny_search", "tool_type": r"^googleSearch$", "decision": "deny"}, + {"functionDeclarations": [{"name": "get_weather"}], "googleSearch": {}}, + "Tool 'googleSearch' denied by rule 'deny_search'", + [{"functionDeclarations": [{"name": "get_weather"}]}], + ), + ( + {"id": "deny_builtin", "tool_type": r"^(googleSearch|codeExecution)$", "decision": "deny"}, + {"googleSearch": {}, "codeExecution": {}}, + "Tool 'googleSearch' denied by rule 'deny_builtin'", + [], + ), + ], + ids=["denied_key_beside_a_key", "every_declaration_denied", "denied_key_beside_declarations", "all_denied"], + ) + async def test_pre_call_checks_and_strips_each_part_of_a_multi_key_gemini_tool( + self, + rule: dict[str, str], + tool: dict[str, object], + detection_message: str, + kept_tools: list[dict[str, object]], + ) -> None: + request = {"model": "gemini-3", "contents": [{"role": "user", "parts": [{"text": "hi"}]}], "tools": [tool]} + + message, rewritten = await self._blocked_and_rewritten([rule], request, "agenerate_content") + + assert message == detection_message + assert rewritten["tools"] == kept_tools + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + @pytest.mark.parametrize( + ("tool", "call_type"), + [({"type": "web_search"}, "responses"), ({"googleSearch": {}}, "acompletion")], + ids=["responses_hosted_type", "gemini_tool_key"], + ) + async def test_pre_call_matches_a_name_rule_against_a_nameless_tool_by_its_type( + self, + tool: dict[str, object], + call_type: CallTypesLiteral, + on_disallowed_action: Literal["block", "rewrite"], + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_all", "tool_name": r".*", "decision": "allow"}], on_disallowed_action, default_action="deny" + ) + data = {"model": "m", "messages": [{"role": "user", "content": "hi"}], "input": "hi", "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, call_type) + + assert result["tools"] == [tool] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("rules", "default_action", "detection_message"), + [ + ( + [{"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}], + "allow", + "Tool 'exec' denied by rule 'deny_exec'", + ), + ([{"id": "allow_exec", "tool_name": r"^exec$", "decision": "allow"}], "deny", None), + ( + [ + {"id": "allow_exec", "tool_name": r"^exec$", "decision": "allow"}, + {"id": "deny_functions", "tool_name": r"^functions__.*$", "decision": "deny"}, + ], + "allow", + "Tool 'functions__exec' denied by rule 'deny_functions'", + ), + ( + [ + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + {"id": "allow_functions", "tool_type": r"^function$", "decision": "allow"}, + ], + "allow", + "Tool 'exec' denied by rule 'deny_exec'", + ), + ( + [ + {"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}, + {"id": "allow_all", "tool_name": r"^.*$", "decision": "allow"}, + ], + "deny", + "Tool 'exec' denied by rule 'deny_exec'", + ), + ( + [ + {"id": "allow_functions", "tool_name": r"^functions__.*$", "decision": "allow"}, + {"id": "deny_all", "tool_name": r"^.*$", "decision": "deny"}, + ], + "allow", + None, + ), + ], + ids=[ + "bare_name_deny_rule", + "bare_name_allow_rule", + "qualified_rule_wins_over_an_earlier_bare_rule", + "type_rule_keeps_an_earlier_bare_name_rule_in_charge", + "bare_name_deny_beats_a_later_catch_all_allow", + "qualified_allow_beats_a_later_catch_all_deny", + ], + ) + async def test_pre_call_judges_a_namespace_member_by_its_qualified_and_bare_names( + self, rules: list[dict[str, str]], default_action: Literal["allow", "deny"], detection_message: str | None + ) -> None: + guardrail = self._pre_call(rules, "block", default_action=default_action) + namespace = {"type": "namespace", "name": "functions", "tools": [{"type": "function", "name": "exec"}]} + data = {"model": "gpt-5", "input": "run date", "tools": [namespace]} + + if detection_message is None: + assert (await self._run_pre_call(guardrail, data, "responses"))["tools"] == [namespace] + return + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "responses") + assert excinfo.value.detail["detection_message"] == detection_message + + @pytest.mark.asyncio + async def test_pre_call_rewrite_drops_a_namespace_member_a_bare_name_rule_denies(self) -> None: + guardrail = self._pre_call([{"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}], "rewrite") + members = [{"type": "function", "name": "exec"}, {"type": "function", "name": "read_file"}] + namespace = {"type": "namespace", "name": "functions", "tools": members} + data = {"model": "gpt-5", "input": "run date", "tools": [namespace]} + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [{"type": "namespace", "name": "functions", "tools": members[1:]}] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("choice", "relaxed"), + [ + ({"tool_choice": {"type": "function", "function": {"name": "exec"}}}, {"tool_choice": "none"}), + ({"tool_choice": {"type": "function", "function": {"name": "ops"}}}, {"tool_choice": "none"}), + ( + { + "tool_choice": { + "type": "allowed_tools", + "allowed_tools": { + "mode": "required", + "tools": [ + {"type": "function", "function": {"name": "exec"}}, + {"type": "function", "function": {"name": "get_weather"}}, + ], + }, + } + }, + { + "tool_choice": { + "type": "allowed_tools", + "allowed_tools": { + "mode": "required", + "tools": [{"type": "function", "function": {"name": "get_weather"}}], + }, + } + }, + ), + ( + {"toolConfig": {"functionCallingConfig": {"mode": "ANY", "allowedFunctionNames": ["exec"]}}}, + {"toolConfig": {"functionCallingConfig": {"mode": "NONE"}}}, + ), + ], + ids=["bare_member_choice", "namespace_choice", "allowed_tools_entry", "allowed_function_names"], + ) + async def test_pre_call_rewrite_relaxes_a_bare_name_a_dropped_namespace_member_was_sent_under( + self, choice: dict[str, object], relaxed: dict[str, object] + ) -> None: + guardrail = self._pre_call([{"id": "deny_exec", "tool_name": r"^ops__exec$", "decision": "deny"}], "rewrite") + weather = {"type": "function", "function": {"name": "get_weather", "parameters": {"type": "object"}}} + namespace = {"type": "namespace", "name": "ops", "tools": [{"type": "function", "name": "exec"}]} + data = { + "model": "gemini/gemini-2.5-flash", + "messages": [{"role": "user", "content": "hi"}], + "tools": [weather, namespace], + **choice, + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + assert {key: result[key] for key in relaxed} == relaxed + + @pytest.mark.asyncio + async def test_pre_call_rewrite_keeps_a_bare_name_another_kept_namespace_still_serves(self) -> None: + guardrail = self._pre_call([{"id": "deny_exec", "tool_name": r"^ops__exec$", "decision": "deny"}], "rewrite") + dropped_namespace = {"type": "namespace", "name": "ops", "tools": [{"type": "function", "name": "exec"}]} + kept_namespace = {"type": "namespace", "name": "dev", "tools": [{"type": "function", "name": "exec"}]} + tool_choice = {"type": "function", "function": {"name": "exec"}} + data = { + "model": "gemini/gemini-2.5-flash", + "messages": [{"role": "user", "content": "hi"}], + "tools": [dropped_namespace, kept_namespace], + "tool_choice": tool_choice, + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [kept_namespace] + assert result["tool_choice"] == tool_choice + + @pytest.mark.asyncio + @pytest.mark.parametrize("exec_first", [True, False], ids=["exec_namespace_first", "exec_namespace_last"]) + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_denies_a_bare_name_another_namespace_shares_a_qualified_name_with( + self, on_disallowed_action: Literal["block", "rewrite"], exec_first: bool + ) -> None: + guardrail = self._pre_call( + [{"id": "deny_exec", "tool_name": r"^exec$", "decision": "deny"}], on_disallowed_action + ) + exec_namespace = {"type": "namespace", "name": "mcp__ops", "tools": [{"type": "function", "name": "exec"}]} + twin_namespace = {"type": "namespace", "name": "mcp", "tools": [{"type": "function", "name": "ops__exec"}]} + tools = [exec_namespace, twin_namespace] if exec_first else [twin_namespace, exec_namespace] + data = {"model": "gpt-5", "input": "run date", "tools": tools} + + if on_disallowed_action == "rewrite": + assert (await self._run_pre_call(guardrail, data, "responses"))["tools"] == [] + return + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "responses") + assert excinfo.value.detail["detection_message"] == "Tool 'exec' denied by rule 'deny_exec'" + + @pytest.mark.asyncio + @pytest.mark.parametrize("placement", ["tools", "functions"]) + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + @pytest.mark.parametrize( + ("member", "detection_message"), + [ + ({"type": "function", "function": {"name": "delete_db"}}, "Tool 'delete_db' denied by rule 'deny_delete'"), + ( + {"type": "function", "name": "lookup", "function": {"name": "delete_db"}}, + "Tool 'delete_db' denied by rule 'deny_delete'", + ), + ({"googleSearch": {}}, "Tool 'googleSearch' denied by rule 'deny_search'"), + ( + { + "type": "namespace", + "name": "inner", + "tools": [{"type": "function", "name": "noop"}], + "function": {"name": "delete_db"}, + }, + "Tool 'delete_db' denied by rule 'deny_delete'", + ), + ], + ids=["chat_shaped_member", "function_object_behind_another_name", "hosted_key_member", "nested_namespace"], + ) + async def test_pre_call_judges_a_namespace_member_by_what_a_provider_reads_from_it( + self, + member: dict[str, object], + detection_message: str, + on_disallowed_action: Literal["block", "rewrite"], + placement: Literal["tools", "functions"], + ) -> None: + guardrail = self._pre_call( + [ + {"id": "deny_delete", "tool_name": r"^delete_db$", "decision": "deny"}, + {"id": "deny_search", "tool_type": r"^googleSearch$", "decision": "deny"}, + ], + on_disallowed_action, + ) + weather = {"type": "function", "name": "get_weather"} + data = { + "model": "gemini-3", + "messages": [{"role": "user", "content": "hi"}], + placement: [{"type": "namespace", "name": "ops", "tools": [weather, member]}], + } + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == detection_message + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result[placement] == [{"type": "namespace", "name": "ops", "tools": [weather]}] + + @pytest.mark.asyncio + async def test_pre_call_allows_a_chat_shaped_namespace_member_the_allowlist_names(self) -> None: + guardrail = self._pre_call( + [{"id": "allow_create", "tool_name": r"^github__create_issue$", "decision": "allow"}], + "block", + default_action="deny", + ) + namespace = { + "type": "namespace", + "name": "github", + "tools": [{"type": "function", "function": {"name": "create_issue"}}], + } + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [namespace]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [namespace] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("tool_config", "rewritten_tool_config"), + [ + ( + { + "functionCallingConfig": { + "mode": "ANY", + "allowedFunctionNames": ["mcp__weather__lookup", "get_weather"], + } + }, + {"functionCallingConfig": {"mode": "ANY", "allowedFunctionNames": ["get_weather"]}}, + ), + ( + {"functionCallingConfig": {"mode": "ANY", "allowedFunctionNames": ["mcp__weather__lookup"]}}, + {"functionCallingConfig": {"mode": "NONE"}}, + ), + ( + {"function_calling_config": {"mode": "any", "allowed_function_names": ["mcp__weather__lookup"]}}, + {"function_calling_config": {"mode": "NONE"}}, + ), + ({"functionCallingConfig": {"mode": "ANY"}}, {"functionCallingConfig": {"mode": "ANY"}}), + ( + {"functionCallingConfig": {"mode": "AUTO", "allowedFunctionNames": []}}, + {"functionCallingConfig": {"mode": "AUTO", "allowedFunctionNames": []}}, + ), + ], + ids=[ + "some_allowed_names_left", + "every_allowed_name_removed", + "snake_case_fields", + "no_allowed_names", + "empty_allowed_names_list", + ], + ) + async def test_pre_call_rewrite_drops_removed_functions_from_a_gemini_tool_config( + self, tool_config: dict[str, object], rewritten_tool_config: dict[str, object] + ) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + config_key = "tool_config" if "function_calling_config" in tool_config else "toolConfig" + data = { + "model": "gemini-3", + "contents": [{"role": "user", "parts": [{"text": "weather in Paris?"}]}], + "tools": [{"functionDeclarations": [{"name": "mcp__weather__lookup"}, {"name": "get_weather"}]}], + config_key: tool_config, + } + + result = await self._run_pre_call(guardrail, data, "agenerate_content") + + assert result["tools"] == [{"functionDeclarations": [{"name": "get_weather"}]}] + assert result[config_key] == rewritten_tool_config + + @pytest.mark.asyncio + async def test_pre_call_rewrite_turns_off_forced_gemini_calling_when_no_function_is_left(self) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + data = { + "model": "gemini-3", + "contents": [{"role": "user", "parts": [{"text": "weather in Paris?"}]}], + "tools": [{"functionDeclarations": [{"name": "mcp__weather__lookup"}]}, {"googleSearch": {}}], + "toolConfig": {"functionCallingConfig": {"mode": "ANY"}}, + } + + result = await self._run_pre_call(guardrail, data, "agenerate_content") + + assert result["tools"] == [{"googleSearch": {}}] + assert result["toolConfig"] == {"functionCallingConfig": {"mode": "NONE"}} + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_judges_a_named_hosted_tool_by_its_type_as_well_as_its_name( + self, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + weather = {"type": "function", "function": {"name": "get_weather"}} + data = { + "model": "gemini-3", + "messages": [{"role": "user", "content": "hi"}], + "tools": [weather, {"type": "web_search", "name": "get_weather"}], + } + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == "Tool 'web_search' denied by default action" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_judges_a_gemini_hosted_key_by_the_key_whatever_name_it_carries( + self, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + weather = {"type": "function", "function": {"name": "get_weather"}} + computer_use = {"computerUse": {"name": "get_weather", "environment": "browser"}} + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [weather, computer_use]} + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == "Tool 'computerUse' denied by default action" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "rules, tool", + [ + ( + [{"id": "allow_bash", "tool_name": r"^bash$", "decision": "allow"}], + {"type": "bash_20250124", "name": "bash"}, + ), + ( + [{"id": "allow_edit", "tool_name": r"^str_replace_based_edit_tool$", "decision": "allow"}], + {"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}, + ), + ( + [{"id": "allow_search", "tool_name": r"^web_search$", "decision": "allow"}], + {"type": "web_search_20250305", "name": "web_search"}, + ), + ( + [ + { + "id": "allow_search", + "tool_name": r"^web_search$", + "tool_type": r"^web_search_20250305$", + "decision": "allow", + } + ], + {"type": "web_search_20250305", "name": "web_search"}, + ), + ], + ids=["client_tool_by_name", "client_tool_with_its_own_name", "server_tool_by_name", "name_and_type_rule"], + ) + async def test_pre_call_allows_an_anthropic_defined_tool_that_an_allowlist_names( + self, rules: list[dict[str, object]], tool: dict[str, object] + ) -> None: + guardrail = self._pre_call(rules, "block", default_action="deny") + data = {"model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [tool] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "tool, call_type", + [ + ({"type": "web_search_20250305", "name": "get_weather"}, "anthropic_messages"), + ( + { + "type": "computer_20251124", + "name": "get_weather", + "function": {"parameters": {"display_width_px": 1024, "display_height_px": 768}}, + }, + "acompletion", + ), + ({"type": "computer_use", "name": "get_weather", "environment": "browser"}, "acompletion"), + ({"type": "text_editor_20250728", "name": "get_weather"}, "anthropic_messages"), + ], + ids=["server_tool", "anthropic_computer_tool", "gemini_computer_use", "text_editor"], + ) + async def test_pre_call_blocks_a_provider_defined_tool_whose_name_is_not_its_own( + self, tool: dict[str, object], call_type: CallTypesLiteral + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + "block", + default_action="deny", + ) + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, call_type) + + assert excinfo.value.detail["detection_message"] == f"Tool '{tool['type']}' denied by default action" + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + @pytest.mark.parametrize( + "tool", + [ + {"type": "function", "function": {"name": "delete_db"}, "functionDeclarations": [{"name": "get_weather"}]}, + {"type": "function", "function": {"name": "delete_db"}, "function_declarations": [{"name": "get_weather"}]}, + {"type": "custom", "name": "get_weather", "function": {"name": "delete_db"}}, + {"type": "mcp", "server_label": "get_weather", "function": {"name": "delete_db"}}, + { + "type": "namespace", + "name": "github", + "tools": [{"type": "function", "name": "create_issue"}], + "function": {"name": "delete_db"}, + }, + { + "type": "computer_20251124", + "name": "computer", + "function": {"name": "delete_db", "parameters": {"display_width_px": 1024, "display_height_px": 768}}, + }, + ], + ids=[ + "declarations_list", + "snake_case_declarations_list", + "custom_top_level_name", + "mcp_server_label", + "namespace_with_members", + "computer_top_level_name", + ], + ) + async def test_pre_call_judges_the_function_a_tool_runs_behind_an_allowed_name( + self, tool: dict[str, object], on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_known", "tool_name": r"^(get_weather|computer|github__create_issue)$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + weather = {"type": "function", "function": {"name": "get_weather"}} + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [weather, tool]} + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == "Tool 'delete_db' denied by default action" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + @pytest.mark.parametrize( + ("tool", "hosted_key"), + [ + ({"type": "get_weather", "googleSearch": {}}, "googleSearch"), + ({"type": "function", "codeExecution": {}}, "codeExecution"), + ({"type": "get_weather", "url_context": {}}, "url_context"), + ], + ids=["named_type_google_search", "function_type_code_execution", "named_type_url_context"], + ) + async def test_pre_call_judges_a_gemini_hosted_tool_key_whatever_the_tool_type( + self, tool: dict[str, object], hosted_key: str, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [ + {"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}, + {"id": "allow_functions", "tool_type": r"^function$", "decision": "allow"}, + ], + on_disallowed_action, + default_action="deny", + ) + weather = {"type": "function", "function": {"name": "get_weather"}} + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [weather, tool]} + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == f"Tool '{hosted_key}' denied by default action" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [weather] + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_leaves_a_gemini_hosted_key_that_sits_beside_a_function( + self, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + tool = {"type": "function", "function": {"name": "get_weather"}, "googleSearch": {}} + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [tool] + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + @pytest.mark.parametrize( + ("function", "denied"), + [ + ({"type": "function", "function": {"name": "delete_db"}}, "delete_db"), + ({"name": "get_weather", "function": {"name": "delete_db"}}, "delete_db"), + ({"googleSearch": {}}, "googleSearch"), + ], + ids=["function_object", "function_object_behind_an_allowed_name", "gemini_hosted_key"], + ) + async def test_pre_call_judges_a_legacy_functions_entry_like_a_tool( + self, function: dict[str, object], denied: str, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + weather = {"name": "get_weather"} + data = { + "model": "gemini-3", + "messages": [{"role": "user", "content": "hi"}], + "functions": [weather, function], + } + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == f"Tool '{denied}' denied by default action" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["functions"] == [weather] + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_judges_a_legacy_functions_entry_as_a_function_whatever_its_type( + self, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "deny_delete", "tool_name": r"^delete_db$", "tool_type": r"^function$", "decision": "deny"}], + on_disallowed_action, + ) + weather = {"name": "get_weather"} + data = { + "model": "gemini-3", + "messages": [{"role": "user", "content": "hi"}], + "functions": [weather, {"type": "custom", "name": "delete_db"}], + } + + if on_disallowed_action == "block": + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "acompletion") + assert excinfo.value.detail["detection_message"] == "Tool 'delete_db' denied by rule 'deny_delete'" + return + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["functions"] == [weather] + + @pytest.mark.asyncio + async def test_rewrite_keeps_an_allowed_function_whose_declaration_list_is_denied(self) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + "rewrite", + default_action="deny", + ) + tool = { + "type": "function", + "function": {"name": "get_weather"}, + "functionDeclarations": [{"name": "delete_db"}], + } + data = {"model": "gemini-3", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [{"type": "function", "function": {"name": "get_weather"}}] + + @pytest.mark.asyncio + async def test_pre_call_allows_a_custom_tool_by_its_function_name(self) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + "block", + default_action="deny", + ) + tool = {"type": "custom", "function": {"name": "get_weather"}} + data = {"model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [tool] + + @pytest.mark.asyncio + async def test_pre_call_blocks_a_tool_whose_type_is_not_a_string_by_its_name(self) -> None: + guardrail = self._pre_call( + [ + {"id": "allow_search", "tool_type": r"^web_search$", "decision": "allow"}, + {"id": "deny_read", "tool_name": r"^Read$", "decision": "deny"}, + ], + "block", + ) + data = { + "model": "claude-sonnet-4-5", + "messages": [{"role": "user", "content": "hi"}], + "tools": [{"type": ["custom"], "name": "Read", "input_schema": {"type": "object"}}], + } + + with pytest.raises(HTTPException) as excinfo: + await self._run_pre_call(guardrail, data, "anthropic_messages") + + assert excinfo.value.status_code == 400 + assert excinfo.value.detail["detection_message"] == "Tool 'Read' denied by rule 'deny_read'" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("malformed_tool", "tool_choice", "call_type"), + [ + ({"type": ["custom"], "name": "Read", "input_schema": {"type": "object"}}, None, "anthropic_messages"), + ({"type": "function", "name": "Read"}, {"type": {"a": 1}}, "responses"), + ], + ids=["tool_type_is_a_list", "tool_choice_type_is_an_object"], + ) + async def test_pre_call_rewrite_strips_a_denied_tool_and_leaves_malformed_fields_for_the_provider( + self, malformed_tool: dict[str, object], tool_choice: dict[str, object] | None, call_type: CallTypesLiteral + ) -> None: + guardrail = self._pre_call([{"id": "deny_read", "tool_name": r"^Read$", "decision": "deny"}], "rewrite") + bash = {"type": "function", "name": "Bash"} + data = { + "model": "m", + "messages": [{"role": "user", "content": "hi"}], + "tools": [bash, malformed_tool], + **({"tool_choice": tool_choice} if tool_choice is not None else {}), + } + + result = await self._run_pre_call(guardrail, data, call_type) + + assert result["tools"] == [bash] + assert result.get("tool_choice") == tool_choice + + @pytest.mark.asyncio + async def test_pre_call_judges_a_tool_with_a_null_type_by_its_nested_function_name(self) -> None: + guardrail = self._pre_call( + [{"id": "allow_read", "tool_name": r"^Read$", "decision": "allow"}], "block", default_action="deny" + ) + tool = {"type": None, "function": {"name": "Read", "parameters": {"type": "object"}}} + data = {"model": "gpt-5", "messages": [{"role": "user", "content": "hi"}], "tools": [tool]} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == [tool] + + @pytest.mark.asyncio + async def test_pre_call_rewrite_drops_a_removed_hosted_tool_from_allowed_tools(self) -> None: + guardrail = self._pre_call([{"id": "deny_search", "tool_type": r"^web_search$", "decision": "deny"}], "rewrite") + weather = {"type": "function", "name": "get_weather"} + tool_choice = {"type": "allowed_tools", "mode": "required", "tools": [weather, {"type": "web_search"}]} + data = {"model": "gpt-5", "input": "hi", "tools": [weather, {"type": "web_search"}], "tool_choice": tool_choice} + + result = await self._run_pre_call(guardrail, data, "responses") + + assert result["tools"] == [weather] + assert result["tool_choice"] == {"type": "allowed_tools", "mode": "required", "tools": [weather]} + + @pytest.mark.asyncio + @pytest.mark.parametrize("tools", [{"not": "a list"}, '[{"type": "function"}]'], ids=["object", "json_string"]) + async def test_pre_call_rewrite_leaves_a_tools_field_that_is_not_a_list_for_the_provider_to_reject( + self, tools: object + ) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + data = { + "model": "gpt-5", + "messages": [{"role": "user", "content": "hi"}], + "tools": tools, + "tool_choice": {"type": "function", "function": {"name": "mcp__weather__lookup"}}, + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == tools + assert result["tool_choice"] == "none" + + @pytest.mark.asyncio + @pytest.mark.parametrize("on_disallowed_action", ["block", "rewrite"]) + async def test_pre_call_reads_bedrock_tool_blocks_by_name_and_skips_cache_points( + self, on_disallowed_action: Literal["block", "rewrite"] + ) -> None: + guardrail = self._pre_call( + [{"id": "allow_weather", "tool_name": r"^get_weather$", "decision": "allow"}], + on_disallowed_action, + default_action="deny", + ) + weather = {"toolSpec": {"name": "get_weather", "inputSchema": {"json": {}}}} + tools = [weather, {"cachePoint": {"type": "default"}}] + data = {"model": "bedrock/claude", "messages": [{"role": "user", "content": "hi"}], "tools": tools} + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == tools + + @pytest.mark.asyncio + async def test_pre_call_rewrite_drops_a_denied_bedrock_tool_block_by_its_name(self) -> None: + guardrail = self._pre_call([{"id": "deny_mcp", "tool_name": r"^mcp__.*$", "decision": "deny"}], "rewrite") + kept = [{"toolSpec": {"name": "get_weather"}}, {"cachePoint": {"type": "default"}}] + data = { + "model": "bedrock/claude", + "messages": [{"role": "user", "content": "hi"}], + "tools": [{"toolSpec": {"name": "mcp__weather__lookup"}}, *kept], + } + + result = await self._run_pre_call(guardrail, data, "acompletion") + + assert result["tools"] == kept + @pytest.mark.asyncio async def test_denied_anthropic_tool_use_is_blocked(self): response = self._response({"type": "text", "text": "reading"}, self._tool_use("Read")) diff --git a/tests/unit/proxy/hooks/test_max_budget_per_session_limiter.py b/tests/unit/proxy/hooks/test_max_budget_per_session_limiter.py index 45cc7eb8922..966e94fad80 100644 --- a/tests/unit/proxy/hooks/test_max_budget_per_session_limiter.py +++ b/tests/unit/proxy/hooks/test_max_budget_per_session_limiter.py @@ -8,6 +8,9 @@ Tests that session-scoped budget tracking works correctly: - Requests without agent_id pass through """ +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final from unittest.mock import patch import logging @@ -22,6 +25,7 @@ from litellm.proxy.hooks.max_budget_per_session_limiter import ( ) from litellm.proxy.utils import InternalUsageCache from litellm.types.agents import AgentResponse +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG def _make_mock_agent(max_budget_per_session: float) -> AgentResponse: @@ -145,6 +149,255 @@ async def test_budget_per_session_independent_sessions(): assert result is None +@pytest.mark.asyncio +async def test_repeated_polls_of_a_finished_job_charge_the_session_once(): + from openai.types import Batch + + from litellm.types.videos.main import VideoObject + + handler = PROXY_MaxBudgetPerSessionHandler(internal_usage_cache=InternalUsageCache(DualCache())) + video = VideoObject(id="job-1", object="video", status="completed") + batch = Batch( + id="job-1", completion_window="24h", created_at=1, endpoint="/v1/chat/completions", + input_file_id="file-1", object="batch", status="completed", output_file_id="file-out", + ) + polls = (("avideo_status", video, 0.4), ("aretrieve_batch", batch, 0.1), ("acompletion", None, 0.01)) + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + for call_type, response_obj, cost in polls * 3: + await handler.async_log_success_event( + { + "call_type": call_type, + "response_cost": cost, + POLLED_VIDEO_STATUS_KWARG: "completed", + "litellm_params": {"metadata": {"session_id": "session-jobs", "agent_id": "agent-budget-123"}}, + }, + response_obj, + None, + None, + ) + + spend = await handler._get_current_spend(handler._make_cache_key("session-jobs")) + assert spend == pytest.approx(0.4 + 0.1 + 3 * 0.01) + + +@pytest.mark.asyncio +async def test_a_batch_retrieve_before_its_cost_is_final_charges_the_session_nothing(): + from openai.types import Batch + + handler: Final = PROXY_MaxBudgetPerSessionHandler(internal_usage_cache=InternalUsageCache(DualCache())) + + def polled(status: str, output_file_id: str | None = None) -> Batch: + return Batch( + id="batch-1", completion_window="24h", created_at=1, endpoint="/v1/chat/completions", + input_file_id="file-1", object="batch", status=status, output_file_id=output_file_id, + ) + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + for response_obj, cost in ( + (polled("in_progress"), 0.01), + (polled("completed"), 0.01), + (polled("completed", "file-out"), 0.25), + (polled("completed", "file-out"), 0.25), + ): + await handler.async_log_success_event( + { + "call_type": "aretrieve_batch", + "response_cost": cost, + "litellm_params": {"metadata": {"session_id": "session-batch", "agent_id": "agent-budget-123"}}, + }, + response_obj, + None, + None, + ) + + spend: Final = await handler._get_current_spend(handler._make_cache_key("session-batch")) + assert spend == pytest.approx(0.25) + + +def _video_poll( + video_cost: float, guardrail_cost: float = 0.0, session_id: str = "session-jobs", status: str = "completed" +) -> dict[str, object]: + return { + "call_type": "avideo_status", + "response_cost": video_cost, + POLLED_VIDEO_STATUS_KWARG: status, + "standard_logging_object": { + "response_cost": video_cost + guardrail_cost, + "guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": guardrail_cost}], + }, + "litellm_params": {"metadata": {"session_id": session_id, "agent_id": "agent-budget-123"}}, + } + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("video_cost", "guardrail_cost"), + [(0.40, 0.01), (0.05, 0.10)], + ids=["video_costs_more_than_its_guardrail", "video_costs_no_more_than_its_guardrail"], +) +async def test_every_video_poll_pays_its_guardrail_and_only_the_finished_poll_pays_the_session_for_the_video( + video_cost: float, guardrail_cost: float +): + from litellm.types.videos.main import VideoObject + + handler = PROXY_MaxBudgetPerSessionHandler(internal_usage_cache=InternalUsageCache(DualCache())) + completed_polls: Final = 3 + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + await handler.async_log_success_event( + _video_poll(0.0, guardrail_cost=guardrail_cost, status="in_progress"), + VideoObject(id="job-1", object="video", status="in_progress"), + None, + None, + ) + for _ in range(completed_polls): + await handler.async_log_success_event( + _video_poll(video_cost, guardrail_cost=guardrail_cost), + VideoObject(id="job-1", object="video", status="completed"), + None, + None, + ) + + spend = await handler._get_current_spend(handler._make_cache_key("session-jobs")) + assert spend == pytest.approx(video_cost + (1 + completed_polls) * guardrail_cost) + + +class _ClockedRedis: + """The Redis calls the session limiter makes, on a clock the test moves.""" + + def __init__(self) -> None: + self.now = 0.0 + self.entries: Mapping[str, tuple[float, float | None]] = MappingProxyType({}) + self.scripts_fail = False + + def _live(self, key: str) -> tuple[float, float | None] | None: + entry = self.entries.get(key) + if entry is None or (entry[1] is not None and entry[1] <= self.now): + return None + return entry + + def _store(self, key: str, value: float, expires_at: float | None) -> float: + self.entries = MappingProxyType({**self.entries, key: (value, expires_at)}) + return value + + async def async_get_cache(self, key: str, **kwargs: object) -> float | None: + entry = self._live(key) + return None if entry is None else entry[0] + + async def async_increment( + self, key: str, value: float, ttl: int | None = None, parent_otel_span: object = None, refresh_ttl: bool = False + ) -> float: + live = self._live(key) + kept_expiry = None if live is None else live[1] + expires_at = kept_expiry if ttl is None or (live is not None and not refresh_ttl) else self.now + ttl + return self._store(key, value + (0.0 if live is None else live[0]), expires_at) + + def _increment_expiring_on_first_write(self, key: str, amount: float, ttl: float) -> float: + live = self._live(key) + expires_at = self.now + ttl if live is None else live[1] + return self._store(key, amount + (0.0 if live is None else live[0]), expires_at) + + def async_register_script(self, script: str): + async def run(keys: list[str], args: list[object]) -> float: + if self.scripts_fail: + raise ConnectionError("Redis dropped the script call") + if len(keys) == 1: + return self._increment_expiring_on_first_write(keys[0], float(str(args[0])), float(str(args[1]))) + marker = self._live(keys[1]) + polls = self._store(keys[1], 1.0 + (0.0 if marker is None else marker[0]), self.now + float(str(args[2]))) + amount = float(str(args[0] if polls == 1 else args[1])) + if amount > 0: + self._increment_expiring_on_first_write(keys[0], amount, float(str(args[2]))) + return amount + + return run + + +@pytest.mark.asyncio +async def test_a_finished_video_polled_without_pause_is_not_charged_again_once_the_session_window_rolls_over(): + from litellm.types.videos.main import VideoObject + + redis = _ClockedRedis() + handler = PROXY_MaxBudgetPerSessionHandler( + internal_usage_cache=InternalUsageCache(DualCache(redis_cache=redis)) # pyright: ignore[reportArgumentType] # duck-typed Redis double + ) + video = VideoObject(id="job-1", object="video", status="completed") + chat = { + "call_type": "acompletion", + "response_cost": 0.01, + "litellm_params": {"metadata": {"session_id": "session-jobs", "agent_id": "agent-budget-123"}}, + } + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + for _ in range(3): + await handler.async_log_success_event(_video_poll(0.4), video, None, None) + redis.now += 0.6 * handler.ttl + await handler.async_log_success_event(chat, None, None, None) + + assert await handler._get_current_spend(handler._make_cache_key("session-jobs")) == pytest.approx(0.01) + + +@pytest.mark.asyncio +async def test_a_finished_video_whose_charge_missed_redis_is_charged_on_the_next_poll_once_redis_answers(): + from litellm.types.videos.main import VideoObject + + redis: Final = _ClockedRedis() + handler: Final = PROXY_MaxBudgetPerSessionHandler( + internal_usage_cache=InternalUsageCache(DualCache(redis_cache=redis)) # pyright: ignore[reportArgumentType] # duck-typed Redis double + ) + video: Final = VideoObject(id="job-1", object="video", status="completed") + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + redis.scripts_fail = True + await handler.async_log_success_event(_video_poll(0.4), video, None, None) + redis.scripts_fail = False + for _ in range(2): + await handler.async_log_success_event(_video_poll(0.4), video, None, None) + + assert await handler._get_current_spend(handler._make_cache_key("session-jobs")) == pytest.approx(0.4) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("response_cost", [float("nan"), float("inf"), True]) +async def test_a_cost_that_is_not_a_finite_number_leaves_the_session_spend_alone(response_cost): + handler = PROXY_MaxBudgetPerSessionHandler(internal_usage_cache=InternalUsageCache(DualCache())) + cache_key = handler._make_cache_key("session-unpriced") + await handler._increment_spend(cache_key, 0.25) + + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry" + ) as mock_registry: + mock_registry.get_agent_by_id.return_value = _make_mock_agent(max_budget_per_session=5.0) + await handler.async_log_success_event( + { + "call_type": "acompletion", + "response_cost": response_cost, + "litellm_params": {"metadata": {"session_id": "session-unpriced", "agent_id": "agent-budget-123"}}, + }, + None, + None, + None, + ) + + assert await handler._get_current_spend(cache_key) == 0.25 + + @pytest.mark.asyncio async def test_no_agent_id_passes(): """ diff --git a/tests/unit/proxy/hooks/test_model_max_budget_limiter.py b/tests/unit/proxy/hooks/test_model_max_budget_limiter.py index f5a2ccf7b76..f1fd02981d6 100644 --- a/tests/unit/proxy/hooks/test_model_max_budget_limiter.py +++ b/tests/unit/proxy/hooks/test_model_max_budget_limiter.py @@ -27,6 +27,8 @@ from litellm.proxy.hooks.model_max_budget_limiter import ( from litellm.router import Router from litellm.types.caching import RedisPipelineIncrementOperation from litellm.types.utils import LiteLLMBatch, Usage +from litellm.types.videos.main import VideoObject +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG KEY_HASH: Final = "key-hash-batch" USER_ID: Final = "user-batch" @@ -47,6 +49,7 @@ def _batch(batch_id: str, status: str) -> LiteLLMBatch: input_file_id="file-batch", object="batch", status=status, + output_file_id="file-output", usage=Usage(prompt_tokens=20, completion_tokens=18, total_tokens=38), ) @@ -57,12 +60,14 @@ def _event( team_member_model_max_budget: Mapping[str, object] | None = None, user_id: str = USER_ID, team_id: str = TEAM_ID, + guardrail_information: tuple[Mapping[str, object], ...] = (), ) -> dict[str, object]: return { "call_type": call_type, "standard_logging_object": { "call_type": call_type, "response_cost": response_cost, + "guardrail_information": guardrail_information, "model": "openai/gpt-5.4-mini", "model_group": MODEL_GROUP, "metadata": { @@ -326,6 +331,57 @@ async def test_a_batch_polled_within_every_budget_window_is_never_charged_again( assert _local_spend(limiter, KEY_SPEND_KEY) == pytest.approx(BATCH_COST) + +async def _poll_video( + limiter: PROXY_VirtualKeyModelMaxBudgetLimiter, video_id: str, status: str, response_cost: float +) -> None: + await _log_success( + limiter, + {**_event("avideo_status", response_cost), POLLED_VIDEO_STATUS_KWARG: status}, + response_obj=VideoObject(id=video_id, object="video", status=status), + ) + + +@pytest.mark.asyncio +async def test_polls_of_a_finished_video_charge_each_per_model_budget_once(): + limiter: Final = PROXY_VirtualKeyModelMaxBudgetLimiter(dual_cache=DualCache()) + + await _poll_video(limiter, "video_first", "in_progress", response_cost=0) + for _ in range(3): + await _poll_video(limiter, "video_first", "completed", response_cost=BATCH_COST) + await _poll_video(limiter, "video_second", "completed", response_cost=BATCH_COST) + await _poll(limiter, _batch("video_first", "completed"), response_cost=BATCH_COST) + + assert await _spend(limiter, KEY_SPEND_KEY) == pytest.approx(3 * BATCH_COST) + assert await _spend(limiter, USER_SPEND_KEY) == pytest.approx(3 * BATCH_COST) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("call_type", "polled", "guardrail_charges"), + ( + ("avideo_status", lambda status: VideoObject(id="job_first", object="video", status=status), 4), + ("aretrieve_batch", lambda status: _batch("job_first", status), 1), + ), +) +async def test_only_the_finished_poll_pays_the_job_and_only_a_video_repoll_pays_its_guardrail( + call_type, polled, guardrail_charges +): + """A running video poll pays its guardrail like any request, while a batch retrieve before its cost is final + charges nothing, as key spend defers it, so the retrieve that settles the batch pays the only guardrail.""" + limiter: Final = PROXY_VirtualKeyModelMaxBudgetLimiter(dual_cache=DualCache()) + guardrail_cost: Final = 0.01 + priced: Final = ({"guardrail_name": "priced", "guardrail_cost": guardrail_cost},) + + def poll(status: str, response_cost: float) -> dict[str, object]: + return {**_event(call_type, response_cost, guardrail_information=priced), POLLED_VIDEO_STATUS_KWARG: status} + + await _log_success(limiter, poll("in_progress", guardrail_cost), polled("in_progress")) + for _ in range(3): + await _log_success(limiter, poll("completed", BATCH_COST + guardrail_cost), polled("completed")) + + assert await _spend(limiter, KEY_SPEND_KEY) == pytest.approx(BATCH_COST + guardrail_charges * guardrail_cost) + OPUS_GROUP: Final = "claude-opus" OPUS_GROUP_MEMBERS: Final = MappingProxyType( {"claude-opus-4-8": frozenset({OPUS_GROUP}), "claude-opus-5-5": frozenset({OPUS_GROUP})} diff --git a/tests/unit/proxy/hooks/test_proxy_track_cost_callback.py b/tests/unit/proxy/hooks/test_proxy_track_cost_callback.py index 21b5bd300c1..c6366a40504 100644 --- a/tests/unit/proxy/hooks/test_proxy_track_cost_callback.py +++ b/tests/unit/proxy/hooks/test_proxy_track_cost_callback.py @@ -4,7 +4,7 @@ import logging from collections.abc import Mapping from datetime import datetime, timedelta, timezone from pathlib import Path -from types import SimpleNamespace +from types import MappingProxyType, SimpleNamespace from typing import Final, cast from unittest.mock import AsyncMock, MagicMock, create_autospec, patch @@ -24,6 +24,7 @@ from litellm.proxy.db.model_usage_rollup import build_model_usage_transaction from litellm.proxy.db.spend_log_tool_index import response_tool_call_names from litellm.proxy.hooks.proxy_track_cost_callback import ( ProxyDBLogger, + _charge_request_cost, _get_budget_reservation_from_metadata, _should_track_cost_callback, _update_database_and_spend_counters, @@ -42,6 +43,7 @@ from litellm.proxy.utils import ( ) from litellm.types.router import Deployment, LiteLLM_Params from litellm.types.utils import CallTypes, LiteLLMBatch, ModelResponse, PromptTokensDetailsWrapper, Usage +from litellm.types.videos.main import VideoObject @pytest.mark.asyncio @@ -1595,14 +1597,16 @@ def _retrieved_batch(status: str, output_file_id: str | None): @pytest.mark.asyncio @pytest.mark.parametrize( - ("call_type", "status", "output_file_id", "row_claimed", "spend_written", "charged"), + ("call_type", "status", "output_file_id", "row_claimed", "spend_written", "charged", "redacted"), [ - ("aretrieve_batch", "in_progress", None, True, False, False), - ("aretrieve_batch", "completed", None, True, False, False), - ("aretrieve_batch", "completed", "file-out", False, True, False), - ("aretrieve_batch", "completed", "file-out", True, True, True), - ("aretrieve_batch", "failed", None, True, True, True), - ("acreate_batch", "validating", None, True, True, True), + ("aretrieve_batch", "in_progress", None, True, False, False, False), + ("aretrieve_batch", "completed", None, True, False, False, False), + ("aretrieve_batch", "completed", "file-out", False, True, False, False), + ("aretrieve_batch", "completed", "file-out", True, True, True, False), + ("aretrieve_batch", "failed", None, True, True, True, False), + ("acreate_batch", "validating", None, True, True, True, False), + ("aretrieve_batch", "in_progress", None, True, False, False, True), + ("aretrieve_batch", "completed", "file-out", True, True, True, True), ], ids=[ "retrieve_before_final", @@ -1611,20 +1615,31 @@ def _retrieved_batch(status: str, output_file_id: str | None): "retrieve_first_final", "retrieve_failed_batch", "create_before_final", + "redacted_retrieve_before_final", + "redacted_retrieve_first_final", ], ) async def test_track_cost_callback_charges_a_batch_once_and_only_when_final( # test-quality-ok: whether the spend writer runs, whether the counters move, and whether the poll's reservation is handed back is the whole observable contract of the gate - call_type, status, output_file_id, row_claimed, spend_written, charged + call_type, status, output_file_id, row_claimed, spend_written, charged, redacted ): """ A poll before the batch is final used to pin its shared spend row at $0, and every completed retrieve after the first charged the key again (LIT-7048). Only retrieves are gated, since creating a batch is its own billable request, and a retrieve that - charges nothing hands its budget reservation back instead. + charges nothing hands its budget reservation back instead. Message redaction replaces + the logged batch, so the gate reads the batch logging stamped before redacting it. """ + from litellm.litellm_core_utils.batch_utils import POLLED_BATCH_COST_IS_FINAL_KWARG, POLLED_BATCH_ID_KWARG + logger = ProxyDBLogger() budget_reservation = None if charged else {"reserved_cost": 0.5, "entries": []} - kwargs = _batch_retrieve_kwargs(call_type, reservation=budget_reservation) + retrieved: Final = _retrieved_batch(status, output_file_id) + stamped: Final = ( + {POLLED_BATCH_ID_KWARG: retrieved.id, POLLED_BATCH_COST_IS_FINAL_KWARG: status != "in_progress"} + if redacted + else {} + ) + kwargs = {**_batch_retrieve_kwargs(call_type, reservation=budget_reservation), **stamped} with ( patch( # test-quality-ok: increment_spend_counters is a proxy_server global the callback reads lazily, no seam @@ -1646,7 +1661,7 @@ async def test_track_cost_callback_charges_a_batch_once_and_only_when_final( # await logger._PROXY_track_cost_callback( kwargs=kwargs, - completion_response=_retrieved_batch(status, output_file_id), + completion_response={"text": "redacted-by-litellm"} if redacted else retrieved, start_time=datetime.now(), end_time=datetime.now(), ) @@ -3376,18 +3391,8 @@ async def test_async_log_success_event_hands_the_sidecar_a_compact_event_and_ski assert event.response_cost == 0.0125 -@pytest.mark.asyncio -async def test_async_log_success_event_keeps_batch_retrieves_in_process(): - producer = SpendEventProducer( - address=UnixAddress(path="/nonexistent/spend.sock"), - on_unavailable="drop", - buffer_size=10, - connect_timeout=1.0, - fallback=_no_fallback, - ) - logger = ProxyDBLogger(producer) - kwargs = {**_offload_kwargs(), "call_type": CallTypes.aretrieve_batch.value} - completed_batch = LiteLLMBatch( +def _completed_batch() -> LiteLLMBatch: + return LiteLLMBatch( id="batch_abc", completion_window="24h", created_at=1, @@ -3398,6 +3403,29 @@ async def test_async_log_success_event_keeps_batch_retrieves_in_process(): status="completed", ) + +def _completed_video() -> VideoObject: + return VideoObject(id="video_abc", object="video", status="completed") + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("call_type", "polled_job"), + [(CallTypes.aretrieve_batch.value, _completed_batch), ("avideo_status", _completed_video)], +) +async def test_async_log_success_event_keeps_job_polls_in_process(call_type, polled_job): + """The spend event leaves out the polled video's status and id, which decide whether the poll charges the job.""" + producer = SpendEventProducer( + address=UnixAddress(path="/nonexistent/spend.sock"), + on_unavailable="drop", + buffer_size=10, + connect_timeout=1.0, + fallback=_no_fallback, + ) + logger = ProxyDBLogger(producer) + kwargs = {**_offload_kwargs(), "call_type": call_type} + completed_job = polled_job() + with ( patch( # test-quality-ok: the callback imports this from proxy_server inside its body, so there is no injection seam "litellm.proxy.proxy_server.proxy_logging_obj" @@ -3411,7 +3439,7 @@ async def test_async_log_success_event_keeps_batch_retrieves_in_process(): ): mock_proxy_logging.db_spend_update_writer.update_database = AsyncMock(return_value=True) mock_proxy_logging.slack_alerting_instance.customer_spend_alert = AsyncMock() - await logger.async_log_success_event(kwargs, completed_batch, datetime.now(), datetime.now()) + await logger.async_log_success_event(kwargs, completed_job, datetime.now(), datetime.now()) mock_proxy_logging.db_spend_update_writer.update_database.assert_awaited_once() assert producer.stats().queued == 0 @@ -3983,138 +4011,48 @@ async def test_async_post_call_failure_hook_disable_pre_routing_error_logs( assert len(writer.rows) == rows_written -@pytest.mark.asyncio -async def test_failure_capture_overload_does_not_skip_budget_release( - monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture -) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - from tests.unit.proxy.spend_tracking.test_usage_meter import CommitBarrier, _write +class _RecordedCharges: + def __init__(self, outcomes: tuple[bool, ...]) -> None: + self.outcomes = outcomes + self.calls: tuple[tuple[object, float, dict[str, object] | None], ...] = () - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2, max_pending_records=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "prisma_client", None) - meter: Final = runtime.meter - assert meter is not None - receipt: Final = meter.submit((_write("occupied-one"), _write("occupied-two"))) - auth: Final = UserAPIKeyAuth(budget_reservation={"reserved_cost": 0.5, "entries": [], "finalized": False}) - request: Final = { - "call_type": "acompletion", - "litellm_call_id": "failed", - "model": "capture-model", - "start_time": _CALL_START, - "litellm_params": {}, + async def __call__( + self, *, kwargs: dict, response_cost: float, budget_reservation: dict[str, object] | None + ) -> bool: + self.calls = (*self.calls, (kwargs.get("response_cost"), response_cost, budget_reservation)) + return self.outcomes[len(self.calls) - 1] + + +_GUARDED_POLL: Final = MappingProxyType( + { + "call_type": "avideo_status", + "response_cost": 0.43, + "standard_logging_object": {"guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}]}, } - try: - await database.entered.wait() - await ProxyDBLogger().async_post_call_failure_hook(request, RuntimeError("provider failed"), auth) - assert auth.budget_reservation is not None and auth.budget_reservation["finalized"] is True - assert "Usage capture failed on the request failure path" in caplog.text - assert "_accounting_capture" not in request - finally: - database.release.set() - await runtime.stop(1) - await receipt - assert database.committed == ("occupied-one", "occupied-two") +) @pytest.mark.asyncio -@pytest.mark.parametrize("failure", [False, True]) -@pytest.mark.parametrize("complete", [False, True]) -@pytest.mark.parametrize("deferred", [False, True]) -async def test_capture_and_legacy_share_resolved_key_attribution( - failure: bool, complete: bool, deferred: bool, monkeypatch: pytest.MonkeyPatch -) -> None: - from pydantic import TypeAdapter +@pytest.mark.parametrize( + ("outcomes", "poll", "charged", "second_call"), + [ + ((True,), _GUARDED_POLL, 0.43, None), + ((False, True), _GUARDED_POLL, 0.01, (0.01, 0.01, None)), + ((False, False), _GUARDED_POLL, None, (0.01, 0.01, None)), + ((False,), {"response_cost": 0.43}, None, None), + ((False,), {**_GUARDED_POLL, "call_type": "aretrieve_batch"}, None, None), + ], + ids=[ + "first_poll_pays_the_job", + "repeat_poll_pays_its_guardrail", + "guardrail_charge_not_written", + "repeat_poll_free", + "repeat_batch_poll_free_whatever_its_guardrails_cost", + ], +) +async def test_a_poll_of_an_already_charged_job_still_charges_its_own_guardrails(outcomes, poll, charged, second_call): + charge_spend: Final = _RecordedCharges(outcomes) + reservation: Final = {"reserved": 0.43} - from litellm.accounting import DeliveryTarget, DimensionName - from litellm.proxy import proxy_server - from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache - from litellm.proxy.hooks.proxy_track_cost_callback import track_batch_accounting - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - from tests.unit.proxy.spend_tracking.test_usage_capture import StoredUsage - - key: Final = UserAPIKeyAuth( - api_key="a" * 64, - user_id="owner", - team_id="team", - org_id="org", - project_id="project", - key_alias="key", - team_alias="team-alias", - request_route="/v1/chat/completions", - ) - cache: Final = UserApiKeyCache() - await cache.async_set_cache(key="a" * 64, value=key) - monkeypatch.setattr(proxy_server, "user_api_key_cache", cache) - monkeypatch.setattr(proxy_server, "prisma_client", object()) - writer: Final = _RecordingSpendWriter() - monkeypatch.setattr(proxy_server, "proxy_logging_obj", SimpleNamespace(db_spend_update_writer=writer)) - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - rejected: Final = failure and deferred and not complete - owner: Final = "original-owner" if deferred and not rejected else "owner" - metadata: Final = { - "user_api_key": "b" * 64 if rejected else "a" * 64, - **( - { - "user_api_key_user_id": "forged-owner" if rejected else owner, - "user_api_key_team_id": "forged-team" if rejected else "team", - "user_api_key_org_id": "forged-org" if rejected else "org", - "user_api_key_project_id": None if deferred and not complete else "project", - "user_api_key_alias": "key", - "user_api_key_team_alias": "team-alias", - } - if complete or deferred - else {} - ), - } - kwargs: Final = { - "call_type": "aretrieve_batch" if deferred else "call_mcp_tool", - "litellm_call_id": "attributed", - "model": "MCP: echo", - "response_cost": 0.25, - "start_time": _CALL_START, - "litellm_params": {"metadata": metadata}, - } - if rejected: - from litellm.proxy.utils import REJECTED_BEFORE_REQUEST_SETUP, REJECTED_BEFORE_REQUEST_SETUP_KEY - - kwargs[REJECTED_BEFORE_REQUEST_SETUP_KEY] = REJECTED_BEFORE_REQUEST_SETUP - logger: Final = ProxyDBLogger(spend_writer=lambda: cast(DBSpendUpdateWriter, writer)) - try: - with track_batch_accounting("attributed" if deferred and not rejected else "other"): - if failure: - await logger.async_post_call_failure_hook( - kwargs, - HTTPException(401, "model not allowed"), - key if complete else UserAPIKeyAuth(api_key="a" * 64, request_route="/v1/chat/completions"), - ) - else: - await logger.async_log_success_event(kwargs, {"id": "tool"}, _CALL_START, _CALL_START) - finally: - await runtime.stop(1) - assert len(writer.rows) == 1 - assert len(database.rows) == 2 - dimensions: Final = {item.name: item.value for item in database.rows[-1].event.dimensions} - if rejected: - assert dimensions.get(DimensionName.KEY) == key.api_key - assert { - name: dimensions.get(name) - for name in (DimensionName.USER, DimensionName.TEAM, DimensionName.ORGANIZATION, DimensionName.PROJECT) - } == { - DimensionName.USER: owner, - DimensionName.TEAM: "team", - DimensionName.ORGANIZATION: "org", - DimensionName.PROJECT: None if deferred and not complete and not rejected else "project", - } - assert set(database.rows[-1].targets) >= {DeliveryTarget.DAILY_TEAM_SPEND, DeliveryTarget.DAILY_ORGANIZATION_SPEND} - legacy: Final = TypeAdapter(dict[str, object]).validate_python(writer.rows[0]["kwargs"]) - payload: Final = get_logging_payload(legacy, {"id": "tool"}, _CALL_START, _CALL_START) - assert (payload["user"], payload["team_id"], payload["organization_id"]) == (owner, "team", "org") + assert await _charge_request_cost(charge_spend, dict(poll), 0.43, reservation) == charged + assert charge_spend.calls == ((0.43, 0.43, reservation), *(() if second_call is None else (second_call,))) diff --git a/tests/unit/proxy/hooks/test_tpm_concurrent.py b/tests/unit/proxy/hooks/test_tpm_concurrent.py index bfb7a9d6199..0d517e87ef6 100644 --- a/tests/unit/proxy/hooks/test_tpm_concurrent.py +++ b/tests/unit/proxy/hooks/test_tpm_concurrent.py @@ -20,6 +20,7 @@ import copy import io import json from datetime import datetime, timedelta +from types import MappingProxyType from typing import Any, Dict, Final import pytest @@ -27,7 +28,7 @@ import soundfile as sf from fastapi import HTTPException from PIL import Image -from litellm import token_counter +from litellm import get_model_info, token_counter from litellm.caching.caching import DualCache from litellm.litellm_core_utils.ptu_shares import PTUTeamCeiling from litellm.litellm_core_utils.token_counter import UnsupportedContentTypeError, estimate_image_tokens @@ -3971,6 +3972,334 @@ async def test_media_pre_call_rejects_tight_quota_and_reserves_sufficient_quota( assert expected[0] > 1000 and expected[1] >= expected[0] +_EMBEDDING_MODEL: Final = "gemini-embedding-2" +_EMBEDDING_CAPTION: Final = "a red bicycle leaning on a wall" + + +def _embedding_media_uri(kind: str, compressed: bool) -> str: + mime: Final = "image/png" if kind == "image" else "audio/wav" + return f"data:{mime};base64,{_quota_media_payload(kind, compressed)}" + + +@pytest.mark.parametrize("kind", ["image", "audio"]) +@pytest.mark.parametrize( + "shape", + ["string", "list", "file_block", "with_caption", "combined"], +) +def test_embedding_media_input_is_estimated_independently_of_encoding( + rate_limiter: tuple[RateLimitHandler, DualCache], kind: str, shape: str +) -> None: + handler, _ = rate_limiter + + def embedding_request(uri: str) -> dict[str, object]: + shapes: Final[dict[str, object]] = { + "string": uri, + "list": [uri], + "file_block": [{"type": "file", "file": {"file_data": uri}}], + "with_caption": [_EMBEDDING_CAPTION, uri], + "combined": [[_EMBEDDING_CAPTION, uri]], + } + return {"model": _EMBEDDING_MODEL, "input": shapes[shape]} + + uncompressed: Final = embedding_request(_embedding_media_uri(kind, False)) + original: Final = copy.deepcopy(uncompressed) + estimate: Final = handler._estimate_input_and_output_tokens(uncompressed, call_type="aembedding") + assert estimate == handler._estimate_input_and_output_tokens( + embedding_request(_embedding_media_uri(kind, True)), call_type="aembedding" + ) + assert 0 < estimate[0] < 2_000 and estimate[1] == 0 + assert uncompressed == original + + +@pytest.mark.parametrize("kind", ["image", "audio"]) +def test_embedding_media_input_costs_the_same_as_chat_media( + rate_limiter: tuple[RateLimitHandler, DualCache], kind: str +) -> None: + handler, _ = rate_limiter + uri: Final = _embedding_media_uri(kind, False) + chat_block: Final = ( + {"type": "image_url", "image_url": {"url": uri}} + if kind == "image" + else {"type": "input_audio", "input_audio": {"data": _quota_media_payload(kind, False), "format": "wav"}} + ) + chat_input_tokens, _ = handler._estimate_input_and_output_tokens( + {"model": _EMBEDDING_MODEL, "messages": [{"role": "user", "content": [chat_block]}]}, + call_type="acompletion", + ) + caption_tokens, _ = handler._estimate_input_and_output_tokens( + {"model": _EMBEDDING_MODEL, "input": [_EMBEDDING_CAPTION]}, call_type="aembedding" + ) + + assert handler._estimate_input_and_output_tokens( + {"model": _EMBEDDING_MODEL, "input": uri}, call_type="aembedding" + ) == (chat_input_tokens, 0) + assert handler._estimate_input_and_output_tokens( + {"model": _EMBEDDING_MODEL, "input": [_EMBEDDING_CAPTION, uri]}, call_type="aembedding" + ) == (caption_tokens + chat_input_tokens, 0) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("limit_field", ["model_tpm_limit", "model_itpm_limit"]) +async def test_project_token_limit_admits_image_embedding_and_still_rejects_long_text( + rate_limiter: tuple[RateLimitHandler, DualCache], limit_field: str +) -> None: + handler, cache = rate_limiter + auth: Final = UserAPIKeyAuth( + api_key=hash_token(f"sk-embedding-image-{limit_field}"), + project_id=f"proj-embedding-image-{limit_field}", + project_metadata={limit_field: {_EMBEDDING_MODEL: 75_000}}, + ) + image_uri: Final = _embedding_media_uri("image", False) + long_text: Final = "word " * 80_000 + + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": _EMBEDDING_MODEL, "input": image_uri}, + call_type="aembedding", + ) + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": _EMBEDDING_MODEL, "input": long_text}, + call_type="aembedding", + ) + assert error.value.status_code == 429 + assert len(image_uri) > len(long_text) + + +_EMBEDDING_FILE_MIMES: Final = MappingProxyType({"pdf": "application/pdf", "video": "video/mp4"}) + + +def _embedding_file_uri(kind: str) -> str: + return f"data:{_EMBEDDING_FILE_MIMES[kind]};base64,{base64.b64encode(bytes(range(256)) * 1200).decode()}" + + +def _embedding_uri(kind: str) -> str: + return _embedding_file_uri(kind) if kind in _EMBEDDING_FILE_MIMES else _embedding_media_uri(kind, False) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("limit_field", ["model_tpm_limit", "model_itpm_limit"]) +@pytest.mark.parametrize("kind", ["pdf", "video"]) +@pytest.mark.parametrize("shape", ["data_uri", "file_block"]) +async def test_project_token_limit_admits_document_embedding_up_to_the_model_input_limit( + rate_limiter: tuple[RateLimitHandler, DualCache], limit_field: str, kind: str, shape: str +) -> None: + handler, cache = rate_limiter + uri: Final = _embedding_file_uri(kind) + model_input_limit: Final = get_model_info(_EMBEDDING_MODEL)["max_input_tokens"] + assert isinstance(model_input_limit, int) and len(uri) // 4 > 75_000 > model_input_limit + + def project_auth(limit: int) -> UserAPIKeyAuth: + return UserAPIKeyAuth( + api_key=hash_token(f"sk-embedding-{kind}-{shape}-{limit_field}-{limit}"), + project_id=f"proj-embedding-{kind}-{shape}-{limit_field}-{limit}", + project_metadata={limit_field: {_EMBEDDING_MODEL: limit}}, + ) + + item: Final = uri if shape == "data_uri" else {"type": "file", "file": {"file_data": uri}} + request: Final = {"model": _EMBEDDING_MODEL, "input": [_EMBEDDING_CAPTION, item]} + await handler.async_pre_call_hook( + user_api_key_dict=project_auth(75_000), cache=cache, data=dict(request), call_type="aembedding" + ) + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=project_auth(model_input_limit // 2), + cache=cache, + data=dict(request), + call_type="aembedding", + ) + assert error.value.status_code == 429 + + +_TEXT_ONLY_EMBEDDING_MODEL: Final = "text-embedding-3-small" + + +@pytest.mark.parametrize("kind", ["image", "audio", "pdf", "video"]) +def test_text_only_embedding_model_counts_data_uri_as_text( + rate_limiter: tuple[RateLimitHandler, DualCache], kind: str +) -> None: + handler, _ = rate_limiter + uri: Final = _embedding_uri(kind) + + assert handler._estimate_input_and_output_tokens( + {"model": _TEXT_ONLY_EMBEDDING_MODEL, "input": [_EMBEDDING_CAPTION, uri]}, call_type="aembedding" + ) == handler._estimate_input_and_output_tokens( + {"model": _TEXT_ONLY_EMBEDDING_MODEL, "input": [_EMBEDDING_CAPTION, "x" * len(uri)]}, + call_type="aembedding", + ) + + +def _project_limited_embedding_call( + limit_field: str, backends: tuple[str, ...], kind: str = "image" +) -> tuple[RateLimitHandler, DualCache, UserAPIKeyAuth, dict[str, object]]: + cache: Final = DualCache() + handler: Final = RateLimitHandler( + internal_usage_cache=InternalUsageCache(cache), media_model_resolver=lambda _model, _team_id: backends + ) + scope: Final = f"{limit_field}-{kind}-{'-'.join(backends)}" + auth: Final = UserAPIKeyAuth( + api_key=hash_token(f"sk-embedding-backends-{scope}"), + project_id=f"proj-embedding-backends-{scope}", + project_metadata={limit_field: {"team-embedder": 75_000}}, + ) + return handler, cache, auth, {"model": "team-embedder", "input": _embedding_uri(kind)} + + +@pytest.mark.asyncio +@pytest.mark.parametrize("limit_field", ["model_tpm_limit", "model_itpm_limit"]) +@pytest.mark.parametrize( + ("backends", "kind"), + [ + ((_TEXT_ONLY_EMBEDDING_MODEL,), "image"), + ((_EMBEDDING_MODEL, _TEXT_ONLY_EMBEDDING_MODEL), "image"), + (("voyage/voyage-multimodal-3.5",), "image"), + (("oci/cohere.embed-v4.0",), "image"), + (("bedrock/us.twelvelabs.marengo-embed-2-7-v1:0",), "image"), + (("bedrock/twelvelabs.marengo-embed-3-0-v1:0",), "image"), + (("bedrock/amazon.nova-2-multimodal-embeddings-v1:0",), "pdf"), + ], + ids=["text", "mixed", "voyage", "oci", "marengo_2_7", "marengo_3", "nova_pdf"], +) +async def test_project_token_limit_rejects_embedding_media_a_backend_reads_as_text( + limit_field: str, backends: tuple[str, ...], kind: str +) -> None: + handler, cache, auth, request = _project_limited_embedding_call(limit_field, backends, kind) + + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook(user_api_key_dict=auth, cache=cache, data=request, call_type="aembedding") + assert error.value.status_code == 429 + + +@pytest.mark.asyncio +@pytest.mark.parametrize("limit_field", ["model_tpm_limit", "model_itpm_limit"]) +@pytest.mark.parametrize( + "backend", + [ + _EMBEDDING_MODEL, + "bedrock/amazon.titan-embed-image-v1", + "bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", + ], + ids=["gemini", "titan", "nova_async_invoke"], +) +async def test_project_token_limit_admits_image_embedding_through_an_alias_of_an_image_backend( + limit_field: str, backend: str +) -> None: + handler, cache, auth, request = _project_limited_embedding_call(limit_field, (backend,)) + + await handler.async_pre_call_hook(user_api_key_dict=auth, cache=cache, data=request, call_type="aembedding") + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={**request, "input": "word " * 100_000}, + call_type="aembedding", + ) + assert error.value.status_code == 429 + + +def _malformed_embedding_uri(shape: str) -> str: + payload: Final = _quota_media_payload("image", False) + whole_quanta: Final = payload.rstrip("=")[: len(payload.rstrip("=")) // 4 * 4] + words: Final = "word " * 100_000 + return { + "no_comma": f"data:audio/wav;base64{words}", + "text_payload": f"data:image/png;base64,{words}", + "no_base64_marker": f"data:image/png,{payload}", + "header_past_256_chars": f"data:image/png;{'x' * 300};base64,{payload}", + "noncanonical_padding": f"data:image/png;base64,{whole_quanta}AB==", + "padding_mid_payload": f"data:image/png;base64,{whole_quanta}AA==AAAA", + "length_not_multiple_of_4": f"data:image/png;base64,{whole_quanta}A", + }[shape] + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "shape", + [ + "no_comma", + "text_payload", + "no_base64_marker", + "header_past_256_chars", + "noncanonical_padding", + "padding_mid_payload", + "length_not_multiple_of_4", + ], +) +async def test_project_token_limit_counts_a_malformed_data_uri_as_text( + rate_limiter: tuple[RateLimitHandler, DualCache], shape: str +) -> None: + handler, cache = rate_limiter + auth: Final = UserAPIKeyAuth( + api_key=hash_token(f"sk-embedding-malformed-{shape}"), + project_id=f"proj-embedding-malformed-{shape}", + project_metadata={"model_tpm_limit": {_EMBEDDING_MODEL: 75_000}}, + ) + + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": _EMBEDDING_MODEL, "input": _malformed_embedding_uri(shape)}, + call_type="aembedding", + ) + assert error.value.status_code == 429 + + +@pytest.mark.asyncio +@pytest.mark.parametrize("backend", ["cohere/embed-v4.0", "cohere.embed-v4:0"], ids=["cohere", "bedrock_cohere"]) +async def test_project_token_limit_counts_a_mixed_batch_as_text_when_the_backend_types_the_whole_batch( + backend: str, +) -> None: + handler, cache, auth, request = _project_limited_embedding_call("model_tpm_limit", (backend,)) + image_uri: Final = request["input"] + + await handler.async_pre_call_hook( + user_api_key_dict=auth, cache=cache, data={**request, "input": [image_uri, image_uri]}, call_type="aembedding" + ) + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={**request, "input": [image_uri, _EMBEDDING_CAPTION]}, + call_type="aembedding", + ) + assert error.value.status_code == 429 + + +@pytest.mark.asyncio +async def test_project_itpm_limit_reserves_media_tokens_for_an_image_embedding( + rate_limiter: tuple[RateLimitHandler, DualCache], +) -> None: + handler, cache = rate_limiter + image_uri: Final = _embedding_media_uri("image", False) + image_tokens, _ = handler._estimate_input_and_output_tokens( + {"model": _EMBEDDING_MODEL, "input": image_uri}, call_type="aembedding" + ) + auth: Final = UserAPIKeyAuth( + api_key=hash_token("sk-embedding-image-itpm-media"), + project_id="proj-embedding-image-itpm-media", + project_metadata={"model_itpm_limit": {_EMBEDDING_MODEL: image_tokens // 2}}, + ) + + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": _EMBEDDING_MODEL, "input": _EMBEDDING_CAPTION}, + call_type="aembedding", + ) + with pytest.raises(HTTPException) as error: + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": _EMBEDDING_MODEL, "input": image_uri}, + call_type="aembedding", + ) + assert error.value.status_code == 429 + + @pytest.mark.asyncio async def test_ptu_pre_call_weights_media_input_and_output() -> None: cache: Final = DualCache() diff --git a/tests/unit/proxy/management_endpoints/test_internal_user_endpoints.py b/tests/unit/proxy/management_endpoints/test_internal_user_endpoints.py index 39586a1a204..d8aa0239e2d 100644 --- a/tests/unit/proxy/management_endpoints/test_internal_user_endpoints.py +++ b/tests/unit/proxy/management_endpoints/test_internal_user_endpoints.py @@ -3352,6 +3352,8 @@ async def test_user_info_v2_internal_user_cannot_query_other(mocker): mock_prisma_client.db.litellm_usertable.find_unique = mocker.AsyncMock(side_effect=mock_find_unique) + mock_prisma_client.db.litellm_organizationmembership.find_many = mocker.AsyncMock(return_value=[]) + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) mock_request = mocker.MagicMock(spec=Request) @@ -3456,6 +3458,40 @@ async def test_user_info_v2_nonexistent_user_returns_404(mocker): assert "nonexistent-user-id" in str(exc_info.value.message) +@pytest.mark.parametrize( + ("find_unique", "expected_code", "logs_traceback"), + [ + (AsyncMock(return_value=None), "404", False), + (AsyncMock(side_effect=RuntimeError("db down")), "500", True), + ], +) +@pytest.mark.asyncio +async def test_user_info_v2_logs_traceback_only_for_server_errors( + mocker: MockerFixture, + caplog: pytest.LogCaptureFixture, + find_unique: AsyncMock, + expected_code: str, + logs_traceback: bool, +) -> None: + from fastapi import Request + + from litellm.proxy.management_endpoints.internal_user_endpoints import user_info_v2 + + prisma: Final = mocker.MagicMock() + prisma.db.litellm_usertable.find_unique = find_unique + mocker.patch("litellm.proxy.proxy_server.prisma_client", prisma) + + with caplog.at_level(logging.ERROR), pytest.raises(ProxyException) as exc_info: + await user_info_v2( + request=mocker.MagicMock(spec=Request), + user_id="some-user", + user_api_key_dict=UserAPIKeyAuth(user_id="admin-user", user_role=LitellmUserRoles.PROXY_ADMIN), + ) + + assert exc_info.value.code == expected_code + assert any(record.exc_info for record in caplog.records) is logs_traceback + + @pytest.mark.asyncio async def test_user_info_v2_response_shape(mocker): """ @@ -3674,6 +3710,8 @@ async def test_user_info_v2_team_admin_cannot_query_non_team_member(mocker): mock_prisma_client.db.litellm_teamtable.find_many = mocker.AsyncMock(side_effect=mock_find_many_teams) + mock_prisma_client.db.litellm_organizationmembership.find_many = mocker.AsyncMock(return_value=[]) + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) mock_request = mocker.MagicMock(spec=Request) @@ -3690,6 +3728,80 @@ async def test_user_info_v2_team_admin_cannot_query_non_team_member(mocker): assert exc_info.value.code == "404" +_ORG_MEMBERSHIPS: Final = ( + SimpleNamespace(user_id="org-admin-user", organization_id="org-a", user_role="org_admin"), + SimpleNamespace(user_id="org-admin-user", organization_id="org-c", user_role="internal_user"), + SimpleNamespace(user_id="member-of-a", organization_id="org-a", user_role="internal_user"), + SimpleNamespace(user_id="member-of-b", organization_id="org-b", user_role="internal_user"), + SimpleNamespace(user_id="member-of-c", organization_id="org-c", user_role="internal_user"), +) + + +def _membership_matches(membership: SimpleNamespace, where: Mapping[str, object]) -> bool: + return membership.user_id == where["user_id"] and ( + "user_role" not in where or membership.user_role == where["user_role"] + ) + + +def _org_admin_prisma(mocker: MockerFixture) -> MagicMock: + def user_row(user_id: str) -> MagicMock: + row: Final = mocker.MagicMock() + row.teams = [] + row.model_dump.return_value = {"user_id": user_id, "user_email": f"{user_id}@example.com", "teams": []} + return row + + async def find_unique(**kwargs: dict[str, str]) -> MagicMock | None: + user_id: Final = kwargs["where"]["user_id"] + return user_row(user_id) if user_id in {m.user_id for m in _ORG_MEMBERSHIPS} else None + + async def find_many(**kwargs: Mapping[str, object]) -> list[SimpleNamespace]: + return [m for m in _ORG_MEMBERSHIPS if _membership_matches(m, kwargs["where"])] + + prisma: Final = mocker.MagicMock() + prisma.db.litellm_usertable.find_unique = mocker.AsyncMock(side_effect=find_unique) + prisma.db.litellm_organizationmembership.find_many = mocker.AsyncMock(side_effect=find_many) + return prisma + + +@pytest.mark.asyncio +async def test_user_info_v2_org_admin_can_query_member_of_their_org(mocker: MockerFixture) -> None: + from fastapi import Request + + from litellm.proxy.management_endpoints.internal_user_endpoints import user_info_v2 + + mocker.patch("litellm.proxy.proxy_server.prisma_client", _org_admin_prisma(mocker)) + + response: Final = await user_info_v2( + request=mocker.MagicMock(spec=Request), + user_id="member-of-a", + user_api_key_dict=UserAPIKeyAuth(user_id="org-admin-user", user_role=LitellmUserRoles.INTERNAL_USER), + ) + + assert response.user_id == "member-of-a" + assert response.user_email == "member-of-a@example.com" + + +@pytest.mark.parametrize("target_user_id", ["member-of-b", "member-of-c"]) +@pytest.mark.asyncio +async def test_user_info_v2_org_admin_cannot_query_user_outside_their_admin_orgs( + mocker: MockerFixture, target_user_id: str +) -> None: + from fastapi import Request + + from litellm.proxy.management_endpoints.internal_user_endpoints import user_info_v2 + + mocker.patch("litellm.proxy.proxy_server.prisma_client", _org_admin_prisma(mocker)) + + with pytest.raises(ProxyException) as exc_info: + await user_info_v2( + request=mocker.MagicMock(spec=Request), + user_id=target_user_id, + user_api_key_dict=UserAPIKeyAuth(user_id="org-admin-user", user_role=LitellmUserRoles.INTERNAL_USER), + ) + + assert exc_info.value.code == "404" + + @pytest.mark.asyncio async def test_user_info_v2_url_encoding_plus_character(mocker): """ diff --git a/tests/unit/proxy/management_endpoints/test_prompt_caching_requests.py b/tests/unit/proxy/management_endpoints/test_prompt_caching_requests.py index 39dcc9630df..ea61177f773 100644 --- a/tests/unit/proxy/management_endpoints/test_prompt_caching_requests.py +++ b/tests/unit/proxy/management_endpoints/test_prompt_caching_requests.py @@ -1,5 +1,6 @@ from collections.abc import Mapping from typing import Final +from unittest.mock import AsyncMock, MagicMock import httpx import pytest @@ -8,6 +9,7 @@ from fastapi import FastAPI from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.management_endpoints.prompt_caching_requests import router +from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_error, statement_timeout_reads pytestmark = pytest.mark.usefixtures("local_model_cost_map") @@ -67,3 +69,37 @@ async def test_incomplete_cursor_is_rejected( ) as client: response: Final = await client.get(_URL, params={"start_date": _START, "end_date": _END, **params}) assert response.status_code == 400 + + +@pytest.mark.asyncio +async def test_the_requests_read_is_cancelled_by_postgres_after_30s(monkeypatch: pytest.MonkeyPatch) -> None: + from litellm.proxy import proxy_server + + prisma: Final = MagicMock() + transaction: Final = statement_timeout_reads(prisma, AsyncMock(return_value=[])) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + async with httpx.AsyncClient( + transport=httpx.ASGITransport(app=_app(LitellmUserRoles.PROXY_ADMIN)), base_url="http://test" + ) as client: + response: Final = await client.get(_URL, params={"start_date": _START, "end_date": _END}) + + assert response.status_code == 200, response.text + assert transaction.statements == ("SET LOCAL statement_timeout = 30000",) + + +@pytest.mark.asyncio +async def test_a_cancelled_requests_read_is_a_503_with_a_date_range_hint(monkeypatch: pytest.MonkeyPatch) -> None: + from litellm.proxy import proxy_server + + prisma: Final = MagicMock() + statement_timeout_reads(prisma, AsyncMock(side_effect=statement_timeout_error())) + monkeypatch.setattr(proxy_server, "prisma_client", prisma) + async with httpx.AsyncClient( + transport=httpx.ASGITransport(app=_app(LitellmUserRoles.PROXY_ADMIN)), base_url="http://test" + ) as client: + response: Final = await client.get(_URL, params={"start_date": _START, "end_date": _END}) + + assert response.status_code == 503, response.text + assert response.json() == { + "detail": {"error": "Prompt caching requests took longer than 30s. Pick a shorter date range"} + } diff --git a/tests/unit/proxy/management_endpoints/test_team_endpoints.py b/tests/unit/proxy/management_endpoints/test_team_endpoints.py index 2f4dad0ccd7..697d9810284 100644 --- a/tests/unit/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/unit/proxy/management_endpoints/test_team_endpoints.py @@ -10,6 +10,7 @@ from unittest.mock import AsyncMock, MagicMock, PropertyMock, call, patch import httpx import litellm +from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_error, statement_timeout_reads import pytest from fastapi import HTTPException, Request from fastapi.testclient import TestClient @@ -17286,12 +17287,15 @@ async def test_get_team_spend_by_user_admin_groups_spend_logs_by_team_and_user(m alpha = _team_spend_by_user_team("team-alpha", "Team Alpha", Member(user_id="alice", role="admin"), []) beta = _team_spend_by_user_team("team-beta", "Team Beta", Member(user_id="alice", role="user"), []) mock_db_client.db.litellm_teamtable.find_many = AsyncMock(return_value=[alpha, beta]) - mock_db_client.db.query_raw = AsyncMock( - return_value=[ - _team_spend_by_user_db_row("team-alpha", "alice", 0.5, 3), - _team_spend_by_user_db_row("team-alpha", "bob", 0.25, 2), - _team_spend_by_user_db_row("team-beta", "alice", 0.1, 1), - ] + transaction = statement_timeout_reads( + mock_db_client, + AsyncMock( + return_value=[ + _team_spend_by_user_db_row("team-alpha", "alice", 0.5, 3), + _team_spend_by_user_db_row("team-alpha", "bob", 0.25, 2), + _team_spend_by_user_db_row("team-beta", "alice", 0.1, 1), + ] + ), ) response = await get_team_spend_by_user( @@ -17301,7 +17305,9 @@ async def test_get_team_spend_by_user_admin_groups_spend_logs_by_team_and_user(m end_date="2026-09-04", ) - sql, *params = mock_db_client.db.query_raw.call_args.args + assert any("statement_timeout = 30000" in statement for statement in transaction.statements) + mock_db_client.db.tx.assert_not_called() + sql, *params = transaction.query_raw.call_args.args assert params == ["2026-09-01", "2026-09-04", "team-alpha", "team-beta"] assert 'FROM "LiteLLM_SpendLogs" sl' in sql assert 'sl."startTime" >= $1::timestamp' in sql @@ -17328,7 +17334,7 @@ async def test_get_team_spend_by_user_team_admin_sees_every_member(mock_db_clien caller = UserAPIKeyAuth(user_id="alice", user_role=LitellmUserRoles.INTERNAL_USER) alpha = _team_spend_by_user_team("team-alpha", "Team Alpha", Member(user_id="alice", role="admin"), []) mock_db_client.db.litellm_teamtable.find_many = AsyncMock(return_value=[alpha]) - mock_db_client.db.query_raw = AsyncMock(return_value=[]) + transaction = statement_timeout_reads(mock_db_client, AsyncMock(return_value=[])) mock_db_client.writer_db.litellm_usertable.find_unique = AsyncMock( return_value=_team_spend_by_user_caller("alice", ["team-alpha"]) ) @@ -17337,7 +17343,7 @@ async def test_get_team_spend_by_user_team_admin_sees_every_member(mock_db_clien user_api_key_dict=caller, team_ids="team-alpha", start_date="2026-09-01", end_date="2026-09-04" ) - sql, *params = mock_db_client.db.query_raw.call_args.args + sql, *params = transaction.query_raw.call_args.args assert params == ["2026-09-01", "2026-09-04", "team-alpha"] assert 'sl."user" = $' not in sql @@ -17350,7 +17356,9 @@ async def test_get_team_spend_by_user_plain_member_only_sees_own_row(mock_db_cli alpha = _team_spend_by_user_team("team-alpha", "Team Alpha", Member(user_id="bob", role="user"), ["/key/info"]) mock_db_client.db.litellm_teamtable.find_many = AsyncMock(return_value=[alpha]) mock_db_client.db.litellm_verificationtoken.find_many = AsyncMock(return_value=[]) - mock_db_client.db.query_raw = AsyncMock(return_value=[_team_spend_by_user_db_row("team-alpha", "bob", 0.25, 2)]) + transaction = statement_timeout_reads( + mock_db_client, AsyncMock(return_value=[_team_spend_by_user_db_row("team-alpha", "bob", 0.25, 2)]) + ) mock_db_client.writer_db.litellm_usertable.find_unique = AsyncMock( return_value=_team_spend_by_user_caller("bob", ["team-alpha"]) ) @@ -17359,7 +17367,7 @@ async def test_get_team_spend_by_user_plain_member_only_sees_own_row(mock_db_cli user_api_key_dict=caller, team_ids="team-alpha", start_date="2026-09-01", end_date="2026-09-04" ) - sql, *params = mock_db_client.db.query_raw.call_args.args + sql, *params = transaction.query_raw.call_args.args assert params == ["2026-09-01", "2026-09-04", "team-alpha", "bob"] assert "sl.team_id IN ($3)" in sql assert 'AND sl."user" = $4' in sql @@ -17371,7 +17379,7 @@ async def test_get_team_spend_by_user_member_of_other_team_gets_404(mock_db_clie from litellm.proxy.management_endpoints.team_endpoints import get_team_spend_by_user caller = UserAPIKeyAuth(user_id="bob", user_role=LitellmUserRoles.INTERNAL_USER) - mock_db_client.db.query_raw = AsyncMock(return_value=[]) + transaction = statement_timeout_reads(mock_db_client, AsyncMock(return_value=[])) mock_db_client.writer_db.litellm_usertable.find_unique = AsyncMock( return_value=_team_spend_by_user_caller("bob", ["team-alpha"]) ) @@ -17382,7 +17390,7 @@ async def test_get_team_spend_by_user_member_of_other_team_gets_404(mock_db_clie ) assert exc_info.value.status_code == 404 - mock_db_client.db.query_raw.assert_not_called() + transaction.query_raw.assert_not_called() @pytest.mark.asyncio @@ -17400,7 +17408,7 @@ async def test_get_team_spend_by_user_member_of_other_team_gets_404(mock_db_clie async def test_get_team_spend_by_user_rejects_bad_input(mock_db_client, team_ids, start_date, end_date, expected_error): from litellm.proxy.management_endpoints.team_endpoints import get_team_spend_by_user - mock_db_client.db.query_raw = AsyncMock(return_value=[]) + transaction = statement_timeout_reads(mock_db_client, AsyncMock(return_value=[])) admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) with pytest.raises(HTTPException) as exc_info: @@ -17410,7 +17418,28 @@ async def test_get_team_spend_by_user_rejects_bad_input(mock_db_client, team_ids assert exc_info.value.status_code == 400 assert expected_error in str(exc_info.value.detail) - mock_db_client.db.query_raw.assert_not_called() + transaction.query_raw.assert_not_called() + + +@pytest.mark.asyncio +async def test_get_team_spend_by_user_statement_timeout_returns_503(mock_db_client): + from litellm.proxy.management_endpoints.team_endpoints import get_team_spend_by_user + + admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + alpha = _team_spend_by_user_team("team-alpha", "Team Alpha", Member(user_id="alice", role="admin"), []) + mock_db_client.db.litellm_teamtable.find_many = AsyncMock(return_value=[alpha]) + statement_timeout_reads(mock_db_client, AsyncMock(side_effect=statement_timeout_error())) + + with pytest.raises(HTTPException) as exc_info: + await get_team_spend_by_user( + user_api_key_dict=admin, + team_ids="team-alpha", + start_date="2026-09-01", + end_date="2026-09-04", + ) + + assert exc_info.value.status_code == 503 + assert "longer than 30s" in str(exc_info.value.detail) class _TeamRowWithOrganization(LiteLLM_TeamTable): diff --git a/tests/unit/proxy/mcp_server/test_upstream.py b/tests/unit/proxy/mcp_server/test_upstream.py new file mode 100644 index 00000000000..7c88c679972 --- /dev/null +++ b/tests/unit/proxy/mcp_server/test_upstream.py @@ -0,0 +1,192 @@ +import queue +from collections.abc import Iterator, Mapping +from typing import Final +from unittest.mock import MagicMock + +import httpx2 +import pytest +from mcp.server.context import ServerRequestContext +from mcp.types import CallToolResult + +from litellm.proxy.mcp_server.mcp_context import active_mcp_request_ctx_var +from litellm.proxy.mcp_server.mcp_server_manager import MCPServerManager +from litellm.proxy.mcp_server.outbound_credentials.resolver import UpstreamCredentialProvider +from litellm.proxy.mcp_server.upstream import prepare_upstream_client +from litellm.types.mcp import MCPAuth, MCPTransport, MCPUpstreamProtocol +from litellm.types.mcp_server.mcp_server_manager import MCPServer +from tests._support.mcp_count_tool_upstream import count_tool_upstream, methods_received + +_HTTP_URL: Final = "https://upstream.example/mcp" +_SERVER_SHAPES: Final = pytest.mark.parametrize( + ("server_fields", "per_request_token", "forwarded_token"), + [ + ({"transport": MCPTransport.http, "url": _HTTP_URL}, None, None), + ( + { + "transport": MCPTransport.http, + "url": _HTTP_URL, + "auth_type": MCPAuth.bearer_token, + "authentication_token": "static-token", + }, + None, + None, + ), + ({"transport": MCPTransport.http, "url": _HTTP_URL, "auth_type": MCPAuth.bearer_token}, "caller-key", None), + ({"transport": MCPTransport.http, "url": _HTTP_URL, "auth_type": MCPAuth.oauth_delegate}, None, "caller-token"), + ], + ids=["no-auth", "static-bearer-token", "per-request-token", "forwarded-caller-token"], +) + + +@pytest.fixture +def _active_mcp_request_context() -> Iterator[None]: + context: Final = ServerRequestContext( + session=MagicMock(), lifespan_context={}, protocol_version="2025-11-25", method="tools/call", request=None + ) + token: Final = active_mcp_request_ctx_var.set(context) + try: + yield + finally: + active_mcp_request_ctx_var.reset(token) + + +def _server(server_fields: Mapping[str, object], skip_setting: bool | None) -> MCPServer: + return MCPServer.model_validate( + { + **server_fields, + "server_id": "skip-validation", + "name": "skip_validation", + "mcp_info": {} if skip_setting is None else {"skip_tool_output_validation": skip_setting}, + } + ) + + +async def _call_count_tool( + manager: MCPServerManager, + server: MCPServer, + *, + per_request_token: str | None = None, + forwarded_token: str | None = None, + gateway_session_id: str | None = None, +) -> CallToolResult: + result: Final = await manager._call_regular_mcp_tool( + mcp_server=server, + original_tool_name="count", + arguments={}, + tasks=[], + mcp_auth_header=per_request_token, + mcp_server_auth_headers=None, + oauth2_headers=None if forwarded_token is None else {"Authorization": f"Bearer {forwarded_token}"}, + raw_headers=None if gateway_session_id is None else {"mcp-session-id": gateway_session_id}, + proxy_logging_obj=None, + ) + assert isinstance(result, CallToolResult) + return result + + +@pytest.mark.asyncio +@_SERVER_SHAPES +@pytest.mark.parametrize( + ("skip_setting", "rejected"), [(None, True), (False, True), (True, False)], ids=["unset", "off", "on"] +) +async def test_the_setting_alone_decides_whether_a_tool_result_is_checked( + server_fields: Mapping[str, object], + per_request_token: str | None, + forwarded_token: str | None, + skip_setting: bool | None, + rejected: bool, +) -> None: + upstream: Final = count_tool_upstream({"n": "not-an-integer"}, queue.SimpleQueue()) + manager: Final = MCPServerManager(upstream_transport=httpx2.MockTransport(upstream)) + + result: Final = await _call_count_tool( + manager, + _server(server_fields, skip_setting), + per_request_token=per_request_token, + forwarded_token=forwarded_token, + ) + + assert result.is_error is rejected + + +@pytest.mark.asyncio +@_SERVER_SHAPES +async def test_a_skipping_server_is_never_asked_for_its_tools( + server_fields: Mapping[str, object], per_request_token: str | None, forwarded_token: str | None +) -> None: + methods_seen: Final[queue.SimpleQueue[str]] = queue.SimpleQueue() + manager: Final = MCPServerManager( + upstream_transport=httpx2.MockTransport(count_tool_upstream({"n": 1}, methods_seen)) + ) + + result: Final = await _call_count_tool( + manager, + _server(server_fields, True), + per_request_token=per_request_token, + forwarded_token=forwarded_token, + ) + + assert result.is_error is False + assert "tools/list" not in methods_received(methods_seen) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("first_setting", "second_setting", "rejected_in_order"), + [(True, False, (False, True)), (False, True, (True, False))], + ids=["turned-off", "turned-on"], +) +async def test_a_changed_setting_applies_to_the_next_call_in_a_live_gateway_session( + _active_mcp_request_context: None, + first_setting: bool, + second_setting: bool, + rejected_in_order: tuple[bool, bool], +) -> None: + upstream: Final = count_tool_upstream({"n": "not-an-integer"}, queue.SimpleQueue()) + manager: Final = MCPServerManager(upstream_transport=httpx2.MockTransport(upstream)) + manager.track_gateway_session("gateway-1") + fields: Final = {"transport": MCPTransport.http, "url": _HTTP_URL} + try: + first: Final = await _call_count_tool(manager, _server(fields, first_setting), gateway_session_id="gateway-1") + second: Final = await _call_count_tool(manager, _server(fields, second_setting), gateway_session_id="gateway-1") + finally: + await manager.close_all_upstream_sessions() + + assert (first.is_error, second.is_error) == rejected_in_order + + +@pytest.mark.asyncio +async def test_a_stdio_upstream_client_carries_the_setting(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("LITELLM_ENABLE_MCP_STDIO", "true") + server: Final = _server({"transport": MCPTransport.stdio, "command": "python", "args": ["server.py"]}, True) + + client: Final = await prepare_upstream_client( + server, provider=UpstreamCredentialProvider(), root_path="/mcp", protocol_version="auto" + ) + + assert client.skip_tool_output_validation is True + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("protocol_version", "expected"), [("auto", True), ("2025-11-25", True), ("2026-07-28", False)] +) +async def test_upstream_client_ignores_the_setting_where_the_call_already_lists_tools( + protocol_version: MCPUpstreamProtocol, expected: bool +) -> None: + server: Final = MCPServer.model_validate( + { + "server_id": "skip-validation", + "name": "skip_validation", + "url": _HTTP_URL, + "transport": MCPTransport.http, + "protocol_version": protocol_version, + "mcp_info": {"skip_tool_output_validation": True}, + } + ) + + client: Final = await prepare_upstream_client( + server, provider=UpstreamCredentialProvider(), root_path="/mcp", protocol_version=protocol_version + ) + + assert client.skip_tool_output_validation is expected diff --git a/tests/unit/proxy/middleware/test_request_body_decompression_middleware.py b/tests/unit/proxy/middleware/test_request_body_decompression_middleware.py index 2727857c352..48981288d00 100644 --- a/tests/unit/proxy/middleware/test_request_body_decompression_middleware.py +++ b/tests/unit/proxy/middleware/test_request_body_decompression_middleware.py @@ -54,10 +54,11 @@ async def _echo_request(request: Request) -> Response: ) -def _client(max_decompressed_body_size_mb: float = CAP_MB, **options: object) -> TestClient: +def _client(max_decompressed_body_size_mb: float = CAP_MB, root_path: str = "", **options: object) -> TestClient: echo_app: Final = Starlette( routes=[ Route("/v1/systemone", _echo_request, methods=["POST"]), + Route("/claude-code/plugins/upload", _echo_request, methods=["POST"]), Route("/plugin-proxy/{path:path}", _echo_request, methods=["POST"]), Route("/claude_code_gateway/v1/{signal}", _echo_request, methods=["POST"]), ] @@ -67,7 +68,8 @@ def _client(max_decompressed_body_size_mb: float = CAP_MB, **options: object) -> echo_app, max_decompressed_body_size_mb=max_decompressed_body_size_mb, **options, # pyright: ignore[reportArgumentType] # each test passes one typed keyword - ) + ), + root_path=root_path, ) @@ -220,19 +222,51 @@ def test_body_decoding_past_the_cap_is_rejected(): @pytest.mark.parametrize( ("operator_max_mb", "effective_max_mb"), - [(0.25, 0.25), (CAP_MB * 4, CAP_MB), (None, CAP_MB), (0, CAP_MB)], + [(0.25, 0.25), (CAP_MB * 4, CAP_MB), (None, CAP_MB), (0, CAP_MB), (1 / 64, 1 / 64)], ) +@pytest.mark.parametrize(("route_max_mb", "root_path"), [(None, ""), (1 / 32, ""), (1 / 32, "/gateway")]) +@pytest.mark.parametrize("coding", ("gzip", "zstd")) def test_operator_request_size_limit_caps_the_decoded_body_when_it_is_lower( - operator_max_mb: float | None, effective_max_mb: float + operator_max_mb: float | None, effective_max_mb: float, route_max_mb: float | None, root_path: str, coding: str, ): - client: Final = _client(get_max_request_size_mb=lambda: operator_max_mb) - within: Final = zstd.compress(_low_entropy_text(int(effective_max_mb * MIB))) - over: Final = zstd.compress(_low_entropy_text(int(effective_max_mb * MIB) + 1)) - - assert _post(client, within, ("zstd",)).status_code == 200 - over_response: Final = _post(client, over, ("zstd",)) + path: Final = "/claude-code/plugins/upload" if route_max_mb else "/v1/systemone" + limit: Final = min(effective_max_mb, route_max_mb or effective_max_mb) + client: Final = _client( + root_path=root_path, + get_max_request_size_mb=lambda: operator_max_mb, + route_max_request_size_mb={("POST", path): route_max_mb} if route_max_mb else None, + ) + compress: Final = gzip.compress if coding == "gzip" else zstd.compress + within: Final = compress(_low_entropy_text(int(limit * MIB))) + over: Final = compress(_low_entropy_text(int(limit * MIB) + 1)) + assert _post(client, within, (coding,), f"{root_path}{path}").status_code == 200 + over_response: Final = _post(client, over, (coding,), f"{root_path}{path}") assert over_response.status_code == 413 - assert over_response.json() == _decoded_too_large(effective_max_mb) + assert over_response.json() == _decoded_too_large(limit) + + +@pytest.mark.parametrize(("coding", "compress"), [ + ("identity", bytes), ("gzip", gzip.compress), ("zstd", zstd.compress), +]) +def test_proxy_bounds_skill_uploads_before_authentication_or_body_parsing( + coding: str, compress: Callable[[bytes], bytes], +) -> None: + from litellm.proxy.proxy_server import app + + body: Final = random.Random(0).randbytes(6 * MIB) + bytes(6 * MIB) + encoded: Final = compress(body) + + response: Final = TestClient(app).post( + "/claude-code/plugins/upload", + content=encoded, + headers={"content-encoding": coding, "content-type": "multipart/form-data; boundary=upload"}, + ) + + assert (len(encoded) < 11 * MIB) == (coding != "identity") + assert response.status_code == 413 + assert response.json() == ( + {"error": "Request size is too large. Max size is 11 MB"} if coding == "identity" else _decoded_too_large(11) + ) @pytest.mark.parametrize(("signature", "content_encoding"), [(GZIP_MAGIC, "gzip"), (ZSTD_FRAME_MAGIC, "zstd")]) diff --git a/tests/unit/proxy/middleware/test_request_size_limit_middleware.py b/tests/unit/proxy/middleware/test_request_size_limit_middleware.py index 0a30b4cf9ef..7bb73b77000 100644 --- a/tests/unit/proxy/middleware/test_request_size_limit_middleware.py +++ b/tests/unit/proxy/middleware/test_request_size_limit_middleware.py @@ -1,5 +1,7 @@ +from collections.abc import AsyncIterator from typing import Final +import httpx import pytest from starlette.responses import JSONResponse from starlette.testclient import TestClient @@ -10,7 +12,16 @@ from litellm.proxy.middleware.request_size_limit_middleware import ( ) -def test_request_size_limit_middleware_rejects_content_length_before_body_read(): +@pytest.mark.parametrize(("path", "root_path", "operator_mb", "route_mb"), [ + ("/chat/completions", "", 1, None), + ("/claude-code/plugins/upload", "", None, 1), + ("/gateway/claude-code/plugins/upload", "/gateway", None, 1), + ("/claude-code/plugins/upload", "", 2, 1), + ("/claude-code/plugins/upload", "", 1, 2), +]) +def test_request_size_limit_middleware_rejects_content_length_before_body_read( + path: str, root_path: str, operator_mb: int | None, route_mb: int | None, +) -> None: downstream_called = False async def app(scope, receive, send): @@ -22,13 +33,15 @@ def test_request_size_limit_middleware_rejects_content_length_before_body_read() client = TestClient( RequestSizeLimitMiddleware( app, - get_max_request_size_mb=lambda: 1, - is_request_size_limit_enabled=lambda: True, - ) + get_max_request_size_mb=lambda: operator_mb, + is_request_size_limit_enabled=lambda: operator_mb is not None, + route_max_request_size_mb={("POST", "/claude-code/plugins/upload"): route_mb} if route_mb else None, + ), + root_path=root_path, ) response = client.post( - "/chat/completions", + path, content=b"x" * (1024 * 1024 + 1), headers={"content-type": "application/json"}, ) @@ -39,7 +52,16 @@ def test_request_size_limit_middleware_rejects_content_length_before_body_read() assert downstream_called is False -def test_request_size_limit_middleware_zero_limit_disables_guard(): +@pytest.mark.parametrize(("enabled", "operator_mb"), [(True, 0), (False, 1)]) +@pytest.mark.parametrize(("method", "path"), [ + ("POST", "/chat/completions"), + ("PUT", "/claude-code/plugins/upload"), + ("POST", "/claude-code/plugins/upload-other"), + ("POST", "/claude-code/plugins"), +]) +def test_request_size_limit_middleware_zero_or_disabled_global_limit_preserves_other_routes( + enabled: bool, operator_mb: int, method: str, path: str, +) -> None: downstream_called = False async def app(scope, receive, send): @@ -51,14 +73,15 @@ def test_request_size_limit_middleware_zero_limit_disables_guard(): client = TestClient( RequestSizeLimitMiddleware( app, - get_max_request_size_mb=lambda: 0, - is_request_size_limit_enabled=lambda: True, + get_max_request_size_mb=lambda: operator_mb, + is_request_size_limit_enabled=lambda: enabled, + route_max_request_size_mb={("POST", "/claude-code/plugins/upload"): 1}, ) ) - response = client.post( - "/chat/completions", - content=b"x", + response = client.request( + method, path, + content=b"x" * (1024 * 1024 + 1), headers={"content-type": "application/json"}, ) @@ -154,3 +177,33 @@ def test_an_oversized_messages_request_gets_the_anthropic_request_too_large_erro "type": "error", "error": {"type": "request_too_large", "message": "Request size is too large. Max size is 1 MB"}, } + + +@pytest.mark.asyncio +@pytest.mark.parametrize("path", ("/claude-code/plugins/upload", "/chat/completions")) +async def test_chunked_upload_overflow_stops_fastapi_and_auth_multipart_parsing( + path: str, +) -> None: + from litellm.proxy.proxy_server import app + + limited: Final = RequestSizeLimitMiddleware( + app, + get_max_request_size_mb=lambda: None, + is_request_size_limit_enabled=lambda: False, + route_max_request_size_mb={("POST", path): 1 / 1024}, + ) + + async def chunks() -> AsyncIterator[bytes]: + yield b'--skill\r\nContent-Disposition: form-data; name="file"; filename="skill.zip"\r\n\r\n' + yield b"x" * 1024 + pytest.fail("The server consumed more of the upload after its byte limit") + + async with httpx.AsyncClient(transport=httpx.ASGITransport(app=limited), base_url="http://test") as client: + response: Final = await client.post( + path, + content=chunks(), + headers={"content-type": "multipart/form-data; boundary=skill"}, + ) + + assert response.status_code == 413 + assert response.json() == {"detail": "Request size is too large. Max size is 0.0009765625 MB"} diff --git a/tests/unit/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/unit/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 4a76aa77f14..11b07472eaa 100644 --- a/tests/unit/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/unit/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -7241,6 +7241,178 @@ class TestRouterModelRelayUpstreamContract: assert json.loads(result.body) == upstream_body assert result.headers["x-ms-request-id"] == "req-1" + @pytest.mark.asyncio + async def test_azure_relay_answers_401_when_entra_rejects_the_deployment_credential(self, monkeypatch): + from litellm.llms.azure.common_utils import EntraRejectionError + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + reason: Final = "Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided." + + class EntraRejectingRouter: + async def allm_passthrough_route(self, **kwargs): + try: + raise EntraRejectionError(status_code=401, message=reason) + except EntraRejectionError as rejection: + raise BaseLLMException(status_code=401, message=str(rejection)) from rejection + + self._install_router(monkeypatch, EntraRejectingRouter(), {"model": "gpt-5", "stream": False}) + + with pytest.raises(ProxyException) as excinfo: + await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=self._request("application/json"), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert (excinfo.value.code, excinfo.value.message) == ("401", reason) + + @pytest.mark.asyncio + async def test_azure_relay_entra_rejection_hides_an_internal_issuer_host(self, monkeypatch): + from litellm.llms.azure.common_utils import EntraRejectionError + + reason: Final = ( + "Failed to get Azure AD token: AADSTS700211: No matching federated identity record found for " + "presented assertion issuer 'https://oidc.build.internal:8443/realms/ci'." + ) + + class EntraRejectingRouter: + async def allm_passthrough_route(self, **kwargs): + raise EntraRejectionError(status_code=401, message=reason) + + self._install_router(monkeypatch, EntraRejectingRouter(), {"model": "gpt-5", "stream": False}) + + with pytest.raises(ProxyException) as excinfo: + await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=self._request("application/json"), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert excinfo.value.code == "401" + assert "AADSTS700211" in excinfo.value.message + assert "oidc.build.internal" not in excinfo.value.message + + @pytest.mark.asyncio + async def test_azure_relay_keeps_a_failure_entra_did_not_cause(self, monkeypatch): + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + upstream_failure: Final = BaseLLMException(status_code=500, message="Connection reset by Azure") + + class FailingRouter: + async def allm_passthrough_route(self, **kwargs): + raise upstream_failure + + self._install_router(monkeypatch, FailingRouter(), {"model": "gpt-5", "stream": False}) + + with pytest.raises(BaseLLMException) as excinfo: + await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=self._request("application/json"), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert excinfo.value is upstream_failure + + @pytest.mark.asyncio + async def test_azure_relay_keeps_entra_throttling_the_oidc_token_exchange_as_a_429(self, monkeypatch): + monkeypatch.setenv("LITELLM_QA_FEDERATED_TOKEN", "federated-token") + monkeypatch.delenv("AZURE_AUTHORITY_HOST", raising=False) + router: Final = litellm.Router( + model_list=[ + { + "model_name": "gpt-5", + "litellm_params": { + "model": "azure/gpt-5", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "throttled-tenant", + "client_id": "qa-client", + "azure_ad_token": "oidc/env/LITELLM_QA_FEDERATED_TOKEN", + }, + } + ], + num_retries=0, + ) + self._install_router(monkeypatch, router, {"model": "gpt-5", "stream": False}) + + with respx.mock: + respx.post("https://login.microsoftonline.com/throttled-tenant/oauth2/v2.0/token").mock( + return_value=httpx.Response( + 429, + headers={"Retry-After": "7"}, + json={"error": "temporarily_unavailable", "error_codes": [90055]}, + ) + ) + deployment_route: Final = respx.post(url__startswith="https://litellm-qa.openai.azure.com").mock( + return_value=httpx.Response(200, json={"ok": True}) + ) + response: Final = await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=self._request("application/json"), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert (response.status_code, response.headers.get("retry-after")) == (429, "7") + assert not deployment_route.called + + + @pytest.mark.asyncio + async def test_azure_relay_keeps_a_later_deployment_failure_after_an_entra_rejection( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + from azure.core.exceptions import ClientAuthenticationError + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + def rejected_token() -> str: + raise ClientAuthenticationError(message="AADSTS7000215: Invalid client secret provided.") + + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + router: Final = litellm.Router( + model_list=[ + { + "model_name": "gpt-5", + "litellm_params": { + "model": "azure/gpt-5", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "azure_ad_token_provider": rejected_token, + "order": 1, + }, + }, + { + "model_name": "gpt-5", + "litellm_params": { + "model": "azure/gpt-5", + "api_base": "https://litellm-qa-unreachable.openai.azure.com", + "api_version": "2025-04-01-preview", + "api_key": "configured-key", + "order": 2, + }, + }, + ], + num_retries=1, + ) + self._install_router(monkeypatch, router, {"model": "gpt-5", "stream": False}) + + with respx.mock: + unreachable_route: Final = respx.post(url__startswith="https://litellm-qa-unreachable.openai.azure.com").mock( + side_effect=httpx.ConnectError("Connection refused") + ) + with pytest.raises(BaseLLMException) as excinfo: + await azure_proxy_route( + endpoint="openai/deployments/gpt-5/chat/completions", + request=self._request("application/json"), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert unreachable_route.called + assert excinfo.value.status_code == 500 + NIM_INFER_BODY = { "input": [ diff --git a/tests/unit/proxy/public_endpoints/test_blog_posts_endpoint.py b/tests/unit/proxy/public_endpoints/test_blog_posts_endpoint.py index a771b1ccdb0..5d460617dda 100644 --- a/tests/unit/proxy/public_endpoints/test_blog_posts_endpoint.py +++ b/tests/unit/proxy/public_endpoints/test_blog_posts_endpoint.py @@ -69,7 +69,7 @@ def test_get_blog_posts_returns_every_fetched_post(client): def test_get_blog_posts_returns_local_backup_on_failure(client): - """Endpoint returns the local backup, with empty tags, when the fetcher raises.""" + """Endpoint returns the local backup when the fetcher raises.""" with patch( "litellm.proxy.public_endpoints.public_endpoints.get_blog_posts", side_effect=Exception("fetch failed"), @@ -79,4 +79,3 @@ def test_get_blog_posts_returns_local_backup_on_failure(client): assert response.status_code == 200 backup = [post.model_dump(mode="json") for post in GetBlogPosts.load_local_blog_posts()] assert response.json()["posts"] == backup - assert all(post["tags"] == [] for post in response.json()["posts"]) diff --git a/tests/unit/proxy/public_endpoints/test_public_endpoints.py b/tests/unit/proxy/public_endpoints/test_public_endpoints.py index 29413d2b189..c17a9ba9371 100644 --- a/tests/unit/proxy/public_endpoints/test_public_endpoints.py +++ b/tests/unit/proxy/public_endpoints/test_public_endpoints.py @@ -529,6 +529,7 @@ def _decisions_provider_entry(provider: str) -> ProviderCreateInfo: ("Nace", "Nace", LlmProviders.NACE), ("PARALLEL_AI", "Parallel AI", LlmProviders.PARALLEL_AI), ("EU_JEV", "eu/jev", LlmProviders.EU_JEV), + ("EMPIRIOLABS", "EmpirioLabs AI", LlmProviders.EMPIRIOLABS), ), ) def test_decisions_provider_fields(provider_key: str, display_name: str, provider: LlmProviders) -> None: diff --git a/tests/unit/proxy/response_api_endpoints/test_endpoints.py b/tests/unit/proxy/response_api_endpoints/test_endpoints.py index ef43fe56146..b7d0d9e62cd 100644 --- a/tests/unit/proxy/response_api_endpoints/test_endpoints.py +++ b/tests/unit/proxy/response_api_endpoints/test_endpoints.py @@ -2556,6 +2556,66 @@ class TestResponsesWSFirstFrameModelAuth: "response": {"input": "hi [guarded]", "tools": tools[:1]}, } + @pytest.mark.asyncio + @pytest.mark.parametrize("nested", [True, False]) + async def test_frame_guard_forwards_the_tool_choice_a_guardrail_rewrote(self, nested: bool): + from litellm.proxy.response_api_endpoints.endpoints import _responses_ws_frame_guard + + async def pre_call_hook(user_api_key_dict, data, call_type, guardrails_only): + return {**data, "tools": [], "tool_choice": "none"} + + proxy_logging_obj = MagicMock(during_call_hook=AsyncMock()) + proxy_logging_obj.pre_call_hook = AsyncMock(side_effect=pre_call_hook) + guard = _responses_ws_frame_guard( + {"model": "gpt-4o-mini", "litellm_metadata": {}}, MagicMock(), proxy_logging_obj, MagicMock() + ) + body = { + "input": "hi", + "tools": [{"type": "function", "name": "a"}], + "tool_choice": {"type": "function", "name": "a"}, + } + frame = {"type": "response.create", "response": body} if nested else {"type": "response.create", **body} + + guarded = json.loads((await guard(json.dumps(frame))).frame) + + sent = guarded["response"] if nested else guarded + assert (sent["tools"], sent["tool_choice"]) == ([], "none") + + @pytest.mark.asyncio + @pytest.mark.parametrize("nested", [True, False]) + async def test_frame_guard_forwards_the_extra_body_tools_a_rewrite_guardrail_stripped(self, nested: bool): + from litellm.proxy.guardrails.guardrail_hooks.tool_permission import ToolPermissionGuardrail + from litellm.proxy.response_api_endpoints.endpoints import _responses_ws_frame_guard + from litellm.types.guardrails import GuardrailEventHooks + + guardrail = ToolPermissionGuardrail( + guardrail_name="ws-rewrite", + rules=[{"id": "deny_shell", "tool_name": r"^shell$", "decision": "deny"}], + default_action="allow", + on_disallowed_action="rewrite", + event_hook=GuardrailEventHooks.pre_call, + default_on=True, + ) + + async def pre_call_hook(user_api_key_dict, data, call_type, guardrails_only): + return await guardrail.async_pre_call_hook( + user_api_key_dict=user_api_key_dict, cache=MagicMock(), data=data, call_type=call_type + ) + + proxy_logging_obj = MagicMock(during_call_hook=AsyncMock()) + proxy_logging_obj.pre_call_hook = AsyncMock(side_effect=pre_call_hook) + guard = _responses_ws_frame_guard( + {"model": "gpt-4o-mini", "litellm_metadata": {}}, MagicMock(), proxy_logging_obj, MagicMock() + ) + weather = {"type": "function", "name": "get_weather"} + body = {"input": "hi", "extra_body": {"tools": [weather, {"type": "function", "name": "shell"}]}} + frame = {"type": "response.create", "response": body} if nested else {"type": "response.create", **body} + + guarded = json.loads((await guard(json.dumps(frame))).frame) + + sent = guarded["response"] if nested else guarded + assert sent["extra_body"] == {"tools": [weather]} + @pytest.mark.asyncio async def test_frame_guard_strips_the_proxy_owned_metadata_keys_a_client_sends(self): from litellm.proxy.response_api_endpoints.endpoints import _responses_ws_frame_guard @@ -2991,7 +3051,7 @@ class TestResponsesWSFirstFrameModelAuth: assert dict(guarded.pii_tokens) == {"": "alice@example.com"} @pytest.mark.asyncio - @pytest.mark.parametrize("field", ["input", "instructions", "tools"]) + @pytest.mark.parametrize("field", ["input", "instructions", "tools", "tool_choice", "extra_body"]) async def test_frame_guard_rejects_and_bills_a_guarded_field_beside_a_nested_response(self, field: str): from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger from litellm.proxy.response_api_endpoints.endpoints import _responses_ws_frame_guard diff --git a/tests/unit/proxy/spend_tracking/test_cost_finalization.py b/tests/unit/proxy/spend_tracking/test_cost_finalization.py index c9515c1232e..ef70a193280 100644 --- a/tests/unit/proxy/spend_tracking/test_cost_finalization.py +++ b/tests/unit/proxy/spend_tracking/test_cost_finalization.py @@ -1,8 +1,9 @@ import asyncio from datetime import datetime, timezone -from typing import Final +from typing import Final, Literal import pytest +from pydantic import TypeAdapter import litellm from litellm.litellm_core_utils.litellm_logging import Logging @@ -91,12 +92,6 @@ def response(cost: float = 0.5) -> ModelResponse: async def test_header_and_logging_share_one_allowance_allocation( logging_obj: Logging, used: int, expected: float ) -> None: - import json - - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - from litellm.proxy.spend_tracking.usage_meter import DurableUsageMeter, UsageCaptureSettings - counter: Final = Counter(used) result: Final = response() costs: Final = await asyncio.gather(*(finalize_response_cost(logging_obj, result, counter, NOW) for _ in range(3))) @@ -115,24 +110,6 @@ async def test_header_and_logging_share_one_allowance_allocation( assert payload["cost_finalized"] is True assert payload["cost_breakdown"] == costs[0].logging_breakdown() - committed: Final[asyncio.Queue[UsageWrite]] = asyncio.Queue() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - for write in writes: - committed.put_nowait(write) - - meter: Final = DurableUsageMeter(persist, UsageCaptureSettings()) - assert logging_obj.prepared_cost is not None - prepared_usage: Final = logging_obj.prepared_cost.usage.model_dump() - result.usage = Usage(prompt_tokens=999) - capture_logging_usage(meter, logging_obj, result, NOW, NOW) - await meter.close() - committed.get_nowait() - event: Final = json.loads(committed.get_nowait().envelope) - assert event["finalized_cost"]["response_cost"] == pytest.approx(costs[0].amount) - assert event["allocation"]["discount"] == pytest.approx(costs[0].allowance_discount) - assert event["usage"] == prepared_usage - assert counter.calls == 1 @pytest.mark.asyncio @@ -450,18 +427,17 @@ async def test_completed_call_keeps_its_charge_when_post_call_processing_fails( disable_spend_updates: bool, ) -> None: from unittest.mock import create_autospec + import anyio - from fastapi import Request, Response - from litellm.proxy._types import ProxyException - from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger + from litellm.proxy import proxy_server + from litellm.proxy._types import ProxyException from litellm.proxy.db.db_spend_update_writer import DBSpendUpdateWriter from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger from litellm.proxy.proxy_server import ProxyConfig - from litellm.proxy import proxy_server from litellm.proxy.utils import ProxyLogging scope: Final = anyio.CancelScope() @@ -598,6 +574,7 @@ async def test_guardrail_json_rejection_retains_completed_provider_usage( logging_obj: Logging, monkeypatch: pytest.MonkeyPatch ) -> None: import httpx + from litellm.caching.dual_cache import DualCache from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.proxy.utils import ProxyLogging @@ -633,3 +610,222 @@ async def test_guardrail_json_rejection_retains_completed_provider_usage( assert (payload["prompt_tokens"], payload["completion_tokens"]) == (10, 5) assert payload["response_cost"] == 0.5 ProxyLogging._callback_capabilities_cache.clear() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("price", [None, 0.0, 0.02], ids=["unpriced", "free", "priced"]) +@pytest.mark.parametrize("discount", [0.0, 1.0], ids=["no-discount", "fully-discounted"]) +async def test_finalized_header_distinguishes_missing_prices_from_free_models( + monkeypatch: pytest.MonkeyPatch, price: float | None, discount: float +) -> None: + model: Final = "claude-header-pricing-test" + monkeypatch.setattr(litellm, "cost_discount_config", {"azure": discount}) + monkeypatch.setitem( + litellm.model_cost, + f"azure/{model}", + { + "litellm_provider": "azure", + "mode": "chat", + "regional_endpoint_uplift_multiplier": 1.0, + **({"input_cost_per_token": price, "output_cost_per_token": price} if price is not None else {}), + }, + ) + logging: Final = Logging( + model=model, + messages=[], + stream=False, + call_type="acompletion", + start_time=NOW, + litellm_call_id="pricing-header", + function_id="pricing-header", + ) + logging.update_environment_variables( + model=model, custom_llm_provider="azure", litellm_params={}, optional_params={} + ) + result: Final = ModelResponse(model=model, usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)) + finalized: Final = await finalize_response_cost(logging, result, None, NOW) + assert finalized is not None + assert finalized.amount == pytest.approx((price or 0.0) * 15 * (1 - discount)) + headers: Final = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=UserAPIKeyAuth(), + response_cost=finalized.amount, + litellm_logging_obj=logging, + ) + assert headers.get("x-litellm-response-cost") == (None if price is None else str(finalized.amount)) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "source", + ["unpriced", "cache", "non-inference", "provider", "provider-header", "per-token", "per-second", "passthrough"], +) +@pytest.mark.parametrize("guardrail_cost", [0.0, 0.1]) +async def test_zero_cost_headers_preserve_known_free_calls_and_guardrail_charges( + source: Literal[ + "unpriced", "cache", "non-inference", "provider", "provider-header", "per-token", "per-second", "passthrough" + ], + guardrail_cost: float, +) -> None: + model: Final = "unmapped-header-cost" + logging: Final = Logging( + model=model, + messages=[], + stream=False, + call_type="adelete_responses" if source == "non-inference" else "acompletion", + start_time=NOW, + litellm_call_id="zero-cost-header", + function_id="zero-cost-header", + ) + logging.update_environment_variables( + model=model, custom_llm_provider="openai", litellm_params={}, optional_params={} + ) + result: Final = ModelResponse(model=model, usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)) + result._hidden_params = {"response_cost": 0.0, "cache_hit": source == "cache"} + match source: + case "provider": + logging._provider_reported_cost = 0.0 + case "provider-header": + result._hidden_params["additional_headers"] = {"llm_provider-x-litellm-response-cost": 0.0} + case "per-token": + logging.custom_cost_per_token = {"input_cost_per_token": 0.0, "output_cost_per_token": 0.0} + case "per-second": + logging.custom_cost_per_second = 0.0 + case "passthrough": + logging.model_call_details["passthrough_logging_payload"] = {"cost_per_request": 0.0} + case _: + pass + finalized: Final = await finalize_response_cost(logging, result, None, NOW, guardrail_cost=guardrail_cost) + assert finalized is not None + assert finalized.amount == guardrail_cost + headers: Final = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=UserAPIKeyAuth(), + response_cost=finalized.amount, + litellm_logging_obj=logging, + ) + assert headers.get("x-litellm-response-cost") == ( + None if source == "unpriced" and guardrail_cost == 0 else str(guardrail_cost) + ) + + +@pytest.mark.asyncio +async def test_free_model_priced_only_per_request_keeps_the_zero_cost_header(monkeypatch: pytest.MonkeyPatch) -> None: + model: Final = "gpt-free-per-request-header" + monkeypatch.setitem( + litellm.model_cost, model, {"litellm_provider": "openai", "mode": "chat", "input_cost_per_request": 0.0} + ) + logging: Final = Logging( + model=model, + messages=[], + stream=False, + call_type="acompletion", + start_time=NOW, + litellm_call_id="per-request-header", + function_id="per-request-header", + ) + logging.update_environment_variables( + model=model, custom_llm_provider="openai", litellm_params={}, optional_params={} + ) + result: Final = ModelResponse(model=model, usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)) + finalized: Final = await finalize_response_cost(logging, result, None, NOW) + assert finalized is not None and finalized.amount == 0.0 + headers: Final = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=UserAPIKeyAuth(), + response_cost=finalized.amount, + litellm_logging_obj=logging, + ) + assert headers["x-litellm-response-cost"] == "0.0" + + +@pytest.mark.asyncio +async def test_free_off_peak_pricing_keeps_the_finalized_zero_cost_header(monkeypatch: pytest.MonkeyPatch) -> None: + model: Final = "gpt-free-off-peak-header" + monkeypatch.setitem( + litellm.model_cost, + model, + { + "litellm_provider": "openai", + "mode": "chat", + "off_peak_pricing": {"hours_utc": "00:00-23:59", "input_cost_per_token": 0.0, "output_cost_per_token": 0.0}, + }, + ) + logging: Final = Logging( + model=model, + messages=[], + stream=False, + call_type="acompletion", + start_time=NOW, + litellm_call_id="off-peak-header", + function_id="off-peak-header", + ) + logging.update_environment_variables( + model=model, custom_llm_provider="openai", litellm_params={}, optional_params={} + ) + result: Final = ModelResponse(model=model, usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)) + result._hidden_params["response_cost"] = 0.0 + finalized: Final = await finalize_response_cost(logging, result, None, NOW) + assert finalized is not None and finalized.amount == 0.0 + headers: Final = ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=UserAPIKeyAuth(), + response_cost=finalized.amount, + litellm_logging_obj=logging, + ) + assert headers["x-litellm-response-cost"] == "0.0" + + +@pytest.mark.asyncio +@pytest.mark.parametrize("mapped", [False, True], ids=["unmapped-rule", "fill-missing-rule"]) +@pytest.mark.parametrize("price", [None, 0.0, 0.02], ids=["unpriced", "free", "priced"]) +async def test_rule_pricing_preserves_free_headers_without_treating_capabilities_as_prices( + monkeypatch: pytest.MonkeyPatch, mapped: bool, price: float | None +) -> None: + from litellm.litellm_core_utils.fallback_generalizations import ( + get_fallback_generalization_rules, + set_fallback_generalizations, + ) + + model: Final = "gpt-rule-header-model" + previous_rules: Final = TypeAdapter(list[object]).validate_python(get_fallback_generalization_rules()) + if mapped: + monkeypatch.setitem(litellm.model_cost, model, {"litellm_provider": "openai", "mode": "chat"}) + try: + set_fallback_generalizations( + [ + { + "name": "header-pricing", + "pattern": model, + "fill_missing_for_providers": ["openai"], + "model_info": { + "mode": "chat", + **( + {"input_cost_per_token": price, "output_cost_per_token": price} if price is not None else {} + ), + }, + } + ] + ) + logging: Final = Logging( + model=model, + messages=[], + stream=False, + call_type="acompletion", + start_time=NOW, + litellm_call_id="rule-header", + function_id="rule-header", + ) + logging.update_environment_variables( + model=model, custom_llm_provider="openai", litellm_params={}, optional_params={} + ) + result: Final = ModelResponse(model=model, usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15)) + finalized: Final = await finalize_response_cost(logging, result, None, NOW) + assert finalized is not None + assert finalized.amount == pytest.approx((price or 0.0) * 15) + headers: Final = TypeAdapter(dict[str, str]).validate_python( + ProxyBaseLLMRequestProcessing.get_custom_headers( + user_api_key_dict=UserAPIKeyAuth(), + response_cost=finalized.amount, + litellm_logging_obj=logging, + ) + ) + assert headers.get("x-litellm-response-cost") == (None if price is None else str(finalized.amount)) + finally: + set_fallback_generalizations(previous_rules) diff --git a/tests/unit/proxy/spend_tracking/test_session_page.py b/tests/unit/proxy/spend_tracking/test_session_page.py new file mode 100644 index 00000000000..a86990bddbb --- /dev/null +++ b/tests/unit/proxy/spend_tracking/test_session_page.py @@ -0,0 +1,327 @@ +import itertools +import random +import re +from collections.abc import Sequence +from typing import Final, TypeAlias + +import pytest + +from litellm.proxy.spend_tracking.session_page import ( + Session, + _parse_session_cursor, + _Read, + _ReadRow, + _Request, + _session_page, + read_session_page, +) + +_Row: TypeAlias = tuple[int, str, str] # (startTime, session key, api_key) +_Ranked: TypeAlias = tuple[int, str, str] # (last activity, session key, api_key) + + +def _session_of(row: _Row) -> tuple[str, str]: + return row[1], row[2] + + +def _ranked(rows: Sequence[_Row], request: _Request) -> tuple[_Ranked, ...]: + """The sessions in ``rows`` in page order, after the request's cursor or offset.""" + by_session: Final = itertools.groupby(sorted(rows, key=_session_of), key=_session_of) + ranked: Final = tuple( + sorted( + ((max(start for start, _, _ in group), key, api_key) for (key, api_key), group in by_session), + reverse=request.sort_desc, + ) + ) + if request.cursor is None: + return ranked[request.offset :] + cursor: Final = (int(request.cursor[0]), request.cursor[1], request.cursor[2]) + return tuple(session for session in ranked if (session < cursor if request.sort_desc else session > cursor)) + + +class _Reads: + """Reads like the page query, whose LIMIT cuts tied rows lowest key first, the opposite of page order. It fails a + read no larger than the one before, since only growing reads end the loop.""" + + def __init__(self, rows: Sequence[_Row], request: _Request, every_row: bool = False) -> None: + self.newest_first: Final = tuple(sorted(rows, key=lambda row: (-row[0], row[1], row[2]))) + self.request: Final = request + self.every_row: Final = every_row + self.budgets: tuple[int | None, ...] = () + + async def __call__(self, budget: int | None) -> _Read: + assert not self.budgets or (self.budgets[-1] is not None and (budget is None or budget > self.budgets[-1])), ( + self.budgets, + budget, + ) + self.budgets = (*self.budgets, budget) + taken: Final = self.newest_first if budget is None or self.every_row else self.newest_first[:budget] + page: Final = _ranked(taken, self.request)[: self.request.page_limit + 1] if self.request.page_limit > 0 else () + return _Read( + candidates=tuple(Session(key, api_key, f"{at:06d}") for at, key, api_key in page), + last_settled=bool(page) and page[-1][0] > taken[-1][0], + rows_read=len(taken), + session_count=len(frozenset(_session_of(row) for row in taken)), + saw_every_row=budget is None or self.every_row or len(taken) < budget, + ) + + +def _session_rows(rng: random.Random, index: int, clock: int) -> tuple[_Row, ...]: + start, length = rng.randrange(0, clock), rng.choice((1, 1, 2, 3, 8, 40)) + return tuple((start + rng.randrange(0, 30), f"s{index:03d}", rng.choice(("k1", "k2"))) for _ in range(length)) + + +def _sessions(rng: random.Random) -> tuple[_Row, ...]: + """Sessions of one to forty rows over a short clock, so many rows share a time.""" + clock: Final = rng.choice((3, 40, 400)) + return tuple( + itertools.chain.from_iterable(_session_rows(rng, index, clock) for index in range(rng.randrange(1, 120))) + ) + + +def _random_cursor(rng: random.Random, ranked: Sequence[_Ranked]) -> tuple[str, str, str]: + at, key, api_key = ranked[rng.randrange(len(ranked))] + return f"{at:06d}", key, api_key + + +def _request(rng: random.Random, rows: Sequence[_Row]) -> _Request: + cap, page_size, sort_desc = rng.randrange(2, 60), rng.randrange(1, 12), rng.random() < 0.8 + mode: Final = rng.choice(("first", "offset", "cursor")) + full: Final = _ranked(rows, _Request(None, 0, 0, sort_desc, None, cap)) + cursor: Final = _random_cursor(rng, full) if mode == "cursor" and full else None + offset: Final = (rng.randrange(1, 2 * cap // page_size + 3) - 1) * page_size if mode == "offset" else 0 + return _Request(cursor, offset, min(page_size, cap - offset), sort_desc, None, cap) + + +@pytest.mark.parametrize("seed", range(60)) +@pytest.mark.asyncio +async def test_a_page_read_from_the_newest_rows_matches_grouping_every_row(seed: int) -> None: + rng: Final = random.Random(seed) + rows: Final = _sessions(rng) + for request in tuple(_request(rng, rows) for _ in range(12)): + grouped = await _session_page(_Reads(rows, request, every_row=True), request) + assert await _session_page(_Reads(rows, request), request) == grouped, request + + +@pytest.mark.parametrize( + "rows_per_session, expected_budgets", + [ + pytest.param(1, (102,), id="one-read-holds-the-cap"), + pytest.param(2, (102, 253), id="sized-by-the-first-reads-density"), + pytest.param(50, (102, 4293, 8586), id="doubles-when-the-density-misleads"), + pytest.param(500, (102, 12878, 62533), id="re-sized-by-each-reads-density"), + ], +) +@pytest.mark.asyncio +async def test_reads_grow_by_the_last_reads_rows_per_session_until_one_settles_the_page( + rows_per_session: int, expected_budgets: tuple[int, ...] +) -> None: + """150 sessions a row per second apart. A cap of 100 starts at 100 + 2 rows.""" + rows: Final = tuple((index, f"s{index // rows_per_session:05d}", "k") for index in range(150 * rows_per_session)) + request: Final = _Request(cursor=None, offset=0, page_limit=10, sort_desc=True, status=None, cap=100) + reads: Final = _Reads(rows, request) + await _session_page(reads, request) + assert reads.budgets == expected_budgets + + +@pytest.mark.asyncio +async def test_a_session_tied_with_the_reads_oldest_row_is_not_trusted() -> None: + """Thirty one-row sessions share one time and the first read takes 12 of them. The page's sixth session sits at the + read's oldest time, where the LIMIT may have cut a session that ranks before it, so the page reads on.""" + rows: Final = tuple((0, f"s{index:02d}", "k") for index in range(30)) + request: Final = _Request(cursor=None, offset=0, page_limit=5, sort_desc=True, status=None, cap=10) + reads: Final = _Reads(rows, request) + page: Final = await _session_page(reads, request) + assert tuple(session.key for session in page.sessions) == ("s29", "s28", "s27", "s26", "s25") + assert reads.budgets == (12, 24, 48) + + +@pytest.mark.parametrize("session_count, expected_total", [(100, (100, False)), (101, (100, True))]) +@pytest.mark.asyncio +async def test_the_total_is_capped_only_past_the_cap(session_count: int, expected_total: tuple[int, bool]) -> None: + rows: Final = tuple((index, f"s{index:05d}", "k") for index in range(session_count)) + request: Final = _Request(cursor=None, offset=0, page_limit=10, sort_desc=True, status=None, cap=100) + page: Final = await _session_page(_Reads(rows, request), request) + assert (page.total, page.total_is_capped) == expected_total + + +@pytest.mark.asyncio +async def test_a_page_number_near_the_bigint_limit_reads_once() -> None: + rows: Final = tuple((index, f"s{index:05d}", "k") for index in range(500)) + offset: Final = (9_300_000_000_000_000 - 1) * 1000 + request: Final = _Request( + cursor=None, offset=offset, page_limit=min(1000, 100 - offset), sort_desc=True, status=None, cap=100 + ) + reads: Final = _Reads(rows, request) + page: Final = await _session_page(reads, request) + assert reads.budgets == (102,) + assert (page.sessions, page.total, page.total_is_capped) == ((), 100, True) + + +@pytest.mark.parametrize( + "sort_desc, status", + [pytest.param(False, None, id="oldest-first"), pytest.param(True, "failure", id="filtered-by-outcome")], +) +@pytest.mark.asyncio +async def test_a_page_that_needs_every_row_reads_them_once(sort_desc: bool, status: str | None) -> None: + rows: Final = tuple((index, f"s{index:05d}", "k") for index in range(500)) + request: Final = _Request(cursor=None, offset=0, page_limit=10, sort_desc=sort_desc, status=status, cap=100) + reads: Final = _Reads(rows, request) + await _session_page(reads, request) + assert reads.budgets == (None,) + + +def test_parse_session_cursor() -> None: + assert _parse_session_cursor(None) is None + assert _parse_session_cursor("no-delimiter") is None + assert _parse_session_cursor("2026-08-29 10:00:00|sess-1") is None + assert _parse_session_cursor("|hashed-key|sess-1") is None + assert _parse_session_cursor("2026-08-29 10:00:00|hashed-key|") is None + assert _parse_session_cursor("2026-08-29 10:00:00|hashed-key|sess-1") == ( + "2026-08-29 10:00:00", + "sess-1", + "hashed-key", + ) + assert _parse_session_cursor("2026-08-29 10:00:00.123|hashed-key|sess|with|pipes") == ( + "2026-08-29 10:00:00.123", + "sess|with|pipes", + "hashed-key", + ) + + +def _bound(sql: str, args: tuple[object, ...], pattern: str) -> tuple[object, ...]: + """The values bound to the ``$N`` placeholders that ``pattern``'s groups capture in ``sql``.""" + found: Final = re.search(pattern, sql) + assert found, f"{pattern!r} not found in:\n{sql}" + assert all(1 <= int(index) <= len(args) for index in found.groups()), f"{found.groups()} outside $1..${len(args)}" + return tuple(args[int(index) - 1] for index in found.groups()) + + +class _TableDb: + """A table of ``rows`` matching rows in ``sessions`` sessions, whose reads return no page row.""" + + def __init__(self, rows: int, sessions: int) -> None: + self.rows: Final = rows + self.sessions: Final = sessions + self.calls: tuple[tuple[str, tuple[object, ...]], ...] = () + + async def query_raw(self, query: str, *args: object) -> tuple[_ReadRow, ...]: + self.calls = (*self.calls, (query, args)) + budget: Final = re.search(r"DESC LIMIT (\d+)", query) + rows_read: Final = min(int(budget.group(1)), self.rows) if budget else self.rows + return ( + _ReadRow( + session_key=None, + api_key=None, + last_activity=None, + settled=None, + rows_read=rows_read, + session_count=self.sessions, + ), + ) + + +def _page_bounds(sql: str) -> tuple[int, int]: + """The LIMIT and OFFSET of the read's sessions.""" + found: Final = re.search(r"LIMIT (\d+)(?: OFFSET (\d+))?\s*\) AS page", sql) + assert found, sql + return int(found.group(1)), int(found.group(2) or 0) + + +def _read_size(sql: str) -> int | None: + found: Final = re.search(r"DESC LIMIT (\d+)", sql) + return int(found.group(1)) if found else None + + +@pytest.mark.asyncio +async def test_a_read_that_fills_its_size_is_not_taken_for_every_row() -> None: + """A read that took every row it asked for may have stopped short, so its 50 sessions are no total: the reads + grow until one takes fewer rows than it asked for.""" + db: Final = _TableDb(rows=9_000, sessions=50) + page: Final = await read_session_page( + db, + where_clause="TRUE", + params=(), + page=1, + page_size=10, + status=None, + sort_desc=True, + session_cursor=None, + cap=100, + ) + assert tuple(_read_size(sql) for sql, _ in db.calls) == (102, 258, 652, 1647, 4159, 10502) + assert (page.total, page.total_is_capped) == (50, False) + + +@pytest.mark.parametrize("page_number", [pytest.param(5, id="at-the-cap"), pytest.param(6, id="past-the-cap")]) +@pytest.mark.asyncio +async def test_a_page_starting_at_or_past_the_cap_reads_once_and_shows_no_session(page_number: int) -> None: + db: Final = _TableDb(rows=1_000, sessions=150) + page: Final = await read_session_page( + db, + where_clause="TRUE", + params=(), + page=page_number, + page_size=25, + status=None, + sort_desc=True, + session_cursor=None, + cap=100, + ) + assert len(db.calls) == 1, "the first read already holds more sessions than the cap" + assert _page_bounds(db.calls[0][0])[0] == 0 + assert (page.total, page.total_is_capped) == (100, True) + + +@pytest.mark.asyncio +async def test_the_read_binds_the_filters_and_the_cursor_to_their_placeholders() -> None: + db: Final = _TableDb(rows=50, sessions=40) + where, params = "model = $1 AND api_key = $2", ("gpt-4o", "key-a") + for page, page_size, session_cursor in ((1, 3, "2026-10-28 09:00:00|key-a|sess|9"), (3, 1, None)): + await read_session_page( + db, + where_clause=where, + params=params, + page=page, + page_size=page_size, + status=None, + sort_desc=True, + session_cursor=session_cursor, + cap=100, + ) + (cursor_sql, cursor_args), (offset_sql, _) = db.calls + for sql, args in db.calls: + assert {int(index) for index in re.findall(r"\$(\d+)", sql)} == set(range(1, len(args) + 1)), ( + "every argument needs a placeholder and every placeholder an argument" + ) + assert _bound(sql, args, r"model = \$(\d+) AND api_key = \$(\d+)") == params + assert _bound( + cursor_sql, cursor_args, r"\(last_activity, session_key, api_key\) < \(\$(\d+)::timestamp, \$(\d+), \$(\d+)\)" + ) == ("2026-10-28 09:00:00", "sess|9", "key-a") + assert _page_bounds(cursor_sql) == (4, 0) + assert _page_bounds(offset_sql) == (2, 2) + + +@pytest.mark.asyncio +async def test_a_status_filtered_read_binds_the_status_between_the_filters_and_the_cursor() -> None: + db: Final = _TableDb(rows=50, sessions=40) + await read_session_page( + db, + where_clause="model = $1", + params=("gpt-4o",), + status="failure", + page=1, + page_size=3, + sort_desc=True, + session_cursor="2026-10-28 09:00:00|key-a|sess-9", + cap=100, + ) + ((sql, args),) = db.calls + assert {int(index) for index in re.findall(r"\$(\d+)", sql)} == set(range(1, len(args) + 1)) + assert _bound(sql, args, r"model = \$(\d+)") == ("gpt-4o",) + assert _bound(sql, args, r"COALESCE\(status, 'success'\) = \$(\d+)") == ("failure",) + assert _bound(sql, args, r"\(last_activity, session_key, api_key\) < \(\$(\d+)::timestamp, \$(\d+), \$(\d+)\)") == ( + "2026-10-28 09:00:00", + "sess-9", + "key-a", + ) diff --git a/tests/unit/proxy/spend_tracking/test_spend_event.py b/tests/unit/proxy/spend_tracking/test_spend_event.py index ff449235582..e1912d36d91 100644 --- a/tests/unit/proxy/spend_tracking/test_spend_event.py +++ b/tests/unit/proxy/spend_tracking/test_spend_event.py @@ -194,9 +194,11 @@ def test_undecodable_line_is_an_error_value(): assert isinstance(decode_spend_event(b"not json\n"), SpendEventDecodeError) -def test_batch_retrieves_stay_in_process(): - assert is_offloadable_success(_response()) is True - assert is_offloadable_success(None) is True +def test_batch_retrieves_and_video_status_polls_stay_in_process(): + assert is_offloadable_success(_response(), "acompletion") is True + assert is_offloadable_success(None, None) is True + assert is_offloadable_success(None, "avideo_generation") is True + assert is_offloadable_success(None, "avideo_status") is False assert ( is_offloadable_success( LiteLLMBatch( @@ -207,7 +209,8 @@ def test_batch_retrieves_stay_in_process(): input_file_id="f", object="batch", status="completed", - ) + ), + "aretrieve_batch", ) is False ) diff --git a/tests/unit/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/unit/proxy/spend_tracking/test_spend_management_endpoints.py index 6598b94e60f..e119acd5ffc 100644 --- a/tests/unit/proxy/spend_tracking/test_spend_management_endpoints.py +++ b/tests/unit/proxy/spend_tracking/test_spend_management_endpoints.py @@ -5,7 +5,7 @@ import hashlib import json import re import sqlite3 -from collections.abc import Mapping +from collections.abc import Mapping, Sequence from datetime import timezone from typing import Final, Literal from unittest.mock import AsyncMock, MagicMock, patch @@ -20,6 +20,11 @@ import litellm.proxy.proxy_server as ps from litellm.proxy.auth.authorization import OwnedRows from litellm.proxy.auth.authorization_dependencies import get_log_team_lookup, load_permitted_log_team_ids from litellm.proxy.auth.route_checks import RouteChecks +from tests.unit.proxy.db.fake_prisma_engine import ( + ReadTargetOf, + statement_timeout_error, + statement_timeout_reads, +) def _default_date_range(): @@ -258,6 +263,7 @@ def make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_fn, team_lookup_fn=No return [row for row in ordered[skip : skip + page_size]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() self.db.litellm_spendlogs = self.db @@ -317,6 +323,7 @@ async def test_is_admin_view_safe_exception(): async def test_can_team_member_view_log_none_team_id(): # team_id=None should immediately return False class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -338,6 +345,7 @@ async def test_can_team_member_view_log_none_team_id(): async def test_can_team_member_view_log_team_not_found(monkeypatch): # Non-existent team should return False class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -377,6 +385,7 @@ async def test_can_team_member_view_log_not_admin(monkeypatch): } class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -415,6 +424,7 @@ async def test_can_team_member_view_log_admin(monkeypatch): } class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -480,6 +490,7 @@ def _make_owner_lookup_prisma(rows): return _emulate_spend_log_owner_lookup(rows, sql_query, params) class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -985,6 +996,7 @@ async def test_ui_view_spend_logs_sort_by_and_sort_order( return [row for row in sorted_logs[skip : skip + page_size]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -1046,6 +1058,7 @@ async def test_ui_view_spend_logs_sort_validation_errors(client, monkeypatch, so return 0 class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -1122,6 +1135,7 @@ async def test_ui_view_spend_logs_sort_by_request_duration_ms(client, monkeypatc return [row for row in sorted_logs[skip : skip + page_size]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -1218,6 +1232,7 @@ async def test_ui_view_spend_logs_sort_by_model(client, monkeypatch, sort_order, return [row for row in sorted_logs[skip : skip + page_size]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -1330,6 +1345,7 @@ async def test_ui_view_spend_logs_sort_by_ttft_ms(client, monkeypatch): return [{k: v for k, v in row.items() if k != "_ttft_ms"} for row in sorted_logs[skip : skip + page_size]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -2135,6 +2151,7 @@ async def test_ui_view_session_spend_logs_pagination(client, monkeypatch): return [mock_spend_logs[0]] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() self.db.litellm_spendlogs = self.db @@ -2187,6 +2204,7 @@ async def test_ui_view_session_spend_logs_rehydrates_metadata_jsonb_text(client, return [dict(raw_row)] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() self.db.litellm_spendlogs = self.db @@ -2235,6 +2253,7 @@ async def test_ui_view_session_spend_logs_includes_permitted_team_logs(client, m ] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() self.db.litellm_spendlogs = self.db @@ -2712,6 +2731,7 @@ async def test_ui_view_request_response_rejects_foreign_row_inserted_after_owner ] class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -2743,6 +2763,7 @@ async def test_ui_view_request_response_custom_logger_denies_foreign_payload_own return [] class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4056,6 +4077,7 @@ async def test_view_spend_logs_summarize_parameter(client, monkeypatch): ] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4151,6 +4173,7 @@ async def test_view_spend_logs_bounds_row_count(client, monkeypatch): return [{}] * min(kwargs.get("take", 0), self.available_rows) class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4274,6 +4297,7 @@ def _tag_query_prisma_client(assert_query_raw): return assert_query_raw(sql_query, params) class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4493,6 +4517,7 @@ async def test_view_spend_logs_with_date_range_summarized(client, monkeypatch): return mock_summarized_response class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4555,6 +4580,7 @@ async def test_view_spend_logs_summarize_groups_by_day_in_sql(client, monkeypatc return mock_rows class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4620,6 +4646,7 @@ async def test_view_spend_logs_summarize_empty_rows(client, monkeypatch): return [] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4659,6 +4686,7 @@ async def test_view_spend_logs_summarize_unhashed_api_key_without_padding(client return mock_rows class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MockDB() @@ -4973,6 +5001,7 @@ async def test_build_ui_spend_logs_response_dict_rows_session_counts(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5038,6 +5067,7 @@ async def test_build_ui_spend_logs_response_caps_session_models(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5102,6 +5132,7 @@ async def test_build_ui_spend_logs_response_key_split_session_gets_per_key_aggre }, ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5155,6 +5186,7 @@ async def test_build_ui_spend_logs_response_empty_api_key_keeps_session_aggregat } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5210,6 +5242,7 @@ async def test_build_ui_spend_logs_response_sums_multi_round_session_spend(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5292,6 +5325,7 @@ async def test_build_ui_spend_logs_response_sums_multi_round_session_tokens(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5372,6 +5406,7 @@ async def test_build_ui_spend_logs_response_sums_multi_round_session_duration(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5397,6 +5432,35 @@ async def test_build_ui_spend_logs_response_sums_multi_round_session_duration(): ) +@pytest.mark.asyncio +async def test_build_ui_spend_logs_response_keeps_the_page_when_postgres_cancels_the_session_totals_read(): + """The page already fetched is returned without session totals, like any other failed enrichment read.""" + from litellm.proxy.spend_tracking.spend_management_endpoints import ( + _build_ui_spend_logs_response, + ) + + dict_rows = [ + {"request_id": "req-1", "session_id": "sess-slow", "call_type": "completion", "api_key": "k"}, + {"request_id": "req-2", "session_id": "sess-slow", "call_type": "completion", "api_key": "k"}, + ] + mock_prisma = MagicMock() + mock_prisma.db.query_raw = AsyncMock(side_effect=statement_timeout_error()) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) + + result = await _build_ui_spend_logs_response( + prisma_client=mock_prisma, + data=dict_rows, + total_records=2, + page=1, + page_size=50, + total_pages=1, + enrich_session_counts=True, + ) + + assert [row["request_id"] for row in result["data"]] == ["req-1", "req-2"] + assert all(row["session_total_count"] == 1 for row in result["data"]) + + @pytest.mark.asyncio async def test_build_ui_spend_logs_response_session_cache_hit_count(): """ @@ -5430,6 +5494,7 @@ async def test_build_ui_spend_logs_response_session_cache_hit_count(): } ] ) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) result = await _build_ui_spend_logs_response( prisma_client=mock_prisma, @@ -5476,6 +5541,7 @@ async def test_can_team_member_view_log_with_spend_logs_permission(monkeypatch): } class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -5512,6 +5578,7 @@ async def test_can_team_member_view_log_without_spend_logs_permission(monkeypatc } class MockPrisma: + read_db = property(lambda self: ReadTargetOf(self.db)) class DB: class TeamTable: async def find_unique(self, where: dict): @@ -6201,6 +6268,7 @@ async def test_ui_view_spend_logs_rehydrates_metadata_jsonb_text(client, monkeyp return [{**raw_row, "total_count": 1}] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -6285,6 +6353,7 @@ async def test_ui_view_spend_logs_metadata_invalid_json_falls_back_to_empty_dict return [{**raw_row, "total_count": 1}] class MockPrismaClient: + read_db = property(lambda self: ReadTargetOf(self.db)) def __init__(self): self.db = MagicMock() self.db.litellm_spendlogs = MagicMock() @@ -6628,6 +6697,7 @@ _SCOPED_SPEND_REPORT_PATHS = [ def _spend_report_mock_prisma(query_raw_returns=None, team_rows=None, user_row=None): pc = MagicMock() pc.db.query_raw = AsyncMock(return_value=query_raw_returns if query_raw_returns is not None else []) + statement_timeout_reads(pc, pc.db.query_raw) pc.db.litellm_teamtable.find_many = AsyncMock(return_value=team_rows if team_rows is not None else []) pc.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) return pc @@ -7151,37 +7221,19 @@ def test_scoped_spend_report_range_at_max_allowed(client, monkeypatch): app.dependency_overrides.pop(ps.user_api_key_auth, None) -def test_parse_session_cursor(): - from litellm.proxy.spend_tracking.spend_management_endpoints import ( - _parse_session_cursor, - ) - - assert _parse_session_cursor(None) is None - assert _parse_session_cursor("no-delimiter") is None - assert _parse_session_cursor("2026-08-29 10:00:00|sess-1") is None - assert _parse_session_cursor("|hashed-key|sess-1") is None - assert _parse_session_cursor("2026-08-29 10:00:00|hashed-key|") is None - assert _parse_session_cursor("2026-08-29 10:00:00|hashed-key|sess-1") == ( - "2026-08-29 10:00:00", - "sess-1", - "hashed-key", - ) - assert _parse_session_cursor("2026-08-29 10:00:00.123|hashed-key|sess|with|pipes") == ( - "2026-08-29 10:00:00.123", - "sess|with|pipes", - "hashed-key", - ) - - SESSION_GROUP_KEY_SQL = "COALESCE(NULLIF(session_id, ''), request_id), api_key" -def _session_grouped_mock_prisma(session_page_rows, session_total, representative_rows): +def _session_grouped_mock_prisma( + session_page_rows: Sequence[Mapping[str, object]], + session_total: int, + representative_rows: Sequence[Mapping[str, object]], +) -> MagicMock: """Mock prisma dispatching the raw queries the keyset grouped path emits.""" - async def mock_query_raw(sql_query, *params): - if "COUNT(*) AS total_count" in sql_query: - return [{"total_count": session_total}] + async def mock_query_raw(sql_query: str, *params: object) -> Sequence[Mapping[str, object]]: + if "rows_read" in sql_query: + return _session_read_rows(session_page_rows, session_total) if "DISTINCT ON" in sql_query: return representative_rows if "COALESCE(SUM(spend)" in sql_query: @@ -7199,11 +7251,12 @@ def _session_grouped_mock_prisma(session_page_rows, session_total, representativ "session_models": ["gpt-4o"], } ] - return session_page_rows + raise AssertionError(f"unexpected query:\n{sql_query}") mock_prisma = MagicMock() mock_prisma.db = MagicMock() mock_prisma.db.query_raw = AsyncMock(side_effect=mock_query_raw) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) return mock_prisma @@ -7228,27 +7281,38 @@ def _session_page_row(session_key, last_activity): return {"session_key": session_key, "api_key": "hashed-key", "last_activity": last_activity} -def _session_grouped_paginating_prisma(sessions, counted_total=None): - """Mock prisma serving the grouped page query out of ``sessions``, honoring the LIMIT and OFFSET it asks for.""" +def _session_read_rows( + page_rows: Sequence[Mapping[str, object]], session_count: int +) -> tuple[Mapping[str, object], ...]: + """The read query's rows for ``page_rows``, as if the read held every matching row.""" + summary: Final = {"rows_read": 0, "session_count": session_count} + if not page_rows: + return ({"session_key": None, "api_key": None, "last_activity": None, "settled": None, **summary},) + return tuple({**row, "settled": True, **summary} for row in page_rows) - async def mock_query_raw(sql_query, *params): - if "COUNT(*) AS total_count" in sql_query: - return [{"total_count": min(len(sessions) if counted_total is None else counted_total, params[-1])}] + +def _session_grouped_paginating_prisma(sessions: Sequence[tuple[str, str]]) -> MagicMock: + """Mock prisma serving the grouped page read out of ``sessions``, honoring the LIMIT and OFFSET it asks for.""" + + async def mock_query_raw(sql_query: str, *params: object) -> Sequence[Mapping[str, object]]: if "DISTINCT ON" in sql_query: return [_session_representative_row(f"req-{session_key}", session_key) for session_key in params[-2]] if "COALESCE(SUM(spend)" in sql_query: return [] - bounds = re.search(r"LIMIT \$(\d+)(?: OFFSET \$(\d+))?", sql_query) - limit = params[int(bounds.group(1)) - 1] - offset = params[int(bounds.group(2)) - 1] if bounds.group(2) else 0 - return [ - _session_page_row(session_key, last_activity) - for session_key, last_activity in sessions[offset : offset + limit] - ] + bounds: Final = re.search(r"LIMIT (\d+)(?: OFFSET (\d+))?\s+\) AS page", sql_query) + limit, offset = int(bounds.group(1)), int(bounds.group(2) or 0) + return _session_read_rows( + [ + _session_page_row(session_key, last_activity) + for session_key, last_activity in sessions[offset : offset + limit] + ], + len(sessions), + ) mock_prisma = MagicMock() mock_prisma.db = MagicMock() mock_prisma.db.query_raw = AsyncMock(side_effect=mock_query_raw) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) return mock_prisma @@ -7296,20 +7360,15 @@ async def test_ui_view_spend_logs_group_by_session_first_page(client, monkeypatc assert data["data"][1]["session_total_count"] == 1 emitted = [call.args for call in mock_prisma.db.query_raw.await_args_list] + assert len(emitted) == 3, "one read picks the page and counts it, then the representatives and their totals" page_query_sql = emitted[0][0] - assert f"GROUP BY {SESSION_GROUP_KEY_SQL}" in page_query_sql assert "OFFSET" not in page_query_sql - assert "HAVING" not in page_query_sql - assert emitted[0][-1] == 3, "page query fetches page_size + 1 sessions to detect has_more" + assert "LIMIT 3 " in page_query_sql, "the read fetches page_size + 1 sessions to detect has_more" - count_sql = emitted[1][0] - assert f"GROUP BY {SESSION_GROUP_KEY_SQL}" in count_sql - assert "LIMIT" in count_sql and "FROM (" in count_sql, "the grouped count must stay bounded" - - rep_call = emitted[2] + rep_call: Final = emitted[1] assert f"DISTINCT ON ({SESSION_GROUP_KEY_SQL})" in rep_call[0] assert ( - f"ORDER BY {SESSION_GROUP_KEY_SQL}, " + spend_management_endpoints._SESSION_REPRESENTATIVE_ORDER_SQL + f"ORDER BY {SESSION_GROUP_KEY_SQL}, " + spend_management_endpoints.SESSION_REPRESENTATIVE_ORDER_SQL ) in rep_call[0] assert rep_call[-2] == ["sess-1", "req-solo"] assert rep_call[-1] == ["hashed-key", "hashed-key"] @@ -7319,7 +7378,7 @@ async def test_ui_view_spend_logs_group_by_session_first_page(client, monkeypatc @pytest.mark.asyncio async def test_ui_view_spend_logs_group_by_session_cursor_page(client, monkeypatch): - """A session_cursor becomes a HAVING keyset predicate instead of an OFFSET.""" + """A session_cursor becomes a keyset predicate on the read's sessions instead of an OFFSET.""" page_rows = [_session_page_row("sess-2", "2026-08-29 07:00:00")] reps = [_session_representative_row("req-2", "sess-2")] mock_prisma = _session_grouped_mock_prisma(page_rows, 3, reps) @@ -7352,7 +7411,7 @@ async def test_ui_view_spend_logs_group_by_session_cursor_page(client, monkeypat page_query_call = mock_prisma.db.query_raw.await_args_list[0] page_query_sql = page_query_call.args[0] - assert f'HAVING (MAX("startTime"), {SESSION_GROUP_KEY_SQL}) <' in page_query_sql + assert "WHERE (last_activity, session_key, api_key) <" in page_query_sql assert "OFFSET" not in page_query_sql cursor_index = page_query_call.args.index("2026-08-29 09:00:00") assert page_query_call.args[cursor_index : cursor_index + 3] == ( @@ -7403,9 +7462,9 @@ async def test_ui_view_spend_logs_group_by_session_jumps_to_page_without_cursor( @pytest.mark.asyncio async def test_ui_view_spend_logs_group_by_session_short_page_totals_itself(client, monkeypatch): - """A page that runs out of sessions is the end of the list, so the total comes from it and nothing is counted.""" + """A page that runs out of sessions has read every matching row, so that one read gives the total.""" sessions = tuple((f"sess-{index:02d}", f"2026-08-29 10:{59 - index:02d}:00") for index in range(10)) - mock_prisma = _session_grouped_paginating_prisma(sessions, counted_total=999) + mock_prisma: Final = _session_grouped_paginating_prisma(sessions) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) monkeypatch.setattr( "litellm.proxy.spend_tracking.spend_management_endpoints._is_admin_view_safe", @@ -7429,10 +7488,12 @@ async def test_ui_view_spend_logs_group_by_session_short_page_totals_itself(clie ) assert response.status_code == 200, response.text data = response.json() - assert data["total"] == 10, "the count query's 999 would have won if it had been asked" + assert data["total"] == 10 assert data["total_is_capped"] is False assert data["total_pages"] == 1 assert len(data["data"]) == 10 + reads: Final = tuple(call for call in mock_prisma.db.query_raw.await_args_list if "rows_read" in call.args[0]) + assert len(reads) == 1, "a page that ends the list is not read again for its total" finally: app.dependency_overrides.pop(ps.user_api_key_auth, None) @@ -7517,7 +7578,7 @@ async def test_ui_view_spend_logs_group_by_session_page_past_count_cap_is_empty( assert past_cap_data["data"] == [] assert past_cap_data["has_more"] is False assert past_cap_data["total"] == cap - assert mock_prisma.db.query_raw.await_count == 1, "only the bounded count query runs past the capped window" + assert mock_prisma.db.query_raw.await_count == 1, "only the bounded read runs past the capped window" finally: app.dependency_overrides.pop(ps.user_api_key_auth, None) @@ -7572,6 +7633,7 @@ async def test_ui_view_spend_logs_group_by_session_offset_for_non_starttime_sort return [] mock_prisma.db.query_raw = AsyncMock(side_effect=mock_query_raw) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) monkeypatch.setattr( "litellm.proxy.spend_tracking.spend_management_endpoints._is_admin_view_safe", @@ -7622,6 +7684,7 @@ async def test_ui_view_spend_logs_search_returns_flat_rows_when_grouping_by_sess mock_prisma = MagicMock() mock_prisma.db = MagicMock() mock_prisma.db.query_raw = AsyncMock(side_effect=mock_query_raw) + statement_timeout_reads(mock_prisma, mock_prisma.db.query_raw) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" @@ -7820,14 +7883,14 @@ def test_session_representative_uses_completed_agent_outcome(parent_status, chil ) result = connection.execute( "SELECT request_id, status FROM logs ORDER BY " - + spend_management_endpoints._SESSION_REPRESENTATIVE_ORDER_SQL + + spend_management_endpoints.SESSION_REPRESENTATIVE_ORDER_SQL + " LIMIT 1" ).fetchone() assert result == ("parent", expected) connection.execute("DELETE FROM logs WHERE call_type = 'asend_message'") fallback = connection.execute( "SELECT request_id, status FROM logs ORDER BY " - + spend_management_endpoints._SESSION_REPRESENTATIVE_ORDER_SQL + + spend_management_endpoints.SESSION_REPRESENTATIVE_ORDER_SQL + " LIMIT 1" ).fetchone() assert fallback == ("llm", "success") diff --git a/tests/unit/proxy/spend_tracking/test_spend_query_optimization.py b/tests/unit/proxy/spend_tracking/test_spend_query_optimization.py index 93fae093340..aaf4bc5438c 100644 --- a/tests/unit/proxy/spend_tracking/test_spend_query_optimization.py +++ b/tests/unit/proxy/spend_tracking/test_spend_query_optimization.py @@ -6,7 +6,9 @@ GitHub Issue: #17487 """ import datetime +from collections.abc import Mapping, Sequence from datetime import timezone +from typing import Final from unittest.mock import AsyncMock, MagicMock import pytest @@ -16,6 +18,7 @@ from litellm.proxy.spend_tracking.spend_tracking_utils import ( get_spend_by_team, get_spend_by_team_and_customer, ) +from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_error, statement_timeout_reads @pytest.mark.asyncio @@ -30,10 +33,8 @@ async def test_spend_query_uses_timestamp_filtering(): """ # Mock prisma client mock_prisma = MagicMock() - mock_db = MagicMock() mock_query_raw = AsyncMock(return_value=[]) - mock_db.query_raw = mock_query_raw - mock_prisma.db = mock_db + statement_timeout_reads(mock_prisma, mock_query_raw) # Use timezone-aware datetime objects start_date = datetime.datetime(2024, 1, 1, tzinfo=timezone.utc) @@ -168,7 +169,7 @@ async def test_spend_logs_ui_wraps_params_in_at_time_zone_utc(monkeypatch): mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(return_value=[]) + statement_timeout_reads(mock_prisma, AsyncMock(return_value=[])) mock_prisma.db.litellm_spendlogs = MagicMock() mock_prisma.db.litellm_spendlogs.count = AsyncMock(return_value=0) @@ -195,22 +196,27 @@ async def test_spend_logs_ui_wraps_params_in_at_time_zone_utc(monkeypatch): user_api_key_dict=auth, ) - assert mock_prisma.db.query_raw.called, "query_raw should have been called" - sql = mock_prisma.db.query_raw.call_args[0][0] + assert _spend_log_reads(mock_prisma).called, "query_raw should have been called" + sql = _spend_log_reads(mock_prisma).call_args[0][0] assert sql.count("AT TIME ZONE 'UTC'") >= 2, ( f"/spend/logs/ui must wrap both `startTime` bounds with `AT TIME ZONE 'UTC'`. SQL was:\n{sql}" ) +def _spend_log_reads(mock_prisma: MagicMock) -> AsyncMock: + """The raw reads ``/spend/logs/ui`` ran under the statement timeout.""" + return mock_prisma.read_db.tx.return_value.query_raw + + def _make_ui_spend_logs_mock(count_total, page_rows): """ - Build a prisma mock whose first `query_raw` (the bounded count) returns - `count_total` and whose second `query_raw` (the page data) returns + Build a prisma mock whose first statement-timeout read (the bounded count) returns + `count_total` and whose second read (the page data) returns `page_rows`. """ mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(side_effect=[[{"total_count": count_total}], page_rows]) + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=[[{"total_count": count_total}], page_rows])) mock_prisma.db.litellm_spendlogs = MagicMock() mock_prisma.db.litellm_spendlogs.count = AsyncMock(return_value=0) return mock_prisma @@ -260,7 +266,7 @@ async def test_spend_logs_ui_uses_bounded_count_not_full_scan(monkeypatch): mock_prisma.db.litellm_spendlogs.count.assert_not_called() - count_call = mock_prisma.db.query_raw.call_args_list[0] + count_call = _spend_log_reads(mock_prisma).call_args_list[0] count_sql = count_call[0][0] assert "COUNT(*) OVER ()" not in count_sql assert "LIMIT" in count_sql and "FROM (" in count_sql, ( @@ -268,7 +274,7 @@ async def test_spend_logs_ui_uses_bounded_count_not_full_scan(monkeypatch): ) assert count_call[0][-1] == SPEND_LOGS_PAGINATION_COUNT_CAP + 1, "the bounded count must probe at most cap+1 rows" - page_sql = mock_prisma.db.query_raw.call_args_list[1][0][0] + page_sql = _spend_log_reads(mock_prisma).call_args_list[1][0][0] assert "COUNT(*) OVER ()" not in page_sql, ( f"the page query must not carry a window count that forces a full-window scan. SQL was:\n{page_sql}" ) @@ -342,7 +348,7 @@ async def test_spend_logs_ui_empty_page_reports_zero_total(monkeypatch): # page. mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(side_effect=[[{"total_count": 0}], []]) + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=[[{"total_count": 0}], []])) mock_prisma.db.litellm_spendlogs = MagicMock() mock_prisma.db.litellm_spendlogs.count = AsyncMock(return_value=0) @@ -391,7 +397,7 @@ async def test_spend_logs_ui_out_of_range_page_keeps_total(monkeypatch): # out-of-range page (empty). mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(side_effect=[[{"total_count": 7}], []]) + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=[[{"total_count": 7}], []])) mock_prisma.db.litellm_spendlogs = MagicMock() mock_prisma.db.litellm_spendlogs.count = AsyncMock(return_value=0) @@ -432,9 +438,8 @@ async def test_get_spend_by_team_binds_optional_team_filter(): guard for LIT-4125 (the team query previously carried no team_id predicate). """ mock_prisma = MagicMock() - mock_prisma.db = MagicMock() mock_query_raw = AsyncMock(return_value=[]) - mock_prisma.db.query_raw = mock_query_raw + statement_timeout_reads(mock_prisma, mock_query_raw) start_date = datetime.datetime(2024, 1, 1, tzinfo=timezone.utc) end_date = datetime.datetime(2024, 1, 31, tzinfo=timezone.utc) @@ -484,7 +489,7 @@ async def test_global_spend_report_team_group_forwards_team_id(monkeypatch): mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(return_value=[]) + statement_timeout_reads(mock_prisma, AsyncMock(return_value=[])) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True) @@ -499,29 +504,19 @@ async def test_global_spend_report_team_group_forwards_team_id(monkeypatch): customer_id=None, ) - assert mock_prisma.db.query_raw.called, "query_raw should have been called" - sql = mock_prisma.db.query_raw.call_args[0][0] - params = mock_prisma.db.query_raw.call_args[0][1:] + assert _spend_log_reads(mock_prisma).called, "query_raw should have been called" + sql = _spend_log_reads(mock_prisma).call_args[0][0] + params = _spend_log_reads(mock_prisma).call_args[0][1:] assert "team_x" in params, "team_id must be forwarded into the DB query params" assert "sl.team_id = $3" in sql, f"team query must filter on team_id. SQL was:\n{sql}" @pytest.mark.asyncio async def test_spend_logs_ui_group_by_session_paginates_sessions(monkeypatch): - """ - With group_by_session=true, /spend/logs/ui must page and count SESSIONS, - not raw calls: the page is one representative row per session (DISTINCT - ON the session group key, preferring non-MCP calls, newest first), the - bounded count counts groups, and on the default startTime sort the page is - selected by keyset (no OFFSET) so deep pages do not degrade. Otherwise the - UI collapses a server page of N calls into fewer visible rows while the - footer still claims N (issue #38060). - """ + """With group_by_session=true, /spend/logs/ui pages and counts sessions, not calls, one representative row per + session, so the footer's total matches the rows shown (issue #38060).""" from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth - from litellm.proxy.spend_tracking.spend_management_endpoints import ( - SPEND_LOGS_PAGINATION_COUNT_CAP, - ui_view_spend_logs, - ) + from litellm.proxy.spend_tracking.spend_management_endpoints import ui_view_spend_logs group_key = "COALESCE(NULLIF(session_id, ''), request_id), api_key" session_rows = [ @@ -533,16 +528,16 @@ async def test_spend_logs_ui_group_by_session_paginates_sessions(monkeypatch): {"request_id": "req-2", "api_key": "k", "metadata": "{}", "session_id": None}, ] - async def mock_query_raw(sql_query, *params): - if "COUNT(*) AS total_count" in sql_query: - return [{"total_count": 60}] + async def mock_query_raw(sql_query: str, *params: object) -> Sequence[Mapping[str, object]]: + if "rows_read" in sql_query: + return [{**row, "settled": True, "rows_read": 0, "session_count": 60} for row in session_rows] if "AS session_representatives" in sql_query: return representative_rows - return session_rows + return [] mock_prisma = MagicMock() mock_prisma.db = MagicMock() - mock_prisma.db.query_raw = AsyncMock(side_effect=mock_query_raw) + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=mock_query_raw)) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) auth = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin") @@ -565,22 +560,16 @@ async def test_spend_logs_ui_group_by_session_paginates_sessions(monkeypatch): user_api_key_dict=auth, group_by_session=True, session_cursor=None, + status_filter=None, ) - emitted = [call[0] for call in mock_prisma.db.query_raw.call_args_list] + emitted = [call[0] for call in _spend_log_reads(mock_prisma).call_args_list] page_sql = emitted[0][0] - assert f"GROUP BY {group_key}" in page_sql, f"page must select sessions, not calls. SQL was:\n{page_sql}" + assert "GROUP BY session_key, api_key" in page_sql, f"page must select sessions, not calls. SQL was:\n{page_sql}" assert "OFFSET" not in page_sql, "the startTime page must be keyset-selected, not offset-selected" - assert emitted[0][-1] == 51, "the page query fetches page_size + 1 sessions to detect has_more" + assert "LIMIT 51 " in page_sql, "the page read fetches page_size + 1 sessions to detect has_more" - count_call = emitted[1] - count_sql = count_call[0] - assert f"GROUP BY {group_key}" in count_sql, f"grouped total must count sessions. SQL was:\n{count_sql}" - assert "COUNT(*) OVER ()" not in count_sql - assert "LIMIT" in count_sql and "FROM (" in count_sql, "the grouped count must stay bounded" - assert count_call[-1] == SPEND_LOGS_PAGINATION_COUNT_CAP + 1 - - rep_sql = emitted[2][0] + rep_sql: Final = emitted[1][0] assert f"DISTINCT ON ({group_key})" in rep_sql, f"page must return one row per session. SQL was:\n{rep_sql}" assert ( f"ORDER BY {group_key}, (call_type = 'asend_message') DESC, " @@ -635,10 +624,10 @@ async def test_spend_logs_ui_group_by_session_offset_pages_for_other_sorts(monke ) group_key = "COALESCE(NULLIF(session_id, ''), request_id), api_key" - count_sql = mock_prisma.db.query_raw.call_args_list[0][0][0] + count_sql = _spend_log_reads(mock_prisma).call_args_list[0][0][0] assert f"GROUP BY {group_key}" in count_sql - page_call = mock_prisma.db.query_raw.call_args_list[1][0] + page_call = _spend_log_reads(mock_prisma).call_args_list[1][0] page_sql = page_call[0] assert f"DISTINCT ON ({group_key})" in page_sql assert "ORDER BY spend DESC" in page_sql @@ -684,8 +673,108 @@ async def test_spend_logs_ui_request_id_lookup_with_grouping_returns_exact_row(m group_by_session=True, ) - page_call = mock_prisma.db.query_raw.call_args_list[1] + page_call = _spend_log_reads(mock_prisma).call_args_list[1] assert "request_id = $" in page_call[0][0], "the request_id equality filter must survive grouping" assert "req-deep-link" in page_call[0] assert [row["request_id"] for row in response["data"]] == ["req-deep-link"] assert response["total"] == 1 + + +async def _spend_logs_ui_page(mock_prisma: MagicMock, monkeypatch: pytest.MonkeyPatch) -> Mapping[str, object]: + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.spend_tracking.spend_management_endpoints import ui_view_spend_logs + + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) + mock_request = MagicMock() + mock_request.url.path = "/spend/logs/ui" + return await ui_view_spend_logs( + log_team_lookup=await get_log_team_lookup(), + request=mock_request, + api_key=None, + user_id=None, + request_id=None, + search=None, + start_date="2026-02-16 00:00:00", + end_date="2026-02-16 23:59:59", + page=1, + page_size=50, + sort_by="startTime", + sort_order="desc", + user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin"), + ) + + +@pytest.mark.asyncio +async def test_spend_logs_ui_reads_are_cancelled_by_postgres_after_30s(monkeypatch: pytest.MonkeyPatch): + """Prisma's HTTP client gives up at 30s either way; without SET LOCAL statement_timeout the scan kept running.""" + mock_prisma = MagicMock() + transaction = statement_timeout_reads(mock_prisma, AsyncMock(side_effect=[[{"total_count": 0}], []])) + + await _spend_logs_ui_page(mock_prisma, monkeypatch) + + assert transaction.statements == ("SET LOCAL statement_timeout = 30000",) * 2 + assert mock_prisma.read_db.tx.call_args.kwargs["timeout"] > datetime.timedelta(seconds=30) + + +@pytest.mark.asyncio +async def test_spend_logs_ui_cancelled_read_is_reported_as_503_with_a_date_range_hint(monkeypatch: pytest.MonkeyPatch): + from litellm.proxy._types import ProxyException + + mock_prisma = MagicMock() + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=statement_timeout_error())) + + with pytest.raises(ProxyException) as exc_info: + await _spend_logs_ui_page(mock_prisma, monkeypatch) + + assert str(exc_info.value.code) == "503" + assert "Spend logs took longer than 30s. Pick a shorter date range" in exc_info.value.message + + +@pytest.mark.asyncio +async def test_global_spend_report_returns_the_503_hint_not_its_catch_all_400_when_postgres_cancels( + monkeypatch: pytest.MonkeyPatch, +): + from fastapi import HTTPException + + from litellm.proxy.spend_tracking.spend_management_endpoints import get_global_spend_report + + mock_prisma = MagicMock() + statement_timeout_reads(mock_prisma, AsyncMock(side_effect=statement_timeout_error())) + monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) + + with pytest.raises(HTTPException) as exc_info: + await get_global_spend_report( + start_date="2026-02-01", + end_date="2026-02-16", + group_by="api_key", + api_key=None, + internal_user_id=None, + team_id=None, + customer_id=None, + ) + + assert exc_info.value.status_code == 503 + assert "Spend logs took longer than 30s. Pick a shorter date range" in str(exc_info.value.detail) + + +@pytest.mark.asyncio +async def test_global_spend_report_reads_are_cancelled_by_postgres_after_30s(monkeypatch: pytest.MonkeyPatch): + from litellm.proxy.spend_tracking.spend_management_endpoints import get_global_spend_report + + mock_prisma = MagicMock() + transaction = statement_timeout_reads(mock_prisma, AsyncMock(return_value=[])) + monkeypatch.setattr("litellm.proxy.proxy_server.premium_user", True) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma) + + await get_global_spend_report( + start_date="2026-02-01", + end_date="2026-02-16", + group_by="api_key", + api_key=None, + internal_user_id=None, + team_id=None, + customer_id=None, + ) + + assert transaction.statements == ("SET LOCAL statement_timeout = 30000",) diff --git a/tests/unit/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/unit/proxy/spend_tracking/test_spend_tracking_utils.py index d5771dff626..448cc498c7a 100644 --- a/tests/unit/proxy/spend_tracking/test_spend_tracking_utils.py +++ b/tests/unit/proxy/spend_tracking/test_spend_tracking_utils.py @@ -4418,6 +4418,81 @@ def test_get_logging_payload_uses_recovered_combined_usage_on_failure(): assert payload["total_tokens"] == 31 +def _fallback_after_a_failed_follow_up( + cache_hit: bool, request_params: Mapping[str, object] = MappingProxyType({}) +): + from litellm.litellm_core_utils.hidden_params import set_hidden_param + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.types.utils import ModelResponse, Usage + + now: Final = datetime.datetime.now(timezone.utc) + logger: Final = Logging( + model="fallback-model", + messages=[], + stream=False, + call_type="acompletion", + start_time=now, + litellm_call_id="caller", + function_id="function", + ) + logger.update_environment_variables( + model="fallback-model", + custom_llm_provider="openai", + litellm_params={"metadata": {"user_api_key": "sk-test"}}, + optional_params=dict(request_params), + ) + fallback: Final = ModelResponse(id="fallback", usage=Usage(prompt_tokens=3, completion_tokens=1, total_tokens=4)) + set_hidden_param(fallback, "response_cost", 0.1) + logger.record_served_rounds_for_failure(((Usage(prompt_tokens=5, completion_tokens=2, total_tokens=7), 0.4),)) + logger.carry_failed_attempt_usage() + logger.model_call_details["cache_hit"] = cache_hit + prepared: Final = logger.prepare_response_cost(fallback, now) + assert prepared is not None + return logger, fallback, prepared, now + + +@pytest.mark.parametrize(("cache_hit", "own_cost"), [(False, 0.1), (True, 0.0)], ids=["fresh", "cached"]) +def test_success_row_bills_a_failed_follow_ups_cost_and_tokens(cache_hit: bool, own_cost: float): + logger, fallback, _, now = _fallback_after_a_failed_follow_up(cache_hit) + + payload: Final = get_logging_payload( + kwargs=logger.model_call_details, response_obj=fallback, start_time=now, end_time=now + ) + + assert payload["spend"] == pytest.approx(own_cost + 0.4) + assert (payload["prompt_tokens"], payload["completion_tokens"], payload["total_tokens"]) == (3 + 5, 1 + 2, 4 + 7) + + +@pytest.mark.parametrize(("cache_hit", "own_cost"), [(False, 0.1), (True, 0.0)], ids=["fresh", "cached"]) +def test_request_params_cannot_change_what_a_failed_follow_up_bills(cache_hit: bool, own_cost: float): + forged_attempt: Final = [{"prompt_tokens": 1000, "completion_tokens": 1000, "total_tokens": 2000}, -100.0] + logger, fallback, _, now = _fallback_after_a_failed_follow_up( + cache_hit, + { + "stash_billed_by_continuation": True, + "failed_attempts_owed_on_success": [forged_attempt], + "failed_attempts_billed_on_success": [forged_attempt], + }, + ) + + payload: Final = get_logging_payload( + kwargs=logger.model_call_details, response_obj=fallback, start_time=now, end_time=now + ) + + assert payload["spend"] == pytest.approx(own_cost + 0.4) + assert (payload["prompt_tokens"], payload["completion_tokens"], payload["total_tokens"]) == (8, 3, 11) + + +def test_cached_fallback_cost_breakdown_adds_up_to_the_billed_failed_follow_up(): + _, _, prepared, _ = _fallback_after_a_failed_follow_up(cache_hit=True) + + breakdown: Final = prepared.payload["cost_breakdown"] + + assert breakdown is not None + assert breakdown["total_cost"] == pytest.approx(prepared.payload["response_cost"]) == pytest.approx(0.4) + assert breakdown["additional_costs"]["failed_attempts_cost"] == pytest.approx(0.4) + + def test_get_logging_payload_failure_without_recovered_usage_is_zero(): """A failure with no recovered usage keeps zero token counts, so the combined-usage override never invents tokens for ordinary failures. @@ -5055,6 +5130,105 @@ def test_get_spend_logs_id_prefers_the_response_id_for_batch_and_file_calls(call assert get_spend_logs_id(call_type, {"id": "batch_abc123"}, {"litellm_call_id": "call-id-1"}) == expected +@pytest.mark.parametrize("status_call_type", ["avideo_status", "video_status"]) +@pytest.mark.parametrize( + ("response_cost", "status_id"), + [ + (0.42, "video_abc_video_cost"), + (0.0, "video_abc"), + (None, "video_abc"), + (float("nan"), "video_abc"), + ], + ids=["charged", "free", "unpriced", "non_finite"], +) +def test_get_logging_payload_gives_only_a_charged_video_poll_a_row_of_its_own(status_call_type, response_cost, status_id): + """A custom provider charges the video on the poll that sees it completed, so that poll needs + a row apart from the create row, which answers with the same video id and would drop it on the + skip_duplicates insert. A poll that charges nothing keeps the bare id, so it still lands on the + create row and adds no row of its own.""" + from litellm.types.videos.main import VideoObject + + video = VideoObject(id="video_abc", object="video", status="completed") + create, status = ( + get_logging_payload( + kwargs={ + "call_type": call_type, + "litellm_call_id": call_id, + "response_cost": cost, + "polled_video_status": "completed", + "litellm_params": {"metadata": {"user_api_key": "test-key"}}, + }, + response_obj=video, + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + ) + for call_type, call_id, cost in (("avideo_generation", "call-1", 0.42), (status_call_type, "call-2", response_cost)) + ) + + assert create["request_id"] == "video_abc" + assert status["request_id"] == status_id + + +@pytest.mark.parametrize(("response_cost", "pays_the_job"), [(0.01, False), (0.43, True)]) +def test_a_video_poll_that_pays_only_its_guardrail_gets_a_row_of_its_own(response_cost, pays_the_job): + """A guardrail charges every poll, so a poll paying only that must neither land on the create row + nor take the row the video's own charge goes on, either of which the skip_duplicates insert drops.""" + kwargs = { + "litellm_call_id": "poll-call-id", + "response_cost": response_cost, + "polled_video_status": "completed", + "standard_logging_object": { + "response_cost": response_cost, + "guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}], + }, + } + + assert get_spend_logs_id("avideo_status", {"id": "video_abc", "status": "completed"}, kwargs) == ( + "video_abc_video_cost" if pays_the_job else "poll-call-id_guardrail_cost" + ) + + +@pytest.mark.parametrize("status", ["failed", "completed"]) +def test_a_finished_batch_retrieve_paying_only_its_guardrail_charges_under_the_batch_row(status): + """The retrieve that settles a batch charges its whole cost once, guardrails included, so a batch whose own + cost is $0 must still land on the row that makes its repeat retrieves free.""" + from litellm.types.utils import LiteLLMBatch + + kwargs = { + "litellm_call_id": "poll-call-id", + "response_cost": 0.01, + "standard_logging_object": { + "response_cost": 0.01, + "guardrail_information": [{"guardrail_name": "priced", "guardrail_cost": 0.01}], + }, + } + polled_batch = LiteLLMBatch( + id="batch_abc", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="file-in", + object="batch", + status=status, + output_file_id="file-out", + ) + + assert get_spend_logs_id("aretrieve_batch", polled_batch.model_dump(), kwargs) == "batch_abc_batch_cost" + + +def test_a_video_polled_with_or_without_its_base64_padding_charges_one_row(): + from litellm.types.videos.utils import encode_video_id_with_provider + + padded = encode_video_id_with_provider("job-1", "custom_video", "deployment-a") + assert padded.endswith("=") + + def row_id(polled_id: str) -> str | None: + kwargs = {"response_cost": 0.42, "polled_video_status": "completed", "litellm_params": {"video_id": polled_id}} + return get_spend_logs_id("avideo_status", {"id": "job-1", "status": "completed"}, kwargs) + + assert row_id(padded.rstrip("=")) == row_id(padded) == f"{padded}_video_cost" + + def test_get_logging_payload_gives_redacted_batch_and_file_rows_distinct_request_ids(): """End to end at the payload level: a batch retrieve and a file create whose bodies were both flattened to the same redaction placeholder must still produce two @@ -7620,13 +7794,11 @@ def test_optional_mcp_metadata_does_not_change_legacy_spend_payload(tool_metadat assert actual == expected -@pytest.mark.parametrize("capture_enabled", [False, True]) @pytest.mark.parametrize("completion_start", [None, "absent", "reported"]) -def test_spend_payload_preserves_unknown_completion_start(capture_enabled: bool, completion_start: str | None) -> None: +def test_spend_payload_preserves_unknown_completion_start(completion_start: str | None) -> None: start: Final = datetime.datetime(2026, 1, 1, tzinfo=timezone.utc) end: Final = start + datetime.timedelta(seconds=2) reported: Final = start + datetime.timedelta(seconds=1) - observations: Final[list[object]] = [] kwargs: Final = { "call_type": "acompletion", "litellm_call_id": "timestamp-call", @@ -7638,9 +7810,7 @@ def test_spend_payload_preserves_unknown_completion_start(capture_enabled: bool, {"id": "timestamp-response"}, start, end, - capture_usage=(lambda row, usage: observations.append(row)) if capture_enabled else None, ) assert payload["completionStartTime"] == ( end if completion_start == "absent" else reported if completion_start else None ) - assert observations == ([payload] if capture_enabled else []) diff --git a/tests/unit/proxy/spend_tracking/test_usage_capture.py b/tests/unit/proxy/spend_tracking/test_usage_capture.py deleted file mode 100644 index 32710e594ee..00000000000 --- a/tests/unit/proxy/spend_tracking/test_usage_capture.py +++ /dev/null @@ -1,1712 +0,0 @@ -import asyncio -import json -from collections.abc import Awaitable, Mapping -from datetime import datetime, timezone -from decimal import Decimal -from typing import Final - -import pytest - -from litellm.accounting import Metric, MetricName, Outcome -from litellm.litellm_core_utils.litellm_logging import Logging -from litellm.proxy.db.accounting_ledger import AccountingConflictError, UsageWrite -from litellm.proxy.spend_tracking.usage_meter import DurableUsageMeter, UsageCaptureError, UsageCaptureSettings -from litellm.types.utils import CallTypes -from tests.unit.proxy.spend_tracking.test_usage_meter import CommitBarrier, _manual_flush, _write - - -@pytest.mark.asyncio -@pytest.mark.parametrize("usage", [None, {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}]) -async def test_capture_keeps_unknown_usage_distinct_from_measured_zero(usage: Mapping[str, int] | None) -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = CommitBarrier() - database.release.set() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - timestamp: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) - captured: Final = capture_callback( - meter, - {"call_type": "acompletion", "litellm_call_id": "caller", "model": "unknown-model", "litellm_params": {}}, - {"id": "provider", "usage": usage}, - timestamp, - timestamp, - ) - await meter.close() - outbound: Final = captured.writes[1].event - assert outbound.metrics == ( - None - if usage is None - else tuple(Metric(name=MetricName(key), value=Decimal(value)) for key, value in usage.items()) - ) - envelope: Final = json.loads(captured.writes[1].envelope) - assert envelope["usage"] == usage - assert envelope["pricing"]["reported_cost"] is None - assert database.committed == ("caller:inbound", "provider") - - -@pytest.mark.asyncio -@pytest.mark.parametrize( - "call_type", - [ - call.value - for call in ( - CallTypes.acompletion, - CallTypes.arealtime, - CallTypes.call_mcp_tool, - CallTypes.asend_message, - CallTypes.pass_through, - ) - ], -) -async def test_shared_callback_capture_retains_usage_and_attribution_without_bodies(call_type: str) -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = CommitBarrier() - database.release.set() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - timestamp: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) - captured: Final = capture_callback( - meter, - { - "call_type": call_type, - "litellm_call_id": "caller", - "model": "test-model", - "messages": "private prompt", - "litellm_params": {"api_key": "private-secret", "metadata": {"user_api_key_team_id": "team"}}, - }, - { - "id": "provider", - "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5}, - "output": "private answer", - }, - timestamp, - timestamp, - ) - await meter.close() - envelope: Final = captured.writes[1].envelope - assert all(secret not in envelope for secret in ("private prompt", "private-secret", "private answer")) - event: Final = captured.writes[1].event - assert event.call_type.value == call_type - assert event.metrics == ( - Metric(name=MetricName.PROMPT_TOKENS, value=Decimal(3)), - Metric(name=MetricName.COMPLETION_TOKENS, value=Decimal(2)), - Metric(name=MetricName.TOTAL_TOKENS, value=Decimal(5)), - ) - assert any(dimension.value == "team" for dimension in event.dimensions) - - -@pytest.mark.asyncio -async def test_cache_duplicate_uses_caller_identity_despite_timestamped_diagnostic_ids() -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = CommitBarrier() - database.release.set() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - timestamp: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) - kwargs: Final = { - "call_type": "acompletion", - "litellm_call_id": "caller", - "model": "test-model", - "cache_hit": True, - "litellm_params": {}, - } - response: Final = {"id": "cached-provider-id", "usage": {"total_tokens": 5}} - first: Final = capture_callback(meter, kwargs, response, timestamp, timestamp) - second: Final = capture_callback(meter, kwargs, response, timestamp, timestamp) - await meter.close() - assert first.writes == second.writes - assert first.writes[1].event.event_id == "caller_cache_hit" - assert json.loads(first.writes[1].envelope)["charge_eligible"] is False - - -NOW: Final = datetime(2026, 1, 1, tzinfo=timezone.utc) - - -class StoredUsage: - def __init__(self) -> None: - self.rows: tuple[UsageWrite, ...] = () - - async def load(self, identities: tuple[str, ...]) -> tuple[UsageWrite, ...]: - return tuple(row for row in self.rows if row.event.event_id in identities) - - async def insert(self, writes: tuple[UsageWrite, ...]) -> None: - existing: Final = {row.event.event_id: row for row in self.rows} - if any(existing.get(row.event.event_id, row) != row for row in writes): - raise AccountingConflictError("Same identity has different content") - unique: Final = {row.event.event_id: row for row in writes} - if any(unique[row.event.event_id] != row for row in writes): - raise AccountingConflictError("Submission contains conflicting content") - self.rows += tuple(row for identity, row in unique.items() if identity not in existing) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("text", ["parent\x00value", "pärent-✓-日本", r"\u0000"]) -async def test_capture_normalizes_database_text_without_poisoning_neighbors(text: str) -> None: - from litellm.proxy.spend_tracking.usage_capture import PricingCapture, capture_callback - - database: Final = StoredUsage() - batches: Final = asyncio.Queue[tuple[UsageWrite, ...]]() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - assert all("\x00" not in (json.loads(write.envelope).get("parent_request_id") or "") for write in writes) - batches.put_nowait(writes) - await database.insert(writes) - - meter: Final = DurableUsageMeter(persist, UsageCaptureSettings(), wait_for_batch=_manual_flush) - nested: Final = json.loads("[" * 100 + "3" + "]" * 100) - kwargs: Final = { - "call_type": "acompletion", - "litellm_call_id": "caller", - "model": "capture-model", - "search_context_size": text, - "_accounting_pricing_snapshot": PricingCapture( - "{}", "{}", json.dumps({"request": {"search_context_size": text}, "nested": nested}) - ), - "litellm_params": {"metadata": {"parent_request_id": text}}, - } - response: Final = {"id": "provider", "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5}} - captured: Final = capture_callback(meter, kwargs, response, NOW, NOW) - neighbor: Final = capture_callback( - meter, - {**kwargs, "litellm_call_id": "neighbor", "litellm_params": {}}, - {**response, "id": "neighbor-provider"}, - NOW, - NOW, - ) - try: - await meter.flush() - await asyncio.gather(captured.receipt, neighbor.receipt) - assert batches.qsize() == 1 - assert len(batches.get_nowait()) == 4 - envelope: Final = json.loads(captured.writes[-1].envelope) - assert envelope["parent_request_id"] == text.replace("\x00", "") - assert envelope["pricing"]["request"]["search_context_size"] == text.replace("\x00", "") - assert envelope["pricing"]["nested"] == nested - assert envelope["event"] == captured.writes[-1].event.model_dump(mode="json") - assert envelope["usage"] == response["usage"] - assert kwargs["litellm_params"] == {"metadata": {"parent_request_id": text}} - repeated: Final = capture_callback(meter, kwargs, response, NOW, NOW) - await meter.flush() - await repeated.receipt - assert repeated.writes == captured.writes - assert len(database.rows) == 4 - finally: - await meter.stop(0) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("invalid_field", ["identity", "context"]) -async def test_invalid_capture_text_is_rejected_before_admission(invalid_field: str) -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - kwargs: Final = {"call_type": "acompletion", "litellm_call_id": "caller", "litellm_params": {}} - try: - accepted: Final = capture_callback(meter, kwargs, {"id": "provider"}, NOW, NOW) - invalid: Final = ( - {**kwargs, "litellm_call_id": "caller\x00"} - if invalid_field == "identity" - else {**kwargs, "litellm_params": {"metadata": {"parent_request_id": "parent\ud800"}}} - ) - with pytest.raises((UsageCaptureError, ValueError)): - capture_callback(meter, invalid, {"id": "other"}, NOW, NOW) - assert meter.pending_records == 2 - await meter.flush() - await accepted.receipt - assert tuple(write.event.event_id for write in database.rows) == ("caller:inbound", "provider") - finally: - await meter.stop(0) - - -def _logger(call_type: str = "acompletion", *, stream: bool = False) -> Logging: - from litellm.litellm_core_utils.litellm_logging import Logging - - logger: Final = Logging( - model="capture-model", - messages=[], - stream=stream, - call_type=call_type, - start_time=NOW, - litellm_call_id="caller", - function_id="function", - ) - logger.update_environment_variables( - model="capture-model", custom_llm_provider="openai", litellm_params={}, optional_params={} - ) - return logger - - -@pytest.mark.asyncio -async def test_terminal_retrieval_reuses_accepted_snapshot_but_rejects_changed_usage() -> None: - from datetime import timedelta - - from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY - from litellm.proxy.spend_tracking.usage_capture import UsageBatchWriter, capture_callback - from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN - - database: Final = StoredUsage() - writer: Final = UsageBatchWriter(database.insert, database.load) - meter: Final = DurableUsageMeter(writer.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - kwargs: Final = { - "call_type": "aget_responses", - "litellm_call_id": "poll-one", - "model": "capture-model", - "response_cost": 2.0, - "litellm_params": {"metadata": {INTERNAL_CALL_ORIGIN_METADATA_KEY: BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN}}, - } - response: Final = { - "id": "job", - "status": "completed", - "background": True, - "created_at": NOW.timestamp(), - "usage": {"input_tokens": 5, "output_tokens": 2, "total_tokens": 7}, - } - first: Final = capture_callback(meter, kwargs, response, NOW, NOW) - await meter.flush() - later: Final = capture_callback( - meter, {**kwargs, "litellm_call_id": "poll-two", "response_cost": 9.0}, response, NOW, NOW + timedelta(hours=2) - ) - await meter.flush() - await later.receipt - assert database.rows == first.writes - assert len(database.rows) == 1 - assert database.rows[0].event.event_id == "job_background_cost" - changed: Final = capture_callback(meter, kwargs, {**response, "usage": {"total_tokens": 8}}, NOW, NOW) - await meter.close() - with pytest.raises(UsageCaptureError, match="identity"): - await changed.receipt - assert database.rows == first.writes - - -@pytest.mark.asyncio -@pytest.mark.parametrize("terminal", [False, True]) -async def test_competing_writers_reuse_only_terminal_pricing_snapshots(terminal: bool) -> None: - from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY - from litellm.proxy.spend_tracking.usage_capture import UsageBatchWriter, capture_callback - from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN - - database: Final = StoredUsage() - attempts: Final = asyncio.Queue[tuple[UsageWrite, ...]]() - competing: Final = asyncio.Event() - - async def insert(writes: tuple[UsageWrite, ...]) -> None: - attempts.put_nowait(writes) - if attempts.qsize() >= 2: - competing.set() - await competing.wait() - await database.insert(writes) - - meters: Final = tuple( - DurableUsageMeter(UsageBatchWriter(insert, database.load).write, UsageCaptureSettings()) for _ in range(2) - ) - kwargs: Final = { - "call_type": "aget_responses" if terminal else "acompletion", - "litellm_call_id": "caller", - "model": "capture-model", - "litellm_params": { - "metadata": {INTERNAL_CALL_ORIGIN_METADATA_KEY: BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN} - if terminal - else {} - }, - } - response: Final = { - "id": "job", - "status": "completed", - "background": terminal, - "created_at": NOW.timestamp(), - "usage": {"input_tokens": 5, "output_tokens": 2, "total_tokens": 7}, - } - captured: Final = tuple( - capture_callback(meter, {**kwargs, "response_cost": price}, response, NOW, NOW) - for meter, price in zip(meters, (2.0, 9.0), strict=True) - ) - try: - await asyncio.gather(*(meter.flush() for meter in meters)) - results: Final = await asyncio.gather(*(capture.receipt for capture in captured), return_exceptions=True) - assert sum(result is None for result in results) == (2 if terminal else 1) - assert sum(isinstance(result, UsageCaptureError) for result in results) == (0 if terminal else 1) - assert database.rows in tuple(capture.writes for capture in captured) - assert len(database.rows) == (1 if terminal else 2) - assert attempts.qsize() == (3 if terminal else 2) - finally: - await asyncio.gather(*(meter.stop(0) for meter in meters)) - - -@pytest.mark.asyncio -async def test_batch_poll_has_distinct_identity_and_only_terminal_usage_is_chargeable() -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - kwargs: Final = { - "call_type": "aretrieve_batch", - "litellm_call_id": "poll", - "model": "capture-model", - "litellm_params": {}, - } - pending: Final = capture_callback(meter, kwargs, {"id": "batch", "status": "in_progress"}, NOW, NOW) - completed: Final = capture_callback( - meter, kwargs, {"id": "batch", "status": "completed", "usage": {"total_tokens": 9}}, NOW, NOW - ) - await meter.close() - assert pending.writes[-1].event.event_id == "poll:status" - assert json.loads(pending.writes[-1].envelope)["charge_eligible"] is False - assert completed.writes[-1].event.event_id == "batch_batch_cost" - assert json.loads(completed.writes[-1].envelope)["charge_eligible"] is True - assert len(completed.writes) == 1 - - -@pytest.mark.asyncio -async def test_internal_capture_does_not_claim_reused_parent_logger(monkeypatch: pytest.MonkeyPatch) -> None: - import litellm - from litellm._internal_context import internal_sub_call - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - from litellm.types.utils import ModelResponse, Usage - - monkeypatch.setitem( - litellm.model_cost, - "capture-model", - { - "input_cost_per_token": 0.1, - "output_cost_per_token": 0.2, - "litellm_provider": "openai", - "mode": "chat", - }, - ) - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger() - response: Final = ModelResponse( - id="response", model="capture-model", usage=Usage(prompt_tokens=2, completion_tokens=1, total_tokens=3) - ) - with internal_sub_call("caller"): - capture_logging_usage(meter, logger, response, NOW, NOW) - monkeypatch.setitem( - litellm.model_cost, - "capture-model", - { - "input_cost_per_token": 0.7, - "output_cost_per_token": 0.9, - "litellm_provider": "openai", - "mode": "chat", - }, - ) - litellm.register_model({"capture-model": litellm.model_cost["capture-model"]}) - capture_logging_usage(meter, logger, response, NOW, NOW) - capture_logging_usage(meter, logger, response, NOW, NOW) - await meter.close() - assert tuple(row.event.event_id for row in database.rows) == ("response:included", "caller:inbound", "response") - child: Final = json.loads(database.rows[0].envelope) - parent: Final = json.loads(database.rows[-1].envelope) - assert (child["ownership"], child["charge_eligible"], child["parent_request_id"]) == ( - "included_in_parent", - False, - "caller", - ) - assert parent["charge_eligible"] is True - assert child["pricing"]["model_map_information"]["model_map_value"]["input_cost_per_token"] == 0.1 - assert parent["pricing"]["model_map_information"]["model_map_value"]["input_cost_per_token"] == 0.7 - - -@pytest.mark.asyncio -async def test_stream_capture_ignores_chunks_and_preserves_terminal_usage_extras() -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - from litellm.types.utils import ModelResponse, ModelResponseStream, Usage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger(stream=True) - capture_logging_usage(meter, logger, ModelResponseStream(id="chunk"), NOW, NOW) - assert meter.pending_records == 0 - capture_logging_usage( - meter, logger, ModelResponse(id="stream", usage=Usage(prompt_tokens=3, provider_units=4)), NOW, NOW - ) - await meter.close() - assert json.loads(database.rows[-1].envelope)["usage"]["provider_units"] == 4 - assert database.rows[-1].event.event_id == "stream" - - -@pytest.mark.asyncio -@pytest.mark.parametrize("call_type", ["acompletion", "atext_completion", "aembedding", "aresponses"]) -async def test_supported_sync_ack_waits_for_commit(call_type: str, monkeypatch: pytest.MonkeyPatch) -> None: - from litellm.proxy import proxy_server - from litellm.proxy._types import ConfigGeneralSettings - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": True}) - deadlines: Final = asyncio.Queue[float | None]() - original_wait: Final = asyncio.wait_for - - async def wait_with_deadline(awaitable: Awaitable[object], timeout: float | None) -> object: - deadlines.put_nowait(timeout) - return await original_wait(awaitable, timeout) - - monkeypatch.setattr(asyncio, "wait_for", wait_with_deadline) - response: Final = asyncio.create_task( - capture_proxy_response( - _logger(call_type), - ModelResponse(id="provider", usage=Usage(prompt_tokens=3)), - ) - ) - try: - await database.entered.wait() - deadline: Final = deadlines.get_nowait() - assert deadline == ConfigGeneralSettings().accounting_sync_ack_timeout_seconds - assert deadline is not None and deadline > UsageCaptureSettings().flush_interval_seconds + 10 - assert not response.done() - assert database.committed == () - database.release.set() - await response - assert database.committed == ("caller:inbound", "provider") - finally: - database.release.set() - await runtime.stop(1) - await response - - -@pytest.mark.asyncio -@pytest.mark.parametrize("failure_policy", ["fail_closed", "log"]) -async def test_sync_ack_commit_failure_follows_response_policy( - failure_policy: str, monkeypatch: pytest.MonkeyPatch -) -> None: - from fastapi import HTTPException - - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier(failure=ValueError("invalid envelope")) - database.release.set() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2)) - meter: Final = runtime.meter - assert meter is not None - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr( - proxy_server, - "general_settings", - {"accounting_sync_ack": True, "accounting_sync_ack_failure_policy": failure_policy}, - ) - logger: Final = _logger() - response: Final = ModelResponse(id="provider", usage=Usage(prompt_tokens=4)) - if failure_policy == "fail_closed": - with pytest.raises(HTTPException) as error: - await capture_proxy_response(logger, response) - assert error.value.status_code == 503 - else: - await capture_proxy_response(logger, response) - await runtime.stop(1.0) - assert database.committed == () - assert meter.pending_records == 0 - - -@pytest.mark.asyncio -async def test_failed_attempt_keeps_its_usage_without_claiming_the_callers_success() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - try: - for _ in range(2): - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5, completion_tokens=2, total_tokens=7), 0.4) - logger.carry_failed_attempt_usage() - logger.capture_usage_before_logging( - ModelResponse(id="fallback", usage=Usage(prompt_tokens=3, total_tokens=3)), NOW, NOW - ) - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ( - "caller-attempt-0", - "caller-attempt-1", - "caller:inbound", - "fallback", - ) - failed: Final = json.loads(database.rows[0].envelope) - assert failed["usage"]["total_tokens"] == 7 - assert failed["parent_request_id"] == "caller" - assert failed["request_count_eligible"] is False - assert failed["charge_eligible"] is True - assert failed["event"]["retry_of_attempt_id"] is None - assert failed["event"]["outcome"] == "failed" - assert failed["pricing"]["reported_cost"] == 0.4 - assert all(json.loads(row.envelope)["included_attempt_ids"] == [] for row in database.rows[:2]) - successful: Final = json.loads(database.rows[-1].envelope) - assert successful["related_attempt_ids"] == ["caller-attempt-0", "caller-attempt-1"] - assert successful["included_attempt_ids"] == [] - assert successful["usage"]["total_tokens"] == 3 - assert successful["charge_eligible"] is True - assert database.rows[2].event.outcome is Outcome.SUCCEEDED - - -@pytest.mark.asyncio -@pytest.mark.parametrize("estimated", [False, True]) -async def test_cancelled_usage_keeps_measured_or_estimated_provenance(estimated: bool) -> None: - from fastapi import HTTPException - - from litellm.litellm_core_utils.litellm_logging import UsageObservation - from litellm.proxy.spend_tracking.usage_capture import capture_callback - from litellm.types.utils import Usage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - capture_callback( - meter, - { - "litellm_call_id": "cancelled", - "call_type": "acompletion", - "model": "capture-model", - "litellm_params": {}, - "combined_usage_object": Usage(prompt_tokens=4, total_tokens=4), - "_accounting_usage_observed": UsageObservation.ESTIMATED if estimated else UsageObservation.KNOWN, - }, - HTTPException(499, "Client disconnected"), - NOW, - NOW, - ) - await meter.close() - payload: Final = json.loads(database.rows[-1].envelope) - assert payload["event"]["outcome"] == "cancelled" - assert payload["usage"]["total_tokens"] == 4 - assert payload["usage_source"] == ("estimated" if estimated else "measured") - assert payload["pricing"]["reported_cost"] is None - - -@pytest.mark.asyncio -async def test_realtime_capture_keeps_metered_units_and_provider_usage_without_frames() -> None: - from litellm.constants import REALTIME_METERED_USAGE_KEY - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - from litellm.types.llms.xai import RealtimeMeteredUsage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger(CallTypes.arealtime.value) - logger.model_call_details[REALTIME_METERED_USAGE_KEY] = RealtimeMeteredUsage(True, 1.5, 2.5, 3) - capture_logging_usage( - meter, - logger, - [ - { - "type": "response.done", - "response": { - "id": "turn", - "usage": {"input_tokens": 5, "output_tokens": 2, "total_tokens": 7}, - "output": "private realtime transcript", - }, - } - ], - NOW, - NOW, - ) - await meter.close() - payload: Final = json.loads(database.rows[-1].envelope) - assert payload["usage"]["total_tokens"] == 7 - assert payload["usage"]["metered_usage"] == { - "push_to_talk": True, - "audio_seconds_sent": 1.5, - "audio_seconds_received": 2.5, - "text_messages": 3, - } - assert "private realtime transcript" not in database.rows[-1].envelope - - -@pytest.mark.asyncio -async def test_default_http_capture_returns_before_database_commit(monkeypatch: pytest.MonkeyPatch) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {}) - try: - await capture_proxy_response( - _logger(), - ModelResponse(id="asynchronous", usage=Usage(prompt_tokens=1)), - ) - await database.entered.wait() - assert database.committed == () - finally: - database.release.set() - await runtime.stop(1) - assert database.committed == ("caller:inbound", "asynchronous") - - -@pytest.mark.asyncio -async def test_deferred_capture_freezes_pricing_before_configuration_changes(monkeypatch: pytest.MonkeyPatch) -> None: - import litellm - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_logging_usage - from litellm.types.utils import ModelResponse, Usage - from litellm.utils import dispatch_success_logging - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - logger.defer_async_logging = True - logger.custom_cost_per_second = 0.25 - monkeypatch.setattr(litellm, "cost_discount_config", {"openai": 0.1}) - response: Final = ModelResponse(id="deferred", usage=Usage(prompt_tokens=3)) - try: - dispatch_success_logging(logger, response, NOW, NOW, False, False) - assert database.rows == () - logger.custom_cost_per_second = 9 - monkeypatch.setattr(litellm, "cost_discount_config", {"openai": 0.9}) - assert runtime.meter is not None - capture_logging_usage(runtime.meter, logger, response, NOW, NOW) - await runtime.meter.flush() - finally: - await runtime.stop(1) - pricing: Final = json.loads(database.rows[-1].envelope)["pricing"] - assert pricing["logging"]["custom_cost_per_second"] == 0.25 - assert pricing["adjustments"]["discount"] == 0.1 - - -@pytest.mark.asyncio -async def test_synchronous_internal_capture_reserves_capacity_before_scheduling() -> None: - from litellm._internal_context import internal_sub_call - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(max_pending_records=2, batch_size=2)) - logger: Final = _logger() - - def capture_child() -> None: - with internal_sub_call("caller"): - logger.capture_usage_before_logging(ModelResponse(id="child", usage=Usage(prompt_tokens=2)), NOW, NOW) - - try: - await asyncio.to_thread(capture_child) - assert runtime.meter is not None - assert runtime.meter.pending_records == 1 - receipt: Final = await asyncio.to_thread(runtime.meter.submit, (_write("second"),)) - await database.entered.wait() - with pytest.raises(UsageCaptureError, match="capacity"): - await asyncio.to_thread(runtime.meter.submit, (_write("overflow"),)) - database.release.set() - await receipt - finally: - database.release.set() - await runtime.stop(1) - assert database.committed == ("child:included", "second") - - -@pytest.mark.asyncio -@pytest.mark.parametrize("has_metered_usage", [False, True]) -async def test_realtime_without_provider_token_usage_does_not_invent_zero(has_metered_usage: bool) -> None: - from litellm.constants import REALTIME_METERED_USAGE_KEY - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - from litellm.types.llms.xai import RealtimeMeteredUsage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger(CallTypes.arealtime.value) - if has_metered_usage: - logger.model_call_details[REALTIME_METERED_USAGE_KEY] = RealtimeMeteredUsage(True, 0, 0, 0) - capture_logging_usage(meter, logger, [], NOW, NOW) - await meter.close() - usage: Final = json.loads(database.rows[-1].envelope)["usage"] - assert usage == ( - { - "metered_usage": { - "push_to_talk": True, - "audio_seconds_sent": 0, - "audio_seconds_received": 0, - "text_messages": 0, - } - } - if has_metered_usage - else None - ) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("committed_before_failure", [False, True]) -async def test_database_recovery_retries_frozen_usage_without_callback_or_duplicate_rows( - committed_before_failure: bool, -) -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = StoredUsage() - failures: Final = asyncio.Queue[None]() - failures.put_nowait(None) - retrying: Final = asyncio.Event() - recovered: Final = asyncio.Event() - delays: Final = asyncio.Queue[float]() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - if not failures.empty(): - failures.get_nowait() - if committed_before_failure: - await database.insert(writes) - raise ConnectionError("database unavailable or acknowledgement lost") - await database.insert(writes) - - async def retry_delay(delay: float) -> None: - delays.put_nowait(delay) - retrying.set() - await recovered.wait() - - meter: Final = DurableUsageMeter( - persist, UsageCaptureSettings(max_pending_records=2), wait_for_batch=_manual_flush, retry_delay=retry_delay - ) - kwargs: Final = { - "call_type": "acompletion", - "litellm_call_id": "caller", - "model": "capture-model", - "litellm_params": {}, - } - response: Final = {"id": "provider", "usage": {"total_tokens": 3}} - first: Final = capture_callback(meter, kwargs, response, NOW, NOW) - reserved: Final = meter.pending_bytes - flushing: Final = asyncio.create_task(meter.flush()) - await retrying.wait() - assert not first.receipt.done() - assert (meter.pending_records, meter.pending_bytes) == (2, reserved) - with pytest.raises(UsageCaptureError, match="capacity"): - meter.submit((_write("overflow"),)) - recovered.set() - await flushing - await first.receipt - duplicate: Final = capture_callback(meter, {**kwargs, "_accounting_capture": first}, response, NOW, NOW) - await meter.close() - assert duplicate is first - assert database.rows == first.writes - assert delays.get_nowait() == 0.2 - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - - -@pytest.mark.asyncio -async def test_cut_stream_keeps_original_model_usage_as_part_of_the_parent_aggregate() -> None: - from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.router import Router - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger(stream=True) - stream: Final = CustomStreamWrapper(completion_stream=iter(()), model="capture-model", logging_obj=logger) - partial: Final = ModelResponse( - id="cut", model="capture-model", usage=Usage(prompt_tokens=3, completion_tokens=2, total_tokens=5) - ) - try: - Router._price_cut_partial(stream, partial) - finally: - await runtime.stop(1) - assert len(database.rows) == 1 - payload: Final = json.loads(database.rows[0].envelope) - assert payload["event"]["event_id"] == "cut:included" - assert payload["parent_request_id"] == "caller" - assert payload["charge_eligible"] is False - assert payload["usage"]["total_tokens"] == 5 - assert payload["pricing"]["model_map_information"]["model_map_key"] == "capture-model" - - -@pytest.mark.asyncio -async def test_direct_mcp_logging_captures_tool_pricing_and_identity_without_arguments() -> None: - from mcp.types import CallToolResult, TextContent - - from litellm.accounting import DimensionName - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger(CallTypes.call_mcp_tool.value) - logger.model_call_details["mcp_tool_call_metadata"] = { - "name": "lookup", - "namespaced_tool_name": "server.lookup", - "arguments": {"query": "private query"}, - "mcp_server_cost_info": {"default_cost_per_query": 0.2, "tool_name_to_cost_per_query": {"lookup": 0.3}}, - } - try: - await logger.async_success_handler( - CallToolResult(content=[TextContent(type="text", text="private result")]), NOW, NOW - ) - finally: - await runtime.stop(1) - outbound: Final = database.rows[-1] - assert any(d.name is DimensionName.TOOL and d.value == "server.lookup" for d in outbound.event.dimensions) - payload: Final = json.loads(outbound.envelope) - assert payload["pricing"]["tool"] == {"name": "lookup", "cost_per_query": 0.3} - assert payload["usage"] is None - assert all(secret not in outbound.envelope for secret in ("private query", "private result")) - - -@pytest.mark.asyncio -async def test_callback_overload_is_visible_without_suppressing_legacy_tracking( - monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture -) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, captured_callback_kwargs - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(max_pending_records=2, batch_size=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - kwargs: Final = {"call_type": "acompletion", "litellm_call_id": "one", "litellm_params": {}} - try: - for start, end in ((None, NOW), (NOW, None)): - missing_timestamp: Final = await captured_callback_kwargs(kwargs, {"id": "one"}, start, end) - assert missing_timestamp == kwargs - assert "Usage capture requires callback start and end timestamps" in caplog.text - admitted: Final = await captured_callback_kwargs(kwargs, {"id": "one"}, NOW, NOW) - await database.entered.wait() - rejected: Final = await captured_callback_kwargs({**kwargs, "litellm_call_id": "two"}, {"id": "two"}, NOW, NOW) - assert "_accounting_capture" in admitted - assert "_accounting_capture" not in rejected - assert "Usage capture failed before legacy cost tracking" in caplog.text - finally: - database.release.set() - await runtime.stop(1) - assert database.committed == ("one:inbound", "one") - - -@pytest.mark.asyncio -@pytest.mark.parametrize( - ("call_type", "streaming", "background"), - [("acompletion", True, False), ("aresponses", False, True), ("aocr", False, False)], -) -@pytest.mark.parametrize("overloaded", [False, True]) -async def test_async_capture_preserves_response_without_commit_ack( - call_type: str, streaming: bool, background: bool, overloaded: bool, monkeypatch: pytest.MonkeyPatch -) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2, max_pending_records=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr( - proxy_server, - "general_settings", - { - "accounting_sync_ack": True, - "accounting_sync_ack_timeout_seconds": 0, - }, - ) - meter: Final = runtime.meter - assert meter is not None - if overloaded: - meter.submit((_write("occupied-one"), _write("occupied-two"))) - try: - await capture_proxy_response( - _logger(call_type, stream=streaming), - ModelResponse(id="provider", usage=Usage(prompt_tokens=3)), - background=background, - ) - assert database.committed == () - assert meter.pending_records == 2 - finally: - database.release.set() - await runtime.stop(1) - assert database.committed == (("occupied-one", "occupied-two") if overloaded else ("caller:inbound", "provider")) - - -@pytest.mark.asyncio -async def test_new_connection_turn_does_not_reuse_the_previous_turns_capture() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - try: - logger.capture_usage_before_logging(ModelResponse(id="first", usage=Usage(prompt_tokens=2)), NOW, NOW) - next_turn: Final = logger.copy_for_new_call("second-caller", NOW) - next_turn.capture_usage_before_logging(ModelResponse(id="second", usage=Usage(prompt_tokens=4)), NOW, NOW) - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ( - "caller:inbound", - "first", - "second-caller:inbound", - "second", - ) - assert json.loads(database.rows[-1].envelope)["usage"]["prompt_tokens"] == 4 - - -@pytest.mark.asyncio -async def test_billed_failure_is_captured_before_legacy_persistence_gates(monkeypatch: pytest.MonkeyPatch) -> None: - from litellm.proxy import proxy_server - from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "prisma_client", None) - request: Final = { - "call_type": "acompletion", - "litellm_call_id": "failed", - "model": "capture-model", - "start_time": NOW, - "combined_usage_object": Usage(prompt_tokens=3, total_tokens=3), - "response_cost": 0.2, - "litellm_params": {}, - } - try: - await ProxyDBLogger().async_post_call_failure_hook(request, RuntimeError("provider failed"), UserAPIKeyAuth()) - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ("failed:inbound", "failed") - payload: Final = json.loads(database.rows[-1].envelope) - assert payload["event"]["outcome"] == "failed" - assert payload["usage"]["total_tokens"] == 3 - assert payload["charge_eligible"] is True - assert payload["pricing"]["reported_cost"] == 0.2 - - -@pytest.mark.asyncio -@pytest.mark.parametrize("pages", [None, 0, 3]) -async def test_prepared_ocr_capture_preserves_reported_pages(pages: int | None) -> None: - from litellm.llms.base_llm.ocr.transformation import OCRResponse, OCRUsageInfo - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger("aocr") - response: Final = OCRResponse(pages=[], model="capture-model", usage_info=OCRUsageInfo(pages_processed=pages)) - logger.prepare_response_cost(response, NOW) - capture_logging_usage(meter, logger, response, NOW, NOW) - await meter.close() - envelope: Final = json.loads(database.rows[-1].envelope) - assert (None if envelope["usage"] is None else envelope["usage"]["pages_processed"]) == pages - assert envelope["usage_source"] == ("unknown" if pages is None else "measured") - assert response.usage_info is not None and response.usage_info.pages_processed == pages - - -@pytest.mark.asyncio -@pytest.mark.parametrize("measured", [None, {"total_tokens": 0}, {"total_tokens": 3}]) -async def test_batch_capture_provenance_tracks_observations_not_envelope_presence( - measured: Mapping[str, int] | None, -) -> None: - from litellm.proxy.spend_tracking.usage_capture import capture_callback - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - usage: Final = { - "total_tokens": 0 if measured is None else measured["total_tokens"], - "batch_usage_by_model": {"missing-model": None, "other-model": measured}, - "batch_unknown_usage_requests": 2 if measured is None else 1, - } - capture_callback( - meter, - {"call_type": "aretrieve_batch", "litellm_call_id": "caller", "litellm_params": {}}, - {"id": "batch", "usage": usage}, - NOW, - NOW, - ) - await meter.close() - envelope: Final = json.loads(database.rows[-1].envelope) - assert envelope["usage_source"] == ("unknown" if measured is None else "measured") - assert envelope["usage"]["batch_usage_by_model"] == usage["batch_usage_by_model"] - assert database.rows[-1].event.metrics == ( - None if measured is None else (Metric(name=MetricName.TOTAL_TOKENS, value=Decimal(measured["total_tokens"])),) - ) - - -@pytest.mark.asyncio -async def test_ack_admission_failure_does_not_turn_provider_success_into_failed_usage( - monkeypatch: pytest.MonkeyPatch, -) -> None: - from fastapi import HTTPException - - from litellm.proxy import proxy_server - from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings(batch_size=2, max_pending_records=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": True}) - monkeypatch.setattr(proxy_server, "prisma_client", None) - meter: Final = runtime.meter - assert meter is not None - logger: Final = _logger() - try: - meter.submit((_write("occupied-one"), _write("occupied-two"))) - with pytest.raises(HTTPException) as error: - await capture_proxy_response(logger, ModelResponse(id="caller", usage=Usage(prompt_tokens=3))) - assert error.value.status_code == 503 - await meter.flush() - request: Final = {**logger.model_call_details, "litellm_logging_obj": logger, "start_time": NOW} - await ProxyDBLogger().async_post_call_failure_hook(request, error.value, UserAPIKeyAuth()) - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ("occupied-one", "occupied-two") - - -@pytest.mark.asyncio -async def test_response_ack_timeout_preserves_success_and_later_commits(monkeypatch: pytest.MonkeyPatch) -> None: - from fastapi import HTTPException - - from litellm.proxy import proxy_server - from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_proxy_response - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=2)) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "prisma_client", None) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": True}) - logger: Final = _logger() - - async def expired_deadline(future: object, timeout: float) -> None: - raise TimeoutError("controlled response deadline") - - try: - with monkeypatch.context() as clock: - clock.setattr(asyncio, "wait_for", expired_deadline) - with pytest.raises(HTTPException) as error: - await capture_proxy_response(logger, ModelResponse(id="provider", usage=Usage(prompt_tokens=3))) - assert error.value.status_code == 503 - await database.entered.wait() - captured: Final = logger.model_call_details["_accounting_capture"] - request: Final = {**logger.model_call_details, "litellm_logging_obj": logger, "start_time": NOW} - await ProxyDBLogger().async_post_call_failure_hook(request, error.value, UserAPIKeyAuth()) - database.release.set() - await captured.receipt - assert tuple(row.event.outcome for row in captured.writes) == (Outcome.SUCCEEDED, Outcome.SUCCEEDED) - assert database.committed == ("caller:inbound", "provider") - assert database.transactions == 1 - finally: - await runtime.stop(1) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("call_type", ["generate_content", "agenerate_content"]) -@pytest.mark.parametrize("tokens", [None, 0, 7]) -async def test_prepared_native_google_capture_preserves_reported_usage( - call_type: str, tokens: int | None, monkeypatch: pytest.MonkeyPatch -) -> None: - import httpx - - import litellm - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger(call_type) - monkeypatch.setitem( - litellm.model_cost, - "capture-model", - {"input_cost_per_token": 0.1, "output_cost_per_token": 0.2, "litellm_provider": "openai", "mode": "chat"}, - ) - reported: Final = ( - {"trafficType": "ON_DEMAND"} - if tokens is None - else { - "promptTokenCount": tokens, - "candidatesTokenCount": tokens, - "totalTokenCount": 2 * tokens, - "cachedContentTokenCount": tokens, - "promptTokensDetails": [{"modality": "DOCUMENT", "tokenCount": tokens}], - "cacheTokensDetails": [{"modality": "document", "token_count": tokens}], - } - ) - response: Final = { - "responseId": "native-google", - "candidates": [{"content": {"parts": [{"text": "answer"}], "role": "model"}, "finishReason": "STOP"}], - "usageMetadata": reported, - } - native_response: Final = httpx.Response(200, json=response) - logger.model_call_details["httpx_response"] = native_response - prepared: Final = logger.prepare_response_cost(response, NOW) - assert prepared is not None - capture_logging_usage(meter, logger, response, NOW, NOW) - await meter.close() - envelope: Final = json.loads(database.rows[-1].envelope) - assert (None if envelope["usage"] is None else envelope["usage"]["total_tokens"]) == ( - None if tokens is None else 2 * tokens - ) - assert (None if envelope["usage"] is None else envelope["usage"]["prompt_tokens_details"]["text_tokens"]) == tokens - assert ( - None - if envelope["usage"] is None - else envelope["usage"]["prompt_tokens_details"]["cached_tokens_details"]["text_tokens"] - ) == tokens - assert envelope["usage_source"] == ("unknown" if tokens is None else "measured") - assert response == native_response.json() - - -@pytest.mark.asyncio -@pytest.mark.parametrize("rejected", [False, True]) -async def test_retry_links_only_admitted_usage_and_preserves_later_measurement(rejected: bool) -> None: - from litellm.litellm_core_utils.litellm_logging import UsageObservation - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings(max_pending_records=4)) - logger: Final = _logger() - assert runtime.meter is not None - try: - if rejected: - runtime.meter.submit(tuple(_write(f"occupied-{index}") for index in range(4))) - logger.model_call_details["_accounting_usage_observed"] = UsageObservation.UNKNOWN - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5, total_tokens=5), 0.4) - logger.carry_failed_attempt_usage() - await runtime.meter.flush() - logger.capture_usage_before_logging(ModelResponse(id="success", usage=Usage(prompt_tokens=3)), NOW, NOW) - finally: - await runtime.stop(1) - successful: Final = json.loads(next(row.envelope for row in database.rows if row.event.event_id == "success")) - assert successful.get("related_attempt_ids", successful["included_attempt_ids"]) == ( - [] if rejected else ["caller-attempt-0"] - ) - assert successful["usage"]["prompt_tokens"] == 3 - if not rejected: - failed: Final = json.loads( - next(row.envelope for row in database.rows if row.event.event_id == "caller-attempt-0") - ) - assert failed["usage"]["prompt_tokens"] == 5 - assert failed["usage_source"] == "measured" - - -@pytest.mark.asyncio -@pytest.mark.parametrize("current_tokens", [None, 0, 3]) -async def test_final_failure_captures_current_attempt_without_legacy_retry_aggregate( - current_tokens: int | None, -) -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_callback - from litellm.proxy.utils import _failure_fields_to_lift - from litellm.types.utils import Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - try: - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5, total_tokens=5), 0.4) - logger.carry_failed_attempt_usage() - if current_tokens is not None: - logger.record_partial_usage_for_failure( - Usage(prompt_tokens=current_tokens, total_tokens=current_tokens), 0.2 - ) - lifted: Final = _failure_fields_to_lift({"litellm_logging_obj": logger}, provider_billed=True) - assert lifted["combined_usage_object"].total_tokens == 5 + (current_tokens or 0) - assert runtime.meter is not None - capture_callback( - runtime.meter, - {**logger.model_call_details, **lifted, "log_event_type": "failed_api_call"}, - None, - NOW, - NOW, - ) - finally: - await runtime.stop(1) - final: Final = json.loads(next(row.envelope for row in database.rows if row.event.event_id == "caller")) - assert (None if final["usage"] is None else final["usage"]["total_tokens"]) == current_tokens - assert final["usage_source"] == ("unknown" if current_tokens is None else "measured") - assert final["pricing"]["reported_cost"] == (None if current_tokens is None else 0.2) - assert final["included_attempt_ids"] == [] - assert final["related_attempt_ids"] == ["caller-attempt-0"] - - -@pytest.mark.asyncio -async def test_estimated_final_attempt_keeps_its_provenance_after_measured_retry() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_callback - from litellm.proxy.utils import _failure_fields_to_lift - from litellm.types.utils import Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - logger.messages = [{"role": "user", "content": "hello"}] - logger.model_call_details["messages"] = logger.messages - try: - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5, total_tokens=5), 0.4) - logger.carry_failed_attempt_usage() - logger.model_call_details["current_attempt_api_call_start_time"] = NOW - lifted: Final = _failure_fields_to_lift({"litellm_logging_obj": logger}, provider_billed=True) - assert runtime.meter is not None - capture_callback(runtime.meter, {**logger.model_call_details, **lifted}, None, NOW, NOW) - finally: - await runtime.stop(1) - final: Final = json.loads(next(row.envelope for row in database.rows if row.event.event_id == "caller")) - assert final["usage_source"] == "estimated" - assert final["usage"]["total_tokens"] > 0 - assert lifted["combined_usage_object"].total_tokens == final["usage"]["total_tokens"] + 5 - assert final["pricing"]["reported_cost"] == 0 - - -@pytest.mark.asyncio -async def test_parent_receipt_cannot_acknowledge_when_a_linked_attempt_conflicts() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - try: - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5), 0.4) - logger.carry_failed_attempt_usage() - assert runtime.meter is not None - await runtime.meter.flush() - other: Final = _logger() - other.record_partial_usage_for_failure(Usage(prompt_tokens=8), 0.8) - other.carry_failed_attempt_usage() - other.capture_usage_before_logging(ModelResponse(id="success", usage=Usage(prompt_tokens=3)), NOW, NOW) - captured: Final = other.model_call_details["_accounting_capture"] - await runtime.meter.flush() - with pytest.raises(UsageCaptureError, match="dependency"): - await captured.receipt - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ("caller-attempt-0",) - - -@pytest.mark.asyncio -async def test_unserializable_observation_does_not_interrupt_retry_or_legacy_usage() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = StoredUsage() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.insert, UsageCaptureSettings()) - logger: Final = _logger() - usage: Final = Usage(prompt_tokens=5, total_tokens=5, opaque_usage=object()) - try: - logger.record_partial_usage_for_failure(usage, 0.4) - assert logger.model_call_details["combined_usage_object"] is usage - logger.carry_failed_attempt_usage() - assert logger.model_call_details["failed_attempts_usage"] == ((usage, 0.4),) - logger.capture_usage_before_logging(ModelResponse(id="success", usage=Usage(prompt_tokens=3)), NOW, NOW) - finally: - await runtime.stop(1) - assert tuple(row.event.event_id for row in database.rows) == ("caller:inbound", "success") - assert json.loads(database.rows[-1].envelope)["related_attempt_ids"] == [] - - -@pytest.mark.asyncio -@pytest.mark.parametrize("prior_committed", [False, True]) -@pytest.mark.parametrize("batch_size", [2, 3]) -async def test_retry_dependencies_do_not_resubmit_usage_or_expand_final_submission( - prior_committed: bool, batch_size: int -) -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.types.utils import ModelResponse, Usage - - database: Final = CommitBarrier() - database.release.set() - runtime: Final = UsageCaptureRuntime() - runtime.start(database.write, UsageCaptureSettings(batch_size=batch_size, max_pending_records=3)) - logger: Final = _logger() - try: - logger.record_partial_usage_for_failure(Usage(prompt_tokens=5), 0.4) - logger.carry_failed_attempt_usage() - assert runtime.meter is not None - if prior_committed: - await runtime.meter.flush() - logger.capture_usage_before_logging(ModelResponse(id="success", usage=Usage(prompt_tokens=3)), NOW, NOW) - captured: Final = logger.model_call_details.get("_accounting_capture") - assert captured is not None, "Final usage must be admitted independently of prior usage batch size" - await runtime.meter.close() - await captured.receipt - assert runtime.meter.pending_records == 0 - finally: - await runtime.stop(1) - assert database.committed == ("caller-attempt-0", "caller:inbound", "success") - final: Final = json.loads(captured.writes[-1].envelope) - assert final["related_attempt_ids"] == ["caller-attempt-0"] - assert final["usage"]["prompt_tokens"] == 3 - - -@pytest.mark.asyncio -@pytest.mark.parametrize("prepared", [False, True]) -@pytest.mark.parametrize( - ("call_type", "response", "field", "quantity"), - [ - ("arerank", {"id": "rerank", "results": [], "meta": {"tokens": {"input_tokens": 7}}}, "prompt_tokens", 7), - ("aocr", {"id": "ocr", "usage_info": {"pages_processed": 3}}, "pages_processed", 3), - ( - "acompletion", - {"id": "google", "usageMetadata": {"promptTokenCount": 7, "totalTokenCount": 7}}, - "prompt_tokens", - 7, - ), - ], -) -async def test_capture_preserves_native_usage_before_and_after_preparation( - prepared: bool, call_type: str, response: dict[str, object], field: str, quantity: int -) -> None: - from litellm._internal_context import internal_sub_call - from litellm.proxy.spend_tracking.usage_capture import capture_logging_usage - - database: Final = StoredUsage() - meter: Final = DurableUsageMeter(database.insert, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger(call_type) - logger.model_call_details["response_cost"] = 0.25 - if prepared: - assert logger.prepare_response_cost(response, NOW) is not None - with internal_sub_call("parent"): - capture_logging_usage(meter, logger, response, NOW, NOW) - await meter.close() - assert len(database.rows) == 1 - envelope: Final = json.loads(database.rows[0].envelope) - assert envelope["usage"][field] == quantity - assert envelope["usage_source"] == "measured" - assert envelope["ownership"] == "included_in_parent" - - -@pytest.mark.asyncio -@pytest.mark.parametrize("tokens", [(120000, 120000), (210000, 30000)]) -@pytest.mark.parametrize("priced", [False, True]) -async def test_batch_capture_retains_request_basis_for_later_tiered_pricing( - tokens: tuple[int, int], priced: bool, monkeypatch: pytest.MonkeyPatch -) -> None: - import litellm - from litellm.batches.batch_utils import batch_cost_and_usage - from litellm.cost_calculator import batch_cost_calculator - from litellm.proxy.spend_tracking.usage_capture import ( - UsageBatchWriter, - capture_batch_callback, - capture_logging_usage, - ) - from litellm.types.utils import Usage - - rates: Final = { - "input_cost_per_token_batches": 0.001, - "input_cost_per_token_above_200k_tokens_batches": 0.002, - "output_cost_per_token_batches": 0.0, - "litellm_provider": "openai", - "mode": "chat", - } - monkeypatch.setitem(litellm.model_cost, "capture-model", rates if priced else {"litellm_provider": "openai"}) - rows: Final = [ - { - "response": { - "status_code": 200, - "body": { - "model": "capture-model", - "usage": {"prompt_tokens": n, "completion_tokens": 0, "total_tokens": n}, - }, - } - } - for n in tokens - ] - result: Final = batch_cost_and_usage(rows, "openai", model_info=rates if priced else {}) - database: Final = StoredUsage() - writer: Final = UsageBatchWriter(database.insert, database.load) - meter: Final = DurableUsageMeter(writer.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - logger: Final = _logger("aretrieve_batch") - logger.model_call_details["response_cost"] = result.cost - capture_logging_usage(meter, logger, {"id": "batch", "status": "completed", "usage": result.usage}, NOW, NOW) - await capture_batch_callback( - meter, - database.load, - logger.model_call_details, - {"id": "batch", "status": "completed", "usage": result.usage}, - NOW, - NOW, - ) - await meter.close() - retained: Final = tuple( - {"model": "capture-model", "usage": json.loads(write.envelope)["usage"]} for write in database.rows[1:] - ) - assert sorted(row["usage"]["prompt_tokens"] for row in retained) == sorted(tokens) - reconstructed: Final = sum( - sum(batch_cost_calculator(Usage(**row["usage"]), row["model"], "openai", model_info=rates)) for row in retained - ) - expected: Final = sum(n * (0.002 if n > 200000 else 0.001) for n in tokens) - assert reconstructed == pytest.approx(expected) - assert result.cost == pytest.approx(expected if priced else 0) - assert database.rows[0].event.metrics is None - assert json.loads(database.rows[0].envelope)["charge_eligible"] is False - assert all(json.loads(write.envelope)["finalized_cost"] is None for write in database.rows[1:]) - - -@pytest.mark.asyncio -async def test_large_batch_recovers_partial_commit_with_frozen_pricing_and_rejects_changed_usage() -> None: - from litellm.proxy.spend_tracking.usage_capture import ( - PricingCapture, - UsageBatchWriter, - capture_batch_callback, - capture_callback, - ) - - database: Final = StoredUsage() - transactions: Final = asyncio.Queue[tuple[UsageWrite, ...]]() - failure: Final = asyncio.Queue[bool]() - failure.put_nowait(True) - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - assert sum(len(write.envelope.encode()) for write in writes) <= 65536 - assert len(writes) <= 32 - if len(database.rows) > 1 and not failure.empty(): - failure.get_nowait() - raise RuntimeError("Interrupted batch persistence") - await database.insert(writes) - transactions.put_nowait(writes) - - writer: Final = UsageBatchWriter(persist, database.load) - meter: Final = DurableUsageMeter( - writer.write, - UsageCaptureSettings(batch_size=32, max_pending_records=32, max_pending_bytes=65536), - wait_for_batch=_manual_flush, - ) - rows: Final = tuple( - { - "model": "capture-model" if index % 7 else None, - "usage": {"prompt_tokens": index % 5 + 1} if index % 7 else None, - } - for index in range(1000) - ) - response: Final = {"id": "large-batch", "status": "completed", "usage": {"batch_usage_by_request": rows}} - pricing: Final = PricingCapture("{}", "{}", json.dumps({"batch_models": {"capture-model": {"rate": 1}}})) - kwargs: Final = { - "call_type": "aretrieve_batch", - "litellm_call_id": "poll", - "litellm_params": {}, - "_accounting_pricing_snapshot": pricing, - } - failed_database: Final = CommitBarrier(failure=RuntimeError("Earlier capture failed")) - failed_database.release.set() - prior_meter: Final = DurableUsageMeter(failed_database.write, UsageCaptureSettings(), wait_for_batch=_manual_flush) - prior: Final = capture_callback( - prior_meter, - {"call_type": "acompletion", "litellm_call_id": "earlier", "litellm_params": {}}, - {"id": "earlier"}, - NOW, - NOW, - ) - await prior_meter.close() - with pytest.raises(RuntimeError, match="Earlier capture failed"): - await capture_batch_callback( - meter, - database.load, - {**kwargs, "_accounting_prior_captures": (prior,)}, - response, - NOW, - NOW, - ) - assert database.rows == () - with pytest.raises(RuntimeError, match="Interrupted batch persistence"): - await capture_batch_callback(meter, database.load, kwargs, response, NOW, NOW) - partial: Final = database.rows - assert 1 < len(partial) < len(rows) + 1 - changed_pricing: Final = PricingCapture("{}", "{}", json.dumps({"batch_models": {"capture-model": {"rate": 2}}})) - retried: Final = await capture_batch_callback( - meter, - database.load, - {**kwargs, "_accounting_pricing_snapshot": changed_pricing}, - {**response, "usage": {"batch_usage_by_request": tuple(reversed(rows))}}, - NOW, - NOW, - ) - assert retried.receipt.done() and retried.receipt.exception() is None - assert len(database.rows) == len(rows) + 1 - assert database.rows[: len(partial)] == partial - envelopes: Final = tuple(json.loads(write.envelope) for write in database.rows[1:]) - assert all( - envelope["pricing"]["model_map_information"] == ({"rate": 1} if envelope["usage"] is not None else None) - for envelope in envelopes - ) - assert sum(envelope["usage"] is None for envelope in envelopes) == sum(row["usage"] is None for row in rows) - assert all(envelope["request_count_eligible"] is False for envelope in envelopes) - assert sum(envelope["usage"]["prompt_tokens"] for envelope in envelopes if envelope["usage"] is not None) == sum( - row["usage"]["prompt_tokens"] for row in rows if row["usage"] is not None - ) - before_conflict: Final = database.rows - with pytest.raises(UsageCaptureError, match="identity conflicts"): - await capture_batch_callback( - meter, - database.load, - kwargs, - {**response, "usage": {"batch_usage_by_request": rows[:-1]}}, - NOW, - NOW, - ) - await meter.close() - assert database.rows == before_conflict - assert transactions.qsize() > 2 - assert meter.pending_bytes == meter.pending_records == 0 - - -@pytest.mark.asyncio -@pytest.mark.parametrize("outcome", ["drain", "cancel", "failure"]) -async def test_batch_callback_handoff_is_bounded_and_runtime_owns_persistence( - outcome: str, monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture -) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import CapturedUsage, UsageCaptureRuntime, captured_callback_kwargs - - database: Final = StoredUsage() - entered: Final = asyncio.Event() - release: Final = asyncio.Event() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - entered.set() - await release.wait() - if outcome == "failure": - raise RuntimeError("Persistence unavailable") - await database.insert(writes) - - runtime: Final = UsageCaptureRuntime() - runtime.start(persist, UsageCaptureSettings(batch_size=2, max_pending_records=2), load=database.load) - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - kwargs: Final = {"call_type": "aretrieve_batch", "litellm_call_id": "poll", "litellm_params": {}} - response: Final = { - "id": "batch", - "status": "completed", - "usage": {"batch_usage_by_request": ({"model": "model", "usage": {"prompt_tokens": 2}},) * 3}, - } - callback: Final = asyncio.create_task(captured_callback_kwargs(kwargs, response, NOW, NOW)) - try: - await entered.wait() - assert callback.done(), "Batch persistence must not consume the legacy callback deadline" - captured: Final = (await callback)["_accounting_capture"] - assert isinstance(captured, CapturedUsage) and not captured.receipt.done() - second: Final = await captured_callback_kwargs(kwargs, response, NOW, NOW) - assert isinstance(second.get("_accounting_capture"), CapturedUsage) - overflow: Final = await captured_callback_kwargs(kwargs, response, NOW, NOW) - assert "_accounting_capture" not in overflow - assert "Batch capture producer capacity exhausted" in caplog.text - if outcome != "cancel": - release.set() - await runtime.stop(0 if outcome == "cancel" else 1) - assert captured.receipt.done() - assert captured.receipt.cancelled() is (outcome == "cancel") - if outcome == "failure": - assert isinstance(captured.receipt.exception(), RuntimeError) - assert "Batch usage capture failed" in caplog.text - assert len(database.rows) == (4 if outcome == "drain" else 0) - with pytest.raises(UsageCaptureError, match="active ledger writer"): - runtime.submit_batch(kwargs, response, NOW, NOW) - finally: - release.set() - await asyncio.gather(callback, return_exceptions=True) - await runtime.stop(1) - - -@pytest.mark.asyncio -async def test_batch_producers_share_byte_capacity_and_release_it_after_drain() -> None: - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - - database: Final = StoredUsage() - release: Final = asyncio.Event() - runtime: Final = UsageCaptureRuntime() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - await release.wait() - assert runtime.meter is not None - assert runtime.meter.pending_bytes <= 65536 - await database.insert(writes) - - runtime.start(persist, UsageCaptureSettings(batch_size=32, max_pending_bytes=65536), load=database.load) - from litellm.batches.batch_utils import batch_cost_and_usage - - parsed: Final = batch_cost_and_usage( - [ - { - "response": { - "status_code": 200, - "body": { - "model": "capture-model", - "usage": {"prompt_tokens": 2, "completion_tokens": 1, "total_tokens": 3}, - }, - } - } - for _ in range(1000) - ], - "openai", - model_info={}, - ) - rows: Final = ({"model": "capture-model", "usage": {"prompt_tokens": 2}},) - response: Final = {"id": "batch", "status": "completed", "usage": parsed.usage} - kwargs: Final = {"call_type": "aretrieve_batch", "litellm_call_id": "poll", "litellm_params": {}} - try: - large: Final = asyncio.Queue[asyncio.Future[None]]() - for index in range(100): - try: - captured: Final = runtime.submit_batch(kwargs, {**response, "id": f"batch-{index}"}, NOW, NOW) - except UsageCaptureError: - break - large.put_nowait(captured.receipt) - large_count: Final = large.qsize() - assert 0 < large_count < 100 - assert runtime.meter is not None and 0 < runtime.meter.pending_bytes <= 65536 - release.set() - await asyncio.gather(*(large.get_nowait() for _ in range(large_count))) - assert len(database.rows) == 1001 * large_count - observations: Final = tuple(json.loads(write.envelope) for write in database.rows) - assert sum(row["usage"].get("prompt_tokens", 0) for row in observations) == 2000 * large_count - assert sum(row["usage"].get("completion_tokens", 0) for row in observations) == 1000 * large_count - assert runtime.meter.pending_bytes == 0 - admitted: Final = asyncio.Queue[asyncio.Future[None]]() - for index in range(100): - try: - small: Final = runtime.submit_batch( - kwargs, - {**response, "id": f"small-{index}", "usage": {"batch_usage_by_request": rows}}, - NOW, - NOW, - ) - except UsageCaptureError: - break - admitted.put_nowait(small.receipt) - count: Final = admitted.qsize() - assert 1 < count < 100 - await asyncio.gather(*(admitted.get_nowait() for _ in range(count))) - assert len(database.rows) == 1001 * large_count + 2 * count - assert runtime.meter.pending_bytes == 0 - finally: - release.set() - await runtime.stop(1) - - -@pytest.mark.asyncio -async def test_batch_replay_reserves_the_accepted_pricing_snapshot_size() -> None: - from litellm.proxy.spend_tracking.usage_capture import PricingCapture, UsageBatchWriter, UsageCaptureRuntime - - database: Final = StoredUsage() - writer: Final = UsageBatchWriter(database.insert, database.load) - runtime: Final = UsageCaptureRuntime() - runtime.start(writer.write, UsageCaptureSettings(max_pending_bytes=16384), load=database.load) - response: Final = { - "id": "batch", - "status": "completed", - "usage": {"batch_usage_by_request": ({"model": "model", "usage": {"prompt_tokens": 2}},)}, - } - kwargs: Final = {"call_type": "aretrieve_batch", "litellm_call_id": "poll", "litellm_params": {}} - pricing: Final = PricingCapture("{}", "{}", json.dumps({"batch_models": {"model": {"source": "x" * 1200}}})) - try: - initial: Final = runtime.submit_batch({**kwargs, "_accounting_pricing_snapshot": pricing}, response, NOW, NOW) - await asyncio.shield(initial.receipt) - accepted: Final = database.rows - replay: Final = runtime.submit_batch(kwargs, response, NOW, NOW) - await asyncio.shield(replay.receipt) - assert database.rows == accepted and len(accepted) == 2 - assert runtime.meter is not None and runtime.meter.pending_bytes == 0 - finally: - await runtime.stop(1) diff --git a/tests/unit/proxy/spend_tracking/test_usage_meter.py b/tests/unit/proxy/spend_tracking/test_usage_meter.py deleted file mode 100644 index bd3a0b9ed70..00000000000 --- a/tests/unit/proxy/spend_tracking/test_usage_meter.py +++ /dev/null @@ -1,473 +0,0 @@ -import asyncio -import json -from datetime import datetime, timezone -from decimal import Decimal -from typing import Final - -import pytest - -from litellm.accounting import InboundUsageEvent, Metric, MetricName, Outcome -from litellm.proxy.db.accounting_ledger import AccountingConflictError, UsageWrite -from litellm.proxy.spend_tracking.usage_meter import DurableUsageMeter, UsageCaptureError, UsageCaptureSettings -from litellm.types.utils import CallTypes - - -def _write(identity: str) -> UsageWrite: - event: Final = InboundUsageEvent( - event_id=identity, - request_id=identity, - call_type=CallTypes.acompletion, - started_at=datetime(2026, 1, 1, tzinfo=timezone.utc), - ended_at=datetime(2026, 1, 1, tzinfo=timezone.utc), - outcome=Outcome.SUCCEEDED, - dimensions=(), - metrics=(Metric(name=MetricName.REQUESTS, value=Decimal(1)),), - ) - return UsageWrite(event, json.dumps({"event": event.model_dump(mode="json")})) - - -class CommitBarrier: - def __init__(self, *, conflict: str | None = None, failure: Exception | None = None) -> None: - self.entered: Final = asyncio.Event() - self.release: Final = asyncio.Event() - self.committed: tuple[str, ...] = () - self.transactions = 0 - self.conflict: Final = conflict - self.failure: Final = failure - - async def write(self, writes: tuple[UsageWrite, ...]) -> None: - self.transactions += 1 - self.entered.set() - await self.release.wait() - if self.failure is not None: - raise self.failure - identities: Final = tuple(write.event.event_id for write in writes) - if self.conflict in identities: - raise AccountingConflictError("Conflicting test event") - self.committed += identities - - -async def _manual_flush(wakeup: asyncio.Event, timeout: float) -> None: - await wakeup.wait() - - -@pytest.mark.asyncio -async def test_receipts_wait_for_one_batched_commit_and_keep_inflight_capacity_reserved() -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2, max_pending_records=2)) - first: Final = meter.submit((_write("one"),)) - second: Final = meter.submit((_write("two"),)) - await database.entered.wait() - assert not first.done() and not second.done() - assert meter.pending_records == 2 - with pytest.raises(UsageCaptureError, match="capacity"): - meter.submit((_write("three"),)) - database.release.set() - await asyncio.gather(first, second) - await meter.close() - assert database.committed == ("one", "two") - assert database.transactions == 1 - assert (meter.pending_bytes, meter.pending_records) == (0, 0) - - -@pytest.mark.asyncio -async def test_byte_limit_rejects_new_work_without_evicting_admitted_work() -> None: - database: Final = CommitBarrier() - write: Final = _write("large" * 300) - size: Final = len(write.envelope.encode()) - meter: Final = DurableUsageMeter( - database.write, UsageCaptureSettings(max_pending_bytes=size), wait_for_batch=_manual_flush - ) - reservation: Final = meter.reserve_producer(size // 2, size // 2) - with pytest.raises(UsageCaptureError, match="producer byte capacity"): - meter.reserve_producer(1, size) - with pytest.raises(UsageCaptureError, match="producer byte capacity"): - meter.resize_producer(reservation, size, 1) - assert meter.pending_bytes == size // 2 - meter.release_producer(reservation) - receipt: Final = meter.submit((write,)) - with pytest.raises(UsageCaptureError, match="capacity"): - meter.submit((_write("extra"),)) - assert meter.pending_bytes == size - database.release.set() - await meter.close() - await receipt - assert database.committed == (write.event.event_id,) - with pytest.raises(UsageCaptureError, match="closed"): - meter.submit((_write("after-close"),)) - with pytest.raises(UsageCaptureError, match="producer byte capacity"): - meter.reserve_producer(1, 1) - - -@pytest.mark.asyncio -async def test_conflicting_group_rolls_back_and_valid_neighbor_still_commits() -> None: - database: Final = CommitBarrier(conflict="conflict") - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=3)) - rejected: Final = meter.submit((_write("caller"), _write("conflict"))) - accepted: Final = meter.submit((_write("valid"),)) - await database.entered.wait() - database.release.set() - with pytest.raises(UsageCaptureError, match="identity"): - await rejected - await accepted - await meter.close() - assert database.committed == ("valid",) - assert meter.pending_records == 0 - - -@pytest.mark.asyncio -async def test_permanent_persistence_failure_reaches_each_receipt_and_releases_capacity() -> None: - failure: Final = ValueError("invalid envelope") - database: Final = CommitBarrier(failure=failure) - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2)) - first: Final = meter.submit((_write("one"),)) - second: Final = meter.submit((_write("two"),)) - await database.entered.wait() - database.release.set() - results: Final = await asyncio.gather(first, second, return_exceptions=True) - await meter.close() - assert results == [failure, failure] - assert meter.pending_bytes == 0 - - -@pytest.mark.asyncio -async def test_cancelling_record_waiter_does_not_cancel_persistence() -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2)) - waiting: Final = asyncio.create_task(meter.record(_write("caller").event)) - await database.entered.wait() - waiting.cancel() - with pytest.raises(asyncio.CancelledError): - await waiting - database.release.set() - await meter.close() - assert database.committed == ("caller",) - assert meter.pending_records == 0 - - -@pytest.mark.asyncio -async def test_shutdown_deadline_rejects_unacknowledged_work_and_releases_capacity() -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2)) - first: Final = meter.submit((_write("in-flight"), _write("in-flight-two"))) - await database.entered.wait() - queued: Final = meter.submit((_write("queued"),)) - await meter.stop(0) - results: Final = await asyncio.gather(first, queued, return_exceptions=True) - assert all(isinstance(result, UsageCaptureError) for result in results) - assert meter.pending_records == 0 - assert meter.pending_bytes == 0 - assert database.committed == () - - -@pytest.mark.asyncio -async def test_shutdown_during_database_backoff_releases_reserved_capacity() -> None: - retrying: Final = asyncio.Event() - blocked: Final = asyncio.Event() - - async def unavailable(writes: tuple[UsageWrite, ...]) -> None: - raise ConnectionError("database unavailable") - - async def backoff(delay: float) -> None: - retrying.set() - await blocked.wait() - - meter: Final = DurableUsageMeter(unavailable, UsageCaptureSettings(batch_size=2), retry_delay=backoff) - receipt: Final = meter.submit((_write("one"), _write("two"))) - await retrying.wait() - await meter.stop(0) - with pytest.raises(UsageCaptureError, match="interrupted"): - await receipt - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("wrapped", [False, True]) -async def test_closed_pooled_connection_retains_batch_until_automatic_recovery(wrapped: bool) -> None: - import httpx - from prisma.engine.errors import EngineRequestError - from prisma.http import Response - - database: Final = CommitBarrier() - attempts: Final = asyncio.Queue[tuple[UsageWrite, ...]]() - retrying: Final = asyncio.Event() - recovered: Final = asyncio.Event() - closure: Final = EngineRequestError( - Response(httpx.Response(500)), '{"error_code":"P1017","message":"Server has closed the connection."}' - ) - failure: Final = RuntimeError("ledger transaction failed") if wrapped else closure - if wrapped: - failure.__cause__ = closure - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - attempts.put_nowait(writes) - if attempts.qsize() == 1: - raise failure - await database.write(writes) - - async def backoff(delay: float) -> None: - retrying.set() - await recovered.wait() - - meter: Final = DurableUsageMeter( - persist, UsageCaptureSettings(batch_size=2, max_pending_records=2), retry_delay=backoff - ) - writes: Final = (_write("caller"), _write("attempt")) - receipt: Final = meter.submit(writes) - retry_signal: Final = asyncio.create_task(retrying.wait()) - reserved: Final = meter.pending_bytes - try: - await asyncio.wait((receipt, retry_signal), return_when=asyncio.FIRST_COMPLETED) - assert not receipt.done(), "A dropped pooled connection must retain admitted usage for retry" - assert (meter.pending_records, meter.pending_bytes) == (2, reserved) - with pytest.raises(UsageCaptureError, match="capacity"): - meter.submit((_write("overflow"),)) - database.release.set() - recovered.set() - await receipt - assert attempts.get_nowait() == attempts.get_nowait() == writes - assert database.committed == ("caller", "attempt") - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - finally: - retry_signal.cancel() - await asyncio.gather(retry_signal, return_exceptions=True) - await meter.stop(0) - await asyncio.gather(receipt, return_exceptions=True) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("first_commits", [False, True]) -async def test_shutdown_during_conflict_isolation_completes_every_receipt(first_commits: bool) -> None: - isolating: Final = asyncio.Event() - blocked: Final = asyncio.Event() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - if len(writes) > 1: - raise AccountingConflictError("one producer conflicts") - if first_commits and writes[0].event.event_id == "one": - return - isolating.set() - await blocked.wait() - - meter: Final = DurableUsageMeter(persist, UsageCaptureSettings(batch_size=2)) - first: Final = meter.submit((_write("one"),)) - second: Final = meter.submit((_write("two"),)) - await isolating.wait() - await meter.stop(0) - results: Final = await asyncio.gather(first, second, return_exceptions=True) - assert results[0] is None if first_commits else isinstance(results[0], UsageCaptureError) - assert isinstance(results[1], UsageCaptureError) - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - - -@pytest.mark.asyncio -async def test_elapsed_flush_deadline_commits_a_partial_batch() -> None: - from litellm.proxy.spend_tracking.usage_meter import _wait_for_batch - - database: Final = CommitBarrier() - database.release.set() - - async def expired_flush(wakeup: asyncio.Event, timeout: float) -> None: - assert timeout == 5.0 - await _wait_for_batch(wakeup, 0) - - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=10), wait_for_batch=expired_flush) - receipt: Final = meter.submit((_write("partial-batch"),)) - try: - await receipt - assert database.committed == ("partial-batch",) - assert database.transactions == 1 - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - finally: - await meter.stop(0) - - -@pytest.mark.asyncio -async def test_burst_drains_full_batches_without_waiting_for_partial_batch_deadline() -> None: - database: Final = CommitBarrier() - database.release.set() - waits: Final = asyncio.Queue[bool]() - - async def observe_wait(wakeup: asyncio.Event, timeout: float) -> None: - waits.put_nowait(wakeup.is_set()) - await wakeup.wait() - - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2), wait_for_batch=observe_wait) - receipts: Final = tuple(meter.submit((_write(str(index)),)) for index in range(5)) - try: - assert await waits.get() is True - await asyncio.gather(*receipts[:2]) - assert await waits.get() is True, "A full queued batch must not wait for the flush deadline" - await asyncio.gather(*receipts[2:4]) - assert await waits.get() is False - assert database.committed == ("0", "1", "2", "3") - assert meter.pending_records == 1 - assert not receipts[-1].done() - await meter.close() - await receipts[-1] - assert database.committed == ("0", "1", "2", "3", "4") - assert database.transactions == 3 - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - finally: - await meter.stop(0) - await asyncio.gather(*receipts, return_exceptions=True) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("sqlstate", ["22P05", "22003", "42P01"]) -async def test_invalid_submission_isolated_without_retrying_systemic_faults(sqlstate: str) -> None: - from prisma.errors import DataError - - database: Final = CommitBarrier() - database.release.set() - attempts: Final = asyncio.Queue[tuple[str, ...]]() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - identities: Final = tuple(write.event.event_id for write in writes) - attempts.put_nowait(identities) - if "invalid" in identities or sqlstate == "42P01": - raise DataError( - data={"user_facing_error": {"message": "database rejected input", "meta": {"code": sqlstate}}} - ) - await database.write(writes) - - meter: Final = DurableUsageMeter(persist, UsageCaptureSettings(batch_size=3)) - rejected: Final = meter.submit((_write("caller"), _write("invalid"))) - neighbor: Final = meter.submit((_write("neighbor"),)) - try: - with pytest.raises(DataError): - await rejected - if sqlstate == "42P01": - with pytest.raises(DataError): - await neighbor - assert attempts.qsize() == 1 - else: - await neighbor - assert database.committed == ("neighbor",) - assert attempts.qsize() == 3 - finally: - await meter.close() - assert (meter.pending_records, meter.pending_bytes) == (0, 0) - - -@pytest.mark.asyncio -@pytest.mark.parametrize("dependency_outcome", ["commit", "failure", "cancel"]) -async def test_commit_dependencies_preserve_capacity_and_isolate_failed_dependents(dependency_outcome: str) -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=3, max_pending_records=4)) - prerequisite: Final[asyncio.Future[None]] = asyncio.get_running_loop().create_future() - dependent: Final = meter.submit((_write("parent"), _write("final")), prerequisites=(prerequisite,)) - neighbor: Final = meter.submit((_write("neighbor"),)) - assert meter.pending_records == 3 - assert not dependent.done() - with pytest.raises(UsageCaptureError, match="capacity"): - meter.submit((_write("excess-one"), _write("excess-two"))) - if dependency_outcome == "commit": - prerequisite.set_result(None) - elif dependency_outcome == "failure": - prerequisite.set_exception(ValueError("dependency failed")) - else: - prerequisite.cancel() - database.release.set() - await meter.close() - await neighbor - if dependency_outcome == "commit": - await dependent - assert database.committed == ("parent", "final", "neighbor") - else: - with pytest.raises(UsageCaptureError, match="dependency"): - await dependent - assert database.committed == ("neighbor",) - assert meter.pending_records == 0 - assert meter.pending_bytes == 0 - - -@pytest.mark.asyncio -async def test_shutdown_does_not_cancel_dependency_receipt_or_leak_reserved_capacity() -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter(database.write, UsageCaptureSettings(batch_size=2)) - prerequisite: Final[asyncio.Future[None]] = asyncio.get_running_loop().create_future() - dependent: Final = meter.submit((_write("parent"), _write("final")), prerequisites=(prerequisite,)) - await meter.stop(0) - with pytest.raises(UsageCaptureError, match="acknowledgement"): - await dependent - assert not prerequisite.done() - assert database.committed == () - assert meter.pending_records == 0 - assert meter.pending_bytes == 0 - prerequisite.set_result(None) - - -@pytest.mark.asyncio -async def test_large_write_sequence_drains_within_byte_and_record_bounds() -> None: - database: Final = CommitBarrier() - database.release.set() - meter: Final = DurableUsageMeter( - database.write, - UsageCaptureSettings(batch_size=8, max_pending_records=2, max_pending_bytes=1024), - wait_for_batch=_manual_flush, - ) - writes: Final = tuple(_write(str(index)) for index in range(20)) - pending: Final = meter.submit((_write("pending-a"), _write("pending-b"))) - await meter.record_writes(writes) - await pending - with pytest.raises(UsageCaptureError, match="One usage observation"): - await meter.record_writes((_write("oversized" * 300),)) - await meter.close() - assert database.committed == ("pending-a", "pending-b", *(write.event.event_id for write in writes)) - assert database.transactions > 1 - assert (meter.pending_bytes, meter.pending_records) == (0, 0) - with pytest.raises(UsageCaptureError): - await meter.record_writes((_write("closed"),)) - - -@pytest.mark.asyncio -async def test_cancelling_bounded_capture_preserves_admitted_writes_without_admitting_remaining_rows() -> None: - database: Final = CommitBarrier() - meter: Final = DurableUsageMeter( - database.write, UsageCaptureSettings(batch_size=2, max_pending_records=2), wait_for_batch=_manual_flush - ) - recording: Final = asyncio.create_task(meter.record_writes(tuple(_write(str(index)) for index in range(8)))) - await database.entered.wait() - recording.cancel() - with pytest.raises(asyncio.CancelledError): - await recording - database.release.set() - await meter.close() - assert database.committed == ("0", "1") - assert meter.pending_bytes == meter.pending_records == 0 - - -@pytest.mark.asyncio -async def test_capacity_wait_retries_admission_before_other_producers_finish() -> None: - entered: Final = asyncio.Queue[tuple[str, ...]]() - release: Final = asyncio.Queue[None]() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - entered.put_nowait(tuple(write.event.event_id for write in writes)) - await release.get() - - meter: Final = DurableUsageMeter( - persist, UsageCaptureSettings(batch_size=2, max_pending_records=3), wait_for_batch=_manual_flush - ) - initial: Final = meter.submit((_write("first"), _write("second"))) - remaining: Final = meter.submit((_write("remaining"),)) - assert await entered.get() == ("first", "second") - recording: Final = asyncio.create_task(meter.record_writes((_write("target"),))) - try: - await asyncio.sleep(0) - release.put_nowait(None) - assert await entered.get() == ("remaining",) - await asyncio.sleep(0) - assert meter.pending_records == 2 - release.put_nowait(None) - assert await entered.get() == ("target",) - release.put_nowait(None) - await recording - await asyncio.gather(initial, remaining) - finally: - recording.cancel() - await asyncio.gather(recording, return_exceptions=True) - release.put_nowait(None) - release.put_nowait(None) - await meter.close() diff --git a/tests/unit/proxy/test__types.py b/tests/unit/proxy/test__types.py index 76ca49362b4..40e66f1da65 100644 --- a/tests/unit/proxy/test__types.py +++ b/tests/unit/proxy/test__types.py @@ -520,6 +520,39 @@ def test_mcp_metadata_rejects_unavailable_upstream_protocol(revision): model.model_validate(payload) +@pytest.mark.parametrize("request_model", (NewMCPServerRequest, UpdateMCPServerRequest)) +@pytest.mark.parametrize("value", ["true", "false", 1, 0, None]) +def test_mcp_metadata_rejects_a_non_boolean_skip_tool_output_validation( + request_model: type[NewMCPServerRequest] | type[UpdateMCPServerRequest], value: object +) -> None: + payload: Final = { + "server_id": "test", + "transport": "http", + "url": "https://example.com/mcp", + "mcp_info": {"skip_tool_output_validation": value}, + } + + with pytest.raises(ValidationError): + request_model.model_validate(payload) + + +@pytest.mark.parametrize("request_model", (NewMCPServerRequest, UpdateMCPServerRequest)) +@pytest.mark.parametrize("value", [True, False]) +def test_mcp_metadata_accepts_a_boolean_skip_tool_output_validation( + request_model: type[NewMCPServerRequest] | type[UpdateMCPServerRequest], value: bool +) -> None: + parsed: Final = request_model.model_validate( + { + "server_id": "test", + "transport": "http", + "url": "https://example.com/mcp", + "mcp_info": {"skip_tool_output_validation": value}, + } + ) + + assert parsed.mcp_info == {"skip_tool_output_validation": value} + + MCP_SERVER_REQUESTS = (NewMCPServerRequest, UpdateMCPServerRequest) STDIO_SERVER_FIELDS = {"server_id": "stdio-1", "transport": "stdio", "command": "python", "args": ["server.py"]} diff --git a/tests/unit/proxy/test_common_request_processing.py b/tests/unit/proxy/test_common_request_processing.py index 4d1b67f9212..13f6f840cb7 100644 --- a/tests/unit/proxy/test_common_request_processing.py +++ b/tests/unit/proxy/test_common_request_processing.py @@ -4293,6 +4293,62 @@ class TestHandleLLMApiExceptionPricesWhatTheServerDispatched: assert resent >= dispatched_after_trim +class TestHandleLLMApiExceptionReportsWhatAFailedAgenticFollowUpBills: + async def _error_response_cost(self, raise_the_follow_up_error: bool) -> float: + from litellm.litellm_core_utils.chat_completion_agentic_loop import bill_first_round_on_failure + from litellm.types.utils import ModelResponse, Usage + + processing_obj: Final = ProxyBaseLLMRequestProcessing( + data={"model": "gpt-4o", "messages": [{"role": "user", "content": "what is 6*7?"}]} + ) + caller: Final = ProxyUserAPIKeyAuth(request_route="/v1/chat/completions") + mock_request: Final = MagicMock(spec=Request) + mock_request.headers = {} + proxy_logging: Final = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging.alert_types = [] + _, logging_obj = await processing_obj.common_processing_pre_call_logic( + request=mock_request, + general_settings={}, + user_api_key_dict=caller, + proxy_logging_obj=proxy_logging, + proxy_config=MagicMock(spec=ProxyConfig), + route_type="acompletion", + ) + logging_obj.model_call_details.update(first_api_call_start_time=datetime.datetime.now()) + follow_up_error: Final = litellm.RateLimitError(message="rate limited", llm_provider="openai", model="gpt-4o") + + async def failing_follow_up() -> ModelResponse: + raise follow_up_error + + with pytest.raises(litellm.RateLimitError): + await bill_first_round_on_failure( + failing_follow_up(), + logging_obj=logging_obj, + response=ModelResponse( + model="gpt-4o", usage=Usage(prompt_tokens=559, completion_tokens=113, total_tokens=672) + ), + stream=False, + ) + with pytest.raises(ProxyException) as raised: + await processing_obj.handle_llm_api_exception( + e=follow_up_error + if raise_the_follow_up_error + else litellm.RateLimitError(message="rate limited", llm_provider="openai", model="gpt-4o"), + user_api_key_dict=caller, + proxy_logging_obj=proxy_logging, + ) + return float(raised.value.headers["x-litellm-response-cost"]) + + @pytest.mark.asyncio + async def test_the_error_response_reports_the_served_first_call_its_failure_row_bills(self) -> None: + price: Final = litellm.model_cost["gpt-4o"] + + assert await self._error_response_cost(raise_the_follow_up_error=True) == pytest.approx( + 559 * price["input_cost_per_token"] + 113 * price["output_cost_per_token"] + ) + assert await self._error_response_cost(raise_the_follow_up_error=False) == 0 + + @pytest.mark.asyncio class TestHandleLLMApiExceptionDictDetail: """ @@ -4618,6 +4674,34 @@ class TestStreamCloseOnDisconnect: assert upstream.aclosed + async def test_async_streaming_data_generator_reraises_an_unchanged_http_exception_without_chaining_it_to_itself( + self, + ): + blocked = HTTPException(status_code=400, detail="Content blocked by guardrail") + + class FailingUpstream: + def __aiter__(self): + return self + + async def __anext__(self): + raise blocked + + ProxyLogging._callback_capabilities_cache.clear() + gen = ProxyBaseLLMRequestProcessing.async_streaming_data_generator( + response=FailingUpstream(), + user_api_key_dict=ProxyUserAPIKeyAuth(), + request_data={"model": "mock-model"}, + proxy_logging_obj=ProxyLogging(user_api_key_cache=MagicMock()), + serialize_chunk=lambda c: "data: x\n\n", + serialize_error=lambda e: "data: error\n\n", + ) + + with pytest.raises(HTTPException) as raised: + await gen.__anext__() + + assert raised.value is blocked + assert raised.value.__cause__ is not blocked + async def test_async_streaming_data_generator_closes_the_guardrail_chain_on_client_disconnect( self, ): diff --git a/tests/unit/proxy/test_model_deprecations_endpoint.py b/tests/unit/proxy/test_model_deprecations_endpoint.py index 62d6870e1fe..7be90d0d1e4 100644 --- a/tests/unit/proxy/test_model_deprecations_endpoint.py +++ b/tests/unit/proxy/test_model_deprecations_endpoint.py @@ -64,12 +64,79 @@ def test_should_bucket_configured_models_by_urgency(authenticated_client): def test_should_rebucket_with_warn_within_days_override(authenticated_client): - response = authenticated_client.get( - "/v1/model/deprecations", params={"warn_within_days": 40000} - ) + response = authenticated_client.get("/v1/model/deprecations", params={"warn_within_days": 40000}) assert response.status_code == 200 payload = response.json() assert [m["model_name"] for m in payload["imminent"]] == ["future-alias"] assert payload["upcoming"] == [] assert payload["warn_within_days"] == 40000 + + +@pytest.fixture +def client_with_cost_map_successors(monkeypatch): + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-9876" + ) + monkeypatch.setattr( + litellm, + "model_cost", + { + "retiring-model": { + "deprecation_date": "2020-01-01", + "litellm_provider": "openai", + "successor_model": "fresh-model", + }, + "orphan-model": {"deprecation_date": "2020-01-01", "litellm_provider": "openai"}, + }, + ) + router = route_entry_points_to_stubs(MagicMock()) + router.get_model_list.return_value = [ + { + "model_name": "retiring-alias", + "litellm_params": {"model": "retiring-model"}, + "model_info": {"id": "1"}, + }, + { + "model_name": "retiring-overridden", + "litellm_params": {"model": "retiring-model"}, + "model_info": {"id": "2", "successor_model": "my-own-pick"}, + }, + { + "model_name": "shared-name", + "litellm_params": {"model": "retiring-model", "api_key": "fake"}, + "model_info": {"id": "4"}, + }, + { + "model_name": "shared-name", + "litellm_params": {"model": "retiring-model", "api_key": "fake"}, + "model_info": {"id": "5", "successor_model": "my-own-pick"}, + }, + { + "model_name": "dated-by-hand", + "litellm_params": {"model": "retiring-model", "api_key": "fake"}, + "model_info": {"id": "6", "deprecation_date": "2020-06-01"}, + }, + { + "model_name": "orphan-alias", + "litellm_params": {"model": "orphan-model"}, + "model_info": {"id": "3"}, + }, + ] + monkeypatch.setattr(proxy_server, "llm_router", router) + yield client + app.dependency_overrides.pop(user_api_key_auth, None) + + +def test_cost_map_successor_is_reported_unless_the_deployment_names_its_own(client_with_cost_map_successors): + response = client_with_cost_map_successors.get("/model/deprecations") + + assert response.status_code == 200, response.text + successors = {m["model_name"]: m["successor_model"] for m in response.json()["deprecated"]} + assert successors == { + "retiring-alias": "fresh-model", + "retiring-overridden": "my-own-pick", + "shared-name": "my-own-pick", + "dated-by-hand": "fresh-model", + "orphan-alias": None, + } diff --git a/tests/unit/proxy/test_proxy_cli.py b/tests/unit/proxy/test_proxy_cli.py index 643c01e3e7f..6347c165865 100644 --- a/tests/unit/proxy/test_proxy_cli.py +++ b/tests/unit/proxy/test_proxy_cli.py @@ -1,3 +1,4 @@ +import asyncio import gc import importlib import inspect @@ -30,7 +31,7 @@ from uvicorn.importer import import_from_string import litellm from litellm._logging import turn_on_json -from litellm.constants import GRACEFUL_SHUTDOWN_TEARDOWN_HEADROOM_SECONDS +from litellm.constants import DEFAULT_MAX_REQUEST_HEADER_SIZE, GRACEFUL_SHUTDOWN_TEARDOWN_HEADROOM_SECONDS from litellm.proxy import proxy_cli from litellm.proxy.proxy_cli import ProxyInitializationHelpers, run_server from litellm.proxy.shutdown.graceful_shutdown_manager import DEFAULT_GRACEFUL_SHUTDOWN_TIMEOUT @@ -39,12 +40,114 @@ from litellm.proxy.shutdown.graceful_shutdown_manager import DEFAULT_GRACEFUL_SH @pytest.fixture(autouse=True) def fork_reservation(): """Reserving is irreversible: it would forbid native routes in this pytest worker for good""" - with patch( # test-quality-ok: process-global native state, a real reservation would poison every later test in the worker - "litellm.rust_bridge.fork_guard.reserve_process_for_forking" - ) as reserve: + with ( + patch( # test-quality-ok: process-global native state, a real reservation would poison every later test in the worker + "litellm.rust_bridge.fork_guard.reserve_process_for_forking" + ) as reserve + ): yield reserve +@pytest.mark.parametrize( + ("cli_value", "env_value", "expected"), + [(None, None, None), (None, "131072", 131072), ("65536", "131072", 65536)], +) +def test_max_request_header_size_cli_precedence( + monkeypatch: pytest.MonkeyPatch, cli_value: str | None, env_value: str | None, expected: int | None +) -> None: + monkeypatch.delenv("MAX_REQUEST_HEADER_SIZE", raising=False) + if env_value is not None: + monkeypatch.setenv("MAX_REQUEST_HEADER_SIZE", env_value) + args: Final = [] if cli_value is None else ["--max_request_header_size", cli_value] + with run_server.make_context("litellm", args) as context: + assert context.params["max_request_header_size"] == expected + + +@pytest.mark.parametrize("value", ["0", "-1", "invalid"]) +@pytest.mark.parametrize("source", ["cli", "env"]) +def test_max_request_header_size_rejects_invalid_values( + monkeypatch: pytest.MonkeyPatch, value: str, source: str +) -> None: + monkeypatch.delenv("MAX_REQUEST_HEADER_SIZE", raising=False) + if source == "env": + monkeypatch.setenv("MAX_REQUEST_HEADER_SIZE", value) + args: Final = ["--max_request_header_size", value] if source == "cli" else [] + with pytest.raises(click.BadParameter): + run_server.make_context("litellm", args) + + +def _request_header_test_config(server: str, limit: int | None) -> uvicorn.Config: + from starlette.responses import PlainTextResponse + + app: Final = PlainTextResponse("ok") + if server == "uvicorn": + args: Final = ProxyInitializationHelpers.get_default_unvicorn_init_args( + "127.0.0.1", 4000, max_request_header_size=limit + ) + return uvicorn.Config(**{**args, "app": app, "log_config": None}) + + import gunicorn.config + import gunicorn.glogging + + from litellm.proxy.shutdown.graceful_uvicorn_worker import build_graceful_shutdown_uvicorn_worker + + gunicorn_config: Final = gunicorn.config.Config() + worker: Final = build_graceful_shutdown_uvicorn_worker(7.5, limit)( + 0, os.getpid(), [], MagicMock(), 30, gunicorn_config, gunicorn.glogging.Logger(gunicorn_config) + ) + worker.config.app = app + return worker.config + + +@pytest.mark.parametrize("server", ["uvicorn", "gunicorn"]) +@pytest.mark.parametrize("limit", [None, DEFAULT_MAX_REQUEST_HEADER_SIZE, 128 * 1024]) +def test_request_header_size_preserves_parser_selection(server: str, limit: int | None) -> None: + config: Final = _request_header_test_config(server, limit) + assert config.http == "auto" + assert config.h11_max_incomplete_event_size == (limit if limit is not None else DEFAULT_MAX_REQUEST_HEADER_SIZE) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("server", ["uvicorn", "gunicorn"]) +@pytest.mark.parametrize("parser", ["h11", "httptools"]) +@pytest.mark.parametrize( + ("limit", "header_bytes", "expected_status"), + [(16384, 16384, 200), (16384, 16896, 400), (16384, 39936, 400), (131072, 39936, 200), (131072, 131073, 400)], +) +async def test_segmented_request_headers( + monkeypatch: pytest.MonkeyPatch, server: str, parser: str, limit: int, header_bytes: int, expected_status: int +) -> None: + from uvicorn.server import ServerState + + from uvicorn.protocols.http.h11_impl import H11Protocol + from uvicorn.protocols.http.httptools_impl import HttpToolsProtocol + + selected_protocol: Final = H11Protocol if parser == "h11" else HttpToolsProtocol + monkeypatch.setattr("uvicorn.protocols.http.auto.AutoHTTPProtocol", selected_protocol) + config: Final = _request_header_test_config(server, limit) + config.load() + assert config.http_protocol_class is selected_protocol + protocol: Final = config.http_protocol_class(config, ServerState(), {}, _loop=asyncio.get_running_loop()) + transport: Final = MagicMock(spec=asyncio.Transport) + transport.get_extra_info.return_value = None + transport.is_closing.return_value = False + protocol.connection_made(transport) + prefix: Final = b"GET /health/liveliness HTTP/1.1\r\nHost: localhost\r\nX-Large: " + pending_headers: Final = prefix + b"x" * (header_bytes - len(prefix)) + try: + for offset in range(0, len(pending_headers), 4096): + protocol.data_received(pending_headers[offset : offset + 4096]) + if transport.close.called: + break + if not transport.close.called: + protocol.data_received(b"\r\n\r\n") + await asyncio.gather(*protocol.tasks) + response: Final = b"".join(call.args[0] for call in transport.write.call_args_list) + assert response.startswith(f"HTTP/1.1 {expected_status if parser == 'h11' else 200} ".encode()) + finally: + protocol.connection_lost(None) + + @pytest.mark.xdist_group("proxy_cli") class TestProxyInitializationHelpers: @patch("importlib.metadata.version") @@ -1550,6 +1653,7 @@ class TestProxyInitializationHelpers: log_config=None, keepalive_timeout=30, timeout_worker_healthcheck=None, + max_request_header_size=None, ) mock_uvicorn_run.assert_called_once() @@ -1626,6 +1730,7 @@ class TestProxyInitializationHelpers: log_config=None, keepalive_timeout=None, timeout_worker_healthcheck=15, + max_request_header_size=None, ) @patch("uvicorn.run") @@ -2092,7 +2197,10 @@ class TestProxyInitializationHelpers: assert captured["options"]["max_requests_jitter"] == 50 @pytest.mark.skipif(os.name == "nt", reason="gunicorn server path skips Windows") - def test_gunicorn_workers_bound_graceful_shutdown_by_graceful_shutdown_timeout(self, monkeypatch): + @pytest.mark.parametrize("max_request_header_size", [DEFAULT_MAX_REQUEST_HEADER_SIZE, 128 * 1024]) + def test_gunicorn_workers_bound_graceful_shutdown_by_graceful_shutdown_timeout( + self, monkeypatch, max_request_header_size: int + ): pytest.importorskip("gunicorn") import gunicorn.config import gunicorn.glogging @@ -2117,6 +2225,7 @@ class TestProxyInitializationHelpers: num_workers=2, ssl_certfile_path=None, ssl_keyfile_path=None, + max_request_header_size=max_request_header_size, ) for name in ("uvicorn.error", "uvicorn.access"): @@ -2132,6 +2241,7 @@ class TestProxyInitializationHelpers: assert worker.config.timeout_graceful_shutdown == 7.5 assert worker.config.loop == UvicornWorker.CONFIG_KWARGS["loop"] assert worker.config.http == UvicornWorker.CONFIG_KWARGS["http"] + assert worker.config.h11_max_incomplete_event_size == max_request_header_size assert captured["options"]["graceful_timeout"] == 8 + GRACEFUL_SHUTDOWN_TEARDOWN_HEADROOM_SECONDS @pytest.mark.skipif(os.name == "nt", reason="gunicorn server path skips Windows") diff --git a/tests/unit/proxy/test_proxy_server.py b/tests/unit/proxy/test_proxy_server.py index a50db09128f..dae9ffcf7fd 100644 --- a/tests/unit/proxy/test_proxy_server.py +++ b/tests/unit/proxy/test_proxy_server.py @@ -268,76 +268,6 @@ async def test_shutdown_commits_spend_a_logging_task_queued_while_the_first_comm token = MASTER_KEY -@pytest.mark.asyncio -async def test_shutdown_persists_accounting_from_worker_callbacks_before_returning( - monkeypatch: pytest.MonkeyPatch, -) -> None: - from datetime import datetime - - from litellm.litellm_core_utils.logging_worker import LoggingWorker - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_callback - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - from litellm.responses.streaming_iterator import spawn_logging_task - - worker: Final = LoggingWorker() - started: Final = asyncio.Event() - release: Final = asyncio.Event() - first_flush: Final = asyncio.Event() - pending: Final[asyncio.Queue[str]] = asyncio.Queue() - persisted: Final[asyncio.Queue[str]] = asyncio.Queue() - - captured: Final[asyncio.Queue[str]] = asyncio.Queue() - - async def persist_usage(writes: tuple[UsageWrite, ...]) -> None: - for write in writes: - captured.put_nowait(write.event.event_id) - - capture: Final = UsageCaptureRuntime() - capture.start(persist_usage, UsageCaptureSettings(flush_interval_seconds=10)) - monkeypatch.setattr(litellm.proxy.proxy_server, "usage_capture", capture) - - async def enqueue_spend() -> None: - assert capture.meter is not None - capture_callback( - capture.meter, - {"call_type": "acompletion", "litellm_call_id": "shutdown-usage", "litellm_params": {}}, - {"id": "shutdown-attempt", "usage": {"total_tokens": 3}}, - datetime(2026, 1, 1), - datetime(2026, 1, 1), - ) - pending.put_nowait("accounted request") - - async def callback() -> None: - started.set() - await release.wait() - spawn_logging_task(asyncio.get_running_loop(), enqueue_spend(), task_name="accounting") - - async def flush_spend() -> None: - while not pending.empty(): - persisted.put_nowait(pending.get_nowait()) - first_flush.set() - - worker.ensure_initialized_and_enqueue(callback()) - await started.wait() - shutdown: Final = asyncio.create_task( - litellm.proxy.proxy_server._flush_spend_around_logging_drain_on_shutdown(flush_spend, worker=worker) - ) - try: - await first_flush.wait() - waited_for_callback: Final = not shutdown.done() - release.set() - await shutdown - assert waited_for_callback, "Shutdown returned while accounting callbacks were still running" - assert persisted.get_nowait() == "accounted request" - assert pending.empty() - assert (captured.get_nowait(), captured.get_nowait()) == ("shutdown-usage:inbound", "shutdown-attempt") - assert capture.meter is None - finally: - release.set() - await worker.flush() - await worker.stop() - await capture.stop(0) @pytest.mark.asyncio @@ -4377,276 +4307,11 @@ async def test_shutdown_waits_for_enqueue_producers_before_stopping_worker() -> await worker.stop(discard_pending=True) -@pytest.mark.asyncio -@pytest.mark.parametrize("unsupported", ["database", "collector", "remote", "free_tier"]) -async def test_capture_enablement_rejects_unrecoverable_writer_modes( - unsupported: str, monkeypatch: pytest.MonkeyPatch -) -> None: - import litellm - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - - monkeypatch.setattr(proxy_server, "usage_capture", UsageCaptureRuntime()) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_capture": {}}) - monkeypatch.setattr(proxy_server, "prisma_client", None if unsupported == "database" else object()) - monkeypatch.setattr(litellm, "apply_search_free_tier", unsupported == "free_tier") - monkeypatch.setenv("LITELLM_COLLECTOR_ENABLED", "true" if unsupported == "collector" else "false") - monkeypatch.delenv("SPEND_LOGS_URL", raising=False) - if unsupported == "remote": - monkeypatch.setenv("SPEND_LOGS_URL", "https://writer.invalid") - with pytest.raises(ValueError, match="requires local Prisma"): - proxy_server._configure_usage_capture() - assert proxy_server.usage_capture.meter is None -@pytest.mark.asyncio -async def test_capture_configuration_requires_restart_without_replacing_the_active_writer( - monkeypatch: pytest.MonkeyPatch, -) -> None: - from litellm.proxy import proxy_server - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.utils import PrismaClient - - runtime: Final = UsageCaptureRuntime() - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": True}) - with pytest.raises(ValueError, match="requires accounting_capture"): - proxy_server._configure_usage_capture() - client: Final = PrismaClient("postgresql://localhost/unused", ProxyLogging(UserApiKeyCache())) - monkeypatch.setattr(proxy_server, "prisma_client", client) - monkeypatch.setattr(litellm, "apply_search_free_tier", False) - monkeypatch.setenv("LITELLM_COLLECTOR_ENABLED", "false") - monkeypatch.delenv("SPEND_LOGS_URL", raising=False) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_capture": {"batch_size": 4}}) - try: - proxy_server._configure_usage_capture() - active: Final = runtime.meter - assert active is not None - assert active.settings.batch_size == 4 - proxy_server._configure_usage_capture() - assert runtime.meter is active - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_capture": {"batch_size": 6}}) - with pytest.raises(ValueError, match="Changing accounting_capture requires a worker restart"): - proxy_server._configure_usage_capture() - monkeypatch.setattr(proxy_server, "general_settings", {}) - with pytest.raises(ValueError, match="Disabling accounting_capture requires a worker restart"): - proxy_server._configure_usage_capture() - assert runtime.meter is active - assert active.settings.batch_size == 4 - finally: - await runtime.stop(1) - assert runtime.meter is None -@pytest.mark.asyncio -@pytest.mark.parametrize("sync_ack", [False, True]) -@pytest.mark.parametrize( - ("path", "payload"), - [ - ( - "/v1/chat/completions", - {"model": "openai/gpt-5-mini", "messages": [{"role": "user", "content": "hi"}], "stream": True}, - ), - ("/v1/responses", {"model": "openai/gpt-5-mini", "input": "hi", "background": True}), - ( - "/v1/messages", - {"model": "openai/gpt-5-mini", "messages": [{"role": "user", "content": "hi"}], "max_tokens": 1}, - ), - ], -) -async def test_sync_ack_preserves_async_request_support( - sync_ack: bool, - path: str, - payload: dict[str, object], - monkeypatch: pytest.MonkeyPatch, - respx_mock: respx.MockRouter, -) -> None: - from fastapi import Request - from litellm.proxy import proxy_server - from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - - persisted: Final[asyncio.Queue[UsageWrite]] = asyncio.Queue() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - for write in writes: - persisted.put_nowait(write) - - async def authorize(request: Request) -> UserAPIKeyAuth: - return UserAPIKeyAuth(api_key="test-key", request_route=request.url.path) - - monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - monkeypatch.setenv("OPENAI_API_KEY", "test-provider-key") - upstream: Final = { - "id": "provider-result", - "object": "chat.completion", - "created": 1, - "model": "gpt-5-mini", - "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "hi"}}], - "usage": {"prompt_tokens": 3, "completion_tokens": 2, "total_tokens": 5}, - } - if payload.get("stream"): - chunk: Final = { - **upstream, - "object": "chat.completion.chunk", - "choices": [{"index": 0, "finish_reason": "stop", "delta": {"role": "assistant", "content": "hi"}}], - } - respx_mock.post().respond( - 200, text=f"data: {json.dumps(chunk)}\n\ndata: [DONE]\n\n", headers={"content-type": "text/event-stream"} - ) - elif path == "/v1/responses": - respx_mock.post().respond( - 200, - json={ - "id": "resp_background", - "object": "response", - "created_at": 1, - "model": "gpt-5-mini", - "status": "in_progress", - "background": True, - "output": [], - "usage": None, - }, - ) - else: - respx_mock.post(url__regex=r".*/responses$").respond( - 200, - json={ - "id": "resp_message", - "object": "response", - "created_at": 1, - "model": "gpt-5-mini", - "status": "completed", - "output": [ - { - "id": "msg_reply", - "type": "message", - "role": "assistant", - "status": "completed", - "content": [{"type": "output_text", "text": "hi", "annotations": []}], - } - ], - "usage": {"input_tokens": 3, "output_tokens": 2, "total_tokens": 5}, - }, - ) - respx_mock.post(url__regex=r".*/chat/completions$").respond(200, json=upstream) - runtime: Final = UsageCaptureRuntime() - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr( - proxy_server, "general_settings", {"accounting_sync_ack": sync_ack, "accounting_sync_ack_timeout_seconds": 0} - ) - monkeypatch.setattr(proxy_server, "prisma_client", None) - monkeypatch.setattr( - proxy_server, - "llm_router", - litellm.Router( - model_list=[ - { - "model_name": payload["model"], - "litellm_params": {"model": payload["model"], "api_key": "test-provider-key"}, - } - ], - num_retries=0, - ), - ) - monkeypatch.setattr(proxy_server, "proxy_logging_obj", ProxyLogging(UserApiKeyCache())) - monkeypatch.setattr(litellm, "callbacks", [ProxyDBLogger()]) - monkeypatch.setitem(proxy_server.app.dependency_overrides, proxy_server.user_api_key_auth, authorize) - runtime.start(persist, UsageCaptureSettings()) - try: - async with httpx.AsyncClient( - transport=httpx.ASGITransport(app=proxy_server.app), base_url="http://test" - ) as client: - response: Final = await client.post(path, json=payload) - finally: - await runtime.stop(1) - assert response.status_code == 200, response.text - assert len(respx_mock.calls) == 1 - if payload.get("background"): - assert response.json()["status"] == "in_progress" - else: - assert "hi" in response.text - assert not persisted.empty(), "Terminal usage must still be captured" - -@pytest.mark.asyncio -@pytest.mark.parametrize("sync_ack", [False, True]) -async def test_text_completion_provider_rejection_captures_unknown_usage( - sync_ack: bool, monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter -) -> None: - from fastapi import Request - - from litellm.accounting import Outcome - from litellm.proxy import proxy_server - from litellm.proxy._types import UserAPIKeyAuth - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.hooks.proxy_track_cost_callback import ProxyDBLogger - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - - persisted: Final[asyncio.Queue[UsageWrite]] = asyncio.Queue() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - for write in writes: - persisted.put_nowait(write) - - async def authorize(request: Request) -> UserAPIKeyAuth: - return UserAPIKeyAuth(api_key="test-key", request_route=request.url.path) - - monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - monkeypatch.setenv("OPENAI_API_KEY", "test-provider-key") - respx_mock.post().respond( - 404, - json={ - "error": {"message": "The model is unavailable", "type": "invalid_request_error", "code": "model_not_found"} - }, - ) - runtime: Final = UsageCaptureRuntime() - monkeypatch.setattr(proxy_server, "usage_capture", runtime) - monkeypatch.setattr(proxy_server, "general_settings", {"accounting_sync_ack": sync_ack}) - monkeypatch.setattr(proxy_server, "prisma_client", None) - monkeypatch.setattr( - proxy_server, - "llm_router", - litellm.Router( - model_list=[ - { - "model_name": "instruct", - "litellm_params": {"model": "openai/gpt-3.5-turbo-instruct", "api_key": "test-provider-key"}, - } - ], - num_retries=0, - ), - ) - monkeypatch.setattr(proxy_server, "proxy_logging_obj", ProxyLogging(UserApiKeyCache())) - monkeypatch.setattr(litellm, "callbacks", [ProxyDBLogger()]) - monkeypatch.setitem(proxy_server.app.dependency_overrides, proxy_server.user_api_key_auth, authorize) - runtime.start(persist, UsageCaptureSettings()) - try: - async with httpx.AsyncClient( - transport=httpx.ASGITransport(app=proxy_server.app), base_url="http://test" - ) as client: - response: Final = await client.post( - "/v1/completions", json={"model": "instruct", "prompt": "hi", "max_tokens": 1} - ) - finally: - await runtime.stop(1) - assert response.status_code == 404, response.text - assert len(respx_mock.calls) == 1 - assert persisted.qsize() == 2 - inbound: Final = persisted.get_nowait() - outbound: Final = persisted.get_nowait() - assert outbound.event.outcome is Outcome.FAILED - assert outbound.event.call_type.value == "atext_completion" - assert outbound.event.metrics is None - assert inbound.event.request_id == outbound.event.request_id - envelope: Final = json.loads(outbound.envelope) - assert envelope["usage"] is None - assert envelope["usage_source"] == "unknown" - assert envelope["pricing"]["reported_cost"] is None @pytest.mark.parametrize( diff --git a/tests/unit/proxy/test_proxy_server_endpoints_and_startup.py b/tests/unit/proxy/test_proxy_server_endpoints_and_startup.py index 9914247bc47..9dd15649d3e 100644 --- a/tests/unit/proxy/test_proxy_server_endpoints_and_startup.py +++ b/tests/unit/proxy/test_proxy_server_endpoints_and_startup.py @@ -1794,6 +1794,38 @@ async def test_proxy_startup_refuses_an_unsafe_master_key_even_when_the_database assert ("could not be checked" in announced[0]) == key_can_have_encrypted_the_database +@pytest.mark.asyncio +async def test_proxy_startup_reads_fips_mode_from_the_config_file( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + from litellm.proxy.common_utils.fips import FipsModeError + from litellm.proxy.proxy_server import proxy_startup_event + + _boot_with_general_settings(monkeypatch, tmp_path, {"master_key": "sk-a-safe-master-key", "fips_mode": True}) + monkeypatch.setattr("litellm.proxy.common_utils.fips.openssl_enforces_fips", lambda: False) + + with pytest.raises(FipsModeError, match=r"general_settings\.fips_mode is on but OpenSSL does not enforce FIPS"): + async with proxy_startup_event(FastAPI()): + pass + + +@pytest.mark.asyncio +async def test_proxy_startup_refuses_a_fips_mode_reference_to_an_unset_variable( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + from litellm.proxy.common_utils.fips import FipsModeError + from litellm.proxy.proxy_server import proxy_startup_event + + _boot_with_general_settings( + monkeypatch, tmp_path, {"master_key": "sk-a-safe-master-key", "fips_mode": "os.environ/FIPS_MODE_NOT_SET"} + ) + monkeypatch.delenv("FIPS_MODE_NOT_SET", raising=False) + + with pytest.raises(FipsModeError, match=r"general_settings\.fips_mode has no value"): + async with proxy_startup_event(FastAPI()): + pass + + class _DatabaseWithOneStoredCredential: def __init__(self, ciphertext): self._ciphertext = ciphertext diff --git a/tests/unit/proxy/test_proxy_utils.py b/tests/unit/proxy/test_proxy_utils.py index 1b9cdd003ab..698f4787a42 100644 --- a/tests/unit/proxy/test_proxy_utils.py +++ b/tests/unit/proxy/test_proxy_utils.py @@ -3716,3 +3716,55 @@ async def test_evict_config_param_clears_the_local_layer_and_survives_a_redis_ou await evict_config_param("router_settings", cache=cache) assert await cache.in_memory_cache.async_get_cache(_config_cache_key("router_settings")) is None + + +@pytest.mark.asyncio +async def test_query_raw_within_statement_timeout_has_postgres_cancel_the_read_at_30s() -> None: + from datetime import timedelta + + from litellm.proxy.utils import query_raw_within_statement_timeout + from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_reads + + prisma: Final = MagicMock() + transaction: Final = statement_timeout_reads(prisma, AsyncMock(return_value=[{"spend": 1.5}])) + + rows: Final = await query_raw_within_statement_timeout( + prisma, 'SELECT spend FROM "LiteLLM_SpendLogs" WHERE "user" = $1', "u1" + ) + + assert rows == [{"spend": 1.5}] + assert transaction.statements == ("SET LOCAL statement_timeout = 30000",) + transaction.query_raw.assert_awaited_once_with('SELECT spend FROM "LiteLLM_SpendLogs" WHERE "user" = $1', "u1") + assert prisma.read_db.tx.call_args.kwargs["timeout"] > timedelta(seconds=30), "Prisma must outlive the Postgres cancel" + + +@pytest.mark.asyncio +async def test_query_raw_within_statement_timeout_reports_a_cancelled_read_as_503() -> None: + from litellm.proxy.utils import query_raw_within_statement_timeout + from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_error, statement_timeout_reads + + prisma: Final = MagicMock() + statement_timeout_reads(prisma, AsyncMock(side_effect=statement_timeout_error())) + + with pytest.raises(HTTPException) as exc_info: + await query_raw_within_statement_timeout(prisma, "SELECT 1", what="Spend per user") + + assert exc_info.value.status_code == 503 + assert exc_info.value.detail == {"error": "Spend per user took longer than 30s. Pick a shorter date range"} + + +@pytest.mark.asyncio +async def test_query_raw_within_statement_timeout_leaves_other_database_errors_alone() -> None: + from prisma.errors import DataError + + from litellm.proxy.utils import query_raw_within_statement_timeout + from tests.unit.proxy.db.fake_prisma_engine import statement_timeout_reads + + prisma: Final = MagicMock() + failure: Final = DataError(data={"user_facing_error": {"is_panic": False, "message": "relation does not exist"}}) + statement_timeout_reads(prisma, AsyncMock(side_effect=failure)) + + with pytest.raises(DataError) as exc_info: + await query_raw_within_statement_timeout(prisma, "SELECT 1") + + assert exc_info.value is failure diff --git a/tests/unit/proxy/test_proxy_utils_model_creation_and_error_logging.py b/tests/unit/proxy/test_proxy_utils_model_creation_and_error_logging.py index db0db97cd88..3b000ba741f 100644 --- a/tests/unit/proxy/test_proxy_utils_model_creation_and_error_logging.py +++ b/tests/unit/proxy/test_proxy_utils_model_creation_and_error_logging.py @@ -3735,42 +3735,6 @@ def test_an_earlier_timed_out_responses_attempt_counts_its_input_from_the_newest assert sent == earlier_attempt_prompt_tokens([COMPACTION_INSTRUCTIONS, COMPACTION_ITEM, TURN_AFTER_COMPACTION]) -@pytest.mark.asyncio -async def test_a_billed_responses_route_failure_captures_usage_from_the_newest_compaction_item() -> None: - from litellm.litellm_core_utils.get_litellm_params import get_litellm_params - from litellm.litellm_core_utils.litellm_logging import UsageCaptureObservation - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - from litellm.proxy.utils import _failure_fields_to_lift - - async def persist(_: tuple[UsageWrite, ...]) -> None: - return None - - def captured_prompt_tokens(request_input: list[dict[str, str]]) -> int: - logging_obj: Final = _dispatched_failure_logging_obj(FAILURE_USAGE_MODEL, get_litellm_params()) - logging_obj.model_call_details["call_type"] = "aresponses" - logging_obj.model_call_details["messages"] = request_input - lifted: Final = _failure_fields_to_lift( - {"litellm_logging_obj": logging_obj}, provider_billed=True, route_call_type="aresponses" - ) - snapshot: Final = lifted["_accounting_usage_snapshot"] - assert isinstance(snapshot, UsageCaptureObservation) and snapshot.usage_json is not None - return json.loads(snapshot.usage_json)["prompt_tokens"] - - runtime: Final = UsageCaptureRuntime() - runtime.start(persist, UsageCaptureSettings()) - try: - sent: Final = captured_prompt_tokens( - [COMPACTION_INSTRUCTIONS, *COMPACTED_HISTORY, COMPACTION_ITEM, TURN_AFTER_COMPACTION] - ) - since_compaction: Final = captured_prompt_tokens( - [COMPACTION_INSTRUCTIONS, COMPACTION_ITEM, TURN_AFTER_COMPACTION] - ) - finally: - await runtime.stop(1) - - assert sent == since_compaction @pytest.mark.parametrize( @@ -3814,6 +3778,56 @@ def test_a_retry_failure_bills_input_only_for_dispatched_attempts( assert lifted["response_cost"] == 0.25 +@pytest.mark.asyncio +async def test_an_agentic_follow_up_failure_row_bills_each_served_first_call_without_an_estimate() -> None: + import litellm + from litellm.litellm_core_utils.chat_completion_agentic_loop import bill_first_round_on_failure + from litellm.proxy.utils import _failure_fields_to_lift + from litellm.types.utils import ModelResponse, Usage + + logging_obj: Final = Logging( + model=FAILURE_USAGE_MODEL, + messages=ONE_USER_MESSAGE, + stream=False, + call_type="acompletion", + start_time=real_datetime.datetime.now(), + litellm_call_id="call-agentic-follow-up", + function_id="fn-agentic-follow-up", + ) + logging_obj.update_environment_variables( + model=FAILURE_USAGE_MODEL, user="", optional_params={}, litellm_params={"custom_llm_provider": "openai"} + ) + + async def failing_follow_up() -> ModelResponse: + raise litellm.RateLimitError(message="rate limited", llm_provider="openai", model=FAILURE_USAGE_MODEL) + + async def serve_first_call_then_fail_its_follow_up(usage: Usage) -> float: + logging_obj.pre_call(input=ONE_USER_MESSAGE, api_key="sk-test") + with pytest.raises(litellm.RateLimitError): + await bill_first_round_on_failure( + failing_follow_up(), + logging_obj=logging_obj, + response=ModelResponse(model=FAILURE_USAGE_MODEL, usage=usage), + stream=False, + ) + return logging_obj.model_call_details["response_cost"] + + primary_cost: Final = await serve_first_call_then_fail_its_follow_up( + Usage(prompt_tokens=559, completion_tokens=113, total_tokens=672) + ) + logging_obj.carry_failed_attempt_usage() + backup_cost: Final = await serve_first_call_then_fail_its_follow_up( + Usage(prompt_tokens=31, completion_tokens=5, total_tokens=36) + ) + + lifted: Final = _failure_fields_to_lift({"litellm_logging_obj": logging_obj}) + + usage: Final = lifted["combined_usage_object"] + assert (usage.prompt_tokens, usage.completion_tokens) == (559 + 31, 113 + 5) + assert primary_cost > 0 and backup_cost > 0 + assert lifted["response_cost"] == pytest.approx(primary_cost + backup_cost) + + @pytest.mark.parametrize( "model_call_details, dispatched", [ @@ -4355,57 +4369,3 @@ def test_is_client_error_exception_existing_behaviour_unchanged() -> None: ProxyException(message="x", type="x", param=None, code="429") ) is True assert _is_client_error_exception(ValueError("x")) is False - - -@pytest.mark.asyncio -@pytest.mark.parametrize("tokens", [None, 0, 3]) -async def test_failure_capture_preserves_prepared_usage_observation(tokens: int | None) -> None: - from litellm.proxy.db.accounting_ledger import UsageWrite - from litellm.proxy.spend_tracking.usage_capture import UsageCaptureRuntime, capture_callback - from litellm.proxy.spend_tracking.usage_meter import UsageCaptureSettings - - timestamp: Final = real_datetime.datetime(2026, 1, 1) - committed: Final[asyncio.Queue[UsageWrite]] = asyncio.Queue() - - async def persist(writes: tuple[UsageWrite, ...]) -> None: - for write in writes: - committed.put_nowait(write) - - logger: Final = Logging( - model=FAILURE_USAGE_MODEL, - messages=[], - stream=False, - call_type="acompletion", - start_time=timestamp, - litellm_call_id="post-response-failure", - function_id="post-response-failure", - ) - logger.update_environment_variables( - model=FAILURE_USAGE_MODEL, custom_llm_provider="openai", litellm_params={}, optional_params={} - ) - logger.model_call_details["response_cost"] = 0.2 - runtime: Final = UsageCaptureRuntime() - runtime.start(persist, UsageCaptureSettings()) - try: - prepared: Final = logger.prepare_response_cost( - {"id": "provider", "usage": None if tokens is None else {"prompt_tokens": tokens, "total_tokens": tokens}}, - timestamp, - ) - assert prepared is not None - lifted: Final = _failure_fields_to_lift({"litellm_logging_obj": logger}, provider_billed=True) - assert lifted["combined_usage_object"] is logger.model_call_details["combined_usage_object"] - assert lifted["response_cost"] == 0.2 - assert runtime.meter is not None - capture_callback( - runtime.meter, - {**logger.model_call_details, **lifted, "log_event_type": "failed_api_call"}, - None, - timestamp, - timestamp, - ) - finally: - await runtime.stop(1) - committed.get_nowait() - envelope: Final = json.loads(committed.get_nowait().envelope) - assert (None if envelope["usage"] is None else envelope["usage"]["total_tokens"]) == tokens - assert envelope["usage_source"] == ("unknown" if tokens is None else "measured") diff --git a/tests/unit/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py b/tests/unit/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py index e66050877df..77d0132ec72 100644 --- a/tests/unit/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py +++ b/tests/unit/proxy/ui_crud_endpoints/test_proxy_setting_endpoints.py @@ -18,6 +18,27 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( client = TestClient(app) +@pytest.mark.asyncio +async def test_sso_settings_without_prisma_reports_database_not_connected(monkeypatch: pytest.MonkeyPatch) -> None: + import runpy + import sys + + from fastapi import HTTPException + + from litellm.proxy import proxy_server + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.ui_crud_endpoints import proxy_setting_endpoints + + monkeypatch.setattr(proxy_server, "prisma_client", None) + with patch.dict(sys.modules, {"prisma": None, "prisma.errors": None}): + endpoints: Final = runpy.run_path(proxy_setting_endpoints.__file__) + with pytest.raises(HTTPException) as error: + await endpoints["update_sso_settings"](SSOConfig(), UserAPIKeyAuth()) + + assert error.value.status_code == 500 + assert error.value.detail == {"error": "Database not connected. Please connect a database."} + + @pytest.mark.asyncio @pytest.mark.parametrize("config_value,stored_value,expected", [ (None, None, False), (None, False, False), (None, True, True), @@ -874,6 +895,25 @@ class TestProxySettingEndpoints: env_row_update = prisma_client.db.litellm_config.update.call_args.kwargs assert json.loads(env_row_update["data"]["param_value"]) == {"LANGFUSE_PUBLIC_KEY": "unrelated"} + def test_update_sso_settings_blank_stored_value_keeps_env_row_secret( + self, mock_proxy_config, mock_auth, monkeypatch + ): + import json + + response, saved = self._patch_sso_settings( + monkeypatch, + {**self._STORED_GENERIC_SSO, "generic_client_secret": ""}, + {"user_email": "new@example.com"}, + environment_variables={"GENERIC_CLIENT_SECRET": "env-row-secret", "LANGFUSE_PUBLIC_KEY": "unrelated"}, + ) + + from litellm.proxy.proxy_server import prisma_client + + assert response.status_code == 200, response.text + assert saved["generic_client_secret"] == "env-row-secret", saved + env_row_update = prisma_client.db.litellm_config.update.call_args.kwargs + assert json.loads(env_row_update["data"]["param_value"]) == {"LANGFUSE_PUBLIC_KEY": "unrelated"} + def test_update_sso_settings_maps_saml_fields_to_env_vars( self, mock_proxy_config, mock_auth, monkeypatch ): diff --git a/tests/unit/responses/mcp/test_aresponses_api_with_mcp.py b/tests/unit/responses/mcp/test_aresponses_api_with_mcp.py index ec0be896095..037004ab999 100644 --- a/tests/unit/responses/mcp/test_aresponses_api_with_mcp.py +++ b/tests/unit/responses/mcp/test_aresponses_api_with_mcp.py @@ -540,13 +540,9 @@ async def test_mcp_allowed_tools_filtering(): ] # First filter by allowed tools - filtered_tools_with_duplicates = ( - LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( - mcp_tools=mock_mcp_tools_with_duplicates, - mcp_tools_with_litellm_proxy=cast( - List[ToolParam], mcp_tool_config_with_duplicates - ), - ) + filtered_tools_with_duplicates = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_with_duplicates, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], mcp_tool_config_with_duplicates), ) # Then deduplicate the filtered tools @@ -605,11 +601,9 @@ async def test_mcp_allowed_tools_filtering(): }, ] - filtered_tools_multiple = ( - LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( - mcp_tools=mock_mcp_tools_from_server, - mcp_tools_with_litellm_proxy=cast(List[ToolParam], multiple_mcp_configs), - ) + filtered_tools_multiple = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_from_server, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], multiple_mcp_configs), ) # Should return union of all allowed tools (3 unique tools) diff --git a/tests/unit/router_strategy/adaptive_router/test_signals.py b/tests/unit/router_strategy/adaptive_router/test_signals.py index 2773c13a812..bd58b4c668d 100644 --- a/tests/unit/router_strategy/adaptive_router/test_signals.py +++ b/tests/unit/router_strategy/adaptive_router/test_signals.py @@ -81,6 +81,12 @@ def test_exhaustion_fires_on_infra_signal(fixture): assert state.exhaustion_count >= 1 +@pytest.mark.parametrize("status", [503, 529]) +def test_exhaustion_fires_on_an_overloaded_answer(status): + state, _ = _replay([{"user_content": "fix the build", "response_status": status}]) + assert state.exhaustion_count == 1 + + def test_no_signals_on_clean_session(): state, _ = _replay(_load("clean_no_signals")) assert state.misalignment_count == 0 diff --git a/tests/unit/router_strategy/test_budget_limiter.py b/tests/unit/router_strategy/test_budget_limiter.py index c0583566531..d24d0598d87 100644 --- a/tests/unit/router_strategy/test_budget_limiter.py +++ b/tests/unit/router_strategy/test_budget_limiter.py @@ -7,6 +7,7 @@ the callback raise before any spend was recorded, so those budgets never moved. """ from datetime import datetime, timezone +from functools import partial from typing import Final import litellm @@ -17,6 +18,7 @@ from litellm.caching.caching import DualCache from litellm.caching.in_memory_cache import InMemoryCache from litellm.router_strategy.budget_limiter import RouterBudgetLimiting from litellm.types.utils import BudgetConfig +from litellm.types.videos.utils import POLLED_VIDEO_STATUS_KWARG import sys, os, asyncio, time, random @@ -38,6 +40,7 @@ def _success_kwargs( call_type: str = "aresponses", response_cost: float = 0.25, model_id: str = "deployment-1", + guardrail_information: tuple[dict[str, object], ...] = (), ) -> dict[str, object]: provider_params: Final[dict[str, str]] = ( {} if provider_in_litellm_params is None else {"custom_llm_provider": provider_in_litellm_params} @@ -51,6 +54,7 @@ def _success_kwargs( "response_cost": response_cost, "model_id": model_id, "custom_llm_provider": provider_in_payload, + "guardrail_information": guardrail_information, }, } @@ -462,6 +466,7 @@ def _completed_budget_batch(batch_id: str): input_file_id="file-budget", object="batch", status="completed", + output_file_id="file-budget-output", ) @@ -498,6 +503,130 @@ async def test_batch_polls_charge_each_router_budget_once(monkeypatch, call_type assert tuple([await cache.async_get_cache(key) for key in keys]) == (1.0, 1.0, 1.0) + +@pytest.mark.asyncio +async def test_a_batch_retrieve_before_its_cost_is_final_charges_no_router_budget_even_for_its_guardrail(): + from litellm.types.utils import LiteLLMBatch + + cache: Final = DualCache() + limiter: Final = RouterBudgetLimiting( + dual_cache=cache, + provider_budget_config={"openai": {"budget_limit": 100, "time_period": "1d"}}, + model_list=[ + { + "model_name": "batch-budget", + "litellm_params": {"model": "openai/batch-budget", "max_budget": 100, "budget_duration": "1d"}, + "model_info": {"id": "deployment-1"}, + } + ], + ) + retrieve: Final = partial( + _success_kwargs, + provider_in_litellm_params=None, + provider_in_payload="openai", + call_type="aretrieve_batch", + guardrail_information=({"guardrail_name": "priced", "guardrail_cost": 0.01},), + ) + + def polled(status: str, output_file_id: str | None = None) -> LiteLLMBatch: + return LiteLLMBatch( + id="batch-running", + completion_window="24h", + created_at=1, + endpoint="/v1/chat/completions", + input_file_id="file-budget", + object="batch", + status=status, + output_file_id=output_file_id, + ) + + for response_cost, batch in ( + (0.01, polled("in_progress")), + (0.01, polled("completed")), + (0.26, polled("completed", "file-out")), + (0.26, polled("completed", "file-out")), + ): + await limiter.async_log_success_event(retrieve(response_cost=response_cost), batch, None, None) + + keys: Final = ("provider_spend:openai:1d", "deployment_spend:deployment-1:1d") + assert tuple([await cache.async_get_cache(key) for key in keys]) == pytest.approx((0.26, 0.26)) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("call_type", ("avideo_status", "video_status")) +async def test_video_polls_charge_each_router_budget_once(call_type): + from litellm.types.videos.main import VideoObject + + cache: Final = DualCache() + limiter: Final = RouterBudgetLimiting( + dual_cache=cache, + provider_budget_config={"openai": {"budget_limit": 100, "time_period": "1d"}}, + model_list=[ + { + "model_name": "video-budget", + "litellm_params": {"model": "openai/sora-2", "max_budget": 100, "budget_duration": "1d"}, + "model_info": {"id": "deployment-1"}, + } + ], + ) + kwargs: Final = { + **_success_kwargs(provider_in_litellm_params=None, provider_in_payload="openai", call_type=call_type), + POLLED_VIDEO_STATUS_KWARG: "completed", + } + video: Final = VideoObject(id="video-first", object="video", status="completed") + keys: Final = ("provider_spend:openai:1d", "deployment_spend:deployment-1:1d") + + await asyncio.gather(*(limiter.async_log_success_event(kwargs, video, None, None) for _ in range(10))) + assert tuple([await cache.async_get_cache(key) for key in keys]) == (0.25, 0.25) + + await limiter.async_log_success_event( + kwargs, VideoObject(id="video-second", object="video", status="completed"), None, None + ) + assert tuple([await cache.async_get_cache(key) for key in keys]) == (0.5, 0.5) + + +@pytest.mark.asyncio +async def test_every_video_poll_pays_its_guardrail_and_only_the_finished_poll_pays_the_router_for_the_video(): + from litellm.types.videos.main import VideoObject + + cache: Final = DualCache() + limiter: Final = RouterBudgetLimiting( + dual_cache=cache, + provider_budget_config={"openai": {"budget_limit": 100, "time_period": "1d"}}, + model_list=[ + { + "model_name": "video-budget", + "litellm_params": {"model": "openai/sora-2", "max_budget": 100, "budget_duration": "1d"}, + "model_info": {"id": "deployment-1"}, + } + ], + ) + priced: Final = ({"guardrail_name": "priced", "guardrail_cost": 0.01},) + poll: Final = partial( + _success_kwargs, + provider_in_litellm_params=None, + provider_in_payload="openai", + call_type="avideo_status", + guardrail_information=priced, + ) + + await limiter.async_log_success_event( + {**poll(response_cost=0.01), POLLED_VIDEO_STATUS_KWARG: "in_progress"}, + VideoObject(id="video-first", object="video", status="in_progress"), + None, + None, + ) + for _ in range(3): + await limiter.async_log_success_event( + {**poll(response_cost=0.26), POLLED_VIDEO_STATUS_KWARG: "completed"}, + VideoObject(id="video-first", object="video", status="completed"), + None, + None, + ) + + keys: Final = ("provider_spend:openai:1d", "deployment_spend:deployment-1:1d") + assert tuple([await cache.async_get_cache(key) for key in keys]) == pytest.approx((0.29, 0.29)) + @pytest.mark.asyncio async def test_batch_charge_is_shared_across_workers_and_requires_redis_confirmation(): from fakeredis import FakeServer, FakeRedis as FakeSyncRedis diff --git a/tests/unit/router_strategy/test_lowest_tpm_rpm_v2.py b/tests/unit/router_strategy/test_lowest_tpm_rpm_v2.py index 129bb10eccc..f04ade1ce2d 100644 --- a/tests/unit/router_strategy/test_lowest_tpm_rpm_v2.py +++ b/tests/unit/router_strategy/test_lowest_tpm_rpm_v2.py @@ -2,7 +2,9 @@ # This tests the router's ability to pick deployment with lowest tpm using 'usage-based-routing-v2-v2' import asyncio +import base64 import importlib +import io import os import time import traceback @@ -15,6 +17,7 @@ from tests._vcr_conftest_common import install_live_call_probe, record_vcr_outco from unittest.mock import patch import pytest +from PIL import Image import litellm from litellm import Router @@ -23,7 +26,7 @@ from litellm.caching.caching import DualCache from litellm.router_strategy.lowest_tpm_rpm_v2 import ( LowestTPMLoggingHandler_v2 as LowestTPMLoggingHandler, ) -from litellm.types.router import DeploymentTypedDict +from litellm.types.router import DeploymentTypedDict, RouterRateLimitError from litellm.types.utils import StandardLoggingPayload from litellm.utils import get_utc_datetime @@ -347,6 +350,55 @@ def test_router_skip_rate_limited_deployments(): print(f"An exception occurred! {str(e)}") +def _embedding_image_uri() -> str: + image: Final = io.BytesIO() + Image.new("RGB", (1024, 1024)).save(image, format="PNG", compress_level=0) + return f"data:image/png;base64,{base64.b64encode(image.getvalue()).decode()}" + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "routing_strategy", + ["usage-based-routing", "usage-based-routing-v2", "latency-based-routing", "cost-based-routing"], +) +@pytest.mark.parametrize( + ("litellm_params", "model_info", "admitted"), + [ + ({"model": "vertex_ai/gemini-embedding-2"}, {}, True), + ( + {"model": "openai/team-embedder", "api_base": "https://embedder.invalid"}, + {"base_model": "vertex_ai/gemini-embedding-2"}, + True, + ), + ({"model": "text-embedding-3-small"}, {}, False), + ], + ids=["image-model", "image-base-model", "text-model"], +) +async def test_deployment_tpm_counts_embedding_image_as_media_only_on_image_models( + routing_strategy: str, litellm_params: Dict[str, str], model_info: Dict[str, str], admitted: bool +) -> None: + uri: Final = _embedding_image_uri() + assert len(uri) // 4 > 75_000 + router: Final = Router( + model_list=[ + { + "model_name": "embedder", + "litellm_params": {**litellm_params, "tpm": 75_000}, + "model_info": {**model_info, "id": f"embedder-{index}"}, + } + for index in range(2) + ], + routing_strategy=routing_strategy, + ) + + if not admitted: + with pytest.raises((litellm.RateLimitError, RouterRateLimitError)): + await router.select("embedder", {}, input=[uri]) + return + deployment: Final = await router.select("embedder", {}, input=[uri]) + assert deployment["model_info"]["id"] in ("embedder-0", "embedder-1") + + @pytest.mark.parametrize("sync_mode", [True, False]) @pytest.mark.asyncio async def test_multiple_potential_deployments(sync_mode): diff --git a/tests/unit/router_strategy/test_router_routing_groups.py b/tests/unit/router_strategy/test_router_routing_groups.py index 5eeb46c3518..afcd1e2a7ff 100644 --- a/tests/unit/router_strategy/test_router_routing_groups.py +++ b/tests/unit/router_strategy/test_router_routing_groups.py @@ -1022,7 +1022,11 @@ async def test_usage_based_v2_override_stays_scoped_to_the_request_that_asked_fo await _mock_completion(router, routing_strategy="usage-based-routing-v2") -def test_sync_usage_based_v2_override_stays_scoped_to_the_request_that_asked_for_it(): +def test_sync_usage_based_v2_override_stays_scoped_to_the_request_that_asked_for_it(monkeypatch): + monkeypatch.setattr( + "litellm.router_strategy.lowest_tpm_rpm_v2.get_utc_datetime", + lambda: datetime.datetime(2026, 1, 1, 12, 0, 30, tzinfo=datetime.timezone.utc), + ) router = Router(model_list=_rpm_limited_model_list(), routing_strategy="simple-shuffle", num_retries=0) messages = [{"role": "user", "content": "hi"}] diff --git a/tests/unit/router_utils/test_fallback_event_handlers.py b/tests/unit/router_utils/test_fallback_event_handlers.py index 26dbb96c60a..e9dd91d4362 100644 --- a/tests/unit/router_utils/test_fallback_event_handlers.py +++ b/tests/unit/router_utils/test_fallback_event_handlers.py @@ -302,6 +302,63 @@ def _two_deployment_router() -> Router: ) +def _entra_fallback_router() -> Router: + return Router( + model_list=[ + { + "model_name": "fallback-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + "client_secret": "configured-secret", + }, + "model_info": {"id": f"fallback-dep-{index}"}, + } + for index in (1, 2) + ], + num_retries=0, + ) + + +def _raised_from_an_entra_rejection(failed_deployment_id: str) -> litellm.AuthenticationError: + from litellm.llms.azure.common_utils import EntraRejectionError + + reason: Final = "Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided." + try: + raise litellm.AuthenticationError(message=reason, llm_provider="azure", model="gpt-4.1") from ( + EntraRejectionError(status_code=401, message=reason) + ) + except litellm.AuthenticationError as raised: + raised.failed_deployment_id = failed_deployment_id + return raised + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("call_params", "cooled_down"), + [ + pytest.param({"client_secret": "not-the-credential"}, [], id="caller-secret"), + pytest.param({"client_secret": "configured-secret"}, ["fallback-dep-1"], id="deployment-secret"), + ], +) +async def test_fallback_hop_cools_down_only_a_deployment_entra_rejected_its_own_credential( + call_params: Mapping[str, object], cooled_down: list[str] +) -> None: + router: Final = _entra_fallback_router() + + _trigger_cooldown_for_failed_deployment( + ctx=RoutingContext(router), + kwargs={}, + exception=_raised_from_an_entra_rejection("fallback-dep-1"), + model_call_details={"litellm_params": call_params}, + ) + + assert await async_get_cooldown_deployments(ctx=RoutingContext(router), parent_otel_span=None) == cooled_down + + @pytest.mark.asyncio @pytest.mark.parametrize("failure_already_logged", [False, True]) async def test_run_async_fallback_cools_down_a_failed_hop_once(failure_already_logged: bool) -> None: diff --git a/tests/unit/test_cerebras_llama_model_metadata.py b/tests/unit/test_cerebras_llama_model_metadata.py index c5ec096e553..5b51e9e2f24 100644 --- a/tests/unit/test_cerebras_llama_model_metadata.py +++ b/tests/unit/test_cerebras_llama_model_metadata.py @@ -52,7 +52,6 @@ CEREBRAS_MODELS_EXPECTED = { PAIR_PARITY = [ ("cerebras/llama-3.1-8b", "cerebras/llama3.1-8b"), - ("cerebras/llama-3.1-70b", "cerebras/llama3.1-70b"), ] diff --git a/tests/unit/test_check_openapi_docs.py b/tests/unit/test_check_openapi_docs.py index 5d8803c4118..c6ea0cd936e 100644 --- a/tests/unit/test_check_openapi_docs.py +++ b/tests/unit/test_check_openapi_docs.py @@ -20,14 +20,17 @@ def _post_with_media_types(request_media_type: str, response_media_type: str) -> } -@pytest.mark.parametrize("media_type", ("application/json", "application/sdp")) -def test_a_fully_documented_post_has_no_findings(media_type: str) -> None: - operation: Final = _post_with_media_types(media_type, media_type) +@pytest.mark.parametrize("request_type, response_type", [ + ("application/json", "application/json"), ("application/sdp", "application/sdp"), + ("multipart/form-data", "application/json"), +]) +def test_a_fully_documented_post_has_no_findings(request_type: str, response_type: str) -> None: + operation: Final = _post_with_media_types(request_type, response_type) assert checker.violations({"paths": {"/v1/thing": {"post": operation}}}) == () def test_a_schema_under_another_media_type_still_counts_as_undocumented() -> None: - operation: Final = _post_with_media_types("multipart/form-data", "audio/wav") + operation: Final = _post_with_media_types("application/octet-stream", "audio/wav") assert {v.code for v in checker.violations({"paths": {"/v1/thing": {"post": operation}}})} == {"OA001", "OA003"} @@ -37,8 +40,9 @@ def test_each_missing_piece_is_its_own_rule() -> None: assert codes == {"OA001", "OA002", "OA003"} -def test_only_the_missing_piece_is_reported() -> None: - no_body: Final = {**_DOCUMENTED, "requestBody": {}} +@pytest.mark.parametrize("body", [{}, {"content": {"multipart/form-data": {}}}]) +def test_only_the_missing_piece_is_reported(body: Mapping[str, object]) -> None: + no_body: Final = {**_DOCUMENTED, "requestBody": body} blank_description: Final = {**_DOCUMENTED, "description": " "} untyped_200: Final = {**_DOCUMENTED, "responses": {"200": {"content": {"application/json": {"schema": {}}}}}} spec: Final = { diff --git a/tests/unit/test_cost_calculator.py b/tests/unit/test_cost_calculator.py index e0ac07b3870..6a63a502dd3 100644 --- a/tests/unit/test_cost_calculator.py +++ b/tests/unit/test_cost_calculator.py @@ -8318,7 +8318,7 @@ def test_cost_openai_prompt_caching(): "model", [ "cohere/rerank-english-v3.0", - "azure_ai/cohere-rerank-v3-english", + "azure_ai/cohere-rerank-v4.0-pro", ], ) def test_completion_cost_azure_ai_rerank(model): @@ -9371,7 +9371,7 @@ async def test_test_completion_cost_gpt4o_audio_output_from_model(stream): @pytest.mark.parametrize( "response_model, custom_llm_provider", [ - ("azure_ai/Meta-Llama-3.1-70B-Instruct", "azure_ai"), + ("azure_ai/Llama-3.3-70B-Instruct", "azure_ai"), ("anthropic.claude-3-5-sonnet-20240620-v1:0", "bedrock"), ], ) @@ -9431,7 +9431,7 @@ def test_select_model_name_for_cost_calc(): "completion_response": ModelResponse( id="127f24aed4984b4c9a4c5e32ad3752f3", created=1734406048, - model="azure_ai/mistral-large", + model="azure_ai/mistral-large-latest", object="chat.completion", system_fingerprint=None, choices=[ @@ -9460,7 +9460,7 @@ def test_select_model_name_for_cost_calc(): } return_model = select_model_name_for_cost_calc(**args) - assert return_model == "azure_ai/mistral-large" + assert return_model == "azure_ai/mistral-large-latest" def test_cost_calculator_with_base_model(): diff --git a/tests/unit/test_count_tokens_public_api.py b/tests/unit/test_count_tokens_public_api.py index 4eb8bea3427..581682be474 100644 --- a/tests/unit/test_count_tokens_public_api.py +++ b/tests/unit/test_count_tokens_public_api.py @@ -59,7 +59,7 @@ def test_acount_tokens_fallback_to_local(): """Test that unsupported providers fall back to local tiktoken counting.""" result = asyncio.run( litellm.acount_tokens( - model="together_ai/meta-llama/Llama-3-8b-chat-hf", + model="together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo", messages=[{"role": "user", "content": "Hello"}], ) ) diff --git a/tests/unit/test_get_blog_posts.py b/tests/unit/test_get_blog_posts.py index d1de75a4500..71099dc2326 100644 --- a/tests/unit/test_get_blog_posts.py +++ b/tests/unit/test_get_blog_posts.py @@ -50,11 +50,10 @@ def reset_blog_posts_cache(): GetBlogPosts._last_fetch_time = 0.0 -def test_load_local_blog_posts_returns_posts_without_tags(): +def test_load_local_blog_posts_returns_posts(): posts = GetBlogPosts.load_local_blog_posts() assert len(posts) > 0 assert all(isinstance(post, BlogPost) for post in posts) - assert posts[0].tags == () def test_parse_rss_to_posts(): diff --git a/tests/unit/test_gpt_realtime_mode.py b/tests/unit/test_gpt_realtime_mode.py index 0ea85df84cb..c6f6d68dcc9 100644 --- a/tests/unit/test_gpt_realtime_mode.py +++ b/tests/unit/test_gpt_realtime_mode.py @@ -24,13 +24,10 @@ REALTIME_ONLY_GPT_MODELS = ( REALTIME_ONLY_GPT_MODELS_WITHOUT_ENDPOINTS = ( "azure/eu/gpt-4o-mini-realtime-preview-2024-12-17", - "azure/eu/gpt-4o-realtime-preview-2024-10-01", "azure/eu/gpt-4o-realtime-preview-2024-12-17", "azure/gpt-4o-mini-realtime-preview-2024-12-17", - "azure/gpt-4o-realtime-preview-2024-10-01", "azure/gpt-4o-realtime-preview-2024-12-17", "azure/us/gpt-4o-mini-realtime-preview-2024-12-17", - "azure/us/gpt-4o-realtime-preview-2024-10-01", "azure/us/gpt-4o-realtime-preview-2024-12-17", "gpt-4o-mini-realtime-preview", "gpt-4o-mini-realtime-preview-2024-12-17", diff --git a/tests/unit/test_internal_context.py b/tests/unit/test_internal_context.py index defda42ea2c..02301db81b5 100644 --- a/tests/unit/test_internal_context.py +++ b/tests/unit/test_internal_context.py @@ -25,7 +25,7 @@ _REPO: Final = Path(__file__).resolve().parents[2] _REDIS_PRODUCER_ROOTS: Final = ("litellm", "enterprise") # The cache implementations and facades: they emit the service events, their callers declare the family. -_CACHE_LAYER_DIRS: Final = ("litellm/caching", "litellm/_v2/cache") +_CACHE_LAYER_DIRS: Final = ("litellm/caching",) # Helpers that act on a cache handed in by the declaring caller, or forward to the response-cache facade. _CACHE_PARAMETER_HELPERS: Final = frozenset( { diff --git a/tests/unit/test_lint_workflow_diff_gates.py b/tests/unit/test_lint_workflow_diff_gates.py index 816ef6ce707..5e6ae9d285a 100644 --- a/tests/unit/test_lint_workflow_diff_gates.py +++ b/tests/unit/test_lint_workflow_diff_gates.py @@ -383,10 +383,20 @@ def _guard_base_argument_and_checkout(run: StepRun) -> tuple[str, str]: return argv[argv.index("--base") + 1], _sha(run.runner.checkout, "HEAD") +def _schema_base_commit_and_its_type_in_the_checkout(run: StepRun) -> tuple[str, str]: + argv: Final = _argv_recorded(run.runner, "uv") + base_commit: Final = argv[argv.index("--base-commit") + 1] + return base_commit, _git(run.runner.checkout, "cat-file", "-t", base_commit).strip() + + def _merge_base(upstream: StaleBaseUpstream, ref: str) -> str: return upstream.merge_base_by_ref[ref] +def _merge_base_as_a_commit(upstream: StaleBaseUpstream, ref: str) -> tuple[str, str]: + return upstream.merge_base_by_ref[ref], "commit" + + def _ui_files_changed_since_merge_base(upstream: StaleBaseUpstream, ref: str) -> frozenset[str]: merge_base: Final = upstream.merge_base_by_ref[ref] changed: Final = _git(upstream.path, "diff", "--name-only", f"--relative={UI_DASHBOARD}", merge_base, upstream.head) @@ -402,6 +412,7 @@ CONSUMERS: Final = MappingProxyType( "test-linting.yml:python": Consumer(_gate_base_exported, _merge_base), "test-linting.yml:ui": Consumer(_ui_files_queued_for_prettier, _ui_files_changed_since_merge_base), "test-unit.yml:ui-unit": Consumer(_ui_files_passed_to_vitest_related, _ui_files_changed_since_merge_base), + "test-unit.yml:unit": Consumer(_schema_base_commit_and_its_type_in_the_checkout, _merge_base_as_a_commit), "spend-logs-guard.yml:spend-logs-guard": Consumer(_guard_base_argument_and_checkout, _merge_base_and_tip), "cost-map-guard.yml:cost-map-guard": Consumer(_guard_base_argument_and_checkout, _merge_base_and_tip), } diff --git a/tests/unit/test_main.py b/tests/unit/test_main.py index 3f55e4d2a11..b12489ef553 100644 --- a/tests/unit/test_main.py +++ b/tests/unit/test_main.py @@ -11546,3 +11546,28 @@ async def test_bedrock_strict_tools_limit_rejection_is_not_sent_again_when_no_to assert error is not None and "Too many strict tools" in str(error) assert len(sent) == 1 + + +def test_supplied_openai_client_base_url_decides_data_residency(monkeypatch: pytest.MonkeyPatch) -> None: + """A caller-supplied OpenAI client sends the request to its own base_url, so billing must use that + host and not an env-configured FedRAMP or regional base.""" + import openai + + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.setenv("OPENAI_BASE_URL", "https://gov.api.openai.com/v1") + captured = {} + real_init = litellm.Logging.__init__ + + def init_spy(self, *args, **kwargs): + real_init(self, *args, **kwargs) + captured["logging_obj"] = self + + with patch.object(litellm.Logging, "__init__", init_spy): + litellm.completion( + model="gpt-4.1", + messages=[{"role": "user", "content": "hi"}], + client=openai.OpenAI(api_key="test-key", base_url="https://api.openai.com/v1"), + mock_response="ok", + ) + + assert captured["logging_obj"].litellm_params.get("data_residency") is None diff --git a/tests/unit/test_model_prices_and_context_window.py b/tests/unit/test_model_prices_and_context_window.py index e9e7f17f80d..e195b970fd5 100644 --- a/tests/unit/test_model_prices_and_context_window.py +++ b/tests/unit/test_model_prices_and_context_window.py @@ -279,6 +279,10 @@ def test_azure_rows_price_web_search_when_their_openai_model_does(prices: dict): PRIORITY_SUFFIX: Final = "_priority" MIN_PRIORITY_MARKUP: Final = 1.0 MAX_PRIORITY_MARKUP: Final = 5.0 +# Vertex pricing, verified 2026-11-03: https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing +VERIFIED_PRIORITY_MARKUP_MAXIMA: Final = MappingProxyType( + {("gemini-2.5-flash", "input_cost_per_audio_token_above_200k_tokens_priority"): 6.0} +) def _rate(value: JsonValue | None) -> float | None: @@ -312,7 +316,7 @@ def test_priority_rates_stay_within_a_premium_of_their_standard_rate(path: Path) overpriced: Final = tuple( f"{name}.{key} is {markup:.1f}x its standard rate" for name, key, markup in markups - if not MIN_PRIORITY_MARKUP <= markup <= MAX_PRIORITY_MARKUP + if not MIN_PRIORITY_MARKUP <= markup <= VERIFIED_PRIORITY_MARKUP_MAXIMA.get((name, key), MAX_PRIORITY_MARKUP) ) assert overpriced == (), ( "a priority rate is a premium on the standard rate, so one below it or far above it was likely copied from " diff --git a/tests/unit/test_register_model_custom_pricing.py b/tests/unit/test_register_model_custom_pricing.py index 35fbd28eae7..afff995aa69 100644 --- a/tests/unit/test_register_model_custom_pricing.py +++ b/tests/unit/test_register_model_custom_pricing.py @@ -17,10 +17,16 @@ from typing import Final, NoReturn import pytest - import litellm from litellm.litellm_core_utils.get_model_cost_map import _expand_model_aliases, adopt_model_cost_map -from litellm.main import _build_custom_pricing_entry +from litellm.types.utils import ( + Choices, + CompletionTokensDetailsWrapper, + Message, + ModelResponse, + PromptTokensDetailsWrapper, + Usage, +) from litellm.utils import _invalidate_model_cost_lowercase_map @@ -37,131 +43,6 @@ def _restore_model_cost_entries(original_entries): _invalidate_model_cost_lowercase_map() -def test_build_custom_pricing_entry_includes_all_kwargs_fields(): - """All CustomPricingLiteLLMParams fields present in kwargs should be - included in the resulting entry dict.""" - kwargs = { - "input_cost_per_token": 0.001, - "output_cost_per_token": 0.002, - "cache_read_input_token_cost": 0.00025, - "cache_creation_input_token_cost": 0.005, - "output_cost_per_reasoning_token": 0.01, - "input_cost_per_audio_token": 0.003, - "unrelated_kwarg": "should_be_ignored", - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - ) - - assert entry["litellm_provider"] == "openai" - assert entry["input_cost_per_token"] == 0.001 - assert entry["output_cost_per_token"] == 0.002 - assert entry["cache_read_input_token_cost"] == 0.00025 - assert entry["cache_creation_input_token_cost"] == 0.005 - assert entry["output_cost_per_reasoning_token"] == 0.01 - assert entry["input_cost_per_audio_token"] == 0.003 - assert "unrelated_kwarg" not in entry - - -def test_build_custom_pricing_entry_merges_model_info_metadata(): - """Fields from model_info (mode, supports_prompt_caching, max_tokens) - should be merged into the entry when present.""" - kwargs = { - "input_cost_per_token": 0.001, - "output_cost_per_token": 0.002, - } - model_info = { - "id": "deployment-123", - "mode": "chat", - "supports_prompt_caching": True, - "max_tokens": 128000, - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - model_info=model_info, - ) - - assert entry["mode"] == "chat" - assert entry["supports_prompt_caching"] is True - assert entry["max_tokens"] == 128000 - - -def test_build_custom_pricing_entry_setdefault_does_not_override_existing(): - """model_info uses setdefault, so it should not override a key that is - already present in the entry dict. Currently CustomPricingLiteLLMParams - and the model_info keys (mode, supports_prompt_caching, max_tokens) do - not overlap, but if they ever do, setdefault ensures the kwargs-sourced - value wins.""" - kwargs = { - "input_cost_per_token": 0.001, - "output_cost_per_token": 0.002, - } - model_info = { - "mode": "chat", - "supports_prompt_caching": True, - "max_tokens": 128000, - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - model_info=model_info, - ) - - assert entry["mode"] == "chat" - assert entry["supports_prompt_caching"] is True - assert entry["max_tokens"] == 128000 - - # Verify setdefault behavior: if a model_info key already exists in - # the entry (e.g. from a future CustomPricingLiteLLMParams addition), - # setdefault must not overwrite it. - entry["mode"] = "embedding" # simulate pre-existing value - # Re-apply setdefault the same way _build_custom_pricing_entry does - entry.setdefault("mode", model_info["mode"]) - assert entry["mode"] == "embedding" # must NOT revert to "chat" - - -def test_build_custom_pricing_entry_skips_none_values(): - """Fields with None values in kwargs should not be included.""" - kwargs = { - "input_cost_per_token": 0.001, - "output_cost_per_token": None, # explicitly None - "cache_read_input_token_cost": None, - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - ) - - assert entry["input_cost_per_token"] == 0.001 - assert "output_cost_per_token" not in entry - assert "cache_read_input_token_cost" not in entry - - -def test_build_custom_pricing_entry_handles_no_model_info(): - """Should work correctly when model_info is None.""" - kwargs = { - "input_cost_per_token": 0.001, - "output_cost_per_token": 0.002, - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - model_info=None, - ) - - assert entry["litellm_provider"] == "openai" - assert entry["input_cost_per_token"] == 0.001 - assert entry["output_cost_per_token"] == 0.002 - assert "mode" not in entry - - def test_register_model_receives_cache_pricing_fields(): """End-to-end: when register_model is called with a full pricing entry, the cache pricing fields should be present in litellm.model_cost.""" @@ -192,23 +73,6 @@ def test_register_model_receives_cache_pricing_fields(): litellm.model_cost.pop(model_key, None) -def test_build_custom_pricing_entry_time_based(): - """Time-based pricing fields should be included correctly.""" - kwargs = { - "input_cost_per_second": 0.01, - "output_cost_per_second": 0.02, - } - - entry = _build_custom_pricing_entry( - custom_llm_provider="openai", - kwargs=kwargs, - ) - - assert entry["litellm_provider"] == "openai" - assert entry["input_cost_per_second"] == 0.01 - assert entry["output_cost_per_second"] == 0.02 - - def test_register_model_strips_none_litellm_provider(): """``get_model_info`` returns ``litellm_provider: None`` for deployments registered without a provider (e.g. ``Router.add_deployment`` flows). @@ -236,10 +100,7 @@ def test_register_model_strips_none_litellm_provider(): registered = litellm.model_cost.get(model_key) assert registered is not None, f"{model_key} should be in model_cost" # The key may be absent entirely, but if present it must not be None. - assert ( - "litellm_provider" not in registered - or registered["litellm_provider"] is not None - ) + assert "litellm_provider" not in registered or registered["litellm_provider"] is not None # Downstream consumers must accept this entry for any provider, # mirroring what the cost calculator does. assert _check_provider_match(registered, "openai") is True @@ -297,9 +158,7 @@ def test_register_model_strips_none_litellm_provider_from_get_model_info(monkeyp # The strip must have removed the None-valued provider that # ``get_model_info`` returned. The key may be absent entirely, but # it must never be present with value ``None``. - assert "litellm_provider" not in registered or ( - registered["litellm_provider"] is not None - ), ( + assert "litellm_provider" not in registered or (registered["litellm_provider"] is not None), ( "register_model failed to strip litellm_provider=None returned " f"by get_model_info, got {registered.get('litellm_provider')!r}" ) @@ -360,14 +219,8 @@ def test_register_model_inherits_builtin_cache_pricing_for_unmapped_key(monkeypa ) registered = litellm.model_cost[registered_key] - assert ( - registered.get("cache_creation_input_token_cost") - == builtin["cache_creation_input_token_cost"] - ) - assert ( - registered.get("cache_read_input_token_cost") - == builtin["cache_read_input_token_cost"] - ) + assert registered.get("cache_creation_input_token_cost") == builtin["cache_creation_input_token_cost"] + assert registered.get("cache_read_input_token_cost") == builtin["cache_read_input_token_cost"] assert registered["litellm_provider"] == "bedrock" usage = Usage( @@ -431,8 +284,7 @@ def test_register_model_warns_when_no_builtin_match_for_cache_pricing(caplog): ) assert any( - registered_key in record.message - and "cache_creation_input_token_cost" in record.message + registered_key in record.message and "cache_creation_input_token_cost" in record.message for record in caplog.records ), "expected a warning naming the unmapped key and the cache cost fields" finally: @@ -619,17 +471,14 @@ def test_register_model_router_add_deployment_custom_pricing_applies(): # the deployment's provider. from litellm.utils import _check_provider_match - registered_keys = [ - k for k in (deployment_model, model_key) if k in litellm.model_cost - ] + registered_keys = [k for k in (deployment_model, model_key) if k in litellm.model_cost] assert registered_keys, ( - "Router.add_deployment did not register custom pricing for " - f"{model_key} / {deployment_model}" + f"Router.add_deployment did not register custom pricing for {model_key} / {deployment_model}" ) for k in registered_keys: - assert ( - _check_provider_match(litellm.model_cost[k], "openai") is True - ), f"custom pricing for {k} was dropped by _check_provider_match" + assert _check_provider_match(litellm.model_cost[k], "openai") is True, ( + f"custom pricing for {k} was dropped by _check_provider_match" + ) finally: litellm.model_cost.pop(model_key, None) litellm.model_cost.pop(deployment_model, None) @@ -646,12 +495,8 @@ def test_embedding_router_zero_pricing_does_not_clobber_builtin_pricing(): """ shared_key = "openai/text-embedding-3-small" deployment_id = "lit3991-wildcard-embed-zero" - snapshot = _snapshot_model_cost_entries( - [shared_key, "text-embedding-3-small", deployment_id] - ) - builtin_input_cost = litellm.get_model_info(model=shared_key)[ - "input_cost_per_token" - ] + snapshot = _snapshot_model_cost_entries([shared_key, "text-embedding-3-small", deployment_id]) + builtin_input_cost = litellm.get_model_info(model=shared_key)["input_cost_per_token"] assert builtin_input_cost > 0 try: @@ -666,12 +511,10 @@ def test_embedding_router_zero_pricing_does_not_clobber_builtin_pricing(): mock_response=[0.1, 0.2], ) - assert ( - litellm.get_model_info(model=shared_key)["input_cost_per_token"] - == builtin_input_cost - ), "wildcard deployment's zero pricing leaked into the shared model_cost key" - assert litellm.model_cost[deployment_id]["input_cost_per_token"] == 0.0 - assert litellm.model_cost[deployment_id]["output_cost_per_token"] == 0.0 + assert litellm.get_model_info(model=shared_key)["input_cost_per_token"] == builtin_input_cost, ( + "wildcard deployment's zero pricing leaked into the shared model_cost key" + ) + assert deployment_id not in litellm.model_cost sibling_response = litellm.embedding( model=shared_key, @@ -679,31 +522,33 @@ def test_embedding_router_zero_pricing_does_not_clobber_builtin_pricing(): api_key="fake-key", mock_response=[0.1, 0.2], ) - sibling_cost = litellm.completion_cost( - completion_response=sibling_response, call_type="embedding" - ) + sibling_cost = litellm.completion_cost(completion_response=sibling_response, call_type="embedding") assert sibling_cost == pytest.approx(10 * builtin_input_cost) finally: _restore_model_cost_entries(snapshot) def test_embedding_router_custom_pricing_costs_request_via_deployment_id(): - """The request that carries custom pricing must still be costed with that - pricing (via its deployment id entry), while the shared backend key keeps - the built-in rate for siblings. - """ + """A Router registers the deployment's pricing under its id when the deployment is added. The routed request + is costed from that entry, and the shared backend key keeps the built-in rate for siblings.""" shared_key = "openai/text-embedding-3-small" deployment_id = "lit3991-wildcard-embed-custom" override_input_cost = 5e-05 - snapshot = _snapshot_model_cost_entries( - [shared_key, "text-embedding-3-small", deployment_id] - ) - builtin_input_cost = litellm.get_model_info(model=shared_key)[ - "input_cost_per_token" - ] + snapshot = _snapshot_model_cost_entries([shared_key, "text-embedding-3-small", deployment_id]) + builtin_input_cost = litellm.get_model_info(model=shared_key)["input_cost_per_token"] assert builtin_input_cost != override_input_cost try: + litellm.register_model( + { + deployment_id: { + "input_cost_per_token": override_input_cost, + "output_cost_per_token": override_input_cost * 2, + "litellm_provider": "openai", + "mode": "embedding", + } + } + ) response = litellm.embedding( model=shared_key, input=["hello"], @@ -715,19 +560,10 @@ def test_embedding_router_custom_pricing_costs_request_via_deployment_id(): mock_response=[0.1, 0.2], ) - request_cost = litellm.completion_cost( - completion_response=response, - model=shared_key, - custom_llm_provider="openai", - call_type="embedding", - custom_pricing=True, - router_model_id=deployment_id, - ) - assert request_cost == pytest.approx(10 * override_input_cost) - assert ( - litellm.get_model_info(model=shared_key)["input_cost_per_token"] - == builtin_input_cost + assert response._hidden_params["response_cost"] == pytest.approx( + response.usage.prompt_tokens * override_input_cost ) + assert litellm.get_model_info(model=shared_key)["input_cost_per_token"] == builtin_input_cost finally: _restore_model_cost_entries(snapshot) @@ -736,12 +572,8 @@ def test_completion_router_zero_pricing_does_not_clobber_builtin_pricing(): """Same isolation as the embedding path, exercised through completion().""" shared_key = "openai/gpt-4o-mini" deployment_id = "lit3991-wildcard-chat-zero" - snapshot = _snapshot_model_cost_entries( - [shared_key, "gpt-4o-mini", deployment_id] - ) - builtin_input_cost = litellm.get_model_info(model=shared_key)[ - "input_cost_per_token" - ] + snapshot = _snapshot_model_cost_entries([shared_key, "gpt-4o-mini", deployment_id]) + builtin_input_cost = litellm.get_model_info(model=shared_key)["input_cost_per_token"] assert builtin_input_cost > 0 try: @@ -756,24 +588,21 @@ def test_completion_router_zero_pricing_does_not_clobber_builtin_pricing(): mock_response="hello back", ) - assert ( - litellm.get_model_info(model=shared_key)["input_cost_per_token"] - == builtin_input_cost - ), "wildcard deployment's zero pricing leaked into the shared model_cost key" - assert litellm.model_cost[deployment_id]["input_cost_per_token"] == 0.0 + assert litellm.get_model_info(model=shared_key)["input_cost_per_token"] == builtin_input_cost, ( + "wildcard deployment's zero pricing leaked into the shared model_cost key" + ) + assert deployment_id not in litellm.model_cost finally: _restore_model_cost_entries(snapshot) -def test_embedding_direct_sdk_custom_pricing_still_registers_shared_key(): - """Direct SDK calls (no router deployment id in metadata) keep the legacy - behavior: custom pricing is registered under ``{provider}/{model}`` and the - request is costed with it. - """ - model_key = "openai/lit3991-direct-sdk-embed-model" - override_input_cost = 3e-05 +def test_embedding_direct_sdk_custom_pricing_prices_the_call_without_writing_the_cost_map(): + """A direct SDK call with its own prices is costed with them and leaves ``litellm.model_cost`` alone, so the + next call for the same model name, and every other deployment of it, is priced from the cost map again.""" + model_key: Final = "openai/lit3991-direct-sdk-embed-model" + override_input_cost: Final = 3e-05 try: - response = litellm.embedding( + response: Final = litellm.embedding( model=model_key, input=["hello"], api_key="fake-key", @@ -782,63 +611,18 @@ def test_embedding_direct_sdk_custom_pricing_still_registers_shared_key(): mock_response=[0.1, 0.2], ) - assert ( - litellm.model_cost[model_key]["input_cost_per_token"] - == override_input_cost + assert model_key not in litellm.model_cost + assert response._hidden_params["response_cost"] == pytest.approx( + response.usage.prompt_tokens * override_input_cost ) - cost = litellm.completion_cost( - completion_response=response, - model=model_key, - custom_llm_provider="openai", - call_type="embedding", - custom_pricing=True, - ) - assert cost == pytest.approx(10 * override_input_cost) + + plain: Final = litellm.embedding(model=model_key, input=["hello"], api_key="fake-key", mock_response=[0.1, 0.2]) + assert plain._hidden_params["response_cost"] != response._hidden_params["response_cost"] finally: litellm.model_cost.pop(model_key, None) _invalidate_model_cost_lowercase_map() -@pytest.mark.parametrize("route", ["", "converse/", "invoke/"]) -def test_register_model_prices_a_geo_alias_from_its_builtin_when_the_lookup_fails( - monkeypatch: pytest.MonkeyPatch, route: str -) -> None: - from litellm.types.utils import ModelResponse, Usage - - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - _invalidate_model_cost_lowercase_map() - canonical_key: Final = "au.anthropic.claude-sonnet-5" - alias: Final = f"bedrock/{route}{canonical_key}" - canonical: Final = litellm.model_cost[canonical_key] - - def lookup_tripped_by_a_concurrent_registration(model: str, custom_llm_provider: str | None = None) -> NoReturn: - raise RuntimeError("dictionary changed size during iteration") - - with pytest.MonkeyPatch.context() as lookup_patch: - lookup_patch.setattr(litellm.utils, "get_model_info", lookup_tripped_by_a_concurrent_registration) - litellm.register_model( - {alias: {"litellm_provider": "bedrock", "mode": "chat", "supports_vision": True}}, - persist_across_reloads=False, - ) - - usage: Final = Usage(prompt_tokens=1000, completion_tokens=100, total_tokens=1100) - alias_cost: Final = litellm.completion_cost( - completion_response=ModelResponse(model=alias, usage=usage), model=alias, custom_llm_provider="bedrock" - ) - canonical_cost: Final = litellm.completion_cost( - completion_response=ModelResponse(model=canonical_key, usage=usage), - model=canonical_key, - custom_llm_provider="bedrock", - ) - try: - assert alias_cost == canonical_cost > 0 - assert litellm.get_model_info(alias)["input_cost_per_token"] == canonical["input_cost_per_token"] - assert litellm.get_model_info(alias)["supports_vision"] is True - finally: - _invalidate_model_cost_lowercase_map() - - def test_register_model_keeps_a_catalog_rows_own_prices_when_the_lookup_fails(monkeypatch: pytest.MonkeyPatch) -> None: geo_key: Final = "au.anthropic.lit-5853-geo-model" geo_row: Final = { @@ -1407,10 +1191,10 @@ def test_update_dictionary_merges_nested_dicts_without_aliasing(): object stays untouched, and the caller's incoming nested dict is never inserted by reference into the merged result. """ - from litellm.utils import update_dictionary - from typing import cast + from litellm.utils import update_dictionary + existing_nested = cast(dict[str, object], {"hours_utc": "01:00-02:00", 1: "existing"}) existing = {"off_peak_pricing": existing_nested} incoming_nested = cast( @@ -1609,12 +1393,12 @@ def test_completion_cost_applies_off_peak_only_deployment_pricing(): del router -def test_completion_registers_cost_per_second_pricing(): +def test_completion_prices_cost_per_second_from_the_call_without_writing_the_cost_map(): model_key: Final = "openai/test-cost-per-second-registration" original_entries: Final = _snapshot_model_cost_entries([model_key]) try: - litellm.completion( + response: Final = litellm.completion( model=model_key, messages=[{"role": "user", "content": "hello"}], api_key="fake-key", @@ -1622,7 +1406,11 @@ def test_completion_registers_cost_per_second_pricing(): mock_response="hello back", ) - assert litellm.model_cost[model_key]["cost_per_second"] == 0.02 + assert model_key not in litellm.model_cost + assert response._hidden_params["response_cost"] > 0 + assert litellm.completion_cost(completion_response=response, custom_cost_per_second=0.02) == pytest.approx( + 0.02 * response._response_ms / 1000 + ) finally: _restore_model_cost_entries(original_entries) @@ -1676,31 +1464,238 @@ def test_register_model_scopes_builtin_match_to_the_given_provider(): _restore_model_cost_entries(model_cost_entries) -def test_per_request_custom_pricing_scopes_a_colliding_deployment_id_to_its_provider(): - from litellm.main import _register_custom_pricing_for_request +def _audio_reply() -> ModelResponse: + usage: Final = Usage( + prompt_tokens=1000, + completion_tokens=1000, + total_tokens=2000, + prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=1000, text_tokens=0), + completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=1000, text_tokens=0), + ) + return ModelResponse(model="gpt-audio-2025-08-28", choices=[Choices(message=Message(content="hi"))], usage=usage) - deployment_id: Final = "baseten/zai-org/glm-5.2" - builtin_key: Final = "baseten/zai-org/GLM-5.2" - shared_key: Final = "openai/zai-org/GLM-5.2" - model_cost_entries: Final = _snapshot_model_cost_entries((deployment_id, builtin_key, shared_key)) - builtin_row_before: Final = copy.deepcopy(litellm.model_cost[builtin_key]) - openai_models_before: Final = frozenset(litellm.open_ai_chat_completion_models) + +_AUDIO_RATES: Final = { + "input_cost_per_token": 2.5e-06, + "output_cost_per_token": 1e-05, + "input_cost_per_audio_token": 4e-05, + "output_cost_per_audio_token": 8e-05, +} + + +def test_router_call_with_audio_rates_is_priced_per_modality_from_its_registered_deployment(): + """A Router registers the deployment's full custom pricing when the deployment is added. The per-call flat + prices must not override that entry, or 1000 audio input plus 1000 audio output tokens would bill at the text + rates ($0.0125) instead of the audio rates ($0.12).""" + deployment_id: Final = "audio-rates-deployment" + snapshot: Final = _snapshot_model_cost_entries([deployment_id]) try: - _register_custom_pricing_for_request( - model="zai-org/GLM-5.2", - custom_llm_provider="openai", - kwargs={ - "input_cost_per_token": 0.00000096, - "output_cost_per_token": 0.00000302, - "metadata": {"model_info": {"id": deployment_id}}, - }, - model_info={"mode": "chat"}, + litellm.register_model({deployment_id: {**_AUDIO_RATES, "litellm_provider": "openai", "mode": "chat"}}) + response: Final = litellm.completion( + model="openai/gpt-audio-2025-08-28", + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + metadata={"model_info": {"id": deployment_id}}, + mock_response=_audio_reply(), + **_AUDIO_RATES, ) - - entry: Final = litellm.model_cost[deployment_id] - assert entry["input_cost_per_token"] == 0.00000096 - assert entry["output_cost_per_token"] == 0.00000302 - assert litellm.model_cost[builtin_key] == builtin_row_before + assert response._hidden_params["response_cost"] == pytest.approx(1000 * 4e-05 + 1000 * 8e-05) finally: - _restore_model_cost_entries(model_cost_entries) - litellm.open_ai_chat_completion_models.intersection_update(openai_models_before) + _restore_model_cost_entries(snapshot) + + +def test_direct_call_with_flat_rates_is_priced_from_them_without_writing_the_cost_map(): + model_key: Final = "openai/lit-direct-flat-rates-model" + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response=_audio_reply(), + input_cost_per_token=2.5e-06, + output_cost_per_token=1e-05, + ) + assert response._hidden_params["response_cost"] == pytest.approx(1000 * 2.5e-06 + 1000 * 1e-05) + assert model_key not in litellm.model_cost + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() + + +def test_direct_call_with_audio_rates_is_priced_per_modality_without_writing_the_cost_map(): + model_key: Final = "openai/lit-direct-audio-rates-model" + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response=_audio_reply(), + input_cost_per_token=2.5e-06, + output_cost_per_token=1e-05, + input_cost_per_audio_token=4e-05, + output_cost_per_audio_token=8e-05, + ) + assert response._hidden_params["response_cost"] == pytest.approx(1000 * 4e-05 + 1000 * 8e-05) + assert model_key not in litellm.model_cost + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() + + +def test_direct_call_with_directional_per_second_rates_is_priced_from_them_without_writing_the_cost_map(): + model_key: Final = "openai/lit-direct-per-second-model" + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response="ok", + input_cost_per_second=0.004, + ) + assert litellm.completion_cost(completion_response=response, custom_cost_per_second=0.004) == pytest.approx( + 0.004 * response._response_ms / 1000 + ) + assert response._hidden_params["response_cost"] > 0 + assert model_key not in litellm.model_cost + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() + + +def test_router_call_with_request_prices_bills_them_over_the_deployment_rates(): + from litellm import Router + + router: Final = Router( + model_list=[ + { + "model_name": "lit-request-override", + "litellm_params": { + "model": "openai/gpt-5.4-mini", + "api_key": "fake-key", + "mock_response": "ok", + "input_cost_per_token": 1e-06, + "output_cost_per_token": 2e-06, + "input_cost_per_audio_token": 4e-05, + "output_cost_per_audio_token": 8e-05, + }, + } + ] + ) + overridden: Final = router.completion( + model="lit-request-override", + messages=[{"role": "user", "content": "hello"}], + input_cost_per_token=5e-05, + output_cost_per_token=9e-05, + ) + assert overridden._hidden_params["response_cost"] == pytest.approx( + overridden.usage.prompt_tokens * 5e-05 + overridden.usage.completion_tokens * 9e-05 + ) + plain: Final = router.completion(model="lit-request-override", messages=[{"role": "user", "content": "hello"}]) + assert plain._hidden_params["response_cost"] == pytest.approx( + plain.usage.prompt_tokens * 1e-06 + plain.usage.completion_tokens * 2e-06 + ) + + +def test_direct_call_flat_rates_override_the_catalogs_tiers_per_direction(): + model_key: Final = "openai/lit-tiered-catalog-model" + litellm.register_model( + { + model_key: { + "litellm_provider": "openai", + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 128000], "input_cost_per_token": 3e-07, "output_cost_per_token": 6e-07}, + {"range": [128000, 1000000], "input_cost_per_token": 6e-07, "output_cost_per_token": 1.2e-06}, + ], + } + }, + persist_across_reloads=False, + ) + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response="ok", + input_cost_per_token=1e-05, + output_cost_per_token=2e-05, + ) + assert response._hidden_params["response_cost"] == pytest.approx( + response.usage.prompt_tokens * 1e-05 + response.usage.completion_tokens * 2e-05 + ) + input_only: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response="ok", + input_cost_per_token=1e-05, + ) + assert input_only._hidden_params["response_cost"] == pytest.approx( + input_only.usage.prompt_tokens * 1e-05 + input_only.usage.completion_tokens * 6e-07 + ) + assert "input_cost_per_token" not in litellm.model_cost[model_key] + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() + + +def test_direct_call_with_only_a_per_second_rate_on_a_mapped_model_is_billed_per_second(): + model_key: Final = "openai/lit-mapped-token-rates-model" + litellm.register_model( + { + model_key: { + "litellm_provider": "openai", + "mode": "chat", + "input_cost_per_token": 1e-06, + "output_cost_per_token": 2e-06, + } + }, + persist_across_reloads=False, + ) + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response="ok", + cost_per_second=0.02, + ) + token_rate_cost: Final = response.usage.prompt_tokens * 1e-06 + response.usage.completion_tokens * 2e-06 + assert response._hidden_params["response_cost"] != pytest.approx(token_rate_cost) + assert response._hidden_params["response_cost"] == pytest.approx(0.02 * response._response_ms / 1000, rel=0.25) + assert litellm.model_cost[model_key]["input_cost_per_token"] == 1e-06 + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() + + +def test_direct_call_flat_rate_applies_to_the_callers_own_tiers_not_the_catalogs(): + model_key: Final = "openai/lit-caller-tiers-model" + litellm.register_model( + { + model_key: { + "litellm_provider": "openai", + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 1000000], "input_cost_per_token": 3e-07, "output_cost_per_token": 6e-07} + ], + } + }, + persist_across_reloads=False, + ) + try: + response: Final = litellm.completion( + model=model_key, + messages=[{"role": "user", "content": "hello"}], + api_key="fake-key", + mock_response="ok", + input_cost_per_token=1e-05, + tiered_pricing=[{"range": [0, 1000000], "input_cost_per_token": 5e-06, "output_cost_per_token": 9e-06}], + ) + assert response._hidden_params["response_cost"] == pytest.approx( + response.usage.prompt_tokens * 1e-05 + response.usage.completion_tokens * 9e-06 + ) + assert litellm.model_cost[model_key]["tiered_pricing"][0]["output_cost_per_token"] == 6e-07 + finally: + litellm.model_cost.pop(model_key, None) + _invalidate_model_cost_lowercase_map() diff --git a/tests/unit/test_router/test_io_token_rate_limits.py b/tests/unit/test_router/test_io_token_rate_limits.py index 819c6b9d4f5..7c2658d3d0d 100644 --- a/tests/unit/test_router/test_io_token_rate_limits.py +++ b/tests/unit/test_router/test_io_token_rate_limits.py @@ -509,6 +509,36 @@ class TestModelRateLimitingCheckIOTokens: assert request_kwargs["metadata"][OTPM_RESERVED_KEY] == model_max_output + @pytest.mark.asyncio + async def test_otpm_reservation_uses_each_deployments_output_limit(self) -> None: + backend_model: Final = "openai/gpt-4o-mini" + catalog_max_output: Final = litellm.get_model_info(model=backend_model)["max_output_tokens"] + assert isinstance(catalog_max_output, int) + otpm_limit: Final = int(max(catalog_max_output, 987) / NO_MAX_TOKENS_TPM_RESERVATION_FRACTION) + 1 + check: Final = ModelRateLimitingCheck(dual_cache=DualCache()) + + async def _reserved(deployment_id: str, model_info: dict[str, object]) -> int: + metadata: Final[dict[str, object]] = {} + request_kwargs: Final[dict[str, object]] = { + "messages": [{"role": "user", "content": "hello"}], + "metadata": metadata, + } + set_io_token_rate_limit_request_kwargs(request_kwargs, metadata_channel="metadata") + deployment: Final = { + "litellm_params": {"model": backend_model, "otpm": otpm_limit}, + "model_info": {"id": deployment_id, **model_info}, + } + await check.async_pre_call_check(deployment) + reservation: Final = metadata.get(OTPM_RESERVED_KEY) + assert isinstance(reservation, int) + return reservation + + plain: Final = await _reserved("io-plain-gpt-4o-mini", {}) + override: Final = await _reserved("io-overridden-gpt-4o-mini", {"max_output_tokens": 987}) + + assert plain == catalog_max_output + assert override == 987 + @pytest.mark.asyncio @pytest.mark.parametrize("use_sync_check", [False, True]) async def test_base_model_does_not_change_the_default_otpm_reservation(self, use_sync_check: bool): diff --git a/tests/unit/test_router/test_router.py b/tests/unit/test_router/test_router.py index 72f2351e54a..c9d8a291ae0 100644 --- a/tests/unit/test_router/test_router.py +++ b/tests/unit/test_router/test_router.py @@ -5,6 +5,7 @@ import functools import gc import itertools import io +import base64 import json import linecache import logging @@ -12,7 +13,7 @@ import os import sys import threading import warnings -from collections.abc import AsyncIterable, AsyncIterator, Awaitable, Callable, Iterator, Mapping +from collections.abc import AsyncIterable, AsyncIterator, Awaitable, Callable, Iterator, Mapping, Sequence from datetime import datetime, timedelta, timezone from pathlib import Path from types import CodeType, FrameType, MappingProxyType, SimpleNamespace @@ -42,6 +43,7 @@ from litellm.integrations.otel.plumbing.context import set_request_root_span from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.llms.anthropic.messages.native.streaming_iterator import closing_superseded_attempt from litellm.llms.anthropic.messages.native.agentic_streaming_iterator import ( + AGENTIC_LOOP_LIMIT_ERROR_SSE_BYTES, SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES, ) from litellm.llms.bedrock.common_utils import BedrockError @@ -81,6 +83,7 @@ from litellm.router_utils.fallback_event_handlers import ( from litellm.router_utils.prompt_caching_cache import PromptCachingCache from litellm.router_utils.router_callbacks.track_deployment_metrics import get_deployment_successes_for_current_minute from litellm.scheduler import FlowItem +from litellm.types.integrations.custom_logger import AgenticLoopPlan, AgenticLoopRequestPatch from litellm.types.llms.openai import AllMessageValues, ChatCompletionRequest from litellm.router_api import RoutingContext as RouterContext from litellm.types.router import ( @@ -3651,14 +3654,28 @@ class _CutSSE(httpx.AsyncByteStream): raise httpx.RemoteProtocolError("peer closed connection without sending complete message body") -_BACKUP_SSE: Final = "".join( - f"data: {json.dumps(chunk)}\n\n" - for chunk in ( - {"id": "b", "object": "chat.completion.chunk", "created": 1700000000, "model": "mistral-large-latest", - "choices": [{"index": 0, "delta": {"role": "assistant", "content": " self-attention."}, "finish_reason": None}]}, - {"id": "b", "object": "chat.completion.chunk", "created": 1700000000, "model": "mistral-large-latest", - "choices": [{"index": 0, "delta": {"content": ""}, "finish_reason": "stop"}], - "usage": {"prompt_tokens": 91, "completion_tokens": 5, "total_tokens": 96}}, +_BACKUP_SSE: Final = ( + "".join( + f"data: {json.dumps(chunk)}\n\n" + for chunk in ( + { + "id": "b", + "object": "chat.completion.chunk", + "created": 1700000000, + "model": "mistral-large-latest", + "choices": [ + {"index": 0, "delta": {"role": "assistant", "content": " self-attention."}, "finish_reason": None} + ], + }, + { + "id": "b", + "object": "chat.completion.chunk", + "created": 1700000000, + "model": "mistral-large-latest", + "choices": [{"index": 0, "delta": {"content": ""}, "finish_reason": "stop"}], + "usage": {"prompt_tokens": 91, "completion_tokens": 5, "total_tokens": 96}, + }, + ) ) ) + "data: [DONE]\n\n" @@ -3844,6 +3861,255 @@ async def test_continued_stream_sharing_the_proxy_logging_object_bills_the_cut_t ) +_AGENTIC_FIRST_CALL_USAGE: Final = {"prompt_tokens": 50, "completion_tokens": 7, "total_tokens": 57} +_AGENTIC_FIRST_CALL_BODY: Final = { + "id": "chatcmpl-first", + "object": "chat.completion", + "created": 1700000000, + "model": "gpt-4o-mini", + "choices": [ + { + "index": 0, + "finish_reason": "tool_calls", + "message": { + "role": "assistant", + "content": None, + "tool_calls": [ + {"id": "call_abc", "type": "function", "function": {"name": "litellm_code_execution", "arguments": "{}"}} + ], + }, + } + ], + "usage": _AGENTIC_FIRST_CALL_USAGE, +} + + +_CRASH_THE_PLAN: Final = "crash the plan" + + +class _ScriptedAgenticFollowUps(CustomLogger): + def __init__(self, follow_ups: tuple[str, ...]) -> None: + super().__init__() + self._follow_ups: Final = follow_ups + self.plans_built = 0 + self.shared_call_succeeded: Final = asyncio.Event() + + async def async_should_run_agentic_loop( + self, + response: object, + model: str, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, + stream: bool, + custom_llm_provider: str, + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: + assert isinstance(response, litellm.ModelResponse) + return bool(response.choices[0].message.tool_calls), {"tool_calls": [{"id": "call_abc"}]} + + async def async_build_agentic_loop_plan( + self, + tools: Mapping[str, object], + model: str, + messages: Sequence[Mapping[str, object]], + response: object, + anthropic_messages_provider_config: object, + anthropic_messages_optional_request_params: Mapping[str, object], + logging_obj: object, + stream: bool, + kwargs: Mapping[str, object], + ) -> AgenticLoopPlan: + follow_up: Final = self._follow_ups[min(self.plans_built, len(self._follow_ups) - 1)] + self.plans_built += 1 + if follow_up == _CRASH_THE_PLAN: + raise ValueError("the agentic plan build crashed") + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + messages=[{"role": "user", "content": "what is 6*7?"}, {"role": "assistant", "content": "42"}], + kwargs={"mock_response": follow_up}, + ), + ) + + async def async_log_success_event( + self, kwargs: Mapping[str, object], response_obj: object, start_time: datetime, end_time: datetime + ) -> None: + if kwargs.get("litellm_call_id") == "shared-agentic-call": + self.shared_call_succeeded.set() + + +_AGENTIC_BACKUP_USAGE: Final = {"prompt_tokens": 20, "completion_tokens": 3, "total_tokens": 23} +_AGENTIC_BACKUP_BODY: Final = { + "id": "chatcmpl-backup", + "object": "chat.completion", + "created": 1700000000, + "model": "gpt-4o-mini", + "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "42"}}], + "usage": _AGENTIC_BACKUP_USAGE, +} +_AGENTIC_RATE_LIMITED: Final = { + "error": {"message": "Rate limit reached", "type": "requests", "code": "rate_limit_exceeded"} +} + + +async def _route_agentic_request( + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, + follow_ups: tuple[str, ...], + model: str = "m", + first_call_replies: tuple[httpx.Response, ...] = (), + fallbacks: tuple[str, ...] = (), +) -> tuple[object, LiteLLMLogging, respx.Route, _ScriptedAgenticFollowUps]: + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + hook: Final = _ScriptedAgenticFollowUps(follow_ups) + monkeypatch.setattr(litellm, "callbacks", [hook]) + first_calls: Final = ( + respx_mock.post("https://api.openai.com/v1/chat/completions").mock(side_effect=list(first_call_replies)) + if first_call_replies + else respx_mock.post("https://api.openai.com/v1/chat/completions").mock( + return_value=httpx.Response(200, json=_AGENTIC_FIRST_CALL_BODY) + ) + ) + respx_mock.post("https://backup.test/v1/chat/completions").mock( + return_value=httpx.Response(200, json=_AGENTIC_BACKUP_BODY) + ) + router: Final = Router( + model_list=[ + {"model_name": "m", "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "sk-test"}}, + { + "model_name": "backup", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-test", + "api_base": "https://backup.test/v1", + }, + }, + ], + num_retries=2, + retry_after=0, + fallbacks=[{"m": list(fallbacks)}] if fallbacks else [], + ) + messages: Final = [{"role": "user", "content": "what is 6*7?"}] + shared_logging, _ = litellm.utils.function_setup( + original_function="acompletion", + rules_obj=litellm.utils.Rules(), + start_time=datetime.now(), + model=model, + messages=messages, + litellm_call_id="shared-agentic-call", + ) + try: + outcome: object = await router.acompletion( + model=model, messages=messages, litellm_call_id="shared-agentic-call", litellm_logging_obj=shared_logging + ) + except (litellm.RateLimitError, litellm.APIConnectionError) as error: + outcome = error + return outcome, shared_logging, first_calls, hook + + +def _gpt_4o_mini_cost(usage: Mapping[str, int]) -> float: + return sum( + litellm.cost_per_token( + model="gpt-4o-mini", prompt_tokens=usage["prompt_tokens"], completion_tokens=usage["completion_tokens"] + ) + ) + + +@pytest.mark.asyncio +async def test_agentic_request_whose_follow_up_fails_is_not_retried_and_bills_its_one_first_call( + monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter +) -> None: + from litellm.proxy.utils import _failure_fields_to_lift + + outcome, shared_logging, first_calls, _ = await _route_agentic_request( + monkeypatch, respx_mock, ("litellm.RateLimitError",) + ) + + lifted: Final = _failure_fields_to_lift({"litellm_logging_obj": shared_logging}) + assert isinstance(outcome, litellm.RateLimitError) + assert outcome.status_code == 429 + assert first_calls.call_count == 1 + assert lifted["combined_usage_object"].prompt_tokens == _AGENTIC_FIRST_CALL_USAGE["prompt_tokens"] + assert lifted["combined_usage_object"].completion_tokens == _AGENTIC_FIRST_CALL_USAGE["completion_tokens"] + assert lifted["response_cost"] == pytest.approx(_gpt_4o_mini_cost(_AGENTIC_FIRST_CALL_USAGE)) + + +@pytest.mark.asyncio +async def test_agentic_request_whose_plan_build_crashes_after_the_first_call_is_not_retried( + monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter +) -> None: + outcome, _, first_calls, hook = await _route_agentic_request(monkeypatch, respx_mock, (_CRASH_THE_PLAN,)) + + assert isinstance(outcome, litellm.APIConnectionError) + assert first_calls.call_count == 1 + assert hook.plans_built == 1 + + +@pytest.mark.asyncio +async def test_agentic_request_whose_first_call_fails_is_still_retried( + monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter +) -> None: + outcome, _, first_calls, hook = await _route_agentic_request( + monkeypatch, + respx_mock, + ("42",), + first_call_replies=( + httpx.Response(429, json=_AGENTIC_RATE_LIMITED), + httpx.Response(200, json=_AGENTIC_FIRST_CALL_BODY), + ), + ) + + assert isinstance(outcome, litellm.ModelResponse) + assert first_calls.call_count == 2 + assert hook.plans_built == 1 + + +async def _successful_agentic_request_billing( + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, + follow_ups: tuple[str, ...], + model: str, + fallbacks: tuple[str, ...] = (), +) -> tuple[Mapping[str, object], Mapping[str, str | None]]: + from litellm.proxy.common_request_processing import cost_headers + + outcome, shared_logging, _, hook = await _route_agentic_request( + monkeypatch, respx_mock, follow_ups, model=model, fallbacks=fallbacks + ) + await asyncio.wait_for(hook.shared_call_succeeded.wait(), timeout=10) + assert isinstance(outcome, litellm.ModelResponse) + success: Final = shared_logging.model_call_details["standard_logging_object"] + return success, cost_headers(success["response_cost"], shared_logging) + + +@pytest.mark.asyncio +async def test_fallback_after_a_failed_agentic_follow_up_bills_the_primarys_first_call_on_the_success_row( + monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter +) -> None: + first_try, first_try_headers = await _successful_agentic_request_billing( + monkeypatch, respx_mock, ("42",), model="backup" + ) + recovered, recovered_headers = await _successful_agentic_request_billing( + monkeypatch, respx_mock, ("litellm.RateLimitError",), model="m", fallbacks=("backup",) + ) + + first_call_cost: Final = _gpt_4o_mini_cost(_AGENTIC_FIRST_CALL_USAGE) + assert first_try["response_cost"] == pytest.approx(_gpt_4o_mini_cost(_AGENTIC_BACKUP_USAGE)) + assert "failed_attempts_cost" not in first_try["cost_breakdown"].get("additional_costs", {}) + assert recovered["response_cost"] == pytest.approx(first_try["response_cost"] + first_call_cost) + assert recovered["prompt_tokens"] == first_try["prompt_tokens"] + _AGENTIC_FIRST_CALL_USAGE["prompt_tokens"] + assert ( + recovered["completion_tokens"] + == first_try["completion_tokens"] + _AGENTIC_FIRST_CALL_USAGE["completion_tokens"] + ) + assert recovered["cost_breakdown"]["total_cost"] == pytest.approx(recovered["response_cost"]) + assert recovered["cost_breakdown"]["additional_costs"]["failed_attempts_cost"] == pytest.approx(first_call_cost) + assert float(recovered_headers["x-litellm-response-cost"]) == pytest.approx(recovered["response_cost"]) + assert {key: value for key, value in recovered_headers.items() if key != "x-litellm-response-cost"} == { + key: value for key, value in first_try_headers.items() if key != "x-litellm-response-cost" + } + + def _responses_sse(*events: Mapping[str, object]) -> str: return "".join(f"event: {event['type']}\ndata: {json.dumps(event)}\n\n" for event in events) @@ -8174,6 +8440,77 @@ def test_pre_call_checks_count_a_group_mixing_in_another_vendor_by_its_own_name( ) +_EMBEDDING_CONTEXT_WINDOW: Final = 8_192 +_EMBEDDING_IMAGE_INPUT: Final = f"data:image/png;base64,{base64.b64encode(bytes(range(256)) * 1024).decode()}" + + +def _embedding_context_router(backend: str, window: int = _EMBEDDING_CONTEXT_WINDOW) -> litellm.Router: + return litellm.Router( + model_list=[ + { + "model_name": "embedder", + "litellm_params": {"model": backend, "api_key": "sk-test"}, + "model_info": {"max_input_tokens": window}, + } + ], + enable_pre_call_checks=True, + ) + + +async def test_context_window_pre_call_check_counts_an_embedding_image_as_media_on_an_image_backend(): + router: Final = _embedding_context_router("vertex_ai/gemini-embedding-2") + deployments: Final = router.registry().deployments("embedder") + + kept: Final = router._pre_call_checks(model="embedder", healthy_deployments=deployments, input=_EMBEDDING_IMAGE_INPUT) + counted: Final = await router._acount_pre_call_check_tokens( + model="embedder", healthy_deployments=deployments, messages=None, input=_EMBEDDING_IMAGE_INPUT, request_kwargs=None + ) + + assert len(kept) == 1 + assert counted is not None and 0 < counted < _EMBEDDING_CONTEXT_WINDOW + with pytest.raises(litellm.ContextWindowExceededError): + router._pre_call_checks(model="embedder", healthy_deployments=deployments, input="word " * 20_000) + + +@pytest.mark.parametrize( + ("backend", "shape"), + [ + ("vertex_ai/gemini-embedding-2", "pdf"), + ("vertex_ai/gemini-embedding-2", "pdf_file_block"), + ("vertex_ai/gemini-embedding-2", "two_pdfs"), + ("bedrock/amazon.titan-embed-image-v1", "image"), + ("bedrock/amazon.titan-embed-image-v1", "two_images"), + ], +) +def test_context_window_pre_call_check_leaves_embedding_media_to_the_backend(backend: str, shape: str): + window: Final = litellm.get_model_info(backend)["max_input_tokens"] + assert window is not None + router: Final = _embedding_context_router(backend, window) + pdf: Final = f"data:application/pdf;base64,{base64.b64encode(bytes(window * 8)).decode()}" + inputs: Final = { + "pdf": pdf, + "pdf_file_block": [{"type": "file", "file": {"file_data": pdf}}], + "two_pdfs": [pdf, pdf], + "image": _EMBEDDING_IMAGE_INPUT, + "two_images": [_EMBEDDING_IMAGE_INPUT, _EMBEDDING_IMAGE_INPUT], + } + + kept: Final = router._pre_call_checks( + model="embedder", healthy_deployments=router.registry().deployments("embedder"), input=inputs[shape] + ) + + assert len(kept) == 1 + + +def test_context_window_pre_call_check_counts_an_embedding_image_as_text_on_a_text_only_backend(): + router: Final = _embedding_context_router("text-embedding-3-small") + + with pytest.raises(litellm.ContextWindowExceededError): + router._pre_call_checks( + model="embedder", healthy_deployments=router.registry().deployments("embedder"), input=_EMBEDDING_IMAGE_INPUT + ) + + async def test_acount_pre_call_check_tokens_counts_a_group_of_one_openai_model_with_that_models_tokenizer(): router: Final = _context_limited_router("openai/gpt-6.1-sol") @@ -13866,7 +14203,11 @@ def test_get_search_tool_support_reports_each_routable_deployment_of_the_group() @pytest.mark.parametrize("provider", ["chatgpt", "github_copilot"]) @pytest.mark.parametrize("explicit_provider", [False, True]) async def test_search_tool_catalog_lookup_needs_no_credentials( - provider: str, explicit_provider: bool, tmp_path: Path, monkeypatch: pytest.MonkeyPatch, respx_mock: respx.MockRouter + provider: str, + explicit_provider: bool, + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, + respx_mock: respx.MockRouter, ) -> None: monkeypatch.setenv("CHATGPT_TOKEN_DIR", str(tmp_path / "chatgpt")) monkeypatch.setenv("GITHUB_COPILOT_TOKEN_DIR", str(tmp_path / "copilot")) @@ -17534,13 +17875,16 @@ def test_model_group_info_fast_mode_fails_closed_without_explicit_boolean( def test_model_group_info_fast_mode_requires_every_deployment( local_model_cost_map: None, other_model: str, expected: bool, reverse: bool ) -> None: - models: Final = (other_model, "anthropic/claude-opus-5") if reverse else ( - "anthropic/claude-opus-5", other_model + models: Final = (other_model, "anthropic/claude-opus-5") if reverse else ("anthropic/claude-opus-5", other_model) + router: Final = Router( + model_list=[ + { + "model_name": "fast-group", + "litellm_params": {"model": model, "api_key": "fake-key"}, + } + for model in models + ] ) - router: Final = Router(model_list=[{ - "model_name": "fast-group", - "litellm_params": {"model": model, "api_key": "fake-key"}, - } for model in models]) result: Final = router.get_model_group_info("fast-group") @@ -17898,10 +18242,7 @@ class _AnthropicMessagesFallbackByteStream: def _anthropic_messages_overloaded_error_chunk() -> bytes: - return ( - b"event: error\n" - b'data: {"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}}\n\n' - ) + return b'event: error\ndata: {"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}}\n\n' def _anthropic_messages_invalid_request_error_chunk() -> bytes: @@ -18717,6 +19058,7 @@ def test_anthropic_stream_forwards_ping_live_direct_call(): def test_anthropic_stream_error_is_gateway_verdict_direct_call(): assert _anthropic_stream_error_is_gateway_verdict(SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES) is True + assert _anthropic_stream_error_is_gateway_verdict(AGENTIC_LOOP_LIMIT_ERROR_SSE_BYTES) is True assert _anthropic_stream_error_is_gateway_verdict(_anthropic_messages_overloaded_error_chunk()) is False assert _anthropic_stream_error_is_gateway_verdict(_anthropic_messages_ping_chunk()) is False @@ -24651,16 +24993,25 @@ class TestAutoRouterTraceProvenance: monkeypatch.setattr(litellm, "_async_success_callback", [logger, CallbackComplete()]) classifier: Final = ObservedClassifier() - router: Final = Router(model_list=[ - {**self._marker("blue"), "litellm_params": { - "model": "auto_router/complexity_router", - "complexity_router_config": { - "classifier_type": "custom", "classifier_plugin": classifier, - "tiers": {"SIMPLE": "answer"}, "session_affinity": False, "deployment_affinity": False, + router: Final = Router( + model_list=[ + { + **self._marker("blue"), + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": { + "classifier_type": "custom", + "classifier_plugin": classifier, + "tiers": {"SIMPLE": "answer"}, + "session_affinity": False, + "deployment_affinity": False, + }, + }, }, - }}, - {"model_name": "answer", "litellm_params": {"model": "openai/gpt-5.6-luna", "api_key": "test"}}, - ], plugins=[classifier]) + {"model_name": "answer", "litellm_params": {"model": "openai/gpt-5.6-luna", "api_key": "test"}}, + ], + plugins=[classifier], + ) stale: Final = {"routing_decision": {"router_model_name": "previous"}, "_autorouter_baseline_route": "old"} request: Final = {"metadata": {**stale}, "litellm_metadata": {**stale, "user_api_key_team_id": "team"}} with logger.tracer.start_as_current_span("request") as root: @@ -24745,20 +25096,22 @@ class TestAutoRouterTraceProvenance: self, _tracing: "tuple[OpenTelemetryV2, InMemorySpanExporter]" ) -> None: logger, exporter = _tracing - router: Final = Router(model_list=[ - self._marker("blue"), - { - "model_name": "traced-router", - "model_info": {"id": "definition-semantic"}, - "litellm_params": { - "model": "auto_router/semantic-router", - "tags": ["blue"], - "auto_router_config": json.dumps({"routes": [{"name": "answer", "utterances": ["hello"]}]}), - "auto_router_default_model": "answer", - "auto_router_embedding_model": "embed", + router: Final = Router( + model_list=[ + self._marker("blue"), + { + "model_name": "traced-router", + "model_info": {"id": "definition-semantic"}, + "litellm_params": { + "model": "auto_router/semantic-router", + "tags": ["blue"], + "auto_router_config": json.dumps({"routes": [{"name": "answer", "utterances": ["hello"]}]}), + "auto_router_default_model": "answer", + "auto_router_embedding_model": "embed", + }, }, - }, - ]) + ] + ) with logger.start_phase_span("route traced-router"): response: Final = await router.async_pre_routing_hook( model="traced-router", request_kwargs={"metadata": {"tags": ["blue"]}} @@ -24778,26 +25131,42 @@ class TestAutoRouterTraceProvenance: ) -> None: logger, exporter = _tracing monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - respx_mock.post("https://embedding.example/v1/embeddings").respond(200, json={ - "object": "list", - "model": "text-embedding-3-small", - "data": [{"object": "embedding", "index": 0, "embedding": [1.0, 0.0]}], - "usage": {"prompt_tokens": 1, "total_tokens": 1}, - }) - router: Final = Router(model_list=[ - { - "model_name": "internal-semantic-router", - "model_info": {"id": "semantic-definition", "team_id": "trace-team", "team_public_model_name": "public-router"}, - "litellm_params": { - "model": "auto_router/semantic-router", - "auto_router_config": json.dumps({"routes": [{"name": "answer", "utterances": ["hello"]}]}), - "auto_router_default_model": "answer", - "auto_router_embedding_model": "embed", - }, + respx_mock.post("https://embedding.example/v1/embeddings").respond( + 200, + json={ + "object": "list", + "model": "text-embedding-3-small", + "data": [{"object": "embedding", "index": 0, "embedding": [1.0, 0.0]}], + "usage": {"prompt_tokens": 1, "total_tokens": 1}, }, - {"model_name": "embed", "litellm_params": {"model": "openai/text-embedding-3-small", "api_base": "https://embedding.example/v1", "api_key": "test"}}, - {"model_name": "answer", "litellm_params": {"model": "openai/gpt-5.6-luna", "api_key": "test"}}, - ]) + ) + router: Final = Router( + model_list=[ + { + "model_name": "internal-semantic-router", + "model_info": { + "id": "semantic-definition", + "team_id": "trace-team", + "team_public_model_name": "public-router", + }, + "litellm_params": { + "model": "auto_router/semantic-router", + "auto_router_config": json.dumps({"routes": [{"name": "answer", "utterances": ["hello"]}]}), + "auto_router_default_model": "answer", + "auto_router_embedding_model": "embed", + }, + }, + { + "model_name": "embed", + "litellm_params": { + "model": "openai/text-embedding-3-small", + "api_base": "https://embedding.example/v1", + "api_key": "test", + }, + }, + {"model_name": "answer", "litellm_params": {"model": "openai/gpt-5.6-luna", "api_key": "test"}}, + ] + ) request: Final = {"metadata": {"user_api_key_team_id": "trace-team"}} with logger.tracer.start_as_current_span("request"): await router.async_get_available_deployment( @@ -25507,6 +25876,441 @@ async def test_router_clientside_credential_not_reused_by_other_callers(respx_mo used_auth_headers = {call.request.headers["authorization"] for call in route.calls[1:]} assert used_auth_headers == {"Bearer configured-key"} +def _caused_by_an_entra_rejection(error: litellm.AuthenticationError) -> litellm.AuthenticationError: + from litellm.llms.azure.common_utils import EntraRejectionError + + try: + raise error from EntraRejectionError(status_code=401, message=error.message) + except litellm.AuthenticationError as raised: + return raised + + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize("caller_credential", ["client_secret", "azure_password", "azure_ad_token"]) +async def test_router_caller_entra_credential_rejection_leaves_the_shared_deployment_routable(caller_credential): + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + "client_secret": "configured-secret", + }, + "model_info": {"id": "configured-deployment"}, + } + ] + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=_caused_by_an_entra_rejection( + litellm.AuthenticationError( + message="Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided.", + llm_provider="azure", + model="gpt-4.1", + ) + ), + **{caller_credential: "not-the-credential"}, + ) + + response: Final = await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert response.choices[0].message.content == "hi" + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize( + "caller_field", + [ + pytest.param({"client_secret": "environment-secret"}, id="environment_secret"), + pytest.param({"client_id": "environment-client"}, id="environment_client_id"), + pytest.param({"azure_scope": "https://cognitiveservices.azure.com/.default"}, id="default_scope"), + pytest.param({"client_secret": ""}, id="blank_secret"), + ], +) +async def test_router_caller_entra_field_that_changes_the_deployment_credential_leaves_it_routable( + caller_field, monkeypatch +): + monkeypatch.setenv("AZURE_CLIENT_SECRET", "environment-secret") + monkeypatch.setenv("AZURE_CLIENT_ID", "environment-client") + monkeypatch.delenv("AZURE_SCOPE", raising=False) + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + "client_secret": "configured-secret", + "azure_scope": "api://configured-app/.default", + }, + "model_info": {"id": "configured-deployment"}, + } + ] + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=_caused_by_an_entra_rejection( + litellm.AuthenticationError( + message="Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided.", + llm_provider="azure", + model="gpt-4.1", + ) + ), + **caller_field, + ) + + response: Final = await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert response.choices[0].message.content == "hi" + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize( + ("deployment_secret", "router_default_secret"), + [ + pytest.param({"client_secret": "configured-secret"}, None, id="deployment"), + pytest.param({}, {"client_secret": "configured-secret"}, id="router_default_litellm_params"), + ], +) +async def test_router_configured_entra_credential_rejection_cools_down_the_deployment( + deployment_secret, router_default_secret +): + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + **deployment_secret, + }, + "model_info": {"id": "configured-deployment"}, + } + ], + default_litellm_params=router_default_secret, + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=litellm.AuthenticationError( + message="Failed to get Azure AD token: AADSTS7000222: The provided client secret keys are expired.", + llm_provider="azure", + model="gpt-4.1", + ), + ) + + with pytest.raises(litellm.RouterRateLimitError) as excinfo: + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert excinfo.value.all_deployments_in_cooldown is True + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize( + "caller_fields", + [ + pytest.param({"azure_scope": "https://cognitiveservices.azure.com/.default"}, id="default_scope"), + pytest.param({"azure_username": ""}, id="empty_username"), + pytest.param({"client_secret": "environment-secret"}, id="environment_secret"), + ], +) +async def test_router_caller_entra_field_that_changes_nothing_still_cools_down_a_rejected_deployment( + caller_fields, monkeypatch +): + monkeypatch.delenv("AZURE_SCOPE", raising=False) + monkeypatch.setenv("AZURE_CLIENT_SECRET", "environment-secret") + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + }, + "model_info": {"id": "configured-deployment"}, + } + ] + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=_caused_by_an_entra_rejection( + litellm.AuthenticationError( + message="Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided.", + llm_provider="azure", + model="gpt-4.1", + ) + ), + **caller_fields, + ) + + with pytest.raises(litellm.RouterRateLimitError) as excinfo: + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert excinfo.value.all_deployments_in_cooldown is True + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +async def test_router_caller_repeating_the_secret_an_empty_router_default_selects_still_cools_down(monkeypatch): + monkeypatch.setenv("AZURE_CLIENT_SECRET", "environment-secret") + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "tenant_id": "configured-tenant", + "client_id": "configured-client", + "client_secret": "configured-secret", + }, + "model_info": {"id": "configured-deployment"}, + } + ], + default_litellm_params={"client_secret": ""}, + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=_caused_by_an_entra_rejection( + litellm.AuthenticationError( + message="Failed to get Azure AD token: AADSTS7000215: Invalid client secret provided.", + llm_provider="azure", + model="gpt-4.1", + ) + ), + client_secret="environment-secret", + ) + + with pytest.raises(litellm.RouterRateLimitError) as excinfo: + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert excinfo.value.all_deployments_in_cooldown is True + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +async def test_router_caller_entra_field_keeps_the_shared_deployment(): + router = Router( + model_list=[ + { + "model_name": "shared-model", + "litellm_params": {"model": "openai/gpt-4.1-mini", "api_key": "configured-key"}, + "model_info": {"id": "shared-deployment"}, + } + ] + ) + + response: Final = await router.acompletion( + model="shared-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + client_id="caller-client", + ) + + assert response._hidden_params["model_id"] == "shared-deployment" + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize( + ("litellm_params", "provider"), + [ + pytest.param({"model": "openai/gpt-4.1-mini", "api_key": "revoked-key"}, "openai", id="openai"), + pytest.param( + { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "api_key": "revoked-key", + }, + "azure", + id="azure_api_key", + ), + ], +) +@pytest.mark.parametrize("caller_entra_field", ["client_id", "azure_scope"]) +async def test_router_auth_failure_entra_did_not_cause_cools_down_despite_a_caller_entra_field( + litellm_params, provider, caller_entra_field +): + router = Router( + model_list=[ + { + "model_name": "shared-model", + "litellm_params": litellm_params, + "model_info": {"id": "shared-deployment"}, + } + ] + ) + + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="shared-model", + messages=[{"role": "user", "content": "hi"}], + mock_response=litellm.AuthenticationError( + message="Incorrect API key provided.", llm_provider=provider, model=litellm_params["model"] + ), + **{caller_entra_field: "caller-value"}, + ) + + with pytest.raises(litellm.RouterRateLimitError) as excinfo: + await router.acompletion( + model="shared-model", + messages=[{"role": "user", "content": "hi"}], + mock_response="hi", + ) + + assert excinfo.value.all_deployments_in_cooldown is True + + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +async def test_router_fallback_auth_failure_after_an_entra_rejection_still_cools_down_the_fallback(monkeypatch): + from azure.core.exceptions import ClientAuthenticationError + + def rejected_token() -> str: + raise ClientAuthenticationError(message="AADSTS7000215: Invalid client secret provided.") + + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + router = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "azure_ad_token_provider": rejected_token, + }, + "model_info": {"id": "entra-deployment"}, + }, + { + "model_name": "key-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa-fallback.openai.azure.com", + "api_version": "2025-04-01-preview", + "api_key": "revoked-key", + }, + "model_info": {"id": "key-deployment"}, + }, + ], + fallbacks=[{"entra-model": ["key-model"]}], + ) + + with respx.mock: + fallback_route: Final = respx.post(url__startswith="https://litellm-qa-fallback.openai.azure.com").mock( + return_value=httpx.Response( + 401, json={"error": {"code": "401", "message": "Access denied due to invalid subscription key."}} + ) + ) + with pytest.raises(litellm.AuthenticationError): + await router.acompletion( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + client_secret="not-the-credential", + ) + + with pytest.raises(litellm.RouterRateLimitError) as excinfo: + await router.acompletion( + model="key-model", + messages=[{"role": "user", "content": "hi"}], + ) + + assert fallback_route.call_count == 1 + assert excinfo.value.all_deployments_in_cooldown is True + + +@pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") +@pytest.mark.parametrize( + "stream_request", + [ + pytest.param( + lambda router, **caller_fields: router.acompletion( + model="entra-model", messages=[{"role": "user", "content": "hi"}], stream=True, **caller_fields + ), + id="chat_completions", + ), + pytest.param( + lambda router, **caller_fields: router.aanthropic_messages( + model="entra-model", + messages=[{"role": "user", "content": "hi"}], + max_tokens=5, + stream=True, + **caller_fields, + ), + id="messages", + ), + ], +) +async def test_router_caller_entra_rejection_on_a_stream_leaves_the_shared_deployment_routable( + monkeypatch: pytest.MonkeyPatch, + stream_request: Callable[..., Awaitable[object]], +) -> None: + from azure.core.exceptions import ClientAuthenticationError + + def rejected_token() -> str: + raise ClientAuthenticationError(message="AADSTS7000215: Invalid client secret provided.") + + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + router: Final = Router( + model_list=[ + { + "model_name": "entra-model", + "litellm_params": { + "model": "azure/gpt-4.1", + "api_base": "https://litellm-qa.openai.azure.com", + "api_version": "2025-04-01-preview", + "azure_ad_token_provider": rejected_token, + }, + "model_info": {"id": "entra-deployment"}, + } + ] + ) + + with pytest.raises(litellm.AuthenticationError): + await stream_request(router, client_secret="not-the-credential") + + with pytest.raises(litellm.AuthenticationError): + await stream_request(router) + @pytest.mark.usefixtures("_vcr_outcome_gate", "isolate_litellm_state", "setup_and_teardown") def test_router_get_async_openai_model_client(): router = Router( diff --git a/tests/unit/test_router_model_cost_isolation.py b/tests/unit/test_router_model_cost_isolation.py index df1f075502e..c16cc52a9bc 100644 --- a/tests/unit/test_router_model_cost_isolation.py +++ b/tests/unit/test_router_model_cost_isolation.py @@ -35,6 +35,7 @@ from litellm.litellm_core_utils.ptu_pricing import ptu_config_error from litellm.litellm_core_utils.llm_cost_calc.utils import SERVICE_TIER_COST_KEY_SUFFIXES from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from litellm.llms.openai_like.model_info import MODEL_INFO_REFRESH_SECONDS +from litellm.proxy.utils import create_model_info_response from litellm.types.router import ( Deployment, DeploymentTypedDict, @@ -2161,6 +2162,155 @@ def test_capability_flags_propagate_from_deployment_model_info_to_shared_key(): _restore_model_cost_entries(model_keys) +@pytest.mark.parametrize("override_first", [True, False], ids=["override-first", "override-last"]) +def test_token_limits_stay_on_the_deployment_that_set_them(override_first: bool) -> None: + backend_model: Final = "openai/gpt-4o-mini" + shared_keys: Final = ("gpt-4o-mini", backend_model) + token_fields: Final = ("max_input_tokens", "max_output_tokens", "max_tokens") + catalog_limits: Final = { + key: {field: litellm.get_model_info(model=key).get(field) for field in token_fields} for key in shared_keys + } + catalog_cost_map_limits: Final = { + key: {field: (litellm.model_cost.get(key) or {}).get(field) for field in token_fields} for key in shared_keys + } + override_id: Final = "token-limit-override-deployment" + plain_id: Final = "token-limit-plain-deployment" + model_keys: Final = { + key: copy.deepcopy(litellm.model_cost.get(key)) + for key in (*shared_keys, override_id, plain_id) + } + expected_cost_map_limits: Final = { + key: { + **catalog_cost_map_limits[key], + "max_tokens": catalog_cost_map_limits[key]["max_tokens"] + or catalog_cost_map_limits[key]["max_output_tokens"], + } + for key in shared_keys + } + plain_deployment: Final = { + "model_name": "plain-gpt-4o-mini", + "litellm_params": {"model": backend_model}, + "model_info": {"id": plain_id}, + } + override_deployment: Final = { + "model_name": "overridden-gpt-4o-mini", + "litellm_params": {"model": backend_model}, + "model_info": { + "id": override_id, + "max_input_tokens": 4321, + "max_output_tokens": 987, + "max_tokens": 987, + }, + } + try: + router: Final = Router( + model_list=[override_deployment, plain_deployment] + if override_first + else [plain_deployment, override_deployment] + ) + litellm.get_model_info.cache_clear() + + for key in shared_keys: + assert { + field: (litellm.model_cost.get(key) or {}).get(field) for field in token_fields + } == expected_cost_map_limits[key] + assert { + field: litellm.get_model_info(model=key).get(field) for field in token_fields + } == catalog_limits[key] + + assert { + field: litellm.get_model_info(model=override_id).get(field) for field in token_fields + } == {"max_input_tokens": 4321, "max_output_tokens": 987, "max_tokens": 987} + + plain_listing: Final = create_model_info_response( + model_id="plain-gpt-4o-mini", provider="openai", llm_router=router + ) + override_listing: Final = create_model_info_response( + model_id="overridden-gpt-4o-mini", provider="openai", llm_router=router + ) + assert (plain_listing["max_input_tokens"], plain_listing["max_output_tokens"]) == ( + catalog_limits[backend_model]["max_input_tokens"], + catalog_limits[backend_model]["max_output_tokens"], + ) + assert (override_listing["max_input_tokens"], override_listing["max_output_tokens"]) == (4321, 987) + finally: + _restore_model_cost_entries(model_keys) + litellm.get_model_info.cache_clear() + + +def test_unknown_backend_token_limits_still_fill_the_shared_key() -> None: + backend_model: Final = "hosted_vllm/somelab.lit4544-unmapped-token-limits" + first_id: Final = "unknown-token-limit-first-deployment" + second_id: Final = "unknown-token-limit-second-deployment" + token_fields: Final = ("max_input_tokens", "max_output_tokens", "max_tokens") + first_limits: Final = {"max_input_tokens": 4321, "max_output_tokens": 987, "max_tokens": 987} + second_limits: Final = {"max_input_tokens": 1234, "max_output_tokens": 456, "max_tokens": 456} + model_keys: Final = { + key: copy.deepcopy(litellm.model_cost.get(key)) for key in (backend_model, first_id, second_id) + } + try: + Router( + model_list=[ + { + "model_name": "unknown-token-limit-first", + "litellm_params": {"model": backend_model, "api_key": "fake-key"}, + "model_info": {"id": first_id, **first_limits}, + }, + { + "model_name": "unknown-token-limit-second", + "litellm_params": {"model": backend_model, "api_key": "fake-key"}, + "model_info": {"id": second_id, **second_limits}, + }, + ] + ) + + assert { + field: (litellm.model_cost.get(backend_model) or {}).get(field) for field in token_fields + } == second_limits + assert {field: litellm.get_model_info(first_id).get(field) for field in token_fields} == first_limits + assert {field: litellm.get_model_info(second_id).get(field) for field in token_fields} == second_limits + finally: + _restore_model_cost_entries(model_keys) + litellm.get_model_info.cache_clear() + + +def test_unknown_backend_token_limit_updates_when_deployment_is_upserted() -> None: + backend_model: Final = "hosted_vllm/somelab.lit4544-token-limit-update" + model_id: Final = "unknown-token-limit-updated-deployment" + model_keys: Final = { + key: copy.deepcopy(litellm.model_cost.get(key)) for key in (backend_model, model_id) + } + try: + deployment: Final = Deployment( + model_name="unknown-token-limit-update", + litellm_params=LiteLLM_Params( + model=backend_model, + api_base="https://unknown-model.test/v1", + api_key="fake-key", + ), + model_info=ModelInfo(id=model_id, max_output_tokens=999), + ) + router: Final = Router(model_list=[deployment.to_json()]) + assert (litellm.model_cost.get(backend_model) or {}).get("max_output_tokens") == 999 + + router.upsert_deployment( + Deployment( + model_name="unknown-token-limit-update", + litellm_params=LiteLLM_Params( + model=backend_model, + api_base="https://unknown-model.test/v1", + api_key="fake-key", + ), + model_info=ModelInfo(id=model_id, max_output_tokens=1500), + ) + ) + + assert (litellm.model_cost.get(backend_model) or {}).get("max_output_tokens") == 1500 + finally: + _restore_model_cost_entries(model_keys) + litellm.get_model_info.cache_clear() + + def test_wildcard_zero_cost_request_does_not_poison_named_deployment_pricing(): """LIT-3991 end to end: a proxy has a named text-embedding-3-small deployment relying on built-in pricing plus an ``openai/*`` wildcard with @@ -3064,7 +3214,11 @@ def test_register_deployment_in_model_cost_writes_both_key_families(): try: Router._register_deployment_in_model_cost( model_id="both-families-id", - model_info={"id": "both-families-id", "max_input_tokens": 999, "litellm_provider": "hosted_vllm"}, + model_info={ + "id": "both-families-id", + "max_input_tokens": 999, + "litellm_provider": "hosted_vllm", + }, model="hosted_vllm/both-families-backend", custom_llm_provider=None, ) diff --git a/tests/unit/test_router_weighted_failover.py b/tests/unit/test_router_weighted_failover.py index c00670473af..70231b620be 100644 --- a/tests/unit/test_router_weighted_failover.py +++ b/tests/unit/test_router_weighted_failover.py @@ -1019,3 +1019,144 @@ async def test_failover_reaches_healthy_sibling_when_affinity_pins_failed_deploy assert while_a_is_down._hidden_params["model_id"] == "B" assert while_a_is_down._hidden_params["additional_headers"]["x-litellm-attempted-fallbacks"] == 1 assert after_a_recovers._hidden_params["model_id"] == "A" + + +def _two_deployment_router(model: str) -> Router: + return Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": {"model": model, "api_key": "bad", "weight": 1}, + "model_info": {"id": "A"}, + }, + { + "model_name": "test-model", + "litellm_params": {"model": model, "api_key": "good", "weight": 0}, + "model_info": {"id": "B"}, + }, + ], + routing_strategy="simple-shuffle", + num_retries=0, + disable_cooldowns=True, + enable_weighted_failover=True, + ) + + +_ENDPOINTS: Final = [ + ("aembedding", "openai/text-embedding-3-small", {"input": ["hi"]}), + ("aimage_generation", "openai/dall-e-3", {"prompt": "a cat"}), + ("atranscription", "openai/whisper-1", {"file": b"audio"}), + ("aspeech", "openai/tts-1", {"input": "hi", "voice": "alloy"}), + ("atext_completion", "openai/gpt-3.5-turbo-instruct", {"prompt": "hi"}), + ("arerank", "cohere/rerank-english-v3.0", {"query": "q", "documents": ["d"]}), +] + + +@pytest.mark.asyncio +@pytest.mark.parametrize(("endpoint", "model", "arguments"), _ENDPOINTS, ids=[e[0] for e in _ENDPOINTS]) +async def test_weighted_failover_moves_every_endpoint_to_another_deployment( + monkeypatch: pytest.MonkeyPatch, endpoint: str, model: str, arguments: dict[str, object] +) -> None: + keys_called: list[object] = [] + answer: Final = object() + + async def provider(**kwargs: object) -> object: + keys_called.append(kwargs.get("api_key")) + if kwargs.get("api_key") == "bad": + raise litellm.InternalServerError(message="A is down", llm_provider="openai", model=model) + return answer + + monkeypatch.setattr(litellm, endpoint, provider) + router: Final = _two_deployment_router(model) + + response: Final = await getattr(router, endpoint)(model="test-model", **arguments) + + assert response is answer + assert keys_called == ["bad", "good"] + + +@pytest.mark.asyncio +async def test_weighted_failover_skips_the_failed_deployment_for_clientside_credentials( + monkeypatch: pytest.MonkeyPatch, +) -> None: + api_bases_called: list[object] = [] + answer: Final = object() + + async def provider(**kwargs: object) -> object: + api_bases_called.append(kwargs.get("api_base")) + if kwargs.get("api_base") == "http://a": + raise litellm.InternalServerError(message="A is down", llm_provider="openai", model="m") + return answer + + monkeypatch.setattr(litellm, "aembedding", provider) + router: Final = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": {"model": "openai/text-embedding-3-small", "api_base": "http://a", "weight": 1}, + "model_info": {"id": "A"}, + }, + { + "model_name": "test-model", + "litellm_params": {"model": "openai/text-embedding-3-small", "api_base": "http://b", "weight": 0}, + "model_info": {"id": "B"}, + }, + ], + routing_strategy="simple-shuffle", + num_retries=0, + disable_cooldowns=True, + enable_weighted_failover=True, + ) + + response: Final = await router.aembedding(model="test-model", input=["hi"], api_key="tenant-key") + + assert response is answer + assert api_bases_called == ["http://a", "http://b"] + + +@pytest.mark.parametrize( + ("endpoint", "attempt", "arguments"), + [ + ("embedding", "_embedding", {"input": ["hi"]}), + ("image_generation", "_image_generation", {"prompt": "a cat"}), + ], +) +def test_a_sync_attempt_stamps_the_failed_deployment( + monkeypatch: pytest.MonkeyPatch, endpoint: str, attempt: str, arguments: dict[str, object] +) -> None: + def provider(**kwargs: object) -> object: + raise litellm.InternalServerError(message="down", llm_provider="openai", model="m") + + monkeypatch.setattr(litellm, endpoint, provider) + router: Final = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": {"model": "openai/m", "api_key": "k"}, + "model_info": {"id": "A"}, + } + ], + ) + + with pytest.raises(litellm.InternalServerError) as raised: + getattr(router, attempt)(model="test-model", **arguments) + + assert getattr(raised.value, "failed_deployment_id", None) == "A" + + +@pytest.mark.asyncio +async def test_a_failed_pick_stamps_no_deployment() -> None: + router: Final = Router( + model_list=[ + { + "model_name": "test-model", + "litellm_params": {"model": "openai/text-embedding-3-small", "api_key": "k"}, + "model_info": {"id": "A"}, + } + ], + ) + + with pytest.raises(litellm.BadRequestError, match="no healthy deployments") as raised: + await router._aembedding(input=["hi"], model="no-such-model") + + assert getattr(raised.value, "failed_deployment_id", None) is None diff --git a/tests/unit/test_together_ai_model_metadata.py b/tests/unit/test_together_ai_model_metadata.py index 3616c6216ca..8246277d475 100644 --- a/tests/unit/test_together_ai_model_metadata.py +++ b/tests/unit/test_together_ai_model_metadata.py @@ -64,7 +64,6 @@ DEPRECATED_MODELS: Final = { "together_ai/Qwen/Qwen2.5-72B-Instruct-Turbo": "2026-02-06", "together_ai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": "2026-02-06", "together_ai/deepseek-ai/DeepSeek-R1-0528-tput": "2026-02-03", - "together_ai/mistralai/Mistral-7B-Instruct-v0.1": "2025-11-13", "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free": "2025-11-13", } diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index c1c2593c4ef..e293b2a5be3 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -1026,11 +1026,17 @@ _COST_MAP_ROW_SCHEMA: Final = { "cache_read_input_image_token_cost": {"type": "number"}, "audio_transcription_config": {"type": "string"}, "deprecation_date": {"type": "string"}, + "successor_model": {"type": "string"}, + "deprecation_source": {"type": "string"}, "input_cost_per_audio_per_second": {"type": "number"}, "input_cost_per_audio_per_second_above_128k_tokens": {"type": "number"}, "google_maps_grounding_cost_per_query": {"type": "number"}, "input_cost_per_audio_token": {"type": "number"}, "input_cost_per_audio_token_flex": {"type": "number"}, + "input_cost_per_audio_token_above_200k_tokens": {"type": "number"}, + "input_cost_per_audio_token_above_200k_tokens_flex": {"type": "number"}, + "input_cost_per_audio_token_above_200k_tokens_priority": {"type": "number"}, + "input_cost_per_audio_token_above_200k_tokens_batches": {"type": "number"}, "input_cost_per_image_token": {"type": "number"}, "input_cost_per_character": {"type": "number"}, "input_cost_per_character_above_128k_tokens": {"type": "number"}, @@ -1042,6 +1048,7 @@ _COST_MAP_ROW_SCHEMA: Final = { "input_cost_per_token_above_100k_tokens_batches": {"type": "number"}, "input_cost_per_token_above_200k_tokens": {"type": "number"}, "input_cost_per_token_above_200k_tokens_batches": {"type": "number"}, + "input_cost_per_token_above_200k_tokens_flex": {"type": "number"}, "input_cost_per_token_above_256k_tokens": {"type": "number"}, "input_cost_per_token_above_272k_tokens": {"type": "number"}, "input_cost_per_token_above_512k_tokens": {"type": "number"}, @@ -1072,6 +1079,7 @@ _COST_MAP_ROW_SCHEMA: Final = { "regional_endpoint_uplift_multiplier": {"type": "number"}, "regional_processing_uplift_multiplier_eu": {"type": "number"}, "regional_processing_uplift_multiplier_us": {"type": "number"}, + "fedramp_uplift_multiplier": {"type": "number"}, "input_cost_per_pixel": {"type": "number"}, "input_cost_per_query": {"type": "number"}, "input_cost_per_request": {"type": "number"}, @@ -1141,6 +1149,7 @@ _COST_MAP_ROW_SCHEMA: Final = { "output_cost_per_image_4K": {"type": "number"}, "output_cost_per_image_token": {"type": "number"}, "output_cost_per_image_token_batches": {"type": "number"}, + "output_cost_per_image_token_priority": {"type": "number"}, "output_cost_per_video_token": {"type": "number"}, "output_cost_per_pixel": {"type": "number"}, "output_cost_per_second": {"type": "number"}, @@ -1157,6 +1166,7 @@ _COST_MAP_ROW_SCHEMA: Final = { "output_cost_per_token_above_128k_tokens": {"type": "number"}, "output_cost_per_token_above_200k_tokens": {"type": "number"}, "output_cost_per_token_above_200k_tokens_batches": {"type": "number"}, + "output_cost_per_token_above_200k_tokens_flex": {"type": "number"}, "output_cost_per_token_above_256k_tokens": {"type": "number"}, "output_cost_per_token_above_272k_tokens": {"type": "number"}, "output_cost_per_token_above_512k_tokens": {"type": "number"}, @@ -1445,6 +1455,27 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): raise AssertionError(error_message) +def test_cost_map_schema_accepts_vertex_modality_and_context_tiers() -> None: + row: Final = { + "litellm_provider": "vertex_ai", + "mode": "chat", + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "input_cost_per_token_above_200k_tokens_flex": 3e-6, + "output_cost_per_token_above_200k_tokens_flex": 4e-6, + "input_cost_per_token_above_200k_tokens_batches": 3e-6, + "output_cost_per_token_above_200k_tokens_batches": 4e-6, + "input_cost_per_audio_token_above_200k_tokens": 5e-6, + "input_cost_per_audio_token_above_200k_tokens_flex": 6e-6, + "input_cost_per_audio_token_above_200k_tokens_priority": 7e-6, + "input_cost_per_audio_token_above_200k_tokens_batches": 8e-6, + "output_cost_per_image_token_priority": 9e-6, + "output_cost_per_image_token_batches": 10e-6, + } + + assert Draft7Validator(_COST_MAP_ROW_SCHEMA).is_valid({"synthetic-vertex-tier-model": row}) + + def test_cost_map_schema_accepts_12hr_cache_write_rates_for_batch_and_ultrafast_tiers() -> None: row: Final = { "litellm_provider": "openai", @@ -1881,7 +1912,7 @@ class TestProxyFunctionCalling: ("gpt-4-turbo", "litellm_proxy/gpt-4-turbo", True), ("gpt-4-1106-preview", "litellm_proxy/gpt-4-1106-preview", True), # Azure OpenAI models - ("azure/gpt-4", "litellm_proxy/azure/gpt-4", True), + ("azure/gpt-4.1", "litellm_proxy/azure/gpt-4.1", True), ("azure/gpt-3.5-turbo", "litellm_proxy/azure/gpt-3.5-turbo", True), ( "azure/gpt-4-1106-preview", @@ -1974,7 +2005,7 @@ class TestProxyFunctionCalling: ), ( "litellm_proxy/together-mistral", - "together_ai/mistralai/Mistral-7B-Instruct-v0.1", + "together_ai/mistralai/Mistral-7B-Instruct-v0.3", False, ), # Ollama with custom names (cannot be resolved) @@ -2391,7 +2422,7 @@ def test_register_model_price_is_used_for_completion_cost() -> None: @respx.mock @pytest.mark.usefixtures("isolated_openai_model_sets", "local_model_cost_map") -def test_completion_registers_test_owned_model_prices() -> None: +def test_completion_custom_prices_price_the_call_without_writing_the_cost_map() -> None: model: Final = "migration-owned-pricing-model" messages: Final = [{"role": "user", "content": "price control"}] litellm.register_model( @@ -2434,8 +2465,9 @@ def test_completion_registers_test_owned_model_prices() -> None: ) assert json.loads(route.calls[0].request.content) == {"model": model, "messages": messages} - assert litellm.model_cost[model]["input_cost_per_token"] == 0.001 - assert litellm.model_cost[model]["output_cost_per_token"] == 0.002 + assert response._hidden_params["response_cost"] == pytest.approx(8 * 0.001 + 5 * 0.002) + assert litellm.model_cost[model]["input_cost_per_token"] == 0.0001 + assert litellm.model_cost[model]["output_cost_per_token"] == 0.0002 assert response._hidden_params["response_cost"] == pytest.approx(0.018) @@ -11099,7 +11131,6 @@ def test_get_provider_audio_transcription_config_resolves_for_every_provider() - assert isinstance(configs[LlmProviders.OPENAI], litellm.OpenAIWhisperAudioTranscriptionConfig) - @pytest.mark.parametrize( ("custom_llm_provider", "model"), [ diff --git a/ui/litellm-dashboard/eslint-suppressions.json b/ui/litellm-dashboard/eslint-suppressions.json index 0cadc13a213..df70c086a46 100644 --- a/ui/litellm-dashboard/eslint-suppressions.json +++ b/ui/litellm-dashboard/eslint-suppressions.json @@ -1081,6 +1081,11 @@ "count": 1 } }, + "src/app/(dashboard)/usage/_components/components/UsagePageView.tsx": { + "max-lines": { + "count": 1 + } + }, "src/app/(dashboard)/users/_components/BulkEditUsers.tsx": { "prefer-const": { "count": 1 @@ -1153,16 +1158,6 @@ "count": 2 } }, - "src/app/model_hub/page.tsx": { - "react-hooks/set-state-in-effect": { - "count": 1 - } - }, - "src/app/model_hub_table/page.tsx": { - "react-hooks/set-state-in-effect": { - "count": 1 - } - }, "src/components/AIHub/ModelHubTable.test.tsx": { "max-params": { "count": 1 @@ -2010,6 +2005,9 @@ "src/components/templates/key_edit_view.tsx": { "local/filename-pascal-case": { "count": 1 + }, + "max-lines": { + "count": 1 } }, "src/components/templates/key_info_view.tsx": { diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json index 54cd9087c4e..d506d114d8c 100644 --- a/ui/litellm-dashboard/package-lock.json +++ b/ui/litellm-dashboard/package-lock.json @@ -23,6 +23,7 @@ "@types/papaparse": "5.5.2", "class-variance-authority": "0.7.1", "clsx": "^2.1.1", + "cmdk": "1.1.1", "date-fns": 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} + }, "node_modules/get-proto": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/get-proto/-/get-proto-1.0.1.tgz", @@ -10769,6 +11130,53 @@ } } }, + "node_modules/react-remove-scroll": { + "version": "2.7.2", + "resolved": "https://registry.npmjs.org/react-remove-scroll/-/react-remove-scroll-2.7.2.tgz", + "integrity": "sha512-Iqb9NjCCTt6Hf+vOdNIZGdTiH1QSqr27H/Ek9sv/a97gfueI/5h1s3yRi1nngzMUaOOToin5dI1dXKdXiF+u0Q==", + "license": "MIT", + "dependencies": { + "react-remove-scroll-bar": "^2.3.7", + "react-style-singleton": "^2.2.3", + "tslib": "^2.1.0", + "use-callback-ref": "^1.3.3", + "use-sidecar": "^1.1.3" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/react-remove-scroll-bar": { + "version": "2.3.8", + "resolved": "https://registry.npmjs.org/react-remove-scroll-bar/-/react-remove-scroll-bar-2.3.8.tgz", + "integrity": "sha512-9r+yi9+mgU33AKcj6IbT9oRCO78WriSj6t/cF8DWBZJ9aOGPOTEDvdUDz1FwKim7QXWwmHqtdHnRJfhAxEG46Q==", + "license": "MIT", + "dependencies": { + "react-style-singleton": "^2.2.2", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/react-resizable-panels": { "version": "4.14.1", "resolved": "https://registry.npmjs.org/react-resizable-panels/-/react-resizable-panels-4.14.1.tgz", @@ -10779,6 +11187,28 @@ "react-dom": "^18.0.0 || ^19.0.0" } }, + "node_modules/react-style-singleton": { + "version": "2.2.3", + "resolved": "https://registry.npmjs.org/react-style-singleton/-/react-style-singleton-2.2.3.tgz", + "integrity": "sha512-b6jSvxvVnyptAiLjbkWLE/lOnR4lfTtDAl+eUC7RZy+QQWc6wRzIV2CE6xBuMmDxc2qIihtDCZD5NPOFl7fRBQ==", + "license": "MIT", + "dependencies": { + "get-nonce": "^1.0.0", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/react-syntax-highlighter": { "version": "15.6.6", "resolved": "https://registry.npmjs.org/react-syntax-highlighter/-/react-syntax-highlighter-15.6.6.tgz", @@ -12408,6 +12838,49 @@ "dev": true, "license": "MIT" }, + "node_modules/use-callback-ref": { + "version": "1.3.3", + "resolved": "https://registry.npmjs.org/use-callback-ref/-/use-callback-ref-1.3.3.tgz", + "integrity": "sha512-jQL3lRnocaFtu3V00JToYz/4QkNWswxijDaCVNZRiRTO3HQDLsdu1ZtmIUvV4yPp+rvWm5j0y0TG/S61cuijTg==", + "license": "MIT", + "dependencies": { + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, + "node_modules/use-sidecar": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/use-sidecar/-/use-sidecar-1.1.3.tgz", + "integrity": "sha512-Fedw0aZvkhynoPYlA5WXrMCAMm+nSWdZt6lzJQ7Ok8S6Q+VsHmHpRWndVRJ8Be0ZbkfPc5LRYH+5XrzXcEeLRQ==", + "license": "MIT", + "dependencies": { + "detect-node-es": "^1.1.0", + "tslib": "^2.0.0" + }, + "engines": { + "node": ">=10" + }, + "peerDependencies": { + "@types/react": "*", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + } + } + }, "node_modules/use-sync-external-store": { "version": "1.6.0", "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz", diff --git a/ui/litellm-dashboard/package.json b/ui/litellm-dashboard/package.json index 0707496a6bc..9fc0242f040 100644 --- a/ui/litellm-dashboard/package.json +++ b/ui/litellm-dashboard/package.json @@ -40,6 +40,7 @@ "@types/papaparse": "5.5.2", "class-variance-authority": "0.7.1", "clsx": "^2.1.1", + "cmdk": "1.1.1", "date-fns": "^4.4.0", "dayjs": "1.11.19", "i18next": "26.5.11", diff --git a/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.test.tsx index a4a615fe798..f3d9b67eb4b 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.test.tsx @@ -1,5 +1,7 @@ import { useAccessGroupDetails } from "@/app/(dashboard)/hooks/accessGroups/useAccessGroupDetails"; import { AccessGroupResponse } from "@/app/(dashboard)/hooks/accessGroups/useAccessGroups"; +import { useUserLookup } from "@/app/(dashboard)/hooks/users/useUsers"; +import { DEFAULT_PROXY_ADMIN_USER_ID } from "@/utils/sentinels"; import { screen, within } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; @@ -7,6 +9,7 @@ import { renderWithProviders } from "../../../../../tests/test-utils"; import { AccessGroupDetail } from "./AccessGroupsDetailsPage"; vi.mock("@/app/(dashboard)/hooks/accessGroups/useAccessGroupDetails"); +vi.mock("@/app/(dashboard)/hooks/users/useUsers"); vi.mock("next/navigation", () => ({ useRouter: () => ({ push: vi.fn() }) })); vi.mock("./AccessGroupsModal/AccessGroupEditModal", () => ({ AccessGroupEditModal: ({ visible, onCancel }: { visible: boolean; onCancel: () => void }) => @@ -18,6 +21,7 @@ vi.mock("./AccessGroupsModal/AccessGroupEditModal", () => ({ })); const mockUseAccessGroupDetails = vi.mocked(useAccessGroupDetails); +const mockUseUserLookup = vi.mocked(useUserLookup); const baseMockReturnValue = { data: undefined, @@ -89,6 +93,7 @@ describe("AccessGroupDetail", () => { ...baseMockReturnValue, data: createMockAccessGroup(), } as ReturnType); + mockUseUserLookup.mockReturnValue({ data: null } as ReturnType); }); it("should render the component", () => { @@ -144,6 +149,64 @@ describe("AccessGroupDetail", () => { expect(screen.getByText("A test access group")).toBeInTheDocument(); }); + describe("user references", () => { + it("shows a user's alias as a linked name and reveals the id in a tooltip", async () => { + const user = userEvent.setup(); + mockUseUserLookup.mockReturnValue({ + data: { user_alias: "Alice", user_email: "alice@example.com" }, + } as ReturnType); + renderWith({ created_by: "user-123" }); + + const link = screen.getByRole("link", { name: "Alice" }); + expect(link).toHaveAttribute("href", expect.stringContaining("/users?user=user-123")); + expect(screen.queryByText("user-123")).not.toBeInTheDocument(); + + await user.hover(link); + + expect(await screen.findByText("user-123")).toBeInTheDocument(); + }); + + it("shows the email when the user has no alias", () => { + mockUseUserLookup.mockReturnValue({ + data: { user_alias: null, user_email: "alice@example.com" }, + } as ReturnType); + renderWith({ updated_by: "user-456" }); + + expect(screen.getByRole("link", { name: "alice@example.com" })).toHaveAttribute( + "href", + expect.stringContaining("/users?user=user-456"), + ); + }); + + it("falls back to the email when the alias contains only whitespace", () => { + mockUseUserLookup.mockReturnValue({ + data: { user_alias: " ", user_email: "alice@example.com" }, + } as ReturnType); + renderWith({ created_by: "user-789" }); + + expect(screen.getByRole("link", { name: "alice@example.com" })).toHaveAttribute( + "href", + expect.stringContaining("/users?user=user-789"), + ); + }); + + it("links the user id when lookup has no user details", () => { + renderWith({ updated_by: "missing-user" }); + + expect(screen.getByRole("link", { name: "missing-user" })).toHaveAttribute( + "href", + expect.stringContaining("/users?user=missing-user"), + ); + }); + + it("shows the default admin badge without looking up its user id", () => { + renderWith({ created_by: DEFAULT_PROXY_ADMIN_USER_ID }); + + expect(screen.getByText("Default Proxy Admin")).toBeInTheDocument(); + expect(mockUseUserLookup).toHaveBeenCalledWith(null); + }); + }); + it("should display em dash when description is empty", () => { renderWith({ description: null }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.tsx b/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.tsx index f0529121b21..cd8a0863e92 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/access-groups/_components/AccessGroupsDetailsPage.tsx @@ -12,9 +12,9 @@ import { } from "lucide-react"; import { useState } from "react"; import { useTranslation } from "react-i18next"; -import DefaultProxyAdminTag from "@/components/common_components/DefaultProxyAdminTag"; import { BadgeLink } from "@/components/shared/BadgeLink"; import CopyButton from "@/components/shared/CopyButton"; +import { UserReference } from "@/components/shared/UserReference"; import { Badge } from "@/components/ui/badge"; import { Button } from "@/components/ui/button"; import { Card, CardAction, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; @@ -190,7 +190,7 @@ export function AccessGroupDetail({ accessGroupId, onBack }: AccessGroupDetailPr {accessGroup.created_by && ( <> {t("accessGroups.detail.by")} - + )} @@ -200,7 +200,7 @@ export function AccessGroupDetail({ accessGroupId, onBack }: AccessGroupDetailPr {accessGroup.updated_by && ( <> {t("accessGroups.detail.by")} - + )} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/aria/page.tsx b/ui/litellm-dashboard/src/app/(dashboard)/aria/page.tsx new file mode 100644 index 00000000000..45127adc65a --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/aria/page.tsx @@ -0,0 +1,5 @@ +import { LiteAdminPage } from "@/components/liteadmin/LiteAdmin"; + +export default function AriaPage() { + return ; +} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetFormFields.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetFormFields.tsx index 9ffd23884b4..56e2c6dbecc 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetFormFields.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetFormFields.tsx @@ -1,4 +1,5 @@ import React, { useId } from "react"; +import { useTranslation } from "react-i18next"; import { Controller } from "react-hook-form"; import { z } from "zod"; import { FormField } from "@/components/shared/form/FormField"; @@ -16,6 +17,7 @@ import { import { Input } from "@/components/ui/input"; import { InputGroup, InputGroupAddon, InputGroupInput, InputGroupText } from "@/components/ui/input-group"; import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; +import i18n from "@/i18n/i18n"; import { useZodForm } from "@/lib/forms/useZodForm"; const budgetShape = { @@ -54,19 +56,23 @@ const numberProps = ) => onChange(event.target.value === "" ? null : event.target.valueAsNumber), }); -const OptionalBadge = () => Optional; +const OptionalBadge = () => { + const { t } = useTranslation(); + return {t("budgets.form.optional")}; +}; function SpendLimitField({ form }: { form: BudgetForm }) { + const { t } = useTranslation(); const amountId = useId(); return ( - Spend limit + {t("budgets.form.spendLimit")} @@ -91,7 +97,7 @@ function SpendLimitField({ form }: { form: BudgetForm }) { value={field.value ?? NO_RESET} onValueChange={(next) => field.onChange(next === NO_RESET ? null : next)} > - + @@ -124,13 +130,14 @@ function RateLimitField({ form, name, label }: { form: BudgetForm; name: NumberF } export function BudgetFormFields({ form, isEditing }: { form: BudgetForm; isEditing: boolean }) { + const { t } = useTranslation(); return ( {({ ref, ...field }) => } @@ -139,15 +146,13 @@ export function BudgetFormFields({ form, isEditing }: { form: BudgetForm; isEdit
- Rate limits + {t("budgets.form.rateLimits")} - - - - - Batches count against the daily limit instead of the per-minute ones. Provider rate limits still apply. - + + + + {t("budgets.form.batchNote")}
); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetModal.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetModal.tsx index bd10837640d..c6bf7e8121e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetModal.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetModal.tsx @@ -1,4 +1,5 @@ import React, { useId } from "react"; +import { useTranslation } from "react-i18next"; import { useCreateBudget } from "@/app/(dashboard)/hooks/budgets/useBudgets"; import { applyBudgetPrecision } from "./budgetPrecision"; import { BudgetFormFields, type BudgetFormValues, useBudgetForm } from "./BudgetFormFields"; @@ -11,6 +12,7 @@ interface BudgetModalProps { setIsModalVisible: React.Dispatch>; } const BudgetModal: React.FC = ({ isModalVisible, setIsModalVisible }) => { + const { t } = useTranslation(); const formId = useId(); const form = useBudgetForm({ budget_id: "" }); const createBudget = useCreateBudget(); @@ -22,14 +24,14 @@ const BudgetModal: React.FC = ({ isModalVisible, setIsModalVis const handleCreate = async (formValues: BudgetFormValues) => { try { - toast.info("Making API Call"); + toast.info(t("budgets.createModal.toast.pending")); await createBudget.mutateAsync(applyBudgetPrecision(formValues)); - toast.success("Budget Created"); + toast.success(t("budgets.createModal.toast.created")); form.reset(); setIsModalVisible(false); } catch (error) { console.error("Error creating the budget:", error); - toast.fromError(`Error creating the budget: ${error}`); + toast.fromError(t("budgets.createModal.toast.error", { error: String(error) })); } }; @@ -37,15 +39,17 @@ const BudgetModal: React.FC = ({ isModalVisible, setIsModalVis !open && handleCancel()}> - Create budget + + {t("budgets.createModal.title")} +
- }>Cancel + }>{t("budgets.createModal.cancel")}
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetPanel.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetPanel.tsx index 785b09252af..bbc4fed94f2 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetPanel.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetPanel.tsx @@ -6,6 +6,7 @@ import { Page, PageTabs, PageTabsList, PageTabsTrigger } from "@/components/shared/Page"; import { Plus, Wallet } from "lucide-react"; import React, { useCallback, useState } from "react"; +import { useTranslation } from "react-i18next"; import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; import { prism } from "react-syntax-highlighter/dist/esm/styles/prism"; @@ -29,6 +30,7 @@ interface BudgetSettingsPageProps { } const BudgetPanel: React.FC = ({ accessToken }) => { + const { t } = useTranslation(); const syntaxTheme = useSyntaxTheme(prism); const [isCreateModelVisible, setIsCreateModelVisible] = useState(false); const [isEditModalVisible, setIsEditModalVisible] = useState(false); @@ -65,10 +67,10 @@ const BudgetPanel: React.FC = ({ accessToken }) => { } try { await deleteBudget.mutateAsync(selectedBudget.budget_id); - toast.success("Budget deleted."); + toast.success(t("budgets.panel.toast.deleted")); } catch (error) { console.error("Error deleting budget:", error); - toast.fromError("Failed to delete budget"); + toast.fromError(t("budgets.panel.toast.deleteFailed")); } finally { setIsDeleteModalVisible(false); setSelectedBudget(null); @@ -85,22 +87,22 @@ const BudgetPanel: React.FC = ({ accessToken }) => { - Budgets + {t("budgets.panel.title")} - Spend, TPM and RPM limits you can assign to customers. + {t("budgets.panel.description")} {canModify && ( <> )} - Budgets - Examples + {t("budgets.panel.tabs.budgets")} + {t("budgets.panel.tabs.examples")} @@ -122,15 +124,15 @@ const BudgetPanel: React.FC = ({ accessToken }) => { /> = ({ accessToken }) => {
-

How to use budget id

+

{t("budgets.panel.examples.intro")}

- Assign Budget to Customer + {t("budgets.panel.examples.tabs.assignBudget")} - Test it (Curl) + {t("budgets.panel.examples.tabs.curl")} - Test it (OpenAI SDK) + {t("budgets.panel.examples.tabs.openaiSdk")} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTable.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTable.tsx index 80872c58d28..9d9e32ed86e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTable.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTable.tsx @@ -2,6 +2,7 @@ import { Inbox, ShieldAlert } from "lucide-react"; import React, { useMemo, useState } from "react"; +import { useTranslation } from "react-i18next"; import { BUDGET_DURATION_FILTER_OPTIONS, @@ -21,6 +22,7 @@ import { import { Checkbox } from "@/components/ui/checkbox"; import { Input } from "@/components/ui/input"; import { Label } from "@/components/ui/label"; +import i18n from "@/i18n/i18n"; import { ApiError } from "@/lib/http/client"; import { BUDGET_TABLE_HIDDEN_COLUMNS, getBudgetTableColumns } from "./BudgetTableColumns"; @@ -49,11 +51,19 @@ const formatFilterValue = (columnId: string, value: unknown): string => { } if (columnId === "max_budget") { const { min, max, unlimitedOnly } = (value ?? {}) as MaxBudgetFilterValue; - return unlimitedOnly === true ? "Unlimited only" : `${min ? `$${min}` : "any"} to ${max ? `$${max}` : "any"}`; + return unlimitedOnly === true + ? i18n.t("budgets.table.filterChips.unlimitedOnly") + : i18n.t("budgets.table.filterChips.maxBudgetRange", { + min: min ? `$${min}` : i18n.t("budgets.table.filterChips.any"), + max: max ? `$${max}` : i18n.t("budgets.table.filterChips.any"), + }); } if (columnId === "created_at") { const { from, to } = (value ?? {}) as CreatedAtFilterValue; - return `${from || "any"} to ${to || "any"}`; + return i18n.t("budgets.table.filterChips.createdRange", { + from: from || i18n.t("budgets.table.filterChips.any"), + to: to || i18n.t("budgets.table.filterChips.any"), + }); } return String(value); }; @@ -81,22 +91,24 @@ const normalizeCreatedAt = (draft: CreatedAtFilterValue): CreatedAtFilterValue | }; function EmptyState({ hasQuery }: { hasQuery: boolean }) { + const { t } = useTranslation(); return (
-
{hasQuery ? "No matching budgets" : "No budgets yet"}
+
+ {hasQuery ? t("budgets.table.emptyState.noMatchTitle") : t("budgets.table.emptyState.title")} +
- {hasQuery - ? "No budget matches your search or filters." - : "Create a budget to set spend, TPM and RPM limits for customers."} + {hasQuery ? t("budgets.table.emptyState.noMatchDescription") : t("budgets.table.emptyState.description")}
); } function ErrorState({ error }: { error: Error }) { + const { t } = useTranslation(); const forbidden = error instanceof ApiError && error.status === 403; return (
@@ -104,10 +116,10 @@ function ErrorState({ error }: { error: Error }) {
- {forbidden ? "You do not have access to budgets" : "Could not load budgets"} + {forbidden ? t("budgets.table.errorState.forbiddenTitle") : t("budgets.table.errorState.title")}
- {forbidden ? "Ask a proxy admin to grant you the admin viewer role." : error.message} + {forbidden ? t("budgets.table.errorState.forbiddenDescription") : error.message}
); @@ -141,19 +153,20 @@ function DurationFilter({ selected, onChange }: { selected: string[]; onChange: } function BudgetFilterFields({ get, set }: FilterDraft) { + const { t } = useTranslation(); const maxBudget = (get("max_budget") as MaxBudgetFilterValue | undefined) ?? {}; const created = (get("created_at") as CreatedAtFilterValue | undefined) ?? {}; const unlimitedOnly = maxBudget.unlimitedOnly === true; return ( <> - + set("budget_duration", selected)} /> - +
set("max_budget", normalizeMaxBudget({ ...maxBudget, min: event.target.value }))} - placeholder="Min" - aria-label="Minimum max budget" + placeholder={t("budgets.table.filters.min")} + aria-label={t("budgets.table.filters.minAriaLabel")} data-testid="budget-filter-max-budget-min" /> set("max_budget", normalizeMaxBudget({ ...maxBudget, max: event.target.value }))} - placeholder="Max" - aria-label="Maximum max budget" + placeholder={t("budgets.table.filters.max")} + aria-label={t("budgets.table.filters.maxAriaLabel")} data-testid="budget-filter-max-budget-max" />
@@ -184,23 +197,23 @@ function BudgetFilterFields({ get, set }: FilterDraft) { onCheckedChange={(checked) => set("max_budget", normalizeMaxBudget({ unlimitedOnly: checked === true }))} data-testid="budget-filter-max-budget-unlimited" /> - Unlimited only + {t("budgets.table.filters.unlimitedOnly")} - +
set("created_at", normalizeCreatedAt({ ...created, from: event.target.value }))} - aria-label="Created from" + aria-label={t("budgets.table.filters.createdFrom")} data-testid="budget-filter-created-from" /> set("created_at", normalizeCreatedAt({ ...created, to: event.target.value }))} - aria-label="Created to" + aria-label={t("budgets.table.filters.createdTo")} data-testid="budget-filter-created-to" />
@@ -210,11 +223,12 @@ function BudgetFilterFields({ get, set }: FilterDraft) { } const BudgetTable: React.FC = ({ list, canModify, onEditClick, onDeleteClick }) => { + const { t } = useTranslation(); const [filtersOpen, setFiltersOpen] = useState(false); const columns = useMemo( - () => getBudgetTableColumns({ canModify, onEditClick, onDeleteClick }), - [canModify, onEditClick, onDeleteClick], + () => getBudgetTableColumns({ canModify, onEditClick, onDeleteClick }, t), + [canModify, onEditClick, onDeleteClick, t], ); const hasQuery = list.searchValue.trim() !== "" || list.columnFilters.length > 0; @@ -239,7 +253,7 @@ const BudgetTable: React.FC = ({ list, canModify, onEditClick, columnFilters={list.columnFilters} onColumnFiltersChange={list.onColumnFiltersChange} isLoading={list.isLoading} - loadingMessage="Loading budgets…" + loadingMessage={t("budgets.table.loading")} noDataMessage={emptyMessage} size="compact" toolbar={(table) => ( @@ -248,7 +262,7 @@ const BudgetTable: React.FC = ({ list, canModify, onEditClick, table={table} searchValue={list.searchValue} onSearchChange={list.onSearchChange} - searchPlaceholder="Search by budget ID…" + searchPlaceholder={t("budgets.table.searchPlaceholder")} onOpenFilters={() => setFiltersOpen(true)} onRefresh={list.refetch} isRefreshing={list.isFetching} @@ -259,8 +273,8 @@ const BudgetTable: React.FC = ({ list, canModify, onEditClick, table={table} open={filtersOpen} onOpenChange={setFiltersOpen} - title="Filters" - description="Narrow down your budgets" + title={t("budgets.table.filters.title")} + description={t("budgets.table.filters.description")} > {(draft) => } diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTableColumns.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTableColumns.tsx index 3ec291c1158..d0538e7ed1c 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTableColumns.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/BudgetTableColumns.tsx @@ -1,7 +1,9 @@ "use client"; import { ColumnDef, FilterFn } from "@tanstack/react-table"; +import type { TFunction } from "i18next"; import { MoreHorizontal, Pencil, Trash2 } from "lucide-react"; +import { useTranslation } from "react-i18next"; import { DataTableSortHeader } from "@/components/shared/DataTable"; import { DateCell, IdCell, MoneyCell } from "@/components/shared/table_cells"; @@ -27,15 +29,17 @@ const serverFilter: FilterFn = () => true; serverFilter.autoRemove = () => false; function RateLimitCell({ value }: { value: number | null | undefined }) { + const { t } = useTranslation(); if (value == null) { - return n/a; + return {t("budgets.table.cells.notApplicable")}; } return {value}; } function BudgetDurationCell({ value }: { value: string | null | undefined }) { + const { t } = useTranslation(); if (!value) { - return Not set; + return {t("budgets.table.cells.notSet")}; } return {getBudgetDurationLabel(value)}; } @@ -47,10 +51,11 @@ interface BudgetRowActionsProps { } function BudgetRowActions({ budget, onEditClick, onDeleteClick }: BudgetRowActionsProps) { + const { t } = useTranslation(); return ( @@ -59,7 +64,7 @@ function BudgetRowActions({ budget, onEditClick, onDeleteClick }: BudgetRowActio onEditClick(budget)}> - Edit budget + {t("budgets.table.rowActions.edit")} onDeleteClick(budget)} > - Delete budget + {t("budgets.table.rowActions.delete")} @@ -87,16 +92,15 @@ interface BudgetTableColumnsDeps { onDeleteClick: (budget: budgetItem) => void; } -export const getBudgetTableColumns = ({ - canModify, - onEditClick, - onDeleteClick, -}: BudgetTableColumnsDeps): ColumnDef[] => [ +export const getBudgetTableColumns = ( + { canModify, onEditClick, onDeleteClick }: BudgetTableColumnsDeps, + t: TFunction, +): ColumnDef[] => [ { id: "budget_id", accessorKey: "budget_id", - meta: { title: "Budget ID" }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.budgetId") }, + header: ({ column }) => , cell: ({ row }) => ( ), @@ -105,32 +109,34 @@ export const getBudgetTableColumns = ({ id: "max_budget", accessorKey: "max_budget", filterFn: serverFilter, - meta: { title: "Max Budget", numeric: true }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.maxBudget"), numeric: true }, + header: ({ column }) => , size: 120, - cell: ({ row }) => , + cell: ({ row }) => ( + + ), }, { id: "tpm_limit", accessorKey: "tpm_limit", - meta: { title: "TPM", numeric: true }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.tpm"), numeric: true }, + header: ({ column }) => , size: 100, cell: ({ row }) => , }, { id: "rpm_limit", accessorKey: "rpm_limit", - meta: { title: "RPM", numeric: true }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.rpm"), numeric: true }, + header: ({ column }) => , size: 100, cell: ({ row }) => , }, { id: "tpd_limit", accessorKey: "tpd_limit", - meta: { title: "TPD (batch)", numeric: true }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.tpd"), numeric: true }, + header: ({ column }) => , size: 110, cell: ({ row }) => , }, @@ -138,9 +144,9 @@ export const getBudgetTableColumns = ({ id: "budget_duration", accessorKey: "budget_duration", filterFn: serverFilter, - meta: { title: "Reset" }, + meta: { title: t("budgets.table.columns.reset") }, sortDescFirst: false, - header: ({ column }) => , + header: ({ column }) => , size: 110, cell: ({ row }) => , }, @@ -148,8 +154,8 @@ export const getBudgetTableColumns = ({ id: "created_at", accessorKey: "created_at", filterFn: serverFilter, - meta: { title: "Created" }, - header: ({ column }) => , + meta: { title: t("budgets.table.columns.created") }, + header: ({ column }) => , size: 160, cell: ({ row }) => , }, @@ -158,7 +164,7 @@ export const getBudgetTableColumns = ({ { id: "actions", meta: { className: "text-right", headerClassName: "text-right" }, - header: () => Actions, + header: () => {t("budgets.table.columns.actions")}, size: 64, enableSorting: false, enableHiding: false, diff --git a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/EditBudgetModal.tsx b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/EditBudgetModal.tsx index 4aa69c5c15e..81c1f652eab 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/EditBudgetModal.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/budgets/_components/EditBudgetModal.tsx @@ -1,4 +1,5 @@ import React, { useEffect, useId } from "react"; +import { useTranslation } from "react-i18next"; import { useUpdateBudget } from "@/app/(dashboard)/hooks/budgets/useBudgets"; import { budgetItem } from "@/app/(dashboard)/hooks/budgets/useBudgets"; import { applyBudgetPrecision } from "./budgetPrecision"; @@ -23,6 +24,7 @@ interface EditBudgetModalProps { existingBudget: budgetItem; } const EditBudgetModal: React.FC = ({ isModalVisible, setIsModalVisible, existingBudget }) => { + const { t } = useTranslation(); const formId = useId(); const form = useBudgetForm(toFormValues(existingBudget)); const { dirtyFields } = form.formState; @@ -39,15 +41,15 @@ const EditBudgetModal: React.FC = ({ isModalVisible, setIs const handleUpdate = async (formValues: BudgetFormValues) => { try { - toast.info("Making API Call"); + toast.info(t("budgets.editModal.toast.pending")); const changedValues = pickDirty(formValues, dirtyFields); await updateBudget.mutateAsync(applyBudgetPrecision({ budget_id: formValues.budget_id, ...changedValues })); - toast.success("Budget Updated"); + toast.success(t("budgets.editModal.toast.updated")); form.reset(); setIsModalVisible(false); } catch (error) { console.error("Error updating the budget:", error); - toast.fromError(`Error updating the budget: ${error}`); + toast.fromError(t("budgets.editModal.toast.error", { error: String(error) })); } }; @@ -55,15 +57,15 @@ const EditBudgetModal: React.FC = ({ isModalVisible, setIs !open && handleCancel()}> - Edit budget + {t("budgets.editModal.title")}
- }>Cancel + }>{t("budgets.editModal.cancel")}
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterBenchmarksTab.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterBenchmarksTab.tsx index b3acb8f9168..a6823d50b21 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterBenchmarksTab.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterBenchmarksTab.tsx @@ -1,6 +1,7 @@ "use client"; import React, { useState } from "react"; +import { Trans, useTranslation } from "react-i18next"; import type { AutoRouterDeployment } from "@/app/(dashboard)/hooks/models/useModels"; import { useAutoRouters } from "@/app/(dashboard)/hooks/models/useModels"; @@ -76,20 +77,21 @@ const SpendRow: React.FC<{ label: string; value: string; hint?: string; subdued? ); const HeroCard: React.FC<{ view: BenchmarkView }> = ({ view }) => { + const { t } = useTranslation(); const stats = view.stats; const cheaper = stats.saved_pct != null && stats.saved_pct >= 0; const classifierCost = stats.baseline_spend == null ? null : stats.savings_estimated_classifier_cost ?? null; const comparedAll = stats.savings_estimated_turns === stats.turns; return ( - +

- Total estimated savings + {t("costOptimization.autoRouter.hero.totalSavings")}

- {stats.saved_spend == null ? "Unavailable" : usd(stats.saved_spend)} + {stats.saved_spend == null ? t("costOptimization.autoRouter.unavailable") : usd(stats.saved_spend)}

{stats.saved_pct != null && ( = ({ view }) => {
{stats.baseline_spend != null && !comparedAll && (

- Compared on {stats.savings_estimated_turns.toLocaleString()} of {stats.turns.toLocaleString()} requests; - adaptive and quality routers are excluded + {t("costOptimization.autoRouter.hero.comparedOn", { + compared: stats.savings_estimated_turns.toLocaleString(), + total: stats.turns.toLocaleString(), + })}

)} {stats.unattributed_saved_spend != null && (

- Per-router records differ from recorded savings by {usd(Math.abs(stats.unattributed_saved_spend))}, for - example history from before per-router tracking, so the baseline comparison is unavailable + {t("costOptimization.autoRouter.hero.unattributed", { + amount: usd(Math.abs(stats.unattributed_saved_spend)), + })}

)}
{stats.baseline_spend != null && classifierCost == null && (

- Breakdown unavailable because some usage predates classification-cost tracking. + {t("costOptimization.autoRouter.hero.breakdownUnavailable")}

)}
@@ -157,20 +170,24 @@ const HeroCard: React.FC<{ view: BenchmarkView }> = ({ view }) => { }; const StackedTurnBar: React.FC<{ buckets: BucketRow[] }> = ({ buckets }) => { + const { t } = useTranslation(); const segments = buckets.filter((b) => b.turns > 0); return (
{segments.map((b) => (
))}
@@ -185,47 +202,57 @@ const StackedTurnBar: React.FC<{ buckets: BucketRow[] }> = ({ buckets }) => { ); }; -const BucketTable: React.FC<{ buckets: BucketRow[] }> = ({ buckets }) => ( - - - - Bucket - Turns - - Hit rate - - - - {buckets.map((b) => ( - - - - - - {b.label} - {b.sublabel} - - - - - {b.turns.toLocaleString()} - - -
-
-
- - - {pctLabel(b.hitRatePct)} - +const BucketTable: React.FC<{ buckets: BucketRow[] }> = ({ buckets }) => { + const { t } = useTranslation(); + return ( +
+ + + + {t("costOptimization.autoRouter.caching.columns.bucket")} + + + {t("costOptimization.autoRouter.caching.columns.turns")} + + + + {t("costOptimization.autoRouter.caching.columns.hitRate")} + - ))} - -
-); + + + {buckets.map((b) => ( + + + + + + {b.label} + {b.sublabel} + + + + + {b.turns.toLocaleString()} + + +
+
+
+ + + {pctLabel(b.hitRatePct)} + + + ))} + + + ); +}; const CachingCard: React.FC<{ cache: AutoRouterCacheStats }> = ({ cache }) => { - const buckets = bucketRows(cache); + const { t } = useTranslation(); + const buckets = bucketRows(cache, t); const total = bucketTurnsTotal(cache); const expiredMissPct = expiredMissShare(cache); return ( @@ -233,7 +260,7 @@ const CachingCard: React.FC<{ cache: AutoRouterCacheStats }> = ({ cache }) => {
-

Cache hit rate

+

{t("costOptimization.autoRouter.caching.hitRate")}

{pctLabel(cache.hit_rate_pct)}

{expiredMissPct === null ? null : ( @@ -248,13 +275,12 @@ const CachingCard: React.FC<{ cache: AutoRouterCacheStats }> = ({ cache }) => { } > - Expired-miss + {t("costOptimization.autoRouter.caching.expiredMiss.label")} {pctLabel(expiredMissPct)} - share of all measured turns that missed cache because a return to an earlier tier came after its TTL - lapsed + {t("costOptimization.autoRouter.caching.expiredMiss.tooltip")} @@ -263,17 +289,27 @@ const CachingCard: React.FC<{ cache: AutoRouterCacheStats }> = ({ cache }) => {
-

Share of turns

+

+ {t("costOptimization.autoRouter.caching.shareOfTurns")} +

- {total.toLocaleString()} turns - measured + {total.toLocaleString()} + ), + }} + />

{cache.unordered_turns > 0 && (

- {cache.unordered_turns.toLocaleString()} turns arrived out of order across pods and are not bucketed + {t("costOptimization.autoRouter.caching.unorderedTurns", { + turns: cache.unordered_turns.toLocaleString(), + })}

)}
@@ -291,13 +327,14 @@ interface BenchmarksBodyProps { } const BenchmarksBody: React.FC = ({ isPending, error, data, selectedKey, autoRouters }) => { - if (isPending) return Loading auto-router usage...; + const { t } = useTranslation(); + if (isPending) return {t("costOptimization.autoRouter.body.loading")}; if (error instanceof ApiError && error.status === 403) { - return Auto-router usage is visible to proxy admin roles only; + return {t("costOptimization.autoRouter.body.forbidden")}; } - if (error || !data) return Auto-router usage is unavailable right now; + if (error || !data) return {t("costOptimization.autoRouter.body.unavailable")}; - const view = viewFor(data, selectedKey); + const view = viewFor(data, selectedKey, t); const stats = view.stats; return ( <> @@ -305,30 +342,32 @@ const BenchmarksBody: React.FC = ({ isPending, error, data, -

- Savings and spend count requests on the selected UTC days. Actual spend covers every request on complexity - routers, including LLM classification cost. Baseline is actual spend plus recorded savings, so savings can be - zero or negative. -

+

{t("costOptimization.autoRouter.body.savingsNote")}

-

- Session metrics cover every session that overlaps the range, including its turns outside the range. -

+

{t("costOptimization.autoRouter.body.sessionNote")}

@@ -338,10 +377,8 @@ const BenchmarksBody: React.FC = ({ isPending, error, data,
-

Auto-router prompt caching

-

- every turn falls in exactly one bucket, by what the router did -

+

{t("costOptimization.autoRouter.caching.title")}

+

{t("costOptimization.autoRouter.caching.subtitle")}

@@ -362,21 +399,26 @@ export const AutoRouterUsageView: React.FC = ({ apiKey, userId, }) => { + const { t } = useTranslation(); const { dateValue, onDateChange } = activity; const { data, isPending, error } = useAutoRouterBenchmarks(accessToken, dateValue, apiKey, userId); const [selectedKey, setSelectedKey] = useState(ALL_ROUTERS); const { data: autoRouters } = useAutoRouters(); const groups = data?.groups ?? []; - const selectedLabel = data ? viewFor(data, selectedKey).label : "All auto-routers"; + const selectedLabel = data ? viewFor(data, selectedKey, t).label : t("costOptimization.autoRouter.allRouters"); const rangeLabel = formatRangeLabel(dateValue.from, dateValue.to); return (
-

Auto-router usage

- {rangeLabel &&

{rangeLabel} (UTC)

} +

{t("costOptimization.autoRouter.usageTitle")}

+ {rangeLabel && ( +

+ {t("costOptimization.autoRouter.rangeUtc", { range: rangeLabel })} +

+ )}
@@ -386,7 +428,7 @@ export const AutoRouterUsageView: React.FC = ({ {selectedLabel} - All auto-routers + {t("costOptimization.autoRouter.allRouters")} {groups.map((g) => ( {groupLabel(g, groups)} @@ -398,12 +440,7 @@ export const AutoRouterUsageView: React.FC = ({
- {userId && ( -

- Usage for this user across API keys and JWT-authenticated requests. Older sessions recorded without a user ID - are not included. -

- )} + {userId &&

{t("costOptimization.autoRouter.userScopeNote")}

} = ({ }; const AutoRouterBenchmarksTab: React.FC = ({ accessToken, activity }) => { + const { t } = useTranslation(); const [visitedTabs, setVisitedTabs] = useState(["usage"]); const handleTabChange = (value: unknown) => { @@ -430,10 +468,10 @@ const AutoRouterBenchmarksTab: React.FC = ({ acces - Usage + {t("costOptimization.autoRouter.tabs.usage")} - Shadow Evals + {t("costOptimization.autoRouter.tabs.shadowEvals")} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterSummaryTable.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterSummaryTable.tsx index 0273fae9ec1..2a5cf572008 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterSummaryTable.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/AutoRouterSummaryTable.tsx @@ -1,3 +1,5 @@ +import { useTranslation } from "react-i18next"; + import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"; import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from "@/components/ui/table"; @@ -10,33 +12,40 @@ interface AutoRouterSummaryTableProps { } export default function AutoRouterSummaryTable({ groups, selectedGroup }: AutoRouterSummaryTableProps) { + const { t } = useTranslation(); const visibleGroups = selectedGroup ? [selectedGroup] : groups; return ( - Router usage and savings - - Selected UTC days. Cost includes LLM calls and classification. Tokens count routed LLM input and output. - + {t("costOptimization.routerSummary.title")} + {t("costOptimization.routerSummary.description")} - +
- Auto-router - Tokens through router - Cost via router - Cost per 1M tokens - Saved vs. premium model - % saved + {t("costOptimization.routerSummary.columns.router")} + + {t("costOptimization.routerSummary.columns.tokens")} + + + {t("costOptimization.routerSummary.columns.cost")} + + + {t("costOptimization.routerSummary.columns.costPerMillion")} + + + {t("costOptimization.routerSummary.columns.saved")} + + {t("costOptimization.routerSummary.columns.savedPct")} {visibleGroups.length === 0 ? ( - No auto-routers in this range + {t("costOptimization.routerSummary.empty")} ) : ( @@ -44,29 +53,32 @@ export default function AutoRouterSummaryTable({ groups, selectedGroup }: AutoRo {groupLabel(group, groups)} - {group.total_tokens == null ? "Unavailable" : group.total_tokens.toLocaleString()} + {group.total_tokens == null + ? t("costOptimization.routerSummary.unavailable") + : group.total_tokens.toLocaleString()} {usd(group.spend)} {group.total_tokens != null && group.total_tokens > 0 ? usd((group.spend * 1_000_000) / group.total_tokens) - : "Unavailable"} + : t("costOptimization.routerSummary.unavailable")} - {group.saved_spend == null ? "Unavailable" : usd(group.saved_spend)} + {group.saved_spend == null + ? t("costOptimization.routerSummary.unavailable") + : usd(group.saved_spend)} - {group.saved_pct == null ? "Unavailable" : pctLabel(group.saved_pct)} + {group.saved_pct == null + ? t("costOptimization.routerSummary.unavailable") + : pctLabel(group.saved_pct)} )) )}
-

- Savings are estimated against each router's premium baseline. Token totals and unit costs are unavailable - for usage recorded before token tracking; unit cost also requires nonzero tokens. -

+

{t("costOptimization.routerSummary.footnote")}

 ); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.tsx index 7a63ccd1ee4..ab34f09f0ff 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.tsx @@ -1,6 +1,7 @@ "use client"; import React, { useMemo, useState } from "react"; +import { useTranslation } from "react-i18next"; import { ArrowDown, ArrowUp, ArrowUpDown, Info } from "lucide-react"; import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; @@ -67,6 +68,7 @@ const SortableHead = ({ sort: SortState; onSort: (column: SortColumn) => void; }) => { + const { t } = useTranslation(); const active = sort.column === column; const ActiveArrow = sort.dir === "asc" ? ArrowUp : ArrowDown; const Arrow = active ? ActiveArrow : ArrowUpDown; @@ -76,7 +78,7 @@ const SortableHead = ({