diff --git a/.circleci/config.yml b/.circleci/config.yml index 9a5b6da77f3..32983c92e27 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -409,14 +409,25 @@ jobs: --verbose \ --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -v -x \ + --cov=./litellm --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" no_output_timeout: 15m + - run: + name: Rename the coverage files + command: | + mv coverage.xml auth_ui_unit_tests_coverage.xml + mv .coverage auth_ui_unit_tests_coverage # Store test results - store_test_results: path: test-results + - persist_to_workspace: + root: . + paths: + - auth_ui_unit_tests_coverage.xml + - auth_ui_unit_tests_coverage litellm_router_testing: # Runs all tests with the "router" keyword docker: @@ -493,13 +504,24 @@ jobs: --verbose \ --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -v -x \ + --cov=./litellm --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" no_output_timeout: 15m + - run: + name: Rename the coverage files + command: | + mv coverage.xml router_unit_tests_coverage.xml + mv .coverage router_unit_tests_coverage # Store test results - store_test_results: path: test-results + - persist_to_workspace: + root: . + paths: + - router_unit_tests_coverage.xml + - router_unit_tests_coverage litellm_assistants_api_testing: # Runs all tests with the "assistants" keyword docker: - *python312_image @@ -2280,10 +2302,11 @@ jobs: - run: name: Combine Coverage command: | - uv tool run --from 'coverage[toml]==7.10.6' coverage combine realtime_translation_coverage ocr_coverage search_coverage logging_coverage audio_coverage local_testing_part1_coverage local_testing_part2_coverage pass_through_unit_tests_coverage batches_coverage guardrails_coverage redis_caching_coverage + uv tool run --from 'coverage[toml]==7.10.6' coverage combine realtime_translation_coverage ocr_coverage search_coverage logging_coverage audio_coverage local_testing_part1_coverage local_testing_part2_coverage pass_through_unit_tests_coverage batches_coverage guardrails_coverage redis_caching_coverage agent_coverage google_generate_content_endpoint_coverage litellm_utils_coverage router_unit_tests_coverage auth_ui_unit_tests_coverage uv tool run --from 'coverage[toml]==7.10.6' coverage xml - codecov/upload: file: ./coverage.xml + flags: circleci ui_build: docker: @@ -2669,6 +2692,8 @@ workflows: - local_testing_part1 - local_testing_part2 - litellm_assistants_api_testing + - litellm_router_unit_testing + - auth_ui_unit_tests - db_migration_disable_update_check: requires: - build_docker_database_image diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml index 64862f3c8f1..7e91341ac77 100644 --- a/.github/workflows/_test-unit-base.yml +++ b/.github/workflows/_test-unit-base.yml @@ -132,4 +132,5 @@ jobs: use_oidc: true directory: coverage-reports root_dir: ${{ github.workspace }} + flags: ${{ inputs.artifact-name }} fail_ci_if_error: false diff --git a/.github/workflows/_test-unit-services-base.yml b/.github/workflows/_test-unit-services-base.yml index 9dd668cbf36..7f973d8cafa 100644 --- a/.github/workflows/_test-unit-services-base.yml +++ b/.github/workflows/_test-unit-services-base.yml @@ -186,4 +186,5 @@ jobs: use_oidc: true directory: coverage-reports root_dir: ${{ github.workspace }} + flags: ${{ inputs.artifact-name }} fail_ci_if_error: false diff --git a/.github/workflows/test-unit-caching-redis.yml b/.github/workflows/test-unit-caching-redis.yml deleted file mode 100644 index ca274324f2f..00000000000 --- a/.github/workflows/test-unit-caching-redis.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: "Unit Tests: Caching (Redis)" - -# Uses cloud Redis credentials — only runs on trusted branches, not PRs. -# This prevents external PRs from accessing Redis credentials. -on: - push: - branches: [main, "litellm_*"] - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -jobs: - caching-redis: - uses: ./.github/workflows/_test-unit-services-base.yml - with: - # Redis-only tests that do NOT require provider API keys. - # Tests needing API keys (test_caching.py, test_caching_ssl.py, test_prometheus_service.py, - # test_router_caching.py) are in Phase 3 integration workflows. - test-path: >- - tests/local_testing/test_dual_cache.py - tests/local_testing/test_redis_batch_optimizations.py - tests/local_testing/test_router_utils.py - workers: 2 - reruns: 2 - timeout-minutes: 20 - enable-redis: true - enable-postgres: false - secrets: - REDIS_HOST: ${{ secrets.REDIS_HOST }} - REDIS_PORT: ${{ secrets.REDIS_PORT }} - REDIS_PASSWORD: ${{ secrets.REDIS_PASSWORD }} - DATABASE_URL: ${{ secrets.DATABASE_URL }} - POSTGRES_USER: ${{ secrets.POSTGRES_USER }} - POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} diff --git a/.gitignore b/.gitignore index 20355a8e4ef..dff64e3c9e9 100644 --- a/.gitignore +++ b/.gitignore @@ -101,4 +101,23 @@ STABILIZATION_TODO.md **/*.storageState.json **/coverage test-config + +# ---------- Terraform ---------- +# Provider binaries + module cache — regenerated by `terraform init`. +**/.terraform/ +# State files often contain secrets (DB passwords, API keys snapshotted from +# data sources). Keep state in a remote backend, never in git. +*.tfstate +*.tfstate.* +*.tfstate.backup +# Plan files can also contain sensitive values (variables in plaintext). +*.tfplan +# User-specific variable inputs — example files (terraform.tfvars.example) are +# tracked because they end in .example, which doesn't match the glob below. +*.tfvars +*.auto.tfvars +crash.log +crash.*.log +# .terraform.lock.hcl is intentionally NOT ignored — it pins provider versions +# and should be committed. .vscode \ No newline at end of file diff --git a/README.md b/README.md index 72fd43925c9..8df351e9303 100644 --- a/README.md +++ b/README.md @@ -292,7 +292,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [CompactifAI (`compactifai`)](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | | | | | | | | | [Custom (`custom`)](https://docs.litellm.ai/docs/providers/custom_llm_server) | ✅ | ✅ | ✅ | | | | | | | | | [Custom OpenAI (`custom_openai`)](https://docs.litellm.ai/docs/providers/openai_compatible) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | -| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | | | | | | | | +| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | | [Databricks (`databricks`)](https://docs.litellm.ai/docs/providers/databricks) | ✅ | ✅ | ✅ | | | | | | | | | [DataRobot (`datarobot`)](https://docs.litellm.ai/docs/providers/datarobot) | ✅ | ✅ | ✅ | | | | | | | | | [Deepgram (`deepgram`)](https://docs.litellm.ai/docs/providers/deepgram) | ✅ | ✅ | ✅ | | | ✅ | | | | | diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 00000000000..c08014fc0ef --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,83 @@ +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a + +FROM $UV_IMAGE AS uvbin + +# ---------- Builder ---------- +FROM $LITELLM_BUILD_IMAGE AS builder + +WORKDIR /app +USER root + +COPY --from=uvbin /uv /uvx /usr/local/bin/ + +RUN apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile + +# UV_COMPILE_BYTECODE=1 precompiles .pyc at install time → faster cold start. +# UV_LINK_MODE=copy avoids hardlink warnings when uv installs from a +# BuildKit cache mount (different filesystem). +# UV_PYTHON_DOWNLOADS=0 force uv to use the apk-installed CPython instead of +# silently pulling a managed interpreter. +ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ + UV_LINK_MODE=copy \ + UV_COMPILE_BYTECODE=1 \ + UV_PYTHON_DOWNLOADS=0 \ + PATH="/app/.venv/bin:${PATH}" + +# Stage 1 — install dependencies only. +RUN --mount=type=cache,target=/root/.cache/uv \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=enterprise/pyproject.toml,target=enterprise/pyproject.toml \ + --mount=type=bind,source=litellm-proxy-extras/pyproject.toml,target=litellm-proxy-extras/pyproject.toml \ + uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ + --extra proxy \ + --extra proxy-runtime \ + --extra extra_proxy \ + --extra semantic-router \ + --python python3 + +# Stage 2 — copy source and install the project + workspace members. +COPY . . + +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-default-groups --no-editable \ + --extra proxy \ + --extra proxy-runtime \ + --extra extra_proxy \ + --extra semantic-router \ + --python python3 + +RUN mkdir -p /home/nonroot && \ + HOME=/home/nonroot prisma generate --schema=./schema.prisma && \ + chown -R nonroot:nonroot /home/nonroot/.cache + +# ---------- Runtime ---------- +FROM $LITELLM_RUNTIME_IMAGE AS runtime + +USER root + +RUN apk add --no-cache bash openssl tzdata python3 libsndfile libatomic + +# wolfi-base ships an unprivileged `nonroot` account (UID/GID 65532) with +# /home/nonroot. We run the backend as that user +WORKDIR /app +ENV HOME=/home/nonroot \ + PATH="/app/.venv/bin:${PATH}" \ + PYTHONPATH="/app" \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 + +COPY --from=builder --chown=nonroot:nonroot /app /app +COPY --from=builder --chown=nonroot:nonroot /home/nonroot/.cache /home/nonroot/.cache + +RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \ + find /app/.venv -type d -path "*/tornado/test" -delete + +USER nonroot + +EXPOSE 4001/tcp + +ENTRYPOINT ["uvicorn", "backend.main:app"] +CMD ["--host", "0.0.0.0", "--port", "4001"] diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 00000000000..4092cd63f69 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,51 @@ +"""UI backend entrypoint. + +Reuses the existing FastAPI app from `litellm.proxy.proxy_server` and trims its +route table to just the management/admin surface used by the dashboard. Purely +additive — no existing module is modified. + +Run with: + uvicorn backend.main:app --host 0.0.0.0 --port 4001 +""" + +from contextlib import asynccontextmanager + +from fastapi.routing import Mount + +# See gateway/main.py for why we assemble DATABASE_URL(s) here before +# importing proxy_server. +from litellm.proxy.db.db_url_settings import DatabaseURLSettings + +DatabaseURLSettings.from_env().apply_to_env() + +from litellm.proxy.proxy_server import app + +from backend.routes.allowlist import BACKEND_EXACT_PATHS, BACKEND_PATH_PREFIXES + + +def _is_backend_route(route) -> bool: + """Keep the route on the backend if its path is in the management surface.""" + path = getattr(route, "path", None) + if path is None: + return False + if isinstance(route, Mount): + # Static UI mounts are served by the dedicated UI container, not here. + return False + if path in BACKEND_EXACT_PATHS: + return True + return any(path.startswith(prefix) for prefix in BACKEND_PATH_PREFIXES) + + +# See gateway/main.py for why the trim runs inside the lifespan instead of at +# module scope. +_proxy_lifespan = app.router.lifespan_context + + +@asynccontextmanager +async def _backend_lifespan(app_): + async with _proxy_lifespan(app_): + app_.router.routes = [r for r in app_.router.routes if _is_backend_route(r)] + yield + + +app.router.lifespan_context = _backend_lifespan diff --git a/backend/routes/__init__.py b/backend/routes/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/backend/routes/allowlist.py b/backend/routes/allowlist.py new file mode 100644 index 00000000000..610ba3dbd69 --- /dev/null +++ b/backend/routes/allowlist.py @@ -0,0 +1,135 @@ +"""Path allowlist for the UI backend (control plane) component. + +The backend exposes management/admin endpoints consumed by the UI: keys, users, +teams, orgs, customers, budgets, tags, workflows, model management, spend & +analytics, settings (router/cache/cost-tracking/fallbacks), SSO/onboarding, +audit logs, debug, enterprise admin, and UI bootstrap helpers (logo, favicon, +.well-known config). + +Anything LLM data-plane is dropped — those run on the gateway component. +""" + +BACKEND_PATH_PREFIXES: tuple[str, ...] = ( + # Identity / access + "/key/", + "/v2/key/", + "/user/", + "/v2/user/", + "/team/", + "/v2/team/", + "/organization/", + "/customer/", + "/end_user/", + "/sso/", + "/login", + "/v2/login", + "/v3/login", + "/logout", + "/token", + "/onboarding/", + "/audit", + "/oauth/", + "/invitation/", + "/jwt/", + # Models & routing config + "/model/", + "/v1/model/info", + "/v2/model/", + "/model_group", + "/model_access_group/", + "/model_hub/", + "/v1/access_group", + "/access_group/", + "/router/", + "/router_settings", + "/adaptive_router/", + "/fallback", + "/fallbacks", + "/cache_settings", + "/cost_tracking", + "/cost/", + "/credentials", + "/credential", + "/provider/budgets", + # Tools / agents (registry & policy admin) + "/v1/tool/", + "/v1/agents", + # Guardrails admin + "/v2/guardrails/", + # MCP server admin + BYOK OAuth flow (UI-initiated) + dynamic per-server endpoints + "/v1/mcp/", + "/test/", + "/{mcp_server_name}/", + # Budgets / tags / workflows / memory mgmt + "/budget/", + "/tag/", + "/workflow/", + "/v1/workflows/", + "/project/", + "/memory/", + "/mcp/", + # Spend / analytics + "/spend/", + "/analytics/", + "/global/", + "/user_agent", + "/usage/", + "/daily/", + # CloudZero cost-export admin (init / settings / export / dry-run / delete) + "/cloudzero/", + # Caching admin + "/cache/", + "/caching/", + # Callbacks / hooks + "/active/callbacks", + "/callbacks", + "/team_callback", + # Alerting / email / IP allowlist + "/alerting/", + "/email/", + "/add/allowed_ip", + "/delete/allowed_ip", + "/get/", + # Enterprise admin + "/enterprise/", + # Debug / config / profiling + "/debug/", + "/config/", + "/memory-usage-in-mem-cache", + "/otel-spans", + "/lazy/", + "/in_product_nudges", + # Admin reload / schedule + "/reload/", + "/schedule/", + "/settings", + "/update/", + "/upload/", + # Dev / admin utilities + "/utils/", + # UI bootstrap helpers (assets the dashboard fetches) + "/get_logo_url", + "/get_image", + "/get_favicon", + "/.well-known/", + "/litellm/.well-known/", + "/ui_discovery/", + "/ui-config", + "/sso_settings", + "/public/", + "/robots.txt", + # Health (k8s probes) + "/health", +) + +BACKEND_EXACT_PATHS: frozenset[str] = frozenset( + { + "/", + "/routes", + "/openapi.json", + "/docs", + "/docs/oauth2-redirect", + "/redoc", + "/fallback/login", + } +) diff --git a/codecov.yaml b/codecov.yaml index 8609d3143d6..58681b884d0 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -3,6 +3,16 @@ codecov: notify: wait_for_ci: false # post as soon as expected uploads arrive, don't wait on CI +# Uploads are flagged per workflow/shard (GHA) or "circleci". carryforward makes +# a re-upload of a flag replace its prior session instead of accumulating a +# conflicting one, and lets a commit reuse a flag from its parent when that flag +# was not re-uploaded. Required because the same commit can receive the +# push-triggered workflows more than once (re-runs / branches cut at the same +# SHA); flagless overlapping sessions made Codecov drop the largest files. +flag_management: + default_rules: + carryforward: true + component_management: individual_components: - component_id: "Router" diff --git a/deploy/charts/litellm-helm/templates/hpa.yaml b/deploy/charts/litellm-helm/templates/hpa.yaml index 71e199c5aeb..fec4d1f5c5e 100644 --- a/deploy/charts/litellm-helm/templates/hpa.yaml +++ b/deploy/charts/litellm-helm/templates/hpa.yaml @@ -12,6 +12,10 @@ spec: name: {{ include "litellm.fullname" . }} minReplicas: {{ .Values.autoscaling.minReplicas }} maxReplicas: {{ .Values.autoscaling.maxReplicas }} + {{- if .Values.autoscaling.behavior }} + behavior: + {{- toYaml .Values.autoscaling.behavior | nindent 4 }} + {{- end }} metrics: {{- if .Values.autoscaling.targetCPUUtilizationPercentage }} - type: Resource diff --git a/deploy/charts/litellm-helm/tests/hpa_tests.yaml b/deploy/charts/litellm-helm/tests/hpa_tests.yaml new file mode 100644 index 00000000000..ec18c3591d3 --- /dev/null +++ b/deploy/charts/litellm-helm/tests/hpa_tests.yaml @@ -0,0 +1,36 @@ +suite: "hpa with behavior" +templates: + - hpa.yaml +tests: + - it: "renders behavior when set" + set: + autoscaling.enabled: true + autoscaling.behavior: + scaleUp: + stabilizationWindowSeconds: 60 + policies: + - type: Pods + value: 2 + periodSeconds: 60 + scaleDown: + stabilizationWindowSeconds: 90 + policies: + - type: Pods + value: 1 + periodSeconds: 60 + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 } + - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 } + +--- +suite: "hpa without behavior" +templates: + - hpa.yaml +tests: + - it: "does not render behavior when not set" + set: + autoscaling.enabled: true + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - isNull: { path: spec.behavior } diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index 9c7c013341b..81558ed5b29 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -184,6 +184,7 @@ autoscaling: maxReplicas: 100 targetCPUUtilizationPercentage: 80 # targetMemoryUtilizationPercentage: 80 + # behavior: {} # Autoscaling with keda is mutually exclusive with hpa keda: diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 9698e7912d4..9f37b52d94c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.40" +version = "0.1.41" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.40" +version = "0.1.41" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile new file mode 100644 index 00000000000..a2ca3d3f83f --- /dev/null +++ b/gateway/Dockerfile @@ -0,0 +1,83 @@ +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a + +FROM $UV_IMAGE AS uvbin + +# ---------- Builder ---------- +FROM $LITELLM_BUILD_IMAGE AS builder + +WORKDIR /app +USER root + +COPY --from=uvbin /uv /uvx /usr/local/bin/ + +RUN apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile + +# UV_COMPILE_BYTECODE=1 precompiles .pyc at install time → faster cold start. +# UV_LINK_MODE=copy avoids hardlink warnings when uv installs from a +# BuildKit cache mount (different filesystem). +# UV_PYTHON_DOWNLOADS=0 force uv to use the apk-installed CPython instead of +# silently pulling a managed interpreter. +ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ + UV_LINK_MODE=copy \ + UV_COMPILE_BYTECODE=1 \ + UV_PYTHON_DOWNLOADS=0 \ + PATH="/app/.venv/bin:${PATH}" + +# Stage 1 — install dependencies only. +RUN --mount=type=cache,target=/root/.cache/uv \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=enterprise/pyproject.toml,target=enterprise/pyproject.toml \ + --mount=type=bind,source=litellm-proxy-extras/pyproject.toml,target=litellm-proxy-extras/pyproject.toml \ + uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ + --extra proxy \ + --extra proxy-runtime \ + --extra extra_proxy \ + --extra semantic-router \ + --python python3 + +# Stage 2 — copy source and install the project + workspace members. +COPY . . + +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-default-groups --no-editable \ + --extra proxy \ + --extra proxy-runtime \ + --extra extra_proxy \ + --extra semantic-router \ + --python python3 + +RUN mkdir -p /home/nonroot && \ + HOME=/home/nonroot prisma generate --schema=./schema.prisma && \ + chown -R nonroot:nonroot /home/nonroot/.cache + +# ---------- Runtime ---------- +FROM $LITELLM_RUNTIME_IMAGE AS runtime + +USER root + +RUN apk add --no-cache bash openssl tzdata python3 libsndfile libatomic + +# wolfi-base ships an unprivileged `nonroot` account (UID/GID 65532) with +# /home/nonroot. We run the proxy as that user. +WORKDIR /app +ENV HOME=/home/nonroot \ + PATH="/app/.venv/bin:${PATH}" \ + PYTHONPATH="/app" \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 + +COPY --from=builder --chown=nonroot:nonroot /app /app +COPY --from=builder --chown=nonroot:nonroot /home/nonroot/.cache /home/nonroot/.cache + +RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \ + find /app/.venv -type d -path "*/tornado/test" -delete + +USER nonroot + +EXPOSE 4000/tcp + +ENTRYPOINT ["sh", "-c", "exec uvicorn gateway.main:app --workers \"${NUM_WORKERS:-1}\" \"$@\"", "--"] +CMD ["--host", "0.0.0.0", "--port", "4000"] diff --git a/gateway/main.py b/gateway/main.py new file mode 100644 index 00000000000..09d30f5da3f --- /dev/null +++ b/gateway/main.py @@ -0,0 +1,59 @@ +"""Gateway entrypoint. + +Reuses the existing FastAPI app from `litellm.proxy.proxy_server` and trims its +route table to just the LLM data-plane surface. The trim is purely additive — +no existing module is modified, the full app continues to work via the legacy +entrypoint (`litellm.proxy.proxy_server:app`). + +Run with: + uvicorn gateway.main:app --host 0.0.0.0 --port 4000 +""" + +from contextlib import asynccontextmanager + +from fastapi.routing import Mount + +# Assemble DATABASE_URL (+ DATABASE_URL_READ_REPLICA) from the discrete +# DATABASE_* env vars before proxy_server imports spin up Prisma. Handles +# both IAM (mint a token) and password auth, writer and reader. The standard +# CLI flow does this in proxy_cli.py; we bypass proxy_cli by uvicorn'ing the +# app directly, so without this Prisma initializes with the placeholder URL +# and every DB-needing endpoint returns "Database not connected". +from litellm.proxy.db.db_url_settings import DatabaseURLSettings + +DatabaseURLSettings.from_env().apply_to_env() + +from litellm.proxy.proxy_server import app + +from gateway.routes.allowlist import GATEWAY_EXACT_PATHS, GATEWAY_PATH_PREFIXES + + +def _is_gateway_route(route) -> bool: + """Keep the route on the gateway if its path is in the LLM data-plane surface.""" + path = getattr(route, "path", None) + if path is None: + return False + if isinstance(route, Mount): + # Gateway never serves the static UI or its asset bundles. + return False + if path in GATEWAY_EXACT_PATHS: + return True + return any(path.startswith(prefix) for prefix in GATEWAY_PATH_PREFIXES) + + +# Wrap proxy_server's existing lifespan so the route trim runs *after* its +# startup hooks (and any plugin code those hooks load) have had a chance to +# register routes. A module-load filter would miss routes added during +# startup; running inside the lifespan, after the inner __aenter__, catches +# them while still completing before uvicorn opens the listener. +_proxy_lifespan = app.router.lifespan_context + + +@asynccontextmanager +async def _gateway_lifespan(app_): + async with _proxy_lifespan(app_): + app_.router.routes = [r for r in app_.router.routes if _is_gateway_route(r)] + yield + + +app.router.lifespan_context = _gateway_lifespan diff --git a/gateway/routes/__init__.py b/gateway/routes/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/gateway/routes/allowlist.py b/gateway/routes/allowlist.py new file mode 100644 index 00000000000..cbbf55c9873 --- /dev/null +++ b/gateway/routes/allowlist.py @@ -0,0 +1,121 @@ +"""Path allowlist for the gateway component. + +The gateway exposes the LLM data-plane surface: chat/completions, embeddings, +audio, batches, files, fine-tuning, rerank, ocr, rag, video, search, image, +responses, vector stores, passthrough providers, realtime websockets, MCP +tool-call endpoints, and operational endpoints (/health, /metrics). + +Any path not listed here is dropped from the gateway process so management/UI +endpoints don't ride on the same pods. + +Versioned data-plane paths are enumerated explicitly rather than allowing a +blanket `/v1/` or `/v2/` prefix — those broad prefixes would otherwise also +match management routes like `/v1/access_group`, `/v1/tool/{tool_name}/logs`, +`/v2/key/info`, etc. +""" + +GATEWAY_PATH_PREFIXES: tuple[str, ...] = ( + # OpenAI-compatible data-plane surface (versioned + unversioned) + "/v1/chat/", + "/chat/", + "/v1/completions", + "/completions", + "/v1/embeddings", + "/embeddings", + "/v1/moderations", + "/moderations", + "/v1/audio/", + "/audio/", + "/v1/images/", + "/images/", + "/v1/files", + "/files", + "/v1/batches", + "/batches", + "/v1/fine_tuning/", + "/fine_tuning/", + "/v1/fine-tuning/", + "/fine-tuning/", + "/v1/responses", + "/responses", + "/v1/threads", + "/threads", + "/v1/assistants", + "/assistants", + "/v1/vector_stores", + "/vector_stores", + "/v1/indexes", + "/v1/models", + "/models", + "/openai/", + "/engines/", + # Anthropic / agentic data-plane surface + "/v1/messages", + "/messages", + "/v1/skills", + "/v1/a2a/", + # LiteLLM-native LLM surface + "/v1/rerank", + "/v2/rerank", + "/rerank", + "/v1/ocr", + "/ocr", + "/v1/rag/", + "/rag/", + "/v1/video", + "/v1/videos", + "/video/", + "/videos", + "/v1/search", + "/search", + "/v1/containers", + "/containers", + "/v1/evals", + "/v1/memory", + "/queue/chat/", + # Google data plane (v1beta is the Google AI Studio version) + "/v1beta/", + "/interactions", + # Provider passthrough + "/anthropic/", + "/azure/", + "/azure_ai/", + "/aws/", + "/bedrock/", + "/cohere/", + "/gemini/", + "/google/", + "/vertex_ai/", + "/vertex-ai/", + "/assemblyai/", + "/eu.assemblyai/", + "/langfuse/", + "/vllm/", + "/mistral/", + "/groq/", + "/voyage/", + "/cursor/", + "/milvus/", + "/openai_passthrough/", + # Dynamic provider / toolset passthrough (path templates) + "/{provider}/", + "/toolset/", + # Realtime / streaming + "/v1/realtime", + "/realtime", + # Health & ops + "/health", + "/metrics", +) + +GATEWAY_EXACT_PATHS: frozenset[str] = frozenset( + { + "/", + "/routes", + "/openapi.json", + "/docs", + "/docs/oauth2-redirect", + "/redoc", + "/test", + } +) diff --git a/helm/litellm/Chart.yaml b/helm/litellm/Chart.yaml new file mode 100644 index 00000000000..e67f5790c7e --- /dev/null +++ b/helm/litellm/Chart.yaml @@ -0,0 +1,8 @@ +apiVersion: v2 +name: litellm +description: LiteLLM componentized — gateway, UI backend, and UI as separate services +type: application +version: 0.1.0 +appVersion: "0.1.0" +annotations: + org.opencontainers.image.source: "https://github.com/BerriAI/litellm" diff --git a/helm/litellm/templates/NOTES.txt b/helm/litellm/templates/NOTES.txt new file mode 100644 index 00000000000..5b939fe480a --- /dev/null +++ b/helm/litellm/templates/NOTES.txt @@ -0,0 +1,49 @@ +LiteLLM componentized — release {{ .Release.Name }} in namespace {{ .Release.Namespace }}. + +Components: +{{- if .Values.gateway.enabled }} + - gateway : Service {{ include "litellm.gateway.fullname" . }} on port {{ .Values.gateway.service.port }} +{{- end }} +{{- if .Values.backend.enabled }} + - backend : Service {{ include "litellm.backend.fullname" . }} on port {{ .Values.backend.service.port }} +{{- end }} +{{- if .Values.ui.enabled }} + - ui : Service {{ include "litellm.ui.fullname" . }} on port {{ .Values.ui.service.port }} +{{- end }} + +Port-forward examples: + kubectl -n {{ .Release.Namespace }} port-forward svc/{{ include "litellm.gateway.fullname" . }} {{ .Values.gateway.service.port }} + kubectl -n {{ .Release.Namespace }} port-forward svc/{{ include "litellm.backend.fullname" . }} {{ .Values.backend.service.port }} + kubectl -n {{ .Release.Namespace }} port-forward svc/{{ include "litellm.ui.fullname" . }} {{ .Values.ui.service.port }} + +Reminders: + - Sensitive values come from Secret references only. Before installing, set: + - masterKey.secretName (Secret with the proxy master key) + - database.writer.{host,port,dbname} (writer connection pieces) + - database.writer.passwordSecret.{name,usernameKey,passwordKey} + (Secret holding the writer DB username + password) + - database.writer.useIAMAuth: true (optional — chart sets IAM_TOKEN_DB_AUTH=true and + omits DATABASE_PASSWORD / DATABASE_URL so the proxy + mints the URL from an IAM token at startup) + - database.reader.host (optional — enables read-replica routing; reader + .passwordSecret.name is required when set, unless + .useIAMAuth is true) + - database.reader.useIAMAuth: true (optional, requires database.writer.useIAMAuth: true — + chart emits DATABASE_*_READ_REPLICA env vars and + omits DATABASE_PASSWORD_READ_REPLICA / + DATABASE_URL_READ_REPLICA so the proxy mints the + reader URL from an IAM token at startup) + - redis.passwordSecret.name (optional — set when redis.host is provided and the + cache requires auth) + - redis.cluster: true (optional — chart sets REDIS_CLUSTER_NODES from + redis.host / redis.port so the proxy's Cache() + constructs a RedisClusterCache; the cluster client + discovers remaining nodes from CLUSTER SLOTS) + - Per-component extras (gateway / backend / ui): + - {component}.extraEnv / envConfigMaps / envSecrets (the latter two are lists of resource names → + envFrom configMapRef / secretRef) + - {component}.logLevel (renders as LITELLM_LOG) + - gateway.config.proxy_config (rendered into a ConfigMap and mounted at + /app/config/config.yaml; gateway reads it via + CONFIG_FILE_PATH) + - Enable ingress.enabled=true to dispatch / → ui, gateway data-plane prefixes → gateway, and the catch-all → backend. diff --git a/helm/litellm/templates/_helpers.tpl b/helm/litellm/templates/_helpers.tpl new file mode 100644 index 00000000000..e2faf42b766 --- /dev/null +++ b/helm/litellm/templates/_helpers.tpl @@ -0,0 +1,245 @@ +{{/* +Common naming + label helpers shared by gateway, backend, and ui templates. +*/}} + +{{- define "litellm.name" -}} +{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +{{- define "litellm.fullname" -}} +{{- if .Values.fullnameOverride -}} +{{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" -}} +{{- else -}} +{{- $name := default .Chart.Name .Values.nameOverride -}} +{{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" -}} +{{- end -}} +{{- end -}} + +{{- define "litellm.gateway.fullname" -}} +{{- printf "%s-gateway" (include "litellm.fullname" .) | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +{{- define "litellm.backend.fullname" -}} +{{- printf "%s-backend" (include "litellm.fullname" .) | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +{{- define "litellm.ui.fullname" -}} +{{- printf "%s-ui" (include "litellm.fullname" .) | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +{{- define "litellm.commonLabels" -}} +app.kubernetes.io/name: {{ include "litellm.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/managed-by: {{ .Release.Service }} +helm.sh/chart: {{ printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" }} +{{- end -}} + +{{/* +Per-component selector labels — used in both Service selectors and Deployment matchLabels. +*/}} +{{- define "litellm.gateway.selectorLabels" -}} +app.kubernetes.io/name: {{ include "litellm.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/component: gateway +{{- end -}} + +{{- define "litellm.backend.selectorLabels" -}} +app.kubernetes.io/name: {{ include "litellm.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/component: backend +{{- end -}} + +{{- define "litellm.ui.selectorLabels" -}} +app.kubernetes.io/name: {{ include "litellm.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/component: ui +{{- end -}} + +{{/* +Shared ServiceAccount name used by all three component Deployments. When +`serviceAccount.create` is true and `serviceAccount.name` is empty, default +to the chart fullname. When `create` is false, fall back to the provided +name or the namespace's `default` SA. +*/}} +{{- define "litellm.serviceAccountName" -}} +{{- if .Values.serviceAccount.create -}} +{{ default (include "litellm.fullname" .) .Values.serviceAccount.name }} +{{- else -}} +{{ default "default" .Values.serviceAccount.name }} +{{- end -}} +{{- end -}} + +{{/* +Master-key + database + redis env block — shared by gateway, backend, and the +migrations Job. + +Invoke with a dict: `(dict "root" $ "component" .Values.gateway)`. `root` is +the chart context (needed for .Values), `component` selects which component's +`extraEnv` / `logLevel` to render. + +Sensitive values (master key, DB username + password, Redis password) come +only from referenced Secrets; the chart never accepts inline values for them. + +The chart never assembles DATABASE_URL itself. It emits only the discrete +DATABASE_HOST/PORT/USER/NAME/SCHEMA (+ DATABASE_PASSWORD for password auth) +vars; the proxy's entrypoint (DatabaseURLSettings in +litellm/proxy/db/db_url_settings.py) builds the URL from them and +percent-encodes the credentials. Assembling the URL here via Kubernetes +`$(VAR)` substitution would embed the raw secret value, corrupting the URL +whenever the password contains a URL-reserved character (@, /, ?, %, +, +...) — as AWS RDS auto-generated passwords routinely do. + +When `database.writer.useIAMAuth: true`, the chart injects +IAM_TOKEN_DB_AUTH=true and omits DATABASE_PASSWORD — the entrypoint mints +the URL from DATABASE_HOST/PORT/USER/NAME plus a short-lived IAM token +instead of a static password. + +The read replica is opt-in via `database.reader.host`. The chart emits +DATABASE_HOST_READ_REPLICA / DATABASE_PORT_READ_REPLICA / +DATABASE_NAME_READ_REPLICA (+ DATABASE_SCHEMA_READ_REPLICA) for both auth +modes, plus DATABASE_USER_READ_REPLICA / DATABASE_PASSWORD_READ_REPLICA for +password auth. When `database.reader.useIAMAuth: true` it omits +DATABASE_PASSWORD_READ_REPLICA and the entrypoint mints the reader URL the +same way. Reader IAM only takes effect when the writer also uses IAM auth +(the proxy gates URL minting on IAM_TOKEN_DB_AUTH, which only the writer +sets). +*/}} +{{- define "litellm.serverEnv" -}} +{{- $root := .root -}} +{{- $component := .component -}} +- name: LITELLM_MASTER_KEY + valueFrom: + secretKeyRef: + name: {{ required "masterKey.secretName is required (the chart no longer accepts an inline master key)" $root.Values.masterKey.secretName }} + key: {{ $root.Values.masterKey.secretKey | default "master-key" }} +{{- if $component.logLevel }} +- name: LITELLM_LOG + value: {{ $component.logLevel | quote }} +{{- end }} +{{- with $root.Values.database.writer }} +- name: DATABASE_HOST + value: {{ required "database.writer.host is required" .host | quote }} +- name: DATABASE_PORT + value: {{ .port | default 5432 | quote }} +- name: DATABASE_USER + valueFrom: + secretKeyRef: + name: {{ required "database.writer.passwordSecret.name is required" .passwordSecret.name }} + key: {{ .passwordSecret.usernameKey | default "username" }} +- name: DATABASE_NAME + value: {{ required "database.writer.dbname is required" .dbname | quote }} +{{- if .schema }} +- name: DATABASE_SCHEMA + value: {{ .schema | quote }} +{{- end }} +{{- if .useIAMAuth }} +- name: IAM_TOKEN_DB_AUTH + value: "true" +{{- else }} +- name: DATABASE_PASSWORD + valueFrom: + secretKeyRef: + name: {{ .passwordSecret.name }} + key: {{ .passwordSecret.passwordKey | default "password" }} +{{- end }} +{{- end }} +{{- with $root.Values.database.reader }} +{{- if .host }} +{{- if and .useIAMAuth (not $root.Values.database.writer.useIAMAuth) }} +{{- fail "database.reader.useIAMAuth requires database.writer.useIAMAuth: true (the proxy gates IAM URL minting on IAM_TOKEN_DB_AUTH, which is only set by the writer)" }} +{{- end }} +- name: DATABASE_HOST_READ_REPLICA + value: {{ .host | quote }} +- name: DATABASE_PORT_READ_REPLICA + value: {{ .port | default 5432 | quote }} +- name: DATABASE_NAME_READ_REPLICA + value: {{ required "database.reader.dbname is required when database.reader.host is set" .dbname | quote }} +{{- if .schema }} +- name: DATABASE_SCHEMA_READ_REPLICA + value: {{ .schema | quote }} +{{- end }} +{{- if .useIAMAuth }} +{{- if .passwordSecret.name }} +- name: DATABASE_USER_READ_REPLICA + valueFrom: + secretKeyRef: + name: {{ .passwordSecret.name }} + key: {{ .passwordSecret.usernameKey | default "username" }} +{{- end }} +{{- else }} +{{- if not .passwordSecret.name }} +{{- fail "database.reader.passwordSecret.name is required when database.reader.host is set" }} +{{- end }} +- name: DATABASE_USER_READ_REPLICA + valueFrom: + secretKeyRef: + name: {{ .passwordSecret.name }} + key: {{ .passwordSecret.usernameKey | default "username" }} +- name: DATABASE_PASSWORD_READ_REPLICA + valueFrom: + secretKeyRef: + name: {{ .passwordSecret.name }} + key: {{ .passwordSecret.passwordKey | default "password" }} +{{- end }} +{{- end }} +{{- end }} +{{/* +The migrations Job (helm.sh/hook: pre-upgrade) is the single owner of +`prisma migrate deploy`. Without this, every gateway/backend pod also runs +Prisma schema-update on startup and contends with the Job — and with each +other — for Prisma's Postgres advisory lock on the writer, which makes the +Job's `migrate deploy` intermittently block until its per-attempt timeout +and retry-exhaust. The Job's entrypoint (migrations/run.py) does not import +proxy_server and never reads DISABLE_SCHEMA_UPDATE, so emitting it here is a +harmless no-op for the Job and authoritative for the app pods. +*/}} +- name: DISABLE_SCHEMA_UPDATE + value: "true" +{{- if $root.Values.redis.host }} +- name: REDIS_HOST + value: {{ $root.Values.redis.host | quote }} +- name: REDIS_PORT + value: {{ $root.Values.redis.port | quote }} +{{- if $root.Values.redis.passwordSecret.name }} +- name: REDIS_PASSWORD + valueFrom: + secretKeyRef: + name: {{ $root.Values.redis.passwordSecret.name }} + key: {{ $root.Values.redis.passwordSecret.passwordKey | default "password" }} +{{- end }} +{{- if $root.Values.redis.cluster }} +{{/* The proxy's Cache() reads REDIS_CLUSTER_NODES as JSON and constructs a + RedisClusterCache when it's set (litellm/caching/caching.py:169-192). + We seed with the single configured endpoint — the cluster client + discovers the remaining nodes from CLUSTER SLOTS at startup. */}} +- name: REDIS_CLUSTER_NODES + value: {{ printf "[{\"host\":%q,\"port\":%v}]" $root.Values.redis.host (int $root.Values.redis.port) | quote }} +{{- end }} +{{- end }} +{{- with $component.extraEnv }} +{{ toYaml . }} +{{- end }} +{{- end -}} + +{{/* +Renders `envFrom:` block for a component's `envConfigMaps` / `envSecrets` +lists. Each entry is a resource name; the chart wires the whole ConfigMap / +Secret into the container's env via configMapRef / secretRef. + +Invoke with just the component dict, e.g. `.Values.gateway`. Emits nothing +when both lists are empty so the container spec stays clean. +*/}} +{{- define "litellm.envFrom" -}} +{{- $component := . -}} +{{- if or $component.envConfigMaps $component.envSecrets }} +envFrom: +{{- range $component.envConfigMaps }} + - configMapRef: + name: {{ . }} +{{- end }} +{{- range $component.envSecrets }} + - secretRef: + name: {{ . }} +{{- end }} +{{- end }} +{{- end -}} diff --git a/helm/litellm/templates/backend/deployment.yaml b/helm/litellm/templates/backend/deployment.yaml new file mode 100644 index 00000000000..e761409f8c4 --- /dev/null +++ b/helm/litellm/templates/backend/deployment.yaml @@ -0,0 +1,60 @@ +{{- if .Values.backend.enabled }} +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "litellm.backend.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: backend +spec: + selector: + matchLabels: + {{- include "litellm.backend.selectorLabels" . | nindent 6 }} + template: + metadata: + {{- with .Values.backend.podAnnotations }} + annotations: + {{- toYaml . | nindent 8 }} + {{- end }} + labels: + {{- include "litellm.backend.selectorLabels" . | nindent 8 }} + spec: + serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . | nindent 8 }} + {{- end }} + containers: + - name: backend + image: "{{ .Values.backend.image.repository }}:{{ .Values.backend.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: {{ .Values.backend.image.pullPolicy }} + ports: + - name: http + containerPort: 4001 + protocol: TCP + env: + {{- include "litellm.serverEnv" (dict "root" $ "component" .Values.backend) | nindent 12 }} + {{- include "litellm.envFrom" .Values.backend | nindent 10 }} + {{- with .Values.backend.livenessProbe }} + livenessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + {{- with .Values.backend.readinessProbe }} + readinessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + resources: + {{- toYaml .Values.backend.resources | nindent 12 }} + {{- with .Values.backend.nodeSelector }} + nodeSelector: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.backend.affinity }} + affinity: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.backend.tolerations }} + tolerations: + {{- toYaml . | nindent 8 }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/backend/hpa.yaml b/helm/litellm/templates/backend/hpa.yaml new file mode 100644 index 00000000000..d02f011d0bb --- /dev/null +++ b/helm/litellm/templates/backend/hpa.yaml @@ -0,0 +1,33 @@ +{{- if and .Values.backend.enabled .Values.backend.hpa.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: {{ include "litellm.backend.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: backend +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: {{ include "litellm.backend.fullname" . }} + minReplicas: {{ .Values.backend.hpa.minReplicas }} + maxReplicas: {{ .Values.backend.hpa.maxReplicas }} + metrics: + {{- if .Values.backend.hpa.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.backend.hpa.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.backend.hpa.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.backend.hpa.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/backend/service.yaml b/helm/litellm/templates/backend/service.yaml new file mode 100644 index 00000000000..d480c654784 --- /dev/null +++ b/helm/litellm/templates/backend/service.yaml @@ -0,0 +1,18 @@ +{{- if .Values.backend.enabled }} +apiVersion: v1 +kind: Service +metadata: + name: {{ include "litellm.backend.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: backend +spec: + type: {{ .Values.backend.service.type }} + ports: + - port: {{ .Values.backend.service.port }} + targetPort: http + protocol: TCP + name: http + selector: + {{- include "litellm.backend.selectorLabels" . | nindent 4 }} +{{- end }} diff --git a/helm/litellm/templates/gateway/configmap.yaml b/helm/litellm/templates/gateway/configmap.yaml new file mode 100644 index 00000000000..d262bf25b87 --- /dev/null +++ b/helm/litellm/templates/gateway/configmap.yaml @@ -0,0 +1,9 @@ +{{- if .Values.gateway.config.create }} +apiVersion: v1 +kind: ConfigMap +metadata: + name: {{ include "litellm.gateway.fullname" . }}-config +data: + config.yaml: | +{{ .Values.gateway.config.proxy_config | toYaml | indent 6 }} +{{- end }} diff --git a/helm/litellm/templates/gateway/deployment.yaml b/helm/litellm/templates/gateway/deployment.yaml new file mode 100644 index 00000000000..935d432342e --- /dev/null +++ b/helm/litellm/templates/gateway/deployment.yaml @@ -0,0 +1,83 @@ +{{- if .Values.gateway.enabled }} +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "litellm.gateway.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: gateway +spec: + selector: + matchLabels: + {{- include "litellm.gateway.selectorLabels" . | nindent 6 }} + template: + metadata: + annotations: + {{- if .Values.gateway.config.create }} + checksum/config: {{ include (print $.Template.BasePath "/gateway/configmap.yaml") . | sha256sum }} + {{- end }} + {{- with .Values.gateway.podAnnotations }} + {{- toYaml . | nindent 8 }} + {{- end }} + labels: + {{- include "litellm.gateway.selectorLabels" . | nindent 8 }} + spec: + serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . | nindent 8 }} + {{- end }} + containers: + - name: gateway + image: "{{ .Values.gateway.image.repository }}:{{ .Values.gateway.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: {{ .Values.gateway.image.pullPolicy }} + ports: + - name: http + containerPort: 4000 + protocol: TCP + env: + {{- include "litellm.serverEnv" (dict "root" $ "component" .Values.gateway) | nindent 12 }} + {{- if .Values.gateway.config.create }} + - name: CONFIG_FILE_PATH + value: /app/config/config.yaml + {{- end }} + {{- if .Values.gateway.numWorkers }} + - name: NUM_WORKERS + value: {{ .Values.gateway.numWorkers | quote }} + {{- end }} + {{- include "litellm.envFrom" .Values.gateway | nindent 10 }} + {{- if .Values.gateway.config.create }} + volumeMounts: + - name: gateway-config + mountPath: /app/config/config.yaml + subPath: config.yaml + {{- end }} + {{- with .Values.gateway.livenessProbe }} + livenessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + {{- with .Values.gateway.readinessProbe }} + readinessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + resources: + {{- toYaml .Values.gateway.resources | nindent 12 }} + {{- if .Values.gateway.config.create }} + volumes: + - name: gateway-config + configMap: + name: {{ include "litellm.gateway.fullname" . }}-config + {{- end }} + {{- with .Values.gateway.nodeSelector }} + nodeSelector: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.gateway.affinity }} + affinity: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.gateway.tolerations }} + tolerations: + {{- toYaml . | nindent 8 }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/gateway/hpa.yaml b/helm/litellm/templates/gateway/hpa.yaml new file mode 100644 index 00000000000..27c4f05ba59 --- /dev/null +++ b/helm/litellm/templates/gateway/hpa.yaml @@ -0,0 +1,33 @@ +{{- if and .Values.gateway.enabled .Values.gateway.hpa.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: {{ include "litellm.gateway.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: gateway +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: {{ include "litellm.gateway.fullname" . }} + minReplicas: {{ .Values.gateway.hpa.minReplicas }} + maxReplicas: {{ .Values.gateway.hpa.maxReplicas }} + metrics: + {{- if .Values.gateway.hpa.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.gateway.hpa.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.gateway.hpa.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.gateway.hpa.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/gateway/service.yaml b/helm/litellm/templates/gateway/service.yaml new file mode 100644 index 00000000000..03a4167a0ab --- /dev/null +++ b/helm/litellm/templates/gateway/service.yaml @@ -0,0 +1,18 @@ +{{- if .Values.gateway.enabled }} +apiVersion: v1 +kind: Service +metadata: + name: {{ include "litellm.gateway.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: gateway +spec: + type: {{ .Values.gateway.service.type }} + ports: + - port: {{ .Values.gateway.service.port }} + targetPort: http + protocol: TCP + name: http + selector: + {{- include "litellm.gateway.selectorLabels" . | nindent 4 }} +{{- end }} diff --git a/helm/litellm/templates/ingress.yaml b/helm/litellm/templates/ingress.yaml new file mode 100644 index 00000000000..30a8e7c974b --- /dev/null +++ b/helm/litellm/templates/ingress.yaml @@ -0,0 +1,153 @@ +{{- if .Values.ingress.enabled -}} +{{- $gatewayName := include "litellm.gateway.fullname" . -}} +{{- $backendName := include "litellm.backend.fullname" . -}} +{{- $uiName := include "litellm.ui.fullname" . -}} +{{- $gatewayPort := .Values.gateway.service.port -}} +{{- $backendPort := .Values.backend.service.port -}} +{{- $uiPort := .Values.ui.service.port -}} +{{/* + Gateway data-plane prefixes — must mirror gateway/routes/allowlist.py. + Versioned paths are listed explicitly to avoid routing management routes + (e.g. /v1/access_group, /v2/key/info, /v1/tool/*, /v1/agents, /v1/workflows, + /v2/user/info, /v2/team/list, /v2/model/info, /v2/login, /v2/guardrails/*, + /v1/mcp/*) onto the gateway via a broad /v1 or /v2 prefix. +*/}} +{{- $gatewayPrefixes := list + "/v1/chat" "/chat" "/v1/completions" "/completions" "/v1/embeddings" "/embeddings" + "/v1/moderations" "/moderations" "/v1/audio" "/audio" "/v1/images" "/images" + "/v1/files" "/files" "/v1/batches" "/batches" "/v1/fine_tuning" "/fine_tuning" + "/v1/fine-tuning" "/fine-tuning" "/v1/responses" "/responses" "/v1/threads" "/threads" + "/v1/assistants" "/assistants" "/v1/vector_stores" "/vector_stores" "/v1/indexes" + "/v1/models" "/models" "/openai" "/engines" + "/v1/messages" "/messages" "/v1/skills" "/v1/a2a" + "/v1/rerank" "/v2/rerank" "/rerank" "/v1/ocr" "/ocr" "/v1/rag" "/rag" + "/v1/video" "/v1/videos" "/video" "/videos" "/v1/search" "/search" + "/v1/containers" "/containers" "/v1/evals" "/v1/memory" "/queue/chat" + "/v1beta" "/interactions" + "/anthropic" "/azure" "/azure_ai" "/aws" "/bedrock" "/cohere" "/gemini" "/google" + "/vertex_ai" "/vertex-ai" "/assemblyai" "/eu.assemblyai" "/langfuse" "/vllm" + "/mistral" "/groq" "/voyage" "/cursor" "/milvus" "/openai_passthrough" + "/toolset" + "/v1/realtime" "/realtime" + "/health" "/metrics" +-}} +{{/* + /test is gateway-only as an EXACT path (GATEWAY_EXACT_PATHS), but its + children /test/connection and /test/tools/list are MCP-server management + endpoints kept only on the backend ("/test/" in BACKEND_PATH_PREFIXES). + A Prefix match here would route /test/* to the gateway, which trims those + routes at startup -> 404. So /test is rendered as a standalone Exact path + and /test/* falls through to the backend catch-all. +*/}} +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: {{ include "litellm.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + {{- with .Values.ingress.annotations }} + annotations: + {{- toYaml . | nindent 4 }} + {{- end }} +spec: + {{- with .Values.ingress.className }} + ingressClassName: {{ . | quote }} + {{- end }} + {{- with .Values.ingress.tls }} + tls: + {{- toYaml . | nindent 4 }} + {{- end }} + rules: + - {{- with .Values.ingress.host }} + host: {{ . | quote }} + {{- end }} + http: + paths: + # --- UI (Next.js static export) --- + - path: / + pathType: Exact + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + - path: /favicon.ico + pathType: Exact + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + - path: /litellm-asset-prefix + pathType: Prefix + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + - path: /_next + pathType: Prefix + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + # /ui/* is where the Next.js SPA serves its login + dashboard + # routes (e.g. /ui/login). Without this, /ui/* falls into the + # catch-all → backend → 404. + - path: /ui + pathType: Prefix + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + # Next.js App Router (output: "export", basePath: "") emits the + # RSC/flight payload for every route as a ROOT-level .txt + # (/index.txt, /teams.txt, /__next._tree.txt, ...). The client + # router fetches these on every soft navigation / prefetch as + # .txt?_rsc= (the query string is irrelevant to path + # matching). They are not under /ui, /_next, or + # /litellm-asset-prefix, so without this rule they fall to the + # backend catch-all → 404 → client-side navigation never settles + # and the login flow spins in an infinite redirect loop + # (/ ⇄ /ui/login). ui/nginx.conf already serves *.txt from the + # export; this rule only routes the request to it. Needs an + # ingress controller whose ImplementationSpecific path is a + # wildcard pattern (AWS ALB: `*` = 0+ chars); this chart targets + # the AWS Load Balancer Controller. + - path: /*.txt + pathType: ImplementationSpecific + backend: + service: + name: {{ $uiName }} + port: + number: {{ $uiPort }} + # --- Gateway data plane --- + # Exact /test only (see the $gatewayPrefixes comment above); + # /test/* MCP management endpoints fall to the backend catch-all. + - path: /test + pathType: Exact + backend: + service: + name: {{ $gatewayName }} + port: + number: {{ $gatewayPort }} + {{- range $gatewayPrefixes }} + - path: {{ . }} + pathType: Prefix + backend: + service: + name: {{ $gatewayName }} + port: + number: {{ $gatewayPort }} + {{- end }} + # --- Catch-all → backend (management API: /key/*, /user/*, /team/*, ...) --- + - path: / + pathType: Prefix + backend: + service: + name: {{ $backendName }} + port: + number: {{ $backendPort }} +{{- end }} diff --git a/helm/litellm/templates/migrations-job.yaml b/helm/litellm/templates/migrations-job.yaml new file mode 100644 index 00000000000..f3dc2ae0236 --- /dev/null +++ b/helm/litellm/templates/migrations-job.yaml @@ -0,0 +1,46 @@ +{{- if .Values.migrationJob.enabled -}} +# Pre-install / pre-upgrade hook that runs `prisma migrate deploy` against +# the writer database before the gateway and backend Deployments are rolled +# out. Required because the gateway and backend both spin up Prisma at +# startup and assume the LiteLLM schema (LiteLLM_Config, +# LiteLLM_VerificationToken, LiteLLM_SpendLogs, ...) already exists. +# +# Running this pre-upgrade closes the window where new application pods would +# otherwise serve traffic against the previous release's unmigrated schema. +apiVersion: batch/v1 +kind: Job +metadata: + name: {{ include "litellm.fullname" . }}-migrations + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: migrations + annotations: + helm.sh/hook: pre-install,pre-upgrade + helm.sh/hook-delete-policy: before-hook-creation + helm.sh/hook-weight: "0" +spec: + backoffLimit: {{ .Values.migrationJob.backoffLimit }} + ttlSecondsAfterFinished: {{ .Values.migrationJob.ttlSecondsAfterFinished }} + template: + metadata: + labels: + {{- include "litellm.commonLabels" . | nindent 8 }} + app.kubernetes.io/component: migrations + spec: + restartPolicy: Never + serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . | nindent 8 }} + {{- end }} + containers: + - name: prisma-migrations + image: "{{ .Values.migrationJob.image.repository }}:{{ .Values.migrationJob.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: {{ .Values.migrationJob.image.pullPolicy }} + env: + {{- include "litellm.serverEnv" (dict "root" $ "component" .Values.migrationJob) | nindent 12 }} + {{- with .Values.migrationJob.resources }} + resources: + {{- toYaml . | nindent 12 }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/serviceaccount.yaml b/helm/litellm/templates/serviceaccount.yaml new file mode 100644 index 00000000000..3c998448ae5 --- /dev/null +++ b/helm/litellm/templates/serviceaccount.yaml @@ -0,0 +1,13 @@ +{{- if .Values.serviceAccount.create -}} +apiVersion: v1 +kind: ServiceAccount +metadata: + name: {{ include "litellm.serviceAccountName" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + {{- with .Values.serviceAccount.annotations }} + annotations: + {{- toYaml . | nindent 4 }} + {{- end }} +automountServiceAccountToken: {{ .Values.serviceAccount.automount }} +{{- end }} diff --git a/helm/litellm/templates/ui/deployment.yaml b/helm/litellm/templates/ui/deployment.yaml new file mode 100644 index 00000000000..549bf61a0dd --- /dev/null +++ b/helm/litellm/templates/ui/deployment.yaml @@ -0,0 +1,70 @@ +{{- if .Values.ui.enabled }} +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "litellm.ui.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: ui +spec: + selector: + matchLabels: + {{- include "litellm.ui.selectorLabels" . | nindent 6 }} + template: + metadata: + {{- with .Values.ui.podAnnotations }} + annotations: + {{- toYaml . | nindent 8 }} + {{- end }} + labels: + {{- include "litellm.ui.selectorLabels" . | nindent 8 }} + spec: + serviceAccountName: {{ include "litellm.serviceAccountName" . }} + {{- with .Values.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . | nindent 8 }} + {{- end }} + containers: + - name: ui + image: "{{ .Values.ui.image.repository }}:{{ .Values.ui.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: {{ .Values.ui.image.pullPolicy }} + ports: + - name: http + containerPort: 3000 + protocol: TCP + env: + {{- if .Values.ui.logLevel }} + - name: LITELLM_LOG + value: {{ .Values.ui.logLevel | quote }} + {{- end }} + {{- if .Values.ui.backendUrl }} + - name: LITELLM_BACKEND_URL + value: {{ .Values.ui.backendUrl | quote }} + {{- end }} + {{- with .Values.ui.extraEnv }} + {{- toYaml . | nindent 12 }} + {{- end }} + {{- include "litellm.envFrom" .Values.ui | nindent 10 }} + {{- with .Values.ui.livenessProbe }} + livenessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + {{- with .Values.ui.readinessProbe }} + readinessProbe: + {{- toYaml . | nindent 12 }} + {{- end }} + resources: + {{- toYaml .Values.ui.resources | nindent 12 }} + {{- with .Values.ui.nodeSelector }} + nodeSelector: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.ui.affinity }} + affinity: + {{- toYaml . | nindent 8 }} + {{- end }} + {{- with .Values.ui.tolerations }} + tolerations: + {{- toYaml . | nindent 8 }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/ui/hpa.yaml b/helm/litellm/templates/ui/hpa.yaml new file mode 100644 index 00000000000..b43eda5ac4a --- /dev/null +++ b/helm/litellm/templates/ui/hpa.yaml @@ -0,0 +1,33 @@ +{{- if and .Values.ui.enabled .Values.ui.hpa.enabled }} +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: {{ include "litellm.ui.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: ui +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: {{ include "litellm.ui.fullname" . }} + minReplicas: {{ .Values.ui.hpa.minReplicas }} + maxReplicas: {{ .Values.ui.hpa.maxReplicas }} + metrics: + {{- if .Values.ui.hpa.targetCPUUtilizationPercentage }} + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: {{ .Values.ui.hpa.targetCPUUtilizationPercentage }} + {{- end }} + {{- if .Values.ui.hpa.targetMemoryUtilizationPercentage }} + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: {{ .Values.ui.hpa.targetMemoryUtilizationPercentage }} + {{- end }} +{{- end }} diff --git a/helm/litellm/templates/ui/service.yaml b/helm/litellm/templates/ui/service.yaml new file mode 100644 index 00000000000..52b539fa00c --- /dev/null +++ b/helm/litellm/templates/ui/service.yaml @@ -0,0 +1,18 @@ +{{- if .Values.ui.enabled }} +apiVersion: v1 +kind: Service +metadata: + name: {{ include "litellm.ui.fullname" . }} + labels: + {{- include "litellm.commonLabels" . | nindent 4 }} + app.kubernetes.io/component: ui +spec: + type: {{ .Values.ui.service.type }} + ports: + - port: {{ .Values.ui.service.port }} + targetPort: http + protocol: TCP + name: http + selector: + {{- include "litellm.ui.selectorLabels" . | nindent 4 }} +{{- end }} diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml new file mode 100644 index 00000000000..92477616a9a --- /dev/null +++ b/helm/litellm/values.yaml @@ -0,0 +1,225 @@ +# LiteLLM helm chart values + +nameOverride: "" +fullnameOverride: "" + +imagePullSecrets: [] + +# Optional Ingress wiring the three component Services behind a single L7 +# entrypoint. Required when serving the static UI bundle over the network. +ingress: + enabled: false + className: "" + annotations: {} + host: "" # optional; if set, becomes the rule's host + tls: [] + +# Shared ServiceAccount used by all three component Deployments. Set +# `create: true` to have the chart provision it (e.g. when wiring an EKS +# Pod Identity association by SA name). Set `name` to use an existing SA +# (chart-created or out-of-band). When both are empty / false, pods run +# with the namespace's `default` SA. +serviceAccount: + create: false + automount: true + annotations: {} + name: "" + +# Pre-install / pre-upgrade Helm hook that runs `prisma migrate deploy` +# against the writer database, creating the LiteLLM schema (tables that +# gateway + backend assume exist at startup: LiteLLM_Config, +# LiteLLM_VerificationToken, LiteLLM_SpendLogs, ...). Disable if your +# pipeline runs migrations out-of-band. +# +# Uses a dedicated `litellm-migrations` image (prisma CLI + the migration +# files from `litellm-proxy-extras`) instead of the backend image, so the +# Job doesn't drag in the rest of the proxy and doesn't run `prisma +# generate` — the migration engine doesn't need the generated client. +migrationJob: + enabled: true + backoffLimit: 4 + ttlSecondsAfterFinished: 120 + resources: {} + image: + repository: ghcr.io/berriai/litellm-migrations + tag: "" # defaults to .Chart.AppVersion + pullPolicy: IfNotPresent + # Extra env appended to the migration container. The migration entrypoint + # uses the v2 resolver by default (no diff-and-force recovery — avoids the + # schema thrashing seen during rolling deploys). To opt back into the v1 + # resolver, append `- name: USE_V2_MIGRATION_RESOLVER` / `value: "false"`. + extraEnv: [] + +# Required: a master key used by gateway + backend to mint/verify proxy tokens. +# Must reference an existing Secret. +masterKey: + secretName: litellm-master-key-secret # name of a Secret containing the master key + secretKey: master-key + +# External Postgres connection. +database: + writer: + host: "" + port: 5432 + dbname: "" + schema: "" + useIAMAuth: false + passwordSecret: + name: litellm-writer-secret + usernameKey: username + passwordKey: password + + # Optional read-replica routing. When `reader.host` is set, the proxy routes + # reads (find_*, count, group_by, query_raw/_first) to this endpoint while + # writes stay on the writer. Leave `reader.host` empty to disable. + reader: + host: "" + port: 5432 + dbname: "" + schema: "" + useIAMAuth: false + passwordSecret: + name: litellm-reader-secret + usernameKey: username + passwordKey: password + +# Optional Redis (caching, rate limiting). Leave host empty to disable. +# +# Set `cluster: true` for Redis Cluster mode (e.g. AWS ElastiCache Cluster, +# self-hosted Redis Cluster). The chart emits REDIS_CLUSTER_NODES from +# `host` / `port` as the single seed; the cluster client discovers the +# remaining nodes from CLUSTER SLOTS at startup. +redis: + cluster: false + host: "" + port: 6379 + passwordSecret: + name: "" # Leave empty for auth-less Redis + passwordKey: password + +# ---------- gateway (LLM data plane) ---------- +gateway: + enabled: true + logLevel: INFO + # Number of uvicorn worker processes per gateway pod. Sets NUM_WORKERS, + # consumed by the gateway image entrypoint. Default is 1. + numWorkers: 1 + extraEnv: [] # Add extra environment variables to the gateway + envConfigMaps: [] # Add extra environment variables to the gateway from config maps + envSecrets: [] # Add extra environment variables to the gateway from secrets + config: + create: true + proxy_config: {} + image: + repository: ghcr.io/berriai/litellm-gateway + tag: "" # defaults to .Chart.AppVersion + pullPolicy: IfNotPresent + service: + type: ClusterIP + port: 4000 + resources: + requests: + cpu: "1" + memory: 4Gi + limits: + cpu: "2" + memory: 4Gi + livenessProbe: + httpGet: { path: /health/liveliness, port: http } + initialDelaySeconds: 10 + periodSeconds: 15 + readinessProbe: + httpGet: { path: /health/readiness, port: http } + initialDelaySeconds: 5 + periodSeconds: 10 + hpa: + enabled: true + minReplicas: 1 + maxReplicas: 10 + targetCPUUtilizationPercentage: 70 + targetMemoryUtilizationPercentage: 80 + podAnnotations: {} + nodeSelector: {} + tolerations: [] + affinity: {} + +# ---------- backend (UI / management API) ---------- +backend: + enabled: true + logLevel: INFO + extraEnv: [] + envConfigMaps: [] + envSecrets: [] + image: + repository: ghcr.io/berriai/litellm-backend + tag: "" + pullPolicy: IfNotPresent + service: + type: ClusterIP + port: 4001 + resources: + requests: + cpu: "1" + memory: 4Gi + limits: + cpu: "2" + memory: 4Gi + livenessProbe: + httpGet: { path: /health/liveliness, port: http } + initialDelaySeconds: 10 + periodSeconds: 15 + readinessProbe: + httpGet: { path: /health/readiness, port: http } + initialDelaySeconds: 5 + periodSeconds: 10 + hpa: + enabled: true + minReplicas: 1 + maxReplicas: 4 + targetCPUUtilizationPercentage: 70 + podAnnotations: {} + nodeSelector: {} + tolerations: [] + affinity: {} + +# ---------- ui (Next.js static dashboard) ---------- +ui: + enabled: true + logLevel: INFO + extraEnv: [] + envConfigMaps: [] + envSecrets: [] + image: + repository: ghcr.io/berriai/litellm-ui + tag: "" + pullPolicy: IfNotPresent + service: + type: ClusterIP + port: 3000 + # The dashboard expects to know where to reach the backend API. Set this to + # the externally-routable URL (typically the ingress host + /api or similar). + backendUrl: "" + resources: + requests: + cpu: 500m + memory: 500Mi + limits: + cpu: "1" + memory: 1Gi + livenessProbe: + httpGet: { path: /, port: http } + initialDelaySeconds: 5 + periodSeconds: 20 + readinessProbe: + httpGet: { path: /, port: http } + initialDelaySeconds: 2 + periodSeconds: 10 + hpa: + enabled: false + minReplicas: 1 + maxReplicas: 3 + targetCPUUtilizationPercentage: 80 + podAnnotations: {} + nodeSelector: {} + tolerations: [] + affinity: {} diff --git a/license_cache.json b/license_cache.json index 4b09afacaa3..803db8fdd1b 100644 --- a/license_cache.json +++ b/license_cache.json @@ -49,5 +49,12 @@ "grpc-google-iam-v1:0.14.3": "Apache 2.0", "h11:0.16.0": "MIT", "requests-toolbelt:1.0.0": "Apache 2.0", - "tornado:6.5.4": "Apache-2.0" + "tornado:6.5.4": "Apache-2.0", + "mlflow:3.11.1": "Copyright 2018 Databricks, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an \"about box\". You should also get your employer (if you work as a programmer) or school, if any, to sign a \"copyright disclaimer\" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read .", + "vcrpy:8.1.1": "MIT", + "langchain-openai:1.1.14": "MIT", + "grpc-google-iam-v1:0.14.4": "Apache 2.0", + "tornado:6.5.5": "Apache-2.0" } \ No newline at end of file diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql new file mode 100644 index 00000000000..3253b63a884 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql @@ -0,0 +1,4 @@ +-- AlterTable +-- Adds the admin-toggleable pause flag used by the router's blocked filter and the +-- credential lookup helpers; defaults to false so existing rows behave unchanged. +ALTER TABLE "LiteLLM_ProxyModelTable" ADD COLUMN IF NOT EXISTS "blocked" BOOLEAN NOT NULL DEFAULT false; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/litellm/__init__.py b/litellm/__init__.py index e1b367fb234..c868ae55b4f 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -416,6 +416,7 @@ custom_prometheus_metadata_labels: List[str] = [] custom_prometheus_tags: List[str] = [] prometheus_metrics_config: Optional[List] = None prometheus_emit_stream_label: bool = False +prometheus_user_budget_label_include_email_alias: bool = False prometheus_end_user_metrics_max_series_per_metric: Optional[int] = 10000 prometheus_end_user_metrics_ttl_seconds: Optional[float] = 3600.0 prometheus_end_user_metrics_cleanup_interval_seconds: Optional[float] = 60.0 @@ -1880,6 +1881,12 @@ if TYPE_CHECKING: from .llms.dashscope.chat.transformation import ( DashScopeChatConfig as DashScopeChatConfig, ) + from .llms.dashscope.embed.transformation import ( + DashScopeEmbeddingConfig as DashScopeEmbeddingConfig, + ) + from .llms.dashscope.rerank.transformation import ( + DashScopeRerankConfig as DashScopeRerankConfig, + ) from .llms.moonshot.chat.transformation import ( MoonshotChatConfig as MoonshotChatConfig, ) diff --git a/litellm/_redis.py b/litellm/_redis.py index 65284162663..5ab551453bb 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -100,6 +100,8 @@ def _get_redis_cluster_kwargs(client=None): "azure_tenant_id", "azure_client_secret", "max_connections", + "socket_timeout", + "socket_connect_timeout", } return available_args diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 3cf1d911d7f..3f4e54382c9 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -87,6 +87,16 @@ class CachingHandlerResponse(BaseModel): in_memory_cache_obj = InMemoryCache() +def _is_chat_completion_cached_dict(cached_result: dict) -> bool: + cached_id = cached_result.get("id") + if isinstance(cached_id, str) and cached_id.startswith("chatcmpl"): + return True + obj = cached_result.get("object") + if isinstance(obj, str): + return obj.startswith("chat.completion") + return "choices" in cached_result + + def _should_defer_streaming_cache_hit_callbacks(*, kwargs: Dict[str, Any]) -> bool: """ When stream=True, do not run success callbacks at cache-hit time. @@ -861,27 +871,47 @@ class LLMCachingHandler: elif (call_type == "aresponses" or call_type == "responses") and isinstance( cached_result, dict ): - from litellm.responses.streaming_iterator import ( - CachedResponsesAPIStreamingIterator, - ) - - response_obj = ResponsesAPIResponse(**cached_result) - if ( - hasattr(response_obj, "_hidden_params") - and response_obj._hidden_params is not None - and isinstance(response_obj._hidden_params, dict) - ): - response_obj._hidden_params["cache_hit"] = True - - if kwargs.get("stream", False) is True: - cached_result = CachedResponsesAPIStreamingIterator( - response=response_obj, - logging_obj=logging_obj, - request_data=kwargs, - call_type=call_type, - ) + use_chat_completion_cache = _is_chat_completion_cached_dict(cached_result) + if use_chat_completion_cache: + if kwargs.get("stream", False) is True: + bridge_call_type = ( + CallTypes.acompletion.value + if call_type == "aresponses" + else CallTypes.completion.value + ) + cached_result = self._convert_cached_stream_response( + cached_result=cached_result, + call_type=bridge_call_type, + logging_obj=logging_obj, + model=model, + ) + else: + cached_result = convert_to_model_response_object( + response_object=cached_result, + model_response_object=ModelResponse(), + ) else: - cached_result = response_obj + from litellm.responses.streaming_iterator import ( + CachedResponsesAPIStreamingIterator, + ) + + response_obj = ResponsesAPIResponse(**cached_result) + if ( + hasattr(response_obj, "_hidden_params") + and response_obj._hidden_params is not None + and isinstance(response_obj._hidden_params, dict) + ): + response_obj._hidden_params["cache_hit"] = True + + if kwargs.get("stream", False) is True: + cached_result = CachedResponsesAPIStreamingIterator( + response=response_obj, + logging_obj=logging_obj, + request_data=kwargs, + call_type=call_type, + ) + else: + cached_result = response_obj if ( hasattr(cached_result, "_hidden_params") diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index ce398ee8288..2de7bda6467 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -37,6 +37,15 @@ class ResponsesToCompletionBridgeHandler: stream = litellm_params.get("stream", False) return bool(stream) + @staticmethod + def _is_preformatted_cached_chat_stream(result: Any) -> bool: + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + + return ( + isinstance(result, CustomStreamWrapper) + and result.custom_llm_provider == "cached_response" + ) + @staticmethod def _coerce_response_object( response_obj: Any, @@ -177,6 +186,8 @@ class ResponsesToCompletionBridgeHandler: **request_data, ) + from litellm.types.utils import ModelResponse + stream = self._resolve_stream_flag(optional_params, litellm_params) if isinstance(result, ResponsesAPIResponse): return self.transformation_handler.transform_response( @@ -192,6 +203,8 @@ class ResponsesToCompletionBridgeHandler: api_key=kwargs.get("api_key"), json_mode=kwargs.get("json_mode"), ) + elif isinstance(result, ModelResponse): + return result elif not stream: responses_api_response = self._collect_response_from_stream(result) return self.transformation_handler.transform_response( @@ -208,6 +221,10 @@ class ResponsesToCompletionBridgeHandler: json_mode=kwargs.get("json_mode"), ) else: + if self._is_preformatted_cached_chat_stream(result): + return self._apply_post_stream_processing( + result, model, custom_llm_provider + ) completion_stream = self.transformation_handler.get_model_response_iterator( streaming_response=result, # type: ignore sync_stream=True, @@ -256,6 +273,8 @@ class ResponsesToCompletionBridgeHandler: aresponses=True, ) + from litellm.types.utils import ModelResponse + stream = self._resolve_stream_flag(optional_params, litellm_params) if isinstance(result, ResponsesAPIResponse): return self.transformation_handler.transform_response( @@ -271,6 +290,8 @@ class ResponsesToCompletionBridgeHandler: api_key=kwargs.get("api_key"), json_mode=kwargs.get("json_mode"), ) + elif isinstance(result, ModelResponse): + return result elif not stream: responses_api_response = await self._collect_response_from_stream_async( result @@ -289,6 +310,10 @@ class ResponsesToCompletionBridgeHandler: json_mode=kwargs.get("json_mode"), ) else: + if self._is_preformatted_cached_chat_stream(result): + return self._apply_post_stream_processing( + result, model, custom_llm_provider + ) completion_stream = self.transformation_handler.get_model_response_iterator( streaming_response=result, # type: ignore sync_stream=False, diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 32423f23314..e3cbf422e5d 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -1141,6 +1141,14 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): event_type = parsed_chunk.get("type") if isinstance(event_type, ResponsesAPIStreamEvents): event_type = event_type.value + + if parsed_chunk.get("object") == "chat.completion.chunk" or ( + event_type is None + and isinstance(parsed_chunk.get("choices"), list) + and parsed_chunk.get("choices") + ): + return ModelResponseStream(**parsed_chunk) + verbose_logger.debug(f"Chat provider: Processing event type: {event_type}") if event_type == "response.created": diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index c0ec148d03a..2257861aff6 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -173,17 +173,45 @@ def _cost_per_token_custom_pricing_helper( prompt_tokens: float = 0, completion_tokens: float = 0, response_time_ms: Optional[float] = 0.0, + cached_tokens: float = 0, + cache_creation_tokens: float = 0, ### CUSTOM PRICING ### custom_cost_per_token: Optional[CostPerToken] = None, custom_cost_per_second: Optional[float] = None, ) -> Optional[Tuple[float, float]]: - """Internal helper function for calculating cost, if custom pricing given""" + """Internal helper function for calculating cost, if custom pricing given. + + prompt_tokens is assumed to include both cached_tokens and cache_creation_tokens + (OpenAI-compatible convention). Anthropic-style usage where prompt_tokens excludes + cache tokens is handled at the caller (cost_per_token) before invoking this helper. + """ if custom_cost_per_token is None and custom_cost_per_second is None: return None if custom_cost_per_token is not None: - input_cost = custom_cost_per_token["input_cost_per_token"] * prompt_tokens - output_cost = custom_cost_per_token["output_cost_per_token"] * completion_tokens + input_cost_per_token = custom_cost_per_token["input_cost_per_token"] + output_cost_per_token = custom_cost_per_token["output_cost_per_token"] + + cache_read_input_token_cost = custom_cost_per_token.get( + "cache_read_input_token_cost", + input_cost_per_token, + ) + cache_creation_input_token_cost = custom_cost_per_token.get( + "cache_creation_input_token_cost", + input_cost_per_token, + ) + + regular_prompt_tokens = max( + prompt_tokens - cached_tokens - cache_creation_tokens, + 0, + ) + + input_cost = ( + regular_prompt_tokens * input_cost_per_token + + cached_tokens * cache_read_input_token_cost + + cache_creation_tokens * cache_creation_input_token_cost + ) + output_cost = completion_tokens * output_cost_per_token return input_cost, output_cost elif custom_cost_per_second is not None: output_cost = custom_cost_per_second * response_time_ms / 1000 # type: ignore @@ -323,10 +351,56 @@ def cost_per_token( # noqa: PLR0915 ) ## CUSTOM PRICING ## + # Normalize cache token counts across providers: + # - OpenAI-compatible: usage.prompt_tokens_details.cached_tokens + # (prompt_tokens already INCLUDES cached_tokens) + # - Anthropic: usage.cache_read_input_tokens / cache_creation_input_tokens + # (prompt_tokens does NOT include these — adjust before calling helper) + _cache_read_tokens: float = 0 + _cache_creation_tokens: float = 0 + _is_anthropic_style = False + + if usage_object is not None: + _pt_details = getattr(usage_object, "prompt_tokens_details", None) + if _pt_details is not None: + _cache_read_tokens = float(getattr(_pt_details, "cached_tokens", 0) or 0) + # OpenAI-compatible providers report cache-write tokens under + # either `cache_write_tokens` (kimi-k2) or `cache_creation_tokens`. + # Mirror db_spend_update_writer to stay symmetric. + _cache_creation_tokens = float( + getattr(_pt_details, "cache_write_tokens", 0) + or getattr(_pt_details, "cache_creation_tokens", 0) + or 0 + ) + + _anthropic_read = getattr(usage_object, "cache_read_input_tokens", None) + _anthropic_create = getattr(usage_object, "cache_creation_input_tokens", None) + if _anthropic_read is not None or _anthropic_create is not None: + _is_anthropic_style = True + if _anthropic_read is not None: + _cache_read_tokens = float(_anthropic_read) + if _anthropic_create is not None: + _cache_creation_tokens = float(_anthropic_create) + + if not _cache_read_tokens and cache_read_input_tokens: + _cache_read_tokens = float(cache_read_input_tokens) + _is_anthropic_style = True + if not _cache_creation_tokens and cache_creation_input_tokens: + _cache_creation_tokens = float(cache_creation_input_tokens) + _is_anthropic_style = True + + # Anthropic reports prompt_tokens as input_tokens (excluding cache tokens). + # Adjust so the helper's "prompt_tokens includes cache tokens" invariant holds. + _normalized_prompt_tokens = float(prompt_tokens) + if _is_anthropic_style: + _normalized_prompt_tokens += _cache_read_tokens + _cache_creation_tokens + response_cost = _cost_per_token_custom_pricing_helper( - prompt_tokens=prompt_tokens, + prompt_tokens=_normalized_prompt_tokens, completion_tokens=completion_tokens, response_time_ms=response_time_ms, + cached_tokens=_cache_read_tokens, + cache_creation_tokens=_cache_creation_tokens, custom_cost_per_second=custom_cost_per_second, custom_cost_per_token=custom_cost_per_token, ) diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 8b005291556..17f5b43c273 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -918,9 +918,11 @@ class GuardrailRaisedException(Exception): guardrail_name: Optional[str] = None, message: str = "", should_wrap_with_default_message: bool = True, + status_code: int = 400, ): default_message = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}" self.guardrail_name = guardrail_name + self.status_code = status_code self.message = default_message if should_wrap_with_default_message else message super().__init__(self.message) @@ -930,12 +932,14 @@ class BlockedPiiEntityError(Exception): self, entity_type: str, guardrail_name: Optional[str] = None, + status_code: int = 400, ): """ Raised when a blocked entity is detected by a guardrail. """ self.entity_type = entity_type self.guardrail_name = guardrail_name + self.status_code = status_code self.message = f"Blocked entity detected: {entity_type} by Guardrail: {guardrail_name}. This entity is not allowed to be used in this request." super().__init__(self.message) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index c937ad0a7bf..82a35f2eedd 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -43,7 +43,11 @@ if TYPE_CHECKING: dc = DualCache() -from litellm.exceptions import ModifyResponseException as ModifyResponseException +from litellm.exceptions import ( + BlockedPiiEntityError, + GuardrailRaisedException, + ModifyResponseException, +) class CustomGuardrail(CustomLogger): @@ -737,12 +741,15 @@ class CustomGuardrail(CustomLogger): (this was logged previously as an API failure - guardrail_failed_to_respond). Guardrails signal intentional blocks by raising: + - GuardrailRaisedException (generic guardrail API, tool permission) + - BlockedPiiEntityError (Presidio PII detection) - HTTPException with status 400 (content policy violation) - ModifyResponseException (passthrough mode violation) """ - if isinstance(e, ModifyResponseException): return True + if isinstance(e, (GuardrailRaisedException, BlockedPiiEntityError)): + return True if ( HTTPException is not None and isinstance(e, HTTPException) diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 41d12907610..ced15a01660 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -59,6 +59,11 @@ LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") LITELLM_METER_NAME = os.getenv("LITELLM_METER_NAME", "litellm") LITELLM_LOGGER_NAME = os.getenv("LITELLM_LOGGER_NAME", "litellm") LITELLM_PROXY_REQUEST_SPAN_NAME = "Received Proxy Server Request" +# OTel-standard names. status is also kept under error.code for back compat. +HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE = "http.response.status_code" +HTTP_ROUTE_ATTRIBUTE = "http.route" +URL_PATH_ATTRIBUTE = "url.path" +PREPROCESSING_DURATION_MS_ATTRIBUTE = "litellm.preprocessing.duration_ms" # Remove the hardcoded LITELLM_RESOURCE dictionary - we'll create it properly later RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request" @@ -667,6 +672,31 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): parent_otel_span = user_api_key_dict.parent_otel_span if parent_otel_span is not None: parent_otel_span.set_status(Status(StatusCode.ERROR)) + + # Stamp structured error attrs on the SERVER span itself; the + # failure path otherwise only sets its status (_handle_failure + # records on the litellm_request child span). Inline import: + # litellm_logging <-> integrations is circular. + from litellm.litellm_core_utils.litellm_logging import ( + StandardLoggingPayloadSetup, + ) + + error_information = StandardLoggingPayloadSetup.get_error_information( + original_exception=original_exception, + traceback_str=traceback_str, + ) + self._record_exception_on_span( + span=parent_otel_span, + kwargs={ + "exception": original_exception, + "standard_logging_object": {"error_information": error_information}, + }, + ) + + # Pre-request latency (request_data carries the propagated + # metadata on the failure path; omitted if it failed before handoff). + self.set_preprocessing_duration_attribute(parent_otel_span, request_data) + _span_name = "Failed Proxy Server Request" # Exception Logging Child Span @@ -703,6 +733,14 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): ctx, _ = self._get_span_context(kwargs, default_span=parent_span) + # Pre-request latency on the SERVER span (success path). + self.set_preprocessing_duration_attribute(parent_span, kwargs) + + # http.response.status_code on the SERVER span (success path). + # A successful proxy response is HTTP 200; the failure path sets + # this from the error code in _record_exception_on_span. + self.set_response_status_code_attribute(parent_span, 200) + # 3. Guardrail span self._create_guardrail_span(kwargs=kwargs, context=ctx) @@ -1069,8 +1107,13 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): "mcp_tool_call_metadata", "vector_store_request_metadata", ]: - if md.get(key) is not None: - common_attrs[f"metadata.{key}"] = str(md[key]) + value = md.get(key) + if value is None: + continue + if isinstance(value, (dict, list)): + common_attrs[f"metadata.{key}"] = safe_dumps(value) + else: + common_attrs[f"metadata.{key}"] = str(value) # get hidden params hidden_params = getattr(std_log, "hidden_params", None) or (std_log or {}).get( @@ -1627,6 +1670,19 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): value=error_information["error_code"], ) + # Also expose under the OTel-standard name as an int + # (error_code is a str, may be non-numeric). + _error_code_val = error_information["error_code"] + if _error_code_val is not None: + try: + self.safe_set_attribute( + span=span, + key=HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE, + value=int(_error_code_val), + ) + except (ValueError, TypeError): + pass + if error_information.get("error_class"): self.safe_set_attribute( span=span, @@ -2889,3 +2945,86 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): context=self.get_traceparent_from_header(headers=headers), kind=self.span_kind.SERVER, ) + + def set_proxy_request_route_attributes( + self, + span: Optional[Span], + *, + url_path: Optional[str] = None, + http_route: Optional[str] = None, + ) -> None: + """ + Set OTel-standard ``http.route`` / ``url.path`` on the proxy SERVER + span. Called from the auth path, the only point where both the + SERVER span and the request are in hand. No-op if span/value missing. + """ + if span is None: + return + if url_path: + self.safe_set_attribute(span=span, key=URL_PATH_ATTRIBUTE, value=url_path) + if http_route: + self.safe_set_attribute( + span=span, key=HTTP_ROUTE_ATTRIBUTE, value=http_route + ) + + def set_response_status_code_attribute( + self, span: Optional[Span], status_code: Optional[int] + ) -> None: + """ + Set OTel-standard ``http.response.status_code`` (int) on the proxy + SERVER span. The failure path sets this from the error code in + ``_record_exception_on_span``; this is the success-path counterpart + so the attribute is present on every SERVER span regardless of + outcome (required by the HTTP semconv, and needed for error-ratio / + status-breakdown dashboards). No-op if span/value missing. + """ + if span is None or status_code is None: + return + self.safe_set_attribute( + span=span, + key=HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE, + value=int(status_code), + ) + + def set_preprocessing_duration_attribute( + self, span: Optional[Span], container: Any + ) -> None: + """ + Set ``litellm.preprocessing.duration_ms`` (proxy-receive -> first + provider handoff) on the proxy SERVER span. ``litellm_received_at`` + rides request metadata; ``first_api_call_start_time`` is the + set-once first-handoff instant (retries/backoff excluded). Works + uniformly for the success (model_call_details) and failure + (request_data) containers. No-op if span/either anchor is missing. + """ + if span is None or not isinstance(container, dict): + return + received_at = None + # first_api_call_start_time is top-level (never in user metadata). + first_handoff = container.get("first_api_call_start_time") + _lp = container.get("litellm_params") + for _md in ( + (_lp or {}).get("metadata") if isinstance(_lp, dict) else None, + container.get("metadata"), + container.get("litellm_metadata"), + ): + if isinstance(_md, dict): + received_at = received_at or _md.get("litellm_received_at") + if received_at is None or first_handoff is None: + return + try: + start_ts = self._to_timestamp(received_at) + end_ts = self._to_timestamp(first_handoff) + except Exception: + return + if start_ts is None or end_ts is None: + return + duration_ms = (end_ts - start_ts) * 1000.0 + # Clock skew → omit rather than emit a negative latency. + if duration_ms < 0: + return + self.safe_set_attribute( + span=span, + key=PREPROCESSING_DURATION_MS_ATTRIBUTE, + value=duration_ms, + ) diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 30af0dcb8ed..2c63455565c 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -3540,6 +3540,10 @@ class PrometheusLogger(CustomLogger): user_object.budget_reset_at = user_info.budget_reset_at if user_object.max_budget is None and user_info.max_budget is not None: user_object.max_budget = user_info.max_budget + if user_info.user_email is not None: + user_object.user_email = user_info.user_email + if user_info.user_alias is not None: + user_object.user_alias = user_info.user_alias return user_object @@ -3556,6 +3560,8 @@ class PrometheusLogger(CustomLogger): """ enum_values = UserAPIKeyLabelValues( user=user.user_id, + user_email=user.user_email or "", + user_alias=user.user_alias or "", ) _labels = prometheus_label_factory( diff --git a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py index 72a3afbc3c5..567e9b523e8 100644 --- a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py +++ b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py @@ -2,7 +2,7 @@ Streaming iterator for transforming Responses API stream to Interactions API stream. """ -from typing import Any, AsyncIterator, Dict, Iterator, Optional, cast +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast from litellm.responses.streaming_iterator import ( BaseResponsesAPIStreamingIterator, @@ -15,6 +15,7 @@ from litellm.types.interactions import ( InteractionsAPIStreamingResponse, ) from litellm.types.llms.openai import ( + ContentPartAddedEvent, OutputTextDeltaEvent, ResponseCompletedEvent, ResponseCreatedEvent, @@ -51,6 +52,7 @@ class LiteLLMResponsesInteractionsStreamingIterator: self.collected_text = "" self.sent_interaction_start = False self.sent_content_start = False + self._pending_events: List[InteractionsAPIStreamingResponse] = [] def _transform_responses_chunk_to_interactions_chunk( self, @@ -80,7 +82,49 @@ class LiteLLMResponsesInteractionsStreamingIterator: ) self.collected_text += delta_text - # Send interaction.start if not sent + # Fallback: emit interaction.start, and queue content.start carrying this + # delta so the first token is preserved in the stream. + if not self.sent_interaction_start: + self.sent_interaction_start = True + self.sent_content_start = True + self._pending_events.append( + InteractionsAPIStreamingResponse( + event_type="content.start", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + ) + return InteractionsAPIStreamingResponse( + event_type="interaction.start", + id=getattr(responses_chunk, "item_id", None) + or f"interaction_{id(self)}", + object="interaction", + status="in_progress", + model=self.model, + ) + + # Fallback: emit content.start if ContentPartAddedEvent never arrived + if not self.sent_content_start: + self.sent_content_start = True + return InteractionsAPIStreamingResponse( + event_type="content.start", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + + # Normal path: emit content.delta with type field + return InteractionsAPIStreamingResponse( + event_type="content.delta", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + + # Handle ContentPartAddedEvent -> content.start (arrives before text deltas) + if isinstance(responses_chunk, ContentPartAddedEvent): + # Fallback: emit interaction.start if ResponseCreatedEvent never arrived if not self.sent_interaction_start: self.sent_interaction_start = True return InteractionsAPIStreamingResponse( @@ -91,8 +135,6 @@ class LiteLLMResponsesInteractionsStreamingIterator: status="in_progress", model=self.model, ) - - # Send content.start if not sent if not self.sent_content_start: self.sent_content_start = True return InteractionsAPIStreamingResponse( @@ -101,14 +143,7 @@ class LiteLLMResponsesInteractionsStreamingIterator: object="content", delta={"type": "text", "text": ""}, ) - - # Send content.delta - return InteractionsAPIStreamingResponse( - event_type="content.delta", - id=getattr(responses_chunk, "item_id", None), - object="content", - delta={"text": delta_text}, - ) + return None # Handle ResponseCreatedEvent or ResponseInProgressEvent -> interaction.start if isinstance(responses_chunk, (ResponseCreatedEvent, ResponseInProgressEvent)): @@ -172,6 +207,10 @@ class LiteLLMResponsesInteractionsStreamingIterator: delattr(self, "_pending_interaction_complete") return pending + # Drain events queued from a prior chunk (e.g. content.start emitted alongside + # the interaction.start fallback for the first OutputTextDeltaEvent). + if self._pending_events: + return self._pending_events.pop(0) # Use a loop instead of recursion to avoid stack overflow sync_iterator = cast( SyncResponsesAPIStreamingIterator, self.responses_stream_iterator @@ -237,6 +276,10 @@ class LiteLLMResponsesInteractionsStreamingIterator: delattr(self, "_pending_interaction_complete") return pending + # Drain events queued from a prior chunk (e.g. content.start emitted alongside + # the interaction.start fallback for the first OutputTextDeltaEvent). + if self._pending_events: + return self._pending_events.pop(0) # Use a loop instead of recursion to avoid stack overflow async_iterator = cast( ResponsesAPIStreamingIterator, self.responses_stream_iterator diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index c73d914e6cc..876f1b167db 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1050,6 +1050,16 @@ class Logging(LiteLLMLoggingBaseClass): ) self.model_call_details["api_call_start_time"] = datetime.datetime.now() + # Set-once first provider-handoff instant. api_call_start_time + # is overwritten on every retry, so it can't measure one-time + # preprocessing; pinning the first attempt excludes retry loops + # + backoff. Logging object only — must NOT go into + # litellm_params["metadata"] (caller request metadata, typed + # Dict[str, str], echoed downstream; a datetime breaks it). + if self.model_call_details.get("first_api_call_start_time") is None: + self.model_call_details["first_api_call_start_time"] = ( + self.model_call_details["api_call_start_time"] + ) # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made callbacks = litellm.input_callback + (self.dynamic_input_callbacks or []) for callback in callbacks: diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index f417b4a5f61..3ee56dfc5ca 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -20,6 +20,7 @@ from typing import ( cast, ) +import litellm from litellm import verbose_logger from litellm.router_utils.batch_utils import InMemoryFile from litellm.types.llms.openai import ( @@ -1170,9 +1171,16 @@ def migrate_file_to_image_url( ChatCompletionImageUrlObject, ) - file_id = message["file"].get("file_id") - file_data = message["file"].get("file_data") - format = message["file"].get("format") + file_sub = message.get("file") + if file_sub is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider=None, + ) + file_id = file_sub.get("file_id") + file_data = file_sub.get("file_data") + format = file_sub.get("format") if not file_id and not file_data: raise ValueError("file_id and file_data are both None") image_url_object = ChatCompletionImageObject( diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index d40ca4e3597..79d527d1eb8 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2057,9 +2057,16 @@ def anthropic_process_openai_file_message( AnthropicMessagesContainerUploadParam, ]: file_message = cast(ChatCompletionFileObject, message) - file_data = file_message["file"].get("file_data") - file_id = file_message["file"].get("file_id") - format = file_message["file"].get("format") + file_sub = file_message.get("file") + if file_sub is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="anthropic", + ) + file_data = file_sub.get("file_data") + file_id = file_sub.get("file_id") + format = file_sub.get("format") if file_data: image_chunk = convert_to_anthropic_image_obj( openai_image_url=file_data, @@ -4879,7 +4886,13 @@ class BedrockConverseMessagesProcessor: @staticmethod def _process_file_message(message: ChatCompletionFileObject) -> BedrockContentBlock: - file_message = message["file"] + file_message = message.get("file") + if file_message is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="bedrock", + ) file_data = file_message.get("file_data") file_id = file_message.get("file_id") @@ -4900,7 +4913,13 @@ class BedrockConverseMessagesProcessor: async def _async_process_file_message( message: ChatCompletionFileObject, ) -> BedrockContentBlock: - file_message = message["file"] + file_message = message.get("file") + if file_message is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="bedrock", + ) file_data = file_message.get("file_data") file_id = file_message.get("file_id") format = file_message.get("format") diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 0602b1c2f62..620bc91732d 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -5,6 +5,7 @@ from typing import Any, Dict, List, Literal, Optional, Union, cast from httpx import Headers, Response +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.secret_managers.main import get_secret_str @@ -263,9 +264,32 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): cancelling_at=None, cancelled_at=None, request_counts=None, - metadata=original_request.get("metadata", {}), + metadata=self._get_openai_compatible_batch_metadata( + original_request.get("metadata", {}) + ), ) + @staticmethod + def _get_openai_compatible_batch_metadata(metadata: Any) -> Dict[str, str]: + """ + OpenAI Batch metadata only accepts string values. + """ + if not isinstance(metadata, dict): + return {} + + sanitized_metadata: Dict[str, str] = {} + for key, value in metadata.items(): + if key == "standard_logging_guardrail_information" or value is None: + continue + + str_key = str(key) + if isinstance(value, str): + sanitized_metadata[str_key] = value + else: + sanitized_metadata[str_key] = safe_dumps(value) + + return sanitized_metadata + def transform_retrieve_batch_request( self, batch_id: str, diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index e4072c24557..c88fa32b6a0 100644 --- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py +++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py @@ -299,9 +299,9 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM): ) def _get_response_stream_shape(self): - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - return BEDROCK_RESPONSE_STREAM_SHAPE + return get_bedrock_response_stream_shape() def _extract_response_content(self, events: InvokeAgentEventList) -> str: """Extract the final response content from parsed events.""" diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 92ca75db95b..7a9916f1f31 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -68,9 +68,9 @@ from litellm.utils import CustomStreamWrapper, get_secret from ..base_aws_llm import BaseAWSLLM from ..common_utils import ( - BEDROCK_RESPONSE_STREAM_SHAPE, BedrockError, ModelResponseIterator, + get_bedrock_response_stream_shape, get_bedrock_tool_name, ) @@ -1828,7 +1828,8 @@ class AWSEventStreamDecoder: yield self._chunk_parser(chunk_data=_data) def _parse_message_from_event(self, event) -> Optional[str]: - if BEDROCK_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_bedrock_response_stream_shape() + if response_stream_shape is None: raise BedrockError( status_code=500, message=( @@ -1837,9 +1838,7 @@ class AWSEventStreamDecoder: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, BEDROCK_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: decoded_body = response_dict["body"].decode() diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 0256d5d4b95..4f4729e4019 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -4,6 +4,7 @@ from __future__ import annotations Common utilities used across bedrock chat/embedding/image generation """ +import functools import json import os from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union @@ -963,10 +964,8 @@ def _load_bedrock_response_stream_shape(): """ Load the ResponseStream shape from botocore's bundled bedrock-runtime schema. - Called once at module import time; the result is stored in - ``BEDROCK_RESPONSE_STREAM_SHAPE`` and reused for the process lifetime. Returns ``None`` if botocore is unavailable or the service model cannot be - loaded, so the module still imports cleanly. + loaded. """ try: from botocore.loaders import Loader @@ -977,15 +976,22 @@ def _load_bedrock_response_stream_shape(): return ServiceModel(service_dict).shape_for("ResponseStream") except Exception as e: verbose_logger.warning( - "litellm: could not pre-load bedrock-runtime response stream shape " + "litellm: could not load bedrock-runtime response stream shape " "— Bedrock event-stream decoding will be unavailable. Error: %s", e, ) return None -# Eagerly resolved once per process — avoids per-instance or per-request disk I/O. -BEDROCK_RESPONSE_STREAM_SHAPE = _load_bedrock_response_stream_shape() +@functools.lru_cache(maxsize=1) +def get_bedrock_response_stream_shape(): + """ + Lazily load and cache the bedrock-runtime ResponseStream shape for the process. + + Avoids importing botocore (and logging warnings) unless Bedrock event-stream + decoding is actually needed. + """ + return _load_bedrock_response_stream_shape() class BedrockEventStreamDecoderBase: @@ -999,7 +1005,8 @@ class BedrockEventStreamDecoderBase: self.parser = EventStreamJSONParser() def _parse_message_from_event(self, event) -> Optional[str]: - if BEDROCK_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_bedrock_response_stream_shape() + if response_stream_shape is None: raise BedrockError( status_code=500, message=( @@ -1008,9 +1015,7 @@ class BedrockEventStreamDecoderBase: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, BEDROCK_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: decoded_body = response_dict["body"].decode() diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py index d00cb74aae0..2c0dc834144 100644 --- a/litellm/llms/bedrock/embed/cohere_transformation.py +++ b/litellm/llms/bedrock/embed/cohere_transformation.py @@ -22,7 +22,7 @@ class BedrockCohereEmbeddingConfig: ) -> dict: for k, v in non_default_params.items(): if k == "encoding_format": - optional_params["embedding_types"] = v + optional_params["embedding_types"] = v if isinstance(v, list) else [v] elif k == "dimensions": optional_params["output_dimension"] = v return optional_params diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py new file mode 100644 index 00000000000..b3b89cbbebf --- /dev/null +++ b/litellm/llms/dashscope/common_utils.py @@ -0,0 +1,28 @@ +""" +Common utilities for the DashScope LLM provider. +""" + +from typing import Optional + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class DashScopeError(BaseLLMException): + """Exception class for DashScope provider errors.""" + + def __init__( + self, + status_code: int, + message: str, + headers: Optional[httpx.Headers] = None, + ): + self.status_code = status_code + self.message = message + self.headers = headers or httpx.Headers() + super().__init__( + status_code=status_code, + message=message, + headers=dict(self.headers), + ) diff --git a/litellm/llms/dashscope/embed/__init__.py b/litellm/llms/dashscope/embed/__init__.py new file mode 100644 index 00000000000..4962b1f3251 --- /dev/null +++ b/litellm/llms/dashscope/embed/__init__.py @@ -0,0 +1,7 @@ +""" +DashScope Embedding Module +""" + +from .transformation import DashScopeEmbeddingConfig + +__all__ = ["DashScopeEmbeddingConfig"] diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py new file mode 100644 index 00000000000..5bc0e5ca817 --- /dev/null +++ b/litellm/llms/dashscope/embed/transformation.py @@ -0,0 +1,191 @@ +""" +Transformation logic from OpenAI /v1/embeddings format to DashScope's /v1/embeddings format. + +Supports +- text-embedding-v4 +- text-embedding-v3 + +Endpoint +- https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings + +Docs - https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api +""" + +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..common_utils import DashScopeError + +DEFAULT_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1" + + +class DashScopeEmbeddingConfig(BaseEmbeddingConfig): + """ + Reference: https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api + + DashScope exposes an OpenAI-compatible /v1/embeddings endpoint, so the + request and response shapes are nearly identical to OpenAI's. + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self, model: str) -> List[str]: + # DashScope's compatible-mode embeddings API accepts the same params as OpenAI. + # `dimensions` / `encoding_format` are only honored by text-embedding-v3 / v4; + # earlier versions silently ignore them server-side. + return ["dimensions", "encoding_format", "user"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool = False, + ) -> dict: + supported = self.get_supported_openai_params(model) + for k, v in non_default_params.items(): + if v is None: + continue + if k in supported: + optional_params[k] = v + # unsupported params are dropped when drop_params=True; + # the upstream _check_valid_arg already raised UnsupportedParamsError + # for drop_params=False before this method is called. + return optional_params + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("DASHSCOPE_API_KEY") + if api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + default_headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}", + } + return {**default_headers, **headers} + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + base = base.rstrip("/") + if base.endswith("/embeddings"): + return base + return f"{base}/embeddings" + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + data: dict = { + "model": model, + "input": input, + } + for key in ("dimensions", "encoding_format", "user"): + value = optional_params.get(key) + if value is not None: + data[key] = value + return data + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + try: + response_json = raw_response.json() + except Exception as e: + raise DashScopeError( + status_code=raw_response.status_code, + message=f"Failed to parse DashScope response as JSON: {str(e)}", + ) + + logging_obj.post_call( + input=request_data.get("input"), + api_key=api_key, + additional_args={"complete_input_dict": request_data}, + original_response=response_json, + ) + + if "error" in response_json: + error = response_json["error"] + message = ( + error.get("message", str(error)) + if isinstance(error, dict) + else str(error) + ) + raise DashScopeError( + status_code=raw_response.status_code, + message=message, + ) + + model_response.object = "list" + model_response.data = response_json.get("data", []) + model_response.model = response_json.get("model", model) + + usage = response_json.get("usage") or {} + prompt_tokens = usage.get("prompt_tokens", 0) + total_tokens = usage.get("total_tokens", prompt_tokens) + setattr( + model_response, + "usage", + Usage( + prompt_tokens=prompt_tokens, + completion_tokens=0, + total_tokens=total_tokens, + ), + ) + + if "id" in response_json: + setattr(model_response, "id", response_json["id"]) + + return model_response + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> BaseLLMException: + if isinstance(headers, dict): + headers = httpx.Headers(headers) + return DashScopeError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/dashscope/rerank/__init__.py b/litellm/llms/dashscope/rerank/__init__.py new file mode 100644 index 00000000000..2a1401f6dc0 --- /dev/null +++ b/litellm/llms/dashscope/rerank/__init__.py @@ -0,0 +1,7 @@ +""" +DashScope Rerank Module +""" + +from .transformation import DashScopeRerankConfig + +__all__ = ["DashScopeRerankConfig"] diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py new file mode 100644 index 00000000000..629f3cf4af7 --- /dev/null +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -0,0 +1,241 @@ +""" +Transformation logic for DashScope's OpenAI-compatible /v1/reranks API. + +Supports +- qwen3-rerank + +(Other DashScope rerankers — gte-rerank-v2 / qwen3-vl-rerank — share the same +endpoint but have not been validated against this transformer. Behavior with +those models is undefined.) + +Endpoint +- https://dashscope.aliyuncs.com/compatible-api/v1/reranks + +Note: chat/embed live under `/compatible-mode/v1/`, but DashScope's rerank +route is exposed under `/compatible-api/v1/reranks` per the docs. Override +with `DASHSCOPE_API_BASE_RERANK` to point at a different host or path. + +Empirically, qwen3-rerank accepts `return_documents=true` and echoes +`results[].document.text` back, even though the public docs list the flag +as supported only for gte-rerank-v2 / qwen3-vl-rerank. + +Docs - https://help.aliyun.com/zh/model-studio/text-rerank-api +""" + +from typing import Any, Dict, List, Optional, Union + +import httpx + +from litellm._uuid import uuid +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.rerank import ( + OptionalRerankParams, + RerankBilledUnits, + RerankResponse, + RerankResponseMeta, + RerankTokens, +) + +from ..common_utils import DashScopeError + +DEFAULT_RERANK_URL = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" + + +class DashScopeRerankConfig(BaseRerankConfig): + """ + Reference: https://help.aliyun.com/zh/model-studio/text-rerank-api + + Targets DashScope's qwen3-rerank model. Request fields: model, query, + documents, top_n, return_documents. Response: results[].index, + results[].relevance_score, optionally results[].document.text (when + return_documents=true), plus a top-level usage.total_tokens counter. + """ + + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: Optional[dict] = None, + ) -> str: + if api_base is None: + api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + + if api_base == DEFAULT_RERANK_URL: + return DEFAULT_RERANK_URL + + cleaned = api_base.rstrip("/") + if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): + return cleaned + + if cleaned.endswith("/v1"): + return f"{cleaned}/reranks" + + # Unknown base: append /reranks rather than silently ignoring the caller's api_base. + return f"{cleaned}/reranks" + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + optional_params: Optional[dict] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("DASHSCOPE_API_KEY") + if api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "content-type": "application/json", + } + return {**default_headers, **headers} + + def get_supported_cohere_rerank_params(self, model: str) -> list: + return ["query", "documents", "top_n", "return_documents"] + + def map_cohere_rerank_params( + self, + non_default_params: Optional[dict], + model: str, + drop_params: bool, + query: str, + documents: List[Union[str, Dict[str, Any]]], + custom_llm_provider: Optional[str] = None, + top_n: Optional[int] = None, + rank_fields: Optional[List[str]] = None, + return_documents: Optional[bool] = True, + max_chunks_per_doc: Optional[int] = None, + max_tokens_per_doc: Optional[int] = None, + ) -> Dict: + # qwen3-rerank accepts query/documents/top_n/return_documents. The + # rest (rank_fields, max_*_per_doc) are silently dropped. + params: OptionalRerankParams = OptionalRerankParams( + query=query, + documents=documents, + ) + if top_n is not None: + params["top_n"] = top_n + if return_documents is not None: + params["return_documents"] = return_documents + return dict(params) + + def transform_rerank_request( + self, + model: str, + optional_rerank_params: Dict, + headers: dict, + litellm_params: Optional[dict] = None, + ) -> dict: + if "query" not in optional_rerank_params: + raise ValueError("query is required for DashScope rerank") + if "documents" not in optional_rerank_params: + raise ValueError("documents is required for DashScope rerank") + + request: Dict[str, Any] = { + "model": model, + "query": optional_rerank_params["query"], + "documents": optional_rerank_params["documents"], + } + if optional_rerank_params.get("top_n") is not None: + request["top_n"] = optional_rerank_params["top_n"] + if optional_rerank_params.get("return_documents") is not None: + request["return_documents"] = optional_rerank_params["return_documents"] + return request + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: Optional[dict] = None, + optional_params: Optional[dict] = None, + litellm_params: Optional[dict] = None, + ) -> RerankResponse: + request_data = request_data or {} + optional_params = optional_params or {} + litellm_params = litellm_params or {} + try: + response_json = raw_response.json() + except Exception: + raise DashScopeError( + status_code=raw_response.status_code, + message=raw_response.text, + ) + + logging_obj.post_call( + input=request_data.get("query"), + api_key=api_key, + additional_args={"complete_input_dict": request_data}, + original_response=response_json, + ) + + # DashScope error envelope: {"code": "...", "message": "...", "request_id": "..."} + if "code" in response_json and "results" not in response_json: + raise DashScopeError( + status_code=raw_response.status_code, + message=response_json.get("message", str(response_json)), + ) + + results = response_json.get("results") + if results is None: + raise DashScopeError( + status_code=raw_response.status_code, + message=f"No results in DashScope rerank response: {response_json}", + ) + + # qwen3-rerank returns: + # {"index": int, "relevance_score": float} + # plus, when return_documents=true was sent: + # "document": {"text": "..."} + # which already matches LiteLLM's RerankResponseDocument shape. + transformed_results: List[dict] = [] + for r in results: + item: Dict[str, Any] = { + "index": r["index"], + "relevance_score": r["relevance_score"], + } + doc = r.get("document") + if isinstance(doc, dict): + item["document"] = doc + elif isinstance(doc, str): + # Defensive: spec says dict, but normalize string-shaped echoes. + item["document"] = {"text": doc} + transformed_results.append(item) + + usage = response_json.get("usage") or {} + total_tokens = usage.get("total_tokens") + billed_units = RerankBilledUnits(total_tokens=total_tokens) + tokens = RerankTokens(input_tokens=total_tokens) + meta = RerankResponseMeta(billed_units=billed_units, tokens=tokens) + + return RerankResponse( + id=response_json.get("id") or str(uuid.uuid4()), + results=transformed_results, # type: ignore + meta=meta, + ) + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> BaseLLMException: + if isinstance(headers, dict): + headers = httpx.Headers(headers) + return DashScopeError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/deepseek/chat/transformation.py b/litellm/llms/deepseek/chat/transformation.py index 5cd8d119542..7ed3e484535 100644 --- a/litellm/llms/deepseek/chat/transformation.py +++ b/litellm/llms/deepseek/chat/transformation.py @@ -2,13 +2,15 @@ Translates from OpenAI's `/v1/chat/completions` to DeepSeek's `/v1/chat/completions` """ -from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload +from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload +import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_messages_with_content_list_to_str_conversion, ) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues +from litellm.utils import supports_reasoning from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -62,6 +64,48 @@ class DeepSeekChatConfig(OpenAIGPTConfig): return optional_params + def _fill_reasoning_content( + self, messages: List[AllMessageValues] + ) -> List[AllMessageValues]: + """ + DeepSeek thinking mode requires `reasoning_content` to be passed back on + every assistant message in multi-turn conversations. If it is missing, + the API returns: + "The reasoning_content in the thinking mode must be passed back to the API." + + For each assistant message that is missing `reasoning_content`: + 1. Promote it from `provider_specific_fields["reasoning_content"]` if present + (LiteLLM stores provider-specific response fields there). + 2. Otherwise inject a single space — the minimum value the API accepts. + """ + result: List[AllMessageValues] = [] + for msg in messages: + if msg.get("role") == "assistant" and not msg.get("reasoning_content"): + patched = dict(cast(dict, msg)) + provider_fields = patched.get("provider_specific_fields") or {} + stored = provider_fields.get("reasoning_content") + if stored: + patched["reasoning_content"] = stored + cleaned = dict(provider_fields) + cleaned.pop("reasoning_content", None) + patched["provider_specific_fields"] = cleaned + else: + litellm.verbose_logger.warning( + "DeepSeek thinking mode: assistant message is missing " + "`reasoning_content` and none was saved in " + "`provider_specific_fields`. A single-space placeholder " + "is being injected to satisfy API validation, but the " + "model will receive a blank reasoning chain for this turn, " + "which may silently degrade multi-turn response quality. " + "Preserve `reasoning_content` from the original assistant " + "response when building multi-turn conversation history." + ) + patched["reasoning_content"] = " " + result.append(cast(AllMessageValues, patched)) + else: + result.append(msg) + return result + @overload def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: Literal[True] @@ -91,6 +135,66 @@ class DeepSeekChatConfig(OpenAIGPTConfig): messages=messages, model=model, is_async=False ) + def _thinking_mode_active(self, model: str, optional_params: dict) -> bool: + """ + Returns True only when thinking mode is actually active for this request: + - model supports reasoning (capability check) + - user explicitly passed thinking={"type": "enabled"} (opt-in check) + """ + return ( + supports_reasoning(model=model, custom_llm_provider="deepseek") + and (optional_params.get("thinking") or {}).get("type") == "enabled" + ) + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Ensures `reasoning_content` is forwarded on assistant messages for + multi-turn thinking-mode conversations (issue #28045). + + Only runs when thinking mode is actually active - guarded by both + supports_reasoning() (model capability) and optional_params["thinking"] + (user explicitly enabled it), preventing spurious injection on models + like deepseek-v3.2 that support thinking as opt-in but not always-on. + """ + if self._thinking_mode_active(model=model, optional_params=optional_params): + messages = self._fill_reasoning_content(messages) + return super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + + async def async_transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Async equivalent of transform_request — applies the same reasoning_content + fix for multi-turn thinking-mode conversations. + """ + if self._thinking_mode_active(model=model, optional_params=optional_params): + messages = self._fill_reasoning_content(messages) + return await super().async_transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: diff --git a/litellm/llms/deepseek/messages/transformation.py b/litellm/llms/deepseek/messages/transformation.py new file mode 100644 index 00000000000..ad60478960e --- /dev/null +++ b/litellm/llms/deepseek/messages/transformation.py @@ -0,0 +1,133 @@ +""" +DeepSeek Anthropic-compatible messages transformation config. +""" + +from typing import Any, Dict, List, Optional, Tuple + +import litellm +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + AnthropicMessagesConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams + + +class DeepSeekAnthropicMessagesConfig(AnthropicMessagesConfig): + """ + DeepSeek exposes an Anthropic-compatible Messages API at + https://api.deepseek.com/anthropic. + + It accepts the native Anthropic Messages conversation shape, including + thinking blocks in assistant history, but rejects Anthropic's explicit + custom-tool discriminator (`{"type": "custom"}`). + """ + + @property + def custom_llm_provider(self) -> Optional[str]: + return "deepseek" + + @staticmethod + def get_api_key(api_key: Optional[str] = None) -> Optional[str]: + return api_key or get_secret_str("DEEPSEEK_API_KEY") or litellm.api_key + + @staticmethod + def get_api_base(api_base: Optional[str] = None) -> str: + return ( + api_base + or get_secret_str("DEEPSEEK_ANTHROPIC_API_BASE") + or get_secret_str("DEEPSEEK_API_BASE") + or "https://api.deepseek.com/anthropic" + ) + + def validate_anthropic_messages_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Tuple[dict, Optional[str]]: + dynamic_api_key = self.get_api_key(api_key=api_key) + + if ( + "x-api-key" not in headers + and "authorization" not in headers + and dynamic_api_key is not None + ): + headers["x-api-key"] = dynamic_api_key + + if "anthropic-version" not in headers: + headers["anthropic-version"] = "2023-06-01" + if "content-type" not in headers: + headers["content-type"] = "application/json" + + headers = self._update_headers_with_anthropic_beta( + headers=headers, + optional_params=optional_params, + custom_llm_provider=self.custom_llm_provider or "deepseek", + ) + + return headers, api_base + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + base_url = self.get_api_base(api_base=api_base).rstrip("/") + + if base_url.endswith("/v1/messages") and "/anthropic/" in base_url: + return base_url + if base_url.endswith("/v1/messages"): + base_url = base_url[: -len("/v1/messages")] + if base_url.endswith("/v1"): + base_url = base_url[: -len("/v1")] + if base_url.endswith("/beta"): + base_url = base_url[: -len("/beta")] + + if not base_url.endswith("/anthropic") and "/anthropic/" not in base_url: + base_url = f"{base_url}/anthropic" + + return f"{base_url}/v1/messages" + + @staticmethod + def _sanitize_tools_for_deepseek(tools: Any) -> Any: + if not isinstance(tools, list): + return tools + + sanitized_tools = [] + for tool in tools: + if isinstance(tool, dict) and tool.get("type") == "custom": + sanitized_tool = dict(tool) + sanitized_tool.pop("type", None) + sanitized_tools.append(sanitized_tool) + else: + sanitized_tools.append(tool) + return sanitized_tools + + def transform_anthropic_messages_request( + self, + model: str, + messages: List[Dict], + anthropic_messages_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + anthropic_messages_request = super().transform_anthropic_messages_request( + model=model, + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params=litellm_params, + headers=headers, + ) + if "tools" in anthropic_messages_request: + anthropic_messages_request["tools"] = self._sanitize_tools_for_deepseek( + anthropic_messages_request["tools"] + ) + return anthropic_messages_request diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index 72569e5c6cd..16e17dcc876 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -1,5 +1,7 @@ from typing import List, Optional, cast +import litellm + from litellm.litellm_core_utils.prompt_templates.factory import ( convert_generic_image_chunk_to_openai_image_obj, convert_to_anthropic_image_obj, @@ -101,7 +103,10 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): return supported_params def _transform_messages( - self, messages: List[AllMessageValues], model: Optional[str] = None + self, + messages: List[AllMessageValues], + model: Optional[str] = None, + litellm_params: Optional[dict] = None, ) -> List[ContentType]: """ Google AI Studio Gemini does not support HTTP/HTTPS URLs for files. @@ -141,14 +146,23 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): img_element["image_url"] = converted_image_url # type: ignore elif element.get("type") == "file": file_element = cast(ChatCompletionFileObject, element) - file_id = file_element["file"].get("file_id") + _file_field = file_element.get("file") + if _file_field is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=model, + llm_provider="gemini", + ) + file_id = _file_field.get("file_id") if file_id and ("http://" in file_id or "https://" in file_id): # Convert HTTP/HTTPS file URL to base64 data try: base64_data = convert_url_to_base64(file_id) - file_element["file"]["file_data"] = base64_data # type: ignore - file_element["file"].pop("file_id", None) # type: ignore + _file_field["file_data"] = base64_data # type: ignore + _file_field.pop("file_id", None) # type: ignore except Exception: # If conversion fails, leave as is and let the API handle it pass - return _gemini_convert_messages_with_history(messages=messages, model=model) + return _gemini_convert_messages_with_history( + messages=messages, model=model, litellm_params=litellm_params + ) diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 6b7ec4dfb1c..5464b5bb7ee 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -287,7 +287,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): content_item["image_url"] = new_image_url_obj elif content_item.get("type") == "file": content_item = cast(ChatCompletionFileObject, content_item) - file_obj = content_item["file"] + file_obj = content_item.get("file") + if file_obj is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="openai", + ) new_file_obj = ChatCompletionFileObjectFile( **{ # type: ignore k: v diff --git a/litellm/llms/sagemaker/common_utils.py b/litellm/llms/sagemaker/common_utils.py index 50c8ee4220e..6c15d642f8c 100644 --- a/litellm/llms/sagemaker/common_utils.py +++ b/litellm/llms/sagemaker/common_utils.py @@ -1,3 +1,4 @@ +import functools import json from typing import AsyncIterator, Iterator, List, Optional, Union @@ -22,14 +23,22 @@ def _load_sagemaker_response_stream_shape(): ) except Exception as e: verbose_logger.warning( - "litellm: could not pre-load sagemaker-runtime response stream shape " + "litellm: could not load sagemaker-runtime response stream shape " "— SageMaker event-stream decoding will be unavailable. Error: %s", e, ) return None -SAGEMAKER_RESPONSE_STREAM_SHAPE = _load_sagemaker_response_stream_shape() +@functools.lru_cache(maxsize=1) +def get_sagemaker_response_stream_shape(): + """ + Lazily load and cache the sagemaker-runtime stream shape for the process. + + Avoids importing botocore (and logging warnings) unless SageMaker event-stream + decoding is actually needed. + """ + return _load_sagemaker_response_stream_shape() class SagemakerError(BaseLLMException): @@ -207,7 +216,8 @@ class AWSEventStreamDecoder: verbose_logger.error(f"Final error parsing accumulated JSON: {e}") def _parse_message_from_event(self, event) -> Optional[str]: - if SAGEMAKER_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_sagemaker_response_stream_shape() + if response_stream_shape is None: raise SagemakerError( status_code=500, message=( @@ -216,9 +226,7 @@ class AWSEventStreamDecoder: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, SAGEMAKER_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: raise ValueError(f"Bad response code, expected 200: {response_dict}") diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 9afa5dec465..f56992a2502 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -6,13 +6,16 @@ Why separate file? Make it easy to see how transformation works import json import os -from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union, cast +import re +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast +from urllib.parse import quote import httpx from pydantic import BaseModel import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.asyncify import asyncify from litellm.litellm_core_utils.prompt_templates.common_utils import ( _get_image_mime_type_from_url, ) @@ -57,6 +60,45 @@ from ..common_utils import ( get_supports_system_message, ) +# Typed as Any to avoid introducing a module-load-time cyclic import to +# vertex_llm_base. The instance is lazily constructed by _get_vertex_base() +# the first time GCS metadata needs to be fetched. +_GCS_METADATA_VERTEX_BASE: Optional[Any] = None +# Shared sync client for GCS JSON API metadata reads so proxy/SSL settings +# from litellm's HTTP stack apply (see Greptile review on PR #27278). +_GCS_METADATA_HTTP_HANDLER: Optional[HTTPHandler] = None +_GEMINI_MIME_TYPE_ALIASES: Dict[str, str] = { + "image/jpg": "image/jpeg", +} + + +def _apply_gemini_mime_type_aliases(mime_type: str) -> str: + """Normalize known MIME aliases only; does not consult the file-type registry. + + Also strips MIME parameters (e.g. ``; charset=utf-8``) so that values + sourced from GCS object metadata (``contentType``) validate correctly. + """ + normalized = mime_type.split(";", 1)[0].strip().lower() + return _GEMINI_MIME_TYPE_ALIASES.get(normalized, normalized) + + +def _get_vertex_base() -> Any: + """Lazily return the shared VertexBase instance to avoid a module-load-time cyclic import.""" + global _GCS_METADATA_VERTEX_BASE + if _GCS_METADATA_VERTEX_BASE is None: + from ..vertex_llm_base import VertexBase + + _GCS_METADATA_VERTEX_BASE = VertexBase() + return _GCS_METADATA_VERTEX_BASE + + +def _get_gcs_metadata_http_handler() -> HTTPHandler: + global _GCS_METADATA_HTTP_HANDLER + if _GCS_METADATA_HTTP_HANDLER is None: + _GCS_METADATA_HTTP_HANDLER = HTTPHandler(timeout=5.0) + return _GCS_METADATA_HTTP_HANDLER + + if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -171,12 +213,299 @@ def _apply_gemini_metadata( return cast(PartType, part_dict) +def _parse_gs_uri(gs_uri: str) -> Tuple[str, str]: + if not gs_uri.startswith("gs://"): + raise ValueError(f"Invalid gs URI: {gs_uri}") + uri_without_scheme = gs_uri[5:] # drop gs:// + uri_parts = uri_without_scheme.split("/", 1) + if len(uri_parts) != 2 or not uri_parts[0] or not uri_parts[1]: + raise ValueError(f"Invalid gs URI: {gs_uri}") + return uri_parts[0], uri_parts[1] + + +def _is_valid_gcs_bucket_name(bucket: str) -> bool: + """ + Validate bucket name against core GCS naming constraints. + """ + bucket_length = len(bucket) + max_bucket_length = 222 if "." in bucket else 63 + if bucket_length < 3 or bucket_length > max_bucket_length: + return False + if "." in bucket and any( + len(label) == 0 or len(label) > 63 for label in bucket.split(".") + ): + return False + if not re.fullmatch(r"[a-z0-9][a-z0-9._-]*[a-z0-9]", bucket): + return False + if ".." in bucket: + return False + if re.fullmatch(r"\d+\.\d+\.\d+\.\d+", bucket): + return False + return True + + +def _gs_uri_requires_content_type_metadata(url: str) -> bool: + """ + True when _process_gemini_media would call _get_gcs_object_content_type + (extension-less gs:// and no explicit format passed into that helper). + """ + if "gs://" not in url: + return False + extension_with_dot = os.path.splitext(url)[-1] + extension = extension_with_dot[1:] if extension_with_dot else "" + return len(extension) == 0 + + +def _image_url_payload_may_need_sync_gcs_metadata_fetch( + raw_image_url: Any, +) -> bool: + """ + True when this image_url value (content-part image_url or assistant ``images[]`` + entry) can trigger a blocking GCS metadata read for MIME resolution. + """ + fmt: Optional[str] = None + url: Optional[str] = None + if isinstance(raw_image_url, dict): + url = raw_image_url.get("url") # type: ignore[assignment] + if not isinstance(url, str): + return False + fmt = ( + raw_image_url.get("format") + or raw_image_url.get("mime_type") + or raw_image_url.get("content_type") + ) + elif isinstance(raw_image_url, str): + url = raw_image_url + else: + return False + if "gs://" not in url or fmt: + return False + return _gs_uri_requires_content_type_metadata(url) + + +def _openai_messages_may_need_sync_gcs_metadata_fetch( + messages: List[AllMessageValues], +) -> bool: + """ + Heuristic: True if any message part can trigger a blocking GCS JSON + metadata read inside _transform_request_body (extension-less gs:// without + explicit MIME hints). Covers user/system ``content`` parts and assistant + ``images`` (same paths as ``_gemini_convert_messages_with_history``). Used + to decide whether ``async_transform_request_body`` should offload the sync + transform via ``asyncify``. + """ + for raw in messages: + msg: Any = raw + if not isinstance(msg, dict) and hasattr(msg, "model_dump"): + msg = msg.model_dump(exclude_none=False) + if not isinstance(msg, dict): + continue + images_field = msg.get("images") + if isinstance(images_field, list): + for image_item in images_field: + if not isinstance(image_item, dict): + continue + if _image_url_payload_may_need_sync_gcs_metadata_fetch( + image_item.get("image_url") + ): + return True + + content = msg.get("content") + if not isinstance(content, list): + continue + for item in content: + if not isinstance(item, dict): + continue + itype = item.get("type") + if itype == "image_url": + if _image_url_payload_may_need_sync_gcs_metadata_fetch( + item.get("image_url") + ): + return True + elif itype == "file": + file_obj = item.get("file") + if not isinstance(file_obj, dict): + continue + fmt = ( + file_obj.get("format") + or file_obj.get("mime_type") + or file_obj.get("content_type") + ) + passed = file_obj.get("file_id") or file_obj.get("file_data") + if ( + isinstance(passed, str) + and "gs://" in passed + and not fmt + and _gs_uri_requires_content_type_metadata(passed) + ): + return True + return False + + +def _get_gcs_object_content_type( + image_url: str, + vertex_project: Optional[str] = None, + vertex_credentials: Optional[Any] = None, +) -> Optional[str]: + """ + Resolve content type from GCS object metadata. + + Only attaches a Bearer token when the caller explicitly supplies Vertex + credentials, to avoid using the server's default Google credentials on + the Gemini API-key (Google AI Studio) path and being used as an oracle + for private GCS object metadata. Without explicit credentials we only + issue an anonymous request, which only succeeds for publicly-readable + objects. + """ + try: + bucket, object_name = _parse_gs_uri(image_url) + except ValueError: + return None + if not _is_valid_gcs_bucket_name(bucket): + return None + + headers: Dict[str, str] = {} + explicit_vertex_auth_provided = ( + vertex_project is not None or vertex_credentials is not None + ) + if explicit_vertex_auth_provided: + try: + access_token, _ = _get_vertex_base().get_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + ) + headers["Authorization"] = f"Bearer {access_token}" + except Exception as e: + raise litellm.BadRequestError( + message=( + "Unable to fetch GCS metadata with provided Vertex credentials/project. " + f"Original error: {str(e)}" + ), + model=None, + llm_provider="vertex_ai", + ) + + # Build the URL via httpx.URL with a fixed scheme/host and URL-encode both + # bucket and object so CodeQL does not flag the interpolation as a + # potential SSRF that could resolve to an arbitrary host. + encoded_bucket = quote(bucket, safe="") + encoded_object = quote(object_name, safe="") + metadata_url = httpx.URL( + scheme="https", + host="storage.googleapis.com", + path=f"/storage/v1/b/{encoded_bucket}/o/{encoded_object}", + params={"fields": "contentType"}, + ) + try: + response = _get_gcs_metadata_http_handler().get( + url=str(metadata_url), + headers=headers or None, + ) + except httpx.RequestError as e: + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "Unable to reach GCS JSON API for object metadata with provided " + f"Vertex credentials. {type(e).__name__}: {e}" + ), + model=None, + llm_provider="vertex_ai", + ) from e + return None + + if response.is_error: + if explicit_vertex_auth_provided: + preview = (response.text or "")[:1024] + raise litellm.BadRequestError( + message=( + "Unable to read GCS object metadata with provided Vertex credentials. " + f"HTTP {response.status_code}. Response body (truncated): {preview!r}" + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + try: + payload = response.json() + except ValueError as e: + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "GCS metadata response was not valid JSON when using provided " + f"Vertex credentials (HTTP {response.status_code}). Error: {e}" + ), + model=None, + llm_provider="vertex_ai", + ) from e + return None + + if not isinstance(payload, dict): + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "GCS metadata response was not a JSON object when using provided " + f"Vertex credentials (HTTP {response.status_code})." + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + content_type = payload.get("contentType") + if isinstance(content_type, str) and len(content_type) > 0: + return content_type + + if explicit_vertex_auth_provided: + preview = (response.text or "")[:1024] + raise litellm.BadRequestError( + message=( + "GCS metadata JSON did not include a non-empty contentType field when " + f"using provided Vertex credentials (HTTP {response.status_code}). " + f"Body (truncated): {preview!r}" + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + +def _normalize_and_validate_gemini_mime_type( + mime_type: str, model: Optional[str] +) -> str: + # Import lazily to avoid a module-level cyclic-import alert with + # litellm.types.files. + from litellm.types.files import get_file_extension_from_mime_type + + normalized_mime_type = _apply_gemini_mime_type_aliases(mime_type) + try: + file_extension = get_file_extension_from_mime_type(normalized_mime_type) + file_type = get_file_type_from_extension(file_extension) + except ValueError: + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {normalized_mime_type}", + model=model, + llm_provider="vertex_ai", + ) + + if not is_gemini_1_5_accepted_file_type(file_type): + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {file_type}", + model=model, + llm_provider="vertex_ai", + ) + + return get_file_mime_type_for_file_type(file_type) + + def _process_gemini_media( image_url: str, format: Optional[str] = None, media_resolution_enum: Optional[Dict[str, str]] = None, model: Optional[str] = None, video_metadata: Optional[Dict[str, Any]] = None, + vertex_project: Optional[str] = None, + vertex_credentials: Optional[Any] = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -193,20 +522,63 @@ def _process_gemini_media( try: # GCS URIs if "gs://" in image_url: - # Figure out file type extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png" extension = extension_with_dot[1:] # Ex: "png" + explicit_gcs_format = False if not format: - file_type = get_file_type_from_extension(extension) + mime_type: Optional[str] = None + # For extension-less gs:// URIs, we cannot infer from path. + # If callers pass `format`/`mime_type`, this branch is skipped. + if extension: + file_type = get_file_type_from_extension(extension) - # Validate the file type is supported by Gemini - if not is_gemini_1_5_accepted_file_type(file_type): - raise Exception(f"File type not supported by gemini - {file_type}") + # Validate the file type is supported by Gemini + if not is_gemini_1_5_accepted_file_type(file_type): + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {file_type}", + model=model, + llm_provider="vertex_ai", + ) - mime_type = get_file_mime_type_for_file_type(file_type) + mime_type = get_file_mime_type_for_file_type(file_type) + else: + mime_type = _get_gcs_object_content_type( + image_url=image_url, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, + ) + if mime_type is None: + raise litellm.BadRequestError( + message=( + f"Unable to determine mime type for gs URI: {image_url}. " + "This gs:// URI has no file extension and GCS metadata " + "lookup failed. Set it explicitly using image_url.format " + "(or image_url.mime_type/content_type) or " + "message.content[].file.format." + ), + model=model, + llm_provider="vertex_ai", + ) else: mime_type = format + explicit_gcs_format = True + if mime_type is None: + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {image_url}", + model=model, + llm_provider="vertex_ai", + ) + if explicit_gcs_format: + # Callers who pass format/mime_type explicitly for gs:// URIs + # rely on pass-through to Gemini (pre-PR behavior). Only apply + # known MIME aliases; skip litellm's file-type registry. + mime_type = _apply_gemini_mime_type_aliases(mime_type) + else: + mime_type = _normalize_and_validate_gemini_mime_type( + mime_type=mime_type, + model=model, + ) file_data = FileDataType(mime_type=mime_type, file_uri=image_url) part: PartType = {"file_data": file_data} return _apply_gemini_metadata( @@ -258,8 +630,6 @@ def _snake_to_camel(snake_str: str) -> str: def _camel_to_snake(camel_str: str) -> str: """Convert camelCase to snake_case""" - import re - return re.sub(r"(? List[ContentType]: """ Converts given messages from OpenAI format to Gemini format @@ -326,6 +697,16 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 msg_i = 0 tool_call_responses = [] + vertex_project = None + vertex_credentials = None + if litellm_params: + vertex_project = litellm_params.get("vertex_project") or litellm_params.get( + "vertex_ai_project" + ) + vertex_credentials = litellm_params.get( + "vertex_credentials" + ) or litellm_params.get("vertex_ai_credentials") + try: while msg_i < len(messages): user_content: List[PartType] = [] @@ -351,20 +732,42 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 img_element = element format: Optional[str] = None media_resolution_enum: Optional[Dict[str, str]] = None - if isinstance(img_element["image_url"], dict): - image_url = img_element["image_url"]["url"] - format = img_element["image_url"].get("format") - detail = img_element["image_url"].get("detail") + raw_image_url = img_element.get("image_url") + if raw_image_url is None: + raise litellm.BadRequestError( + message="Invalid message content: element type is 'image_url' but 'image_url' field is missing ", + model=model, + llm_provider="vertex_ai", + ) + if isinstance(raw_image_url, dict): + image_url = raw_image_url.get("url") + if image_url is None: + raise litellm.BadRequestError( + message="Invalid message content: element type is 'image_url' but 'url' field is missing inside 'image_url' ", + model=model, + llm_provider="vertex_ai", + ) + # TypedDict does not declare mime_type/content_type; + # read via Dict[str, Any] for caller-provided MIME fields. + image_url_dict = cast(Dict[str, Any], raw_image_url) + format = ( + image_url_dict.get("format") + or image_url_dict.get("mime_type") + or image_url_dict.get("content_type") + ) + detail = image_url_dict.get("detail") media_resolution_enum = ( _convert_detail_to_media_resolution_enum(detail) ) else: - image_url = img_element["image_url"] + image_url = raw_image_url _part = _process_gemini_media( image_url=image_url, format=format, media_resolution_enum=media_resolution_enum, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) elif element["type"] == "input_audio": @@ -390,15 +793,31 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 image_url=openai_image_str, format=audio_format_modified, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) elif element["type"] == "file": file_element = cast(ChatCompletionFileObject, element) - file_id = file_element["file"].get("file_id") - format = file_element["file"].get("format") - file_data = file_element["file"].get("file_data") - detail = file_element["file"].get("detail") - video_metadata = file_element["file"].get("video_metadata") + _file_field = file_element.get("file") + if _file_field is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=model, + llm_provider="vertex_ai", + ) + # TypedDict does not declare mime_type/content_type; + # read via Dict[str, Any] for caller-provided MIME fields. + file_dict = cast(Dict[str, Any], _file_field) + file_id = file_dict.get("file_id") + format = ( + file_dict.get("format") + or file_dict.get("mime_type") + or file_dict.get("content_type") + ) + file_data = file_dict.get("file_data") + detail = file_dict.get("detail") + video_metadata = file_dict.get("video_metadata") passed_file = file_id or file_data if passed_file is None: raise Exception( @@ -417,13 +836,23 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 model=model, media_resolution_enum=media_resolution_enum, video_metadata=video_metadata, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) - except Exception: - raise Exception( - "Unable to determine mime type for file_id: {}, set this explicitly using message[{}].content[{}].file.format".format( - file_id, msg_i, element_idx - ) + except litellm.BadRequestError: + raise + except Exception as e: + raise litellm.BadRequestError( + message=( + f"Unable to determine mime type for file: " + f"{file_id or 'provided data'}, set this explicitly " + f"using message[{msg_i}].content[{element_idx}].file.format " + f"(or file.mime_type/content_type). " + f"Original error: {str(e)}" + ), + model=model, + llm_provider="vertex_ai", ) user_content.extend(_parts) elif _message_content is not None and isinstance(_message_content, str): @@ -528,7 +957,11 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 image_url_obj = image_item.get("image_url") if isinstance(image_url_obj, dict): assistant_image_url = image_url_obj.get("url") - format = image_url_obj.get("format") + format = ( + image_url_obj.get("format") + or image_url_obj.get("mime_type") + or image_url_obj.get("content_type") + ) detail = image_url_obj.get("detail") media_resolution_enum = ( _convert_detail_to_media_resolution_enum(detail) @@ -539,6 +972,8 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 format=format, media_resolution_enum=media_resolution_enum, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) assistant_content.append(_part) @@ -713,11 +1148,11 @@ def _transform_request_body( # noqa: PLR0915 try: if custom_llm_provider == "gemini": content = litellm.GoogleAIStudioGeminiConfig()._transform_messages( - messages=messages, model=model + messages=messages, model=model, litellm_params=litellm_params ) else: content = litellm.VertexGeminiConfig()._transform_messages( - messages=messages, model=model + messages=messages, model=model, litellm_params=litellm_params ) tools: Optional[Tools] = optional_params.pop("tools", None) tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None) @@ -893,6 +1328,20 @@ async def async_transform_request_body( vertex_auth_header=vertex_auth_header, ) + if _openai_messages_may_need_sync_gcs_metadata_fetch(messages): + # _transform_request_body may issue a sync httpx.get (up to 5s timeout) + # via _get_gcs_object_content_type to fetch GCS object metadata. Run the + # whole sync transformation on a worker thread so it does not block the + # async event loop. + return await asyncify(_transform_request_body)( + messages=messages, + model=model, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + cached_content=cached_content, + optional_params=optional_params, + ) + return _transform_request_body( messages=messages, model=model, 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 6278de662f8..49c1c335467 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 @@ -2533,9 +2533,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return model_response def _transform_messages( - self, messages: List[AllMessageValues], model: Optional[str] = None + self, + messages: List[AllMessageValues], + model: Optional[str] = None, + litellm_params: Optional[dict] = None, ) -> List[ContentType]: - return _gemini_convert_messages_with_history(messages=messages, model=model) + return _gemini_convert_messages_with_history( + messages=messages, model=model, litellm_params=litellm_params + ) def get_error_class( self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] @@ -3139,6 +3144,31 @@ class ModelResponseIterator: self.cumulative_tool_call_index: int = 0 self.has_seen_tool_calls: bool = False + @staticmethod + def _check_streaming_error(chunk: dict) -> None: + """Detect embedded errors (e.g. 429 RESOURCE_EXHAUSTED) in streaming chunks and raise VertexAIError.""" + if "error" not in chunk: + return + error_data = chunk["error"] + if not isinstance(error_data, dict): + raise VertexAIError( + status_code=500, + message=f"Unexpected error format in mid-stream chunk: {error_data}", + ) + raw_code = error_data.get("code", 500) + if raw_code is None: + raw_code = 500 + try: + error_code = int(raw_code) + except (TypeError, ValueError): + error_code = 500 + error_message = error_data.get("message", "Unknown error") + error_status = error_data.get("status", "UNKNOWN") + raise VertexAIError( + status_code=error_code, + message=f"{error_status} - {error_message}", + ) + def _apply_stream_candidates( self, _candidates: List[Candidates], @@ -3256,6 +3286,11 @@ class ModelResponseIterator: def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]: try: verbose_logger.debug(f"RAW GEMINI CHUNK: {chunk}") + + # Detect mid-stream error chunks (e.g. 429 RESOURCE_EXHAUSTED). + # Vertex AI can return errors as HTTP 200 but with an "error" field in the SSE body. + self._check_streaming_error(chunk) + from litellm.types.utils import ModelResponseStream processed_chunk = GenerateContentResponseBody(**chunk) # type: ignore diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index 123d925f7c1..eb67e3aa828 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -292,22 +292,15 @@ class VertexAIPartnerModels(VertexBase): Returns: Dict containing token count information """ - try: - import vertexai - except Exception as e: - raise VertexAIError( - status_code=400, - message=f"""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`. Got error: {e}""", - ) - - if not ( - hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") - ): - raise VertexAIError( - status_code=400, - message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", - ) - + # Note: we intentionally do not import `vertexai` (the Gemini SDK shipped + # by `google-cloud-aiplatform`) on this path. Partner models such as + # Claude on Vertex use the Anthropic Messages API protocol directly via + # `:rawPredict`, and `VertexAIPartnerModelsTokenCounter` reaches that + # endpoint with an authenticated httpx client — it never touches the + # Gemini SDK. Requiring `google-cloud-aiplatform>=1.38` here turned a + # SDK-free Anthropic-protocol call into a hard dependency on the Gemini + # SDK (see #28084), breaking `/v1/messages/count_tokens` for Claude-on- + # Vertex on any LiteLLM install without that extra. Stay SDK-free. try: from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import ( VertexAIPartnerModelsTokenCounter, diff --git a/litellm/main.py b/litellm/main.py index c3d1c2e05b0..b5364f8ba17 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5720,6 +5720,33 @@ def embedding( # noqa: PLR0915 aembedding=aembedding, headers=headers, ) + elif custom_llm_provider == "dashscope": + dashscope_key = ( + api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY") + ) + if dashscope_key is None: + raise ValueError( + "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + if extra_headers is not None and isinstance(extra_headers, dict): + headers = extra_headers + else: + headers = {} + response = base_llm_http_handler.embedding( + model=model, + input=input, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + api_base=api_base, + optional_params=optional_params, + litellm_params={}, + model_response=EmbeddingResponse(), + api_key=dashscope_key, + client=client, + aembedding=aembedding, + headers=headers, + ) elif custom_llm_provider == "ovhcloud": api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY") api_base = ( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 543172b8381..fb5bfa6cf4e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -2112,6 +2112,380 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "azure_ai/gpt-5.4": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-2026-03-05": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro-2026-03-05": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-mini-2026-03-17": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano-2026-03-17": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, "azure_ai/model_router": { "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index e6691329709..8541a691e88 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -30,23 +30,43 @@ _DEFAULT_PORTS = {"http": 80, "https": 443} _TRUSTED_REDIRECT_ORIGINS_ENV = "MCP_TRUSTED_REDIRECT_ORIGINS" +_warned_invalid_proxy_base_url: Optional[str] = None + + +def _resolve_proxy_base_url_env() -> Optional[str]: + global _warned_invalid_proxy_base_url + configured = os.environ.get("PROXY_BASE_URL", "").strip() + if not configured: + return None + parsed = urlparse(configured) + if parsed.scheme in ("http", "https") and parsed.netloc: + normalized = urlunparse((parsed.scheme, parsed.netloc, parsed.path, "", "", "")) + return normalized.rstrip("/") + if _warned_invalid_proxy_base_url != configured: + verbose_logger.warning( + "PROXY_BASE_URL=%r is not a valid http(s) URL (missing scheme " + "or host) and will be ignored for MCP OAuth origin resolution. " + "Set it to a full URL like https://litellm.example.com.", + configured, + ) + _warned_invalid_proxy_base_url = configured + return None + + def get_request_base_url(request: Request) -> str: """ Get the base URL for the request, considering X-Forwarded-* headers. - X-Forwarded-Proto / X-Forwarded-Host / X-Forwarded-Port are only honoured - when the request comes from a configured trusted proxy - (``use_x_forwarded_for`` enabled AND caller in ``mcp_trusted_proxy_ranges``). - Otherwise the request's literal ``base_url`` is returned, so an - untrusted caller cannot poison OAuth-discovery / redirect_uri values - by injecting headers. - - Args: - request: FastAPI Request object - - Returns: - The reconstructed base URL (e.g., "https://proxy.example.com") + Resolution order: ``PROXY_BASE_URL`` env var, then X-Forwarded-* when + the caller is a trusted proxy (``use_x_forwarded_for`` enabled AND + caller in ``mcp_trusted_proxy_ranges``), otherwise the request's + literal ``base_url``. Untrusted callers cannot poison OAuth-discovery + / redirect_uri values by injecting headers. """ + configured = _resolve_proxy_base_url_env() + if configured: + return configured + base_url = str(request.base_url).rstrip("/") parsed = urlparse(base_url) @@ -284,4 +304,22 @@ def validate_trusted_redirect_uri(request: Request, redirect_uri: str) -> None: if _matches_trusted_origin_entry(redirect_netloc, entry): return + verbose_logger.warning( + "MCP OAuth: rejecting redirect_uri %r as invalid_request. " + "Computed proxy base=%r (PROXY_BASE_URL=%r). " + "Inbound headers: X-Forwarded-Proto=%r X-Forwarded-Host=%r " + "X-Forwarded-Port=%r Host=%r. " + "Trusted-redirect-origins env=%r. " + "If this should be accepted, either align ingress X-Forwarded-* " + "with the browser URL, set PROXY_BASE_URL to your public origin, " + "or add the redirect_uri host to MCP_TRUSTED_REDIRECT_ORIGINS.", + redirect_uri, + proxy_base, + os.environ.get("PROXY_BASE_URL"), + request.headers.get("X-Forwarded-Proto"), + request.headers.get("X-Forwarded-Host"), + request.headers.get("X-Forwarded-Port"), + request.headers.get("Host"), + os.environ.get(_TRUSTED_REDIRECT_ORIGINS_ENV), + ) raise HTTPException(status_code=400, detail="invalid_request") diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 3e20cf89e47..46e13ca9931 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

404

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+0:{"buildId":"LpD6ruZoEpvYpT5IvMEoa","tree":{"name":"","paramType":null,"paramKey":"","hasRuntimePrefetch":false,"slots":{"children":{"name":"(dashboard)","paramType":null,"paramKey":"(dashboard)","hasRuntimePrefetch":false,"slots":{"children":{"name":"virtual-keys","paramType":null,"paramKey":"virtual-keys","hasRuntimePrefetch":false,"slots":{"children":{"name":"__PAGE__","paramType":null,"paramKey":"__PAGE__","hasRuntimePrefetch":false,"slots":null,"isRootLayout":false}},"isRootLayout":false}},"isRootLayout":false}},"isRootLayout":true},"staleTime":300} diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d4fa497698a..c989f5dff13 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -4549,6 +4549,7 @@ class PrismaCompatibleUpdateDBModel(TypedDict, total=False): model_name: str litellm_params: str model_info: str + blocked: bool updated_at: str updated_by: str diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index ee86e923924..637a4a070c4 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -522,6 +522,25 @@ def get_request_route(request: Request) -> str: return str(request.url.path) +def get_request_route_template(request: Request) -> Optional[str]: + """ + Return the low-cardinality route template, e.g. + ``/v1/threads/{thread_id}/runs`` (vs. the literal path from + ``get_request_route``). FastAPI sets ``scope["route"]`` before endpoint + dependencies run. Returns None if unavailable (unmatched path, Mount). + """ + try: + scope = request.scope + if not isinstance(scope, dict): + return None + route = scope.get("route") + template = getattr(route, "path", None) + return template if isinstance(template, str) and template else None + except Exception as e: + verbose_proxy_logger.debug(f"error on get_request_route_template: {str(e)}") + return None + + @lru_cache(maxsize=256) def normalize_request_route(route: str) -> str: """ diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 03167c5a2dc..30b5d36e14a 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -51,6 +51,7 @@ from litellm.proxy.auth.auth_utils import ( get_end_user_id_from_request_body, get_model_from_request, get_request_route, + get_request_route_template, normalize_request_route, pre_db_read_auth_checks, route_in_additonal_public_routes, @@ -682,6 +683,12 @@ async def _user_api_key_auth_builder( # noqa: PLR0915 parent_otel_span: Optional[Span] = None start_time = datetime.now() + # Stash the proxy-receive instant for the pre-request latency calc — + # the OTel Span API exposes no start-time getter, so propagate it. + try: + request.state.litellm_received_at = start_time + except Exception: + pass route: str = get_request_route(request=request) valid_token: Optional[UserAPIKeyAuth] = None custom_auth_api_key: bool = False @@ -723,6 +730,12 @@ async def _user_api_key_auth_builder( # noqa: PLR0915 headers=_safe_get_request_headers(request), ) ) + # `route` is the literal path; template from the matched route. + open_telemetry_logger.set_proxy_request_route_attributes( + parent_otel_span, + url_path=route, + http_route=get_request_route_template(request), + ) ### USER-DEFINED AUTH FUNCTION ### if enterprise_custom_auth is not None: diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 7611c9c9692..e7f14df5294 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -69,6 +69,35 @@ else: ProxyLogging = Any +def _extract_cache_read_tokens(usage_obj: dict) -> int: + """ + Anthropic: top-level cache_read_input_tokens field. + OpenAI-compatible (moonshotai, openai, deepseek, etc.): prompt_tokens_details.cached_tokens. + """ + explicit = usage_obj.get("cache_read_input_tokens", 0) or 0 + if explicit: + return int(explicit) + details = usage_obj.get("prompt_tokens_details") or {} + return int(details.get("cached_tokens", 0) or 0) + + +def _extract_cache_creation_tokens(usage_obj: dict) -> int: + """ + Anthropic: top-level cache_creation_input_tokens field. + OpenAI-compatible (kimi-k2 etc.): prompt_tokens_details.cache_write_tokens + or prompt_tokens_details.cache_creation_tokens. + """ + explicit = usage_obj.get("cache_creation_input_tokens", 0) or 0 + if explicit: + return int(explicit) + details = usage_obj.get("prompt_tokens_details") or {} + return int( + details.get("cache_write_tokens", 0) + or details.get("cache_creation_tokens", 0) + or 0 + ) + + class DBSpendUpdateWriter: """ Module responsible for @@ -1992,12 +2021,8 @@ class DBSpendUpdateWriter: api_requests=1, successful_requests=1 if request_status == "success" else 0, failed_requests=1 if request_status != "success" else 0, - cache_read_input_tokens=usage_obj.get("cache_read_input_tokens", 0) - or 0, - cache_creation_input_tokens=usage_obj.get( - "cache_creation_input_tokens", 0 - ) - or 0, + cache_read_input_tokens=_extract_cache_read_tokens(usage_obj), + cache_creation_input_tokens=_extract_cache_creation_tokens(usage_obj), ) return daily_transaction except Exception as e: diff --git a/litellm/proxy/db/db_url_settings.py b/litellm/proxy/db/db_url_settings.py new file mode 100644 index 00000000000..58478db5e2e --- /dev/null +++ b/litellm/proxy/db/db_url_settings.py @@ -0,0 +1,263 @@ +"""Assemble DATABASE_URL (+ optional DATABASE_URL_READ_REPLICA) from env. + +The CLI (`proxy_cli.py`) assembles ``DATABASE_URL`` from discrete +``DATABASE_*`` env vars before Prisma initializes. The componentized +entrypoints (gateway / backend / migrations) bypass the CLI by uvicorn'ing +the app directly, so they call ``DatabaseURLSettings.from_env().apply_to_env()`` +to do the same thing before importing ``proxy_server``. + +The env var names this module reads are exactly the ones emitted by the +``helm/litellm`` chart's ``litellm.serverEnv`` block +(``helm/litellm/templates/_helpers.tpl``). Both auth styles and both +endpoints are covered: + + * IAM auth (``IAM_TOKEN_DB_AUTH`` truthy): mint a short-lived RDS IAM + token and embed it as the password. The writer URL is always + (re)written because the token is freshly minted on every startup. The + chart omits ``DATABASE_PASSWORD`` in this mode. + * Password auth: build a percent-encoded URL from ``DATABASE_PASSWORD``. + The chart emits the discrete ``DATABASE_*`` fields (never a + pre-assembled URL), so URL-reserved characters in the password survive + instead of corrupting the URL. A pre-existing ``DATABASE_URL`` — e.g. + one an operator pinned via ``extraEnv`` — is left untouched and wins. + +The read replica is opt-in via ``DATABASE_HOST_READ_REPLICA`` and never +clobbers a pre-existing ``DATABASE_URL_READ_REPLICA``, so an IAM writer can +run alongside a password-auth reader (or a precomputed reader URL). Reader +IAM is gated on the single global ``IAM_TOKEN_DB_AUTH`` flag — the chart +only emits the reader IAM env vars when the writer also uses IAM auth. +Reader-side fields fall back to the writer's user / name / schema / port / +password when their ``*_READ_REPLICA`` counterpart is unset. +""" + +import os +import urllib.parse +from typing import Optional, cast + +from pydantic import AliasChoices, Field +from pydantic_settings import BaseSettings, SettingsConfigDict + +# Imported as a module (not `from ... import generate_iam_auth_token`) so the +# AWS-touching token mint stays patchable at its canonical location in tests. +from litellm.proxy.auth import rds_iam_token + +_IAM_ENV_KEY = "IAM_TOKEN_DB_AUTH" +_DEFAULT_PG_PORT = "5432" + + +class DatabaseURLSettings(BaseSettings): + """Discrete ``DATABASE_*`` env vars, loaded once at process start. + + Field names are internal; ``validation_alias`` pins each one to the exact + env var the helm chart emits. ``DATABASE_USER`` doubles as + ``DATABASE_USERNAME`` for parity with ``construct_database_url_from_env_vars``. + """ + + model_config = SettingsConfigDict(case_sensitive=False, extra="ignore") + + iam_token_db_auth: bool = Field(default=False, validation_alias=_IAM_ENV_KEY) + + # Writer + database_url: Optional[str] = Field(default=None, validation_alias="DATABASE_URL") + database_host: Optional[str] = Field(default=None, validation_alias="DATABASE_HOST") + database_port: str = Field( + default=_DEFAULT_PG_PORT, validation_alias="DATABASE_PORT" + ) + database_user: Optional[str] = Field( + default=None, + validation_alias=AliasChoices("DATABASE_USER", "DATABASE_USERNAME"), + ) + database_name: Optional[str] = Field(default=None, validation_alias="DATABASE_NAME") + database_schema: Optional[str] = Field( + default=None, validation_alias="DATABASE_SCHEMA" + ) + database_password: Optional[str] = Field( + default=None, validation_alias="DATABASE_PASSWORD" + ) + + # Read replica + database_url_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_URL_READ_REPLICA" + ) + database_host_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_HOST_READ_REPLICA" + ) + database_port_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_PORT_READ_REPLICA" + ) + database_user_read_replica: Optional[str] = Field( + default=None, + validation_alias=AliasChoices( + "DATABASE_USER_READ_REPLICA", "DATABASE_USERNAME_READ_REPLICA" + ), + ) + database_name_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_NAME_READ_REPLICA" + ) + database_schema_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_SCHEMA_READ_REPLICA" + ) + database_password_read_replica: Optional[str] = Field( + default=None, validation_alias="DATABASE_PASSWORD_READ_REPLICA" + ) + + @classmethod + def from_env(cls) -> "DatabaseURLSettings": + """Load the settings from ``os.environ`` (read at call time).""" + return cls() + + def build_writer_url(self) -> Optional[str]: + """Return the writer URL to set, or ``None`` to leave it as-is. + + Raises ``RuntimeError`` (naming the offending vars) when IAM auth is + enabled but a required field is missing — the proxy cannot recover + from this and a clear startup error beats a Prisma connect failure. + """ + if self.iam_token_db_auth: + missing = [ + env + for env, val in ( + ("DATABASE_HOST", self.database_host), + ("DATABASE_USER", self.database_user), + ("DATABASE_NAME", self.database_name), + ) + if not val + ] + if missing: + raise RuntimeError( + "IAM_TOKEN_DB_AUTH is enabled but required DB env var(s) " + f"are unset: {', '.join(missing)}. Set them so the writer " + "DATABASE_URL can be assembled with a minted IAM token." + ) + host = cast(str, self.database_host) + user = cast(str, self.database_user) + name = cast(str, self.database_name) + # IAM token is already URL-quoted by generate_iam_auth_token; + # user/name embedded raw (parity with proxy_cli.py / IAMEndpoint). + token = rds_iam_token.generate_iam_auth_token( + db_host=host, db_port=self.database_port, db_user=user + ) + url = f"postgresql://{user}:{token}@{host}:{self.database_port}/{name}" + if self.database_schema: + url += f"?schema={self.database_schema}" + return url + + # Password auth: an operator-pinned DATABASE_URL always wins. + if self.database_url: + return None + if self.database_host and self.database_user and self.database_name: + return self._password_url( + user=self.database_user, + password=self.database_password, + host=self.database_host, + port=self.database_port, + name=self.database_name, + schema=self.database_schema, + ) + return None + + def build_reader_url(self) -> Optional[str]: + """Return the read-replica URL to set, or ``None`` to leave it as-is. + + Opt-in via ``DATABASE_HOST_READ_REPLICA``; never clobbers a + pre-existing ``DATABASE_URL_READ_REPLICA``. Reader fields fall back + to the writer's values. + """ + if not self.database_host_read_replica: + return None # reader is opt-in + if self.database_url_read_replica: + return None # never clobber an operator-supplied reader URL + + host = self.database_host_read_replica + port = self.database_port_read_replica or self.database_port + user = self.database_user_read_replica or self.database_user + name = self.database_name_read_replica or self.database_name + schema = self.database_schema_read_replica or self.database_schema + password = self.database_password_read_replica or self.database_password + + if self.iam_token_db_auth: + missing = [ + env + for env, val in ( + ("DATABASE_USER[_READ_REPLICA]", user), + ("DATABASE_NAME[_READ_REPLICA]", name), + ) + if not val + ] + if missing: + raise RuntimeError( + "IAM_TOKEN_DB_AUTH is enabled and DATABASE_HOST_READ_REPLICA " + "is set, but the reader could not resolve: " + f"{', '.join(missing)} (no *_READ_REPLICA value and no " + "writer fallback). Set the reader fields or the writer " + "defaults." + ) + user = cast(str, user) + name = cast(str, name) + token = rds_iam_token.generate_iam_auth_token( + db_host=host, db_port=port, db_user=user + ) + url = f"postgresql://{user}:{token}@{host}:{port}/{name}" + if schema: + url += f"?schema={schema}" + return url + + if user and name: + return self._password_url( + user=user, + password=password, + host=host, + port=port, + name=name, + schema=schema, + ) + return None + + @staticmethod + def _password_url( + *, + user: str, + password: Optional[str], + host: str, + port: str, + name: str, + schema: Optional[str], + ) -> str: + """Percent-encode credentials into a ``postgresql://`` URL. + + Parity with ``construct_database_url_from_env_vars`` in + ``proxy/utils.py``; ``password`` may be empty for a passwordless URL. + """ + quote = urllib.parse.quote_plus + user_p = quote(user) + name_p = quote(name) + if password: + url = f"postgresql://{user_p}:{quote(password)}@{host}:{port}/{name_p}" + else: + url = f"postgresql://{user_p}@{host}:{port}/{name_p}" + if schema: + url += f"?schema={schema}" + return url + + def apply_to_env(self) -> bool: + """Write the assembled URL(s) into ``os.environ``. + + Returns True iff this call set ``DATABASE_URL`` (IAM mint, or + password auth that assembled a fresh URL). False means there was + nothing to do — an operator-pinned URL, or no discrete fields. + """ + wrote_writer = False + writer_url = self.build_writer_url() + if writer_url is not None: + os.environ["DATABASE_URL"] = writer_url + if self.iam_token_db_auth: + # Normalize the toggle so downstream readers (PrismaWrapper's + # IAM refresh) reliably see IAM on, regardless of spelling. + os.environ[_IAM_ENV_KEY] = "True" + wrote_writer = True + + reader_url = self.build_reader_url() + if reader_url is not None: + os.environ["DATABASE_URL_READ_REPLICA"] = reader_url + + return wrote_writer diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 7cd099a7296..8cb9a11ffee 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -1651,6 +1651,12 @@ async def add_litellm_data_to_request( # noqa: PLR0915 ) data[_metadata_variable_name]["headers"] = _headers data[_metadata_variable_name]["endpoint"] = str(request.url) + # Carry the proxy-receive instant via metadata (like `endpoint`) so the + # OTel layer can compute pre-request latency, including on the failure + # path after the logging object is popped. + data[_metadata_variable_name]["litellm_received_at"] = getattr( + request.state, "litellm_received_at", None + ) # OTEL Controls / Tracing # Add the OTEL Parent Trace before sending it LiteLLM diff --git a/litellm/proxy/management_endpoints/common_utils.py b/litellm/proxy/management_endpoints/common_utils.py index a43d15a580f..dc27e87726a 100644 --- a/litellm/proxy/management_endpoints/common_utils.py +++ b/litellm/proxy/management_endpoints/common_utils.py @@ -1,6 +1,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from fastapi import HTTPException, status +from pydantic import BaseModel from litellm._logging import verbose_proxy_logger from litellm.caching import DualCache @@ -53,6 +54,37 @@ def require_caller_user_id_for_non_admin( return user_api_key_dict.user_id +def _check_passthrough_routes_caller_permission( + data: BaseModel, + user_api_key_dict: UserAPIKeyAuth, + *, + entity: str = "key", +) -> None: + """ + Only proxy admins may set `allowed_passthrough_routes` (top-level or under + `metadata`) — it short-circuits the role-based route gate, so keys and teams + must be gated identically. + """ + # view-only admins excluded by design; blocked upstream from writes anyway + if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value: + return + if getattr(data, "allowed_passthrough_routes", None): + raise HTTPException( + status_code=403, + detail={ + "error": f"Only proxy admins can set `allowed_passthrough_routes` on a {entity}." + }, + ) + metadata = getattr(data, "metadata", None) + if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"): + raise HTTPException( + status_code=403, + detail={ + "error": f"Only proxy admins can set `metadata.allowed_passthrough_routes` on a {entity}." + }, + ) + + def _is_user_team_admin( user_api_key_dict: UserAPIKeyAuth, team_obj: LiteLLM_TeamTable ) -> bool: diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 7ff706eb91e..2ab147043d9 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -55,6 +55,7 @@ from litellm.proxy.common_utils.rbac_utils import check_org_admin_can_generate_k from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time from litellm.proxy.hooks.key_management_event_hooks import KeyManagementEventHooks from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, _is_user_org_admin_for_team, _is_user_team_admin, _set_object_metadata_field, @@ -548,36 +549,6 @@ def _check_allowed_routes_caller_permission( ) -def _check_passthrough_routes_caller_permission( - data: BaseModel, - user_api_key_dict: UserAPIKeyAuth, -) -> None: - """ - Only proxy admins may set `allowed_passthrough_routes` on a key, either at - the top level of the request or nested under `metadata`. - - The route gate evaluates passthrough access ahead of the standard role - gate, so the field is restricted to admins to keep that ordering safe. - """ - if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value: - return - if getattr(data, "allowed_passthrough_routes", None): - raise HTTPException( - status_code=403, - detail={ - "error": "Only proxy admins can set `allowed_passthrough_routes` on a key." - }, - ) - metadata = getattr(data, "metadata", None) - if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"): - raise HTTPException( - status_code=403, - detail={ - "error": "Only proxy admins can set `metadata.allowed_passthrough_routes` on a key." - }, - ) - - async def validate_team_id_used_in_service_account_request( team_id: Optional[str], prisma_client: Optional[PrismaClient], diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index af84bc123ff..472306eb818 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -150,6 +150,9 @@ def update_db_model( model_info[key] = value.isoformat() prisma_compatible_model_dict["model_info"] = json.dumps(model_info) + if updated_patch.blocked is not None: + prisma_compatible_model_dict["blocked"] = updated_patch.blocked + return prisma_compatible_model_dict @@ -230,6 +233,20 @@ async def patch_model( premium_user=premium_user, ) + # Pause/resume (`blocked`) is a proxy-admin-only privilege. Team admins + # passed the auth check above for team-scoped models, but they must not + # be able to unblock (or block) a model their proxy admin has paused. + if ( + patch_data.blocked is not None + and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN + ): + raise ProxyException( + message="Only proxy admins can change a model's blocked flag.", + type=ProxyErrorTypes.auth_error.value, + code=status.HTTP_403_FORBIDDEN, + param="blocked", + ) + # Handle team model updates with proper alias management update_data = await _update_team_model_in_db( db_model=db_model, diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index 35e3d196e9e..86c4d6dcd9a 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -73,6 +73,7 @@ from litellm.proxy.auth.auth_checks import ( ) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, _is_user_org_admin_for_team, _is_user_team_admin, _set_object_metadata_field, @@ -1049,6 +1050,10 @@ async def new_team( # noqa: PLR0915 Member(role="admin", user_id=user_api_key_dict.user_id) ) + _check_passthrough_routes_caller_permission( + data, user_api_key_dict, entity="team" + ) + ## ADD TO MODEL TABLE _model_id = None if data.model_aliases is not None and isinstance(data.model_aliases, dict): @@ -1646,6 +1651,10 @@ async def update_team( # noqa: PLR0915 user_api_key_dict=user_api_key_dict, ) + _check_passthrough_routes_caller_permission( + data, user_api_key_dict, entity="team" + ) + if data.soft_budget is not None: max_budget_to_check = ( data.max_budget diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index a744885b3bb..ec038dd4b3e 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -20,6 +20,7 @@ from typing import ( TYPE_CHECKING, Any, AsyncGenerator, + Callable, Dict, List, Literal, @@ -4726,6 +4727,7 @@ class ProxyConfig: if _id is not None: model.model_info["id"] = _id model.model_info["db_model"] = True + model.model_info["blocked"] = bool(getattr(model, "blocked", False)) if premium_user is True: # seeing "created_at", "updated_at", "created_by", "updated_by" is a LiteLLM Enterprise Feature @@ -6931,6 +6933,15 @@ async def async_data_generator( # noqa: PLR0915 if isinstance(chunk, BaseModel): chunk = _serialize_streaming_chunk(chunk) + elif isinstance(chunk, bytes): + # Some upstream streaming iterators (e.g. AsyncGoogleGenAIGenerateContentStreamingIterator + # for /v1beta/.../streamGenerateContent) yield raw SSE bytes from Gemini. + # Decode to str so the f-string below does not emit a Python b'...' literal, + # and pass already-formatted SSE through unchanged to avoid double "data:" prefix. + chunk = chunk.decode("utf-8", errors="replace") + if chunk.startswith(("data:", "event:", ":")): + yield chunk if chunk.endswith("\n\n") else chunk + "\n\n" + continue elif isinstance(chunk, str) and chunk.startswith("data: "): error_message = chunk break @@ -8104,6 +8115,11 @@ async def model_list( proxy_logging_obj=proxy_logging_obj, ) + # Compute once — used in both branches below to hide paused models from the listing. + blocked_names = ( + llm_router.get_fully_blocked_model_names() if llm_router is not None else set() + ) + # If scope=expand and user has admin privileges, return all proxy models if should_expand_scope: # Get all proxy models as if user is a proxy admin @@ -8136,6 +8152,10 @@ async def model_list( only_model_access_groups=only_model_access_groups or False, ) + # Hide paused models from the public listing (admins manage them via /model/info) + if blocked_names: + all_models = [m for m in all_models if m not in blocked_names] + # Build response data with all proxy models model_data = [] for model in all_models: @@ -8169,6 +8189,10 @@ async def model_list( user_api_key_cache=user_api_key_cache, ) + # Hide paused models from the public listing (admins manage them via /model/info) + if blocked_names: + all_models = [m for m in all_models if m not in blocked_names] + # Build response data model_data = [] for model in all_models: @@ -10905,13 +10929,130 @@ def _enrich_model_info_with_litellm_data( return model +async def _get_caller_byok_team_scope( + user_api_key_dict: Optional[UserAPIKeyAuth], + prisma_client: Optional[Any], +) -> Optional[Set[str]]: + """ + Return the team IDs whose BYOK rows the caller is allowed to see via + `/v2/model/info` search results. + + `None` means "no scoping" — used for admins and for callers/paths that + have already been scoped upstream (or in tests that supply their own + pre-filtered input set). A returned set (possibly empty) means BYOK rows + must have `model_info.team_id` ∈ that set, otherwise they belong to a + team the caller is not a member of and must be dropped. + """ + if user_api_key_dict is None or prisma_client is None: + return None + if user_api_key_dict.user_role in ( + LitellmUserRoles.PROXY_ADMIN, + LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + ): + return None + user_id = user_api_key_dict.user_id + if user_id is None: + return set() + try: + user_row = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) + except Exception: + verbose_proxy_logger.exception( + "Failed to look up caller teams while scoping BYOK search; " + "defaulting to no team access." + ) + return set() + if user_row is None: + return set() + return set(user_row.teams or []) + + +# Hard cap on rows the DB-side BYOK search may pull when results need to be +# sorted across the full match set. Without this, an authenticated caller +# can hit `/v2/model/info?search=&sortBy=` and force the +# proxy to materialize and decrypt every matching BYOK row on each request. +_SORTED_SEARCH_DB_FETCH_CAP = 500 + + +async def _fetch_db_models_for_search( + prisma_client: Any, + proxy_config: Any, + search_lower: str, + db_model_ids_in_router: Set[str], + router_models_count: int, + page: int, + size: int, + sort_by: Optional[str], + is_byok_outside_caller_teams: Callable[[Dict[str, Any]], bool], +) -> Tuple[List[Dict[str, Any]], int]: + """ + Run the bounded DB query that backs `/v2/model/info?search=`. Returns + `(decrypted_models, total_count)` where `total_count` is the cheap + `count(...)` of rows matching `search` (not yet team-scoped) so the + UI's pagination stays accurate without materializing every row. + + Earlier iterations also OR'd a JSON-path match on + `model_info.team_public_model_name` to surface BYOK rows that live + only in the DB. That branch fell back to `string_contains: ""` + because Prisma's JSON `string_contains` is case-sensitive on + Postgres, which let any authenticated caller force a full BYOK-table + read via `/v2/model/info?search=x`. We rely on the router-side + filter for `team_public_model_name` instead and keep the DB cost + bounded by `search`. + """ + db_where_condition: Dict[str, Any] = { + "model_name": {"contains": search_lower, "mode": "insensitive"} + } + if db_model_ids_in_router: + db_where_condition["model_id"] = {"not": {"in": list(db_model_ids_in_router)}} + + # Unsorted searches only need enough DB rows to fill the current + # page after counting router-side matches. Sorted searches need + # ordering across the full match set, so fall back to a hard cap. + if sort_by: + take_limit = _SORTED_SEARCH_DB_FETCH_CAP + else: + take_limit = max(0, page * size - router_models_count) + + db_models_total_count = await prisma_client.db.litellm_proxymodeltable.count( + where=db_where_condition + ) + + db_models_raw: list = [] + if take_limit > 0: + db_models_raw = await prisma_client.db.litellm_proxymodeltable.find_many( + where=db_where_condition, + take=take_limit, + ) + + # Scope BYOK rows to the caller's allowed teams so non-admin callers + # can't enumerate other teams' BYOK metadata via `?search=...`. + matching_db_rows = [ + m + for m in db_models_raw + if not is_byok_outside_caller_teams( + m.model_info if isinstance(m.model_info, dict) else {} + ) + ] + + decrypted: List[Dict[str, Any]] = [] + for db_model in matching_db_rows: + decrypted_models = proxy_config.decrypt_model_list_from_db([db_model]) + if decrypted_models: + decrypted.extend(decrypted_models) + + return decrypted, db_models_total_count + + async def _apply_search_filter_to_models( all_models: List[Dict[str, Any]], search: str, - page: int, - size: int, prisma_client: Optional[Any], proxy_config: Any, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, + page: int = 1, + size: int = 50, sort_by: Optional[str] = None, ) -> Tuple[List[Dict[str, Any]], Optional[int]]: """ @@ -10920,11 +11061,19 @@ async def _apply_search_filter_to_models( Args: all_models: List of models to filter search: Search term (case-insensitive) - page: Current page number - size: Page size prisma_client: Prisma client for database queries proxy_config: Proxy config for decrypting models - sort_by: Optional sort field - if provided, fetch all matching models instead of paginating at DB level + user_api_key_dict: Caller identity used to scope BYOK matches to + teams the caller belongs to. When omitted (None), no team + scoping is applied — pass it from request handlers that expose + this function to non-admin callers. + page: Current page number (1-indexed). Used with ``size`` to bound + the DB ``find_many(take=...)`` so a broad search term can't + force a full table read + decrypt on every request. + size: Page size. See ``page``. + sort_by: Sort field. When set, results must be sorted across the + full match set, so the DB fetch is capped at + ``_SORTED_SEARCH_DB_FETCH_CAP`` instead of one page. Returns: Tuple of (filtered_models, total_count). total_count is None if not searching. @@ -10934,9 +11083,43 @@ async def _apply_search_filter_to_models( search_lower = search.lower().strip() - # Filter models in router by search term + allowed_team_ids = await _get_caller_byok_team_scope( + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + ) + + def _is_byok_outside_caller_teams(model_info_dict: Dict[str, Any]) -> bool: + # `team_id` is only set on team BYOK rows. Non-team rows fall + # through unaffected — they are gated by other paths (router + # membership, direct_access, include_team_models). + if allowed_team_ids is None: + return False + team_id = model_info_dict.get("team_id") + if team_id is None: + return False + return team_id not in allowed_team_ids + + def _model_matches_search(m: Dict[str, Any]) -> bool: + # Team BYOK models persist an internal `model_name` + # (e.g. `model_name_{team_id}_{uuid}`) and expose the user-facing + # name via `model_info.team_public_model_name`. Match both so the + # name shown in the UI is searchable. + if search_lower in (m.get("model_name") or "").lower(): + return True + team_public_model_name = (m.get("model_info") or {}).get( + "team_public_model_name" + ) or "" + return search_lower in team_public_model_name.lower() + + # Filter models in router by search term, dropping BYOK rows that + # belong to teams the caller is not a member of so search can't leak + # other teams' models when the request omits `include_team_models` / + # `teamId`. filtered_router_models = [ - m for m in all_models if search_lower in m.get("model_name", "").lower() + m + for m in all_models + if _model_matches_search(m) + and not _is_byok_outside_caller_teams(m.get("model_info") or {}) ] # Separate filtered models into config vs db models, and track db model IDs @@ -10958,91 +11141,30 @@ async def _apply_search_filter_to_models( router_models_count = config_models_count + db_models_in_router_count # Query database for additional models with search term - db_models = [] - db_models_total_count = 0 - models_needed_for_page = size * page - - # Only query database if prisma_client is available + db_models: List[Dict[str, Any]] = [] if prisma_client is not None: try: - # Build where condition for database query - db_where_condition: Dict[str, Any] = { - "model_name": { - "contains": search_lower, - "mode": "insensitive", - } - } - # Exclude models already in router if we have any - if db_model_ids_in_router: - db_where_condition["model_id"] = { - "not": {"in": list(db_model_ids_in_router)} - } - - # Get total count of matching database models - db_models_total_count = ( - await prisma_client.db.litellm_proxymodeltable.count( - where=db_where_condition - ) + db_models, db_models_total_count = await _fetch_db_models_for_search( + prisma_client=prisma_client, + proxy_config=proxy_config, + search_lower=search_lower, + db_model_ids_in_router=db_model_ids_in_router, + router_models_count=router_models_count, + page=page, + size=size, + sort_by=sort_by, + is_byok_outside_caller_teams=_is_byok_outside_caller_teams, ) - - # Calculate total count for search results search_total_count = router_models_count + db_models_total_count - - # If sorting is requested, we need to fetch ALL matching models to sort correctly - # Otherwise, we can optimize by only fetching what's needed for the current page - if sort_by: - # Fetch all matching database models for sorting - if db_models_total_count > 0: - db_models_raw = ( - await prisma_client.db.litellm_proxymodeltable.find_many( - where=db_where_condition, - take=db_models_total_count, # Fetch all matching models - ) - ) - - # Convert database models to router format - for db_model in db_models_raw: - decrypted_models = proxy_config.decrypt_model_list_from_db( - [db_model] - ) - if decrypted_models: - db_models.extend(decrypted_models) - else: - # Fetch database models if we need more for the current page - if router_models_count < models_needed_for_page: - models_to_fetch = min( - models_needed_for_page - router_models_count, - db_models_total_count, - ) - - if models_to_fetch > 0: - db_models_raw = ( - await prisma_client.db.litellm_proxymodeltable.find_many( - where=db_where_condition, - take=models_to_fetch, - ) - ) - - # Convert database models to router format - for db_model in db_models_raw: - decrypted_models = proxy_config.decrypt_model_list_from_db( - [db_model] - ) - if decrypted_models: - db_models.extend(decrypted_models) except Exception as e: verbose_proxy_logger.exception( f"Error querying database models with search: {str(e)}" ) - # If error, use router models count as fallback search_total_count = router_models_count else: - # If no prisma_client, only use router models search_total_count = router_models_count - # Combine all models - filtered_models = filtered_router_models + db_models - return filtered_models, search_total_count + return filtered_router_models + db_models, search_total_count def _normalize_datetime_for_sorting(dt: Any) -> Optional[datetime]: @@ -11118,6 +11240,15 @@ def _sort_models( model_info = model.get("model_info", {}) if sort_by == "model_name": + # Team BYOK models persist an internal `model_name` (e.g. + # `model_name_{team_id}_{uuid}`) and expose the user-facing + # name via `model_info.team_public_model_name` — same as the + # UI's getDisplayModelName. Sort by the displayed name so + # BYOK rows interleave alphabetically with non-BYOK rows + # instead of clumping at the end on their opaque IDs. + team_public_model_name = model_info.get("team_public_model_name") + if team_public_model_name: + return str(team_public_model_name).lower() return model.get("model_name", "").lower() elif sort_by == "created_at": @@ -11302,28 +11433,81 @@ async def _gather_team_accessible_model_ids( return team_accessible_model_ids +async def _authorize_team_id_query( + team_id: str, + user_api_key_dict: UserAPIKeyAuth, + prisma_client: PrismaClient, +) -> None: + """ + `teamId` arrives untrusted via the /v2/model/info query string and the + filter below includes BYOK rows solely on `model_info.team_id == team_id`. + Without this guard, any authenticated user who knows (or guesses) another + team's id could enumerate that team's BYOK model metadata. Allow only + proxy admins or members of the requested team. + """ + if user_api_key_dict.user_role in ( + LitellmUserRoles.PROXY_ADMIN, + LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + ): + return + + user_id = user_api_key_dict.user_id + if user_id is None: + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + try: + user_row = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) + except Exception: + verbose_proxy_logger.exception( + "Failed to look up caller teams while authorizing teamId filter" + ) + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + + if user_row is None or team_id not in (user_row.teams or []): + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + + async def _filter_models_by_team_id( all_models: List[Dict[str, Any]], team_id: str, prisma_client: PrismaClient, llm_router: Router, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, ) -> List[Dict[str, Any]]: """ Filter models by team ID. Returns models where: - - direct_access is True, OR - - team_id is in access_via_team_ids - - Also searches config and database for models accessible to the team. + - team_id matches the model's BYOK team_id, OR + - team_id is in access_via_team_ids, OR + - model_id is reachable via team.models / access groups Args: all_models: List of models to filter team_id: Team ID to filter by prisma_client: Prisma client for database queries llm_router: Router instance for config queries + user_api_key_dict: Caller auth context. When provided, the caller must + be a proxy admin or a member of `team_id`; otherwise raises 403. Returns: Filtered list of models """ + if user_api_key_dict is not None: + await _authorize_team_id_query( + team_id=team_id, + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + ) + team_object = await _load_team_object_for_model_filter(team_id, prisma_client) if team_object is None: return [] @@ -11332,26 +11516,30 @@ async def _filter_models_by_team_id( team_object, team_id, prisma_client, llm_router ) - # Filter models based on direct_access or access_via_team_ids - # Models are already enriched with these fields before this function is called + # When filtering by a specific team we want exactly the models that team + # can use: its BYOK rows and the deployments resolved from team.models / + # access groups. `direct_access` describes the viewer's own permissions + # (the admin path sets it on every non-team model) and must NOT widen the + # team's visible set, otherwise selecting a team in the UI still shows + # every public model the admin can call. filtered_models = [] for _model in all_models: model_info = _model.get("model_info", {}) model_id = model_info.get("id", None) - # Include if direct_access is True - if model_info.get("direct_access", False): + # BYOK rows owned by this team are always accessible to it, even if + # they haven't been re-added to team.models for some reason. + if model_info.get("team_id") == team_id: filtered_models.append(_model) continue - # Include if team_id is in access_via_team_ids access_via_team_ids = model_info.get("access_via_team_ids", []) if isinstance(access_via_team_ids, list) and team_id in access_via_team_ids: filtered_models.append(_model) continue - # Also include if model_id is in team_accessible_model_ids (from config/db search) - # This catches models that might not have been enriched with access_via_team_ids yet + # Catches models resolved from team.models / access groups that + # weren't enriched with access_via_team_ids upstream. if model_id and model_id in team_accessible_model_ids: filtered_models.append(_model) @@ -11493,10 +11681,11 @@ async def model_info_v2( all_models, search_total_count = await _apply_search_filter_to_models( all_models=all_models, search=search or "", - page=page, - size=size, prisma_client=prisma_client, proxy_config=proxy_config, + user_api_key_dict=user_api_key_dict, + page=page, + size=size, sort_by=sortBy, ) @@ -11532,6 +11721,7 @@ async def model_info_v2( team_id=teamId.strip(), prisma_client=prisma_client, llm_router=llm_router, + user_api_key_dict=user_api_key_dict, ) # Update search_total_count after teamId filter is applied search_total_count = len(all_models) diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index f86cde87401..06fd35448f1 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -430,7 +430,11 @@ async def route_request( # noqa: PLR0915 - Complex routing function, refactorin deployment = llm_router.get_deployment_by_model_group_name( model_group_name=model ) - if deployment and deployment.litellm_params: + if ( + deployment + and deployment.litellm_params + and not llm_router._is_deployment_blocked(deployment) + ): deployment_creds = deployment.litellm_params.model_dump( exclude_none=True ) diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 871f084c58d..32c887f17b2 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1984,6 +1984,17 @@ class ProxyLogging: original_exception=original_exception, ) + # Lift the first-handoff instant onto request_data (top-level + # internal key, not metadata) so failure-path callbacks can still + # compute preprocessing latency after the logging object is popped. + _logging_obj = request_data.get("litellm_logging_obj") + if _logging_obj is not None: + _first_handoff = getattr(_logging_obj, "model_call_details", {}).get( + "first_api_call_start_time" + ) + if _first_handoff is not None: + request_data["first_api_call_start_time"] = _first_handoff + # Remove before callbacks iterate — not serialisable request_data.pop("litellm_logging_obj", None) diff --git a/litellm/router.py b/litellm/router.py index 64a6b34e26a..a51d9d2ad26 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -30,6 +30,7 @@ from typing import ( List, Literal, Optional, + Set, Tuple, Union, cast, @@ -6996,12 +6997,11 @@ class Router: unhealthy_deployments = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) - healthy_deployments: list = [] - for deployment in _all_deployments: - if deployment["model_info"]["id"] in unhealthy_deployments: - continue - else: - healthy_deployments.append(deployment) + unhealthy_set = set(unhealthy_deployments) + healthy_deployments: list = [ + d for d in _all_deployments if d["model_info"]["id"] not in unhealthy_set + ] + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) return healthy_deployments, _all_deployments @@ -7029,10 +7029,12 @@ class Router: ) # Convert to set for O(1) lookup instead of O(n) unhealthy_deployments_set = set(unhealthy_deployments) - healthy_deployments: list = [] - for deployment in _all_deployments: - if deployment["model_info"]["id"] not in unhealthy_deployments_set: - healthy_deployments.append(deployment) + healthy_deployments: list = [ + d + for d in _all_deployments + if d["model_info"]["id"] not in unhealthy_deployments_set + ] + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) return healthy_deployments, _all_deployments def routing_strategy_pre_call_checks(self, deployment: dict): @@ -8181,10 +8183,14 @@ class Router: def get_deployment_credentials(self, model_id: str) -> Optional[dict]: """ - Returns -> dict of credentials for a given model id + Returns -> dict of credentials for a given model id. + + Returns None if the deployment is paused via `LiteLLM_ProxyModelTable.blocked`, + so file/batch/passthrough callers that resolve credentials directly cannot keep + using a paused deployment. """ deployment = self.get_deployment(model_id=model_id) - if deployment is None: + if deployment is None or self._is_deployment_blocked(deployment): return None return CredentialLiteLLMParams( **deployment.litellm_params.model_dump(exclude_none=True) @@ -8229,7 +8235,9 @@ class Router: Returns: Dictionary containing api_key, api_base, custom_llm_provider, etc. - Returns None if model not found. + Returns None if model not found, or if the resolved deployment is + paused via `LiteLLM_ProxyModelTable.blocked` (so passthrough callers + cannot bypass an admin pause by resolving credentials directly). Example: credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm") @@ -8255,7 +8263,7 @@ class Router: elif isinstance(deployment_dict, Deployment): deployment = deployment_dict - if deployment is None: + if deployment is None or self._is_deployment_blocked(deployment): return None # Get basic credentials @@ -9282,6 +9290,29 @@ class Router: return model_names + def get_fully_blocked_model_names(self) -> Set[str]: + """ + Returns the set of model_names where every backing deployment has `blocked=True`. + + Used by `/v1/models` to hide paused models from client listings while still + surfacing them on admin endpoints (e.g. `/model/info`). A model with at least + one non-blocked deployment is still serviceable and remains visible. + """ + deployments = self.get_model_list() or [] + blocked_by_name: Dict[str, bool] = {} + for deployment in deployments: + name = deployment.get("model_name") or "" + if not name: + continue + is_blocked = (deployment.get("model_info") or {}).get("blocked") is True + if name in blocked_by_name: + blocked_by_name[name] = blocked_by_name[name] and is_blocked + else: + blocked_by_name[name] = is_blocked + return { + name for name, fully_blocked in blocked_by_name.items() if fully_blocked + } + def _get_team_specific_model( self, deployment: DeploymentTypedDict, team_id: Optional[str] = None ) -> Optional[str]: @@ -10170,6 +10201,12 @@ class Router: ) if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) return healthy_deployments # Health-check-based filtering (before cooldown) @@ -10203,6 +10240,8 @@ class Router: ) healthy_deployments = _pre_cooldown_deployments + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) + healthy_deployments = await self.async_callback_filter_deployments( model=model, healthy_deployments=healthy_deployments, @@ -10426,6 +10465,12 @@ class Router: # 3. If specific deployment returned, verify if it supports pass-through if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) litellm_params = healthy_deployments.get("litellm_params", {}) if litellm_params.get("use_in_pass_through"): return healthy_deployments @@ -10594,6 +10639,12 @@ class Router: ) if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) return healthy_deployments parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs( @@ -10624,6 +10675,8 @@ class Router: ) healthy_deployments = _pre_cooldown_deployments + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) + # filter pre-call checks if self.enable_pre_call_checks and messages is not None: healthy_deployments = self._pre_call_checks( @@ -10743,6 +10796,12 @@ class Router: # 2. If the returned is a specific deployment (Dict), verify and return directly if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) litellm_params = healthy_deployments.get("litellm_params", {}) if litellm_params.get("use_in_pass_through"): return healthy_deployments @@ -10782,6 +10841,9 @@ class Router: healthy_deployments=pass_through_deployments, cooldown_deployments=cooldown_deployments, ) + pass_through_deployments = self._filter_blocked_deployments( + pass_through_deployments + ) # 5. Apply pre-call checks (if enabled) if self.enable_pre_call_checks and messages is not None: @@ -10871,6 +10933,36 @@ class Router: if deployment["model_info"]["id"] not in cooldown_set ] + def _filter_blocked_deployments( + self, healthy_deployments: List[Dict] + ) -> List[Dict]: + """ + Filters out deployments that an admin has paused via `LiteLLM_ProxyModelTable.blocked`. + + Applied alongside the cooldown filter on every routing entry point that calls + `_common_checks_available_deployment` directly — the primary sync/async path, + the sync pass-through path, and the retry / health-check helpers — so paused + deployments never serve a request. The async pass-through path inherits this + filter through its delegation to `async_get_healthy_deployments`. + """ + return [ + deployment + for deployment in healthy_deployments + if (deployment.get("model_info") or {}).get("blocked") is not True + ] + + @staticmethod + def _is_deployment_blocked(deployment: "Deployment") -> bool: + """ + Returns True when a `Deployment` Pydantic instance carries the admin-paused + flag. Used by credential-lookup helpers so passthrough file / batch endpoints + cannot bypass the pause by resolving credentials directly. + """ + model_info = getattr(deployment, "model_info", None) + if model_info is None: + return False + return getattr(model_info, "blocked", None) is True + async def _async_filter_health_check_unhealthy_deployments( self, healthy_deployments: List[Dict], diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index 43a287f29bc..7b5c5ab2969 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -160,6 +160,7 @@ class UserAPIKeyLabelNames(Enum): END_USER = "end_user" USER = "user" USER_EMAIL = "user_email" + USER_ALIAS = "user_alias" API_KEY_HASH = "hashed_api_key" API_KEY_ALIAS = "api_key_alias" TEAM = "team" @@ -533,17 +534,9 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.USER.value, ] - litellm_user_max_budget_metric = [ - UserAPIKeyLabelNames.USER.value, - ] + litellm_user_max_budget_metric = litellm_remaining_user_budget_metric - litellm_user_budget_remaining_hours_metric = [ - UserAPIKeyLabelNames.USER.value, - ] - - litellm_user_budget_remaining_hours_metric = [ - UserAPIKeyLabelNames.USER.value, - ] + litellm_user_budget_remaining_hours_metric = litellm_remaining_user_budget_metric litellm_remaining_api_key_requests_for_model = [ UserAPIKeyLabelNames.API_KEY_HASH.value, @@ -730,6 +723,22 @@ class PrometheusMetricLabels: ): custom_labels.append(UserAPIKeyLabelNames.STREAM.value) + _user_budget_metrics = { + "litellm_remaining_user_budget_metric", + "litellm_user_max_budget_metric", + "litellm_user_budget_remaining_hours_metric", + } + if ( + label_name in _user_budget_metrics + and litellm.prometheus_user_budget_label_include_email_alias is True + ): + for label in [ + UserAPIKeyLabelNames.USER_EMAIL.value, + UserAPIKeyLabelNames.USER_ALIAS.value, + ]: + if label not in default_labels and label not in custom_labels: + custom_labels.append(label) + if label_name in PrometheusMetricLabels._org_label_metrics: for label in [ UserAPIKeyLabelNames.ORG_ID.value, @@ -759,6 +768,7 @@ class UserAPIKeyLabelValues: end_user: Optional[str] = None user: Optional[str] = None user_email: Optional[str] = None + user_alias: Optional[str] = None hashed_api_key: Optional[str] = None api_key_alias: Optional[str] = None team: Optional[str] = None diff --git a/litellm/types/router.py b/litellm/types/router.py index 926815ba317..6601f552b52 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -133,6 +133,9 @@ class ModelInfo(BaseModel): # the model_name that can be used by the team when making LLM calls team_public_model_name: Optional[str] = None + # admin-toggled pause flag; mirrors LiteLLM_ProxyModelTable.blocked + blocked: Optional[bool] = None + def __init__(self, id: Optional[Union[str, int]] = None, **params): if id is None: id = str(uuid.uuid4()) # Generate a UUID if id is None or not provided @@ -323,6 +326,7 @@ class updateDeployment(BaseModel): model_name: Optional[str] = None litellm_params: Optional[updateLiteLLMParams] = None model_info: Optional[ModelInfo] = None + blocked: Optional[bool] = None model_config = ConfigDict(protected_namespaces=()) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 400edcac889..6084f14e2df 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -107,9 +107,13 @@ class LiteLLMCommonStrings(Enum): SupportedCacheControls = ["ttl", "s-maxage", "no-cache", "no-store"] -class CostPerToken(TypedDict): - input_cost_per_token: float - output_cost_per_token: float +class CostPerToken(TypedDict, total=False): + # Required base rates — kept under total=False so we can mark them + # Required individually while leaving the cache rates NotRequired. + input_cost_per_token: Required[float] + output_cost_per_token: Required[float] + cache_read_input_token_cost: float + cache_creation_input_token_cost: float class ProviderField(TypedDict): diff --git a/litellm/utils.py b/litellm/utils.py index cefd348078b..001c89fee4c 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8402,6 +8402,12 @@ class ProviderConfigManager: ) return VolcEngineEmbeddingConfig() + elif litellm.LlmProviders.DASHSCOPE == provider: + from litellm.llms.dashscope.embed.transformation import ( + DashScopeEmbeddingConfig, + ) + + return DashScopeEmbeddingConfig() elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SNOWFLAKE == provider: @@ -8481,6 +8487,12 @@ class ProviderConfigManager: return litellm.VoyageRerankConfig() elif litellm.LlmProviders.WATSONX == provider: return litellm.IBMWatsonXRerankConfig() + elif litellm.LlmProviders.DASHSCOPE == provider: + from litellm.llms.dashscope.rerank.transformation import ( + DashScopeRerankConfig, + ) + + return DashScopeRerankConfig() return litellm.CohereRerankConfig() @staticmethod @@ -8527,6 +8539,12 @@ class ProviderConfigManager: ) return MinimaxMessagesConfig() + elif litellm.LlmProviders.DEEPSEEK == provider: + from litellm.llms.deepseek.messages.transformation import ( + DeepSeekAnthropicMessagesConfig, + ) + + return DeepSeekAnthropicMessagesConfig() return None @staticmethod diff --git a/migrations/Dockerfile b/migrations/Dockerfile new file mode 100644 index 00000000000..2160514251a --- /dev/null +++ b/migrations/Dockerfile @@ -0,0 +1,98 @@ +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a + +FROM $UV_IMAGE AS uvbin + +# ---------- Builder ---------- +# +# Minimal install for `prisma migrate deploy`. We deliberately skip the heavy +# `proxy-runtime` (otel, sentry, ddtrace, pypdf, google-genai, anthropic-vertex, +# ...) and `semantic-router` extras that the gateway/backend pull in — the +# migration engine doesn't need them. We DO install `--extra proxy` so the +# DB-URL helper from `litellm.proxy.auth.rds_iam_token` is importable, which +# is how the gateway and backend assemble `DATABASE_URL` at pod startup when +# `IAM_TOKEN_DB_AUTH=true` (see backend/main.py:17, gateway/main.py:22). And +# `--extra extra_proxy` provides the `prisma` CLI + the secret-manager +# backends `litellm.secret_managers.main` lazily imports. +# +# `prisma generate` runs once at BUILD time to (a) install the Node-based +# Prisma CLI into the binary cache and (b) download the migration / query +# engine binaries. The Python client it also produces is unused by this +# image's runtime entrypoint — that's fine, it's a few hundred KB and the +# alternative (`prisma py fetch`) doesn't reliably trigger engine downloads +# under nodeenv. Crucially we do NOT run `prisma generate` at RUNTIME; the +# old migration job did, on every pod start, which is the wasteful behaviour +# the componentization is fixing. +FROM $LITELLM_BUILD_IMAGE AS builder + +WORKDIR /app +USER root + +COPY --from=uvbin /uv /uvx /usr/local/bin/ + +RUN apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile + +ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ + UV_LINK_MODE=copy \ + UV_COMPILE_BYTECODE=1 \ + UV_PYTHON_DOWNLOADS=0 \ + PATH="/app/.venv/bin:${PATH}" + +# Stage 1 — install third-party deps only (cached by pyproject.toml/uv.lock). +RUN --mount=type=cache,target=/root/.cache/uv \ + --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ + --mount=type=bind,source=uv.lock,target=uv.lock \ + --mount=type=bind,source=enterprise/pyproject.toml,target=enterprise/pyproject.toml \ + --mount=type=bind,source=litellm-proxy-extras/pyproject.toml,target=litellm-proxy-extras/pyproject.toml \ + uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ + --extra proxy \ + --extra extra_proxy \ + --python python3 + +# Stage 2 — copy source and install the project + workspace members. +COPY . . + +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-default-groups --no-editable \ + --extra proxy \ + --extra extra_proxy \ + --python python3 + +COPY migrations/run.py /app/run.py + +# Pre-warm the Prisma binary cache so the Job pod doesn't reach the +# internet on first start. This matches what the backend Dockerfile does: +# `prisma generate` runs nodeenv (downloads Node), installs the prisma npm +# CLI, downloads the engine binaries for each `binaryTarget` in +# schema.prisma, AND emits the generated Python client. We don't need the +# client at runtime — the migration job invokes `prisma migrate deploy` +# via subprocess — but having it cached is harmless and the alternative +# (`prisma py fetch`) doesn't reliably trigger engine downloads. +RUN mkdir -p /home/nonroot && \ + HOME=/home/nonroot prisma generate --schema=./schema.prisma && \ + chown -R nonroot:nonroot /home/nonroot/.cache + +# ---------- Runtime ---------- +FROM $LITELLM_RUNTIME_IMAGE AS runtime + +USER root + +RUN apk add --no-cache bash openssl tzdata python3 libsndfile libatomic + +# wolfi-base ships an unprivileged `nonroot` account (UID/GID 65532). The +# Prisma engine binaries are dynamically linked against libssl/libcrypto, so +# openssl stays in the runtime layer. +WORKDIR /app +ENV HOME=/home/nonroot \ + PATH="/app/.venv/bin:${PATH}" \ + PYTHONPATH="/app" \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 + +COPY --from=builder --chown=nonroot:nonroot /app /app +COPY --from=builder --chown=nonroot:nonroot /home/nonroot/.cache /home/nonroot/.cache + +USER nonroot + +ENTRYPOINT ["python3", "/app/run.py"] diff --git a/migrations/run.py b/migrations/run.py new file mode 100644 index 00000000000..7ea80d48719 --- /dev/null +++ b/migrations/run.py @@ -0,0 +1,67 @@ +"""Entrypoint for the migrations Job container. + +Runs `prisma migrate deploy` against the LiteLLM writer database using the +recovery logic in `litellm_proxy_extras.ProxyExtrasDBManager.setup_database` +(P3005 baseline + P3009/P3018 idempotent-error handling, retries, etc.). + +Env vars: + DATABASE_URL required unless it can be assembled at + startup from the discrete DATABASE_* vars + (password auth) or minted from an IAM token + (`IAM_TOKEN_DB_AUTH=true`) + DIRECT_URL optional — used by `migrate diff` when the + primary URL is a pooler (e.g. Neon -pooler) + USE_V2_MIGRATION_RESOLVER "false" → fall back to the v1 resolver + (legacy diff-and-force recovery). Defaults + to "true": the v2 resolver avoids the schema + thrashing seen during rolling deploys when + two LiteLLM versions contend for the same DB. + USE_PRISMA_DB_PUSH "true" → use `prisma db push` instead of + `migrate deploy`. Default false. +""" + +import os +import sys + +from litellm.proxy.db.db_url_settings import DatabaseURLSettings +from litellm_proxy_extras._logging import logger +from litellm_proxy_extras.utils import ProxyExtrasDBManager, str_to_bool + + +def main() -> int: + # Assemble DATABASE_URL from the discrete DATABASE_* env vars, matching + # the gateway/backend startup path (IAM mint or password auth). Leaves an + # operator-pinned DATABASE_URL untouched. + DatabaseURLSettings.from_env().apply_to_env() + + if not os.getenv("DATABASE_URL"): + logger.error( + "DATABASE_URL is not set and could not be assembled from the " + "DATABASE_* env vars — cannot run migrations." + ) + return 1 + + # v2 is the safer default for componentized deploys: it skips the + # diff-and-force recovery from v1 that caused schema thrashing during + # rolling deploys. Set USE_V2_MIGRATION_RESOLVER=false to opt back into v1. + use_v2 = str_to_bool(os.getenv("USE_V2_MIGRATION_RESOLVER", "true")) + use_db_push = str_to_bool(os.getenv("USE_PRISMA_DB_PUSH")) + + logger.info( + "Starting prisma migration job (use_migrate=%s, use_v2_resolver=%s)", + not use_db_push, + use_v2, + ) + ok = ProxyExtrasDBManager.setup_database( + use_migrate=not use_db_push, + use_v2_resolver=use_v2, + ) + if not ok: + logger.error("Migration job failed after retries.") + return 1 + logger.info("Migration job completed successfully.") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 3eabb82093b..94f0f1e78d3 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -2141,6 +2141,380 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "azure_ai/gpt-5.4": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-2026-03-05": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro-2026-03-05": { + "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, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-mini-2026-03-17": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano-2026-03-17": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, "azure_ai/model_router": { "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, diff --git a/pyproject.toml b/pyproject.toml index 48effe0fa29..f63770105dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.85.0" +version = "1.86.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.14" @@ -57,12 +57,13 @@ proxy = [ "azure-storage-blob==12.28.0", "mcp==1.26.0", "litellm-proxy-extras==0.4.72", - "litellm-enterprise==0.1.40", + "litellm-enterprise==0.1.41", "RestrictedPython==8.1", "rich==13.9.4", "polars==1.38.1", "soundfile==0.12.1", "pyroscope-io==0.8.16; sys_platform != 'win32'", + "pydantic-settings>=2.14.1", ] extra_proxy = [ "prisma==0.11.0", @@ -250,7 +251,7 @@ source-exclude = [ profile = "black" [tool.commitizen] -version = "1.85.0" +version = "1.86.0" version_files = [ "pyproject.toml:^version", ] diff --git a/schema.prisma b/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/schema.prisma +++ b/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/terraform/litellm/README.md b/terraform/litellm/README.md new file mode 100644 index 00000000000..5ca704b96dd --- /dev/null +++ b/terraform/litellm/README.md @@ -0,0 +1,159 @@ +# LiteLLM Terraform stacks + +Two self-contained Terraform root modules that deploy the **componentized** +LiteLLM proxy — the gateway, backend, and UI as three independent containers +(see `helm/litellm/` for the canonical chart with the same split). + +| Stack | Compute | Database (writer + reader) | Cache | Object store | Public entrypoint | +| ------ | ----------- | ---------------------------------- | ----------- | ------------ | ------------------ | +| `aws/` | ECS Fargate | Aurora Postgres (IAM auth) | ElastiCache | S3 | Application LB | +| `gcp/` | Cloud Run | Cloud SQL Postgres (password auth) | Memorystore | GCS | External HTTPS LB | + +Each stack creates its own VPC and managed data stores — drop in a tfvars +file and run `terraform apply`. Both stacks support a typed `proxy_config` +input (mirrors `helm/litellm`'s `gateway.config.proxy_config`) and per-component +extra env vars / secret-manager refs. + +## Components + +The proxy is split into three deployables: + +| Component | Default image | Port | Role | +| --------- | ---------------------------------------- | ---- | -------------------------------------------------------------------- | +| `gateway` | `ghcr.io/berriai/litellm-gateway:main-stable` | 4000 | LLM data plane (`/v1/chat/completions`, `/v1/embeddings`, …) | +| `backend` | `ghcr.io/berriai/litellm-backend:main-stable` | 4001 | Management API (`/key/*`, `/user/*`, `/team/*`, `/model/*`, …) | +| `ui` | `ghcr.io/berriai/litellm-ui:main-stable` | 3000 | Static Next.js dashboard served by nginx | + +The load balancer routes gateway path prefixes (mirrored verbatim from +`gateway/routes/allowlist.py`) to the gateway, UI asset paths (`/`, +`/litellm-asset-prefix/*`, `/_next/*`, `/favicon.ico`) to the UI, and +everything else to the backend. + +## Architecture + +### AWS (`terraform/litellm/aws/`) + +``` + ┌───────────────────────────────────────┐ + │ Public Internet │ + └─────────────────┬─────────────────────┘ + │ HTTP/80 + ┌───────────────▼───────────────┐ + │ Application Load Balancer │ + │ (path-routing listener) │ + └─┬─────────────┬─────────────┬─┘ + │ │ │ + UI assets, / │ /v1/chat, │ /key/* │ + /_next/*, … │ /v1/embed, │ /user/* │ + │ … │ … │ + ┌─────────────▼───┐ ┌──────▼──────┐ ┌───▼──────────────┐ + │ ECS Service │ │ ECS Service │ │ ECS Service │ + │ (ui) │ │ (gateway) │ │ (backend) │ + │ Fargate :3000 │ │ Fargate:4000│ │ Fargate :4001 │ + └─────────────────┘ └──────┬──────┘ └────────┬─────────┘ + │ │ + ┌─── private subnets (one per AZ) ──────────────────────┐ + │ │ + │ ┌────────────────────────┐ ┌────────────────┐ │ + │ │ Aurora Postgres │ │ ElastiCache │ │ + │ │ cluster (IAM auth) │ │ Redis (1 node)│ │ + │ │ ┌───────┐ ┌───────┐ │ └────────────────┘ │ + │ │ │writer │ │reader │ │ │ + │ │ └───────┘ └───────┘ │ ┌────────────────┐ │ + │ └────────────────────────┘ │ S3 bucket │ │ + │ │ (versioned) │ │ + │ ┌────────────────────────┐ └────────────────┘ │ + │ │ Secrets Manager │ │ + │ │ • LITELLM_MASTER_KEY │ ┌────────────────┐ │ + │ │ • DB master password │ │ One-off ECS │ │ + │ │ • user-supplied API │ │ task: prisma │ │ + │ │ keys (referenced) │ │ migrate deploy │ │ + │ └────────────────────────┘ └────────────────┘ │ + │ │ + └─── VPC ───────────────────────────────────────────────┘ + │ NAT gateway in one public subnet + ▼ + egress to LLM providers +``` + +### GCP (`terraform/litellm/gcp/`) + +``` + ┌───────────────────────────────────────┐ + │ Public Internet │ + └─────────────────┬─────────────────────┘ + │ HTTP/80 + ┌───────────────▼───────────────┐ + │ External HTTPS Load Balancer │ + │ (global, URL map routing) │ + └─┬─────────────┬─────────────┬─┘ + │ │ │ + │ Serverless NEGs (one per service) + │ │ │ + ┌─────────────▼───┐ ┌──────▼──────┐ ┌───▼──────────────┐ + │ Cloud Run │ │ Cloud Run │ │ Cloud Run │ + │ (ui) │ │ (gateway) │ │ (backend) │ + │ :3000 │ │ :4000 │ │ :4001 │ + └─────────────────┘ └──────┬──────┘ └────────┬─────────┘ + │ │ + │ Serverless VPC Access connector + ┌─── VPC (private services access range) ──────────────────┐ + │ │ + │ ┌────────────────────────┐ ┌──────────────────┐ │ + │ │ Cloud SQL Postgres │ │ Memorystore │ │ + │ │ ┌───────┐ ┌───────┐ │ │ Redis │ │ + │ │ │writer │ │reader │ │ └──────────────────┘ │ + │ │ └───────┘ └───────┘ │ │ + │ └────────────────────────┘ ┌──────────────────┐ │ + │ │ GCS bucket │ │ + │ ┌────────────────────────┐ │ (versioned) │ │ + │ │ Secret Manager │ └──────────────────┘ │ + │ │ • LITELLM_MASTER_KEY │ │ + │ │ • DB password │ ┌──────────────────┐ │ + │ │ • user-supplied API │ │ Cloud Run Job: │ │ + │ │ keys (referenced) │ │ prisma migrate │ │ + │ └────────────────────────┘ │ deploy │ │ + │ └──────────────────┘ │ + └──────────────────────────────────────────────────────────┘ +``` + +## Images + +Both stacks take per-component image references as variables. The defaults +point at the public `ghcr.io/berriai/litellm-:main-stable` +images, so the stack is runnable end-to-end without pre-flight setup — +pin to a specific tag for production: + +- **AWS** can pull from any registry the task execution role can reach. + The role gets `AmazonECSTaskExecutionRolePolicy` attached, which grants + ECR pull permissions for repositories in the same account. + +- **GCP Cloud Run** can only pull from Artifact Registry or + `gcr.io`-style registries. To use images hosted elsewhere, mirror them + into Artifact Registry first. + +## Migrations + +LiteLLM's proxy runs `prisma migrate deploy` at startup, but on first apply +the gateway/backend can race the empty database. Both stacks expose a +one-off migration task that runs `python litellm/proxy/prisma_migration.py` +against the backend image: + +- AWS: an `aws_ecs_task_definition` (`litellm-migrations`). Run with + `aws ecs run-task` — the command is printed in `terraform output`. +- GCP: a `google_cloud_run_v2_job` (`litellm-migrations`). Run with + `gcloud run jobs execute` — the command is printed in `terraform output`. + +Run the migration job once after the first `terraform apply` and before the +gateway/backend services start serving traffic. + +## What's not included + +- TLS certificates / custom domains. Both stacks expose plain-HTTP load + balancers; bring your own ACM cert (AWS) or managed cert (GCP) and wire + it into the LB resource. +- Remote state backends. Default local state — add an `s3` or `gcs` + backend block to `versions.tf` when graduating to a team environment. +- Observability beyond the cloud provider's defaults (CloudWatch logs on + AWS, Cloud Logging on GCP). Wire your own Prometheus / Datadog / Langfuse + via the `*_extra_env` variables. diff --git a/terraform/litellm/aws/.terraform.lock.hcl b/terraform/litellm/aws/.terraform.lock.hcl new file mode 100644 index 00000000000..30b2a194c4c --- /dev/null +++ b/terraform/litellm/aws/.terraform.lock.hcl @@ -0,0 +1,45 @@ +# This file is maintained automatically by "terraform init". +# Manual edits may be lost in future updates. + +provider "registry.terraform.io/hashicorp/aws" { + version = "5.100.0" + constraints = "~> 5.60" + hashes = [ + "h1:Ijt7pOlB7Tr7maGQIqtsLFbl7pSMIj06TVdkoSBcYOw=", + "zh:054b8dd49f0549c9a7cc27d159e45327b7b65cf404da5e5a20da154b90b8a644", + "zh:0b97bf8d5e03d15d83cc40b0530a1f84b459354939ba6f135a0086c20ebbe6b2", + "zh:1589a2266af699cbd5d80737a0fe02e54ec9cf2ca54e7e00ac51c7359056f274", + "zh:6330766f1d85f01ae6ea90d1b214b8b74cc8c1badc4696b165b36ddd4cc15f7b", + "zh:7c8c2e30d8e55291b86fcb64bdf6c25489d538688545eb48fd74ad622e5d3862", + "zh:99b1003bd9bd32ee323544da897148f46a527f622dc3971af63ea3e251596342", + "zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425", + "zh:9f8b909d3ec50ade83c8062290378b1ec553edef6a447c56dadc01a99f4eaa93", + "zh:aaef921ff9aabaf8b1869a86d692ebd24fbd4e12c21205034bb679b9caf883a2", + "zh:ac882313207aba00dd5a76dbd572a0ddc818bb9cbf5c9d61b28fe30efaec951e", + "zh:bb64e8aff37becab373a1a0cc1080990785304141af42ed6aa3dd4913b000421", + "zh:dfe495f6621df5540d9c92ad40b8067376350b005c637ea6efac5dc15028add4", + "zh:f0ddf0eaf052766cfe09dea8200a946519f653c384ab4336e2a4a64fdd6310e9", + "zh:f1b7e684f4c7ae1eed272b6de7d2049bb87a0275cb04dbb7cda6636f600699c9", + "zh:ff461571e3f233699bf690db319dfe46aec75e58726636a0d97dd9ac6e32fb70", + ] +} + +provider "registry.terraform.io/hashicorp/random" { + version = "3.8.1" + constraints = "~> 3.6" + hashes = [ + "h1:u8AKlWVDTH5r9YLSeswoVEjiY72Rt4/ch7U+61ZDkiQ=", + "zh:08dd03b918c7b55713026037c5400c48af5b9f468f483463321bd18e17b907b4", + "zh:0eee654a5542dc1d41920bbf2419032d6f0d5625b03bd81339e5b33394a3e0ae", + "zh:229665ddf060aa0ed315597908483eee5b818a17d09b6417a0f52fd9405c4f57", + "zh:2469d2e48f28076254a2a3fc327f184914566d9e40c5780b8d96ebf7205f8bc0", + "zh:37d7eb334d9561f335e748280f5535a384a88675af9a9eac439d4cfd663bcb66", + "zh:741101426a2f2c52dee37122f0f4a2f2d6af6d852cb1db634480a86398fa3511", + "zh:78d5eefdd9e494defcb3c68d282b8f96630502cac21d1ea161f53cfe9bb483b3", + "zh:a902473f08ef8df62cfe6116bd6c157070a93f66622384300de235a533e9d4a9", + "zh:b85c511a23e57a2147355932b3b6dce2a11e856b941165793a0c3d7578d94d05", + "zh:c5172226d18eaac95b1daac80172287b69d4ce32750c82ad77fa0768be4ea4b8", + "zh:dab4434dba34aad569b0bc243c2d3f3ff86dd7740def373f2a49816bd2ff819b", + "zh:f49fd62aa8c5525a5c17abd51e27ca5e213881d58882fd42fec4a545b53c9699", + ] +} diff --git a/terraform/litellm/aws/README.md b/terraform/litellm/aws/README.md new file mode 100644 index 00000000000..8638ea800ec --- /dev/null +++ b/terraform/litellm/aws/README.md @@ -0,0 +1,254 @@ +# LiteLLM on AWS (ECS Fargate) + +Deploys the componentized LiteLLM proxy on AWS: + +- **VPC** with public + private subnets across the AZs you pass in, one NAT gateway +- **Aurora Postgres** cluster — one writer instance + one reader instance, **IAM database authentication enabled** +- **ElastiCache Redis** (private, replication group with multi-AZ failover and at-rest + in-transit encryption) for caching + rate limiting +- **S3 bucket** (private, versioned, SSE-S3) — exposed to gateway + backend as `S3_BUCKET_NAME` / `S3_REGION_NAME` for cache backend, request log archival, and `/v1/files` storage +- **Secrets Manager** entries for `LITELLM_MASTER_KEY` (auto-generated, `sk-…`) and the Aurora master password (bootstrap-only) +- **ECS Fargate cluster** running three services — `gateway`, `backend`, `ui` +- **Application Load Balancer** (public, HTTP/80) with path-based routing: + - LLM data-plane prefixes (`/v1/chat/*`, `/v1/embeddings`, …) → `gateway` + - UI assets (`/`, `/_next/*`, `/litellm-asset-prefix/*`, …) → `ui` + - Everything else (management API: `/key/*`, `/user/*`, …) → `backend` +- **One-off migration task** (`litellm-migrations`) that runs `prisma migrate deploy` from the dedicated `ghcr.io/berriai/litellm-migrations` image + +## Aurora + IAM auth + +The cluster runs with `iam_database_authentication_enabled = true`. Enabling +that on the cluster doesn't by itself let any Postgres user log in with an IAM +token — you also need to `CREATE USER ... GRANT rds_iam` once. `bootstrap.tf` +does this automatically during `terraform apply` via a one-shot Fargate task +(`postgres:16-alpine` running the bootstrap SQL with the master password from +Secrets Manager). The SQL is idempotent, so re-applies are safe. + +The same apply also runs the prisma schema migration via the existing +`litellm-migrations` task definition, and the gateway/backend services +`depends_on` the migration so they don't start until the schema is in place. + +At runtime, the proxy assembles `DATABASE_URL` from `DATABASE_HOST/PORT/USER/NAME` +plus a short-lived IAM token — see `litellm/proxy/auth/rds_iam_token.py`. The +task role has `rds-db:connect` scoped to the IAM-authed user on the cluster. + +**Break-glass.** If you need to run the bootstrap or migration by hand (e.g., +to re-apply against an externally provisioned cluster), `db_bootstrap_sql` and +`migration_run_command` are still exposed as outputs. + +**Prerequisite.** `terraform apply` shells out to `aws ecs run-task` / +`aws ecs wait` in `local-exec` provisioners, so the machine running terraform +needs the `aws` CLI installed and authenticated. + +## Configuring the proxy + +### `proxy_config` (preferred) + +Mirrors the helm chart's `gateway.config.proxy_config`. The map is YAML-encoded +and base64-passed to gateway, backend, and the migration task; each container +decodes it to `/tmp/litellm-config.yaml` at startup and sets `CONFIG_FILE_PATH` +to match. + +```hcl +proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + } +} +``` + +LiteLLM resolves `os.environ/` references in the YAML against the +container's environment. That means provider API keys belong in +`*_extra_secrets` (next section), and your YAML just references them by name. + +### Extra env vars + +Non-sensitive plaintext (feature flags, observability hosts, etc.): + +```hcl +gateway_extra_env = { + LANGFUSE_HOST = "https://us.cloud.langfuse.com" +} +backend_extra_env = { + STORE_MODEL_IN_DB = "True" +} +``` + +### Extra secrets (API keys) + +Sensitive values — provider API keys, third-party tokens — live in **existing +Secrets Manager secrets**. Reference them by ARN: + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" + ANTHROPIC_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:anthropic-api-key-GhIjKl" +} +``` + +What happens under the hood: +- The execution role auto-gains `secretsmanager:GetSecretValue` on every ARN + listed here. +- ECS resolves each secret at task launch and injects its value into the + container as the env var named on the left. +- The `proxy_config` YAML references the resulting env var via + `os.environ/OPENAI_API_KEY`. + +To pluck a single field out of a JSON secret, use ECS's `:fieldName::` suffix: + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "arn:…:secret:provider-keys-AbCdEf:openai_api_key::" +} +``` + +To create the secret beforehand: + +```bash +aws secretsmanager create-secret \ + --name openai-api-key \ + --secret-string "sk-proj-..." +``` + +## Tenant deployment + +Every resource the stack creates is named `${tenant}-litellm-${env}` (or +that plus a per-resource suffix), so multiple tenants and multiple +environments coexist in the same account as long as the `(tenant, env)` +pair differs: + +| `tenant` | `env` | Example resource name | +| -------- | ------- | ---------------------------------- | +| `acme` | `stage` | `acme-litellm-stage-gateway` | +| `acme` | `prod` | `acme-litellm-prod-master-key` | +| `globex` | `dev` | `globex-litellm-dev-license` | + +For a per-tenant instance, the only inputs that change are the tenant +slug, env, and the two pre-issued secrets: + +```bash +export TF_VAR_litellm_master_key="sk-..." # the tenant's master key +export TF_VAR_litellm_license="lic-..." # their LITELLM_LICENSE + +terraform apply \ + -var "region=us-west-2" \ + -var 'azs=["us-west-2a","us-west-2b"]' \ + -var "tenant=acme" \ + -var "env=stage" +``` + +Both `litellm_master_key` and `litellm_license` are optional: +- Omit `litellm_master_key` → the stack auto-generates a random `sk-…` + value (trial/dev path). +- Omit `litellm_license` → no license secret is created and gateway/ + backend run without `LITELLM_LICENSE` (OSS-only). + +Use `TF_VAR_*` env vars rather than tfvars files for these — values +written to a tfvars file end up in `terraform.tfstate` and any committed +example files. + +## Quick start + +```bash +cd terraform/litellm/aws +cp terraform.tfvars.example terraform.tfvars +# Edit: region, tenant, env, azs, *_image, proxy_config, gateway_extra_secrets. + +terraform init +terraform apply +``` + +That single apply provisions everything, runs the DB user bootstrap, runs the +schema migration, and only then starts the gateway/backend services. When it +returns, the stack is serving traffic. + +```bash +terraform output alb_url +# UI login: admin / +aws secretsmanager get-secret-value \ + --secret-id "$(terraform output -raw master_key_secret_arn)" \ + --query SecretString --output text +``` + +## Image pulls + +The defaults pull from `ghcr.io/berriai/litellm-:v1.86.0-dev`, +which is anonymous-readable. There are four images: `litellm-gateway`, +`litellm-backend`, `litellm-ui`, and `litellm-migrations` (slim image used +only by the one-off migration task — runs `prisma migrate deploy` against +the writer DB and exits). Bump them together when bumping LiteLLM. To pull +from a private registry: + +- **ECR (same account)**: the execution role already has + `AmazonECSTaskExecutionRolePolicy`, which grants ECR pull for repos in + the same account. No extra config needed. +- **ECR (cross-account)**: attach a policy to the execution role allowing + `ecr:GetAuthorizationToken` + `ecr:BatchGetImage` on the foreign repo + ARNs. +- **Other private registries** (GHCR with a PAT, Docker Hub, …): create a + secret holding `{"auths":{"":{"auth":""}}}` + in Secrets Manager and set `repositoryCredentials.credentialsParameter` + on the task def container — extend `ecs.tf` accordingly. + +## TLS + +`terraform plan` refuses to provision an HTTP-only ALB by default — TLS +is the supported posture. Two paths: + +**Production / staging — provide an ACM certificate:** + +1. Create or import an ACM cert in `var.region` covering the DNS name you + plan to point at the ALB. +2. Set `acm_certificate_arn = "arn:aws:acm:..."` in tfvars and apply. + +Result: a 443 listener carries the path-routing rules; the 80 listener +serves a permanent 301 redirect to HTTPS, so HTTP clients are +automatically upgraded. + +**Trial / dev — explicitly opt into HTTP-only:** + +Set `allow_plaintext_alb = true` in tfvars. Without this flag, plan fails +with a clear error pointing at the precondition. Intended for short-lived +trial / dev stacks only. + +## Storage and database retention + +Three opt-in tripwires guard against accidental data loss on +`terraform destroy`: + +- **`skip_final_snapshot`** (Aurora; default `false`) — destroying the + cluster takes a `-final-` snapshot first. +- **`s3_force_destroy`** (S3 bucket holding request log archives, + `/v1/files` content, and the S3 cache backend; default `false`) — + `terraform destroy` against a non-empty bucket fails. + +Flip either to `true` only for ephemeral / CI stacks where you accept +losing the contents. + +## Files + +| File | What's in it | +| ----------------- | --------------------------------------------------------------------- | +| `versions.tf` | Terraform + provider version constraints | +| `providers.tf` | AWS provider (region + default tags) | +| `variables.tf` | All input variables | +| `locals.tf` | Path-prefix lists for ALB routing (mirror of `helm/.../ingress.yaml`) | +| `network.tf` | VPC, subnets, IGW, NAT, route tables, security groups | +| `secrets.tf` | Secrets Manager entries + random passwords | +| `rds.tf` | Aurora Postgres cluster + writer / reader instances | +| `redis.tf` | ElastiCache Redis | +| `s3.tf` | S3 bucket + task-role policy scoped to it | +| `iam.tf` | Task execution + task roles, including `rds-db:connect` | +| `ecs.tf` | ECS cluster, task definitions, services for the three components | +| `alb.tf` | ALB, listener, target groups, path-routing rules | +| `migrations.tf` | One-off migration task definition | +| `outputs.tf` | DNS name, secret ARN, bootstrap SQL, migration `run-task` command | diff --git a/terraform/litellm/aws/alb.tf b/terraform/litellm/aws/alb.tf new file mode 100644 index 00000000000..de0d9c2310f --- /dev/null +++ b/terraform/litellm/aws/alb.tf @@ -0,0 +1,179 @@ +resource "aws_lb" "this" { + name = local.name + load_balancer_type = "application" + internal = false + security_groups = [aws_security_group.alb.id] + subnets = aws_subnet.public[*].id + + idle_timeout = 120 +} + +locals { + # When an ACM cert ARN is provided we provision a 443 listener carrying + # the path-routing rules and downgrade the 80 listener to a redirect. + tls_enabled = var.acm_certificate_arn != "" + rules_listener_arn = local.tls_enabled ? aws_lb_listener.https[0].arn : aws_lb_listener.http.arn +} + +# Target groups — one per component. IP target type because Fargate tasks +# are addressed by ENI IP, not instance. + +resource "aws_lb_target_group" "gateway" { + name = "${local.name}-gateway" + port = 4000 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/health/readiness" + matcher = "200-299" + interval = 30 + timeout = 10 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +resource "aws_lb_target_group" "backend" { + name = "${local.name}-backend" + port = 4001 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/health/readiness" + matcher = "200-299" + interval = 30 + timeout = 10 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +resource "aws_lb_target_group" "ui" { + name = "${local.name}-ui" + port = 3000 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/healthz" + matcher = "200-299" + interval = 30 + timeout = 5 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +# HTTP listener. When TLS is enabled this only serves a permanent +# 301 redirect to HTTPS; otherwise it carries the path-routing rules +# (default → backend). +resource "aws_lb_listener" "http" { + load_balancer_arn = aws_lb.this.arn + port = 80 + protocol = "HTTP" + + default_action { + type = local.tls_enabled ? "redirect" : "forward" + + dynamic "redirect" { + for_each = local.tls_enabled ? [1] : [] + content { + port = "443" + protocol = "HTTPS" + status_code = "HTTP_301" + } + } + + target_group_arn = local.tls_enabled ? null : aws_lb_target_group.backend.arn + } + + # Default-deny on the HTTP-only path: TLS is the supported posture. + # Operators must either supply an ACM cert or explicitly opt in. + lifecycle { + precondition { + condition = local.tls_enabled || var.allow_plaintext_alb + error_message = "ALB has no HTTPS listener. Either set `acm_certificate_arn` to enable TLS, or set `allow_plaintext_alb = true` to opt into HTTP-only (trial / dev only)." + } + } +} + +# HTTPS listener. Only created when an ACM cert ARN is supplied — terminates +# TLS and carries the same default + path-routing rules. +resource "aws_lb_listener" "https" { + count = local.tls_enabled ? 1 : 0 + load_balancer_arn = aws_lb.this.arn + port = 443 + protocol = "HTTPS" + ssl_policy = "ELBSecurityPolicy-TLS13-1-2-2021-06" + certificate_arn = var.acm_certificate_arn + + default_action { + type = "forward" + target_group_arn = aws_lb_target_group.backend.arn + } +} + +# UI exact paths (/, /favicon.ico, /ui) — priority 10. +resource "aws_lb_listener_rule" "ui_exact" { + listener_arn = local.rules_listener_arn + priority = 10 + + action { + type = "forward" + target_group_arn = aws_lb_target_group.ui.arn + } + + condition { + path_pattern { + values = local.ui_exact_paths + } + } +} + +# UI prefix paths (/_next/*, /litellm-asset-prefix/*, /assets/*, /ui/*) — priority 20. +resource "aws_lb_listener_rule" "ui_prefix" { + listener_arn = local.rules_listener_arn + priority = 20 + + action { + type = "forward" + target_group_arn = aws_lb_target_group.ui.arn + } + + condition { + path_pattern { + values = local.ui_path_prefixes + } + } +} + +# Gateway prefix rules — one per chunk-of-5 because ALB caps a path-pattern +# condition at 5 values. Priorities 100..(100 + N). +resource "aws_lb_listener_rule" "gateway" { + for_each = { for idx, chunk in local.gateway_path_chunks : idx => chunk } + + listener_arn = local.rules_listener_arn + priority = 100 + tonumber(each.key) + + action { + type = "forward" + target_group_arn = aws_lb_target_group.gateway.arn + } + + condition { + path_pattern { + values = each.value + } + } +} diff --git a/terraform/litellm/aws/autoscaling.tf b/terraform/litellm/aws/autoscaling.tf new file mode 100644 index 00000000000..71b6c24fac7 --- /dev/null +++ b/terraform/litellm/aws/autoscaling.tf @@ -0,0 +1,105 @@ +# Application Auto Scaling for the three ECS services. Mirrors the HPA values +# baked into the helm chart at helm/litellm/values.yaml: +# +# gateway: 1-10 replicas, target 70% CPU + 80% memory +# backend: 1-4 replicas, target 70% CPU +# ui: 1-3 replicas, target 80% CPU (off by default; nginx static export) +# +# Each service gets a scalable target plus one target-tracking policy per metric. +# When autoscaling is disabled (count=0) the resources collapse cleanly out of +# the plan; the service's desired_count from ecs.tf stays in effect. + +# ---------- Gateway ---------- +resource "aws_appautoscaling_target" "gateway" { + count = var.gateway_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.gateway.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.gateway_min_capacity + max_capacity = var.gateway_max_capacity +} + +resource "aws_appautoscaling_policy" "gateway_cpu" { + count = var.gateway_autoscaling_enabled ? 1 : 0 + name = "${local.name}-gateway-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.gateway[0].service_namespace + resource_id = aws_appautoscaling_target.gateway[0].resource_id + scalable_dimension = aws_appautoscaling_target.gateway[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.gateway_cpu_target + } +} + +resource "aws_appautoscaling_policy" "gateway_memory" { + # Memory policy is optional; set gateway_memory_target = 0 to omit it. + count = var.gateway_autoscaling_enabled && var.gateway_memory_target > 0 ? 1 : 0 + name = "${local.name}-gateway-memory" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.gateway[0].service_namespace + resource_id = aws_appautoscaling_target.gateway[0].resource_id + scalable_dimension = aws_appautoscaling_target.gateway[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageMemoryUtilization" + } + target_value = var.gateway_memory_target + } +} + +# ---------- Backend ---------- +resource "aws_appautoscaling_target" "backend" { + count = var.backend_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.backend.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.backend_min_capacity + max_capacity = var.backend_max_capacity +} + +resource "aws_appautoscaling_policy" "backend_cpu" { + count = var.backend_autoscaling_enabled ? 1 : 0 + name = "${local.name}-backend-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.backend[0].service_namespace + resource_id = aws_appautoscaling_target.backend[0].resource_id + scalable_dimension = aws_appautoscaling_target.backend[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.backend_cpu_target + } +} + +# ---------- UI ---------- +resource "aws_appautoscaling_target" "ui" { + count = var.ui_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.ui.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.ui_min_capacity + max_capacity = var.ui_max_capacity +} + +resource "aws_appautoscaling_policy" "ui_cpu" { + count = var.ui_autoscaling_enabled ? 1 : 0 + name = "${local.name}-ui-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.ui[0].service_namespace + resource_id = aws_appautoscaling_target.ui[0].resource_id + scalable_dimension = aws_appautoscaling_target.ui[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.ui_cpu_target + } +} diff --git a/terraform/litellm/aws/bootstrap.tf b/terraform/litellm/aws/bootstrap.tf new file mode 100644 index 00000000000..e9a56dedbb5 --- /dev/null +++ b/terraform/litellm/aws/bootstrap.tf @@ -0,0 +1,185 @@ +# Auto-runs the two manual steps that used to follow `terraform apply`: +# +# 1. Create the IAM-authed Postgres user (litellm_app) — uses the postgres:16 +# image with the master password from Secrets Manager. +# 2. Run prisma migrate deploy — reuses the existing aws_ecs_task_definition +# .migrations task def from migrations.tf. +# +# Both are invoked via `terraform_data` provisioners. Gateway/backend services +# in ecs.tf depend on `terraform_data.migration`, so on a fresh apply they +# don't start until the schema is in place — no crash-loop window. +# +# Triggers: +# - bootstrap_db re-runs if the Aurora cluster is recreated, or if the +# bootstrap task definition (image/SQL) changes. +# - migration re-runs if the migration task def revision changes (e.g., new +# backend image with new prisma migration files) or if bootstrap re-ran. +# +# Requires `aws` CLI on the machine running terraform. For laptop usage that's +# fine; for CI/CD the runner image needs `aws`. + +# ---------- IAM ---------- +# Execution role can already read the runtime secrets (master_key, user-provided +# extras — see iam.tf). The DB master password lives in a separate secret used +# only here, so we grant access in an additive policy. +resource "aws_iam_policy" "bootstrap_secrets" { + name = "${local.name}-bootstrap-secrets-access" + policy = jsonencode({ + Version = "2012-10-17" + Statement = [{ + Effect = "Allow" + Action = ["secretsmanager:GetSecretValue"] + Resource = [aws_secretsmanager_secret.db_master_password.arn] + }] + }) +} + +resource "aws_iam_role_policy_attachment" "task_execution_bootstrap_secrets" { + role = aws_iam_role.task_execution.name + policy_arn = aws_iam_policy.bootstrap_secrets.arn +} + +# ---------- Bootstrap task def ---------- +resource "aws_cloudwatch_log_group" "bootstrap_db" { + name = "/ecs/${local.name}/bootstrap-db" + retention_in_days = var.log_retention_days +} + +locals { + # Idempotent: CREATE USER is wrapped in DO/EXCEPTION; GRANTs are + # idempotent by definition (re-granting is a no-op). Safe to re-run on + # any subsequent apply. + bootstrap_sql = <<-SQL + DO $$ + BEGIN + CREATE USER ${var.db_username}; + EXCEPTION WHEN duplicate_object THEN NULL; + END $$; + GRANT rds_iam TO ${var.db_username}; + GRANT ALL PRIVILEGES ON DATABASE ${var.db_name} TO ${var.db_username}; + GRANT ALL ON SCHEMA public TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO ${var.db_username}; + SQL +} + +resource "aws_ecs_task_definition" "bootstrap_db" { + family = "${local.name}-bootstrap-db" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = 256 + memory = 512 + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([{ + name = "psql" + image = "postgres:16-alpine" + essential = true + + environment = [ + { name = "PGHOST", value = aws_rds_cluster.this.endpoint }, + { name = "PGPORT", value = tostring(aws_rds_cluster.this.port) }, + { name = "PGUSER", value = var.db_master_username }, + { name = "PGDATABASE", value = var.db_name }, + { name = "BOOTSTRAP_SQL", value = local.bootstrap_sql }, + ] + secrets = [ + # `:password::` extracts the password field out of the JSON secret. + { name = "PGPASSWORD", valueFrom = "${aws_secretsmanager_secret.db_master_password.arn}:password::" }, + ] + + entryPoint = ["sh", "-c"] + command = ["echo \"$BOOTSTRAP_SQL\" | psql -v ON_ERROR_STOP=1"] + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.bootstrap_db.name + awslogs-region = var.region + awslogs-stream-prefix = "bootstrap" + } + } + }]) +} + +# ---------- Bootstrap trigger ---------- +resource "terraform_data" "bootstrap_db" { + triggers_replace = { + cluster_resource_id = aws_rds_cluster.this.cluster_resource_id + task_def_revision = aws_ecs_task_definition.bootstrap_db.revision + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + CLUSTER = aws_ecs_cluster.this.name + TASK_DEF = aws_ecs_task_definition.bootstrap_db.arn + SUBNETS = join(",", aws_subnet.private[*].id) + SG = aws_security_group.tasks.id + REGION = var.region + LOG_GRP = aws_cloudwatch_log_group.bootstrap_db.name + } + command = <<-EOT + set -euo pipefail + task_arn=$(aws ecs run-task --region "$REGION" --cluster "$CLUSTER" \ + --launch-type FARGATE --task-definition "$TASK_DEF" \ + --network-configuration "awsvpcConfiguration={subnets=[$SUBNETS],securityGroups=[$SG],assignPublicIp=DISABLED}" \ + --query 'tasks[0].taskArn' --output text) + echo "bootstrap task: $task_arn" + aws ecs wait tasks-stopped --region "$REGION" --cluster "$CLUSTER" --tasks "$task_arn" + task_id=$(echo "$task_arn" | awk -F/ '{print $NF}') + exit_code=$(aws ecs describe-tasks --region "$REGION" --cluster "$CLUSTER" --tasks "$task_id" \ + --query 'tasks[0].containers[0].exitCode' --output text) + if [ "$exit_code" != "0" ]; then + echo "Bootstrap failed (exit=$exit_code). Logs: $LOG_GRP" >&2 + exit 1 + fi + EOT + } + + depends_on = [ + aws_rds_cluster_instance.writer, + aws_iam_role_policy_attachment.task_execution_bootstrap_secrets, + ] +} + +# ---------- Migration trigger ---------- +# Reuses the task definition from migrations.tf — this resource just invokes +# it and waits. +resource "terraform_data" "migration" { + triggers_replace = { + task_def_revision = aws_ecs_task_definition.migrations.revision + bootstrap_id = terraform_data.bootstrap_db.id + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + CLUSTER = aws_ecs_cluster.this.name + TASK_DEF = aws_ecs_task_definition.migrations.arn + SUBNETS = join(",", aws_subnet.private[*].id) + SG = aws_security_group.tasks.id + REGION = var.region + LOG_GRP = aws_cloudwatch_log_group.migrations.name + } + command = <<-EOT + set -euo pipefail + task_arn=$(aws ecs run-task --region "$REGION" --cluster "$CLUSTER" \ + --launch-type FARGATE --task-definition "$TASK_DEF" \ + --network-configuration "awsvpcConfiguration={subnets=[$SUBNETS],securityGroups=[$SG],assignPublicIp=DISABLED}" \ + --query 'tasks[0].taskArn' --output text) + echo "migration task: $task_arn" + aws ecs wait tasks-stopped --region "$REGION" --cluster "$CLUSTER" --tasks "$task_arn" + task_id=$(echo "$task_arn" | awk -F/ '{print $NF}') + exit_code=$(aws ecs describe-tasks --region "$REGION" --cluster "$CLUSTER" --tasks "$task_id" \ + --query 'tasks[0].containers[0].exitCode' --output text) + if [ "$exit_code" != "0" ]; then + echo "Migration failed (exit=$exit_code). Logs: $LOG_GRP" >&2 + exit 1 + fi + EOT + } + + depends_on = [terraform_data.bootstrap_db] +} diff --git a/terraform/litellm/aws/ecs.tf b/terraform/litellm/aws/ecs.tf new file mode 100644 index 00000000000..a6d2350c681 --- /dev/null +++ b/terraform/litellm/aws/ecs.tf @@ -0,0 +1,347 @@ +resource "aws_ecs_cluster" "this" { + name = local.name + + setting { + name = "containerInsights" + value = "enabled" + } +} + +resource "aws_cloudwatch_log_group" "gateway" { + name = "/ecs/${local.name}/gateway" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "backend" { + name = "/ecs/${local.name}/backend" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "ui" { + name = "/ecs/${local.name}/ui" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "migrations" { + name = "/ecs/${local.name}/migrations" + retention_in_days = var.log_retention_days +} + +# Shared env block fed to gateway, backend, and the migration task. Mirrors +# the helm chart's `litellm.serverEnv` helper on the IAM-auth branch: +# DATABASE_URL is assembled at runtime by +# litellm/proxy/auth/rds_iam_token.py::init_iam_db_url_from_env from +# HOST/PORT/USER/NAME plus an IAM-signed token, so no DB password is needed +# in the task definition. +locals { + shared_env = [ + { name = "IAM_TOKEN_DB_AUTH", value = "true" }, + { name = "DATABASE_HOST", value = aws_rds_cluster.this.endpoint }, + { name = "DATABASE_PORT", value = tostring(aws_rds_cluster.this.port) }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + { name = "DATABASE_HOST_READ_REPLICA", value = aws_rds_cluster.this.reader_endpoint }, + { name = "DATABASE_PORT_READ_REPLICA", value = tostring(aws_rds_cluster.this.port) }, + { name = "REDIS_HOST", value = aws_elasticache_replication_group.this.primary_endpoint_address }, + { name = "REDIS_PORT", value = tostring(aws_elasticache_replication_group.this.port) }, + # transit_encryption_enabled = true on the replication group means the + # proxy must connect via rediss://. _redis.get_redis_url_from_environment + # honors REDIS_SSL to flip the scheme. + { name = "REDIS_SSL", value = "true" }, + # S3 bucket — referenced from proxy_config via os.environ/S3_BUCKET_NAME + # (e.g. cache backend, request log archival, /files passthrough). + { name = "S3_BUCKET_NAME", value = aws_s3_bucket.this.bucket }, + { name = "S3_REGION_NAME", value = var.region }, + # boto3 inside generate_iam_auth_token reads AWS_REGION_NAME first, then + # AWS_REGION. Set both for compatibility. + { name = "AWS_REGION", value = var.region }, + { name = "AWS_REGION_NAME", value = var.region }, + ] + + shared_secrets = concat( + [ + { name = "LITELLM_MASTER_KEY", valueFrom = aws_secretsmanager_secret.master_key.arn }, + ], + var.litellm_license == "" ? [] : [ + { name = "LITELLM_LICENSE", valueFrom = aws_secretsmanager_secret.license[0].arn }, + ], + ) + + # Backend-only managed secrets. UI_PASSWORD is consumed by the management + # API (UI login flow) and has no use on the gateway data plane. + backend_managed_secrets = var.ui_password == "" ? [] : [ + { name = "UI_PASSWORD", valueFrom = aws_secretsmanager_secret.ui_password[0].arn }, + ] + + gateway_extra_env_list = [ + for k, v in var.gateway_extra_env : { name = k, value = v } + ] + backend_extra_env_list = [ + for k, v in var.backend_extra_env : { name = k, value = v } + ] + + backend_default_env = [ + { name = "STORE_MODEL_IN_DB", value = "true" }, + ] + gateway_extra_secrets_list = [ + for k, v in var.gateway_extra_secrets : { name = k, valueFrom = v } + ] + backend_extra_secrets_list = [ + for k, v in var.backend_extra_secrets : { name = k, valueFrom = v } + ] + + # Mirrors the helm chart's gateway.config.create / configmap pattern. + # ECS Fargate has no ConfigMap analogue, so we pass the YAML as a + # base64-encoded env var and decode it at container start via a tiny + # python shim that prepends the image's normal uvicorn entrypoint. + proxy_config_enabled = length(keys(var.proxy_config)) > 0 + proxy_config_b64 = local.proxy_config_enabled ? base64encode(yamlencode(var.proxy_config)) : "" + + proxy_config_env = local.proxy_config_enabled ? [ + { name = "LITELLM_PROXY_CONFIG_B64", value = local.proxy_config_b64 }, + { name = "CONFIG_FILE_PATH", value = "/tmp/litellm-config.yaml" }, + ] : [] + + # Gateway always needs --workers wired in (no NUM_WORKERS env var support + # in the image entrypoint). When proxy_config is enabled we also have to + # decode the base64 config first, so the command goes through `sh -c`; + # otherwise we keep the image's ENTRYPOINT and only override `command`. + gateway_uvicorn_args = "--host 0.0.0.0 --port 4000 --workers ${var.gateway_num_workers}" + backend_uvicorn_args = "--host 0.0.0.0 --port 4001" + + gateway_proxy_overrides = local.proxy_config_enabled ? { + entryPoint = ["sh", "-c"] + command = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\" && exec uvicorn gateway.main:app ${local.gateway_uvicorn_args}" + ] + } : { + # Mirror the image's ENTRYPOINT so we can append --workers via command. + entryPoint = ["uvicorn", "gateway.main:app"] + command = split(" ", local.gateway_uvicorn_args) + } + + backend_proxy_overrides = local.proxy_config_enabled ? { + entryPoint = ["sh", "-c"] + command = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\" && exec uvicorn backend.main:app ${local.backend_uvicorn_args}" + ] + } : {} +} + +# ---------- Gateway ---------- +resource "aws_ecs_task_definition" "gateway" { + family = "${local.name}-gateway" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.gateway_cpu + memory = var.gateway_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([ + merge( + { + name = "gateway" + image = var.gateway_image + essential = true + + portMappings = [{ containerPort = 4000, protocol = "tcp" }] + environment = concat( + local.shared_env, + local.gateway_extra_env_list, + local.proxy_config_env, + ) + secrets = concat(local.shared_secrets, local.gateway_extra_secrets_list) + + # Container-level healthCheck intentionally omitted — the wolfi + # runtime image doesn't ship curl/wget. The ALB target group polls + # /health/readiness. + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.gateway.name + awslogs-region = var.region + awslogs-stream-prefix = "gateway" + } + } + }, + local.gateway_proxy_overrides, + ) + ]) +} + +resource "aws_ecs_service" "gateway" { + name = "${local.name}-gateway" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.gateway.arn + desired_count = var.gateway_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.gateway.arn + container_name = "gateway" + container_port = 4000 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + # desired_count is owned by Application Auto Scaling once enabled (autoscaling.tf). + # Terraform sets the initial value from var.gateway_desired_count, then steps aside. + lifecycle { + ignore_changes = [desired_count] + } + + # Don't start until the schema migration has run. Otherwise the proxy + # boots, Prisma fails on the missing tables, and ECS thrashes the task. + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + terraform_data.migration, + ] +} + +# ---------- Backend ---------- +resource "aws_ecs_task_definition" "backend" { + family = "${local.name}-backend" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.backend_cpu + memory = var.backend_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([ + merge( + { + name = "backend" + image = var.backend_image + essential = true + + portMappings = [{ containerPort = 4001, protocol = "tcp" }] + environment = concat( + local.shared_env, + local.backend_default_env, + local.backend_extra_env_list, + local.proxy_config_env, + ) + secrets = concat(local.shared_secrets, local.backend_managed_secrets, local.backend_extra_secrets_list) + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.backend.name + awslogs-region = var.region + awslogs-stream-prefix = "backend" + } + } + }, + local.backend_proxy_overrides, + ) + ]) +} + +resource "aws_ecs_service" "backend" { + name = "${local.name}-backend" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.backend.arn + desired_count = var.backend_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.backend.arn + container_name = "backend" + container_port = 4001 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + lifecycle { + ignore_changes = [desired_count] + } + + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + terraform_data.migration, + ] +} + +# ---------- UI ---------- +# task_role is deliberately the unprivileged ui_task — the UI has no DB, +# S3, or Secrets Manager dependency, and inheriting the shared `task` +# role would expose every data-plane secret to a compromised UI +# container via the task metadata endpoint. +resource "aws_ecs_task_definition" "ui" { + family = "${local.name}-ui" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.ui_cpu + memory = var.ui_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.ui_task.arn + + container_definitions = jsonencode([ + { + name = "ui" + image = var.ui_image + essential = true + portMappings = [{ containerPort = 3000, protocol = "tcp" }] + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.ui.name + awslogs-region = var.region + awslogs-stream-prefix = "ui" + } + } + } + ]) +} + +resource "aws_ecs_service" "ui" { + name = "${local.name}-ui" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.ui.arn + desired_count = var.ui_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.ui.arn + container_name = "ui" + container_port = 3000 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + lifecycle { + ignore_changes = [desired_count] + } + + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + ] +} diff --git a/terraform/litellm/aws/iam.tf b/terraform/litellm/aws/iam.tf new file mode 100644 index 00000000000..504e0fe1d63 --- /dev/null +++ b/terraform/litellm/aws/iam.tf @@ -0,0 +1,114 @@ +# ECS task execution role — used by the agent to pull images, write logs, +# and resolve secrets at task start. +data "aws_iam_policy_document" "task_assume" { + statement { + actions = ["sts:AssumeRole"] + principals { + type = "Service" + identifiers = ["ecs-tasks.amazonaws.com"] + } + } +} + +resource "aws_iam_role" "task_execution" { + name = "${local.name}-task-execution" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} + +resource "aws_iam_role_policy_attachment" "task_execution" { + role = aws_iam_role.task_execution.name + policy_arn = "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy" +} + +# User-provided extra secrets may be passed as the bare secret ARN +# ("arn:aws:secretsmanager:...:secret:name-AbCdEf") or the JSON-key form +# ECS supports — fully spelled out as +# "arn:...:secret:name-AbCdEf:jsonKey:versionStage:versionId" with any of +# the trailing parts blank ("...:jsonKey::" being the most common). The IAM +# policy resource must always be the bare ARN, so we split on ':' and keep +# the first 7 components — robust to any combination of empty/non-empty +# version-stage/version-id suffixes that a regex would otherwise have to +# enumerate. +locals { + extra_secret_value_froms = concat( + values(var.gateway_extra_secrets), + values(var.backend_extra_secrets), + ) + + extra_secret_arns = distinct([ + for v in local.extra_secret_value_froms : + join(":", slice(split(":", v), 0, 7)) + ]) +} + +# Execution role can read the managed secrets + any caller-provided extras +# so ECS can resolve them when launching tasks. Image pulls inherit the +# managed AmazonECSTaskExecutionRolePolicy. +data "aws_iam_policy_document" "secrets_access" { + statement { + actions = ["secretsmanager:GetSecretValue"] + resources = concat( + [aws_secretsmanager_secret.master_key.arn], + aws_secretsmanager_secret.license[*].arn, + aws_secretsmanager_secret.ui_password[*].arn, + local.extra_secret_arns, + ) + } +} + +resource "aws_iam_policy" "secrets_access" { + name = "${local.name}-secrets-access" + policy = data.aws_iam_policy_document.secrets_access.json +} + +resource "aws_iam_role_policy_attachment" "task_execution_secrets" { + role = aws_iam_role.task_execution.name + policy_arn = aws_iam_policy.secrets_access.arn +} + +# ---------- Task role ---------- +# +# Assumed by the running container. Gets `rds-db:connect` so the proxy can +# mint IAM-signed Postgres tokens for the app user. Layer additional +# policies here (e.g. Bedrock invoke, S3 read) when the proxy needs them. + +resource "aws_iam_role" "task" { + name = "${local.name}-task" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} + +data "aws_caller_identity" "current" {} + +data "aws_iam_policy_document" "rds_iam_connect" { + statement { + actions = ["rds-db:connect"] + resources = [ + "arn:aws:rds-db:${var.region}:${data.aws_caller_identity.current.account_id}:dbuser:${aws_rds_cluster.this.cluster_resource_id}/${var.db_username}", + ] + } +} + +resource "aws_iam_policy" "rds_iam_connect" { + name = "${local.name}-rds-iam-connect" + policy = data.aws_iam_policy_document.rds_iam_connect.json +} + +resource "aws_iam_role_policy_attachment" "task_rds_iam_connect" { + role = aws_iam_role.task.name + policy_arn = aws_iam_policy.rds_iam_connect.arn +} + +# ---------- UI task role ---------- +# +# The UI is static nginx with no DB, S3, or Secrets Manager dependencies, +# so it deliberately does NOT inherit the shared `task` role's +# rds-db:connect / S3 / extra-secrets policies. Empty policy set — the +# only thing exposed via the task metadata endpoint is an identity that +# can't reach any LiteLLM data-plane resource. The shared +# `task_execution` role still pulls the image and writes logs (its +# credentials aren't surfaced to the container). + +resource "aws_iam_role" "ui_task" { + name = "${local.name}-ui-task" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} diff --git a/terraform/litellm/aws/locals.tf b/terraform/litellm/aws/locals.tf new file mode 100644 index 00000000000..85c3b6eaaad --- /dev/null +++ b/terraform/litellm/aws/locals.tf @@ -0,0 +1,75 @@ +# Gateway path prefixes — mirrored verbatim from gateway/routes/allowlist.py +# and the helm ingress in helm/litellm/templates/ingress.yaml. Anything not in +# this list and not a UI asset path falls through to the backend (management +# API) catch-all rule on the ALB. +# +# ALB listener rules cap path-pattern conditions at 5 values per rule, so we +# chunk this list and emit one rule per chunk. +locals { + # Every resource the stack creates is named `-litellm-` + # (or that with a per-resource suffix). Computed once here so the rest of + # the stack can reference local.name. + name = "${var.tenant}-litellm-${var.env}" + + gateway_path_prefixes = [ + "/v1/chat/*", "/chat/*", + "/v1/completions*", "/completions*", + "/v1/embeddings*", "/embeddings*", + "/v1/moderations*", "/moderations*", + "/v1/audio/*", "/audio/*", + "/v1/images/*", "/images/*", + "/v1/files*", "/files*", + "/v1/batches*", "/batches*", + "/v1/fine_tuning/*", "/fine_tuning/*", + "/v1/fine-tuning/*", "/fine-tuning/*", + "/v1/responses*", "/responses*", + "/v1/threads*", "/threads*", + "/v1/assistants*", "/assistants*", + "/v1/vector_stores*", "/vector_stores*", + "/v1/indexes*", + "/v1/models*", "/models*", + "/openai/*", "/engines/*", + "/v1/messages*", "/messages*", + "/v1/skills/*", "/v1/a2a/*", + "/v1/rerank*", "/v2/rerank*", "/rerank*", + "/v1/ocr*", "/ocr*", + "/v1/rag/*", "/rag/*", + "/v1/video/*", "/v1/videos/*", "/video/*", "/videos/*", + "/v1/search*", "/search*", + "/v1/containers/*", "/containers/*", + "/v1/evals/*", + "/v1/memory/*", + "/queue/chat/*", + "/v1beta/*", + "/interactions/*", + "/anthropic/*", "/azure/*", "/azure_ai/*", "/aws/*", "/bedrock/*", + "/cohere/*", "/gemini/*", "/google/*", + "/vertex_ai/*", "/vertex-ai/*", + "/assemblyai/*", "/eu.assemblyai/*", + "/langfuse/*", "/vllm/*", + "/mistral/*", "/groq/*", "/voyage/*", "/cursor/*", "/milvus/*", + "/openai_passthrough/*", + "/toolset/*", + "/v1/realtime*", "/realtime*", + "/health*", "/metrics", "/test*", + ] + + # Static UI asset prefixes — handled by the UI service, not the backend + # catch-all. /favicon.ico and / are also UI but added as exact rules. + ui_path_prefixes = [ + "/litellm-asset-prefix/*", + "/_next/*", + "/assets/*", + "/ui/*", + ] + + ui_exact_paths = [ + "/", + "/favicon.ico", + "/ui", + ] + + # ALB rules accept ≤ 5 path-pattern values per condition. Chunk the prefix + # list so each chunk becomes one rule. + gateway_path_chunks = chunklist(local.gateway_path_prefixes, 5) +} diff --git a/terraform/litellm/aws/migrations.tf b/terraform/litellm/aws/migrations.tf new file mode 100644 index 00000000000..fc4e2ce0cab --- /dev/null +++ b/terraform/litellm/aws/migrations.tf @@ -0,0 +1,45 @@ +# Task definition for the dedicated litellm-migrations image. Mirrors the +# pre-install/pre-upgrade Helm hook in helm/litellm/templates/migrations-job.yaml. +# +# The image (built from migrations/Dockerfile) ships with +# `ENTRYPOINT ["python3", "/app/run.py"]`. run.py assembles DATABASE_URL from +# the discrete DATABASE_* env vars (IAM auth here) via DatabaseURLSettings, +# then calls ProxyExtrasDBManager.setup_database() — i.e. `prisma migrate +# deploy` with the v2 resolver and P3005/P3009/P3018 recovery. It does NOT +# read CONFIG_FILE_PATH, the master key, or DISABLE_SCHEMA_UPDATE, so we +# don't pass them. +# +# Invoked automatically by `terraform_data.migration` in bootstrap.tf during +# every apply (after the IAM-authed user has been created). The +# `migration_run_command` output is preserved for break-glass manual re-runs. +resource "aws_ecs_task_definition" "migrations" { + family = "${local.name}-migrations" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + # Prisma's Node + Rust engine plus the v2 migration resolver routinely + # peaks well above 1 GiB while applying the schema. 4 GiB gives plenty + # of headroom; CPU stays low because `prisma migrate deploy` is + # single-threaded. + cpu = 512 + memory = 4096 + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([{ + name = "migrations" + image = var.migrations_image + essential = true + + # No entryPoint/command override — the image's ENTRYPOINT runs run.py. + environment = local.shared_env + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.migrations.name + awslogs-region = var.region + awslogs-stream-prefix = "migrations" + } + } + }]) +} diff --git a/terraform/litellm/aws/network.tf b/terraform/litellm/aws/network.tf new file mode 100644 index 00000000000..d5ed49c1b8a --- /dev/null +++ b/terraform/litellm/aws/network.tf @@ -0,0 +1,172 @@ +data "aws_availability_zones" "available" { + state = "available" +} + +resource "aws_vpc" "this" { + cidr_block = var.vpc_cidr + enable_dns_hostnames = true + enable_dns_support = true + + tags = { Name = local.name } +} + +resource "aws_internet_gateway" "this" { + vpc_id = aws_vpc.this.id + tags = { Name = local.name } +} + +# Public subnets (ALB + NAT). One per AZ. +resource "aws_subnet" "public" { + count = length(var.azs) + vpc_id = aws_vpc.this.id + cidr_block = cidrsubnet(var.vpc_cidr, 8, count.index) + availability_zone = var.azs[count.index] + map_public_ip_on_launch = true + + tags = { Name = "${local.name}-public-${var.azs[count.index]}" } +} + +# Private subnets (ECS tasks, RDS, ElastiCache). One per AZ, separate from +# public range. +resource "aws_subnet" "private" { + count = length(var.azs) + vpc_id = aws_vpc.this.id + cidr_block = cidrsubnet(var.vpc_cidr, 8, count.index + 10) + availability_zone = var.azs[count.index] + + tags = { Name = "${local.name}-private-${var.azs[count.index]}" } +} + +resource "aws_eip" "nat" { + domain = "vpc" + tags = { Name = "${local.name}-nat" } + + depends_on = [aws_internet_gateway.this] +} + +# Single NAT gateway in the first public subnet. For HA, replicate per AZ — +# adds ~$30/mo per gateway, so off by default for a baseline deployment. +resource "aws_nat_gateway" "this" { + allocation_id = aws_eip.nat.id + subnet_id = aws_subnet.public[0].id + + tags = { Name = local.name } + + depends_on = [aws_internet_gateway.this] +} + +resource "aws_route_table" "public" { + vpc_id = aws_vpc.this.id + + route { + cidr_block = "0.0.0.0/0" + gateway_id = aws_internet_gateway.this.id + } + + tags = { Name = "${local.name}-public" } +} + +resource "aws_route_table_association" "public" { + count = length(var.azs) + subnet_id = aws_subnet.public[count.index].id + route_table_id = aws_route_table.public.id +} + +resource "aws_route_table" "private" { + vpc_id = aws_vpc.this.id + + route { + cidr_block = "0.0.0.0/0" + nat_gateway_id = aws_nat_gateway.this.id + } + + tags = { Name = "${local.name}-private" } +} + +resource "aws_route_table_association" "private" { + count = length(var.azs) + subnet_id = aws_subnet.private[count.index].id + route_table_id = aws_route_table.private.id +} + +# ---------- Security groups ---------- + +resource "aws_security_group" "alb" { + name = "${local.name}-alb" + description = "Inbound HTTP/HTTPS to the LiteLLM ALB." + vpc_id = aws_vpc.this.id + + ingress { + description = "HTTP from anywhere" + from_port = 80 + to_port = 80 + protocol = "tcp" + cidr_blocks = ["0.0.0.0/0"] + } + + ingress { + description = "HTTPS from anywhere" + from_port = 443 + to_port = 443 + protocol = "tcp" + cidr_blocks = ["0.0.0.0/0"] + } + + egress { + description = "All egress" + from_port = 0 + to_port = 0 + protocol = "-1" + cidr_blocks = ["0.0.0.0/0"] + } +} + +resource "aws_security_group" "tasks" { + name = "${local.name}-tasks" + description = "ECS tasks (gateway/backend/ui)." + vpc_id = aws_vpc.this.id + + ingress { + description = "ALB to tasks" + from_port = 0 + to_port = 65535 + protocol = "tcp" + security_groups = [aws_security_group.alb.id] + } + + egress { + description = "All egress (LLM providers, RDS, Redis)" + from_port = 0 + to_port = 0 + protocol = "-1" + cidr_blocks = ["0.0.0.0/0"] + } +} + +resource "aws_security_group" "rds" { + name = "${local.name}-rds" + description = "RDS Postgres - tasks only." + vpc_id = aws_vpc.this.id + + ingress { + description = "Postgres from ECS tasks" + from_port = 5432 + to_port = 5432 + protocol = "tcp" + security_groups = [aws_security_group.tasks.id] + } +} + +resource "aws_security_group" "redis" { + name = "${local.name}-redis" + description = "ElastiCache Redis - tasks only." + vpc_id = aws_vpc.this.id + + ingress { + description = "Redis from ECS tasks" + from_port = 6379 + to_port = 6379 + protocol = "tcp" + security_groups = [aws_security_group.tasks.id] + } +} diff --git a/terraform/litellm/aws/outputs.tf b/terraform/litellm/aws/outputs.tf new file mode 100644 index 00000000000..9c36b1a7e0f --- /dev/null +++ b/terraform/litellm/aws/outputs.tf @@ -0,0 +1,72 @@ +output "alb_dns_name" { + description = "Public DNS name of the LiteLLM ALB." + value = aws_lb.this.dns_name +} + +output "alb_url" { + description = "Proxy URL. Switches scheme based on whether acm_certificate_arn is set; the underlying DNS name is the ALB. The dashboard is served at /, the API at /v1/*." + value = "${local.tls_enabled ? "https" : "http"}://${aws_lb.this.dns_name}" +} + +output "ecs_cluster" { + description = "ECS cluster name." + value = aws_ecs_cluster.this.name +} + +output "aurora_writer_endpoint" { + description = "Aurora writer endpoint (cluster endpoint). Used by gateway/backend as DATABASE_HOST." + value = aws_rds_cluster.this.endpoint +} + +output "aurora_reader_endpoint" { + description = "Aurora reader endpoint. Used by gateway/backend as DATABASE_HOST_READ_REPLICA." + value = aws_rds_cluster.this.reader_endpoint +} + +output "redis_endpoint" { + description = "ElastiCache Redis primary endpoint (TLS, transit_encryption_enabled = true)." + value = "${aws_elasticache_replication_group.this.primary_endpoint_address}:${aws_elasticache_replication_group.this.port}" +} + +output "s3_bucket" { + description = "S3 bucket name. Exposed to gateway + backend as S3_BUCKET_NAME / S3_REGION_NAME. Reference from proxy_config via `os.environ/S3_BUCKET_NAME`." + value = aws_s3_bucket.this.bucket +} + +output "master_key_secret_arn" { + description = "Secrets Manager ARN holding LITELLM_MASTER_KEY. Fetch with `aws secretsmanager get-secret-value --secret-id `." + value = aws_secretsmanager_secret.master_key.arn +} + +output "db_master_password_secret_arn" { + description = "Secrets Manager ARN holding the Aurora master credentials (bootstrap-only). Used to create the IAM-authed application user." + value = aws_secretsmanager_secret.db_master_password.arn +} + +# Pre-baked SQL to run once as the master user, creating the IAM-authed +# application user that gateway/backend/migration tasks will authenticate as. +output "db_bootstrap_sql" { + description = "Run this once as the master DB user (after the first apply) to create the IAM-authed app user." + value = <<-SQL + CREATE USER ${var.db_username}; + GRANT rds_iam TO ${var.db_username}; + GRANT ALL PRIVILEGES ON DATABASE ${var.db_name} TO ${var.db_username}; + GRANT ALL ON SCHEMA public TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO ${var.db_username}; + SQL +} + +# Pre-baked command for running the one-off migration task. ECS run-task +# needs the subnet + SG IDs at call time, so we render the full command. +output "migration_run_command" { + description = "Shell command that runs the one-off prisma migration task against Aurora. Run this once, after the bootstrap SQL above, before sending traffic." + value = format( + "aws ecs run-task --cluster %s --launch-type FARGATE --task-definition %s --network-configuration 'awsvpcConfiguration={subnets=[%s],securityGroups=[%s],assignPublicIp=DISABLED}' --region %s", + aws_ecs_cluster.this.name, + aws_ecs_task_definition.migrations.arn, + join(",", aws_subnet.private[*].id), + aws_security_group.tasks.id, + var.region, + ) +} diff --git a/terraform/litellm/aws/providers.tf b/terraform/litellm/aws/providers.tf new file mode 100644 index 00000000000..5e7d506c23f --- /dev/null +++ b/terraform/litellm/aws/providers.tf @@ -0,0 +1,13 @@ +provider "aws" { + region = var.region + + default_tags { + tags = merge( + { + "litellm:stack" = local.name + "managed-by" = "terraform" + }, + var.tags, + ) + } +} diff --git a/terraform/litellm/aws/rds.tf b/terraform/litellm/aws/rds.tf new file mode 100644 index 00000000000..8e3b70a8d62 --- /dev/null +++ b/terraform/litellm/aws/rds.tf @@ -0,0 +1,85 @@ +# Aurora Postgres cluster with one writer + one reader instance, IAM +# database authentication enabled. +# +# Important: enabling IAM auth on the cluster does not by itself grant any +# Postgres user the ability to log in with an IAM token. After the first +# apply, connect as the master user (password lives in Secrets Manager — +# see `master_user_secret_arn` in outputs) and run, once: +# +# CREATE USER {var.db_username}; +# GRANT rds_iam TO {var.db_username}; +# GRANT ALL PRIVILEGES ON DATABASE {var.db_name} TO {var.db_username}; +# GRANT ALL ON SCHEMA public TO {var.db_username}; +# +# After that, the gateway/backend/migration tasks (which authenticate as +# `{var.db_username}` via IAM-signed tokens) can connect. The master user +# itself is a superuser and Postgres refuses to grant `rds_iam` to +# superusers — keep it for break-glass only. + +resource "aws_db_subnet_group" "this" { + name = "${local.name}-db" + subnet_ids = aws_subnet.private[*].id +} + +resource "aws_rds_cluster_parameter_group" "this" { + name = "${local.name}-cluster-pg" + family = "aurora-postgresql${split(".", var.db_engine_version)[0]}" + description = "LiteLLM Aurora Postgres cluster parameters." +} + +resource "aws_rds_cluster" "this" { + cluster_identifier = local.name + engine = "aurora-postgresql" + engine_mode = "provisioned" + engine_version = var.db_engine_version + database_name = var.db_name + master_username = var.db_master_username + master_password = random_password.db_master_password.result + db_subnet_group_name = aws_db_subnet_group.this.name + vpc_security_group_ids = [aws_security_group.rds.id] + db_cluster_parameter_group_name = aws_rds_cluster_parameter_group.this.name + + iam_database_authentication_enabled = true + storage_encrypted = true + apply_immediately = true + + # Final-snapshot guard. With the safe default (skip_final_snapshot = false), + # `terraform destroy` takes a snapshot named `-final-` + # before dropping the cluster. The short SHA disambiguates repeated + # destroy/recreate cycles so each snapshot has a unique name. + skip_final_snapshot = var.skip_final_snapshot + final_snapshot_identifier = var.skip_final_snapshot ? null : "${local.name}-final-${substr(md5(local.name), 0, 8)}" + + backup_retention_period = 7 + preferred_backup_window = "07:00-09:00" +} + +resource "aws_rds_cluster_instance" "writer" { + identifier = "${local.name}-writer" + cluster_identifier = aws_rds_cluster.this.id + instance_class = var.db_instance_class + engine = aws_rds_cluster.this.engine + engine_version = aws_rds_cluster.this.engine_version + + publicly_accessible = false + performance_insights_enabled = true + + # Promotion tier 0 — first in line during failover, so this instance stays + # the writer unless it goes unhealthy. + promotion_tier = 0 +} + +resource "aws_rds_cluster_instance" "reader" { + identifier = "${local.name}-reader" + cluster_identifier = aws_rds_cluster.this.id + instance_class = var.db_instance_class + engine = aws_rds_cluster.this.engine + engine_version = aws_rds_cluster.this.engine_version + + publicly_accessible = false + performance_insights_enabled = true + + # Higher promotion tier — won't be picked as writer during a failover + # unless the writer instance itself is gone. + promotion_tier = 15 +} diff --git a/terraform/litellm/aws/redis.tf b/terraform/litellm/aws/redis.tf new file mode 100644 index 00000000000..2a6fab2d89f --- /dev/null +++ b/terraform/litellm/aws/redis.tf @@ -0,0 +1,33 @@ +resource "aws_elasticache_subnet_group" "this" { + name = "${local.name}-redis" + subnet_ids = aws_subnet.private[*].id +} + +# Replication group (not aws_elasticache_cluster, which is the +# Memcached / single-node Redis resource and can't be upgraded in-place +# to HA). With redis_num_replicas >= 1 we get automatic_failover_enabled +# + multi_az_enabled; at_rest_encryption_enabled and +# transit_encryption_enabled are on unconditionally so Redis traffic is +# TLS-protected — the proxy connects via the rediss:// scheme thanks to +# REDIS_SSL=true in the shared task env (see ecs.tf). +resource "aws_elasticache_replication_group" "this" { + replication_group_id = "${local.name}-redis" + description = "LiteLLM ElastiCache Redis" + + engine = "redis" + engine_version = "7.1" + node_type = var.redis_node_type + num_cache_clusters = 1 + var.redis_num_replicas + parameter_group_name = "default.redis7" + port = 6379 + + subnet_group_name = aws_elasticache_subnet_group.this.name + security_group_ids = [aws_security_group.redis.id] + + automatic_failover_enabled = var.redis_num_replicas >= 1 + multi_az_enabled = var.redis_num_replicas >= 1 + at_rest_encryption_enabled = true + transit_encryption_enabled = true + + apply_immediately = true +} diff --git a/terraform/litellm/aws/s3.tf b/terraform/litellm/aws/s3.tf new file mode 100644 index 00000000000..375bc73bb71 --- /dev/null +++ b/terraform/litellm/aws/s3.tf @@ -0,0 +1,80 @@ +# General-purpose S3 bucket for the proxy. LiteLLM uses S3 for: +# - Cache backend (cache_params.s3_bucket_name in proxy_config) +# - Request log archival (S3_REQUEST_LOGS_BUCKET_NAME) +# - /v1/files endpoint passthrough storage +# +# The bucket name + region are exposed to gateway + backend as S3_BUCKET_NAME +# / S3_REGION_NAME so proxy_config can reference them via +# `os.environ/S3_BUCKET_NAME`. The task role is scoped to this bucket only. + +resource "random_id" "s3_suffix" { + byte_length = 4 +} + +resource "aws_s3_bucket" "this" { + bucket = "${local.name}-${random_id.s3_suffix.hex}" + + # Default false → `terraform destroy` refuses on a non-empty bucket so + # cached responses, archived request logs, and /v1/files storage stay put. + # Flip to true only for ephemeral / CI stacks (`var.s3_force_destroy`). + force_destroy = var.s3_force_destroy +} + +resource "aws_s3_bucket_versioning" "this" { + bucket = aws_s3_bucket.this.id + versioning_configuration { + status = "Enabled" + } +} + +resource "aws_s3_bucket_server_side_encryption_configuration" "this" { + bucket = aws_s3_bucket.this.id + + rule { + apply_server_side_encryption_by_default { + sse_algorithm = "AES256" + } + } +} + +resource "aws_s3_bucket_public_access_block" "this" { + bucket = aws_s3_bucket.this.id + + block_public_acls = true + block_public_policy = true + ignore_public_acls = true + restrict_public_buckets = true +} + +# Task role gains object-level read/write on this bucket. Bucket-level perms +# (list/location) are also scoped to this bucket only. +data "aws_iam_policy_document" "s3_access" { + statement { + actions = [ + "s3:ListBucket", + "s3:GetBucketLocation", + ] + resources = [aws_s3_bucket.this.arn] + } + + statement { + actions = [ + "s3:GetObject", + "s3:PutObject", + "s3:DeleteObject", + "s3:AbortMultipartUpload", + "s3:ListMultipartUploadParts", + ] + resources = ["${aws_s3_bucket.this.arn}/*"] + } +} + +resource "aws_iam_policy" "s3_access" { + name = "${local.name}-s3-access" + policy = data.aws_iam_policy_document.s3_access.json +} + +resource "aws_iam_role_policy_attachment" "task_s3_access" { + role = aws_iam_role.task.name + policy_arn = aws_iam_policy.s3_access.arn +} diff --git a/terraform/litellm/aws/secrets.tf b/terraform/litellm/aws/secrets.tf new file mode 100644 index 00000000000..dd13fdc1239 --- /dev/null +++ b/terraform/litellm/aws/secrets.tf @@ -0,0 +1,86 @@ +resource "random_password" "master_key" { + length = 48 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# Master DB password — used once to bootstrap the IAM-authed application +# user (see rds.tf header). Runtime services authenticate via IAM tokens +# and never read this secret. +resource "random_password" "db_master_password" { + length = 32 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# LITELLM_MASTER_KEY — must begin with `sk-` per the proxy's validator. +resource "aws_secretsmanager_secret" "master_key" { + name = "${local.name}-master-key" + description = "LITELLM_MASTER_KEY for gateway + backend." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "master_key" { + secret_id = aws_secretsmanager_secret.master_key.id + # When the operator passes litellm_master_key, use it verbatim. Otherwise + # fall back to the auto-generated `sk-…` value (trial / OSS path). + secret_string = coalesce(var.litellm_master_key, "sk-${random_password.master_key.result}") +} + +# LITELLM_LICENSE — only created when the operator supplies one. The +# task-execution role gets GetSecretValue via iam.tf, and gateway + backend +# pick the env var up through shared_secrets in ecs.tf. +resource "aws_secretsmanager_secret" "license" { + count = var.litellm_license == "" ? 0 : 1 + + name = "${local.name}-license" + description = "LITELLM_LICENSE for gateway + backend." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = aws_secretsmanager_secret.license[0].id + secret_string = var.litellm_license +} + +# UI_PASSWORD — backend-only. Same pattern as license: only created when +# the operator supplies one. The execution role gets GetSecretValue via +# iam.tf, and the backend task picks the env var up through +# backend_managed_secrets in ecs.tf. +resource "aws_secretsmanager_secret" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + name = "${local.name}-ui-password" + description = "UI_PASSWORD for the backend (UI admin login)." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = aws_secretsmanager_secret.ui_password[0].id + secret_string = var.ui_password +} + +resource "aws_secretsmanager_secret" "db_master_password" { + name = "${local.name}-db-master-password" + description = "Aurora master-user password - bootstrap only. Runtime auth is IAM-token." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "db_master_password" { + secret_id = aws_secretsmanager_secret.db_master_password.id + secret_string = jsonencode({ + username = var.db_master_username + password = random_password.db_master_password.result + host = aws_rds_cluster.this.endpoint + port = aws_rds_cluster.this.port + dbname = var.db_name + }) +} diff --git a/terraform/litellm/aws/terraform.tfvars.example b/terraform/litellm/aws/terraform.tfvars.example new file mode 100644 index 00000000000..2be573949ef --- /dev/null +++ b/terraform/litellm/aws/terraform.tfvars.example @@ -0,0 +1,88 @@ +region = "us-west-2" +azs = ["us-west-2a", "us-west-2b"] + +# Resource naming: every AWS resource the stack creates is named +# `${tenant}-litellm-${env}` (or that plus a per-resource suffix). E.g. +# tenant="acme" + env="stage" → ALB `acme-litellm-stage`, ECS service +# `acme-litellm-stage-gateway`, etc. +tenant = "acme" +env = "stage" + +# Tenant-supplied secrets. Prefer TF_VAR_litellm_master_key / +# TF_VAR_litellm_license / TF_VAR_ui_password env vars so the values don't +# end up in a committed tfvars file. All three are optional — when +# omitted the stack auto-generates a master key, runs without a license, +# and falls back to LITELLM_MASTER_KEY for UI login. +# litellm_master_key = "sk-..." +# litellm_license = "lic-..." +# ui_password = "..." + +# TLS: provide an ACM cert for production. Without one, plan fails unless +# allow_plaintext_alb = true is set explicitly (trial/dev only). +# acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..." +# allow_plaintext_alb = true + +# Storage retention: false (default) makes `terraform destroy` refuse on a +# non-empty bucket. Flip to true only for ephemeral / CI stacks. +# s3_force_destroy = false + +# Component images. Defaults pin all four to the same GHCR release tag — +# bump them together when bumping LiteLLM. Override here to pull from a +# private registry or to mix-and-match versions. +# gateway_image = "ghcr.io/berriai/litellm-gateway:1.86.0-dev" +# backend_image = "ghcr.io/berriai/litellm-backend:1.86.0-dev" +# ui_image = "ghcr.io/berriai/litellm-ui:1.86.0-dev" +# migrations_image = "ghcr.io/berriai/litellm-migrations:1.86.0-dev" + +# Per-task sizing for the gateway. Defaults are 1 vCPU / 4 GiB / 1 worker. +# uvicorn rule of thumb for CPU-bound work is (2 * vCPU) + 1 workers. +# gateway_cpu = 1024 # 1024 = 1 vCPU +# gateway_memory = 4096 # MiB +# gateway_num_workers = 1 + +# ---------- proxy_config (mirrors helm gateway.config.proxy_config) ---------- +# proxy_config = { +# model_list = [ +# { +# model_name = "gpt-4o" +# litellm_params = { +# model = "openai/gpt-4o" +# api_key = "os.environ/OPENAI_API_KEY" +# } +# }, +# { +# model_name = "claude-sonnet-4-6" +# litellm_params = { +# model = "anthropic/claude-sonnet-4-6" +# api_key = "os.environ/ANTHROPIC_API_KEY" +# } +# }, +# ] +# general_settings = { +# master_key = "os.environ/LITELLM_MASTER_KEY" +# database_url = "os.environ/DATABASE_URL" +# } +# } + +# ---------- Extra env / secrets ---------- +# Plain-text env vars (non-sensitive). Land directly in the ECS task def. +# gateway_extra_env = { +# LANGFUSE_HOST = "https://us.cloud.langfuse.com" +# } + +# Backend env vars commonly tuned in prod: SSO redirect, docs branding, +# UI admin username. UI_PASSWORD is its own first-class var (see top). +# backend_extra_env = { +# AUTO_REDIRECT_UI_LOGIN_TO_SSO = "true" +# DOCS_TITLE = "Acme LiteLLM" +# UI_USERNAME = "admin" +# } + +# Provider API keys, sourced from existing Secrets Manager entries. The +# execution role auto-gains GetSecretValue on each ARN listed here. The +# values you reference above as `os.environ/OPENAI_API_KEY` must appear +# here. Same shape works for backend_extra_secrets. +# gateway_extra_secrets = { +# OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" +# ANTHROPIC_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:anthropic-api-key-GhIjKl" +# } diff --git a/terraform/litellm/aws/variables.tf b/terraform/litellm/aws/variables.tf new file mode 100644 index 00000000000..946cd7ebbf3 --- /dev/null +++ b/terraform/litellm/aws/variables.tf @@ -0,0 +1,458 @@ +variable "region" { + description = "AWS region to deploy into." + type = string +} + +variable "tenant" { + description = "Tenant slug — used as the prefix for every AWS resource the stack creates. Combined with var.env to form `-litellm-` (e.g. `acme-litellm-stage`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,20}$", var.tenant)) + error_message = "tenant must be 1-21 chars, lower-kebab-case, starting with a letter." + } +} + +variable "env" { + description = "Environment suffix appended to every resource name (e.g. `stage`, `prod`, `dev`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,8}$", var.env)) + error_message = "env must be 1-9 chars, lower-kebab-case, starting with a letter." + } +} + +variable "tags" { + description = "Additional tags merged into the provider default_tags." + type = map(string) + default = {} +} + +# ---------- Tenant-supplied secrets ---------- +# +# Both default to "" so the stack stays usable for trial / OSS deploys. +# Set via TF_VAR_litellm_master_key / TF_VAR_litellm_license to keep the +# values out of state files committed to a VCS. + +variable "litellm_master_key" { + description = <<-EOT + Pre-existing LITELLM_MASTER_KEY (must begin with `sk-`). When set, this + value is written to the master-key Secrets Manager entry. When empty, + the stack auto-generates a random `sk-…` key (preserving today's + trial-deploy behavior). + EOT + type = string + default = "" + sensitive = true +} + +variable "litellm_license" { + description = <<-EOT + LiteLLM enterprise license string. When set, the stack creates a + `-litellm--license` Secrets Manager entry, grants the + task-execution role GetSecretValue on it, and exposes its value to + gateway + backend as `LITELLM_LICENSE`. Leave empty for OSS-only deploys. + EOT + type = string + default = "" + sensitive = true +} + +variable "ui_password" { + description = <<-EOT + UI admin password. When set, the stack creates a + `-litellm--ui-password` Secrets Manager entry, grants the + task-execution role GetSecretValue on it, and exposes its value to the + backend as `UI_PASSWORD`. Pair with `backend_extra_env.UI_USERNAME` to + set the matching username. Leave empty to skip — the proxy then falls + back to the LITELLM_MASTER_KEY for UI login. + EOT + type = string + default = "" + sensitive = true +} + +# ---------- Networking ---------- + +variable "vpc_cidr" { + description = "CIDR block for the VPC." + type = string + default = "10.40.0.0/16" +} + +variable "azs" { + description = "Availability zones to spread subnets across. At least 2 required for RDS and ALB." + type = list(string) + validation { + condition = length(var.azs) >= 2 + error_message = "Provide at least 2 availability zones." + } +} + +# ---------- Component images ---------- +# +# Defaults pin the four componentized images at the same release tag on +# GHCR. Override on a per-component basis in tfvars when bumping; bump them +# together when bumping the LiteLLM release. + +variable "gateway_image" { + description = "Container image for the gateway (data plane, port 4000). Tag must match a tag actually published to GHCR — the split images use the `v`-prefixed semver convention." + type = string + default = "ghcr.io/berriai/litellm-gateway:v1.86.0-dev" +} + +variable "backend_image" { + description = "Container image for the backend (management API, port 4001)." + type = string + default = "ghcr.io/berriai/litellm-backend:v1.86.0-dev" +} + +variable "ui_image" { + description = "Container image for the UI (nginx static export, port 3000)." + type = string + default = "ghcr.io/berriai/litellm-ui:v1.86.0-dev" +} + +variable "migrations_image" { + description = <<-EOT + Container image for the one-off prisma migration task. Built from + `migrations/Dockerfile` — slim image whose ENTRYPOINT runs + `python3 /app/run.py` (assembles DATABASE_URL from DATABASE_* env vars + via DatabaseURLSettings, then runs `prisma migrate deploy`). Should track + the same release tag as gateway/backend/ui. + EOT + type = string + default = "ghcr.io/berriai/litellm-migrations:v1.86.0-dev" +} + +# ---------- Service sizing ---------- + +variable "gateway_cpu" { + description = "Fargate CPU units for the gateway task (1024 = 1 vCPU)." + type = number + default = 1024 +} + +variable "gateway_memory" { + description = "Fargate memory (MiB) for the gateway task." + type = number + default = 4096 +} + +variable "gateway_desired_count" { + description = "Desired number of gateway tasks." + type = number + default = 2 +} + +variable "gateway_num_workers" { + description = "uvicorn worker processes per gateway task (passed as --workers). Size relative to gateway_cpu — uvicorn recommends ~(2 × vCPU) + 1 for CPU-bound work." + type = number + default = 1 + + validation { + condition = var.gateway_num_workers >= 1 + error_message = "gateway_num_workers must be >= 1." + } +} + +variable "backend_cpu" { + description = "Fargate CPU units for the backend task (1024 = 1 vCPU)." + type = number + default = 1024 +} + +variable "backend_memory" { + description = "Fargate memory (MiB) for the backend task. The proxy_server import chain alone needs >1 GiB; 4 GiB matches gateway." + type = number + default = 4096 +} + +variable "backend_desired_count" { + description = "Desired number of backend tasks." + type = number + default = 1 +} + +variable "ui_cpu" { + description = "Fargate CPU units for the UI task." + type = number + default = 256 +} + +variable "ui_memory" { + description = "Fargate memory (MiB) for the UI task." + type = number + default = 512 +} + +variable "ui_desired_count" { + description = "Desired number of UI tasks." + type = number + default = 1 +} + +# ---------- Autoscaling ---------- +# Defaults mirror helm/litellm/values.yaml HPAs. The "*_desired_count" vars +# above seed the initial task count; once autoscaling is enabled, the service's +# desired_count is left to Application Auto Scaling (ecs.tf ignores future +# changes to it). + +variable "gateway_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the gateway service." + type = bool + default = true +} + +variable "gateway_min_capacity" { + description = "Minimum gateway task count under autoscaling." + type = number + default = 1 +} + +variable "gateway_max_capacity" { + description = "Maximum gateway task count under autoscaling." + type = number + default = 10 +} + +variable "gateway_cpu_target" { + description = "Target average CPU utilization (%) for the gateway autoscaling policy." + type = number + default = 70 +} + +variable "gateway_memory_target" { + description = "Target average memory utilization (%) for the gateway autoscaling policy. Set 0 to skip the memory policy and scale on CPU only." + type = number + default = 80 +} + +variable "backend_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the backend service." + type = bool + default = true +} + +variable "backend_min_capacity" { + description = "Minimum backend task count under autoscaling." + type = number + default = 1 +} + +variable "backend_max_capacity" { + description = "Maximum backend task count under autoscaling." + type = number + default = 4 +} + +variable "backend_cpu_target" { + description = "Target average CPU utilization (%) for the backend autoscaling policy." + type = number + default = 70 +} + +variable "ui_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the UI service. Off by default — UI is a static nginx export and one task is usually enough." + type = bool + default = false +} + +variable "ui_min_capacity" { + description = "Minimum UI task count under autoscaling." + type = number + default = 1 +} + +variable "ui_max_capacity" { + description = "Maximum UI task count under autoscaling." + type = number + default = 3 +} + +variable "ui_cpu_target" { + description = "Target average CPU utilization (%) for the UI autoscaling policy." + type = number + default = 80 +} + +# ---------- RDS ---------- + +variable "db_instance_class" { + description = "Aurora instance class for both writer and reader." + type = string + default = "db.r6g.large" +} + +variable "db_engine_version" { + description = "Aurora Postgres engine version. Major version drives the parameter-group family (aurora-postgresql)." + type = string + default = "16.4" +} + +variable "db_name" { + description = "Initial database name created on the Aurora cluster." + type = string + default = "litellm" +} + +variable "db_master_username" { + description = "Aurora master (superuser) username — used only to bootstrap the IAM-authed application user." + type = string + default = "postgres" +} + +variable "db_username" { + description = "IAM-authed Postgres user the proxy connects as. Must be CREATEd in the cluster and granted the rds_iam role — see terraform/litellm/aws/README.md." + type = string + default = "litellm_app" +} + +# ---------- Redis ---------- + +variable "redis_node_type" { + description = "ElastiCache node type." + type = string + default = "cache.t4g.small" +} + +variable "redis_num_replicas" { + description = "Number of read replicas in the Redis replication group. The primary plus this many replicas form the cluster — set to 0 for a single-node dev deployment, 1+ for HA. multi_az_enabled and automatic_failover_enabled require >= 1." + type = number + default = 1 + + validation { + condition = var.redis_num_replicas >= 0 + error_message = "redis_num_replicas must be >= 0." + } +} + +# ---------- TLS ---------- + +variable "acm_certificate_arn" { + description = <<-EOT + ACM certificate ARN for the ALB's HTTPS listener. When set, the stack + provisions a 443 listener carrying the same path-routing rules as the 80 + listener, and the 80 listener is rewritten to redirect HTTP→HTTPS. Leave + empty ("") to disable TLS (must combine with `allow_plaintext_alb = true` + for the plan to succeed — see README.md "TLS"). + EOT + type = string + default = "" +} + +variable "allow_plaintext_alb" { + description = <<-EOT + Opt into HTTP-only mode on the ALB (port 80, no TLS). Default false: + `terraform plan` fails when `acm_certificate_arn = ""` so the operator + must either provide an ACM cert or consciously opt out. Intended for + short-lived trial / dev stacks only. + EOT + type = bool + default = false +} + +# ---------- RDS ---------- + +variable "skip_final_snapshot" { + description = "Skip the Aurora final snapshot on `terraform destroy`. Default false — destroying the cluster takes a snapshot first so data is recoverable. Set true only for ephemeral / CI environments where you accept permanent data loss on destroy." + type = bool + default = false +} + +variable "s3_force_destroy" { + description = <<-EOT + Allow `terraform destroy` to delete the S3 bucket even when it still + contains objects (request log archives, /v1/files storage, S3 cache + backend). Default false — destroying a non-empty bucket fails, acting + as a tripwire against accidental data loss. Set true only for + ephemeral / CI environments. Mirrors the safety posture of + `skip_final_snapshot` on Aurora. + EOT + type = bool + default = false +} + +# ---------- Extra env ---------- + +variable "gateway_extra_env" { + description = <<-EOT + Additional plain-text env vars for the gateway container. Use this for + non-sensitive config (LANGFUSE_HOST, custom feature flags, …). For API + keys, use gateway_extra_secrets instead. + EOT + type = map(string) + default = {} +} + +variable "backend_extra_env" { + description = "Additional plain-text env vars for the backend container." + type = map(string) + default = {} +} + +variable "gateway_extra_secrets" { + description = <<-EOT + Extra env vars sourced from AWS Secrets Manager. Map of env-var name to + Secrets Manager ARN. Pass the bare secret ARN to inject the whole secret + string as the env var value, or append ":::" to extract a single + JSON field (ECS docs). + + Example for OPENAI_API_KEY: + gateway_extra_secrets = { + OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" + } + + The stack's task execution role automatically gains GetSecretValue on every + ARN referenced here (suffix-stripped). + EOT + type = map(string) + default = {} +} + +variable "backend_extra_secrets" { + description = "Same shape as gateway_extra_secrets, but layered onto the backend container." + type = map(string) + default = {} +} + +variable "proxy_config" { + description = <<-EOT + LiteLLM proxy config (the contents of config.yaml). Mirrors the helm + chart's `gateway.config.proxy_config` value. Passed to gateway, backend, + and the migration task as a base64-encoded env var and decoded to + /tmp/litellm-config.yaml at container start; CONFIG_FILE_PATH is set + automatically. + + Example: + proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + ui_username = "admin" + } + } + + Leave empty ({}) to skip mounting a config — the proxy then runs with + defaults. Use the "os.environ/" syntax in the YAML to reference + env vars provided by *_extra_env or *_extra_secrets. + EOT + type = any + default = {} +} + +variable "log_retention_days" { + description = "CloudWatch log retention for the three services." + type = number + default = 30 +} diff --git a/terraform/litellm/aws/versions.tf b/terraform/litellm/aws/versions.tf new file mode 100644 index 00000000000..73b88e91dce --- /dev/null +++ b/terraform/litellm/aws/versions.tf @@ -0,0 +1,14 @@ +terraform { + required_version = ">= 1.6.0" + + required_providers { + aws = { + source = "hashicorp/aws" + version = "~> 5.60" + } + random = { + source = "hashicorp/random" + version = "~> 3.6" + } + } +} diff --git a/terraform/litellm/gcp/.terraform.lock.hcl b/terraform/litellm/gcp/.terraform.lock.hcl new file mode 100644 index 00000000000..013391b53d4 --- /dev/null +++ b/terraform/litellm/gcp/.terraform.lock.hcl @@ -0,0 +1,62 @@ +# This file is maintained automatically by "terraform init". +# Manual edits may be lost in future updates. + +provider "registry.terraform.io/hashicorp/google" { + version = "6.50.0" + constraints = "~> 6.10" + hashes = [ + "h1:79CwMTsp3Ud1nOl5hFS5mxQHyT0fGVye7pqpU0PPlHI=", + "zh:1f3513fcfcbf7ca53d667a168c5067a4dd91a4d4cccd19743e248ff31065503c", + "zh:3da7db8fc2c51a77dd958ea8baaa05c29cd7f829bd8941c26e2ea9cb3aadc1e5", + "zh:3e09ac3f6ca8111cbb659d38c251771829f4347ab159a12db195e211c76068bb", + "zh:7bb9e41c568df15ccf1a8946037355eefb4dfb4e35e3b190808bb7c4abae547d", + "zh:81e5d78bdec7778e6d67b5c3544777505db40a826b6eb5abe9b86d4ba396866b", + "zh:8d309d020fb321525883f5c4ea864df3d5942b6087f6656d6d8b3a1377f340fc", + "zh:93e112559655ab95a523193158f4a4ac0f2bfed7eeaa712010b85ebb551d5071", + "zh:d3efe589ffd625b300cef5917c4629513f77e3a7b111c9df65075f76a46a63c7", + "zh:d4a4d672bbef756a870d8f32b35925f8ce2ef4f6bbd5b71a3cb764f1b6c85421", + "zh:e13a86bca299ba8a118e80d5f84fbdd708fe600ecdceea1a13d4919c068379fe", + "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c", + "zh:fec30c095647b583a246c39d557704947195a1b7d41f81e369ba377d997faef6", + ] +} + +provider "registry.terraform.io/hashicorp/google-beta" { + version = "6.50.0" + constraints = "~> 6.10" + hashes = [ + "h1:P2GiUJM1frlPtBViwKn1A9V2dVBdGuWcX80w9TdH8ZE=", + "zh:18b442bd0a05321d39dda1e9e3f1bdede4e61bc2ac62cc7a67037a3864f75101", + "zh:2e387c51455862828bec923a3ec81abf63a4d998da470cf00e09003bda53d668", + "zh:3942e708fa84ebe54996086f4b1398cb747fe19cbcd0be07ace528291fb35dee", + "zh:496287dd48b34ae6197cb1f887abeafd07c33f389dbe431bb01e24846754cfdd", + "zh:6eca885419969ce5c2a706f34dce1f10bde9774757675f2d8a92d12e5a1be390", + "zh:710dbef826c3fe7f76f844dae47937e8e4c1279dd9205ec4610be04cf3327244", + "zh:777ebf44b24bfc7bdbf770dc089f1a72f143b4718fdedb8c6bd75983115a1ec2", + "zh:9c8703bba37b8c7ad857efc3513392c5a096c519397c1cb822d7612f38e4262f", + "zh:c4f1d3a73de2702277c99d5348ad6d374705bcfdd367ad964ff4cfd2cf06c281", + "zh:eca8df11af3f5a948492d5b8b5d01b4ec705aad10bc30ec1524205508ae28393", + "zh:f41e7fd5f2628e8fd6b8ea136366923858f54428d1729898925469b862c275c2", + "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c", + ] +} + +provider "registry.terraform.io/hashicorp/random" { + version = "3.8.1" + constraints = "~> 3.6" + hashes = [ + "h1:u8AKlWVDTH5r9YLSeswoVEjiY72Rt4/ch7U+61ZDkiQ=", + "zh:08dd03b918c7b55713026037c5400c48af5b9f468f483463321bd18e17b907b4", + "zh:0eee654a5542dc1d41920bbf2419032d6f0d5625b03bd81339e5b33394a3e0ae", + "zh:229665ddf060aa0ed315597908483eee5b818a17d09b6417a0f52fd9405c4f57", + "zh:2469d2e48f28076254a2a3fc327f184914566d9e40c5780b8d96ebf7205f8bc0", + "zh:37d7eb334d9561f335e748280f5535a384a88675af9a9eac439d4cfd663bcb66", + "zh:741101426a2f2c52dee37122f0f4a2f2d6af6d852cb1db634480a86398fa3511", + "zh:78d5eefdd9e494defcb3c68d282b8f96630502cac21d1ea161f53cfe9bb483b3", + "zh:a902473f08ef8df62cfe6116bd6c157070a93f66622384300de235a533e9d4a9", + "zh:b85c511a23e57a2147355932b3b6dce2a11e856b941165793a0c3d7578d94d05", + "zh:c5172226d18eaac95b1daac80172287b69d4ce32750c82ad77fa0768be4ea4b8", + "zh:dab4434dba34aad569b0bc243c2d3f3ff86dd7740def373f2a49816bd2ff819b", + "zh:f49fd62aa8c5525a5c17abd51e27ca5e213881d58882fd42fec4a545b53c9699", + ] +} diff --git a/terraform/litellm/gcp/README.md b/terraform/litellm/gcp/README.md new file mode 100644 index 00000000000..504cfa066e4 --- /dev/null +++ b/terraform/litellm/gcp/README.md @@ -0,0 +1,296 @@ +# LiteLLM on GCP (Cloud Run) + +Deploys the componentized LiteLLM proxy on GCP: + +- **VPC** + Private Services Access range + a Serverless VPC Access connector + so Cloud Run can reach private IPs +- **Cloud SQL for PostgreSQL** — primary instance + cross-zone read replica, + password auth via Secret Manager +- **Memorystore (Redis)** for caching + rate limiting, private IP only +- **GCS bucket** — private, versioned, uniform IAM; exposed as `GCS_BUCKET_NAME` +- **Secret Manager** entries for `LITELLM_MASTER_KEY` and `DATABASE_PASSWORD` +- **Cloud Run v2** services for `gateway` (port 4000), `backend` (port 4001), + and `ui` (port 3000), all using a shared runtime service account +- **Cloud Run Job** (`litellm-migrations`) that runs `prisma migrate deploy` from the dedicated `ghcr.io/berriai/litellm-migrations` image +- **External global HTTP(S) load balancer** with serverless NEGs and a URL + map mirroring the helm-chart ingress path routing: + - LLM data-plane prefixes → `gateway` + - UI asset paths → `ui` + - Everything else → `backend` + +## Image pulls + +There are four images: `litellm-gateway`, `litellm-backend`, `litellm-ui`, +and `litellm-migrations` (slim image used only by the one-off Cloud Run +Job — runs `prisma migrate deploy` against the writer DB and exits). +Bump them together when bumping LiteLLM. + +Cloud Run only accepts images from Artifact Registry, `[region.]gcr.io`, +or `docker.io` — `ghcr.io` URIs are rejected at apply time. The four +images are published to GHCR upstream, so any real deploy needs an +Artifact Registry remote repository pointed at GHCR. + +**One-time setup (per project):** create a remote repo and let Cloud Run +pull through it. + +```bash +gcloud artifacts repositories create litellm \ + --repository-format=docker \ + --location=us-central1 \ + --mode=remote-repository \ + --remote-repo-config-desc="GitHub Container Registry passthrough" \ + --remote-docker-repo=https://ghcr.io +``` + +Then point the stack at it via `image_registry`: + +```hcl +image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai" +image_tag = "v1.86.0-dev" +``` + +The four `litellm-:${image_tag}` URIs are composed from those +two vars. Set `gateway_image` / `backend_image` / `ui_image` / +`migrations_image` only if you need a per-component override (custom +build, different tag). + +Two further notes: + +- The runtime SAs the stack creates do **not** need + `roles/artifactregistry.reader` — Cloud Run pulls images using the + per-project serverless agent + (`service-@serverless-robot-prod.iam.gserviceaccount.com`), + not the runtime SA. +- For a fully air-gapped option, mirror the images into a regular AR + repository instead of a remote repo: + + ```bash + for c in gateway backend ui migrations; do + docker pull ghcr.io/berriai/litellm-$c: + docker tag ghcr.io/berriai/litellm-$c: \ + us-central1-docker.pkg.dev/$PROJECT/litellm/$c: + docker push us-central1-docker.pkg.dev/$PROJECT/litellm/$c: + done + ``` + + then set `image_registry = "us-central1-docker.pkg.dev/$PROJECT/litellm"` + (drop the `/berriai` suffix — the mirrored layout has no org segment). + +## Database authentication + +LiteLLM's `init_iam_db_url_from_env()` mints **AWS RDS** tokens via boto3 — +it doesn't speak GCP IAM. To IAM-auth against Cloud SQL from Cloud Run you'd +need the Cloud SQL Auth Proxy as a sidecar, which complicates the service +spec. This stack therefore uses **password authentication**: + +- A random password is generated and stored in Secret Manager + (`-db-password`). +- Each Cloud Run service receives the password as `DATABASE_PASSWORD` via + `value_source.secret_key_ref`. +- The container's entrypoint shim assembles `DATABASE_URL` (and + `DATABASE_URL_READ_REPLICA`) from `DATABASE_HOST` / `DATABASE_PASSWORD` + before exec'ing uvicorn — so the password never appears in the service + spec or in logs. + +If you need GCP-native IAM auth later, add `cloud-sql-proxy` as a sidecar +container under `template.template.containers` (Cloud Run v2 supports +multiple containers) and replace the password-based URL with the proxy's +Unix socket. + +## Configuring the proxy + +### `proxy_config` + +Mirrors the helm chart's `gateway.config.proxy_config`. The map is +YAML-encoded and base64-passed to gateway, backend, and the migration job; +each container decodes it to `/tmp/litellm-config.yaml` at startup and sets +`CONFIG_FILE_PATH`. + +```hcl +proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + } +} +``` + +LiteLLM resolves `os.environ/` references against the container +environment. Provider API keys belong in `*_extra_secrets` and are +referenced from the YAML by env-var name. + +### Extra env / secrets + +Non-sensitive env vars: + +```hcl +gateway_extra_env = { + LANGFUSE_HOST = "https://us.cloud.langfuse.com" +} +``` + +Sensitive values — create the secret in Secret Manager first, then reference +its resource ID: + +```bash +echo -n "sk-proj-..." | gcloud secrets create openai-api-key --data-file=- +``` + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "projects/my-gcp-project/secrets/openai-api-key" +} +``` + +The Cloud Run runtime SA auto-gains `roles/secretmanager.secretAccessor` on +every secret referenced. **Pass the bare secret resource ID only** — +`projects/.../secrets/openai-api-key`, never the version-suffixed form +`projects/.../secrets/openai-api-key/versions/3`. The Cloud Run +`secret_key_ref` binding and the stack's IAM `secret_id` grant both +reject the version suffix; version is always resolved as `latest`. If +you need a pinned version, edit `local.gateway_extra_secret_kv` in +`cloudrun.tf` directly to set `version = "3"` for the entry in question. + +## Tenant deployment + +Every resource the stack creates is named `${tenant}-litellm-${env}` (or +that plus a per-resource suffix), so multiple tenants and multiple +environments coexist in the same project as long as the `(tenant, env)` +pair differs: + +| `tenant` | `env` | Example resource name | +| -------- | ------- | ---------------------------------- | +| `acme` | `stage` | `acme-litellm-stage-gateway` | +| `acme` | `prod` | `acme-litellm-prod-master-key` | +| `globex` | `dev` | `globex-litellm-dev-license` | + +For a per-tenant instance, the only inputs that change are the tenant +slug, env, and the two pre-issued secrets: + +```bash +export TF_VAR_litellm_master_key="sk-..." # the tenant's master key +export TF_VAR_litellm_license="lic-..." # their LITELLM_LICENSE + +terraform apply \ + -var "project=my-gcp-project" \ + -var "region=us-central1" \ + -var "tenant=acme" \ + -var "env=stage" +``` + +Both `litellm_master_key` and `litellm_license` are optional: +- Omit `litellm_master_key` → the stack auto-generates a random `sk-…` + value (trial/dev path). +- Omit `litellm_license` → no license secret is created and gateway/ + backend run without `LITELLM_LICENSE` (OSS-only). + +Use `TF_VAR_*` env vars rather than tfvars files for these — values +written to a tfvars file end up in `terraform.tfstate` and any committed +example files. + +## Quick start + +```bash +cd terraform/litellm/gcp +cp terraform.tfvars.example terraform.tfvars +# Edit: project, region, tenant, env, *_image, proxy_config, gateway_extra_secrets. + +terraform init +terraform apply +``` + +That single apply provisions everything, runs the prisma schema migration via +the Cloud Run job (auto-triggered by `bootstrap.tf`), and only then starts the +gateway/backend services. When it returns, the stack is serving traffic. + +```bash +terraform output lb_url +# UI login: admin / +gcloud secrets versions access latest --secret="$(terraform output -raw master_key_secret_id)" +``` + +The `migration_run_command` output is preserved for break-glass manual re-runs. + +**Prerequisite**: `gcloud` must be authenticated (`gcloud auth login`) and the +required APIs must be enabled (run, sqladmin, redis, secretmanager, +vpcaccess, compute, servicenetworking, storage, artifactregistry). + +## TLS + +`terraform plan` refuses to provision an HTTP-only LB by default — TLS +is the supported posture. Two paths: + +**Production / staging — set `lb_domains`:** + +1. `terraform apply` once with `allow_plaintext_lb = true` (intentional + chicken-and-egg escape hatch) to provision the LB and read the anycast + IP from `terraform output -raw lb_ip`. +2. Point each DNS name you want to serve from at that IP. +3. Set `lb_domains = ["proxy.example.com"]` and remove + `allow_plaintext_lb`; re-apply. + +Result: a 443 forwarding rule with a Google-managed cert covering each +listed domain; the 80 forwarding rule is rewritten to serve a permanent +301 redirect to HTTPS, so HTTP clients are automatically upgraded. The +managed cert sits in `PROVISIONING` for ~15-60 min on first apply until +DNS propagation completes — `gcloud compute ssl-certificates describe +-litellm--cert` shows the state. + +**Trial / dev — explicitly opt into HTTP-only:** + +Set `allow_plaintext_lb = true` and leave `lb_domains = []`. Without the +flag, plan fails with a clear error pointing at the precondition. +Intended for short-lived trial / dev stacks only. + +## Storage and database retention + +Two opt-in tripwires guard against accidental data loss on +`terraform destroy`: + +- **`cloudsql_deletion_protection`** (Cloud SQL writer + reader; + default `true`) — destroy fails with a clear error rather than + dropping the database. +- **`gcs_force_destroy`** (GCS bucket holding request log archives, + `/v1/files` content, and the GCS cache backend; default `false`) — + `terraform destroy` against a non-empty bucket fails. + +Flip `cloudsql_deletion_protection` to `false` or `gcs_force_destroy` to +`true` only for ephemeral / CI stacks where you accept losing the data. + +## Redis encryption + +Memorystore runs with `transit_encryption_mode = "SERVER_AUTHENTICATION"`, +so the proxy connects via `rediss://`. The instance's self-signed CA cert +(`server_ca_certs[0].cert`) is shipped to gateway + backend as +`REDIS_CA_PEM_B64`; their entrypoint shell decodes it to `/tmp/redis-ca.pem` +before uvicorn starts and points `REDIS_SSL_CA_CERTS` at that path. No +extra config needed — but if you ever swap Memorystore for an external +Redis, override `REDIS_HOST`/`REDIS_PORT` and either drop these env vars +or point them at your own CA. + +## Files + +| File | What's in it | +| ----------------- | -------------------------------------------------------------------- | +| `versions.tf` | Terraform + provider version constraints | +| `providers.tf` | Google + Google-Beta providers | +| `variables.tf` | All input variables | +| `locals.tf` | Path-prefix lists (mirror of `helm/.../ingress.yaml`) + proxy_config helpers | +| `network.tf` | VPC, subnet, PSA range, Serverless VPC connector | +| `secrets.tf` | Secret Manager entries + random master_key | +| `cloudsql.tf` | Cloud SQL writer + read replica + app user + password secret | +| `redis.tf` | Memorystore Redis (private IP) | +| `gcs.tf` | GCS bucket + objectAdmin binding | +| `iam.tf` | Runtime SA + Cloud SQL client + Secret Manager accessor | +| `cloudrun.tf` | 3 Cloud Run services + Cloud Run Job for migrations | +| `load_balancer.tf`| External HTTPS LB, serverless NEGs, URL map for path routing | +| `outputs.tf` | LB IP, service URLs, secret IDs, migration `execute` command | diff --git a/terraform/litellm/gcp/bootstrap.tf b/terraform/litellm/gcp/bootstrap.tf new file mode 100644 index 00000000000..47ad885ff12 --- /dev/null +++ b/terraform/litellm/gcp/bootstrap.tf @@ -0,0 +1,43 @@ +# Auto-runs the prisma schema migration as part of `terraform apply`. Mirrors +# the AWS stack's terraform_data.migration in spirit. Cloud SQL doesn't need a +# separate user-bootstrap step because google_sql_user.app already creates the +# application user — so the only post-cluster work is the migration. +# +# Gateway/backend Cloud Run services depend on this resource (in cloudrun.tf) +# so they don't go live until the schema is in place. +# +# Triggers: +# - re-runs if the migrations image changes (new release ships new prisma +# migration files). +# - re-runs if the migration job is recreated. +# +# Requires `gcloud` on the machine running terraform, with user creds live +# enough to invoke Cloud Run admin APIs (`gcloud auth login`). + +resource "terraform_data" "migration" { + triggers_replace = { + job_id = google_cloud_run_v2_job.migrations.id + job_image = local.migrations_image + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + JOB = google_cloud_run_v2_job.migrations.name + REGION = var.region + PROJECT = var.project + } + command = <<-EOT + set -euo pipefail + gcloud run jobs execute "$JOB" \ + --region "$REGION" \ + --project "$PROJECT" \ + --wait + EOT + } + + depends_on = [ + google_cloud_run_v2_job.migrations, + google_sql_user.app, + ] +} diff --git a/terraform/litellm/gcp/cloudrun.tf b/terraform/litellm/gcp/cloudrun.tf new file mode 100644 index 00000000000..28e1145b081 --- /dev/null +++ b/terraform/litellm/gcp/cloudrun.tf @@ -0,0 +1,430 @@ +# Three Cloud Run v2 services + one Cloud Run v2 job for migrations. +# All four use the same service account and the same VPC connector for +# private egress to Cloud SQL + Memorystore. + +locals { + # Memorystore exposes a self-signed CA cert per instance; we ship it as + # a base64 env var and decode it to a file at container startup so the + # rediss:// connection can validate. Public cert, not sensitive. + redis_ca_pem_b64 = base64encode(google_redis_instance.this.server_ca_certs[0].cert) + + shared_env_kv = [ + { name = "DATABASE_HOST", value = google_sql_database_instance.writer.private_ip_address }, + { name = "DATABASE_PORT", value = "5432" }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + { name = "DATABASE_HOST_READ_REPLICA", value = google_sql_database_instance.reader.private_ip_address }, + { name = "DATABASE_PORT_READ_REPLICA", value = "5432" }, + { name = "REDIS_HOST", value = google_redis_instance.this.host }, + { name = "REDIS_PORT", value = tostring(google_redis_instance.this.port) }, + # _redis.get_redis_url_from_environment honors REDIS_SSL to flip the + # scheme to rediss://; REDIS_SSL_CA_CERTS is mapped via + # _get_redis_env_kwarg_mapping → ssl_ca_certs on the redis-py client. + { name = "REDIS_SSL", value = "true" }, + { name = "REDIS_SSL_CA_CERTS", value = "/tmp/redis-ca.pem" }, + { name = "REDIS_CA_PEM_B64", value = local.redis_ca_pem_b64 }, + { name = "GCS_BUCKET_NAME", value = google_storage_bucket.this.name }, + ] + + # 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 + # job has its own narrower env list — see migrations_env_secrets below). + shared_env_secrets = concat( + [ + { name = "LITELLM_MASTER_KEY", secret = google_secret_manager_secret.master_key.id, version = "latest" }, + { name = "DATABASE_PASSWORD", secret = google_secret_manager_secret.db_password.id, version = "latest" }, + ], + var.litellm_license == "" ? [] : [ + { name = "LITELLM_LICENSE", secret = google_secret_manager_secret.license[0].id, version = "latest" }, + ], + ) + + # Backend-only managed secrets. UI_PASSWORD is consumed by the management + # API (UI login flow) and has no use on the gateway data plane. + backend_managed_env_secrets = var.ui_password == "" ? [] : [ + { name = "UI_PASSWORD", secret = google_secret_manager_secret.ui_password[0].id, version = "latest" }, + ] + + # Per-component extras (from variables). + gateway_extra_env_kv = [ + for k, v in var.gateway_extra_env : { name = k, value = v } + ] + backend_extra_env_kv = [ + for k, v in var.backend_extra_env : { name = k, value = v } + ] + + backend_default_env_kv = [ + { name = "STORE_MODEL_IN_DB", value = "true" }, + ] + gateway_extra_secret_kv = [ + for k, v in var.gateway_extra_secrets : { name = k, secret = v, version = "latest" } + ] + backend_extra_secret_kv = [ + for k, v in var.backend_extra_secrets : { name = k, secret = v, version = "latest" } + ] + + # Shell fragments composed with && so any failure short-circuits the + # whole startup instead of falling through to `exec uvicorn`. The + # python step is only included when the caller provided a proxy_config. + proxy_config_fragment = local.proxy_config_enabled ? [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\"" + ] : [] + + # Decode the Memorystore CA cert (passed as REDIS_CA_PEM_B64) to the + # path REDIS_SSL_CA_CERTS points at, so the redis-py client can validate + # the rediss:// handshake. + redis_ca_fragment = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['REDIS_SSL_CA_CERTS']).write_bytes(base64.b64decode(os.environ['REDIS_CA_PEM_B64']))\"" + ] + + database_url_fragment = [ + "export DATABASE_URL=\"postgresql://$${DATABASE_USER}:$${DATABASE_PASSWORD}@$${DATABASE_HOST}:$${DATABASE_PORT}/$${DATABASE_NAME}\"", + "export DATABASE_URL_READ_REPLICA=\"postgresql://$${DATABASE_USER}:$${DATABASE_PASSWORD}@$${DATABASE_HOST_READ_REPLICA}:$${DATABASE_PORT_READ_REPLICA}/$${DATABASE_NAME}\"", + ] + + gateway_args = join(" && ", concat( + local.proxy_config_fragment, + local.redis_ca_fragment, + local.database_url_fragment, + ["exec uvicorn gateway.main:app --host 0.0.0.0 --port 4000"], + )) + + backend_args = join(" && ", concat( + local.proxy_config_fragment, + local.redis_ca_fragment, + local.database_url_fragment, + ["exec uvicorn backend.main:app --host 0.0.0.0 --port 4001"], + )) + + # Env shipped to the migrations Job. The migrations image runs run.py + # which assembles DATABASE_URL from these discrete vars itself, so we + # only need writer-side DB env (no read replica, no proxy_config, no + # master key). + migrations_env_kv = [ + { name = "DATABASE_HOST", value = google_sql_database_instance.writer.private_ip_address }, + { name = "DATABASE_PORT", value = "5432" }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + ] + + migrations_env_secrets = [ + { name = "DATABASE_PASSWORD", secret = google_secret_manager_secret.db_password.id, version = "latest" }, + ] +} + +# ---------- Gateway ---------- +resource "google_cloud_run_v2_service" "gateway" { + name = "${local.name}-gateway" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.runtime.email + max_instance_request_concurrency = var.gateway_max_instance_request_concurrency + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + scaling { + min_instance_count = var.gateway_min_instances + max_instance_count = var.gateway_max_instances + } + + containers { + image = local.gateway_image + command = ["sh", "-c"] + args = [local.gateway_args] + + ports { + container_port = 4000 + } + + resources { + limits = { + cpu = var.gateway_cpu + memory = var.gateway_memory + } + } + + dynamic "env" { + for_each = concat(local.shared_env_kv, local.gateway_extra_env_kv, local.proxy_config_env) + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = concat(local.shared_env_secrets, local.gateway_extra_secret_kv) + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + + startup_probe { + http_get { + path = "/health/readiness" + port = 4000 + } + initial_delay_seconds = 10 + period_seconds = 10 + timeout_seconds = 5 + failure_threshold = 12 + } + + liveness_probe { + http_get { + path = "/health/liveliness" + port = 4000 + } + period_seconds = 30 + timeout_seconds = 5 + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.master_key, + google_secret_manager_secret_iam_member.db_password, + google_secret_manager_secret_iam_member.license, + google_secret_manager_secret_iam_member.extras, + google_sql_user.app, + # Don't go live until the schema is migrated; otherwise the proxy boots, + # fails on missing tables, and Cloud Run keeps cold-restarting. + terraform_data.migration, + ] +} + +# ---------- Backend ---------- +resource "google_cloud_run_v2_service" "backend" { + name = "${local.name}-backend" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.runtime.email + max_instance_request_concurrency = var.backend_max_instance_request_concurrency + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + scaling { + min_instance_count = var.backend_min_instances + max_instance_count = var.backend_max_instances + } + + containers { + image = local.backend_image + command = ["sh", "-c"] + args = [local.backend_args] + + ports { + container_port = 4001 + } + + resources { + limits = { + cpu = var.backend_cpu + memory = var.backend_memory + } + } + + dynamic "env" { + for_each = concat(local.shared_env_kv, local.backend_default_env_kv, local.backend_extra_env_kv, local.proxy_config_env) + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = concat(local.shared_env_secrets, local.backend_managed_env_secrets, local.backend_extra_secret_kv) + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + + startup_probe { + http_get { + path = "/health/readiness" + port = 4001 + } + initial_delay_seconds = 10 + period_seconds = 10 + timeout_seconds = 5 + failure_threshold = 12 + } + + liveness_probe { + http_get { + path = "/health/liveliness" + port = 4001 + } + period_seconds = 30 + timeout_seconds = 5 + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.master_key, + google_secret_manager_secret_iam_member.db_password, + google_secret_manager_secret_iam_member.license, + google_secret_manager_secret_iam_member.ui_password, + google_secret_manager_secret_iam_member.extras, + google_sql_user.app, + terraform_data.migration, + ] +} + +# ---------- UI ---------- +# Static nginx — no DB, no Redis, no secrets. Runs as ui_runtime, a SA +# with zero IAM bindings, so a compromised UI container can't pivot to +# Secret Manager / Cloud SQL via the metadata service. +resource "google_cloud_run_v2_service" "ui" { + name = "${local.name}-ui" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.ui_runtime.email + max_instance_request_concurrency = var.ui_max_instance_request_concurrency + + scaling { + min_instance_count = var.ui_min_instances + max_instance_count = var.ui_max_instances + } + + containers { + image = local.ui_image + + ports { + container_port = 3000 + } + + resources { + limits = { + cpu = var.ui_cpu + memory = var.ui_memory + } + } + + startup_probe { + http_get { + path = "/healthz" + port = 3000 + } + initial_delay_seconds = 5 + period_seconds = 10 + timeout_seconds = 3 + failure_threshold = 6 + } + } + } +} + +# Allow the LB (any unauthenticated traffic from the configured serverless +# NEG) to invoke the Cloud Run services. The actual auth is in the proxy +# (LITELLM_MASTER_KEY); these IAM bindings just open up Cloud Run's invoker +# gate so the LB request makes it to the container. +resource "google_cloud_run_v2_service_iam_member" "gateway_allusers" { + project = var.project + location = google_cloud_run_v2_service.gateway.location + name = google_cloud_run_v2_service.gateway.name + role = "roles/run.invoker" + member = "allUsers" +} + +resource "google_cloud_run_v2_service_iam_member" "backend_allusers" { + project = var.project + location = google_cloud_run_v2_service.backend.location + name = google_cloud_run_v2_service.backend.name + role = "roles/run.invoker" + member = "allUsers" +} + +resource "google_cloud_run_v2_service_iam_member" "ui_allusers" { + project = var.project + location = google_cloud_run_v2_service.ui.location + name = google_cloud_run_v2_service.ui.name + role = "roles/run.invoker" + member = "allUsers" +} + +# ---------- Migrations job ---------- +# Dedicated litellm-migrations image — slim, ENTRYPOINT runs run.py which +# assembles DATABASE_URL from the DATABASE_* env vars and runs `prisma +# migrate deploy`. No proxy_config, no master key, no shell wrapper. +resource "google_cloud_run_v2_job" "migrations" { + name = "${local.name}-migrations" + location = var.region + + template { + template { + service_account = google_service_account.runtime.email + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + containers { + image = local.migrations_image + + # Prisma's Node + Rust engine plus the v2 migration resolver + # routinely peaks above 1 GiB while applying the schema, so 2 GiB + # is the floor — 1 GiB OOM-kills mid-migrate. CPU stays at 1 vCPU + # (Cloud Run requires >= 1 with concurrency > 1, and `prisma + # migrate deploy` is single-threaded so more buys nothing). + resources { + limits = { + cpu = "1000m" + memory = "4Gi" + } + } + + dynamic "env" { + for_each = local.migrations_env_kv + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = local.migrations_env_secrets + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.db_password, + google_sql_user.app, + ] +} diff --git a/terraform/litellm/gcp/cloudsql.tf b/terraform/litellm/gcp/cloudsql.tf new file mode 100644 index 00000000000..70939c049c3 --- /dev/null +++ b/terraform/litellm/gcp/cloudsql.tf @@ -0,0 +1,102 @@ +# Cloud SQL for PostgreSQL — one primary + one read replica. +# +# Note on auth: LiteLLM's IAM-auth helper (rds_iam_token.py) mints AWS RDS +# tokens via boto3 and doesn't speak GCP IAM. Cloud SQL IAM auth from Cloud +# Run requires the Cloud SQL Auth Proxy as a sidecar, which complicates the +# Cloud Run service spec. We instead use password auth: a random password +# lives in Secret Manager and is injected into the Cloud Run services as +# DATABASE_PASSWORD. The writer's DATABASE_URL is assembled inside the +# container at startup; the reader URL is built from the replica's IP. + +resource "google_sql_database_instance" "writer" { + name = local.name + region = var.region + database_version = var.db_version + + depends_on = [google_service_networking_connection.psa] + + settings { + # ENTERPRISE accepts the db-custom-* and db-n1-* tiers we default to. + # ENTERPRISE_PLUS only accepts db-perf-optimized-* and is ~3x cost — set + # var.db_edition = "ENTERPRISE_PLUS" + change var.db_tier together if you + # want it. + edition = var.db_edition + tier = var.db_tier + availability_type = "REGIONAL" + disk_size = 20 + disk_autoresize = true + + backup_configuration { + enabled = true + point_in_time_recovery_enabled = true + start_time = "07:00" + } + + ip_configuration { + ipv4_enabled = false + private_network = google_compute_network.this.id + } + + insights_config { + query_insights_enabled = true + record_application_tags = true + record_client_address = true + } + } + + deletion_protection = var.cloudsql_deletion_protection +} + +resource "google_sql_database_instance" "reader" { + name = "${local.name}-reader" + region = var.region + database_version = var.db_version + master_instance_name = google_sql_database_instance.writer.name + + depends_on = [google_service_networking_connection.psa] + + settings { + edition = var.db_edition + tier = var.db_tier + availability_type = "ZONAL" + disk_autoresize = true + + ip_configuration { + ipv4_enabled = false + private_network = google_compute_network.this.id + } + } + + deletion_protection = var.cloudsql_deletion_protection +} + +resource "google_sql_database" "this" { + name = var.db_name + instance = google_sql_database_instance.writer.name +} + +resource "random_password" "db_password" { + length = 32 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +resource "google_sql_user" "app" { + name = var.db_username + instance = google_sql_database_instance.writer.name + password = random_password.db_password.result +} + +resource "google_secret_manager_secret" "db_password" { + secret_id = "${local.name}-db-password" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "db_password" { + secret = google_secret_manager_secret.db_password.id + secret_data = random_password.db_password.result +} diff --git a/terraform/litellm/gcp/gcs.tf b/terraform/litellm/gcp/gcs.tf new file mode 100644 index 00000000000..86511d38a31 --- /dev/null +++ b/terraform/litellm/gcp/gcs.tf @@ -0,0 +1,29 @@ +# General-purpose GCS bucket — same role as the AWS S3 bucket. The bucket +# name is exposed to gateway + backend as GCS_BUCKET_NAME; reference it +# from proxy_config via `os.environ/GCS_BUCKET_NAME`. + +resource "random_id" "bucket_suffix" { + byte_length = 4 +} + +resource "google_storage_bucket" "this" { + name = "${var.project}-${local.name}-${random_id.bucket_suffix.hex}" + location = var.region + uniform_bucket_level_access = true + force_destroy = var.gcs_force_destroy + + versioning { + enabled = true + } + + public_access_prevention = "enforced" + + labels = var.labels +} + +# Cloud Run runtime SA gains object admin on this bucket only. +resource "google_storage_bucket_iam_member" "runtime" { + bucket = google_storage_bucket.this.name + role = "roles/storage.objectAdmin" + member = "serviceAccount:${google_service_account.runtime.email}" +} diff --git a/terraform/litellm/gcp/iam.tf b/terraform/litellm/gcp/iam.tf new file mode 100644 index 00000000000..93a9997ed7a --- /dev/null +++ b/terraform/litellm/gcp/iam.tf @@ -0,0 +1,71 @@ +# Runtime SA used by the gateway, backend, and migration job — has Cloud +# SQL client + Secret Manager accessor on every managed/extra secret. The +# UI deliberately uses a *different* SA (below) so a compromised UI +# container can't read master_key / db_password / license / ui_password / +# provider creds via the metadata service. +resource "google_service_account" "runtime" { + account_id = "${local.name}-runtime" + display_name = "LiteLLM Cloud Run runtime" +} + +# UI runtime SA — no role bindings. The UI is static nginx with no DB, +# Redis, or Secret Manager dependencies, so its task identity should not +# be able to read any of those. Cloud Run pulls the UI image via the +# project's serverless service agent (not this SA), so it doesn't need +# artifactregistry.reader either. +resource "google_service_account" "ui_runtime" { + account_id = "${local.name}-ui-runtime" + display_name = "LiteLLM Cloud Run UI runtime (no data-plane access)" +} + +# Cloud SQL client — lets the Cloud Run services connect to the instance +# over private IP via the VPC connector. +resource "google_project_iam_member" "runtime_cloudsql" { + project = var.project + role = "roles/cloudsql.client" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# Secret Manager accessor — managed secrets first (split out as separate +# resources because their IDs are computed-at-apply and can't drive a +# for_each). +resource "google_secret_manager_secret_iam_member" "master_key" { + secret_id = google_secret_manager_secret.master_key.id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +resource "google_secret_manager_secret_iam_member" "db_password" { + secret_id = google_secret_manager_secret.db_password.id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# License secret accessor — only created when var.litellm_license is set. +resource "google_secret_manager_secret_iam_member" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = google_secret_manager_secret.license[0].id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# UI password secret accessor — only created when var.ui_password is set. +resource "google_secret_manager_secret_iam_member" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = google_secret_manager_secret.ui_password[0].id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# User-supplied extras. Dedupe on the secret resource ID — two different +# env-var names could reference the same secret, and we want exactly one +# IAM binding per (secret, role, member) tuple in state. +resource "google_secret_manager_secret_iam_member" "extras" { + for_each = toset(values(merge(var.gateway_extra_secrets, var.backend_extra_secrets))) + + secret_id = each.value + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} diff --git a/terraform/litellm/gcp/load_balancer.tf b/terraform/litellm/gcp/load_balancer.tf new file mode 100644 index 00000000000..b0081786f13 --- /dev/null +++ b/terraform/litellm/gcp/load_balancer.tf @@ -0,0 +1,185 @@ +# External global HTTP(S) load balancer fronting all three Cloud Run +# services. URL map mirrors the helm-chart ingress path routing: +# - LLM data-plane paths → gateway +# - UI asset paths → ui +# - Everything else → backend (management API: /key/*, /user/*, …) +# +# By default the LB serves plain HTTP on port 80. Set var.lb_domains to a +# list of DNS names already pointing at lb_ip and the stack provisions a +# Google-managed SSL cert + 443 forwarding rule, and the 80 forwarding rule +# is rewritten to redirect HTTP→HTTPS via a redirect-only URL map. + +locals { + tls_enabled = length(var.lb_domains) > 0 +} + +resource "google_compute_global_address" "lb" { + name = "${local.name}-lb-ip" +} + +# Serverless NEGs — one per Cloud Run service. +resource "google_compute_region_network_endpoint_group" "gateway" { + name = "${local.name}-gateway-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.gateway.name + } +} + +resource "google_compute_region_network_endpoint_group" "backend" { + name = "${local.name}-backend-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.backend.name + } +} + +resource "google_compute_region_network_endpoint_group" "ui" { + name = "${local.name}-ui-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.ui.name + } +} + +# Backend services wrap each NEG. +resource "google_compute_backend_service" "gateway" { + name = "${local.name}-gateway-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.gateway.id + } +} + +resource "google_compute_backend_service" "backend" { + name = "${local.name}-backend-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.backend.id + } +} + +resource "google_compute_backend_service" "ui" { + name = "${local.name}-ui-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.ui.id + } +} + +# URL map. Default → backend (management API). Path matchers route the +# gateway and UI prefixes elsewhere. +resource "google_compute_url_map" "this" { + name = local.name + default_service = google_compute_backend_service.backend.id + + host_rule { + hosts = ["*"] + path_matcher = "main" + } + + path_matcher { + name = "main" + default_service = google_compute_backend_service.backend.id + + # UI paths (catch them before any /v1/* gateway rules so /favicon.ico + # and / take precedence). + path_rule { + paths = local.ui_path_prefixes + service = google_compute_backend_service.ui.id + } + + # Gateway path prefixes. GCP URL maps cap a path_rule at 10 path globs, + # so chunk into rules of 10. + dynamic "path_rule" { + for_each = { for idx, chunk in chunklist(local.gateway_path_prefixes, 10) : idx => chunk } + content { + paths = path_rule.value + service = google_compute_backend_service.gateway.id + } + } + } +} + +# Permanent HTTP→HTTPS redirect URL map. Only attached to the port-80 +# target proxy when TLS is enabled; otherwise the regular path-routing +# URL map is attached to the HTTP proxy and everything stays plaintext. +resource "google_compute_url_map" "https_redirect" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-redirect" + + default_url_redirect { + https_redirect = true + redirect_response_code = "MOVED_PERMANENTLY_DEFAULT" + strip_query = false + } +} + +resource "google_compute_target_http_proxy" "this" { + name = "${local.name}-http" + url_map = local.tls_enabled ? google_compute_url_map.https_redirect[0].id : google_compute_url_map.this.id + + # Default-deny on the HTTP-only path: TLS is the supported posture. + # Operators must either supply DNS names or explicitly opt in. + lifecycle { + precondition { + condition = local.tls_enabled || var.allow_plaintext_lb + error_message = "LB has no HTTPS forwarding rule. Either set `lb_domains` to a list of DNS names you want a Google-managed cert for, or set `allow_plaintext_lb = true` to opt into HTTP-only (trial / dev only)." + } + } +} + +resource "google_compute_global_forwarding_rule" "http" { + name = "${local.name}-http" + ip_protocol = "TCP" + port_range = "80" + load_balancing_scheme = "EXTERNAL_MANAGED" + ip_address = google_compute_global_address.lb.address + target = google_compute_target_http_proxy.this.id +} + +# ---------- HTTPS (gated on var.lb_domains) ---------- +# +# Google-managed certs require each listed domain to resolve to lb_ip +# *before* the cert provisions; on first apply the cert sits in +# PROVISIONING for ~15-60 min until DNS propagates. The LB starts serving +# 443 immediately, but cert handshakes fail until the managed cert +# transitions to ACTIVE. + +resource "google_compute_managed_ssl_certificate" "this" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-cert" + + managed { + domains = var.lb_domains + } +} + +resource "google_compute_target_https_proxy" "this" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-https" + url_map = google_compute_url_map.this.id + ssl_certificates = [google_compute_managed_ssl_certificate.this[0].id] +} + +resource "google_compute_global_forwarding_rule" "https" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-https" + ip_protocol = "TCP" + port_range = "443" + load_balancing_scheme = "EXTERNAL_MANAGED" + ip_address = google_compute_global_address.lb.address + target = google_compute_target_https_proxy.this[0].id +} diff --git a/terraform/litellm/gcp/locals.tf b/terraform/litellm/gcp/locals.tf new file mode 100644 index 00000000000..2d1231fb197 --- /dev/null +++ b/terraform/litellm/gcp/locals.tf @@ -0,0 +1,79 @@ +# Gateway path prefixes — mirrored verbatim from gateway/routes/allowlist.py +# and helm/litellm/templates/ingress.yaml. URL maps use the "path matcher" +# rule with `paths` lists; up to 10 path globs per rule, up to 50 rules +# per matcher. Easily fits the gateway list in one rule per chunk-of-10. +locals { + # Every resource the stack creates is named `${tenant}-litellm-${env}` + # (or that with a per-resource suffix). Computed once here so the rest of + # the stack can reference local.name. + name = "${var.tenant}-litellm-${var.env}" + + gateway_path_prefixes = [ + "/v1/chat/*", "/chat/*", + "/v1/completions*", "/completions*", + "/v1/embeddings*", "/embeddings*", + "/v1/moderations*", "/moderations*", + "/v1/audio/*", "/audio/*", + "/v1/images/*", "/images/*", + "/v1/files*", "/files*", + "/v1/batches*", "/batches*", + "/v1/fine_tuning/*", "/fine_tuning/*", + "/v1/fine-tuning/*", "/fine-tuning/*", + "/v1/responses*", "/responses*", + "/v1/threads*", "/threads*", + "/v1/assistants*", "/assistants*", + "/v1/vector_stores*", "/vector_stores*", + "/v1/indexes*", + "/v1/models*", "/models*", + "/openai/*", "/engines/*", + "/v1/messages*", "/messages*", + "/v1/skills/*", "/v1/a2a/*", + "/v1/rerank*", "/v2/rerank*", "/rerank*", + "/v1/ocr*", "/ocr*", + "/v1/rag/*", "/rag/*", + "/v1/video/*", "/v1/videos/*", "/video/*", "/videos/*", + "/v1/search*", "/search*", + "/v1/containers/*", "/containers/*", + "/v1/evals/*", + "/v1/memory/*", + "/queue/chat/*", + "/v1beta/*", + "/interactions/*", + "/anthropic/*", "/azure/*", "/azure_ai/*", "/aws/*", "/bedrock/*", + "/cohere/*", "/gemini/*", "/google/*", + "/vertex_ai/*", "/vertex-ai/*", + "/assemblyai/*", "/eu.assemblyai/*", + "/langfuse/*", "/vllm/*", + "/mistral/*", "/groq/*", "/voyage/*", "/cursor/*", "/milvus/*", + "/openai_passthrough/*", + "/toolset/*", + "/v1/realtime*", "/realtime*", + "/health*", "/metrics", "/test*", + ] + + ui_path_prefixes = [ + "/", + "/favicon.ico", + "/litellm-asset-prefix/*", + "/_next/*", + "/assets/*", + "/ui", + "/ui/*", + ] + + proxy_config_enabled = length(keys(var.proxy_config)) > 0 + proxy_config_b64 = local.proxy_config_enabled ? base64encode(yamlencode(var.proxy_config)) : "" + + proxy_config_env = local.proxy_config_enabled ? [ + { name = "LITELLM_PROXY_CONFIG_B64", value = local.proxy_config_b64 }, + { name = "CONFIG_FILE_PATH", value = "/tmp/litellm-config.yaml" }, + ] : [] + + # Resolved image URIs: per-component override wins, otherwise compose + # from image_registry + image_tag. Cloud Run only accepts AR / gcr.io / + # docker.io paths — see variables.tf for the full constraint list. + gateway_image = var.gateway_image != "" ? var.gateway_image : "${var.image_registry}/litellm-gateway:${var.image_tag}" + backend_image = var.backend_image != "" ? var.backend_image : "${var.image_registry}/litellm-backend:${var.image_tag}" + ui_image = var.ui_image != "" ? var.ui_image : "${var.image_registry}/litellm-ui:${var.image_tag}" + migrations_image = var.migrations_image != "" ? var.migrations_image : "${var.image_registry}/litellm-migrations:${var.image_tag}" +} diff --git a/terraform/litellm/gcp/network.tf b/terraform/litellm/gcp/network.tf new file mode 100644 index 00000000000..a1ccaed02f9 --- /dev/null +++ b/terraform/litellm/gcp/network.tf @@ -0,0 +1,46 @@ +resource "google_compute_network" "this" { + name = local.name + auto_create_subnetworks = false + routing_mode = "REGIONAL" +} + +resource "google_compute_subnetwork" "this" { + name = "${local.name}-${var.region}" + region = var.region + network = google_compute_network.this.id + ip_cidr_range = var.subnet_cidr + private_ip_google_access = true +} + +# Private Services Access (PSA) range for Cloud SQL + Memorystore. Both +# managed services peer with the VPC over the connection below using +# addresses from this range. +resource "google_compute_global_address" "psa" { + name = "${local.name}-psa" + purpose = "VPC_PEERING" + address_type = "INTERNAL" + prefix_length = 16 + network = google_compute_network.this.id +} + +resource "google_service_networking_connection" "psa" { + network = google_compute_network.this.id + service = "servicenetworking.googleapis.com" + reserved_peering_ranges = [google_compute_global_address.psa.name] +} + +# Serverless VPC Access connector — required so Cloud Run can reach +# Cloud SQL / Memorystore private IPs via the PSA range. +# +# min/max instances are required by the API now (you can't just set +# machine_type alone). Defaults: 2 e2-micro instances scale up to 3 — fine +# for low-to-moderate Cloud Run egress; bump max if your services push +# heavy private-network traffic. +resource "google_vpc_access_connector" "this" { + name = "${local.name}-conn" + region = var.region + network = google_compute_network.this.name + ip_cidr_range = var.vpc_connector_cidr + min_instances = 2 + max_instances = 3 +} diff --git a/terraform/litellm/gcp/outputs.tf b/terraform/litellm/gcp/outputs.tf new file mode 100644 index 00000000000..df25215adcc --- /dev/null +++ b/terraform/litellm/gcp/outputs.tf @@ -0,0 +1,64 @@ +output "lb_ip" { + description = "Global anycast IP of the external HTTPS load balancer." + value = google_compute_global_address.lb.address +} + +output "lb_url" { + description = "Proxy URL. Switches scheme based on whether lb_domains is set; when TLS is enabled the URL points at the first listed domain (since managed certs are tied to the hostname, not the anycast IP). The dashboard is served at /, the API at /v1/*." + value = local.tls_enabled ? "https://${var.lb_domains[0]}" : "http://${google_compute_global_address.lb.address}" +} + +output "gateway_service_url" { + description = "Default Cloud Run URL for the gateway (bypasses the LB)." + value = google_cloud_run_v2_service.gateway.uri +} + +output "backend_service_url" { + description = "Default Cloud Run URL for the backend (bypasses the LB)." + value = google_cloud_run_v2_service.backend.uri +} + +output "ui_service_url" { + description = "Default Cloud Run URL for the UI (bypasses the LB)." + value = google_cloud_run_v2_service.ui.uri +} + +output "cloudsql_writer_ip" { + description = "Private IP of the Cloud SQL writer." + value = google_sql_database_instance.writer.private_ip_address +} + +output "cloudsql_reader_ip" { + description = "Private IP of the Cloud SQL read replica." + value = google_sql_database_instance.reader.private_ip_address +} + +output "redis_endpoint" { + description = "Memorystore Redis endpoint." + value = "${google_redis_instance.this.host}:${google_redis_instance.this.port}" +} + +output "gcs_bucket" { + description = "GCS bucket name. Exposed to gateway + backend as GCS_BUCKET_NAME. Reference from proxy_config via `os.environ/GCS_BUCKET_NAME`." + value = google_storage_bucket.this.name +} + +output "master_key_secret_id" { + description = "Secret Manager resource ID holding LITELLM_MASTER_KEY. Fetch with `gcloud secrets versions access latest --secret=`." + value = google_secret_manager_secret.master_key.secret_id +} + +output "db_password_secret_id" { + description = "Secret Manager resource ID holding the Cloud SQL app-user password." + value = google_secret_manager_secret.db_password.secret_id +} + +output "migration_run_command" { + description = "Shell command that executes the one-off migration job against Cloud SQL. Run this once after the first apply." + value = format( + "gcloud run jobs execute %s --region %s --project %s --wait", + google_cloud_run_v2_job.migrations.name, + var.region, + var.project, + ) +} diff --git a/terraform/litellm/gcp/providers.tf b/terraform/litellm/gcp/providers.tf new file mode 100644 index 00000000000..fd1584463f8 --- /dev/null +++ b/terraform/litellm/gcp/providers.tf @@ -0,0 +1,9 @@ +provider "google" { + project = var.project + region = var.region +} + +provider "google-beta" { + project = var.project + region = var.region +} diff --git a/terraform/litellm/gcp/redis.tf b/terraform/litellm/gcp/redis.tf new file mode 100644 index 00000000000..f7e174ecbae --- /dev/null +++ b/terraform/litellm/gcp/redis.tf @@ -0,0 +1,20 @@ +resource "google_redis_instance" "this" { + name = local.name + tier = var.redis_tier + memory_size_gb = var.redis_memory_size_gb + region = var.region + + authorized_network = google_compute_network.this.id + connect_mode = "PRIVATE_SERVICE_ACCESS" + + redis_version = "REDIS_7_0" + + # In-transit encryption between Cloud Run and Memorystore. The instance + # exposes its self-signed CA via `server_ca_certs` (read in cloudrun.tf + # and passed to the proxy as REDIS_CA_PEM_B64); the proxy decodes it to + # /tmp/redis-ca.pem at startup and uses it to validate the rediss:// + # handshake. Mirrors `transit_encryption_enabled = true` on AWS. + transit_encryption_mode = "SERVER_AUTHENTICATION" + + depends_on = [google_service_networking_connection.psa] +} diff --git a/terraform/litellm/gcp/secrets.tf b/terraform/litellm/gcp/secrets.tf new file mode 100644 index 00000000000..80312e06a91 --- /dev/null +++ b/terraform/litellm/gcp/secrets.tf @@ -0,0 +1,62 @@ +resource "random_password" "master_key" { + length = 48 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# LITELLM_MASTER_KEY (sk-…) lives in Secret Manager. The Cloud Run service +# account gets accessor permission on it (see iam.tf). +resource "google_secret_manager_secret" "master_key" { + secret_id = "${local.name}-master-key" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "master_key" { + secret = google_secret_manager_secret.master_key.id + # When the operator passes litellm_master_key, use it verbatim. Otherwise + # fall back to the auto-generated `sk-…` value (trial / OSS path). + secret_data = coalesce(var.litellm_master_key, "sk-${random_password.master_key.result}") +} + +# LITELLM_LICENSE — only created when the operator supplies one. The runtime +# SA gets accessor permission via iam.tf, and gateway + backend pick it up +# through shared_env_secrets in cloudrun.tf. +resource "google_secret_manager_secret" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = "${local.name}-license" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret = google_secret_manager_secret.license[0].id + secret_data = var.litellm_license +} + +# UI_PASSWORD — backend-only. Same pattern as license: only created when +# the operator supplies one. The runtime SA gets accessor permission via +# iam.tf, and the backend service picks the env var up through +# backend_managed_env_secrets in cloudrun.tf. +resource "google_secret_manager_secret" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = "${local.name}-ui-password" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret = google_secret_manager_secret.ui_password[0].id + secret_data = var.ui_password +} diff --git a/terraform/litellm/gcp/terraform.tfvars.example b/terraform/litellm/gcp/terraform.tfvars.example new file mode 100644 index 00000000000..5c22a14c6d6 --- /dev/null +++ b/terraform/litellm/gcp/terraform.tfvars.example @@ -0,0 +1,77 @@ +project = "my-gcp-project" +region = "us-central1" + +# Resource naming: every GCP resource the stack creates is named +# `${tenant}-litellm-${env}` (or that plus a per-resource suffix). E.g. +# tenant="acme" + env="stage" → Cloud Run service `acme-litellm-stage-gateway`, +# Cloud SQL instance `acme-litellm-stage`, etc. +tenant = "acme" +env = "stage" + +# Tenant-supplied secrets. Prefer TF_VAR_litellm_master_key / +# TF_VAR_litellm_license / TF_VAR_ui_password env vars so the values don't +# end up in a committed tfvars file. All three are optional — when +# omitted the stack auto-generates a master key, runs without a license, +# and falls back to LITELLM_MASTER_KEY for UI login. +# litellm_master_key = "sk-..." +# litellm_license = "lic-..." +# ui_password = "..." + +# TLS: provide DNS names already pointing at the LB IP for a Google-managed +# cert. Without one, plan fails unless allow_plaintext_lb = true is set +# explicitly (trial/dev only). +# lb_domains = ["proxy.example.com"] +# allow_plaintext_lb = true + +# Storage and database retention. Defaults are safe — destroy preserves +# data. Flip these only for ephemeral / CI stacks. +# cloudsql_deletion_protection = true # default: refuse destroy on the DB +# gcs_force_destroy = false # default: refuse destroy on a non-empty bucket + +# Component images. Defaults pin all four to the same GHCR release tag — +# bump them together when bumping LiteLLM. To use private images, mirror +# them into Artifact Registry first — Cloud Run only authenticates against +# AR / gcr.io. +# gateway_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/gateway:1.86.0-dev" +# backend_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/backend:1.86.0-dev" +# ui_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/ui:1.86.0-dev" +# migrations_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/migrations:1.86.0-dev" + +# ---------- proxy_config (mirrors helm gateway.config.proxy_config) ---------- +# proxy_config = { +# model_list = [ +# { +# model_name = "gpt-4o" +# litellm_params = { +# model = "openai/gpt-4o" +# api_key = "os.environ/OPENAI_API_KEY" +# } +# }, +# ] +# general_settings = { +# master_key = "os.environ/LITELLM_MASTER_KEY" +# database_url = "os.environ/DATABASE_URL" +# } +# } + +# ---------- Extra env / secrets ---------- +# Plain-text env vars (non-sensitive). Land directly in the Cloud Run service spec. +# gateway_extra_env = { +# LANGFUSE_HOST = "https://us.cloud.langfuse.com" +# } + +# Backend env vars commonly tuned in prod: SSO redirect, docs branding, +# UI admin username. UI_PASSWORD is its own first-class var (see top). +# backend_extra_env = { +# AUTO_REDIRECT_UI_LOGIN_TO_SSO = "true" +# DOCS_TITLE = "Acme LiteLLM" +# UI_USERNAME = "admin" +# } + +# Provider API keys — Secret Manager resource IDs (NOT secret values). The +# Cloud Run SA auto-gains roles/secretmanager.secretAccessor on every +# secret listed here. Same shape works for backend_extra_secrets. +# gateway_extra_secrets = { +# OPENAI_API_KEY = "projects/my-gcp-project/secrets/openai-api-key" +# ANTHROPIC_API_KEY = "projects/my-gcp-project/secrets/anthropic-api-key" +# } diff --git a/terraform/litellm/gcp/variables.tf b/terraform/litellm/gcp/variables.tf new file mode 100644 index 00000000000..fe726b0317a --- /dev/null +++ b/terraform/litellm/gcp/variables.tf @@ -0,0 +1,405 @@ +variable "project" { + description = "GCP project ID." + type = string +} + +variable "region" { + description = "GCP region for VPC, Cloud SQL, Memorystore, Cloud Run, and the LB IP." + type = string + default = "us-central1" +} + +variable "tenant" { + description = "Tenant slug — used as the prefix for every GCP resource the stack creates. Combined with var.env to form `-litellm-` (e.g. `acme-litellm-stage`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,20}$", var.tenant)) + error_message = "tenant must be 1-21 chars, lower-kebab-case, starting with a letter." + } +} + +variable "env" { + description = "Environment suffix appended to every resource name (e.g. `stage`, `prod`, `dev`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,8}$", var.env)) + error_message = "env must be 1-9 chars, lower-kebab-case, starting with a letter." + } +} + +variable "labels" { + description = "Resource labels merged into every label-supporting resource." + type = map(string) + default = { + "managed-by" = "terraform" + } +} + +# ---------- Tenant-supplied secrets ---------- +# +# Both default to "" so the stack stays usable for trial / OSS deploys. +# Set via TF_VAR_litellm_master_key / TF_VAR_litellm_license to keep the +# values out of state files committed to a VCS. + +variable "litellm_master_key" { + description = <<-EOT + Pre-existing LITELLM_MASTER_KEY (must begin with `sk-`). When set, this + value is written to the master-key Secret Manager entry. When empty, + the stack auto-generates a random `sk-…` key (preserving today's + trial-deploy behavior). + EOT + type = string + default = "" + sensitive = true +} + +variable "litellm_license" { + description = <<-EOT + LiteLLM enterprise license string. When set, the stack creates a + `-litellm--license` Secret Manager entry, grants the + runtime SA accessor on it, and exposes its value to gateway + backend + as `LITELLM_LICENSE`. Leave empty for OSS-only deploys. + EOT + type = string + default = "" + sensitive = true +} + +variable "ui_password" { + description = <<-EOT + UI admin password. When set, the stack creates a + `-litellm--ui-password` Secret Manager entry, grants the + runtime SA accessor on it, and exposes its value to the backend as + `UI_PASSWORD`. Pair with `backend_extra_env.UI_USERNAME` to set the + matching username. Leave empty to skip — the proxy then falls back to + the LITELLM_MASTER_KEY for UI login. + EOT + type = string + default = "" + sensitive = true +} + +# ---------- Networking ---------- + +variable "subnet_cidr" { + description = "Primary CIDR block for the LiteLLM subnet." + type = string + default = "10.40.0.0/16" +} + +variable "vpc_connector_cidr" { + description = "CIDR for the Serverless VPC Access connector. /28 required." + type = string + default = "10.41.0.0/28" +} + +# ---------- Component images ---------- +# +# Cloud Run only pulls from Artifact Registry, [region.]gcr.io, or +# docker.io — it rejects arbitrary registries (notably ghcr.io) at apply +# time. The four images live on GHCR upstream, so any real deploy must +# either set `image_registry` to an Artifact Registry remote repository +# pointed at ghcr.io (e.g. `us-central1-docker.pkg.dev/my-proj/litellm/berriai`) +# or override the per-component `*_image` vars individually with full URIs. + +variable "image_registry" { + description = <<-EOT + Registry path prefix used to compose the four LiteLLM image URIs as + `/litellm-:`. The default + (`ghcr.io/berriai`) only works on registries Cloud Run accepts — for + GHCR-backed deploys, create an Artifact Registry remote repository + pointed at `https://ghcr.io` and set this to that repo's path + (e.g. `us-central1-docker.pkg.dev///berriai`). + Per-component overrides (`gateway_image`, `backend_image`, `ui_image`, + `migrations_image`) bypass this entirely when set. + EOT + type = string + default = "ghcr.io/berriai" +} + +variable "image_tag" { + description = "Tag applied to all four litellm-* images when composed from `image_registry`. Bump in lockstep when bumping LiteLLM. Must match a tag actually published to GHCR — the split images use the `v`-prefixed semver convention (e.g. `v1.86.0-dev`)." + type = string + default = "v1.86.0-dev" +} + +variable "gateway_image" { + description = "Full image URI for the gateway. Empty (default) composes from `image_registry` + `image_tag`. Public images or Artifact Registry only — Cloud Run won't authenticate against arbitrary private registries." + type = string + default = "" +} + +variable "backend_image" { + description = "Full image URI for the backend. Empty (default) composes from `image_registry` + `image_tag`." + type = string + default = "" +} + +variable "ui_image" { + description = "Full image URI for the UI. Empty (default) composes from `image_registry` + `image_tag`." + type = string + default = "" +} + +variable "migrations_image" { + description = <<-EOT + Full image URI for the one-off prisma migration Cloud Run Job. Empty + (default) composes from `image_registry` + `image_tag` as + `litellm-migrations`. Built from `migrations/Dockerfile` — slim image + whose ENTRYPOINT runs `python3 /app/run.py` (assembles DATABASE_URL + from DATABASE_* env vars via DatabaseURLSettings, then runs + `prisma migrate deploy`). Should track the same release tag as + gateway/backend/ui. + EOT + type = string + default = "" +} + +# ---------- Service sizing ---------- + +variable "gateway_cpu" { + description = "Cloud Run CPU per gateway instance." + type = string + default = "1000m" +} + +variable "gateway_memory" { + description = "Cloud Run memory per gateway instance." + type = string + default = "4Gi" +} + +# Cloud Run autoscales out of the box (request-rate driven). The min/max +# bounds mirror the HPA replica bounds in helm/litellm/values.yaml so each +# stack scales over the same range. Cloud Run has no direct CPU-utilization +# target; the request-concurrency knob below is the closest analog. + +variable "gateway_min_instances" { + description = "Lower bound on gateway Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "gateway_max_instances" { + description = "Upper bound on gateway Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 10 +} + +variable "gateway_max_instance_request_concurrency" { + description = "Concurrent requests one gateway instance handles before Cloud Run scales out. Cloud Run v2 default is 80; lower it for LLM streams that pin a worker for tens of seconds." + type = number + default = 80 +} + +variable "backend_cpu" { + description = "Cloud Run CPU per backend instance. Cloud Run rejects sub-1 CPU when `backend_max_instance_request_concurrency > 1`, so the default is 1000m. Lower this only if you also drop concurrency to 1." + type = string + default = "1000m" +} + +variable "backend_memory" { + description = "Cloud Run memory per backend instance." + type = string + default = "4Gi" +} + +variable "backend_min_instances" { + description = "Lower bound on backend Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "backend_max_instances" { + description = "Upper bound on backend Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 4 +} + +variable "backend_max_instance_request_concurrency" { + description = "Concurrent requests one backend instance handles before Cloud Run scales out." + type = number + default = 80 +} + +variable "ui_cpu" { + description = "Cloud Run CPU per UI instance. Cloud Run rejects sub-1 CPU when `ui_max_instance_request_concurrency > 1`, so the default is 1000m. Lower this only if you also drop concurrency to 1 (which makes nginx scale 1:1 with traffic — almost never what you want)." + type = string + default = "1000m" +} + +variable "ui_memory" { + description = "Cloud Run memory per UI instance. Cloud Run rejects `< 512Mi` when CPU is always-allocated (the default whenever `ui_min_instances > 0`), so the default is 512Mi." + type = string + default = "512Mi" +} + +variable "ui_min_instances" { + description = "Lower bound on UI Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "ui_max_instances" { + description = "Upper bound on UI Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 3 +} + +variable "ui_max_instance_request_concurrency" { + description = "Concurrent requests one UI instance handles before Cloud Run scales out. The UI is static nginx, so this can be high." + type = number + default = 200 +} + +# ---------- Cloud SQL ---------- + +variable "db_tier" { + description = "Cloud SQL tier (machine type) for the writer instance." + type = string + default = "db-custom-2-7680" +} + +variable "db_edition" { + description = "Cloud SQL edition. ENTERPRISE accepts the db-custom-* and db-n1-* tiers. ENTERPRISE_PLUS only accepts db-perf-optimized-* tiers and is ~3x cost — change db_tier in lockstep when switching." + type = string + default = "ENTERPRISE" + + validation { + condition = contains(["ENTERPRISE", "ENTERPRISE_PLUS"], var.db_edition) + error_message = "db_edition must be ENTERPRISE or ENTERPRISE_PLUS." + } +} + +variable "db_version" { + description = "Cloud SQL Postgres version." + type = string + default = "POSTGRES_16" +} + +variable "db_name" { + description = "Initial database created on the Cloud SQL instance." + type = string + default = "litellm" +} + +variable "db_username" { + description = "Application Postgres user (password-auth). Password is auto-generated and stored in Secret Manager." + type = string + default = "litellm_app" +} + +variable "lb_domains" { + description = <<-EOT + DNS names for a Google-managed SSL certificate fronting the LB. When + non-empty, the stack provisions a 443 forwarding rule + HTTPS target + proxy + managed cert covering these domains, and the existing 80 + forwarding rule serves a permanent 301 redirect to HTTPS. Leave empty + ([]) to disable TLS (must combine with `allow_plaintext_lb = true` for + the plan to succeed — see README.md "TLS"). Each domain must already + resolve to the LB's anycast IP (`lb_ip` output) for managed-cert + provisioning to succeed. + EOT + type = list(string) + default = [] +} + +variable "allow_plaintext_lb" { + description = <<-EOT + Opt into HTTP-only mode on the load balancer (port 80, no TLS). + Default false: `terraform plan` fails when `lb_domains = []` so the + operator must either provide DNS names for a managed cert or + consciously opt out. Intended for short-lived trial / dev stacks only. + EOT + type = bool + default = false +} + +variable "cloudsql_deletion_protection" { + description = "Cloud SQL instance-level deletion protection (writer + reader). Default true — `terraform destroy` (and `terraform apply` operations that replace the instance) will fail with a clear error rather than silently dropping the database. Set false only for ephemeral / CI environments." + type = bool + default = true +} + +variable "gcs_force_destroy" { + description = <<-EOT + Allow `terraform destroy` to delete the GCS bucket even when it still + contains objects (request log archives, /v1/files storage, GCS cache + backend). Default false — destroying a non-empty bucket fails, acting + as a tripwire against accidental data loss. Set true only for + ephemeral / CI environments. Mirrors `s3_force_destroy` on AWS and + `cloudsql_deletion_protection` on the database side. + EOT + type = bool + default = false +} + +# ---------- Memorystore (Redis) ---------- + +variable "redis_tier" { + description = "Memorystore tier — STANDARD_HA for production, BASIC for dev." + type = string + default = "STANDARD_HA" +} + +variable "redis_memory_size_gb" { + type = number + default = 1 +} + +# ---------- Extras / proxy_config ---------- + +variable "gateway_extra_env" { + description = "Plain-text env vars layered onto the gateway." + type = map(string) + default = {} +} + +variable "backend_extra_env" { + description = "Plain-text env vars layered onto the backend." + type = map(string) + default = {} +} + +variable "gateway_extra_secrets" { + description = <<-EOT + Extra env vars sourced from Google Secret Manager, applied to the + gateway. Map of env-var name to the Secret Manager **secret resource + ID** (`projects//secrets/` — *not* a version resource + ID; the Cloud Run secret_key_ref binding and the stack's IAM grant + both reject `/versions/` suffixes). Versions are always resolved + as `latest`; if you need a pinned version, edit + `local.gateway_extra_secret_kv` in `cloudrun.tf` directly. + + Example: + gateway_extra_secrets = { + OPENAI_API_KEY = "projects/my-proj/secrets/openai-api-key" + } + + The Cloud Run service account auto-gains roles/secretmanager.secretAccessor + on each secret listed here. + EOT + type = map(string) + default = {} +} + +variable "backend_extra_secrets" { + description = "Same shape as gateway_extra_secrets (secret resource ID, version always `latest`), layered onto the backend." + type = map(string) + default = {} +} + +variable "proxy_config" { + description = <<-EOT + LiteLLM proxy config (contents of config.yaml). Mirrors the helm chart's + `gateway.config.proxy_config`. Passed to gateway, backend, and the + migration job as a base64-encoded env var and decoded to + /tmp/litellm-config.yaml at container start; CONFIG_FILE_PATH is set + automatically. Reference env-injected secrets from the YAML via + `os.environ/`. Leave empty ({}) to skip. + EOT + type = any + default = {} +} diff --git a/terraform/litellm/gcp/versions.tf b/terraform/litellm/gcp/versions.tf new file mode 100644 index 00000000000..a630c59afd0 --- /dev/null +++ b/terraform/litellm/gcp/versions.tf @@ -0,0 +1,18 @@ +terraform { + required_version = ">= 1.6.0" + + required_providers { + google = { + source = "hashicorp/google" + version = "~> 6.10" + } + google-beta = { + source = "hashicorp/google-beta" + version = "~> 6.10" + } + random = { + source = "hashicorp/random" + version = "~> 3.6" + } + } +} diff --git a/tests/_vcr_conftest_common.py b/tests/_vcr_conftest_common.py index a179a21ba69..cb43f1abbdd 100644 --- a/tests/_vcr_conftest_common.py +++ b/tests/_vcr_conftest_common.py @@ -36,6 +36,75 @@ SAFE_BODY_MATCHER_NAME = "safe_body" KEY_FINGERPRINT_MATCHER_NAME = "key_fingerprint" KEY_FINGERPRINT_HEADER = "x-litellm-key-fp" +VCR_DIAG_DIR_ENV = "LITELLM_VCR_DIAG_DIR" +VCR_DIAG_DIR_DEFAULT = "test-results/vcr-diagnostics" + + +def _vcr_diag_dir() -> str: + return os.environ.get(VCR_DIAG_DIR_ENV) or VCR_DIAG_DIR_DEFAULT + + +def vcr_diag_write_line(msg: str) -> None: + try: + directory = _vcr_diag_dir() + os.makedirs(directory, exist_ok=True) + path = os.path.join(directory, f"{os.getpid()}.log") + with open(path, "a", encoding="utf-8") as fh: + fh.write(msg.rstrip("\n") + "\n") + except OSError: + pass + + +def reset_vcr_diag_dir() -> None: + if os.environ.get("PYTEST_XDIST_WORKER"): + return + directory = _vcr_diag_dir() + if not os.path.isdir(directory): + return + try: + names = os.listdir(directory) + except OSError: + return + for name in names: + if name.endswith(".log"): + try: + os.remove(os.path.join(directory, name)) + except OSError: + pass + + +def emit_vcr_diagnostic_log(terminalreporter) -> None: + directory = _vcr_diag_dir() + if not os.path.isdir(directory): + return + try: + files = sorted(f for f in os.listdir(directory) if f.endswith(".log")) + except OSError: + return + if not files: + return + terminalreporter.write_sep("=", "VCR DIAGNOSTIC LOG", bold=True) + terminalreporter.write_line( + f" source dir: {directory} (also archived as a CI artifact)" + ) + for name in files: + path = os.path.join(directory, name) + try: + with open(path, "r", encoding="utf-8") as fh: + content = fh.read() + except OSError as exc: + terminalreporter.write_line( + f" [failed to read {name}: {type(exc).__name__}: {exc}]" + ) + continue + if not content.strip(): + continue + terminalreporter.write_sep("-", name, bold=False) + for line in content.splitlines(): + terminalreporter.write_line(line) + terminalreporter.write_sep("=", bold=True) + + # Intentionally narrower than ``FILTERED_REQUEST_HEADERS``: AWS SigV4 headers # carry secrets but their values rotate on every call, so fingerprinting them # would defeat caching. @@ -91,6 +160,32 @@ VCR_IMAGE_B64_PLACEHOLDER = "dGVzdA==" VCR_FIXED_MULTIPART_BOUNDARY = "vcr-static-boundary" +def pin_httpx_multipart_boundary(monkeypatch) -> None: + try: + import httpx._multipart as _httpx_multipart + except ImportError: + return + + _original_init = _httpx_multipart.MultipartStream.__init__ + + def _init_with_fixed_boundary(self, data, files, boundary=None, **kwargs): + if boundary is None: + boundary = VCR_FIXED_MULTIPART_BOUNDARY.encode("ascii") + return _original_init(self, data=data, files=files, boundary=boundary, **kwargs) + + monkeypatch.setattr( + _httpx_multipart.MultipartStream, "__init__", _init_with_fixed_boundary + ) + + +@pytest.fixture(scope="session", autouse=True) +def _pin_multipart_boundary(): + monkeypatch = pytest.MonkeyPatch() + pin_httpx_multipart_boundary(monkeypatch) + yield + monkeypatch.undo() + + def _scrub_response(response): if not isinstance(response, dict): return response @@ -139,9 +234,17 @@ def _strip_image_b64_payloads(response): preserves all those checks while shrinking cassettes by ~99%. """ if not isinstance(response, dict): + vcr_diag_write_line( + f"[vcr-strip-b64] response is {type(response).__name__!r}, not " + "dict; skipping b64 scrub" + ) return response body = response.get("body") if not isinstance(body, dict): + vcr_diag_write_line( + f"[vcr-strip-b64] response['body'] is {type(body).__name__!r}, " + "not dict; skipping b64 scrub" + ) return response raw = body.get("string") if raw is None: @@ -151,12 +254,20 @@ def _strip_image_b64_payloads(response): try: text = bytes(raw).decode("utf-8") except UnicodeDecodeError: + vcr_diag_write_line( + "[vcr-strip-b64] response body bytes are not valid UTF-8; " + "skipping b64 scrub" + ) return response was_bytes = True elif isinstance(raw, str): text = raw was_bytes = False else: + vcr_diag_write_line( + f"[vcr-strip-b64] response['body']['string'] is " + f"{type(raw).__name__!r}, not bytes/str; skipping b64 scrub" + ) return response try: @@ -186,6 +297,35 @@ def _before_record_response(response): return filter_non_2xx_response(_scrub_response(_strip_image_b64_payloads(response))) +def _canonical_body(request) -> tuple[bytes, str]: + pre_type = type(getattr(request, "body", None)).__name__ + _materialize_iterable_body(request) + body = getattr(request, "body", None) + if body is None: + return b"", pre_type + if isinstance(body, bytes): + return body, pre_type + if isinstance(body, bytearray): + return bytes(body), pre_type + if isinstance(body, str): + return body.encode("utf-8"), pre_type + if isinstance(body, (dict, list)): + try: + return ( + json.dumps(body, sort_keys=True, separators=(",", ":")).encode("utf-8"), + pre_type, + ) + except (TypeError, ValueError): + pass + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + vcr_diag_write_line( + f"[vcr-canonical-body] FALLBACK: {method} {uri} body type " + f"{type(body).__name__!r} not coerced to bytes; comparing as b''" + ) + return b"", pre_type + + def _safe_body_matcher(r1, r2) -> None: """Compare request bodies as bytes; never invokes ``json.loads``. @@ -195,27 +335,47 @@ def _safe_body_matcher(r1, r2) -> None: This matcher is strictly more conservative — the only equivalence it gives up vs. the default is "JSON key order doesn't matter". """ - body1 = getattr(r1, "body", None) - body2 = getattr(r2, "body", None) + body1, pre1 = _canonical_body(r1) + body2, pre2 = _canonical_body(r2) if body1 == body2: return - - def _to_bytes(b): - if b is None: - return b"" - if isinstance(b, bytes): - return b - if isinstance(b, str): - return b.encode("utf-8") - return None - - n1 = _to_bytes(body1) - n2 = _to_bytes(body2) - if n1 is not None and n2 is not None and n1 == n2: - return + _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2) raise AssertionError("request bodies differ") +def _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2) -> None: + def _describe(label, asbytes, pre_type): + return ( + f" {label}: pre_canonical_type={pre_type!r} length={len(asbytes)} " + f"sha256={hashlib.sha256(asbytes).hexdigest()} " + f"preview={asbytes[:120]!r}" + ) + + method_a = getattr(r1, "method", "?") + method_b = getattr(r2, "method", "?") + url_a = getattr(r1, "uri", getattr(r1, "url", "?")) + url_b = getattr(r2, "uri", getattr(r2, "url", "?")) + lines = [ + "[vcr-safe-body-matcher] request body mismatch", + f" request[a]: {method_a} {url_a}", + f" request[b]: {method_b} {url_b}", + _describe("body[a]", body1, pre1), + _describe("body[b]", body2, pre2), + ] + if body1 != body2: + offset = next( + (i for i in range(min(len(body1), len(body2))) if body1[i] != body2[i]), + min(len(body1), len(body2)), + ) + start = max(0, offset - 100) + end_a = min(len(body1), offset + 100) + end_b = min(len(body2), offset + 100) + lines.append(f" first divergent byte offset: {offset}") + lines.append(f" window[a] @ {start}..{end_a}: {body1[start:end_a]!r}") + lines.append(f" window[b] @ {start}..{end_b}: {body2[start:end_b]!r}") + vcr_diag_write_line("\n".join(lines)) + + def _iter_header_values(headers, name: str): if headers is None: return @@ -271,6 +431,13 @@ def _compute_key_fingerprint(request) -> str: stable = _stable_key_value(header_name, text) parts.append(f"{header_name}={stable}") if not parts: + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + vcr_diag_write_line( + f"[vcr-key-fingerprint] no API key header found on {method} " + f"{uri}; falling back to 'no-key'. If this request should have " + "carried auth, something earlier in the pipeline stripped it." + ) return "no-key" digest = hashlib.sha256("\n".join(parts).encode("utf-8")).hexdigest() return digest[:16] @@ -360,6 +527,13 @@ def _normalize_multipart_boundary(request) -> None: elif isinstance(body, str): new_body = body.replace(current_boundary, VCR_FIXED_MULTIPART_BOUNDARY) else: + vcr_diag_write_line( + f"[vcr-multipart-normalize] body normalization SKIPPED: " + f"body type {type(body).__name__!r} is not bytes/bytearray/str. " + f"content-type={content_type_value!r}. " + f"Recorded body will retain the random boundary substring " + f"and the safe_body matcher will miss on the next run." + ) return try: @@ -389,6 +563,7 @@ def _before_record_request(request): headers = getattr(request, "headers", None) if headers is None: return request + _materialize_iterable_body(request) if not any(_iter_header_values(headers, KEY_FINGERPRINT_HEADER)): fingerprint = _compute_key_fingerprint(request) try: @@ -400,6 +575,56 @@ def _before_record_request(request): return request +def _materialize_iterable_body(request) -> None: + body = getattr(request, "body", None) + if body is None or isinstance(body, (bytes, bytearray, str)): + return + if not hasattr(body, "__next__"): + return + try: + chunks = list(body) + except TypeError: + return + + out = _coalesce_chunks_to_bytes(chunks) + if out is None: + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + first_type = type(chunks[0]).__name__ if chunks else "empty" + vcr_diag_write_line( + f"[vcr-materialize] FALLBACK: {method} {uri} chunk type " + f"{first_type!r} not coerced to bytes; storing b''" + ) + out = b"" + + try: + request.body = out + except (AttributeError, TypeError): + pass + + for attr in ("_was_iter", "_was_file"): + try: + setattr(request, attr, False) + except (AttributeError, TypeError): + pass + + +def _coalesce_chunks_to_bytes(chunks): + if not chunks: + return b"" + first = chunks[0] + try: + if isinstance(first, int): + return bytes(chunks) + if isinstance(first, (bytes, bytearray)): + return b"".join(c if isinstance(c, bytes) else bytes(c) for c in chunks) + if isinstance(first, str): + return "".join(chunks).encode("utf-8") + except (TypeError, ValueError): + return None + return None + + def _key_fingerprint_matcher(r1, r2) -> None: def _fp(req): for value in _iter_header_values( @@ -410,7 +635,17 @@ def _key_fingerprint_matcher(r1, r2) -> None: return value if isinstance(value, str) else str(value) return "no-key" - if _fp(r1) != _fp(r2): + fp1, fp2 = _fp(r1), _fp(r2) + if fp1 != fp2: + method_a = getattr(r1, "method", "?") + method_b = getattr(r2, "method", "?") + url_a = getattr(r1, "uri", getattr(r1, "url", "?")) + url_b = getattr(r2, "uri", getattr(r2, "url", "?")) + vcr_diag_write_line( + "[vcr-key-fingerprint-matcher] API key fingerprints differ\n" + f" request[a]: {method_a} {url_a} fingerprint={fp1!r}\n" + f" request[b]: {method_b} {url_b} fingerprint={fp2!r}" + ) raise AssertionError("API key fingerprints differ") diff --git a/tests/_vcr_redis_persister.py b/tests/_vcr_redis_persister.py index 7fdb7267a38..373cb66696a 100644 --- a/tests/_vcr_redis_persister.py +++ b/tests/_vcr_redis_persister.py @@ -159,9 +159,20 @@ def make_redis_persister( raise CassetteNotFoundError() from exc if data is None: raise CassetteNotFoundError() - if isinstance(data, bytes): - data = data.decode("utf-8") - return deserialize(data, serializer) + try: + if isinstance(data, bytes): + data = data.decode("utf-8") + return deserialize(data, serializer) + except Exception as exc: + _record_cache_failure("load", exc) + msg = ( + f"VCR redis load failed for {cassette_path}; cached " + f"payload is corrupt, treating as cache miss: " + f"{type(exc).__name__}: {exc}" + ) + _log.warning(msg) + warnings.warn(msg, VCRCassetteCacheWarning, stacklevel=2) + raise CassetteNotFoundError() from exc @staticmethod def save_cassette(cassette_path, cassette_dict, serializer): diff --git a/tests/audio_tests/conftest.py b/tests/audio_tests/conftest.py index ff47853d494..c4ff576e5bd 100644 --- a/tests/audio_tests/conftest.py +++ b/tests/audio_tests/conftest.py @@ -5,14 +5,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -44,6 +47,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -57,3 +61,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/audio_tests/test_whisper.py b/tests/audio_tests/test_whisper.py index cdf079f8cb4..243d27614b1 100644 --- a/tests/audio_tests/test_whisper.py +++ b/tests/audio_tests/test_whisper.py @@ -23,12 +23,21 @@ pwd = os.path.dirname(os.path.realpath(__file__)) print(pwd) file_path = os.path.join(pwd, "gettysburg.wav") - -audio_file = open(file_path, "rb") - - file2_path = os.path.join(pwd, "eagle.wav") -audio_file2 = open(file2_path, "rb") + +with open(file_path, "rb") as _f: + _GETTYSBURG_BYTES = _f.read() +with open(file2_path, "rb") as _f: + _EAGLE_BYTES = _f.read() + + +def _audio_file(): + return ("gettysburg.wav", _GETTYSBURG_BYTES, "audio/wav") + + +def _audio_file2(): + return ("eagle.wav", _EAGLE_BYTES, "audio/wav") + load_dotenv() @@ -44,7 +53,7 @@ async def _run_transcription( ): transcript = await litellm.atranscription( model=model, - file=audio_file, + file=_audio_file(), api_key=api_key, api_base=api_base, response_format=response_format, @@ -101,7 +110,7 @@ async def test_transcription_caching(): response_1 = await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) await asyncio.sleep(5) @@ -110,7 +119,7 @@ async def test_transcription_caching(): response_2 = await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) print("response_1", response_1) @@ -122,7 +131,7 @@ async def test_transcription_caching(): response_3 = await litellm.atranscription( model="whisper-1", - file=audio_file2, + file=_audio_file2(), ) print("response_3", response_3) print("response3 hidden params", response_3._hidden_params) @@ -146,7 +155,7 @@ async def test_whisper_log_pre_call(): with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call: await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) mock_log_pre_call.assert_called_once() @@ -165,7 +174,7 @@ async def test_whisper_log_pre_call(): with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call: await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) mock_log_pre_call.assert_called_once() @@ -177,7 +186,7 @@ async def test_gpt_4o_transcribe(): from unittest.mock import patch, MagicMock await litellm.atranscription( - model="openai/gpt-4o-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json" ) @@ -187,7 +196,9 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test GPT-4o mini transcribe response = await litellm.atranscription( - model="openai/gpt-4o-mini-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-mini-transcribe", + file=_audio_file(), + response_format="json", ) # Check that the response contains the correct model in hidden params @@ -198,7 +209,7 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test GPT-4o transcribe response2 = await litellm.atranscription( - model="openai/gpt-4o-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json" ) # Check that the response contains the correct model in hidden params @@ -209,7 +220,7 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test traditional whisper-1 still works response3 = await litellm.atranscription( - model="openai/whisper-1", file=audio_file, response_format="json" + model="openai/whisper-1", file=_audio_file(), response_format="json" ) # Check that the response contains the correct model in hidden params @@ -262,7 +273,7 @@ async def test_azure_transcribe_model_mapping(): # Make the transcription call response = await litellm.atranscription( model="azure/whisper-1", - file=audio_file, + file=_audio_file(), response_format="json", api_key="test-api-key", api_base="https://my-endpoint-europe-berri-992.openai.azure.com/", diff --git a/tests/code_coverage_tests/liccheck.ini b/tests/code_coverage_tests/liccheck.ini index 5a09403c570..3d53ecede7b 100644 --- a/tests/code_coverage_tests/liccheck.ini +++ b/tests/code_coverage_tests/liccheck.ini @@ -90,6 +90,7 @@ jinja2: >=3.1.4 # BSD 3-Clause License litellm-proxy-extras: >=0.1.1 # MIT License litellm-enterprise: >=0.1.1 # LiteLLM Enterprise License a2a-sdk: >=0.3.22 # Apache 2.0 license +pydantic-settings: >=2.14.1 # MIT License manually verified (uses PEP 639 License-Expression: MIT, not the legacy License field, so liccheck reports it as unknown) anyio: >=4.5.0 # Unknown license httpx-aiohttp: >=0.1.4 # Unknown license backoff: >=2.2.1 # Unknown license diff --git a/tests/guardrails_tests/conftest.py b/tests/guardrails_tests/conftest.py index eb563699b2b..f2f65645c3d 100644 --- a/tests/guardrails_tests/conftest.py +++ b/tests/guardrails_tests/conftest.py @@ -16,14 +16,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -55,6 +58,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -160,3 +164,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/image_gen_tests/conftest.py b/tests/image_gen_tests/conftest.py index 93dec98e708..9f808c11161 100644 --- a/tests/image_gen_tests/conftest.py +++ b/tests/image_gen_tests/conftest.py @@ -9,14 +9,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -58,6 +61,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/image_gen_tests/test_image_edits.py b/tests/image_gen_tests/test_image_edits.py index 656b8a69117..ca8ec3bbe32 100644 --- a/tests/image_gen_tests/test_image_edits.py +++ b/tests/image_gen_tests/test_image_edits.py @@ -103,12 +103,6 @@ class BaseLLMImageEditTest(ABC): pwd = os.path.dirname(os.path.realpath(__file__)) -# Image fixtures must be regenerated per access — module-level -# ``open(...)`` handles get consumed after a single multipart upload, leaving -# subsequent tests in the same process to send empty bodies. That non-determinism -# (a) blows the recorded cassette past ``MAX_EPISODES_PER_CASSETTE`` so the -# persister refuses to save (see ``tests/_vcr_redis_persister.py``), and -# (b) re-bills the live image edit endpoint on every CI run. def _read_image_bytes(filename: str) -> bytes: with open(os.path.join(pwd, filename), "rb") as f: return f.read() @@ -119,32 +113,20 @@ _LITELLM_SITE_BYTES = _read_image_bytes("litellm_site.png") def _make_test_images() -> list: - """Return a fresh pair of image streams seeded with the fixture bytes. + return [_ISHAAN_GITHUB_BYTES, _LITELLM_SITE_BYTES] - Use this everywhere you'd previously have used the module-level - ``TEST_IMAGES``. Each call returns brand new ``BytesIO`` objects whose - file pointers start at 0, so multipart uploads encode the full image - bytes on every test invocation. Parametrized and ``flaky``-retried - test methods call ``get_base_image_edit_call_args`` once per - invocation, so a fresh stream per call is sufficient — the factory - must not auto-rewind on EOF or the SDK's multipart writer will read - the same bytes forever (worker OOM). - """ + +def _make_single_test_image() -> bytes: + return _ISHAAN_GITHUB_BYTES + + +def get_test_images_as_bytesio(): return [ BytesIO(_ISHAAN_GITHUB_BYTES), BytesIO(_LITELLM_SITE_BYTES), ] -def _make_single_test_image() -> BytesIO: - return BytesIO(_ISHAAN_GITHUB_BYTES) - - -def get_test_images_as_bytesio(): - """Helper function to get test images as BytesIO objects""" - return _make_test_images() - - class TestOpenAIImageEditGPTImage1(BaseLLMImageEditTest): """ Concrete implementation of BaseLLMImageEditTest for OpenAI image edits. @@ -710,10 +692,9 @@ async def test_multiple_image_edit_with_different_formats(): try: prompt = "Create a cohesive artistic style across all images" - # Test with mixed BytesIO and file objects mixed_images = [ - _make_single_test_image(), # File object - get_test_images_as_bytesio()[1], # BytesIO object + _make_single_test_image(), + get_test_images_as_bytesio()[1], ] result = await aimage_edit( diff --git a/tests/litellm_utils_tests/conftest.py b/tests/litellm_utils_tests/conftest.py index 08745c99c07..418ee76a399 100644 --- a/tests/litellm_utils_tests/conftest.py +++ b/tests/litellm_utils_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -86,6 +89,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/llm_responses_api_testing/conftest.py b/tests/llm_responses_api_testing/conftest.py index 2a08db57149..1928b540dad 100644 --- a/tests/llm_responses_api_testing/conftest.py +++ b/tests/llm_responses_api_testing/conftest.py @@ -13,14 +13,17 @@ sys.path.insert( import litellm # noqa: E402 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/llm_translation/conftest.py b/tests/llm_translation/conftest.py index 5fcd31aa32d..d346dae4308 100644 --- a/tests/llm_translation/conftest.py +++ b/tests/llm_translation/conftest.py @@ -18,14 +18,17 @@ sys.path.insert( import litellm # noqa: E402 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -73,6 +76,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -82,6 +86,7 @@ def pytest_runtest_logreport(report): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) # --------------------------------------------------------------------------- diff --git a/tests/llm_translation/realtime/test_openai_realtime.py b/tests/llm_translation/realtime/test_openai_realtime.py index c5f77de6beb..fc9f938b4cd 100644 --- a/tests/llm_translation/realtime/test_openai_realtime.py +++ b/tests/llm_translation/realtime/test_openai_realtime.py @@ -101,7 +101,9 @@ async def test_openai_realtime_direct_call_no_intent(): try: await litellm._arealtime( - model="openai/gpt-4o-realtime-preview", + # OpenAI shut down the gpt-4o-realtime-preview family (incl. the + # undated alias) on 2026-05-07; gpt-realtime is the GA successor. + model="openai/gpt-realtime", websocket=websocket_client, api_key=os.environ.get("OPENAI_API_KEY"), timeout=60, @@ -249,14 +251,16 @@ async def test_openai_realtime_direct_call_with_intent(): websocket_client = RealTimeWebSocketClient() caught_exception = None + # OpenAI shut down the gpt-4o-realtime-preview family (incl. the undated + # alias) on 2026-05-07; gpt-realtime is the GA successor. query_params: RealtimeQueryParams = { - "model": "openai/gpt-4o-realtime-preview", + "model": "openai/gpt-realtime", "intent": "chat", } try: await litellm._arealtime( - model="openai/gpt-4o-realtime-preview", + model="openai/gpt-realtime", websocket=websocket_client, api_key=os.environ.get("OPENAI_API_KEY"), query_params=query_params, diff --git a/tests/llm_translation/realtime/test_openai_realtime_simple.py b/tests/llm_translation/realtime/test_openai_realtime_simple.py index 5522d843e42..073c1ce11af 100644 --- a/tests/llm_translation/realtime/test_openai_realtime_simple.py +++ b/tests/llm_translation/realtime/test_openai_realtime_simple.py @@ -21,7 +21,10 @@ class TestOpenAIRealtime(BaseRealtimeTest): """ def get_model(self) -> str: - return "gpt-4o-realtime-preview" + # OpenAI shut down the entire gpt-4o-realtime-preview family + # (including the undated alias) on 2026-05-07. gpt-realtime is the + # current GA realtime model. + return "gpt-realtime" def get_api_key_env_var(self) -> str: return "OPENAI_API_KEY" diff --git a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py index 170440f6b9f..50cedba2ac0 100644 --- a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py +++ b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py @@ -26,9 +26,7 @@ from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.types.guardrails import GuardrailEventHooks OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") -OPENAI_REALTIME_URL = ( - "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview-2024-12-17" -) +OPENAI_REALTIME_URL = "wss://api.openai.com/v1/realtime?model=gpt-realtime" pytestmark = pytest.mark.skipif( not OPENAI_API_KEY, @@ -192,10 +190,35 @@ async def test_text_message_blocked_by_guardrail_no_ai_response(): len(transcript_deltas) >= 1 ), f"Expected guardrail message in transcript delta, got: {event_types}" - # 3. No *real* AI response should have been generated. - # The guardrail may produce its own response (e.g. "Content blocked: ...") - # via response.cancel + conversation.item.create + response.create. - # We allow the guardrail's own block message but NOT original AI content. + # 3. No *real* AI response to the blocked content should have been + # generated. The original user message is blocked BEFORE it is + # forwarded to OpenAI, so the only thing the model ever sees is the + # guardrail's "say exactly: " prompt + # (see realtime_streaming.py). Two safe outcomes are possible: + # - the model voices the block message verbatim (older realtime + # snapshots did this -> text contains "blocked"), or + # - the model declines to repeat it (gpt-realtime tends to refuse + # verbatim-repeat instructions, e.g. "I'm sorry, but I can't + # repeat that message."). + # Both mean the blocked prompt itself was never answered, so we + # accept either. The hard invariant is that the blocked phrase must + # never leak into AI output, and the model must not have produced a + # normal answer to the user (which would have neither a block nor a + # refusal marker). + safe_markers = ( + "block", + "guardrail", + "content filter", + "policy", + "can't repeat", + "cannot repeat", + "won't repeat", + "can't assist", + "can't help", + "unable to", + "i'm sorry", + "i am sorry", + ) done_events = [e for e in client_events if e.get("type") == "response.done"] for done in done_events: output = done.get("response", {}).get("output", []) @@ -205,11 +228,19 @@ async def test_text_message_blocked_by_guardrail_no_ai_response(): for c in item.get("content", []) ] real_ai_text = " ".join(ai_texts).strip() - # Allow guardrail-generated block messages (contain "Content blocked" or "blocked") if real_ai_text: assert ( - "blocked" in real_ai_text.lower() - or "guardrail" in real_ai_text.lower() + BLOCKED_PHRASE not in real_ai_text + ), f"Blocked phrase leaked into AI response: {real_ai_text!r}" + normalized_ai_text = ( + real_ai_text.lower() + .replace("\u2019", "'") + .replace("\u2018", "'") + .replace("\u201c", '"') + .replace("\u201d", '"') + ) + assert any( + marker in normalized_ai_text for marker in safe_markers ), f"AI responded with non-guardrail content even though message was blocked: {real_ai_text!r}" finally: diff --git a/tests/llm_translation/reasoning_effort_grid/__init__.py b/tests/llm_translation/reasoning_effort_grid/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/llm_translation/reasoning_effort_grid/conftest.py b/tests/llm_translation/reasoning_effort_grid/conftest.py new file mode 100644 index 00000000000..4ea2cd1d9b9 --- /dev/null +++ b/tests/llm_translation/reasoning_effort_grid/conftest.py @@ -0,0 +1,38 @@ +from typing import Any, Dict, List, Optional + +import pytest + +import litellm +from litellm.integrations.custom_logger import CustomLogger + + +class _WireBodyCapture(CustomLogger): + def __init__(self) -> None: + super().__init__() + self.records: List[Dict[str, Any]] = [] + + def log_pre_api_call(self, model, messages, kwargs): + self.records.append( + { + "model": model, + "body": kwargs.get("additional_args", {}).get("complete_input_dict"), + "api_base": kwargs.get("additional_args", {}).get("api_base"), + } + ) + + async def async_log_pre_api_call(self, model, messages, kwargs): + self.log_pre_api_call(model, messages, kwargs) + + def latest(self) -> Optional[Dict[str, Any]]: + return self.records[-1] if self.records else None + + +@pytest.fixture() +def wire_capture(): + capture = _WireBodyCapture() + previous = list(litellm.callbacks) + litellm.callbacks = previous + [capture] + try: + yield capture + finally: + litellm.callbacks = previous diff --git a/tests/llm_translation/reasoning_effort_grid/grid_spec.py b/tests/llm_translation/reasoning_effort_grid/grid_spec.py new file mode 100644 index 00000000000..ed5346dad71 --- /dev/null +++ b/tests/llm_translation/reasoning_effort_grid/grid_spec.py @@ -0,0 +1,289 @@ +from dataclasses import dataclass, field +from typing import Dict, FrozenSet, List, Optional, Tuple + + +OMIT = object() + + +@dataclass(frozen=True) +class CellExpectation: + status: int + thinking_type: object + output_config_effort: object = OMIT + thinking_budget_tokens: object = OMIT + max_tokens: object = OMIT + + +@dataclass(frozen=True) +class ModelEntry: + alias: str + model: str + mode: str + extra_params: Tuple[Tuple[str, str], ...] = field(default_factory=tuple) + required_env: FrozenSet[str] = field(default_factory=frozenset) + caps: FrozenSet[str] = field(default_factory=frozenset) + + def params(self) -> Dict[str, str]: + return dict(self.extra_params) + + +EFFORTS: Tuple[str, ...] = ( + "__omit__", + "none", + "minimal", + "low", + "medium", + "high", + "xhigh", + "max", + "disabled", + "invalid", + "", +) + +_BUDGET_TOKENS: Dict[str, int] = { + "minimal": 1024, + "low": 1024, + "medium": 2048, + "high": 4096, + "xhigh": 8192, + "max": 16384, +} + +_ADAPTIVE_EFFORT_LABEL: Dict[str, str] = { + "minimal": "low", + "low": "low", + "medium": "medium", + "high": "high", + "xhigh": "xhigh", + "max": "max", +} + +_BAD_REQUEST_EFFORTS: FrozenSet[str] = frozenset({"disabled", "invalid", ""}) + + +def expected(model: ModelEntry, effort: str) -> CellExpectation: + if effort in ("__omit__", "none"): + if model.mode == "budget": + return CellExpectation(status=200, thinking_type=OMIT, max_tokens=8192) + return CellExpectation(status=200, thinking_type=OMIT) + + if effort in _BAD_REQUEST_EFFORTS: + return CellExpectation(status=400, thinking_type=OMIT) + + if effort in ("xhigh", "max"): + cap = f"supports_{effort}_reasoning_effort" + if cap not in model.caps: + return CellExpectation(status=400, thinking_type=OMIT) + + if model.mode == "adaptive": + return CellExpectation( + status=200, + thinking_type="adaptive", + output_config_effort=_ADAPTIVE_EFFORT_LABEL[effort], + ) + + return CellExpectation( + status=200, + thinking_type="enabled", + thinking_budget_tokens=_BUDGET_TOKENS[effort], + max_tokens=8192, + ) + + +_ANTHROPIC_REQ = frozenset({"ANTHROPIC_API_KEY"}) +_AZURE_FOUNDRY_REQ = frozenset({"AZURE_FOUNDRY_API_BASE", "AZURE_FOUNDRY_API_KEY"}) +_VERTEX_REQ = frozenset({"VERTEX_PROJECT"}) +_BEDROCK_REQ = frozenset({"AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY"}) + + +_CAPS_OPUS_4_7: FrozenSet[str] = frozenset( + {"supports_xhigh_reasoning_effort", "supports_max_reasoning_effort"} +) +_CAPS_4_6: FrozenSet[str] = frozenset({"supports_max_reasoning_effort"}) +_CAPS_NONE: FrozenSet[str] = frozenset() + + +ANTHROPIC_DIRECT_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="claude-opus-4-7", + model="anthropic/claude-opus-4-7", + mode="adaptive", + required_env=_ANTHROPIC_REQ, + caps=_CAPS_OPUS_4_7, + ), + ModelEntry( + alias="claude-sonnet-4-6", + model="anthropic/claude-sonnet-4-6", + mode="adaptive", + required_env=_ANTHROPIC_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="claude-haiku-4-5", + model="anthropic/claude-haiku-4-5", + mode="budget", + required_env=_ANTHROPIC_REQ, + caps=_CAPS_NONE, + ), +) + + +AZURE_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="azure-claude-opus-4-7", + model="azure_ai/claude-opus-4-7", + mode="adaptive", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_OPUS_4_7, + ), + ModelEntry( + alias="azure-claude-opus-4-6", + model="azure_ai/claude-opus-4-6", + mode="adaptive", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="azure-claude-sonnet-4-6", + model="azure_ai/claude-sonnet-4-6", + mode="adaptive", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="azure-claude-haiku-4-5", + model="azure_ai/claude-haiku-4-5", + mode="budget", + required_env=_AZURE_FOUNDRY_REQ, + caps=_CAPS_NONE, + ), +) + + +VERTEX_AI_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="vertex-claude-opus-4-7", + model="vertex_ai/claude-opus-4-7", + mode="adaptive", + extra_params=(("vertex_location", "global"),), + required_env=_VERTEX_REQ, + caps=_CAPS_OPUS_4_7, + ), + ModelEntry( + alias="vertex-claude-opus-4-6", + model="vertex_ai/claude-opus-4-6", + mode="adaptive", + extra_params=(("vertex_location", "us-east5"),), + required_env=_VERTEX_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="vertex-claude-sonnet-4-6", + model="vertex_ai/claude-sonnet-4-6", + mode="adaptive", + extra_params=(("vertex_location", "us-east5"),), + required_env=_VERTEX_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="vertex-claude-haiku-4-5", + model="vertex_ai/claude-haiku-4-5", + mode="budget", + extra_params=(("vertex_location", "us-east5"),), + required_env=_VERTEX_REQ, + caps=_CAPS_NONE, + ), +) + + +BEDROCK_CONVERSE_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="bedrock-claude-opus-4-7", + model="bedrock/converse/us.anthropic.claude-opus-4-7", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_OPUS_4_7, + ), + ModelEntry( + alias="bedrock-claude-opus-4-6", + model="bedrock/converse/us.anthropic.claude-opus-4-6-v1", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="bedrock-claude-sonnet-4-6", + model="bedrock/converse/us.anthropic.claude-sonnet-4-6", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="bedrock-claude-sonnet-4-5", + model="bedrock/converse/us.anthropic.claude-sonnet-4-5-20250929-v1:0", + mode="budget", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_NONE, + ), +) + + +BEDROCK_INVOKE_CHAT_MODELS: Tuple[ModelEntry, ...] = ( + ModelEntry( + alias="bedrock-invoke-claude-opus-4-6", + model="bedrock/invoke/us.anthropic.claude-opus-4-6-v1", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="bedrock-invoke-claude-sonnet-4-6", + model="bedrock/invoke/us.anthropic.claude-sonnet-4-6", + mode="adaptive", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_4_6, + ), + ModelEntry( + alias="bedrock-invoke-claude-opus-4-5", + model="bedrock/invoke/us.anthropic.claude-opus-4-5-20251101-v1:0", + mode="budget", + extra_params=(("aws_region_name", "us-east-1"),), + required_env=_BEDROCK_REQ, + caps=_CAPS_NONE, + ), +) + + +BEDROCK_INVOKE_MESSAGES_MODELS: Tuple[ModelEntry, ...] = BEDROCK_INVOKE_CHAT_MODELS + + +@dataclass(frozen=True) +class Route: + name: str + models: Tuple[ModelEntry, ...] + + +ROUTES: Tuple[Route, ...] = ( + Route("anthropic_direct", ANTHROPIC_DIRECT_MODELS), + Route("azure_ai", AZURE_AI_MODELS), + Route("vertex_ai", VERTEX_AI_MODELS), + Route("bedrock_converse", BEDROCK_CONVERSE_MODELS), + Route("bedrock_invoke_chat", BEDROCK_INVOKE_CHAT_MODELS), + Route("bedrock_invoke_messages", BEDROCK_INVOKE_MESSAGES_MODELS), +) + + +def all_cells() -> List[Tuple[str, ModelEntry, str, CellExpectation]]: + cells: List[Tuple[str, ModelEntry, str, CellExpectation]] = [] + for route in ROUTES: + for model in route.models: + for effort in EFFORTS: + cells.append((route.name, model, effort, expected(model, effort))) + return cells diff --git a/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py new file mode 100644 index 00000000000..28e2e402d67 --- /dev/null +++ b/tests/llm_translation/reasoning_effort_grid/test_reasoning_effort_grid.py @@ -0,0 +1,207 @@ +import json +import os +from typing import Any, Dict, List, Optional, Tuple + +import pytest + +import litellm +from litellm.exceptions import BadRequestError + +from .grid_spec import ( + OMIT, + ROUTES, + CellExpectation, + ModelEntry, + all_cells, +) + + +_PROMPT_MESSAGES: List[Dict[str, str]] = [ + {"role": "user", "content": "Step by step, calculate 47 * 53. Show your work."} +] + + +def _required_env_missing(model: ModelEntry) -> Optional[str]: + missing = [key for key in model.required_env if not os.environ.get(key)] + if missing: + return "missing env: " + ", ".join(sorted(missing)) + return None + + +def _max_tokens_for(model: ModelEntry) -> int: + return 200 if model.mode == "adaptive" else 8192 + + +def _build_completion_kwargs(model: ModelEntry, effort: str) -> Dict[str, Any]: + kwargs: Dict[str, Any] = { + "model": model.model, + "messages": _PROMPT_MESSAGES, + "max_tokens": _max_tokens_for(model), + } + kwargs.update(model.params()) + if effort != "__omit__": + kwargs["reasoning_effort"] = effort + if model.model.startswith("vertex_ai/"): + kwargs["vertex_project"] = os.environ["VERTEX_PROJECT"] + if model.model.startswith("azure_ai/"): + kwargs["api_base"] = os.environ["AZURE_FOUNDRY_API_BASE"] + kwargs["api_key"] = os.environ["AZURE_FOUNDRY_API_KEY"] + return kwargs + + +def _converse_subbody(body: Dict[str, Any]) -> Dict[str, Any]: + return body.get("additionalModelRequestFields", body) + + +def _max_tokens_from_body(body: Dict[str, Any], route_name: str) -> Optional[int]: + if route_name == "bedrock_converse": + return body.get("inferenceConfig", {}).get("maxTokens") + return body.get("max_tokens") + + +def _assert_cell( + route_name: str, + body: Optional[Dict[str, Any]], + status: int, + cell: CellExpectation, +) -> None: + assert status == cell.status, f"expected status={cell.status}, got status={status}" + + if cell.status != 200: + return + + assert body is not None, "wire body was not captured for a 200-status cell" + subbody = _converse_subbody(body) if route_name == "bedrock_converse" else body + thinking = subbody.get("thinking") + output_config = subbody.get("output_config") + + if cell.thinking_type is OMIT: + assert thinking is None, f"expected thinking omitted, got {thinking!r}" + else: + assert thinking is not None, "expected thinking present, got omit" + assert thinking.get("type") == cell.thinking_type, ( + f"expected thinking.type={cell.thinking_type!r}, " + f"got {thinking.get('type')!r}" + ) + + if cell.output_config_effort is OMIT: + assert ( + output_config is None or "effort" not in output_config + ), f"expected output_config.effort omitted, got {output_config!r}" + else: + assert output_config is not None, ( + f"expected output_config.effort={cell.output_config_effort!r}, " + "got output_config omitted" + ) + assert output_config.get("effort") == cell.output_config_effort, ( + f"expected output_config.effort={cell.output_config_effort!r}, " + f"got {output_config.get('effort')!r}" + ) + + if cell.thinking_budget_tokens is not OMIT: + assert thinking is not None + assert thinking.get("budget_tokens") == cell.thinking_budget_tokens, ( + f"expected thinking.budget_tokens={cell.thinking_budget_tokens!r}, " + f"got {thinking.get('budget_tokens')!r}" + ) + + if cell.max_tokens is not OMIT: + wire_max = _max_tokens_from_body(body, route_name) + assert ( + wire_max == cell.max_tokens + ), f"expected max_tokens={cell.max_tokens!r}, got {wire_max!r}" + + +_PARAMS: List[Tuple[str, ModelEntry, str, CellExpectation]] = all_cells() + + +def _cell_id(case: Tuple[str, ModelEntry, str, CellExpectation]) -> str: + route_name, model, effort, _ = case + effort_label = "__empty__" if effort == "" else effort + return f"{route_name}-{model.alias}-{effort_label}" + + +_PARAM_IDS: List[str] = [_cell_id(case) for case in _PARAMS] + + +def _classify_status(exc: Exception) -> int: + if isinstance(exc, BadRequestError): + return 400 + code = getattr(exc, "status_code", None) + if isinstance(code, int): + return code + return 500 + + +async def _call_chat(model: ModelEntry, effort: str) -> Tuple[int, Optional[Exception]]: + kwargs = _build_completion_kwargs(model, effort) + try: + await litellm.acompletion(**kwargs) + return 200, None + except Exception as exc: + return _classify_status(exc), exc + + +async def _call_messages( + model: ModelEntry, effort: str +) -> Tuple[int, Optional[Exception]]: + kwargs = _build_completion_kwargs(model, effort) + try: + await litellm.anthropic_messages(**kwargs) + return 200, None + except Exception as exc: + return _classify_status(exc), exc + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("route_name", "model", "effort", "cell"), _PARAMS, ids=_PARAM_IDS +) +async def test_reasoning_effort_grid( + route_name: str, + model: ModelEntry, + effort: str, + cell: CellExpectation, + wire_capture, +) -> None: + skip_reason = _required_env_missing(model) + if skip_reason: + pytest.skip(skip_reason) + + if route_name == "bedrock_invoke_messages": + status, exc = await _call_messages(model, effort) + else: + status, exc = await _call_chat(model, effort) + + record = wire_capture.latest() + body = record["body"] if record else None + if route_name == "bedrock_converse" and isinstance(body, str): + body = json.loads(body) + + try: + _assert_cell(route_name, body, status, cell) + except AssertionError: + if exc is not None: + raise AssertionError( + f"underlying exception ({type(exc).__name__}): {exc}" + ) from None + raise + + +def test_grid_cell_count() -> None: + assert len(_PARAMS) == 21 * 11, ( + f"expected 231 cells (21 provider x model combos x 11 efforts), " + f"got {len(_PARAMS)}" + ) + + +def test_grid_route_coverage() -> None: + route_names = {route.name for route in ROUTES} + assert route_names == { + "anthropic_direct", + "azure_ai", + "vertex_ai", + "bedrock_converse", + "bedrock_invoke_chat", + "bedrock_invoke_messages", + } diff --git a/tests/llm_translation/test_deepseek_completion.py b/tests/llm_translation/test_deepseek_completion.py index da402a51b68..2ede5d3f3f8 100644 --- a/tests/llm_translation/test_deepseek_completion.py +++ b/tests/llm_translation/test_deepseek_completion.py @@ -176,3 +176,113 @@ def test_completion_cost_deepseek(): pass except Exception as e: pytest.fail(f"Error occurred: {e}") + + +def test_deepseek_fill_reasoning_content_multiturn(): + """ + Unit test for _fill_reasoning_content. + Reproduces issue #28045: DeepSeek thinking mode fails in multi-turn conversations + because reasoning_content is not passed back to the API. + """ + from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig + + config = DeepSeekChatConfig() + + # Case 1: assistant message already has reasoning_content — should be left as-is + messages_with_rc = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi", "reasoning_content": "I thought about it"}, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_with_rc) + assert result[1]["reasoning_content"] == "I thought about it" + + # Case 2: assistant message has reasoning_content in provider_specific_fields — should be promoted + messages_with_psf = [ + {"role": "user", "content": "Hello"}, + { + "role": "assistant", + "content": "Hi", + "provider_specific_fields": {"reasoning_content": "stored thinking"}, + }, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_with_psf) + assert result[1]["reasoning_content"] == "stored thinking" + # Should be removed from provider_specific_fields to avoid duplication + assert "reasoning_content" not in result[1].get("provider_specific_fields", {}) + + # Case 3: assistant message has no reasoning_content anywhere — should inject placeholder + messages_no_rc = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"}, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_no_rc) + assert result[1]["reasoning_content"] == " " + + # Case 4: non-assistant messages should never be touched + messages_user_only = [ + {"role": "user", "content": "Hello"}, + {"role": "system", "content": "You are helpful"}, + ] + result = config._fill_reasoning_content(messages_user_only) + assert "reasoning_content" not in result[0] + assert "reasoning_content" not in result[1] + + +def test_deepseek_fill_reasoning_content_guard_in_transform_request(): + """ + _fill_reasoning_content must only run when BOTH conditions are true: + 1. supports_reasoning() is True for the model + 2. thinking mode is explicitly enabled in optional_params ({"type": "enabled"}) + + This prevents spurious injection on models like deepseek-v3.2 that support + thinking as opt-in but not always-on. Addresses oss-pr-review-agent feedback + on PR #28057. + """ + from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig + + config = DeepSeekChatConfig() + + messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"}, + {"role": "user", "content": "Follow up"}, + ] + + # Case 1: reasoning model + thinking enabled -> injection should happen + result = config.transform_request( + model="deepseek-reasoner", + messages=messages, + optional_params={"thinking": {"type": "enabled"}}, + litellm_params={}, + headers={}, + ) + assert result["messages"][1].get("reasoning_content") == " ", ( + "reasoning_content should be injected when thinking is enabled" + ) + + # Case 2: reasoning model + thinking NOT in optional_params -> no injection + result = config.transform_request( + model="deepseek-reasoner", + messages=messages, + optional_params={}, + litellm_params={}, + headers={}, + ) + assert "reasoning_content" not in result["messages"][1], ( + "reasoning_content should not be injected when thinking is not enabled" + ) + + # Case 3: non-reasoning model + thinking enabled -> no injection + result = config.transform_request( + model="deepseek-chat", + messages=messages, + optional_params={"thinking": {"type": "enabled"}}, + litellm_params={}, + headers={}, + ) + assert "reasoning_content" not in result["messages"][1], ( + "reasoning_content should not be injected for non-reasoning models" + ) diff --git a/tests/llm_translation/test_gemini.py b/tests/llm_translation/test_gemini.py index 01e40628441..bd340aa63be 100644 --- a/tests/llm_translation/test_gemini.py +++ b/tests/llm_translation/test_gemini.py @@ -1362,8 +1362,12 @@ def test_anthropic_thinking_param_to_gemini_3_provider_defaults(): ) # For Gemini 3, should not force thinkingLevel by default - assert "thinkingLevel" not in result, "Should not force thinkingLevel for Gemini 3" - assert "thinkingBudget" not in result, "Should NOT have thinkingBudget for Gemini 3" + assert ( + "thinkingLevel" not in result + ), "Should not force thinkingLevel for Gemini 3" + assert ( + "thinkingBudget" not in result + ), "Should NOT have thinkingBudget for Gemini 3" assert result["includeThoughts"] is True # Test 2: Anthropic thinking disabled for Gemini 3 @@ -1395,7 +1399,10 @@ def test_anthropic_thinking_param_to_gemini_3_provider_defaults(): ) assert result_zero["includeThoughts"] is False - assert "thinkingLevel" not in result_zero or result_zero.get("thinkingLevel") is None + assert ( + "thinkingLevel" not in result_zero + or result_zero.get("thinkingLevel") is None + ) # Test 4: Gemini 3 flash-preview should also follow provider defaults by default result_gemini3flashpreview = VertexGeminiConfig._map_thinking_param( @@ -1525,8 +1532,12 @@ def test_anthropic_thinking_param_via_map_openai_params(): # Check that thinkingConfig was created without forced thinkingLevel assert "thinkingConfig" in result, "Should have thinkingConfig in optional_params" thinking_config = result["thinkingConfig"] - assert "thinkingLevel" not in thinking_config, "Should not force thinkingLevel for Gemini 3 by default" - assert "thinkingBudget" not in thinking_config, "Should NOT have thinkingBudget for Gemini 3" + assert ( + "thinkingLevel" not in thinking_config + ), "Should not force thinkingLevel for Gemini 3 by default" + assert ( + "thinkingBudget" not in thinking_config + ), "Should NOT have thinkingBudget for Gemini 3" assert thinking_config["includeThoughts"] is True # Test with Gemini 2 model diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 469516407c8..80e764147bb 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -262,3 +262,44 @@ class TestNvidiaNim(BaseLLMRerankTest): def get_expected_cost(self) -> float: """Nvidia NIM rerank models are free (cost = 0.0)""" return 0.0 + + @pytest.mark.asyncio() + @pytest.mark.parametrize("sync_mode", [True, False]) + async def test_basic_rerank(self, sync_mode, monkeypatch): + """ + Override the base live rerank test with a mocked HTTP layer. + + NVIDIA reached end-of-life for the hosted + nvidia/llama-3.2-nv-rerankqa-1b-v2 rerank API on 2026-05-18 and + published no replacement model, so a live call now returns HTTP 410 + ("Gone"). NVIDIA's hosted catalog rotates on a schedule, so pointing + at another live model would only defer the same failure. Mock the + transport instead (same pattern as + test_nvidia_nim_rerank_ranking_endpoint above) so the request/response + transformation and cost calculation stay covered offline. + """ + monkeypatch.setenv("NVIDIA_NIM_API_KEY", "fake-api-key") + + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.headers = {} + mock_response.text = "" + mock_response.json.return_value = { + "rankings": [ + {"index": 0, "logit": 0.95}, + {"index": 1, "logit": 0.75}, + ], + "usage": {"total_tokens": 7}, + } + + with ( + patch( + "litellm.llms.custom_httpx.http_handler.HTTPHandler.post", + return_value=mock_response, + ), + patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=mock_response, + ), + ): + await super().test_basic_rerank(sync_mode=sync_mode) diff --git a/tests/local_testing/conftest.py b/tests/local_testing/conftest.py index 0ff7dff668a..6a746041f15 100644 --- a/tests/local_testing/conftest.py +++ b/tests/local_testing/conftest.py @@ -22,14 +22,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -84,6 +87,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -93,6 +97,7 @@ def pytest_runtest_logreport(report): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) # --------------------------------------------------------------------------- diff --git a/tests/local_testing/test_caching_handler.py b/tests/local_testing/test_caching_handler.py index 2b6712cbaa3..0f4539162a2 100644 --- a/tests/local_testing/test_caching_handler.py +++ b/tests/local_testing/test_caching_handler.py @@ -25,6 +25,7 @@ from unittest.mock import AsyncMock, patch, MagicMock from litellm.caching.caching_handler import ( LLMCachingHandler, CachingHandlerResponse, + _is_chat_completion_cached_dict, _should_defer_streaming_cache_hit_callbacks, ) from litellm.caching.caching import LiteLLMCacheType @@ -40,6 +41,7 @@ from litellm.types.utils import ( from litellm.types.llms.openai import ResponsesAPIResponse from datetime import timedelta, datetime from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm._logging import verbose_logger import logging @@ -1072,6 +1074,70 @@ def test_convert_cached_streaming_responses_result_to_iterator(): ) +def test_is_chat_completion_cached_dict(): + assert _is_chat_completion_cached_dict( + {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "other", "object": "chat.completion.chunk", "choices": []} + ) + assert not _is_chat_completion_cached_dict( + {"id": "resp_abc", "object": "response", "output": []} + ) + + +def test_convert_cached_aresponses_bridge_chat_completion_stream(): + """ + openai/responses chat-completions bridge caches ModelResponse JSON on aresponses + cache keys; replay must not call ResponsesAPIResponse(**chatcmpl_dict). + """ + caching_handler = LLMCachingHandler( + original_function=aresponses, request_kwargs={}, start_time=datetime.now() + ) + logging_obj = LiteLLMLogging( + litellm_call_id=str(datetime.now()), + call_type=CallTypes.aresponses.value, + model="gpt-5.4", + messages=[], + function_id=str(uuid.uuid4()), + stream=True, + start_time=datetime.now(), + ) + cached_result = { + "id": "chatcmpl-bridge-cache-test", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 7, + "completion_tokens": 11, + "total_tokens": 18, + }, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.aresponses.value, + kwargs={ + "model": "gpt-5.4", + "stream": True, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, CustomStreamWrapper) + + def test_convert_cached_streaming_reasoning_result_to_iterator(): caching_handler = LLMCachingHandler( original_function=responses, request_kwargs={}, start_time=datetime.now() diff --git a/tests/logging_callback_tests/conftest.py b/tests/logging_callback_tests/conftest.py index cdb9200bc83..6dde85f2ca7 100644 --- a/tests/logging_callback_tests/conftest.py +++ b/tests/logging_callback_tests/conftest.py @@ -19,14 +19,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -79,6 +82,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -229,3 +233,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/ocr_tests/conftest.py b/tests/ocr_tests/conftest.py index 66970b8579f..94790bd7aa3 100644 --- a/tests/ocr_tests/conftest.py +++ b/tests/ocr_tests/conftest.py @@ -12,14 +12,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -51,6 +54,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -64,3 +68,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/otel_tests/test_prometheus.py b/tests/otel_tests/test_prometheus.py index c9490af07cb..90c71037609 100644 --- a/tests/otel_tests/test_prometheus.py +++ b/tests/otel_tests/test_prometheus.py @@ -610,22 +610,18 @@ def extract_user_budget_metrics(metrics_text: str, user_id: str) -> Dict[str, fl # Escape user_id for regex pattern matching escaped_user_id = re.escape(user_id) - # Get remaining budget - remaining_pattern = ( - f'litellm_remaining_user_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)' - ) + # Get remaining budget (user_email and user_alias may also be present as labels) + remaining_pattern = rf'litellm_remaining_user_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' remaining_match = re.search(remaining_pattern, metrics_text) metrics["remaining"] = float(remaining_match.group(1)) if remaining_match else None # Get total budget - total_pattern = ( - f'litellm_user_max_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)' - ) + total_pattern = rf'litellm_user_max_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' total_match = re.search(total_pattern, metrics_text) metrics["total"] = float(total_match.group(1)) if total_match else None # Get remaining hours - hours_pattern = f'litellm_user_budget_remaining_hours_metric{{user="{escaped_user_id}"}} ([0-9.]+)' + hours_pattern = rf'litellm_user_budget_remaining_hours_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' hours_match = re.search(hours_pattern, metrics_text) metrics["remaining_hours"] = float(hours_match.group(1)) if hours_match else None diff --git a/tests/pass_through_unit_tests/conftest.py b/tests/pass_through_unit_tests/conftest.py index 42a95343eb7..390e14b7f11 100644 --- a/tests/pass_through_unit_tests/conftest.py +++ b/tests/pass_through_unit_tests/conftest.py @@ -5,14 +5,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -56,6 +59,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/router_unit_tests/conftest.py b/tests/router_unit_tests/conftest.py index fe976515c92..6a8f3e589f4 100644 --- a/tests/router_unit_tests/conftest.py +++ b/tests/router_unit_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -97,6 +100,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -123,3 +127,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/search_tests/conftest.py b/tests/search_tests/conftest.py index e06d3e95eee..78ba19a7724 100644 --- a/tests/search_tests/conftest.py +++ b/tests/search_tests/conftest.py @@ -13,14 +13,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -65,3 +69,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/test_callbacks_on_proxy.py b/tests/test_callbacks_on_proxy.py index 0b55d820532..17c0db9260f 100644 --- a/tests/test_callbacks_on_proxy.py +++ b/tests/test_callbacks_on_proxy.py @@ -9,12 +9,155 @@ import pytest import asyncio import aiohttp import os +import re import dotenv +from collections import Counter from dotenv import load_dotenv import pytest load_dotenv() +# A *leak* is sustained, monotonic growth of one callback TYPE across the whole +# sampling window. A one-time bump that then plateaus is benign pollution from +# other tests sharing this proxy (this suite runs `pytest -n 4` against a single +# proxy container, so other workers legitimately add team/key-scoped callbacks +# while this test sleeps). We therefore sample N times and only flag a type +# whose normalized count never decreases, grows in >=2 distinct intervals, and +# nets >= LEAK_MIN_NET_GROWTH overall. +NUM_SAMPLES = 4 +SAMPLE_INTERVAL_SECONDS = 20 +LEAK_MIN_NET_GROWTH = 5 +LEAK_MIN_GROWING_INTERVALS = 2 +# A routing-strategy switch / alerting config is a *known, bounded, one-time* +# registration (CCI diagnostic 2026-05-16: total 85->95 on the first interval +# after switching to latency-based-routing, then flat at 95 for 2.5 min under +# load). We absorb that step by settling before the baseline sample, so only +# growth *after* the deliberate perturbation can count as a leak. +SETTLE_SECONDS = 30 + +# Strip instance-identity noise so N leaked instances of one class collapse to +# one rising counter instead of N opaque, unrelated-looking strings. +_ADDR_RE = re.compile(r" at 0x[0-9a-fA-F]+") +_OBJ_RE = re.compile(r"<([\w.]+) object") + + +def _normalize_callback(cb_str: str) -> str: + """Reduce a callback's str() to a stable type key (drops 0x… addresses).""" + s = _ADDR_RE.sub("", cb_str) + m = _OBJ_RE.search(s) + if m: + return m.group(1).split(".")[-1] + # bound methods: ">" -> "Cls.m" + bm = re.search(r"bound method ([\w.]+)", s) + if bm: + return bm.group(1) + return s.strip() + + +def _summarize(all_litellm_callbacks) -> Counter: + return Counter(_normalize_callback(str(c)) for c in all_litellm_callbacks) + + +def _detect_leaks(samples): + """ + samples: list[Counter] taken in time order. + + Returns {callback_type: [counts across samples]} for types that grew + monotonically (never decreased), in >=LEAK_MIN_GROWING_INTERVALS intervals, + and netted >=LEAK_MIN_NET_GROWTH overall — i.e. a real leak, not a one-shot + step from a parallel test. + """ + leaks = {} + all_types = set().union(*[set(s) for s in samples]) if samples else set() + for t in all_types: + series = [s.get(t, 0) for s in samples] + deltas = [b - a for a, b in zip(series, series[1:])] + net = series[-1] - series[0] + non_decreasing = all(d >= 0 for d in deltas) + growing_intervals = sum(1 for d in deltas if d > 0) + if ( + non_decreasing + and net >= LEAK_MIN_NET_GROWTH + and growing_intervals >= LEAK_MIN_GROWING_INTERVALS + ): + leaks[t] = series + return leaks + + +def _terminal_suspects(samples): + """ + Types whose net growth clears the threshold monotonically but is confined + to the *final* interval — `growing_intervals == 1` with that one growing + interval being the last. `_detect_leaks`' `>= 2` guard silently passes + these, so a real leak that accumulates entirely in the last sampled window + is indistinguishable from a one-time terminal step *without one more + sample*. Returns the set of such types so the caller can re-confirm. + """ + suspects = set() + all_types = set().union(*[set(s) for s in samples]) if samples else set() + for t in all_types: + series = [s.get(t, 0) for s in samples] + deltas = [b - a for a, b in zip(series, series[1:])] + if not deltas: + continue + net = series[-1] - series[0] + non_decreasing = all(d >= 0 for d in deltas) + growing = [i for i, d in enumerate(deltas) if d > 0] + if ( + non_decreasing + and net >= LEAK_MIN_NET_GROWTH + and growing == [len(deltas) - 1] + ): + suspects.add(t) + return suspects + + +async def _detect_leaks_confirmed(session, samples): + """ + `_detect_leaks`, plus a single confirmation sample when growth is confined + to the final interval (see `_terminal_suspects`). A genuine ongoing leak + keeps climbing -> now grows in >= 2 intervals -> flagged; a one-time + terminal registration plateaus -> still 1 growing interval -> ignored. + Returns `(leaks, samples)` (samples may have one extra entry appended). + """ + leaks = _detect_leaks(samples) + if not leaks and _terminal_suspects(samples): + await asyncio.sleep(SAMPLE_INTERVAL_SECONDS) + _, _, all_cb = await get_active_callbacks(session=session) + samples = samples + [_summarize(all_cb)] + leaks = _detect_leaks(samples) + return leaks, samples + + +def _format_report(samples, leaks) -> str: + lines = ["Callback count per type across samples (time order):"] + all_types = sorted(set().union(*[set(s) for s in samples])) + for t in all_types: + series = [s.get(t, 0) for s in samples] + marker = " <-- LEAK" if t in leaks else "" + lines.append(f" {t}: {series}{marker}") + totals = [sum(s.values()) for s in samples] + lines.append(f"TOTAL callbacks per sample: {totals}") + if leaks: + lines.append( + "Leaking callback types (sustained monotonic growth): " + + ", ".join(sorted(leaks)) + ) + return "\n".join(lines) + + +async def _sample_callbacks(session, num_samples, interval): + """Take `num_samples` callback snapshots `interval`s apart.""" + samples = [] + alerts = [] + for i in range(num_samples): + if i > 0: + await asyncio.sleep(interval) + num_cb, num_alert, all_cb = await get_active_callbacks(session=session) + samples.append(_summarize(all_cb)) + alerts.append(num_alert) + return samples, alerts + async def config_update(session, routing_strategy=None): url = "http://0.0.0.0:4000/config/update" @@ -97,105 +240,65 @@ async def get_current_routing_strategy(session): @pytest.mark.asyncio @pytest.mark.order1 +@pytest.mark.flaky(reruns=2, reruns_delay=5) async def test_check_num_callbacks(): """ - Test 1: num callbacks should NOT increase over time - -> check current callbacks - -> sleep for 30 seconds - -> check current callbacks - -> sleep for 30 seconds - -> check current callbacks + PROD invariant: no callback TYPE should grow without bound over time. + + This suite runs `pytest -n 4` against one shared proxy, so the raw count is + noisy — other workers legitimately add team/key-scoped callbacks that then + plateau. We settle first, then sample several times, and only fail on + *sustained, monotonic* per-type growth (a genuine leak), naming the type. """ - from litellm._uuid import uuid - async with aiohttp.ClientSession() as session: - await asyncio.sleep(30) - num_callbacks_1, _, all_litellm_callbacks_1 = await get_active_callbacks( - session=session - ) - assert num_callbacks_1 > 0 - await asyncio.sleep(30) + # Absorb proxy warmup / in-flight parallel registration before baseline. + await asyncio.sleep(SETTLE_SECONDS) - num_callbacks_2, _, all_litellm_callbacks_2 = await get_active_callbacks( - session=session + samples, _ = await _sample_callbacks( + session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS ) - print("all_litellm_callbacks_1", all_litellm_callbacks_1) + assert sum(samples[0].values()) > 0, "expected some callbacks registered" - print( - "diff in callbacks=", - set(all_litellm_callbacks_1) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_1 - num_callbacks_2) <= 4 - - await asyncio.sleep(30) - - num_callbacks_3, _, all_litellm_callbacks_3 = await get_active_callbacks( - session=session - ) - - print( - "diff in callbacks = all_litellm_callbacks3 - all_litellm_callbacks2 ", - set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_3 - num_callbacks_2) <= 4 + leaks, samples = await _detect_leaks_confirmed(session, samples) + report = _format_report(samples, leaks) + print(report) + assert not leaks, f"Callback leak detected.\n{report}" @pytest.mark.asyncio @pytest.mark.order2 +@pytest.mark.flaky(reruns=2, reruns_delay=5) async def test_check_num_callbacks_on_lowest_latency(): """ - Test 1: num callbacks should NOT increase over time - -> Update to lowest latency - -> check current callbacks - -> sleep for 30s - -> check current callbacks - -> sleep for 30s - -> check current callbacks - -> update back to original routing-strategy + Same PROD invariant as test_check_num_callbacks, but after switching the + router to latency-based-routing. That switch is a *known, bounded* one-time + registration (it adds the latency strategy handler + Slack alerting); we + settle past it before baselining so only post-switch growth counts as a + leak. Also asserts the alerting count is stable. """ - from litellm._uuid import uuid - async with aiohttp.ClientSession() as session: await asyncio.sleep(30) original_routing_strategy = await get_current_routing_strategy(session=session) await config_update(session=session, routing_strategy="latency-based-routing") - await asyncio.sleep(30) + try: + # Absorb the deliberate one-time config/update registration step. + await asyncio.sleep(SETTLE_SECONDS) - num_callbacks_1, num_alerts_1, all_litellm_callbacks_1 = ( - await get_active_callbacks(session=session) - ) + samples, alerts = await _sample_callbacks( + session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS + ) - await asyncio.sleep(30) - - num_callbacks_2, num_alerts_2, all_litellm_callbacks_2 = ( - await get_active_callbacks(session=session) - ) - - print( - "diff in callbacks all_litellm_callbacks_2 - all_litellm_callbacks_1 =", - set(all_litellm_callbacks_2) - set(all_litellm_callbacks_1), - ) - - assert abs(num_callbacks_1 - num_callbacks_2) <= 4 - - await asyncio.sleep(30) - - num_callbacks_3, num_alerts_3, all_litellm_callbacks_3 = ( - await get_active_callbacks(session=session) - ) - - print( - "diff in callbacks all_litellm_callbacks_3 - all_litellm_callbacks_2 =", - set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_2 - num_callbacks_3) <= 4 - - assert num_alerts_1 == num_alerts_2 == num_alerts_3 - - await config_update(session=session, routing_strategy=original_routing_strategy) + leaks, samples = await _detect_leaks_confirmed(session, samples) + report = _format_report(samples, leaks) + print(report) + assert not leaks, f"Callback leak detected.\n{report}" + assert ( + len(set(alerts)) == 1 + ), f"alerting count changed across samples: {alerts}" + finally: + await config_update( + session=session, routing_strategy=original_routing_strategy + ) diff --git a/tests/test_litellm/caching/test_caching_handler.py b/tests/test_litellm/caching/test_caching_handler.py index 742a4f410d4..3eb949d7f29 100644 --- a/tests/test_litellm/caching/test_caching_handler.py +++ b/tests/test_litellm/caching/test_caching_handler.py @@ -232,3 +232,207 @@ def test_combine_usage_handles_none_details(): combined = llm_caching_handler.combine_usage(usage_a, usage_c) assert combined.prompt_tokens_details is not None assert combined.prompt_tokens_details.image_count == 1 + + +def test_is_chat_completion_cached_dict(): + from litellm.caching.caching_handler import _is_chat_completion_cached_dict + + assert _is_chat_completion_cached_dict( + {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "other", "object": "chat.completion.chunk", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "no-object", "choices": [{"index": 0}]} + ) + assert not _is_chat_completion_cached_dict( + {"id": "resp_abc", "object": "response", "output": []} + ) + + +def _build_logging_obj(call_type: str, stream: bool): + import uuid as _uuid + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging + + return LiteLLMLogging( + litellm_call_id=str(datetime.now()), + call_type=call_type, + model="gpt-5.4", + messages=[], + function_id=str(_uuid.uuid4()), + stream=stream, + start_time=datetime.now(), + ) + + +def test_convert_cached_aresponses_bridge_chat_completion_stream(): + """openai/responses chat-completions bridge: streaming cache hit replays as chat stream.""" + from litellm import aresponses + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=aresponses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "chatcmpl-bridge-cache-test", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18}, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.aresponses.value, + kwargs={ + "model": "gpt-5.4", + "stream": True, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=_build_logging_obj(CallTypes.aresponses.value, stream=True), + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, CustomStreamWrapper) + + +def test_convert_cached_responses_bridge_chat_completion_nonstream(): + """openai/responses chat-completions bridge: non-streaming cache hit replays as ModelResponse.""" + from litellm import responses + from litellm.types.utils import CallTypes, ModelResponse + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "chatcmpl-bridge-nonstream", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18}, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={ + "model": "gpt-5.4", + "stream": False, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False), + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hi!" + + +def test_convert_cached_responses_legacy_nonstream_path(): + """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) falls through legacy path.""" + from litellm import responses + from litellm.types.llms.openai import ResponsesAPIResponse + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "resp_legacy_nonstream", + "created_at": int(time.time()), + "status": "completed", + "model": "gpt-4o", + "object": "response", + "output": [ + { + "type": "message", + "id": "msg_legacy", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "legacy response", + "annotations": [], + } + ], + } + ], + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={"model": "gpt-4o", "input": "hi", "stream": False}, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False), + model="gpt-4o", + args=(), + ) + + assert isinstance(result, ResponsesAPIResponse) + assert result.id == "resp_legacy_nonstream" + + +def test_convert_cached_responses_legacy_stream_path(): + """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) on stream falls through legacy path.""" + from litellm import responses + from litellm.responses.streaming_iterator import ( + CachedResponsesAPIStreamingIterator, + ) + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "resp_legacy_stream", + "created_at": int(time.time()), + "status": "completed", + "model": "gpt-4o", + "object": "response", + "output": [ + { + "type": "message", + "id": "msg_legacy_stream", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "legacy stream", + "annotations": [], + } + ], + } + ], + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={"model": "gpt-4o", "input": "hi", "stream": True}, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=True), + model="gpt-4o", + args=(), + ) + + assert isinstance(result, CachedResponsesAPIStreamingIterator) diff --git a/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py new file mode 100644 index 00000000000..734033ed6be --- /dev/null +++ b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py @@ -0,0 +1,150 @@ +import os +import sys +from datetime import datetime +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +from litellm.completion_extras.litellm_responses_transformation.handler import ( + ResponsesToCompletionBridgeHandler, +) +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper +from litellm.types.utils import ModelResponse + + +def test_is_preformatted_cached_chat_stream_true(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream) + is True + ) + + +def test_is_preformatted_cached_chat_stream_false_wrong_provider(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "openai" + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream) + is False + ) + + +def test_is_preformatted_cached_chat_stream_false_wrong_type(): + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream( + {"object": "chat.completion.chunk"} + ) + is False + ) + + +def _bridge_kwargs(stream: bool): + logging_obj = LiteLLMLogging( + litellm_call_id="test-call", + call_type="completion", + model="gpt-5.4", + messages=[{"role": "user", "content": "hi"}], + function_id="fn-id", + stream=stream, + start_time=datetime.now(), + ) + return { + "model": "gpt-5.4", + "custom_llm_provider": "openai", + "messages": [{"role": "user", "content": "hi"}], + "optional_params": {"stream": stream}, + "litellm_params": {}, + "headers": {}, + "model_response": ModelResponse(), + "logging_obj": logging_obj, + } + + +def test_completion_returns_cached_model_response_directly(): + """Non-streaming bridge cache hit: responses() returns a ModelResponse -> bridge returns it as-is.""" + cached = ModelResponse(id="chatcmpl-cached-nonstream", model="gpt-5.4") + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.responses", return_value=cached), + ): + result = bridge.completion(**_bridge_kwargs(stream=False)) + + assert result is cached + + +@pytest.mark.asyncio +async def test_acompletion_returns_cached_model_response_directly(): + cached = ModelResponse(id="chatcmpl-cached-nonstream-async", model="gpt-5.4") + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.aresponses", new=AsyncMock(return_value=cached)), + ): + result = await bridge.acompletion(**_bridge_kwargs(stream=False)) + + assert result is cached + + +def test_completion_skips_rewrapping_preformatted_cached_chat_stream(): + """Streaming bridge cache hit returning CustomStreamWrapper(cached_response) -> bridge skips re-wrapping.""" + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.responses", return_value=stream), + patch.object( + bridge, + "_apply_post_stream_processing", + side_effect=lambda s, *a, **kw: s, + ) as post, + ): + result = bridge.completion(**_bridge_kwargs(stream=True)) + + post.assert_called_once() + assert result is stream + + +@pytest.mark.asyncio +async def test_acompletion_skips_rewrapping_preformatted_cached_chat_stream(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.aresponses", new=AsyncMock(return_value=stream)), + patch.object( + bridge, + "_apply_post_stream_processing", + side_effect=lambda s, *a, **kw: s, + ) as post, + ): + result = await bridge.acompletion(**_bridge_kwargs(stream=True)) + + post.assert_called_once() + assert result is stream diff --git a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py index 009f432fca1..05bdc40112c 100644 --- a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py +++ b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py @@ -230,3 +230,30 @@ def test_transform_request_drops_user_metadata_with_additional_drop_params(): assert "metadata" not in result assert result["litellm_metadata"]["internal_key"] == "secret" + + +def test_translate_responses_chunk_passthrough_chat_completion_chunk(): + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + OpenAiResponsesToChatCompletionStreamIterator, + ) + + chat_chunk = { + "id": "chatcmpl-cache-passthrough", + "object": "chat.completion.chunk", + "created": 1779104834, + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "delta": {"role": "assistant", "content": "Hi! How can I help?"}, + "finish_reason": None, + } + ], + } + + result = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream( + chat_chunk + ) + + assert result.choices[0].delta.content == "Hi! How can I help?" + assert result.choices[0].finish_reason is None diff --git a/tests/test_litellm/integrations/test_opentelemetry.py b/tests/test_litellm/integrations/test_opentelemetry.py index 27356038cd0..87298b5a7a3 100644 --- a/tests/test_litellm/integrations/test_opentelemetry.py +++ b/tests/test_litellm/integrations/test_opentelemetry.py @@ -1,3 +1,4 @@ +import asyncio import json import os import sys @@ -4625,3 +4626,317 @@ class TestOpenTelemetrySpanDedupe(unittest.TestCase): 2, f"Two distinct guardrail invocations expected, got {len(guardrail_spans)}", ) + + +class TestOpenTelemetryHttpStatusCodeAttribute(unittest.TestCase): + """PR 1: the failure recorder also exposes the HTTP status under the + OTel-standard ``http.response.status_code`` (as an int), while keeping the + legacy ``error.code`` for back-compat and leaving span status untouched. + """ + + def _record(self, error_information): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + span = tracer.start_span("Received Proxy Server Request") + kwargs = { + "exception": ValueError("boom"), + "standard_logging_object": {"error_information": error_information}, + } + otel._record_exception_on_span(span=span, kwargs=kwargs) + span.end() + + finished = exporter.get_finished_spans() + assert len(finished) == 1 + return finished[0] + + def test_401_sets_int_status_code_and_error_type(self): + span = self._record({"error_code": "401", "error_class": "AuthenticationError"}) + assert span.attributes["http.response.status_code"] == 401 + assert isinstance(span.attributes["http.response.status_code"], int) + assert span.attributes["error.type"] == "AuthenticationError" + + def test_429_terminal(self): + span = self._record({"error_code": "429"}) + assert span.attributes["http.response.status_code"] == 429 + + def test_500_sets_status_code_and_records_exception_event(self): + span = self._record({"error_code": "500"}) + assert span.attributes["http.response.status_code"] == 500 + assert any(e.name == "exception" for e in span.events) + + def test_legacy_error_code_still_present_no_regression(self): + span = self._record({"error_code": "401"}) + assert span.attributes["error.code"] == "401" + + def test_non_numeric_error_code_omits_status_code(self): + span = self._record({"error_code": "ContextWindowExceeded"}) + assert "http.response.status_code" not in span.attributes + # legacy attribute still set so existing dashboards don't regress + assert span.attributes["error.code"] == "ContextWindowExceeded" + + def test_empty_error_code_omits_status_code(self): + span = self._record({"error_code": ""}) + assert "http.response.status_code" not in span.attributes + + def test_recorder_does_not_touch_span_status(self): + span = self._record({"error_code": "401"}) + assert span.status.status_code == trace.StatusCode.UNSET + + +class TestOpenTelemetryFailureHookStampsServerSpan(unittest.TestCase): + """Error attributes must land on the SERVER span dashboards query. + ``_handle_failure`` records on the litellm_request child span, so + ``async_post_call_failure_hook`` — which holds the SERVER span via + ``user_api_key_dict.parent_otel_span`` — is where it gets stamped. + """ + + def _run_hook(self, exception): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + otel.tracer = tracer + server_span = tracer.start_span("Received Proxy Server Request") + + user_api_key_dict = MagicMock() + user_api_key_dict.parent_otel_span = server_span + + asyncio.run( + otel.async_post_call_failure_hook( + request_data={}, + original_exception=exception, + user_api_key_dict=user_api_key_dict, + traceback_str="trace", + ) + ) + + finished = {s.name: s for s in exporter.get_finished_spans()} + assert "Received Proxy Server Request" in finished + return finished["Received Proxy Server Request"] + + def test_server_span_gets_int_status_code_and_error_type(self): + class _Boom(Exception): + status_code = 500 + + span = self._run_hook(_Boom("upstream blew up")) + assert span.attributes["http.response.status_code"] == 500 + assert isinstance(span.attributes["http.response.status_code"], int) + assert span.attributes["error.type"] == "_Boom" + assert span.attributes["error.code"] == "500" # legacy, string + assert span.status.status_code == trace.StatusCode.ERROR + + def test_non_numeric_code_omits_status_code_no_crash(self): + class _Boom(Exception): + code = "ContextWindowExceeded" + + span = self._run_hook(_Boom("bad")) + assert "http.response.status_code" not in span.attributes + assert span.attributes["error.code"] == "ContextWindowExceeded" + + def test_no_parent_span_is_noop(self): + otel = OpenTelemetry() + otel.tracer = MagicMock() + user_api_key_dict = MagicMock() + user_api_key_dict.parent_otel_span = None + # Must not raise when there is no SERVER span (e.g. pre-auth 401). + asyncio.run( + otel.async_post_call_failure_hook( + request_data={}, + original_exception=ValueError("x"), + user_api_key_dict=user_api_key_dict, + traceback_str=None, + ) + ) + + +class TestOpenTelemetrySetProxyRequestRouteAttributes(unittest.TestCase): + """http.route (template) + url.path (literal) must land on the SERVER + span. The logging handlers write the litellm_request child span, so + this is set from the auth path on the freshly-created SERVER span. + """ + + def _set(self, **kwargs): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + span = tracer.start_span("Received Proxy Server Request") + otel.set_proxy_request_route_attributes(span, **kwargs) + span.end() + return exporter.get_finished_spans()[0] + + def test_sets_named_template_and_literal(self): + span = self._set( + url_path="/v1/threads/abc123/runs", + http_route="/v1/threads/{thread_id}/runs", + ) + # Exact OTel-standard names — NOT metadata.* (naming regression guard). + assert span.attributes["url.path"] == "/v1/threads/abc123/runs" + assert span.attributes["http.route"] == "/v1/threads/{thread_id}/runs" + assert span.attributes["http.route"] != span.attributes["url.path"] + assert "metadata.http_route" not in span.attributes + + def test_flat_route_template_equals_literal(self): + span = self._set( + url_path="/v1/chat/completions", + http_route="/v1/chat/completions", + ) + assert span.attributes["http.route"] == "/v1/chat/completions" + assert span.attributes["url.path"] == "/v1/chat/completions" + + def test_missing_http_route_omits_only_that_attribute(self): + span = self._set(url_path="/v1/chat/completions", http_route=None) + assert span.attributes["url.path"] == "/v1/chat/completions" + assert "http.route" not in span.attributes + + def test_missing_both_sets_nothing(self): + span = self._set(url_path=None, http_route=None) + assert "url.path" not in span.attributes + assert "http.route" not in span.attributes + + def test_none_span_is_noop(self): + otel = OpenTelemetry() + # Mirrors the Langfuse-override path (create span returns None). + otel.set_proxy_request_route_attributes(None, url_path="/x", http_route="/x") + + +class TestOpenTelemetrySetResponseStatusCodeAttribute(unittest.TestCase): + """http.response.status_code must land on the SERVER span on the + success path too (failure path sets it in _record_exception_on_span). + Without this the attribute is failure-only, so error-ratio / + status-breakdown dashboards have no 2xx bucket. + """ + + def _set(self, status_code): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + span = tracer.start_span("Received Proxy Server Request") + otel.set_response_status_code_attribute(span, status_code) + span.end() + return exporter.get_finished_spans()[0] + + def test_success_sets_int_200(self): + span = self._set(200) + # Exact OTel-standard name, stored as int (regression guard). + assert span.attributes["http.response.status_code"] == 200 + assert isinstance(span.attributes["http.response.status_code"], int) + + def test_none_status_code_omits_attribute(self): + span = self._set(None) + assert "http.response.status_code" not in span.attributes + + def test_none_span_is_noop(self): + otel = OpenTelemetry() + # Mirrors the Langfuse-override path (create span returns None). + otel.set_response_status_code_attribute(None, 200) + + +class TestOpenTelemetryPreprocessingDuration(unittest.TestCase): + """litellm.preprocessing.duration_ms (proxy-receive -> first provider + handoff) on the SERVER span. Read from container metadata so the + success (model_call_details) and failure (request_data) paths work + uniformly. Excludes retries via the set-once first_api_call_start_time. + """ + + def _span(self): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + return tracer.start_span("Received Proxy Server Request"), exporter + + def _attr(self, span, exporter): + span.end() + return exporter.get_finished_spans()[0].attributes + + def test_success_shape_model_call_details(self): + # success path: first_api_call_start_time top-level, + # received-at under litellm_params.metadata + received = datetime(2026, 1, 1, 0, 0, 0) + handoff = datetime(2026, 1, 1, 0, 0, 0, 250000) # +250ms + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute( + span, + { + "first_api_call_start_time": handoff, + "litellm_params": {"metadata": {"litellm_received_at": received}}, + }, + ) + attrs = self._attr(span, exp) + self.assertAlmostEqual( + attrs["litellm.preprocessing.duration_ms"], 250.0, places=1 + ) + + def test_failure_shape_request_data(self): + # failure path: request_data with first_api_call_start_time lifted + # to the TOP LEVEL by the proxy (off the logging object, before it + # is popped) and received-at riding the metadata variable. The + # user metadata sub-dict is never used for the handoff anchor. + received = datetime(2026, 1, 1, 0, 0, 0) + handoff = datetime(2026, 1, 1, 0, 0, 0, 30000) # +30ms + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute( + span, + { + "first_api_call_start_time": handoff, + "metadata": {"litellm_received_at": received}, + }, + ) + attrs = self._attr(span, exp) + self.assertAlmostEqual( + attrs["litellm.preprocessing.duration_ms"], 30.0, places=1 + ) + + def test_missing_received_at_omits(self): + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute( + span, {"first_api_call_start_time": datetime(2026, 1, 1)} + ) + assert "litellm.preprocessing.duration_ms" not in self._attr(span, exp) + + def test_missing_handoff_omits(self): + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute( + span, {"metadata": {"litellm_received_at": datetime(2026, 1, 1)}} + ) + assert "litellm.preprocessing.duration_ms" not in self._attr(span, exp) + + def test_negative_duration_omitted(self): + # clock skew: handoff before receive -> omit, not a negative value + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute( + span, + { + "first_api_call_start_time": datetime(2026, 1, 1, 0, 0, 0), + "metadata": {"litellm_received_at": datetime(2026, 1, 1, 0, 0, 5)}, + }, + ) + assert "litellm.preprocessing.duration_ms" not in self._attr(span, exp) + + def test_none_span_is_noop(self): + OpenTelemetry().set_preprocessing_duration_attribute( + None, {"first_api_call_start_time": datetime(2026, 1, 1)} + ) + + def test_non_dict_container_is_noop(self): + otel = OpenTelemetry() + span, exp = self._span() + otel.set_preprocessing_duration_attribute(span, None) + assert "litellm.preprocessing.duration_ms" not in self._attr(span, exp) diff --git a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py index 19ae819c85a..12f30ab6024 100644 --- a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py +++ b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py @@ -460,6 +460,104 @@ async def test_assemble_user_object_does_not_override_metadata_max_budget( ), "max_budget from metadata must not be replaced by the DB value" +async def test_assemble_user_object_populates_user_email_and_alias_from_db( + prometheus_logger, +): + db_user = MagicMock() + db_user.max_budget = None + db_user.budget_reset_at = None + db_user.user_email = "alice@example.com" + db_user.user_alias = "Alice" + + with patch("litellm.proxy.auth.auth_checks.get_user_object") as mock_get_user: + mock_get_user.return_value = db_user + user_object = await prometheus_logger._assemble_user_object( + user_id="user-abc-123", + spend=10.0, + max_budget=None, + response_cost=0.5, + ) + + assert user_object.user_email == "alice@example.com" + assert user_object.user_alias == "Alice" + + +def test_set_user_budget_metrics_default_no_email_alias_labels( + prometheus_logger, +): + """By default (flag off), only user label is emitted.""" + import litellm + from litellm.proxy._types import LiteLLM_UserTable + + litellm.prometheus_user_budget_label_include_email_alias = False + + user = LiteLLM_UserTable( + user_id="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + spend=25.0, + max_budget=100.0, + budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + ) + + prometheus_logger.litellm_remaining_user_budget_metric = MagicMock() + prometheus_logger.litellm_user_max_budget_metric = MagicMock() + prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock() + + prometheus_logger._set_user_budget_metrics(user) + + prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + ) + + +def test_set_user_budget_metrics_includes_user_email_and_alias_labels_when_opted_in( + prometheus_logger, +): + """When prometheus_user_budget_label_include_email_alias=True, email+alias labels appear.""" + import litellm + from litellm.proxy._types import LiteLLM_UserTable + + litellm.prometheus_user_budget_label_include_email_alias = True + + user = LiteLLM_UserTable( + user_id="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + spend=25.0, + max_budget=100.0, + budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + ) + + prometheus_logger.litellm_remaining_user_budget_metric = MagicMock() + prometheus_logger.litellm_user_max_budget_metric = MagicMock() + prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock() + + try: + prometheus_logger._set_user_budget_metrics(user) + + prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + prometheus_logger.litellm_remaining_user_budget_metric.labels().set.assert_called_once_with( + 75.0 + ) + prometheus_logger.litellm_user_max_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + prometheus_logger.litellm_user_budget_remaining_hours_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + finally: + litellm.prometheus_user_budget_label_include_email_alias = False + + async def test_set_user_budget_metrics_after_api_request_no_inf_when_metadata_budget_none( prometheus_logger, ): diff --git a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py index 758ff3ea38e..0ef97e25e44 100644 --- a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py +++ b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py @@ -9,15 +9,24 @@ Covers credential leak prevention changes: import os import sys -from unittest.mock import patch +from unittest.mock import MagicMock, patch import pytest sys.path.insert(0, os.path.abspath("../../..")) +from litellm.interactions.litellm_responses_transformation.streaming_iterator import ( + LiteLLMResponsesInteractionsStreamingIterator, +) from litellm.llms.gemini.interactions.transformation import ( GoogleAIStudioInteractionsConfig, ) +from litellm.types.llms.openai import ( + ContentPartAddedEvent, + OutputTextDeltaEvent, + ResponseCompletedEvent, + ResponseCreatedEvent, +) from litellm.types.router import GenericLiteLLMParams _PATCH_GET_API_KEY = "litellm.llms.gemini.common_utils.GeminiModelInfo.get_api_key" @@ -113,6 +122,186 @@ class TestGetCompleteUrl: ) +class TestStreamingIterator: + def _make_iterator(self) -> LiteLLMResponsesInteractionsStreamingIterator: + return LiteLLMResponsesInteractionsStreamingIterator( + model="gpt-5.4", + litellm_custom_stream_wrapper=MagicMock(), + request_input="hi", + optional_params={}, + ) + + def _make_text_delta( + self, text: str, item_id: str = "item_1" + ) -> OutputTextDeltaEvent: + event = MagicMock(spec=OutputTextDeltaEvent) + event.delta = text + event.item_id = item_id + return event + + def _make_part_added(self, item_id: str = "item_1") -> ContentPartAddedEvent: + event = MagicMock(spec=ContentPartAddedEvent) + event.item_id = item_id + return event + + def _make_response_created(self) -> ResponseCreatedEvent: + event = MagicMock(spec=ResponseCreatedEvent) + event.response = MagicMock(id="resp_123") + return event + + def test_content_delta_includes_type_field(self): + """content.delta events must carry delta.type='text' so the UI can display them.""" + it = self._make_iterator() + it.sent_interaction_start = True + it.sent_content_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_text_delta("Hello") + ) + + assert chunk is not None + assert chunk.event_type == "content.delta" + assert chunk.delta == {"type": "text", "text": "Hello"} + + def test_response_part_added_emits_content_start(self): + """ContentPartAddedEvent (arrives before text deltas) should emit content.start + so the first OutputTextDeltaEvent immediately emits content.delta without dropping text. + """ + it = self._make_iterator() + it.sent_interaction_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added() + ) + + assert chunk is not None + assert chunk.event_type == "content.start" + assert it.sent_content_start is True + + def test_first_text_delta_not_dropped_when_part_added_seen(self): + """After ContentPartAddedEvent, the first text delta must yield content.delta + (not content.start), preserving the token text.""" + it = self._make_iterator() + it.sent_interaction_start = True + it._transform_responses_chunk_to_interactions_chunk(self._make_part_added()) + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_text_delta("Hello") + ) + + assert chunk is not None + assert chunk.event_type == "content.delta" + assert chunk.delta is not None + assert chunk.delta.get("text") == "Hello" + + def test_part_added_emits_interaction_start_fallback_when_not_sent(self): + """If ContentPartAddedEvent arrives before any ResponseCreatedEvent, + the iterator must emit interaction.start before content.start to honor + the documented event ordering contract.""" + it = self._make_iterator() + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added(item_id="item_42") + ) + + assert chunk is not None + assert chunk.event_type == "interaction.start" + assert chunk.id == "item_42" + assert chunk.status == "in_progress" + assert chunk.model == "gpt-5.4" + assert it.sent_interaction_start is True + assert it.sent_content_start is False + + def test_part_added_returns_none_when_already_started(self): + """A second ContentPartAddedEvent (after content.start was already emitted) + should be a no-op so we don't re-emit content.start.""" + it = self._make_iterator() + it.sent_interaction_start = True + it.sent_content_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added() + ) + + assert chunk is None + + def test_part_added_without_item_id_falls_back_to_self_id(self): + """When ContentPartAddedEvent has no item_id and we emit the interaction.start + fallback, the id must default to an interaction_ string.""" + it = self._make_iterator() + event = MagicMock(spec=ContentPartAddedEvent) + event.item_id = None + + chunk = it._transform_responses_chunk_to_interactions_chunk(event) + + assert chunk is not None + assert chunk.event_type == "interaction.start" + assert chunk.id == f"interaction_{id(it)}" + + def test_first_text_delta_not_dropped_when_no_prior_start_events(self): + """When OutputTextDeltaEvent arrives before any ResponseCreatedEvent or + ContentPartAddedEvent, the iterator must emit interaction.start *and* + immediately follow with a content.start that carries this delta's text, + so the first token is never silently dropped from the stream.""" + events = [ + self._make_text_delta("Hello"), + self._make_text_delta(" World"), + ] + wrapper = MagicMock() + wrapper.__iter__ = lambda self: iter(events) + wrapper.__next__ = lambda self, _it=iter(events): next(_it) + it = LiteLLMResponsesInteractionsStreamingIterator( + model="gpt-5.4", + litellm_custom_stream_wrapper=wrapper, + request_input="hi", + optional_params={}, + ) + + first = it._transform_responses_chunk_to_interactions_chunk(events[0]) + assert first is not None + assert first.event_type == "interaction.start" + assert it.sent_interaction_start is True + assert it.sent_content_start is True + assert len(it._pending_events) == 1 + pending = it._pending_events[0] + assert pending.event_type == "content.start" + assert pending.delta == {"type": "text", "text": "Hello"} + + second = it._transform_responses_chunk_to_interactions_chunk(events[1]) + assert second is not None + assert second.event_type == "content.delta" + assert second.delta == {"type": "text", "text": " World"} + + +class TestTransformRequest: + def test_stream_param_included_in_request_body(self, config): + """When stream=True is in optional_params, the request body must include it + so the proxy forwards the SSE streaming flag to Google's backend.""" + body = config.transform_request( + model="gemini-2.5-flash", + agent=None, + input="Hello", + optional_params={"stream": True}, + litellm_params=GenericLiteLLMParams(api_key="test-key"), + headers={}, + ) + + assert body.get("stream") is True + assert body.get("input") == "Hello" + + def test_stream_false_not_included_when_absent(self, config): + body = config.transform_request( + model="gemini-2.5-flash", + agent=None, + input="Hello", + optional_params={}, + litellm_params=GenericLiteLLMParams(api_key="test-key"), + headers={}, + ) + + assert "stream" not in body + + class TestInteractionOperationUrls: """Test that get/delete/cancel interaction URLs exclude API key.""" diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index e84baf5e137..c6961477a58 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -2685,3 +2685,50 @@ def test_success_handler_unified_helper_runs_for_typed_results(): ) mock_calc.assert_called_once() assert logging_obj.model_call_details["response_cost"] == expected_cost + + +class TestFirstApiCallStartTimeSetOnce: + """first_api_call_start_time pins the FIRST provider handoff so + preprocessing latency excludes retries/backoff (api_call_start_time is + overwritten on every attempt). It is set ONLY on the logging object's + model_call_details. It must never be written into + litellm_params["metadata"] — that is the caller's request metadata, + echoed back into provider request bodies, spend logs, and batch + objects (typed Dict[str, str]); a datetime there breaks them. The + proxy failure path lifts it off the logging object into request_data + separately (see proxy/utils.py), not via this dict. + """ + + def _logging_obj(self): + obj = LitellmLogging( + model="gpt-4", + messages=[{"role": "user", "content": "hi"}], + stream=False, + call_type="completion", + start_time=time.time(), + litellm_call_id="set-once-1", + function_id="f1", + ) + obj.model_call_details["litellm_params"] = {"metadata": {}} + return obj + + def test_set_once_survives_retry_and_never_touches_user_metadata(self): + obj = self._logging_obj() + user_meta = obj.model_call_details["litellm_params"]["metadata"] + + obj.pre_call(input="hi", api_key="sk-test") + first = obj.model_call_details["first_api_call_start_time"] + assert first == obj.model_call_details["api_call_start_time"] + # Set on the logging object only — user metadata untouched. + assert user_meta == {} + assert ( + "first_api_call_start_time" not in obj.model_call_details["litellm_params"] + ) + + time.sleep(0.002) # ensure a distinct retry timestamp + obj.pre_call(input="hi", api_key="sk-test") + + # retry advanced api_call_start_time but NOT first_api_call_start_time + assert obj.model_call_details["api_call_start_time"] > first + assert obj.model_call_details["first_api_call_start_time"] == first + assert user_meta == {} diff --git a/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py new file mode 100644 index 00000000000..8de47331614 --- /dev/null +++ b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py @@ -0,0 +1,119 @@ +""" +Test that BedrockBatchesConfig._get_openai_compatible_batch_metadata +sanitizes non-string metadata values injected by proxy guardrail hooks. + +The OpenAI Batch Pydantic model requires metadata: Dict[str, str]. +Proxy hooks (Model Armor, OpenAI Moderations, queue time tracking) inject +dicts, floats, and other non-string values that cause a ValidationError +when constructing LiteLLMBatch. This test suite verifies the sanitization +layer prevents that. +""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + +class TestGetOpenaiCompatibleBatchMetadata: + """Tests for _get_openai_compatible_batch_metadata.""" + + def test_string_values_pass_through_unchanged(self): + metadata = {"user_key": "user_value", "run_id": "abc123"} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert result == {"user_key": "user_value", "run_id": "abc123"} + + def test_dict_values_serialized_to_json_string(self): + metadata = { + "_model_armor_response": { + "sanitizationResult": {"filterMatchState": "MATCH_FOUND"} + } + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "_model_armor_response" in result + assert isinstance(result["_model_armor_response"], str) + assert "MATCH_FOUND" in result["_model_armor_response"] + + def test_float_values_serialized_to_string(self): + metadata = {"queue_time_seconds": 0.5} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert result == {"queue_time_seconds": "0.5"} + + def test_none_values_excluded(self): + metadata = {"key": "value", "empty": None} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "empty" not in result + assert result == {"key": "value"} + + def test_standard_logging_guardrail_information_excluded(self): + metadata = { + "standard_logging_guardrail_information": {"some": "logging_data"}, + "user_key": "keep_me", + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "standard_logging_guardrail_information" not in result + assert result == {"user_key": "keep_me"} + + def test_non_dict_input_returns_empty_dict(self): + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(None) == {} + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata("string") == {} + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(123) == {} + + def test_empty_dict_returns_empty_dict(self): + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata({}) == {} + + def test_mixed_metadata_from_guardrails(self): + """Simulate real metadata contaminated by proxy guardrails.""" + metadata = { + "_model_armor_response": {"sanitizationResult": {"key": "val"}}, + "_model_armor_status": "success", + "_openai_moderation_response": {"id": "mod-123", "flagged": False}, + "queue_time_seconds": 1.23, + "headers": {"Authorization": "Bearer sk-xxx"}, + "standard_logging_guardrail_information": {"internal": True}, + "user_metadata_key": "user_value", + "none_field": None, + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + + # All values must be strings + for key, value in result.items(): + assert isinstance(value, str), f"metadata[{key!r}] is {type(value)}, not str" + + # Excluded keys + assert "standard_logging_guardrail_information" not in result + assert "none_field" not in result + + # Preserved keys + assert result["_model_armor_status"] == "success" + assert result["user_metadata_key"] == "user_value" + + def test_result_compatible_with_litellm_batch(self): + """Verify sanitized metadata can construct a LiteLLMBatch without error.""" + import time + + from litellm.types.utils import LiteLLMBatch + + metadata = { + "_model_armor_response": {"blocked": True}, + "queue_time_seconds": 0.05, + "user_key": "value", + } + sanitized = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + + # This would raise ValidationError before the fix + batch = LiteLLMBatch( + id="arn:aws:bedrock:us-east-1:123:model-invocation-job/test", + object="batch", + endpoint="/v1/chat/completions", + input_file_id="file-123", + completion_window="24h", + status="validating", + created_at=int(time.time()), + metadata=sanitized, + ) + assert batch.metadata == sanitized diff --git a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py index c67a8712340..9955851132c 100644 --- a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py +++ b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py @@ -957,3 +957,50 @@ def test_titan_image_embedding_cost_uses_per_image_rate(): assert response.usage is not None assert response.usage.prompt_tokens_details is not None assert response.usage.prompt_tokens_details.image_count == 1 + + +@pytest.mark.parametrize( + "encoding_format,expected_embedding_types", + [ + ("float", ["float"]), + ("base64", ["base64"]), + (["float", "int8"], ["float", "int8"]), + ], +) +def test_bedrock_cohere_embedding_types_wrapped_as_list( + encoding_format, expected_embedding_types +): + """ + Bedrock Cohere expects `embedding_types` as a JSON array, not a raw string. + + Regression test for: Bedrock returns + Malformed input request: #/embedding_types: expected type: JSONArray, found: String + when `encoding_format` is passed as a string. + """ + litellm.set_verbose = True + client = HTTPHandler() + model = "bedrock/cohere.embed-multilingual-v3" + + with patch.object(client, "post") as mock_post: + mock_response = Mock() + mock_response.status_code = 200 + mock_response.text = json.dumps(cohere_embedding_response) + mock_response.json = lambda: json.loads(mock_response.text) + mock_post.return_value = mock_response + + response = litellm.embedding( + model=model, + input=test_input, + encoding_format=encoding_format, + client=client, + aws_region_name="us-east-1", + aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com", + api_key="test-bearer-token-12345", + ) + + assert isinstance(response, litellm.EmbeddingResponse) + + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + assert "embedding_types" in request_body + assert request_body["embedding_types"] == expected_embedding_types + assert isinstance(request_body["embedding_types"], list) diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py index 8fa9290d3de..c39fb427a01 100644 --- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py +++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py @@ -14,18 +14,46 @@ from litellm.llms.bedrock.common_utils import BedrockModelInfo # --------------------------------------------------------------------------- # -# BEDROCK_RESPONSE_STREAM_SHAPE eager-load tests # +# get_bedrock_response_stream_shape lazy-load tests # # --------------------------------------------------------------------------- # -def test_bedrock_response_stream_shape_loaded_at_import(): +@pytest.fixture(autouse=True) +def _reset_bedrock_response_stream_shape_cache(): + """Prevent lru_cache leakage between tests in this module.""" + import litellm.llms.bedrock.common_utils as mod + + mod.get_bedrock_response_stream_shape.cache_clear() + yield + mod.get_bedrock_response_stream_shape.cache_clear() + + +def test_bedrock_response_stream_shape_lazy_loads_once(): """ - BEDROCK_RESPONSE_STREAM_SHAPE is resolved at module import time. + get_bedrock_response_stream_shape() loads from botocore at most once per process. + """ + from unittest.mock import MagicMock, patch + + import litellm.llms.bedrock.common_utils as mod + + sentinel = MagicMock() + with patch.object( + mod, "_load_bedrock_response_stream_shape", return_value=sentinel + ) as mock_load: + assert mod.get_bedrock_response_stream_shape() is sentinel + assert mod.get_bedrock_response_stream_shape() is sentinel + mock_load.assert_called_once() + + +def test_bedrock_response_stream_shape_loaded_on_first_access(): + """ + get_bedrock_response_stream_shape() loads once on first use. In a standard environment with botocore installed it must be non-None. """ - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + pytest.importorskip("botocore") + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert BEDROCK_RESPONSE_STREAM_SHAPE is not None + assert get_bedrock_response_stream_shape() is not None def test_bedrock_response_stream_shape_load_failure_returns_none(): @@ -38,6 +66,7 @@ def test_bedrock_response_stream_shape_load_failure_returns_none(): import litellm.llms.bedrock.common_utils as mod + pytest.importorskip("botocore") with patch( "botocore.loaders.Loader.load_service_model", side_effect=Exception("no data"), @@ -51,31 +80,29 @@ def test_bedrock_response_stream_shape_is_structure_shape(): The loaded shape should be the botocore StructureShape for ResponseStream, not a plain dict or any other type. """ + pytest.importorskip("botocore") from botocore.model import StructureShape - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert BEDROCK_RESPONSE_STREAM_SHAPE is not None, ( - "BEDROCK_RESPONSE_STREAM_SHAPE is None — botocore may not be installed" - ) - shape: StructureShape = BEDROCK_RESPONSE_STREAM_SHAPE # remove Optional + loaded_shape = get_bedrock_response_stream_shape() + assert ( + loaded_shape is not None + ), "get_bedrock_response_stream_shape() is None — botocore may not be installed" + shape: StructureShape = loaded_shape assert isinstance(shape, StructureShape) assert shape.name == "ResponseStream" -def test_bedrock_response_stream_shape_same_object_across_imports(): +def test_bedrock_response_stream_shape_same_object_across_calls(): """ - Both bedrock modules that use the shape must reference the identical object — - confirming the constant is not re-loaded per import. + Repeated calls must return the identical cached object. """ - from litellm.llms.bedrock.chat.invoke_handler import ( - BEDROCK_RESPONSE_STREAM_SHAPE as invoke_shape, - ) - from litellm.llms.bedrock.common_utils import ( - BEDROCK_RESPONSE_STREAM_SHAPE as common_shape, - ) + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert common_shape is invoke_shape + first = get_bedrock_response_stream_shape() + second = get_bedrock_response_stream_shape() + assert first is second def test_bedrock_event_stream_decoder_base_uses_module_shape(): @@ -95,19 +122,23 @@ def test_bedrock_event_stream_decoder_base_uses_module_shape(): def test_bedrock_parse_message_from_event_raises_on_none_shape(): """ - When BEDROCK_RESPONSE_STREAM_SHAPE is None (botocore unavailable), + When get_bedrock_response_stream_shape() returns None (botocore unavailable), _parse_message_from_event must raise BedrockError before touching the botocore parser — not an opaque AttributeError from inside botocore. """ from unittest.mock import MagicMock, patch import litellm.llms.bedrock.common_utils as mod - from litellm.llms.bedrock.common_utils import BedrockError, BedrockEventStreamDecoderBase + from litellm.llms.bedrock.common_utils import ( + BedrockError, + BedrockEventStreamDecoderBase, + ) - decoder = BedrockEventStreamDecoderBase() + decoder = BedrockEventStreamDecoderBase.__new__(BedrockEventStreamDecoderBase) + decoder.parser = MagicMock() mock_event = MagicMock() - with patch.object(mod, "BEDROCK_RESPONSE_STREAM_SHAPE", None): + with patch.object(mod, "get_bedrock_response_stream_shape", return_value=None): with pytest.raises(BedrockError) as exc_info: decoder._parse_message_from_event(mock_event) diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py new file mode 100644 index 00000000000..5e4d0177e8d --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py @@ -0,0 +1,141 @@ +""" +Unit tests for DashScope embedding transformation. +""" + +import json +import os +import sys +from unittest.mock import MagicMock + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.dashscope.common_utils import DashScopeError +from litellm.llms.dashscope.embed.transformation import ( + DEFAULT_API_BASE, + DashScopeEmbeddingConfig, +) +from litellm.types.utils import EmbeddingResponse + + +def test_validate_environment_and_url(): + config = DashScopeEmbeddingConfig() + headers = config.validate_environment( + headers={}, + model="text-embedding-v4", + messages=[], + optional_params={}, + litellm_params={}, + api_key="sk-test", + ) + assert headers["Authorization"] == "Bearer sk-test" + + url = config.get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v4", + optional_params={}, + litellm_params={}, + ) + assert url == f"{DEFAULT_API_BASE}/embeddings" + + +def test_transform_embedding_request(): + config = DashScopeEmbeddingConfig() + data = config.transform_embedding_request( + model="text-embedding-v4", + input=["风急天高猿啸哀"], + optional_params={"dimensions": 1024, "encoding_format": "float"}, + headers={}, + ) + assert data == { + "model": "text-embedding-v4", + "input": ["风急天高猿啸哀"], + "dimensions": 1024, + "encoding_format": "float", + } + + +def test_transform_embedding_response_success(): + config = DashScopeEmbeddingConfig() + payload = { + "data": [ + {"embedding": [0.1, 0.2], "index": 0, "object": "embedding"}, + ], + "model": "text-embedding-v4", + "object": "list", + "usage": {"prompt_tokens": 5, "total_tokens": 5}, + "id": "73591b79-xxxx", + } + raw = httpx.Response( + status_code=200, + content=json.dumps(payload).encode("utf-8"), + request=httpx.Request("POST", "https://example.com"), + ) + result = config.transform_embedding_response( + model="text-embedding-v4", + raw_response=raw, + model_response=EmbeddingResponse(), + logging_obj=MagicMock(), + api_key="sk-x", + request_data={"input": ["a"]}, + optional_params={}, + litellm_params={}, + ) + assert result.model == "text-embedding-v4" + assert len(result.data) == 1 + assert result.usage.prompt_tokens == 5 + + +def test_transform_embedding_request_user_param(): + config = DashScopeEmbeddingConfig() + data = config.transform_embedding_request( + model="text-embedding-v4", + input=["hello"], + optional_params={"user": "user-123"}, + headers={}, + ) + assert data["user"] == "user-123" + + +def test_map_openai_params_drops_unsupported_with_drop_params(): + config = DashScopeEmbeddingConfig() + result = config.map_openai_params( + non_default_params={"dimensions": 512, "unknown_param": "value"}, + optional_params={}, + model="text-embedding-v4", + drop_params=True, + ) + assert result == {"dimensions": 512} + assert "unknown_param" not in result + + +def test_transform_embedding_response_error(): + config = DashScopeEmbeddingConfig() + payload = { + "error": { + "message": "Incorrect API key provided.", + "type": "invalid_request_error", + "code": "invalid_api_key", + } + } + raw = httpx.Response( + status_code=401, + content=json.dumps(payload).encode("utf-8"), + request=httpx.Request("POST", "https://example.com"), + ) + with pytest.raises(DashScopeError) as exc: + config.transform_embedding_response( + model="text-embedding-v4", + raw_response=raw, + model_response=EmbeddingResponse(), + logging_obj=MagicMock(), + api_key="sk-bad", + request_data={"input": ["a"]}, + optional_params={}, + litellm_params={}, + ) + assert exc.value.status_code == 401 + assert "Incorrect API key" in exc.value.message diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py new file mode 100644 index 00000000000..0e8d58b6530 --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py @@ -0,0 +1,328 @@ +""" +Unit tests for DashScope rerank transformation. +""" + +import json +import os +import sys +from unittest.mock import MagicMock + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.dashscope.common_utils import DashScopeError +from litellm.llms.dashscope.rerank.transformation import ( + DEFAULT_RERANK_URL, + DashScopeRerankConfig, +) +from litellm.types.rerank import RerankResponse + + +class TestDashScopeRerankURL: + def setup_method(self): + self.config = DashScopeRerankConfig() + + def test_default_url(self): + url = self.config.get_complete_url(api_base=None, model="qwen3-rerank") + assert url == DEFAULT_RERANK_URL + + def test_explicit_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://dashscope.aliyuncs.com/compatible-mode/v1", + model="qwen3-rerank", + ) + assert url == "https://dashscope.aliyuncs.com/compatible-mode/v1/reranks" + + def test_intl_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + model="qwen3-rerank", + ) + assert url == "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/reranks" + + def test_already_complete_url_passthrough(self): + full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" + assert self.config.get_complete_url(api_base=full, model="qwen3-rerank") == full + + def test_trailing_slash_stripped(self): + full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks/" + assert self.config.get_complete_url( + api_base=full, model="qwen3-rerank" + ) == full.rstrip("/") + + def test_custom_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://my-proxy.example.com/v1", model="qwen3-rerank" + ) + assert url == "https://my-proxy.example.com/v1/reranks" + + +class TestDashScopeRerankRequest: + def setup_method(self): + self.config = DashScopeRerankConfig() + + def test_validate_environment_with_explicit_key(self): + headers = self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key="sk-test" + ) + assert headers["Authorization"] == "Bearer sk-test" + assert headers["content-type"] == "application/json" + + def test_validate_environment_missing_key(self, monkeypatch): + monkeypatch.delenv("DASHSCOPE_API_KEY", raising=False) + with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"): + self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key=None + ) + + def test_validate_environment_falls_back_to_env(self, monkeypatch): + monkeypatch.setenv("DASHSCOPE_API_KEY", "env-key") + headers = self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key=None + ) + assert headers["Authorization"] == "Bearer env-key" + + def test_supported_params(self): + assert self.config.get_supported_cohere_rerank_params("qwen3-rerank") == [ + "query", + "documents", + "top_n", + "return_documents", + ] + + def test_map_params_drops_unsupported(self): + # qwen3-rerank accepts query/documents/top_n/return_documents. + # rank_fields and max_*_per_doc are silently dropped. + params = self.config.map_cohere_rerank_params( + non_default_params={}, + model="qwen3-rerank", + drop_params=False, + query="什么是文本排序模型", + documents=["d1", "d2"], + top_n=2, + rank_fields=["title"], + return_documents=True, + max_chunks_per_doc=5, + max_tokens_per_doc=100, + ) + assert params == { + "query": "什么是文本排序模型", + "documents": ["d1", "d2"], + "top_n": 2, + "return_documents": True, + } + + def test_transform_request_full(self): + body = self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={ + "query": "如何制作美味的苹果派?", + "documents": ["a", "b"], + "top_n": 5, + "return_documents": True, + }, + headers={}, + ) + assert body == { + "model": "qwen3-rerank", + "query": "如何制作美味的苹果派?", + "documents": ["a", "b"], + "top_n": 5, + "return_documents": True, + } + + def test_transform_request_omits_unset_optional(self): + body = self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"query": "q", "documents": ["a"]}, + headers={}, + ) + assert "top_n" not in body + assert "return_documents" not in body + + def test_transform_request_requires_query(self): + with pytest.raises(ValueError, match="query"): + self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"documents": ["a"]}, + headers={}, + ) + + def test_transform_request_requires_documents(self): + with pytest.raises(ValueError, match="documents"): + self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"query": "q"}, + headers={}, + ) + + +class TestDashScopeRerankResponse: + def setup_method(self): + self.config = DashScopeRerankConfig() + self.logging = MagicMock() + + def _resp(self, body, status_code=200): + return httpx.Response( + status_code=status_code, + content=json.dumps(body).encode(), + request=httpx.Request("POST", "https://example.com"), + ) + + def test_success_response(self): + body = { + "object": "list", + "results": [ + {"index": 0, "relevance_score": 0.93}, + {"index": 2, "relevance_score": 0.34}, + ], + "model": "qwen3-rerank", + "id": "85ba5752", + "usage": {"total_tokens": 79}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + api_key="sk", + request_data={"query": "q"}, + ) + assert out.id == "85ba5752" + assert out.results == [ + {"index": 0, "relevance_score": 0.93}, + {"index": 2, "relevance_score": 0.34}, + ] + assert out.meta == { + "billed_units": {"total_tokens": 79}, + "tokens": {"input_tokens": 79}, + } + + def test_response_with_return_documents_real_payload(self): + # Verbatim sample from a real qwen3-rerank call with return_documents=true. + body = { + "object": "list", + "results": [ + { + "document": { + "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。" + }, + "index": 1, + "relevance_score": 0.8304247466067356, + }, + { + "document": { + "text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。" + }, + "index": 3, + "relevance_score": 0.7142660211908354, + }, + ], + "model": "qwen3-rerank", + "id": "e191b077-97c4-9929-b121-c2fbd2c7b0af", + "usage": {"total_tokens": 192}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + request_data={"query": "如何制作美味的苹果派?"}, + ) + assert out.id == "e191b077-97c4-9929-b121-c2fbd2c7b0af" + assert out.results == [ + { + "index": 1, + "relevance_score": 0.8304247466067356, + "document": { + "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。" + }, + }, + { + "index": 3, + "relevance_score": 0.7142660211908354, + "document": {"text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。"}, + }, + ] + assert out.meta == { + "billed_units": {"total_tokens": 192}, + "tokens": {"input_tokens": 192}, + } + + def test_response_string_document_normalized(self): + # Defensive path: if a future API revision returns a bare string, + # normalize to {"text": ...} so downstream code stays consistent. + body = { + "results": [{"index": 0, "relevance_score": 0.9, "document": "hello"}], + "model": "qwen3-rerank", + "usage": {"total_tokens": 5}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert out.results[0]["document"] == {"text": "hello"} + + def test_missing_id_generates_uuid(self): + body = {"results": [{"index": 0, "relevance_score": 0.5}], "usage": {}} + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert out.id is not None and len(out.id) > 0 + + def test_error_envelope_raises(self): + body = { + "code": "InvalidApiKey", + "message": "Invalid API-key provided.", + "request_id": "fb53", + } + with pytest.raises(DashScopeError) as exc_info: + self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body, status_code=401), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert "Invalid API-key provided." in str(exc_info.value) + + def test_non_json_response_raises(self): + bad = httpx.Response( + status_code=500, + content=b"bad gateway", + request=httpx.Request("POST", "https://example.com"), + ) + with pytest.raises(DashScopeError): + self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=bad, + model_response=RerankResponse(), + logging_obj=self.logging, + ) + + def test_get_error_class(self): + err = self.config.get_error_class( + error_message="boom", status_code=500, headers={} + ) + assert isinstance(err, DashScopeError) + assert err.status_code == 500 + + +class TestProviderConfigManagerDispatch: + def test_dashscope_returns_rerank_config(self): + import litellm + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_rerank_config( + model="qwen3-rerank", + provider=litellm.LlmProviders.DASHSCOPE, + api_base=None, + present_version_params=[], + ) + assert isinstance(cfg, DashScopeRerankConfig) diff --git a/tests/test_litellm/llms/deepseek/__init__.py b/tests/test_litellm/llms/deepseek/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/deepseek/messages/__init__.py b/tests/test_litellm/llms/deepseek/messages/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py new file mode 100644 index 00000000000..7c5f0483ded --- /dev/null +++ b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py @@ -0,0 +1,189 @@ +import litellm +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + AnthropicMessagesConfig, +) +from litellm.llms.deepseek.messages.transformation import ( + DeepSeekAnthropicMessagesConfig, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.utils import ProviderConfigManager + + +def test_deepseek_provider_uses_anthropic_messages_config(): + config = ProviderConfigManager.get_provider_anthropic_messages_config( + model="deepseek-v4-pro", + provider=litellm.LlmProviders.DEEPSEEK, + ) + + assert isinstance(config, DeepSeekAnthropicMessagesConfig) + assert config.custom_llm_provider == "deepseek" + + +def test_deepseek_anthropic_messages_config_defaults(): + config = DeepSeekAnthropicMessagesConfig() + + assert config.custom_llm_provider == "deepseek" + assert config.get_api_base() == "https://api.deepseek.com/anthropic" + + +def test_anthropic_provider_keeps_default_config_for_deepseek_named_model(): + config = ProviderConfigManager.get_provider_anthropic_messages_config( + model="deepseek-v4-pro", + provider=litellm.LlmProviders.ANTHROPIC, + ) + + assert isinstance(config, AnthropicMessagesConfig) + assert not isinstance(config, DeepSeekAnthropicMessagesConfig) + + +def test_deepseek_anthropic_messages_url_defaults_to_anthropic_endpoint(): + config = DeepSeekAnthropicMessagesConfig() + + assert ( + config.get_complete_url( + api_base=None, + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/anthropic/v1", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/anthropic", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/v1", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/v1/messages", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + + +def test_deepseek_anthropic_messages_headers_use_deepseek_key(): + config = DeepSeekAnthropicMessagesConfig() + + headers, api_base = config.validate_anthropic_messages_environment( + headers={}, + model="deepseek-v4-pro", + messages=[], + optional_params={}, + litellm_params={}, + api_key="sk-deepseek", + api_base="https://example.test/anthropic", + ) + + assert api_base == "https://example.test/anthropic" + assert headers["x-api-key"] == "sk-deepseek" + assert headers["anthropic-version"] == "2023-06-01" + assert headers["content-type"] == "application/json" + + +def test_deepseek_anthropic_messages_preserves_thinking_and_sanitizes_custom_tools(): + config = DeepSeekAnthropicMessagesConfig() + messages = [ + { + "role": "user", + "content": "Use the tool.", + }, + { + "role": "assistant", + "content": [ + { + "type": "thinking", + "thinking": "I should call the tool.", + "signature": "sig", + }, + { + "type": "tool_use", + "id": "toolu_123", + "name": "get_weather", + "input": {"city": "Sao Paulo"}, + }, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_123", + "content": "Sunny", + } + ], + }, + ] + + request = config.transform_anthropic_messages_request( + model="deepseek-v4-pro", + messages=messages, + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "thinking": {"type": "enabled", "budget_tokens": 1024}, + "tools": [ + { + "type": "custom", + "name": "get_weather", + "description": "Get weather", + "input_schema": {"type": "object"}, + }, + { + "type": "web_search_20260209", + "name": "web_search", + "max_uses": 1, + }, + ], + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert request["messages"] == messages + assert request["thinking"] == {"type": "enabled", "budget_tokens": 1024} + assert request["tools"][0] == { + "name": "get_weather", + "description": "Get weather", + "input_schema": {"type": "object"}, + } + assert request["tools"][1]["type"] == "web_search_20260209" diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py index 9d7706557b5..7e13459bca1 100644 --- a/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py +++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py @@ -12,18 +12,46 @@ from litellm.llms.sagemaker.completion.transformation import SagemakerConfig # --------------------------------------------------------------------------- # -# SAGEMAKER_RESPONSE_STREAM_SHAPE eager-load tests # +# get_sagemaker_response_stream_shape lazy-load tests # # --------------------------------------------------------------------------- # -def test_sagemaker_response_stream_shape_loaded_at_import(): +@pytest.fixture(autouse=True) +def _reset_sagemaker_response_stream_shape_cache(): + """Prevent lru_cache leakage between tests in this module.""" + import litellm.llms.sagemaker.common_utils as mod + + mod.get_sagemaker_response_stream_shape.cache_clear() + yield + mod.get_sagemaker_response_stream_shape.cache_clear() + + +def test_sagemaker_response_stream_shape_lazy_loads_once(): """ - SAGEMAKER_RESPONSE_STREAM_SHAPE is resolved at module import time. + get_sagemaker_response_stream_shape() loads from botocore at most once per process. + """ + from unittest.mock import MagicMock, patch + + import litellm.llms.sagemaker.common_utils as mod + + sentinel = MagicMock() + with patch.object( + mod, "_load_sagemaker_response_stream_shape", return_value=sentinel + ) as mock_load: + assert mod.get_sagemaker_response_stream_shape() is sentinel + assert mod.get_sagemaker_response_stream_shape() is sentinel + mock_load.assert_called_once() + + +def test_sagemaker_response_stream_shape_loaded_on_first_access(): + """ + get_sagemaker_response_stream_shape() loads once on first use. In a standard environment with botocore installed it must be non-None. """ - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + pytest.importorskip("botocore") + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None + assert get_sagemaker_response_stream_shape() is not None def test_sagemaker_response_stream_shape_load_failure_returns_none(): @@ -36,6 +64,7 @@ def test_sagemaker_response_stream_shape_load_failure_returns_none(): import litellm.llms.sagemaker.common_utils as mod + pytest.importorskip("botocore") with patch( "botocore.loaders.Loader.load_service_model", side_effect=Exception("no data"), @@ -49,14 +78,16 @@ def test_sagemaker_response_stream_shape_is_structure_shape(): The loaded shape should be the botocore StructureShape for InvokeEndpointWithResponseStreamOutput, not a plain dict or any other type. """ + pytest.importorskip("botocore") from botocore.model import StructureShape - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None, ( - "SAGEMAKER_RESPONSE_STREAM_SHAPE is None — botocore may not be installed" - ) - shape: StructureShape = SAGEMAKER_RESPONSE_STREAM_SHAPE # remove Optional + shape = get_sagemaker_response_stream_shape() + assert ( + shape is not None + ), "get_sagemaker_response_stream_shape() is None — botocore may not be installed" + shape: StructureShape = shape # remove Optional assert isinstance(shape, StructureShape) assert shape.name == "InvokeEndpointWithResponseStreamOutput" @@ -64,29 +95,25 @@ def test_sagemaker_response_stream_shape_is_structure_shape(): def test_sagemaker_response_stream_shape_not_reloaded_on_new_decoder(): """ Creating multiple AWSEventStreamDecoder instances must not trigger - additional botocore Loader calls — the shape is resolved once at import - time and reused. + additional botocore Loader calls — the shape is cached after first access. """ - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - decoder_a = AWSEventStreamDecoder(model="test-model-a") - decoder_b = AWSEventStreamDecoder(model="test-model-b") + decoder_a = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) + decoder_b = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) - # Both decoders should use the same pre-loaded shape object (identity check) assert "_response_stream_shape_cache" not in decoder_a.__dict__ assert "_response_stream_shape_cache" not in decoder_b.__dict__ - # The module constant is still the same object - from litellm.llms.sagemaker.common_utils import ( - SAGEMAKER_RESPONSE_STREAM_SHAPE as shape_after, - ) - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is shape_after + first = get_sagemaker_response_stream_shape() + second = get_sagemaker_response_stream_shape() + assert first is second def test_sagemaker_parse_message_from_event_raises_on_none_shape(): """ - When SAGEMAKER_RESPONSE_STREAM_SHAPE is None (botocore unavailable), - _parse_message_from_event must raise ValueError before touching the + When get_sagemaker_response_stream_shape() returns None (botocore unavailable), + _parse_message_from_event must raise SagemakerError before touching the botocore parser — not an opaque AttributeError from inside botocore. """ from unittest.mock import MagicMock, patch @@ -94,10 +121,14 @@ def test_sagemaker_parse_message_from_event_raises_on_none_shape(): import litellm.llms.sagemaker.common_utils as mod from litellm.llms.sagemaker.common_utils import SagemakerError - decoder = AWSEventStreamDecoder(model="test-model") + decoder = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) + decoder.model = "test-model" + decoder.parser = MagicMock() + decoder.content_blocks = [] + decoder.is_messages_api = None mock_event = MagicMock() - with patch.object(mod, "SAGEMAKER_RESPONSE_STREAM_SHAPE", None): + with patch.object(mod, "get_sagemaker_response_stream_shape", return_value=None): with pytest.raises(SagemakerError) as exc_info: decoder._parse_message_from_event(mock_event) diff --git a/tests/test_litellm/llms/test_file_content_block.py b/tests/test_litellm/llms/test_file_content_block.py new file mode 100644 index 00000000000..5552c1a4d68 --- /dev/null +++ b/tests/test_litellm/llms/test_file_content_block.py @@ -0,0 +1,433 @@ +""" +Tests for handling malformed or invalid 'file' content blocks (missing or null +`file` sub-field, HTTP file_id URLs for Google AI Studio). + +Regression tests for: +- litellm/llms/vertex_ai/gemini/transformation.py +- litellm/llms/gemini/chat/transformation.py +- litellm/litellm_core_utils/prompt_templates/common_utils.py + (migrate_file_to_image_url raises on missing `file`; file-id helpers skip non-OpenAI shapes) +- litellm/litellm_core_utils/prompt_templates/factory.py (Bedrock + Anthropic) +- litellm/llms/openai/chat/gpt_transformation.py +""" + +import asyncio +import copy +from typing import List, cast + +import pytest + +import litellm +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + get_file_ids_from_messages, + migrate_file_to_image_url, + update_messages_with_model_file_ids, +) +from litellm.litellm_core_utils.prompt_templates.factory import ( + BedrockConverseMessagesProcessor, + anthropic_process_openai_file_message, +) +from litellm.llms.gemini.chat.transformation import GoogleAIStudioGeminiConfig +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, +) +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionFileObject, + OpenAIMessageContentListBlock, +) + +_MALFORMED_MESSAGES_RAW = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file"}, # Missing required "file" sub-field + ], + } +] + +_WELL_FORMED_MESSAGES_RAW = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": {"file_id": "file-abc123", "format": "pdf"}, + }, + ], + } +] + +MALFORMED_FILE_OBJECT: ChatCompletionFileObject = cast( + ChatCompletionFileObject, {"type": "file"} +) + +EXPLICIT_NULL_FILE_OBJECT: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": None}, +) + + +def _malformed() -> List[AllMessageValues]: + return copy.deepcopy(cast(List[AllMessageValues], _MALFORMED_MESSAGES_RAW)) + + +def _well_formed() -> List[AllMessageValues]: + return copy.deepcopy(cast(List[AllMessageValues], _WELL_FORMED_MESSAGES_RAW)) + + +def _explicit_null_file_in_content() -> List[AllMessageValues]: + return copy.deepcopy( + cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file", "file": None}, + ], + } + ], + ) + ) + + +# --------------------------------------------------------------------------- +# vertex_ai/gemini/transformation.py +# --------------------------------------------------------------------------- + + +def test_gemini_convert_messages_malformed_file_raises_bad_request(): + """_gemini_convert_messages_with_history should raise BadRequestError (not KeyError) + when a content block has type='file' but no 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + _gemini_convert_messages_with_history( + messages=_malformed(), + model="gemini-2.0-flash", + ) + + +def test_gemini_convert_messages_explicit_null_file_field_raises_bad_request(): + """Explicit JSON null for `file` must be rejected like a missing `file` key.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + _gemini_convert_messages_with_history( + messages=_explicit_null_file_in_content(), + model="gemini-2.0-flash", + ) + + +# --------------------------------------------------------------------------- +# gemini/chat/transformation.py - GoogleAIStudioGeminiConfig +# --------------------------------------------------------------------------- + + +def test_google_ai_studio_transform_messages_malformed_file_raises_bad_request(): + """GoogleAIStudioGeminiConfig._transform_messages should raise BadRequestError + when a content block has type='file' but no 'file' sub-field.""" + config = GoogleAIStudioGeminiConfig() + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._transform_messages(messages=_malformed(), model="gemini-2.0-flash") + + +def test_google_ai_studio_transform_messages_explicit_null_file_field_raises_bad_request(): + """Explicit JSON null for `file` must be rejected like a missing `file` key.""" + config = GoogleAIStudioGeminiConfig() + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._transform_messages( + messages=_explicit_null_file_in_content(), model="gemini-2.0-flash" + ) + + +def test_google_ai_studio_transform_messages_http_file_id_converts_to_base64(monkeypatch): + """Google AI Studio rejects raw HTTP(S) file URLs; _transform_messages should + fetch and replace them with base64 `file_data` before conversion.""" + # Data URL shape so downstream Gemini media parsing accepts the inlined bytes + # (mirrors real `convert_url_to_base64` output from `_process_image_response`). + fake_file_data = "data:application/pdf;base64,aGVsbG8=" + + def _fake_convert_url_to_base64(url: str) -> str: + assert url == "https://example.com/doc.pdf" + return fake_file_data + + monkeypatch.setattr( + "litellm.llms.gemini.chat.transformation.convert_url_to_base64", + _fake_convert_url_to_base64, + ) + messages = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": { + "file_id": "https://example.com/doc.pdf", + "format": "pdf", + }, + }, + ], + } + ], + ) + config = GoogleAIStudioGeminiConfig() + config._transform_messages(messages=messages, model="gemini-2.0-flash") + content = messages[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + file_field = file_block.get("file") + assert isinstance(file_field, dict) + assert file_field.get("file_data") == fake_file_data + assert "file_id" not in file_field + + +def test_google_ai_studio_transform_messages_http_file_id_convert_failure_leaves_file_unchanged( + monkeypatch, +): + """If convert_url_to_base64 fails, the Studio prep step must not mutate the block + (see try/except in GoogleAIStudioGeminiConfig._transform_messages).""" + https_id = "https://example.com/missing.pdf" + + def _raise(_url: str) -> str: + raise litellm.ImageFetchError("simulated fetch failure") + + monkeypatch.setattr( + "litellm.llms.gemini.chat.transformation.convert_url_to_base64", + _raise, + ) + messages = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": { + "file_id": https_id, + "format": "application/pdf", + }, + }, + ], + } + ], + ) + config = GoogleAIStudioGeminiConfig() + config._transform_messages(messages=messages, model="gemini-2.0-flash") + content = messages[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + file_field = file_block.get("file") + assert isinstance(file_field, dict) + assert file_field.get("file_id") == https_id + assert file_field.get("format") == "application/pdf" + assert "file_data" not in file_field + + +# --------------------------------------------------------------------------- +# common_utils.py - update_messages_with_model_file_ids +# --------------------------------------------------------------------------- + + +def test_update_messages_with_model_file_ids_malformed_skips_non_openai_file_block(): + """Non-OpenAI file blocks (e.g. missing nested `file` dict) are skipped so callers + relying on LangChain v1 / provider-native shapes are not rejected here.""" + messages = _malformed() + result = update_messages_with_model_file_ids( + messages=messages, + model_id="some-model", + model_file_id_mapping={}, + ) + assert result == messages + content = result[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + assert "file" not in file_block + + +def test_update_messages_with_model_file_ids_well_formed_updates(): + """update_messages_with_model_file_ids should update file_id for well-formed blocks.""" + mapping = {"file-abc123": {"some-model": "provider-file-xyz"}} + result = update_messages_with_model_file_ids( + messages=_well_formed(), + model_id="some-model", + model_file_id_mapping=mapping, + ) + content = result[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if c.get("type") == "file") + assert file_block.get("file", {}).get("file_id") == "provider-file-xyz" + + +# --------------------------------------------------------------------------- +# common_utils.py - get_file_ids_from_messages +# --------------------------------------------------------------------------- + + +def test_get_file_ids_from_messages_malformed_skips_non_openai_file_block(): + """Blocks with type='file' but no OpenAI `file` sub-dict yield no extracted ids.""" + assert get_file_ids_from_messages(messages=_malformed()) == [] + + +def test_get_file_ids_from_messages_well_formed_returns_ids(): + """get_file_ids_from_messages should extract file_id from well-formed blocks.""" + messages: List[AllMessageValues] = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file", "file": {"file_id": "file-abc123", "format": "pdf"}}, + ], + } + ], + ) + result = get_file_ids_from_messages(messages=messages) + assert result == ["file-abc123"] + + +# --------------------------------------------------------------------------- +# factory.py - BedrockConverseMessagesProcessor (sync + async) +# --------------------------------------------------------------------------- + + +def test_bedrock_process_file_message_malformed_raises_bad_request(): + """_process_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + BedrockConverseMessagesProcessor._process_file_message(MALFORMED_FILE_OBJECT) + + +def test_bedrock_process_file_message_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + BedrockConverseMessagesProcessor._process_file_message(EXPLICIT_NULL_FILE_OBJECT) + + +def test_bedrock_async_process_file_message_malformed_raises_bad_request(): + """_async_process_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + + async def _run() -> None: + with pytest.raises( + litellm.BadRequestError, match="missing the required 'file' field" + ): + await BedrockConverseMessagesProcessor._async_process_file_message( + MALFORMED_FILE_OBJECT + ) + + asyncio.run(_run()) + + +def test_bedrock_async_process_file_message_explicit_null_file_field_raises_bad_request(): + async def _run() -> None: + with pytest.raises( + litellm.BadRequestError, match="missing the required 'file' field" + ): + await BedrockConverseMessagesProcessor._async_process_file_message( + EXPLICIT_NULL_FILE_OBJECT + ) + + asyncio.run(_run()) + + +# --------------------------------------------------------------------------- +# openai/chat/gpt_transformation.py +# --------------------------------------------------------------------------- + + +def test_openai_apply_common_transform_malformed_file_raises_bad_request(): + """_apply_common_transform_content_item should raise BadRequestError (not KeyError) + when a content block has type='file' but no 'file' sub-field.""" + config = OpenAIGPTConfig() + malformed_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, {"type": "file"} + ) + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._apply_common_transform_content_item(malformed_block) + + +def test_openai_apply_common_transform_explicit_null_file_field_raises_bad_request(): + config = OpenAIGPTConfig() + explicit_null_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, + {"type": "file", "file": None}, + ) + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._apply_common_transform_content_item(explicit_null_block) + + +def test_openai_apply_common_transform_well_formed_file_does_not_raise(): + """_apply_common_transform_content_item should not raise for well-formed file blocks.""" + config = OpenAIGPTConfig() + well_formed_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ) + result = config._apply_common_transform_content_item(well_formed_block) + assert result.get("type") == "file" + file_field = cast(ChatCompletionFileObject, result).get("file", {}) + assert file_field.get("file_id") == "file-abc123" + + +# --------------------------------------------------------------------------- +# factory.py - anthropic_process_openai_file_message +# --------------------------------------------------------------------------- + + +def test_anthropic_process_openai_file_message_malformed_raises_bad_request(): + """anthropic_process_openai_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + anthropic_process_openai_file_message(MALFORMED_FILE_OBJECT) + + +def test_anthropic_process_openai_file_message_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + anthropic_process_openai_file_message(EXPLICIT_NULL_FILE_OBJECT) + + +def test_anthropic_process_openai_file_message_well_formed_file_id_does_not_raise(): + """anthropic_process_openai_file_message should not raise for a well-formed file_id block.""" + well_formed: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ) + result = anthropic_process_openai_file_message(well_formed) + assert result.get("type") in ("document", "image", "container_upload") + + +# --------------------------------------------------------------------------- +# common_utils.py - migrate_file_to_image_url +# --------------------------------------------------------------------------- + + +def test_migrate_file_to_image_url_malformed_raises_bad_request(): + """migrate_file_to_image_url should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + migrate_file_to_image_url(MALFORMED_FILE_OBJECT) + + +def test_migrate_file_to_image_url_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + migrate_file_to_image_url(EXPLICIT_NULL_FILE_OBJECT) + + +def test_migrate_file_to_image_url_well_formed_returns_image_url(): + """migrate_file_to_image_url should return an image_url block for a well-formed file.""" + well_formed: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": {"file_id": "file-abc123", "format": "png"}}, + ) + result = migrate_file_to_image_url(well_formed) + assert result.get("type") == "image_url" + image_url = result.get("image_url", {}) + assert isinstance(image_url, dict) + assert image_url.get("url") == "file-abc123" diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py new file mode 100644 index 00000000000..10fc68ecaad --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py @@ -0,0 +1,52 @@ +import pytest +from typing import List, cast + +import litellm +from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, +) +from litellm.types.llms.openai import AllMessageValues + + +def test_missing_image_url_field_raises_bad_request_error(): + """When element type is 'image_url' but 'image_url' field is missing, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url"}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'image_url' field is missing" in str(exc_info.value) + + +def test_missing_url_inside_image_url_dict_raises_bad_request_error(): + """When image_url is a dict but 'url' key is absent, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": {"detail": "high"}}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'url' field is missing inside" in str(exc_info.value) + + +def test_explicit_null_image_url_raises_bad_request_error(): + """When image_url key is present but explicitly null, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": None}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'image_url' field is missing" in str(exc_info.value) + + +def test_empty_dict_image_url_raises_bad_request_error(): + """When image_url is an empty dict (no url), a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": {}}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'url' field is missing inside" in str(exc_info.value) diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 353d19b0198..1e0ad04c3c2 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -4290,3 +4290,444 @@ def test_transform_response_does_not_leak_body_on_parse_failure(): msg = str(exc_info.value) assert "secret content" not in msg assert "Error converting to valid response block" in msg + + +def test_chunk_parser_raises_on_429_error_chunk(): + """Test chunk_parser raises VertexAIError on 429 RESOURCE_EXHAUSTED error chunk""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 429, + "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.", + "status": "RESOURCE_EXHAUSTED", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + assert "RESOURCE_EXHAUSTED" in exc_info.value.message + assert "Resource exhausted" in exc_info.value.message + + +def test_chunk_parser_raises_on_500_error_chunk(): + """Test chunk_parser raises VertexAIError on 500 INTERNAL error chunk""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 500, + "message": "Internal error encountered.", + "status": "INTERNAL", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "INTERNAL" in exc_info.value.message + + +def test_chunk_parser_raises_on_error_chunk_with_minimal_fields(): + """Test chunk_parser handles error chunks with missing optional fields""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 429, + "message": "Resource exhausted.", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + + +def test_chunk_parser_normal_chunk_unaffected_by_error_check(): + """Test that normal streaming chunks still work correctly after error check addition""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + normal_chunk = { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "Hello"}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 5, + "candidatesTokenCount": 1, + "totalTokenCount": 6, + }, + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + result = streaming_obj.chunk_parser(normal_chunk) + assert result is not None + assert len(result.choices) > 0 + assert result.choices[0].delta.content == "Hello" + + +def test_chunk_parser_raises_on_non_dict_error(): + """Test chunk_parser raises VertexAIError when chunk['error'] is not a dict""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": "something went wrong"} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + + +def test_chunk_parser_raises_on_string_error_code(): + """Test chunk_parser correctly converts string error code to int""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + # code field is a string "429" rather than an int + error_chunk = { + "error": { + "code": "429", + "message": "Resource exhausted.", + "status": "RESOURCE_EXHAUSTED", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + assert isinstance(exc_info.value.status_code, int) + + +def test_chunk_parser_error_chunk_explicit_null_code_uses_500(): + """JSON null for code must not call int(None); status defaults to 500.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": None, + "message": "Something went wrong.", + "status": "UNKNOWN", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Something went wrong" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_numeric_code_defaults_to_500(): + """Non-numeric code must not become ValueError -> RuntimeError in __next__.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": "NOT_A_NUMBER", + "message": "Malformed.", + "status": "INVALID", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Malformed" in exc_info.value.message + + +def test_chunk_parser_error_chunk_empty_dict_defaults_to_500(): + """Empty error object {} uses default code 500 and default message/status strings.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": {}} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "UNKNOWN" in exc_info.value.message + assert "Unknown error" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_dict_int_value(): + """Non-dict error payloads (e.g. bare JSON number) must raise with status 500, not TypeError.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": 503} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + assert "503" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_dict_null_value(): + """JSON null for error must hit the non-dict branch (same as int/string).""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": None} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + + +def test_mid_stream_429_error_raises_during_iteration(): + """ + Simulate a full streaming scenario: normal thinking chunks arrive first, + then a 429 RESOURCE_EXHAUSTED error chunk arrives mid-stream. + Verify that ModelResponseIterator raises VertexAIError during iteration. + """ + import json + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + # Simulate Vertex AI SSE stream: normal chunks followed by a 429 error chunk + normal_chunk_1 = json.dumps( + { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "Let me think about this...", "thought": True}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15, + }, + "modelVersion": "gemini-3.1-flash-image-preview", + } + ) + + normal_chunk_2 = json.dumps( + { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "I'll generate the image now.", "thought": True}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 12, + "totalTokenCount": 22, + }, + } + ) + + error_chunk = json.dumps( + { + "error": { + "code": 429, + "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.", + "status": "RESOURCE_EXHAUSTED", + } + } + ) + + # Build a mock SSE stream (lines returned by iter_lines) + sse_lines = iter([normal_chunk_1, normal_chunk_2, error_chunk]) + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=sse_lines, + sync_stream=True, + logging_obj=logging_obj, + ) + + # Iterate the stream: first chunks should succeed, then 429 error should be raised + results = [] + with pytest.raises(VertexAIError) as exc_info: + for chunk in streaming_obj: + if chunk is not None: + results.append(chunk) + + # Verify: received normal chunks before the error + assert ( + len(results) >= 1 + ), "Should have received at least 1 normal chunk before the error" + + # Verify: 429 error is properly raised + assert exc_info.value.status_code == 429 + assert "RESOURCE_EXHAUSTED" in str(exc_info.value.message) diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py index 2e9629f95de..be0e59e8b7d 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py @@ -1219,6 +1219,32 @@ def test_process_gemini_media(): mime_type="image/jpeg", file_uri="gs://bucket/image" ) + # Test gs url without extension using mime_type from image_url object + image_message = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "gs://bucket/image-without-extension", + "mime_type": "image/png", + }, + } + ], + } + ] + from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, + ) + + converted = _gemini_convert_messages_with_history( + messages=image_message, model="gemini-2.5-flash" + ) + assert converted[0]["parts"][0]["file_data"] == FileDataType( + mime_type="image/png", file_uri="gs://bucket/image-without-extension" + ) + # Test HTTPS JPG URL https_result = _process_gemini_media("https://example.com/image.jpg") print("https_result JPG", https_result) @@ -1256,6 +1282,7 @@ def test_process_gemini_media(): assert base64_result["inline_data"]["data"] == "/9j/4AAQSkZJRg..." + def test_get_image_mime_type_from_url(): """Test the _get_image_mime_type_from_url function for different image URLs""" from litellm.llms.vertex_ai.gemini.transformation import ( diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py new file mode 100644 index 00000000000..e0eccad80e2 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py @@ -0,0 +1,466 @@ +"""Vertex Gemini: extensionless gs:// MIME + GCS metadata tests. + +Split from test_vertex.py to satisfy CI per-file size limits. +""" +import asyncio +import os +import sys +import time + +from dotenv import load_dotenv + +load_dotenv() + +import pytest + +import litellm +from unittest.mock import MagicMock, patch + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm.llms.vertex_ai.gemini.transformation import _process_gemini_media + + +def test_process_gemini_media_gcs_explicit_format_octet_stream_and_alias(): + """Explicit format bypasses registry; image/jpg alias still applies.""" + from litellm.types.llms.vertex_ai import FileDataType + + r1 = _process_gemini_media( + "gs://bucket/object-no-ext", + format="application/octet-stream", + ) + assert r1["file_data"] == FileDataType( + mime_type="application/octet-stream", + file_uri="gs://bucket/object-no-ext", + ) + r2 = _process_gemini_media("gs://bucket/object-no-ext", format="image/jpg") + assert r2["file_data"] == FileDataType( + mime_type="image/jpeg", + file_uri="gs://bucket/object-no-ext", + ) + + +def test_process_gemini_media_gcs_without_extension_errors_and_metadata_mock(): + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value=None, + ): + with pytest.raises(litellm.BadRequestError) as exc: + _process_gemini_media("gs://bucket/image-without-extension") + assert "Unable to determine mime type for gs URI" in str(exc.value) + + from litellm.types.llms.vertex_ai import FileDataType + + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="image/jpeg", + ) as m: + r = _process_gemini_media("gs://bucket/image-without-extension") + assert r["file_data"] == FileDataType( + mime_type="image/jpeg", file_uri="gs://bucket/image-without-extension" + ) + m.assert_called() + + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="image/jpg", + ): + r_alias = _process_gemini_media("gs://bucket/image-without-extension") + assert r_alias["file_data"]["mime_type"] == "image/jpeg" + + +def test_process_gemini_media_rejects_gcs_metadata_mime_not_supported_by_gemini(): + """Non-empty GCS contentType that fails _normalize_and_validate_gemini_mime_type.""" + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="application/x-litellm-unit-test-unknown-mime", + ): + with pytest.raises( + litellm.BadRequestError, + match="File type not supported by gemini", + ): + _process_gemini_media("gs://bucket/object-without-extension") + + +def test_file_block_uses_mime_type_alias_for_extensionless_gcs(): + from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, + ) + from litellm.types.llms.vertex_ai import FileDataType + + messages = [ + { + "role": "user", + "content": [ + { + "type": "file", + "file": { + "file_id": "gs://bucket/no-extension-object", + "mime_type": "application/pdf", + }, + } + ], + } + ] + converted = _gemini_convert_messages_with_history( + messages=messages, model="gemini-2.5-flash" + ) + assert converted[0]["parts"][0]["file_data"] == FileDataType( + mime_type="application/pdf", file_uri="gs://bucket/no-extension-object" + ) + + +@pytest.mark.parametrize( + "bucket,expected", + [ + (("a." * 110) + "aa", True), + ("ab", False), + ("a" * 64, False), + ("ab..cd", False), + ("1.2.3.4", False), + ("192.168.0.1", False), + ("Bucket-Upper", False), + ("bucket@name", False), + ("bucket name", False), + ("-mybucket", False), + ("mybucket-", False), + (".mybucket", False), + ("mybucket.", False), + ], +) +def test_is_valid_gcs_bucket_name_matrix(bucket, expected): + from litellm.llms.vertex_ai.gemini.transformation import _is_valid_gcs_bucket_name + + assert _is_valid_gcs_bucket_name(bucket) is expected + + +def test_get_gcs_object_content_type_explicit_vertex_success_and_token_failure(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("test-token", "test-project") + resp = MagicMock() + resp.is_error = False + resp.status_code = 200 + resp.json.return_value = {"contentType": "image/png"} + http = MagicMock() + http.get.return_value = resp + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + assert ( + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/image-without-extension", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + == "image/png" + ) + mock_v.get_access_token.assert_called_once_with( + credentials="credential-json", + project_id="project-123", + ) + + mock_v2 = MagicMock() + mock_v2.get_access_token.side_effect = Exception("token failure") + with patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2): + with pytest.raises( + litellm.BadRequestError, + match="Unable to fetch GCS metadata with provided Vertex credentials/project", + ): + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/image-without-extension", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + + +def test_get_gcs_object_content_type_http_error_explicit_vs_anonymous(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("t", "p") + err_resp = MagicMock() + err_resp.is_error = True + err_resp.status_code = 403 + err_resp.text = '{"error":{"message":"Permission denied"}}' + http = MagicMock() + http.get.return_value = err_resp + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + with pytest.raises(litellm.BadRequestError, match="HTTP 403") as ei: + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/obj", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + assert "Permission denied" in str(ei.value) + + mock_v2 = MagicMock() + anon_err = MagicMock() + anon_err.is_error = True + anon_err.status_code = 403 + anon_err.text = "Forbidden" + http2 = MagicMock() + http2.get.return_value = anon_err + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http2, + ), + ): + assert ( + gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object") + is None + ) + mock_v2.get_access_token.assert_not_called() + + +def test_get_gcs_object_content_type_anonymous_success_no_auth_header(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + ok = MagicMock() + ok.is_error = False + ok.status_code = 200 + ok.json.return_value = {"contentType": "image/jpeg"} + http = MagicMock() + http.get.return_value = ok + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + assert ( + gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object") + == "image/jpeg" + ) + mock_v.get_access_token.assert_not_called() + hdrs = http.get.call_args.kwargs.get("headers") + assert hdrs is None or "Authorization" not in hdrs + + +def test_async_transform_request_body_offloads_extensionless_gs_not_plain_text(): + from litellm.llms.vertex_ai.gemini import transformation as gemini_transformation + + messages = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image-without-extension"}, + } + ], + } + ] + + def slow_http_get(*args, **kwargs): + time.sleep(0.5) + response = MagicMock() + response.is_error = False + response.status_code = 200 + response.raise_for_status.return_value = None + response.json.return_value = {"contentType": "image/png"} + return response + + async def fake_check_and_create_cache(self, **kwargs): + return kwargs["messages"], kwargs["optional_params"], None + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("token", "project") + mock_http = MagicMock() + mock_http.get.side_effect = slow_http_get + + async def run_scenario() -> float: + async def concurrent_sleep() -> float: + start = time.monotonic() + await asyncio.sleep(0.05) + return time.monotonic() - start + + task = asyncio.create_task( + gemini_transformation.async_transform_request_body( + gemini_api_key=None, + messages=messages, + api_base=None, + model="gemini-2.5-flash", + client=None, + timeout=None, + extra_headers=None, + optional_params={}, + logging_obj=MagicMock(), + custom_llm_provider="vertex_ai", + litellm_params={}, + vertex_project=None, + vertex_location=None, + vertex_auth_header=None, + ) + ) + elapsed = await concurrent_sleep() + await task + return elapsed + + with ( + patch.object(gemini_transformation, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=mock_http, + ), + patch( + "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching." + "ContextCachingEndpoints.async_check_and_create_cache", + new=fake_check_and_create_cache, + ), + ): + sleep_elapsed = asyncio.run(run_scenario()) + + assert sleep_elapsed < 0.4, ( + f"Event loop blocked for {sleep_elapsed:.3f}s; " + "async_transform_request_body did not offload sync GCS metadata" + ) + + async def fake_cache2(self, **kwargs): + return kwargs["messages"], kwargs["optional_params"], None + + async def run_plain(): + with patch( + "litellm.llms.vertex_ai.gemini.transformation.asyncify", + side_effect=AssertionError("asyncify must not run without extensionless gs://"), + ): + return await gemini_transformation.async_transform_request_body( + gemini_api_key=None, + messages=[{"role": "user", "content": "hello"}], + api_base=None, + model="gemini-2.5-flash", + client=None, + timeout=None, + extra_headers=None, + optional_params={}, + logging_obj=MagicMock(), + custom_llm_provider="vertex_ai", + litellm_params={}, + vertex_project=None, + vertex_location=None, + vertex_auth_header=None, + ) + + with patch( + "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching." + "ContextCachingEndpoints.async_check_and_create_cache", + new=fake_cache2, + ): + body = asyncio.run(run_plain()) + assert body is not None and "contents" in body + + +@pytest.mark.parametrize( + "messages,expected", + [ + ([{"role": "user", "content": "hello"}], False), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image-without-extension"}, + } + ], + } + ], + True, + ), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image.png"}, + } + ], + } + ], + False, + ), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "gs://bucket/image-without-extension", + "mime_type": "image/png", + }, + } + ], + } + ], + False, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [ + {"image_url": {"url": "gs://bucket/gen-without-extension"}}, + ], + } + ], + True, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [{"image_url": {"url": "gs://bucket/gen.png"}}], + } + ], + False, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [ + { + "image_url": { + "url": "gs://bucket/gen-no-ext", + "mime_type": "image/png", + }, + } + ], + } + ], + False, + ), + ], +) +def test_openai_messages_may_need_sync_gcs_metadata_fetch_matrix(messages, expected): + from litellm.llms.vertex_ai.gemini.transformation import ( + _openai_messages_may_need_sync_gcs_metadata_fetch, + ) + + assert _openai_messages_may_need_sync_gcs_metadata_fetch(messages) is expected diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py new file mode 100644 index 00000000000..b483a75a939 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py @@ -0,0 +1,121 @@ +""" +Regression tests for #28084: + +`VertexAIPartnerModels.count_tokens` (for Claude / Mistral / Llama on Vertex) +used to gate on `import vertexai` even though the actual count-tokens path goes +through `VertexAIPartnerModelsTokenCounter.handle_count_tokens_request`, which +talks to the publisher's `:rawPredict` endpoint over plain httpx and never +touches the Gemini SDK. The unused gate broke `/v1/messages/count_tokens` for +any LiteLLM install that did not pull in `google-cloud-aiplatform` (which is +not in the default `proxy` / `proxy-dev` extras). + +These tests pin the absence of that gate by: + +1. simulating `vertexai` being unimportable and verifying the partner-model + path does not raise the historical "vertexai import failed" error before + reaching the network/auth layer, and +2. asserting that import of the partner-model count-tokens handler module by + itself does not pull `vertexai` into `sys.modules`. +""" + +import sys + +import pytest + +from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import ( + VertexAIPartnerModelsTokenCounter, +) +from litellm.llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels + + +@pytest.mark.asyncio +async def test_count_tokens_does_not_require_vertexai_sdk(monkeypatch): + """Even when `import vertexai` would fail, count_tokens must not raise the + historical "vertexai import failed" gate. The downstream handler talks to + `:rawPredict` over httpx with an access token — no Gemini SDK needed.""" + + # Simulate `vertexai` being unimportable, regardless of what is actually on + # the test environment's sys.path. + monkeypatch.setitem(sys.modules, "vertexai", None) + monkeypatch.setitem(sys.modules, "vertexai.preview", None) + + captured = {} + + async def fake_ensure_access_token( + self, credentials, project_id, custom_llm_provider + ): + return "fake-token", "fake-project" + + def fake_build_endpoint(self, model, project_id, vertex_location, api_base=None): + captured["model_to_endpoint"] = model + return "https://fake-endpoint" + + monkeypatch.setattr( + VertexAIPartnerModelsTokenCounter, + "_ensure_access_token_async", + fake_ensure_access_token, + ) + monkeypatch.setattr( + VertexAIPartnerModelsTokenCounter, + "_build_count_tokens_endpoint", + fake_build_endpoint, + ) + + class FakeResponse: + status_code = 200 + + def json(self): + return {"input_tokens": 9} + + class FakeClient: + async def post(self, url, headers=None, json=None, **kwargs): + captured["url"] = url + captured["headers"] = headers + captured["json"] = json + return FakeResponse() + + import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod + + monkeypatch.setattr( + handler_mod, "get_async_httpx_client", lambda **kwargs: FakeClient() + ) + + result = await VertexAIPartnerModels().count_tokens( + model="claude-sonnet-4-6", + messages=[{"role": "user", "content": "hello"}], + litellm_params={"vertex_location": "us-east5"}, + vertex_project="test-project", + vertex_location="us-east5", + vertex_credentials=None, + ) + + # We should reach the publisher endpoint and parse its response, not raise + # the vertexai-import gate. + assert result == { + "input_tokens": 9, + "tokenizer_used": "vertex_ai_partner_models", + } + assert captured["headers"] == {"Authorization": "Bearer fake-token"} + assert captured["model_to_endpoint"] == "claude-sonnet-4-6" + + +def test_handler_module_does_not_import_vertexai_sdk(): + """Importing the partner-model count-tokens handler must not load the + Gemini SDK into sys.modules. Operators who only need Claude-on-Vertex + token counting should not pay for `google-cloud-aiplatform`.""" + + # Force-evict any prior load so this assertion measures what THIS module + # pulls in, not what an unrelated earlier test did. + for mod in list(sys.modules): + if mod == "vertexai" or mod.startswith("vertexai."): + sys.modules.pop(mod, None) + + # Re-import the handler module to verify it stays SDK-free. + import importlib + + import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod + + importlib.reload(handler_mod) + + leaked = [m for m in sys.modules if m == "vertexai" or m.startswith("vertexai.")] + assert leaked == [], f"unexpected vertexai SDK imports: {leaked}" diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 581324d47d2..b06cc7f0f12 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -1278,6 +1278,188 @@ def test_xff_misconfig_warning_emitted_once(caplog): ), f"expected exactly one warning, got {len(matching)}: {[r.getMessage() for r in matching]}" +def test_get_request_base_url_honors_proxy_base_url_env(monkeypatch): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP discoverable endpoints not available") + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "10.0.0.7" + headers = { + "X-Forwarded-Proto": "https", + "X-Forwarded-Host": "litellm-internal:4000", + "X-Forwarded-Port": "9999", + } + mock_request.headers.get = lambda name, default=None: headers.get(name, default) + + monkeypatch.setenv("PROXY_BASE_URL", "https://litellm.example.com") + assert get_request_base_url(mock_request) == "https://litellm.example.com" + + monkeypatch.setenv("PROXY_BASE_URL", "https://litellm.example.com/") + assert get_request_base_url(mock_request) == "https://litellm.example.com" + + +def test_validate_trusted_redirect_uri_logs_diagnostic_on_rejection( + caplog, monkeypatch +): + try: + from fastapi import HTTPException, Request + + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + validate_trusted_redirect_uri, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + monkeypatch.delenv("PROXY_BASE_URL", raising=False) + monkeypatch.delenv("MCP_TRUSTED_REDIRECT_ORIGINS", raising=False) + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "203.0.113.5" + headers = { + "X-Forwarded-Proto": "https", + "X-Forwarded-Host": "litellm.example.com", + "X-Forwarded-Port": "443", + "Host": "litellm-internal:4000", + } + mock_request.headers.get = lambda name, default=None: headers.get(name, default) + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + with pytest.raises(HTTPException) as exc_info: + validate_trusted_redirect_uri( + mock_request, + "https://litellm.example.com/ui/mcp/oauth/callback", + ) + assert exc_info.value.status_code == 400 + assert exc_info.value.detail == "invalid_request" + + matching = [r for r in caplog.records if "rejecting redirect_uri" in r.getMessage()] + assert len(matching) == 1, ( + "expected exactly one diagnostic warning, got " + f"{[r.getMessage() for r in caplog.records]}" + ) + msg = matching[0].getMessage() + assert "https://litellm.example.com/ui/mcp/oauth/callback" in msg + assert "litellm-internal:4000" in msg + assert "X-Forwarded-Host" in msg + + +@pytest.mark.parametrize( + "bad_value", + [ + "litellm.example.com", + "litellm.example.com/", + "://litellm.example.com", + "ftp://litellm.example.com", + "https://", + "not a url at all", + ], +) +def test_get_request_base_url_rejects_malformed_proxy_base_url( + bad_value, monkeypatch, caplog +): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server import oauth_utils + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + oauth_utils._warned_invalid_proxy_base_url = None + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "127.0.0.1" + mock_request.headers.get = lambda name, default=None: default + + monkeypatch.setenv("PROXY_BASE_URL", bad_value) + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + result = get_request_base_url(mock_request) + + assert result == "http://litellm-internal:4000", ( + f"malformed PROXY_BASE_URL={bad_value!r} should be ignored, " f"got {result!r}" + ) + matching = [ + r + for r in caplog.records + if "PROXY_BASE_URL" in r.getMessage() and "ignored" in r.getMessage() + ] + assert len(matching) == 1, ( + "expected one diagnostic for malformed PROXY_BASE_URL, got " + f"{[r.getMessage() for r in caplog.records]}" + ) + assert ( + repr(bad_value) in matching[0].getMessage() + or bad_value in matching[0].getMessage() + ) + + +def test_get_request_base_url_malformed_proxy_base_url_warning_is_one_shot( + monkeypatch, caplog +): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server import oauth_utils + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + oauth_utils._warned_invalid_proxy_base_url = None + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "127.0.0.1" + mock_request.headers.get = lambda name, default=None: default + + monkeypatch.setenv("PROXY_BASE_URL", "litellm.example.com") + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + for _ in range(5): + get_request_base_url(mock_request) + + matching = [ + r + for r in caplog.records + if "PROXY_BASE_URL" in r.getMessage() and "ignored" in r.getMessage() + ] + assert ( + len(matching) == 1 + ), f"expected exactly one warning across 5 calls, got {len(matching)}" + + # ------------------------------------------------------------------- # Tests for scopes_supported when mcp_server.scopes is None # ------------------------------------------------------------------- diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index 70e8812c99a..08035fb7173 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -19,6 +19,7 @@ from litellm.proxy.auth.auth_utils import ( get_model_from_request, get_project_model_rpm_limit, get_project_model_tpm_limit, + get_request_route_template, is_request_body_safe, ) @@ -1573,3 +1574,44 @@ class TestPricingInjectionBlocked: ) is True ) + + +class TestGetRequestRouteTemplate: + """get_request_route_template returns the low-cardinality FastAPI route + template (e.g. /v1/threads/{thread_id}/runs) for http.route, distinct + from the literal url.path. None when unavailable.""" + + def _request(self, scope): + req = MagicMock() + req.scope = scope + return req + + def test_returns_route_template(self): + route = MagicMock() + route.path = "/v1/threads/{thread_id}/runs" + req = self._request({"route": route, "path": "/v1/threads/abc123/runs"}) + # template, not the literal path — two thread IDs share this value + assert get_request_route_template(req) == "/v1/threads/{thread_id}/runs" + + def test_scope_not_dict_returns_none(self): + assert get_request_route_template(self._request("not-a-dict")) is None + + def test_no_route_in_scope_returns_none(self): + assert get_request_route_template(self._request({"path": "/x"})) is None + + def test_route_without_str_path_returns_none(self): + route = MagicMock() + route.path = 12345 # not a str + assert get_request_route_template(self._request({"route": route})) is None + + def test_route_with_empty_path_returns_none(self): + route = MagicMock() + route.path = "" + assert get_request_route_template(self._request({"route": route})) is None + + def test_exception_returns_none(self): + req = MagicMock() + type(req).scope = property( + lambda self: (_ for _ in ()).throw(RuntimeError("boom")) + ) + assert get_request_route_template(req) is None diff --git a/tests/test_litellm/proxy/db/test_db_url_settings.py b/tests/test_litellm/proxy/db/test_db_url_settings.py new file mode 100644 index 00000000000..9e348c3988f --- /dev/null +++ b/tests/test_litellm/proxy/db/test_db_url_settings.py @@ -0,0 +1,271 @@ +"""Tests for ``DatabaseURLSettings``. + +The model assembles ``DATABASE_URL`` (and optionally +``DATABASE_URL_READ_REPLICA``) from the discrete ``DATABASE_*`` env vars +emitted by the ``helm/litellm`` chart, before Prisma initializes. It covers +both IAM auth (mint a short-lived token) and password auth, for both the +writer and the read replica. + +The reader URL is opt-in via ``DATABASE_HOST_READ_REPLICA`` and must not +clobber a pre-existing ``DATABASE_URL_READ_REPLICA``. A pre-existing +``DATABASE_URL`` (password auth) is likewise left untouched. +""" + +import os +from unittest.mock import patch + +import pytest + +from litellm.proxy.db.db_url_settings import DatabaseURLSettings + + +def _apply() -> bool: + """Run the production call path: load from env, write to env.""" + return DatabaseURLSettings.from_env().apply_to_env() + + +@pytest.fixture(autouse=True) +def _scrub_db_env(monkeypatch): + """Remove every env var the model reads so tests start from a clean slate.""" + for var in ( + "IAM_TOKEN_DB_AUTH", + "DATABASE_URL", + "DATABASE_URL_READ_REPLICA", + "DATABASE_HOST", + "DATABASE_PORT", + "DATABASE_USER", + "DATABASE_USERNAME", + "DATABASE_NAME", + "DATABASE_SCHEMA", + "DATABASE_PASSWORD", + "DATABASE_HOST_READ_REPLICA", + "DATABASE_PORT_READ_REPLICA", + "DATABASE_USER_READ_REPLICA", + "DATABASE_USERNAME_READ_REPLICA", + "DATABASE_NAME_READ_REPLICA", + "DATABASE_SCHEMA_READ_REPLICA", + "DATABASE_PASSWORD_READ_REPLICA", + ): + monkeypatch.delenv(var, raising=False) + + +def _stub_iam_token(token: str = "FAKE_TOKEN"): + """Patch the AWS-touching token mint so tests don't need boto3 / network.""" + return patch( + "litellm.proxy.auth.rds_iam_token.generate_iam_auth_token", + return_value=token, + ) + + +# --------------------------------------------------------------------------- +# IAM auth +# --------------------------------------------------------------------------- + + +def test_returns_false_when_nothing_configured(monkeypatch): + """No env mutation, no error — just a False return.""" + assert _apply() is False + assert "DATABASE_URL" not in os.environ + + +def test_assembles_writer_url_when_iam_enabled(monkeypatch): + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + + with _stub_iam_token("WRITER_TOKEN"): + assert _apply() is True + + assert ( + os.environ["DATABASE_URL"] + == "postgresql://litellm:WRITER_TOKEN@writer.example.com:5432/litellm_db" + ) + # Reader was never configured, so it must not have been set. + assert "DATABASE_URL_READ_REPLICA" not in os.environ + + +def test_missing_writer_envs_raises(monkeypatch): + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + # DATABASE_HOST intentionally unset. + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + + with pytest.raises(RuntimeError, match="DATABASE_HOST"): + _apply() + + +def test_reader_url_assembled_when_host_set_and_url_unset(monkeypatch): + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_HOST_READ_REPLICA", "reader.example.com") + + with _stub_iam_token("READER_TOKEN"): + _apply() + + assert ( + os.environ["DATABASE_URL_READ_REPLICA"] + == "postgresql://litellm:READER_TOKEN@reader.example.com:5432/litellm_db" + ) + + +def test_reader_url_not_clobbered_when_already_set(monkeypatch): + """If the operator pinned DATABASE_URL_READ_REPLICA (e.g. a non-IAM + reader), the model must leave it untouched even though + DATABASE_HOST_READ_REPLICA is also set.""" + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_HOST_READ_REPLICA", "reader.example.com") + monkeypatch.setenv( + "DATABASE_URL_READ_REPLICA", + "postgresql://app:secret@reader.example.com:5432/litellm_db", + ) + + with _stub_iam_token("READER_TOKEN"): + _apply() + + assert ( + os.environ["DATABASE_URL_READ_REPLICA"] + == "postgresql://app:secret@reader.example.com:5432/litellm_db" + ) + + +def test_reader_url_skipped_when_host_unset(monkeypatch): + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + + with _stub_iam_token("WRITER_TOKEN"): + _apply() + + assert "DATABASE_URL_READ_REPLICA" not in os.environ + + +def test_reader_field_fallbacks_default_to_writer_values(monkeypatch): + """When *_READ_REPLICA fields are unset (other than host), they fall + back to the writer's user / name / schema.""" + monkeypatch.setenv("IAM_TOKEN_DB_AUTH", "true") + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_SCHEMA", "public") + monkeypatch.setenv("DATABASE_HOST_READ_REPLICA", "reader.example.com") + + with _stub_iam_token("READER_TOKEN"): + _apply() + + assert ( + os.environ["DATABASE_URL_READ_REPLICA"] + == "postgresql://litellm:READER_TOKEN@reader.example.com:5432/litellm_db?schema=public" + ) + + +# --------------------------------------------------------------------------- +# Password auth +# --------------------------------------------------------------------------- + + +def test_assembles_writer_url_from_password(monkeypatch): + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "s3cr3t") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL"] + == "postgresql://litellm:s3cr3t@writer.example.com:5432/litellm_db" + ) + + +def test_writer_password_is_percent_encoded(monkeypatch): + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "p@ss/w:rd") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL"] + == "postgresql://litellm:p%40ss%2Fw%3Ard@writer.example.com:5432/litellm_db" + ) + + +def test_writer_url_not_clobbered_when_already_set(monkeypatch): + """An operator-pinned DATABASE_URL (e.g. helm's $(VAR) assembly) always + wins over the discrete fields.""" + monkeypatch.setenv( + "DATABASE_URL", "postgresql://pinned:url@db.example.com:5432/litellm_db" + ) + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "s3cr3t") + + assert _apply() is False + assert ( + os.environ["DATABASE_URL"] + == "postgresql://pinned:url@db.example.com:5432/litellm_db" + ) + + +def test_writer_url_passwordless(monkeypatch): + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL"] + == "postgresql://litellm@writer.example.com:5432/litellm_db" + ) + + +def test_database_username_alias(monkeypatch): + """DATABASE_USERNAME is accepted as an alias for DATABASE_USER (parity + with construct_database_url_from_env_vars).""" + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USERNAME", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "s3cr3t") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL"] + == "postgresql://litellm:s3cr3t@writer.example.com:5432/litellm_db" + ) + + +def test_password_reader_falls_back_to_writer_password(monkeypatch): + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "s3cr3t") + monkeypatch.setenv("DATABASE_HOST_READ_REPLICA", "reader.example.com") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL_READ_REPLICA"] + == "postgresql://litellm:s3cr3t@reader.example.com:5432/litellm_db" + ) + + +def test_password_reader_uses_own_credentials(monkeypatch): + monkeypatch.setenv("DATABASE_HOST", "writer.example.com") + monkeypatch.setenv("DATABASE_USER", "litellm") + monkeypatch.setenv("DATABASE_NAME", "litellm_db") + monkeypatch.setenv("DATABASE_PASSWORD", "s3cr3t") + monkeypatch.setenv("DATABASE_HOST_READ_REPLICA", "reader.example.com") + monkeypatch.setenv("DATABASE_USER_READ_REPLICA", "litellm_ro") + monkeypatch.setenv("DATABASE_PASSWORD_READ_REPLICA", "ro_pw") + + assert _apply() is True + assert ( + os.environ["DATABASE_URL_READ_REPLICA"] + == "postgresql://litellm_ro:ro_pw@reader.example.com:5432/litellm_db" + ) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py index e01038cd35f..6ec793a1bb0 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py @@ -553,6 +553,7 @@ class TestGuardrailActions: # Verify the exception has the clean error message (no wrapper) assert str(exc_info.value) == "Content contains harmful instructions" assert exc_info.value.guardrail_name == "generic_guardrail_api" + assert exc_info.value.status_code == 400 @pytest.mark.asyncio async def test_action_intervened_modifies_content( diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py index f0bf4578636..b65f6305b77 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py @@ -1446,3 +1446,175 @@ class TestGetTeamDeployments: result = await _get_team_deployments(team_id, prisma_client) assert len(result) == 1 assert result[0] is dep1 + + +def _build_db_model_for_blocked_test(): + from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo + + return Deployment( + model_name="gpt-4o", + litellm_params=LiteLLM_Params(model="openai/gpt-4o"), + model_info=ModelInfo(id="dep-0"), + ) + + +class TestUpdateDBModelBlocked: + """`update_db_model` must thread `blocked` through to the Prisma payload only + when the caller explicitly set it — PATCH semantics: an absent field means + "leave the stored value untouched".""" + + def test_update_db_model_passes_blocked_true_to_db(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(blocked=True), + ) + assert result["blocked"] is True + + def test_update_db_model_passes_blocked_false_to_db(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(blocked=False), + ) + assert result["blocked"] is False + + def test_update_db_model_omits_blocked_when_patch_is_none(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(), + ) + assert "blocked" not in result + + +class TestGetModelInfoWithIdBlocked: + """`ProxyConfig.get_model_info_with_id` must propagate the DB-level `blocked` + column into the in-memory `model_info` dict so the router filter can read it.""" + + def test_get_model_info_with_id_propagates_blocked_true(self): + from litellm.proxy.proxy_server import ProxyConfig + + model = MagicMock() + model.model_id = "dep-1" + model.model_info = {} + model.blocked = True + info = ProxyConfig().get_model_info_with_id(model=model, db_model=True) + assert info.id == "dep-1" + assert getattr(info, "blocked") is True + + def test_get_model_info_with_id_defaults_blocked_to_false_when_missing(self): + from litellm.proxy.proxy_server import ProxyConfig + + model = MagicMock(spec=["model_id", "model_info"]) + model.model_id = "dep-2" + model.model_info = {} + info = ProxyConfig().get_model_info_with_id(model=model, db_model=True) + assert getattr(info, "blocked") is False + + +class TestPatchModelBlockedAuthGate: + """Only proxy admins may flip `blocked` — team admins authorized for + team-scoped models via `can_user_make_model_call` must still be rejected + when they attempt to toggle the pause flag.""" + + @pytest.mark.asyncio + async def test_team_admin_cannot_toggle_blocked(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + patch_model, + ) + + non_admin = UserAPIKeyAuth( + user_id="team_admin", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + existing_row = MagicMock() + existing_row.litellm_params = {"model": "openai/gpt-4o-mini"} + existing_row.model_dump.return_value = { + "model_name": "gpt-4o-mini", + "litellm_params": existing_row.litellm_params, + "model_info": {"id": "m1"}, + } + existing_row.model_dump_json.return_value = "{}" + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock( + return_value=existing_row + ) + + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), + patch("litellm.proxy.proxy_server.llm_router", MagicMock()), + patch("litellm.proxy.proxy_server.store_model_in_db", True), + patch("litellm.proxy.proxy_server.premium_user", True), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + ): + with pytest.raises(Exception) as exc_info: + await patch_model( + model_id="m1", + patch_data=updateDeployment(blocked=True), + user_api_key_dict=non_admin, + ) + err = exc_info.value + assert getattr(err, "param", "") == "blocked" + assert "proxy admin" in getattr(err, "message", "").lower() + + @pytest.mark.asyncio + async def test_proxy_admin_can_toggle_blocked(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + patch_model, + ) + + admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + existing_row = MagicMock() + existing_row.litellm_params = {"model": "openai/gpt-4o-mini"} + existing_row.model_dump.return_value = { + "model_name": "gpt-4o-mini", + "litellm_params": existing_row.litellm_params, + "model_info": {"id": "m1"}, + } + existing_row.model_dump_json.return_value = "{}" + updated_row = MagicMock() + updated_row.model_dump_json.return_value = "{}" + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock( + return_value=existing_row + ) + mock_prisma.db.litellm_proxymodeltable.update = AsyncMock( + return_value=updated_row + ) + + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), + patch("litellm.proxy.proxy_server.llm_router", MagicMock()), + patch("litellm.proxy.proxy_server.store_model_in_db", True), + patch("litellm.proxy.proxy_server.premium_user", True), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.clear_cache", + new=AsyncMock(return_value=None), + ), + ): + result = await patch_model( + model_id="m1", + patch_data=updateDeployment(blocked=True), + user_api_key_dict=admin, + ) + assert result is updated_row + mock_prisma.db.litellm_proxymodeltable.update.assert_awaited_once() diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index 5c7bbc46c95..b450a262907 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -7943,3 +7943,103 @@ async def test_team_member_me_returns_404_for_unknown_team(mock_db_client): user_api_key_dict=caller_auth, ) assert exc_info.value.status_code == 404 + + +def _non_admin_auth(): + return UserAPIKeyAuth( + user_id="u-team-admin", user_role=LitellmUserRoles.INTERNAL_USER + ) + + +def test_check_passthrough_routes_caller_permission_team(): + from litellm.proxy._types import NewTeamRequest + from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, + ) + + admin = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN) + non_admin = _non_admin_auth() + + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=["/foo/*"]), admin, entity="team" + ) + + _check_passthrough_routes_caller_permission( + NewTeamRequest(), non_admin, entity="team" + ) + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=[]), non_admin, entity="team" + ) + + with pytest.raises(HTTPException) as exc: + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=["/admin/*"]), + non_admin, + entity="team", + ) + assert exc.value.status_code == 403 + assert "allowed_passthrough_routes" in str(exc.value.detail) + assert "team" in str(exc.value.detail) + + with pytest.raises(HTTPException) as exc: + _check_passthrough_routes_caller_permission( + NewTeamRequest(metadata={"allowed_passthrough_routes": ["/admin/*"]}), + non_admin, + entity="team", + ) + assert exc.value.status_code == 403 + assert "metadata.allowed_passthrough_routes" in str(exc.value.detail) + + +@pytest.mark.asyncio +async def test_new_team_blocks_non_admin_passthrough_routes(mock_db_client): + """A non-proxy-admin cannot self-grant pass-through routes via /team/new.""" + mock_db_client.db.litellm_teamtable.count = AsyncMock(return_value=0) + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException + from litellm.proxy.management_endpoints.team_endpoints import new_team + + with patch( + "litellm.proxy.management_endpoints.team_endpoints._check_user_team_limits", + AsyncMock(return_value=None), + ): + with pytest.raises(ProxyException) as exc: + await new_team( + data=NewTeamRequest( + team_alias="t", allowed_passthrough_routes=["/admin/*"] + ), + http_request=MagicMock(spec=Request), + user_api_key_dict=_non_admin_auth(), + ) + assert str(exc.value.code) == "403" + assert "allowed_passthrough_routes" in str(exc.value.message) + + +@pytest.mark.asyncio +async def test_update_team_blocks_non_admin_passthrough_routes(mock_db_client): + """Even a team manager (non-proxy-admin) cannot set pass-through routes via + /team/update — the gate runs after _verify_team_access.""" + from fastapi import Request + + from litellm.proxy._types import ProxyException, UpdateTeamRequest + from litellm.proxy.management_endpoints.team_endpoints import update_team + + existing = MagicMock() + existing.model_dump.return_value = {"team_id": "t1"} + mock_db_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=existing) + + with patch( + "litellm.proxy.management_endpoints.team_endpoints._verify_team_access", + AsyncMock(return_value=None), + ): + with pytest.raises(ProxyException) as exc: + await update_team( + data=UpdateTeamRequest( + team_id="t1", allowed_passthrough_routes=["/admin/*"] + ), + http_request=MagicMock(spec=Request), + user_api_key_dict=_non_admin_auth(), + ) + assert str(exc.value.code) == "403" + assert "allowed_passthrough_routes" in str(exc.value.message) diff --git a/tests/test_litellm/proxy/test_component_allowlists.py b/tests/test_litellm/proxy/test_component_allowlists.py new file mode 100644 index 00000000000..d20e1781169 --- /dev/null +++ b/tests/test_litellm/proxy/test_component_allowlists.py @@ -0,0 +1,76 @@ +"""Coverage test for the gateway / backend component allowlists. + +The componentization scaffold splits the proxy FastAPI app into two runtime +components by trimming the route table inside a wrapped lifespan context: + + gateway.main -> only paths matched by gateway/routes/allowlist.py + backend.main -> only paths matched by backend/routes/allowlist.py + +If either allowlist drops a path that was reachable on the monolithic app, +clients hitting that path on the corresponding pod get a 404. This test +guarantees that the union of the two trimmed route sets equals the full set +of routes on the proxy app — i.e. no endpoint is dropped on the floor. + +The test reproduces the same predicate that ``gateway/main.py`` and +``backend/main.py`` use, without importing them. The component modules wrap +the shared ``app.router.lifespan_context``; importing them in the test process +would chain wrappers and corrupt the snapshot. +""" + +import os +import sys + +# Importing ``litellm.proxy.proxy_server`` runs its module-level setup, which +# reads ``DATABASE_URL`` (Prisma) and ``LITELLM_MASTER_KEY``. Tier-zero CI +# runners don't set these. We pin throwaway values before the import so the +# test never depends on a live database or master key. +os.environ.setdefault("DATABASE_URL", "sqlite:///:memory:") +os.environ.setdefault("LITELLM_MASTER_KEY", "sk-test-component-allowlist") + +from fastapi.routing import Mount + +# gateway/ and backend/ live at the repo root, not inside litellm/. +_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..")) +if _REPO_ROOT not in sys.path: + sys.path.insert(0, _REPO_ROOT) + +from backend.routes.allowlist import BACKEND_EXACT_PATHS, BACKEND_PATH_PREFIXES +from gateway.routes.allowlist import GATEWAY_EXACT_PATHS, GATEWAY_PATH_PREFIXES +from litellm.proxy.proxy_server import app + + +def _component_paths(routes, exact_paths, path_prefixes) -> set[str]: + """Reproduce ``gateway.main._is_gateway_route`` / ``backend.main._is_backend_route``.""" + out: set[str] = set() + for route in routes: + if isinstance(route, Mount): + continue + path = getattr(route, "path", None) + if path is None: + continue + if path in exact_paths or any(path.startswith(p) for p in path_prefixes): + out.add(path) + return out + + +def test_gateway_plus_backend_covers_full_app(): + """Every route on the proxy app must be served by gateway or backend.""" + all_paths = { + getattr(r, "path") + for r in app.router.routes + if not isinstance(r, Mount) and getattr(r, "path", None) is not None + } + gateway_paths = _component_paths( + app.router.routes, GATEWAY_EXACT_PATHS, GATEWAY_PATH_PREFIXES + ) + backend_paths = _component_paths( + app.router.routes, BACKEND_EXACT_PATHS, BACKEND_PATH_PREFIXES + ) + + uncovered = all_paths - (gateway_paths | backend_paths) + + assert not uncovered, ( + f"{len(uncovered)} route(s) are not exposed on either component. " + f"Update gateway/routes/allowlist.py or backend/routes/allowlist.py to cover:\n " + + "\n ".join(sorted(uncovered)) + ) diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index bedc9c1f6e8..6d10d2a6353 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -1169,6 +1169,434 @@ def test_add_team_models_to_all_models(): assert result == {"gpt-4-model-2": {"team1"}} +@pytest.mark.asyncio +async def test_apply_search_filter_matches_team_public_model_name(): + """ + Regression test: team BYOK models persist an internal model_name + (e.g. `model_name_{team_id}_{uuid}`) and surface the user-facing name + via `model_info.team_public_model_name`. The /v2/model/info search + filter must match that public name so BYOK rows appear in results. + """ + from litellm.proxy.proxy_server import _apply_search_filter_to_models + + byok_model = { + "model_name": "model_name_team-abc-123_4a6b8", + "litellm_params": {"model": "claude-sonnet-4-5"}, + "model_info": { + "id": "byok-id-1", + "team_id": "team-abc-123", + "team_public_model_name": "team-claude-sonnet", + "db_model": True, + }, + } + unrelated_model = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": {"id": "normal-id-1", "db_model": False}, + } + + # Search matching only team_public_model_name should still include BYOK + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="claude", + prisma_client=None, + proxy_config=MagicMock(), + ) + filtered_ids = {m["model_info"]["id"] for m in filtered} + assert "byok-id-1" in filtered_ids + assert "normal-id-1" not in filtered_ids + + # Search by internal model_name still matches as before + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="model_name_team-abc-123", + prisma_client=None, + proxy_config=MagicMock(), + ) + assert [m["model_info"]["id"] for m in filtered] == ["byok-id-1"] + + # Non-matching search returns nothing + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="gemini", + prisma_client=None, + proxy_config=MagicMock(), + ) + assert filtered == [] + + +@pytest.mark.asyncio +async def test_apply_search_filter_scopes_byok_to_caller_teams(): + """ + Regression test: `/v2/model/info?search=...` must not leak BYOK rows + from teams the caller is not a member of. Even with a bounded + `model_name`-contains DB query, a non-admin caller could otherwise + see other teams' BYOK rows that happen to match by internal name. + The post-fetch team scope drops those. + """ + from litellm.proxy.proxy_server import _apply_search_filter_to_models + + # In-router BYOK rows: one in the caller's team, one in someone else's. + caller_team_byok = { + "model_name": "model_name_team-mine_internal", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-mine", + "team_id": "team-mine", + "team_public_model_name": "claude-sonnet-prod", + "db_model": True, + }, + } + other_team_byok = { + "model_name": "model_name_team-other_internal", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-other", + "team_id": "team-other", + "team_public_model_name": "claude-sonnet-staging", + "db_model": True, + }, + } + # Non-team row stays in the router-side result regardless of teams. + public_model = { + "model_name": "claude-public", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": {"id": "public-id", "db_model": False}, + } + + # DB-only BYOK rows fetched by the over-broad JSON branch. + db_caller_row = MagicMock() + db_caller_row.model_id = "byok-db-mine" + db_caller_row.model_name = "model_name_team-mine_db" + db_caller_row.model_info = { + "id": "byok-db-mine", + "team_id": "team-mine", + "team_public_model_name": "Claude DB Mine", + "db_model": True, + } + db_other_row = MagicMock() + db_other_row.model_id = "byok-db-other" + db_other_row.model_name = "model_name_team-other_db" + db_other_row.model_info = { + "id": "byok-db-other", + "team_id": "team-other", + "team_public_model_name": "Claude DB Other", + "db_model": True, + } + + prisma_client = MagicMock() + prisma_client.db.litellm_proxymodeltable.count = AsyncMock(return_value=2) + prisma_client.db.litellm_proxymodeltable.find_many = AsyncMock( + return_value=[db_caller_row, db_other_row] + ) + caller_user_row = MagicMock() + caller_user_row.teams = ["team-mine"] + prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=caller_user_row + ) + + proxy_config = MagicMock() + proxy_config.decrypt_model_list_from_db = lambda rows: [ + { + "model_name": r.model_name, + "model_info": r.model_info, + "litellm_params": {"model": "claude-sonnet"}, + } + for r in rows + ] + + non_admin = MagicMock(spec=UserAPIKeyAuth) + non_admin.user_role = LitellmUserRoles.INTERNAL_USER + non_admin.user_id = "user-mine" + + filtered, total_count = await _apply_search_filter_to_models( + all_models=[caller_team_byok, other_team_byok, public_model], + search="claude", + prisma_client=prisma_client, + proxy_config=proxy_config, + user_api_key_dict=non_admin, + ) + + filtered_ids = {m["model_info"]["id"] for m in filtered} + assert "byok-mine" in filtered_ids + assert "byok-db-mine" in filtered_ids + assert "public-id" in filtered_ids + assert "byok-other" not in filtered_ids, ( + "router-side BYOK from another team must be dropped from search " + "when caller doesn't belong to that team" + ) + assert "byok-db-other" not in filtered_ids, ( + "DB-only BYOK from another team must be dropped from search when " + "caller doesn't belong to that team" + ) + # total_count is router_models_count (2: caller_team_byok + public_model, + # other_team_byok dropped router-side) + DB count (2 from the mocked + # `count()`). The DB count is the *unscoped* match count; non-admin + # team scoping applies only to the returned page so the count can be + # over-reported, but it must never under-report (callers can paginate + # within the bound). + assert total_count == 4 + + # Admins keep the un-scoped view across teams. + admin = MagicMock(spec=UserAPIKeyAuth) + admin.user_role = LitellmUserRoles.PROXY_ADMIN + admin.user_id = "admin-1" + + filtered_admin, _ = await _apply_search_filter_to_models( + all_models=[caller_team_byok, other_team_byok, public_model], + search="claude", + prisma_client=prisma_client, + proxy_config=proxy_config, + user_api_key_dict=admin, + ) + admin_ids = {m["model_info"]["id"] for m in filtered_admin} + assert "byok-other" in admin_ids + assert "byok-db-other" in admin_ids + + +@pytest.mark.asyncio +async def test_apply_search_filter_bounds_db_fetch_by_page_and_cap(): + """ + Regression test: a broad search term must not force a full BYOK-table + read + decrypt on each request. + + * Unsorted searches: `find_many(take=N)` where N is just enough to + fill the current page after counting router-side matches. + * Sorted searches: `find_many(take=cap)` falls back to + `_SORTED_SEARCH_DB_FETCH_CAP` so ordering still works across a + large match set without scanning the whole table. + """ + from litellm.proxy.proxy_server import ( + _SORTED_SEARCH_DB_FETCH_CAP, + _apply_search_filter_to_models, + ) + + prisma_client = MagicMock() + prisma_client.db.litellm_proxymodeltable.count = AsyncMock(return_value=10_000) + prisma_client.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + proxy_config = MagicMock() + proxy_config.decrypt_model_list_from_db = lambda rows: [] + + # Unsorted: page=1, size=50, no router-side matches -> take must be 50. + await _apply_search_filter_to_models( + all_models=[], + search="model", + prisma_client=prisma_client, + proxy_config=proxy_config, + page=1, + size=50, + sort_by=None, + ) + take = prisma_client.db.litellm_proxymodeltable.find_many.call_args.kwargs["take"] + assert take == 50, "unsorted search must take just one page's worth of rows" + + # Sorted: still bounded, but by the hard cap rather than the page. + prisma_client.db.litellm_proxymodeltable.find_many.reset_mock() + await _apply_search_filter_to_models( + all_models=[], + search="model", + prisma_client=prisma_client, + proxy_config=proxy_config, + page=1, + size=50, + sort_by="model_name", + ) + take = prisma_client.db.litellm_proxymodeltable.find_many.call_args.kwargs["take"] + assert take == _SORTED_SEARCH_DB_FETCH_CAP + assert take < 10_000, "sorted search must cap below the full match set" + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_excludes_viewer_direct_access(): + """ + Regression test: when the UI picks a specific team in the Current Team + selector, the model list must show only that team's BYOK rows + the + models assigned to the team. The admin viewer's `direct_access` flag + (set on every non-team model upstream) must NOT widen the team's + visible set, or selecting team-111 still shows every public model. + """ + from litellm.proxy.proxy_server import _filter_models_by_team_id + + public_model = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": { + "id": "public-id", + # admin viewer has direct_access on this public model + "direct_access": True, + # team-111 is NOT in access_via_team_ids -> shouldn't show for team-111 + "access_via_team_ids": ["team-222"], + }, + } + team111_byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-team-111", + "team_id": "team-111", + "team_public_model_name": "team-claude", + "access_via_team_ids": ["team-111"], + }, + } + team222_byok = { + "model_name": "model_name_team-222_uuid", + "litellm_params": {"model": "claude-haiku"}, + "model_info": { + "id": "byok-team-222", + "team_id": "team-222", + "team_public_model_name": "team-haiku", + "access_via_team_ids": ["team-222"], + }, + } + + prisma = MagicMock() + team_db = MagicMock() + team_db.model_dump.return_value = { + "team_id": "team-111", + "team_alias": "Team 111", + # specific models list that doesn't include the BYOK's internal name + "models": ["some-other-model"], + "access_group_ids": None, + } + prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=team_db) + prisma.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + router = MagicMock() + router.get_model_access_groups = MagicMock(return_value={}) + # team-111 only resolves "some-other-model", which has no deployments + router.get_model_list = MagicMock(return_value=[]) + + filtered = await _filter_models_by_team_id( + all_models=[public_model, team111_byok, team222_byok], + team_id="team-111", + prisma_client=prisma, + llm_router=router, + ) + visible_ids = sorted(m["model_info"]["id"] for m in filtered) + + assert "byok-team-111" in visible_ids, "team-111's own BYOK must always be visible" + assert "byok-team-222" not in visible_ids, "must not leak other teams' BYOK" + assert ( + "public-id" not in visible_ids + ), "viewer's direct_access must not widen the team's visible set" + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_rejects_non_member(): + """ + Regression test: /v2/model/info?teamId=X includes BYOK rows solely on + `model_info.team_id == X`. Without an auth check, any authenticated user + could enumerate another team's BYOK metadata by guessing its id. Callers + that are neither proxy admins nor members of `team_id` must get 403. + """ + from fastapi import HTTPException + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _filter_models_by_team_id + + byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude"}, + "model_info": {"id": "byok-team-111", "team_id": "team-111"}, + } + + prisma = MagicMock() + # Caller is in team-222 only + user_row = MagicMock() + user_row.teams = ["team-222"] + prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + + caller = UserAPIKeyAuth( + user_id="alice", + user_role=LitellmUserRoles.INTERNAL_USER, + api_key="sk-test", + ) + + with pytest.raises(HTTPException) as excinfo: + await _filter_models_by_team_id( + all_models=[byok], + team_id="team-111", + prisma_client=prisma, + llm_router=MagicMock(), + user_api_key_dict=caller, + ) + assert excinfo.value.status_code == 403 + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_allows_team_member(): + """ + A caller who IS a member of `team_id` must be allowed to filter, and + should see that team's BYOK rows. + """ + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _filter_models_by_team_id + + byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude"}, + "model_info": {"id": "byok-team-111", "team_id": "team-111"}, + } + + prisma = MagicMock() + user_row = MagicMock() + user_row.teams = ["team-111", "team-999"] + prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + team_db = MagicMock() + team_db.model_dump.return_value = { + "team_id": "team-111", + "team_alias": "Team 111", + "models": [], + "access_group_ids": None, + } + prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=team_db) + prisma.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + router = MagicMock() + router.get_model_access_groups = MagicMock(return_value={}) + router.get_model_list = MagicMock(return_value=[byok]) + + caller = UserAPIKeyAuth( + user_id="bob", + user_role=LitellmUserRoles.INTERNAL_USER, + api_key="sk-test", + ) + + result = await _filter_models_by_team_id( + all_models=[byok], + team_id="team-111", + prisma_client=prisma, + llm_router=router, + user_api_key_dict=caller, + ) + assert [m["model_info"]["id"] for m in result] == ["byok-team-111"] + + +@pytest.mark.asyncio +async def test_caller_byok_team_scope_treats_view_only_admin_as_unscoped(): + """ + Regression test: `PROXY_ADMIN_VIEW_ONLY` is an admin role + ("can login, view all own keys, view all spend"). Search results for + this role must show BYOK rows across all teams, not be silently scoped + to the user-id's `teams` field — that path narrows results to whatever + teams the admin happens to be a member of, regressing pre-PR behavior. + """ + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _get_caller_byok_team_scope + + caller = UserAPIKeyAuth( + user_id="view-admin", + user_role=LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + api_key="sk-test", + ) + scope = await _get_caller_byok_team_scope( + user_api_key_dict=caller, + prisma_client=MagicMock(), + ) + assert scope is None, "PROXY_ADMIN_VIEW_ONLY must be unscoped, like PROXY_ADMIN" + + @pytest.mark.asyncio async def test_add_access_group_models_to_team_models(): """ @@ -4637,6 +5065,66 @@ async def test_async_data_generator_uses_direct_stream_fast_path_without_callbac mock_response.aclose.assert_awaited_once() +@pytest.mark.asyncio +async def test_async_data_generator_passes_through_google_native_sse_bytes(): + """ + Google-native streamGenerateContent yields raw SSE bytes; they must not be + re-wrapped as data: b'data: {...}'. + """ + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.proxy_server import async_data_generator + from litellm.proxy.utils import ProxyLogging + + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_request_data = { + "model": "gemini-2.0-flash", + "messages": [{"role": "user", "content": "test"}], + } + gemini_event = b'data: {"candidates": [{"content": "hi"}]}\n\n' + gemini_event_without_terminator = b'data: {"candidates": [{"content": "there"}]}' + raw_payload = b'{"partial": true}' + + class MockStream: + def __aiter__(self): + return self._stream() + + async def _stream(self): + yield gemini_event + yield gemini_event_without_terminator + yield raw_payload + + async def aclose(self): + pass + + mock_response = MockStream() + mock_response.aclose = AsyncMock() + mock_proxy_logging_obj = MagicMock(spec=ProxyLogging) + mock_proxy_logging_obj.has_streaming_callbacks.return_value = False + mock_proxy_logging_obj.needs_iterator_wrap.return_value = False + mock_proxy_logging_obj.needs_per_chunk_streaming_hook.return_value = False + mock_proxy_logging_obj.async_post_call_streaming_iterator_hook = MagicMock() + mock_proxy_logging_obj.async_post_call_streaming_hook = AsyncMock() + mock_proxy_logging_obj.post_call_failure_hook = AsyncMock() + + with patch("litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj): + with patch.object(ProxyLogging, "_fire_deferred_stream_logging"): + yielded_data = [] + async for data in async_data_generator( + mock_response, mock_user_api_key_dict, mock_request_data + ): + yielded_data.append(data) + + yielded_text = [ + chunk.decode("utf-8") if isinstance(chunk, bytes) else chunk + for chunk in yielded_data + ] + assert yielded_text[0] == gemini_event.decode("utf-8") + assert yielded_text[1] == gemini_event_without_terminator.decode("utf-8") + "\n\n" + assert yielded_text[2] == f'data: {raw_payload.decode("utf-8")}\n\n' + assert "b'data:" not in "".join(yielded_text) + assert yielded_text[-1] == "data: [DONE]\n\n" + + @pytest.mark.asyncio async def test_async_data_generator_cleanup_on_normal_completion(): """ @@ -6922,3 +7410,80 @@ class TestTransformRequestBannedParams: f"Expected 400 for banned param '{banned}', " f"got {response.status_code}: {response.json()}" ) + + +class TestSortModelsByDisplayName: + """Regression: team BYOK rows persist an internal `model_name` like + `model_name_{team_id}_{uuid}` and expose the user-facing name via + `model_info.team_public_model_name`. Sorting must use the displayed + name so BYOK rows interleave with non-BYOK rows alphabetically — + otherwise they clump at the end on their opaque IDs even though the + UI shows them under a normal-looking name. + """ + + def test_byok_models_sort_by_team_public_model_name(self): + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "claude-haiku-4-5", "model_info": {}}, + { + # Opaque internal name; UI displays team_public_model_name. + "model_name": "model_name_team-1_abc123", + "model_info": {"team_public_model_name": "anthropic/claude"}, + }, + {"model_name": "gpt-4o", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="asc" + ) + displayed_order = [ + m["model_info"].get("team_public_model_name") or m["model_name"] + for m in sorted_models + ] + assert displayed_order == [ + "anthropic/claude", + "claude-haiku-4-5", + "gpt-4o", + ] + + def test_byok_models_sort_descending_by_display_name(self): + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "claude-haiku-4-5", "model_info": {}}, + { + "model_name": "model_name_team-1_zzz", + "model_info": {"team_public_model_name": "zeta/model"}, + }, + {"model_name": "gpt-4o", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="desc" + ) + displayed_order = [ + m["model_info"].get("team_public_model_name") or m["model_name"] + for m in sorted_models + ] + assert displayed_order == [ + "zeta/model", + "gpt-4o", + "claude-haiku-4-5", + ] + + def test_empty_team_public_model_name_falls_back_to_model_name(self): + # Empty string for team_public_model_name (not None) must still + # fall back to model_name — otherwise BYOK rows with a blank + # display name would sort to the top. + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "alpha", "model_info": {"team_public_model_name": ""}}, + {"model_name": "beta", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="asc" + ) + assert [m["model_name"] for m in sorted_models] == ["alpha", "beta"] diff --git a/tests/test_litellm/proxy/test_proxy_utils.py b/tests/test_litellm/proxy/test_proxy_utils.py index 2605eadba7a..7a2b20bd8fb 100644 --- a/tests/test_litellm/proxy/test_proxy_utils.py +++ b/tests/test_litellm/proxy/test_proxy_utils.py @@ -264,3 +264,60 @@ def test_enrich_http_exception_callback_without_guardrail_name_noop(): exc = HTTPException(status_code=400, detail={"error": "x"}) _enrich_http_exception_with_guardrail_context(exc, StubCallback()) assert exc.detail == {"error": "x"} + + +class TestPostCallFailureHookLiftsFirstApiCallStartTime: + """post_call_failure_hook lifts first_api_call_start_time off the + logging object into request_data (an internal top-level key) before + the non-serialisable logging object is popped, so failure-path + callbacks (OTel preprocessing latency) can still read it. It must + never land in request_data["metadata"] (user request metadata, + echoed downstream and typed Dict[str, str] in batch objects). + """ + + async def _run(self, request_data): + from unittest.mock import AsyncMock, patch + + from litellm.proxy._types import UserAPIKeyAuth + + proxy_logging_obj = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging_obj.alert_types = [] # skip alerting branch + with patch.object(proxy_logging_obj, "update_request_status", new=AsyncMock()): + await proxy_logging_obj.post_call_failure_hook( + request_data=request_data, + original_exception=Exception("boom"), + user_api_key_dict=UserAPIKeyAuth(), + ) + + @pytest.mark.asyncio + async def test_lifts_to_top_level_and_pops_logging_obj(self): + handoff = real_datetime.datetime(2026, 1, 1, 0, 0, 0) + logging_obj = MagicMock() + logging_obj.model_call_details = {"first_api_call_start_time": handoff} + user_meta = {} + request_data = { + "litellm_logging_obj": logging_obj, + "metadata": user_meta, + } + await self._run(request_data) + + assert request_data["first_api_call_start_time"] == handoff + assert "litellm_logging_obj" not in request_data + # user metadata is never touched + assert user_meta == {} + assert "first_api_call_start_time" not in request_data["metadata"] + + @pytest.mark.asyncio + async def test_no_logging_obj_is_noop(self): + request_data = {"metadata": {}} + await self._run(request_data) + assert "first_api_call_start_time" not in request_data + + @pytest.mark.asyncio + async def test_logging_obj_without_anchor_is_noop(self): + logging_obj = MagicMock() + logging_obj.model_call_details = {} + request_data = {"litellm_logging_obj": logging_obj} + await self._run(request_data) + assert "first_api_call_start_time" not in request_data + assert "litellm_logging_obj" not in request_data diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index d5c1132c5fd..1be4abbec6e 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -2057,3 +2057,317 @@ def test_openrouter_gemini_3_1_flash_lite_preview_pricing(): assert model_info["output_cost_per_token"] == 1.5e-06 assert model_info["max_input_tokens"] == 1048576 assert model_info["max_output_tokens"] == 65536 + + +def test_custom_pricing_applies_cache_read_input_cost(): + """ + Bug 1 reproduction: custom_cost_per_token with cache_read_input_token_cost + should bill cached prompt tokens at the cache rate, not the full input rate. + """ + usage = Usage( + prompt_tokens=6074, + completion_tokens=285, + total_tokens=6359, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=3456, + audio_tokens=0, + ), + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + }, + ) + + expected = (6074 - 3456) * 0.0000025 + 3456 * 0.00000025 + 285 * 0.000015 + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details(): + """ + OpenAI-compatible providers report cache-write tokens under + prompt_tokens_details.cache_creation_tokens. The custom-pricing helper must + bill those at cache_creation_input_token_cost, not the full input rate. + """ + pt_details = PromptTokensDetailsWrapper(cached_tokens=1000, audio_tokens=0) + pt_details.cache_creation_tokens = 500 + + usage = Usage( + prompt_tokens=4000, + completion_tokens=100, + total_tokens=4100, + prompt_tokens_details=pt_details, + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + "cache_creation_input_token_cost": 0.000003125, + }, + ) + + expected = ( + (4000 - 1000 - 500) * 0.0000025 + + 1000 * 0.00000025 + + 500 * 0.000003125 + + 100 * 0.000015 + ) + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens_alias(): + """ + Some OpenAI-compatible providers (e.g. kimi-k2) emit cache-write tokens as + `cache_write_tokens` rather than `cache_creation_tokens`. The cost + calculator must mirror db_spend_update_writer and accept either name — + otherwise daily aggregation counts the tokens but the per-request cost + bills them at the full input rate. + + Drives `cost_per_token` directly with a SimpleNamespace usage stub so the + `cache_write_tokens` alias survives the call (Pydantic's Usage init + rebuilds prompt_tokens_details and drops dynamic attributes). + """ + from types import SimpleNamespace + + from litellm.cost_calculator import cost_per_token + + pt_details = SimpleNamespace(cached_tokens=1000, cache_write_tokens=500) + usage_stub = SimpleNamespace( + prompt_tokens=4000, + completion_tokens=100, + total_tokens=4100, + prompt_tokens_details=pt_details, + cache_read_input_tokens=None, + cache_creation_input_tokens=None, + ) + + prompt_cost, completion_cost = cost_per_token( + model="moonshotai/kimi-k2", + prompt_tokens=4000, + completion_tokens=100, + custom_llm_provider="openai", + usage_object=usage_stub, + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + "cache_creation_input_token_cost": 0.000003125, + }, + ) + + expected_prompt = ( + (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 + ) + expected_completion = 100 * 0.000015 + + assert prompt_cost == pytest.approx(expected_prompt) + assert completion_cost == pytest.approx(expected_completion) + + +# --------------------------------------------------------------------------- +# Bug 2 — db_spend_update_writer cache token extraction helpers. +# --------------------------------------------------------------------------- + + +def test_extract_cache_read_tokens_anthropic_top_level(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + usage_obj = { + "prompt_tokens": 100, + "cache_read_input_tokens": 80, + "prompt_tokens_details": {"cached_tokens": 80}, + } + # Anthropic top-level value should win over prompt_tokens_details fallback. + assert _extract_cache_read_tokens(usage_obj) == 80 + + +def test_extract_cache_read_tokens_openai_compatible_fallback(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + # Anthropic field absent — fall back to prompt_tokens_details.cached_tokens. + usage_obj = { + "prompt_tokens": 22583, + "prompt_tokens_details": {"cached_tokens": 22016}, + } + assert _extract_cache_read_tokens(usage_obj) == 22016 + + +def test_extract_cache_read_tokens_zero_when_missing(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + assert _extract_cache_read_tokens({}) == 0 + assert _extract_cache_read_tokens({"cache_read_input_tokens": None}) == 0 + assert ( + _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}}) + == 0 + ) + + +def test_extract_cache_creation_tokens_anthropic_top_level(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + usage_obj = { + "prompt_tokens": 100, + "cache_creation_input_tokens": 50, + "prompt_tokens_details": {"cache_write_tokens": 50}, + } + # Anthropic top-level should short-circuit the fallback. + assert _extract_cache_creation_tokens(usage_obj) == 50 + + +def test_extract_cache_creation_tokens_openai_cache_write_alias(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + # kimi-k2 emits cache_write_tokens. + usage_obj = { + "prompt_tokens": 1000, + "prompt_tokens_details": {"cache_write_tokens": 200}, + } + assert _extract_cache_creation_tokens(usage_obj) == 200 + + +def test_extract_cache_creation_tokens_openai_cache_creation_alias(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + # Other OpenAI-compatible providers emit cache_creation_tokens. + usage_obj = { + "prompt_tokens": 1000, + "prompt_tokens_details": {"cache_creation_tokens": 300}, + } + assert _extract_cache_creation_tokens(usage_obj) == 300 + + +def test_extract_cache_creation_tokens_zero_when_missing(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + assert _extract_cache_creation_tokens({}) == 0 + assert _extract_cache_creation_tokens({"cache_creation_input_tokens": None}) == 0 + assert ( + _extract_cache_creation_tokens( + {"prompt_tokens_details": {"cache_write_tokens": None}} + ) + == 0 + ) + + +def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted(): + """ + Anthropic providers report cache tokens at the top level of Usage, and + `prompt_tokens` EXCLUDES them. The helper expects `prompt_tokens` to + include cache tokens, so cost_per_token must adjust before invoking it — + otherwise regular_prompt_tokens goes negative and clamps to 0. + """ + usage = Usage( + prompt_tokens=2000, + completion_tokens=100, + total_tokens=2100, + cache_read_input_tokens=1500, + cache_creation_input_tokens=300, + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="anthropic/claude-3-5-sonnet", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="anthropic/claude-3-5-sonnet", + custom_llm_provider="anthropic", + custom_cost_per_token={ + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.0000003, + "cache_creation_input_token_cost": 0.00000375, + }, + ) + + # Anthropic prompt_tokens=2000 excludes cache. After normalization the + # helper sees 2000 + 1500 + 300 = 3800, of which 2000 are uncached. + expected = 2000 * 0.000003 + 1500 * 0.0000003 + 300 * 0.00000375 + 100 * 0.000015 + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_without_cache_keys_preserves_legacy_behavior(): + """ + Backward compatibility: when custom_cost_per_token omits both cache rates, + cached tokens must be billed at input_cost_per_token (matching the pre-fix + behavior) so existing callers see no change. + """ + usage = Usage( + prompt_tokens=1000, + completion_tokens=100, + total_tokens=1100, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=400, + audio_tokens=0, + ), + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + }, + ) + + # All 1000 prompt tokens billed at input rate, regardless of cached_tokens. + expected = 1000 * 0.0000025 + 100 * 0.000015 + + assert cost == pytest.approx(expected) diff --git a/tests/test_litellm/test_guardrail_exception_status_codes.py b/tests/test_litellm/test_guardrail_exception_status_codes.py new file mode 100644 index 00000000000..c4df1295580 --- /dev/null +++ b/tests/test_litellm/test_guardrail_exception_status_codes.py @@ -0,0 +1,66 @@ +""" +Tests for guardrail exception status codes. + +GuardrailRaisedException and BlockedPiiEntityError must carry +``status_code = 400`` so the proxy exception handler +(``getattr(e, "status_code", 500)``) returns HTTP 400 instead of 500 +for intentional guardrail blocks. +""" + +from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException + + +class TestGuardrailRaisedExceptionStatusCode: + """GuardrailRaisedException should default to status_code=400.""" + + def test_default_status_code(self): + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="blocked", + ) + assert exc.status_code == 400 + + def test_custom_status_code(self): + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="rate limited", + status_code=429, + ) + assert exc.status_code == 429 + + def test_getattr_fallback_resolves_to_400(self): + """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it + resolves to 400, not the 500 default.""" + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="blocked", + ) + assert getattr(exc, "status_code", 500) == 400 + + +class TestBlockedPiiEntityErrorStatusCode: + """BlockedPiiEntityError should default to status_code=400.""" + + def test_default_status_code(self): + exc = BlockedPiiEntityError( + entity_type="CREDIT_CARD", + guardrail_name="presidio", + ) + assert exc.status_code == 400 + + def test_custom_status_code(self): + exc = BlockedPiiEntityError( + entity_type="SSN", + guardrail_name="presidio", + status_code=403, + ) + assert exc.status_code == 403 + + def test_getattr_fallback_resolves_to_400(self): + """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it + resolves to 400, not the 500 default.""" + exc = BlockedPiiEntityError( + entity_type="PHONE_NUMBER", + guardrail_name="presidio", + ) + assert getattr(exc, "status_code", 500) == 400 diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py index 282c9d72d51..a89e30a0e06 100644 --- a/tests/test_litellm/test_redis.py +++ b/tests/test_litellm/test_redis.py @@ -1,5 +1,4 @@ import json -import os from unittest.mock import MagicMock, patch import pytest @@ -165,6 +164,13 @@ def test_max_connections_in_cluster_kwargs(): ), "max_connections should be in available Redis cluster kwargs" +def test_socket_timeouts_in_cluster_kwargs(): + """Test that Redis cluster clients can receive socket timeout configuration""" + kwargs = _get_redis_cluster_kwargs() + assert "socket_timeout" in kwargs + assert "socket_connect_timeout" in kwargs + + def test_get_redis_async_client_with_connection_pool(): """Test that connection_pool parameter is properly passed to Redis client""" # Create a mock connection pool diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 48facace528..d8be527689e 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -3697,3 +3697,173 @@ def test_try_early_resolve_deployments_for_model_not_in_names(): default_router.default_deployment["litellm_params"]["model"] == "openai/will-be-overridden" ) + + +def _router_with_two_deployments(blocked_flags): + import litellm + + model_list = [] + for idx, blocked in enumerate(blocked_flags): + model_list.append( + { + "model_name": "gpt-4o", + "litellm_params": {"model": f"openai/gpt-4o-{idx}"}, + "model_info": {"id": f"dep-{idx}", "blocked": blocked}, + } + ) + return litellm.Router(model_list=model_list) + + +def test_get_fully_blocked_model_names_marks_name_when_all_deployments_blocked(): + router = _router_with_two_deployments([True, True]) + assert router.get_fully_blocked_model_names() == {"gpt-4o"} + + +def test_get_fully_blocked_model_names_keeps_name_when_partial_blocked(): + router = _router_with_two_deployments([True, False]) + assert router.get_fully_blocked_model_names() == set() + + +def test_get_fully_blocked_model_names_treats_missing_key_as_unblocked(): + import litellm + + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o", + "litellm_params": {"model": "openai/gpt-4o"}, + "model_info": {"id": "dep-0"}, + } + ] + ) + assert router.get_fully_blocked_model_names() == set() + + +@pytest.mark.asyncio +async def test_async_get_healthy_deployments_skips_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + healthy, all_dep = await router._async_get_healthy_deployments( + model="gpt-4o", parent_otel_span=None + ) + healthy_ids = [d["model_info"]["id"] for d in healthy] + assert "dep-0" not in healthy_ids + assert "dep-1" in healthy_ids + assert len(all_dep) == 2 + + +def test_get_healthy_deployments_sync_skips_blocked_deployment(): + router = _router_with_two_deployments([False, True]) + healthy, all_dep = router._get_healthy_deployments( + model="gpt-4o", parent_otel_span=None + ) + healthy_ids = [d["model_info"]["id"] for d in healthy] + assert "dep-0" in healthy_ids + assert "dep-1" not in healthy_ids + assert len(all_dep) == 2 + + +def test_filter_blocked_deployments_drops_blocked_keeps_unblocked(): + router = _router_with_two_deployments([True, False]) + filtered = router._filter_blocked_deployments(router.get_model_list() or []) + ids = [d["model_info"]["id"] for d in filtered] + assert ids == ["dep-1"] + + +@pytest.mark.asyncio +async def test_public_async_get_healthy_deployments_skips_blocked_on_primary_path(): + router = _router_with_two_deployments([True, False]) + deployments = await router.async_get_healthy_deployments( + model="gpt-4o", request_kwargs={} + ) + assert isinstance(deployments, list) + ids = [d["model_info"]["id"] for d in deployments] + assert "dep-0" not in ids + assert "dep-1" in ids + + +def test_public_get_available_deployment_skips_blocked_on_primary_path(): + router = _router_with_two_deployments([True, False]) + deployment = router.get_available_deployment(model="gpt-4o", request_kwargs={}) + assert deployment["model_info"]["id"] == "dep-1" + + +def test_get_available_deployment_raises_when_addressed_dict_is_blocked(): + import litellm + + router = _router_with_two_deployments([True, True]) + with pytest.raises(litellm.ServiceUnavailableError): + router.get_available_deployment(model="dep-0", request_kwargs={}) + + +def _router_with_two_pass_through_deployments(blocked_flags): + import litellm + + model_list = [] + for idx, blocked in enumerate(blocked_flags): + model_list.append( + { + "model_name": "gpt-4o", + "litellm_params": { + "model": f"openai/gpt-4o-{idx}", + "api_key": "sk-fake-for-tests", + "use_in_pass_through": True, + }, + "model_info": {"id": f"pt-{idx}", "blocked": blocked}, + } + ) + return litellm.Router(model_list=model_list) + + +def test_get_available_deployment_for_pass_through_skips_blocked(): + router = _router_with_two_pass_through_deployments([True, False]) + deployment = router.get_available_deployment_for_pass_through( + model="gpt-4o", request_kwargs={} + ) + assert deployment["model_info"]["id"] == "pt-1" + + +def test_get_available_deployment_for_pass_through_raises_when_dict_blocked(): + import litellm + + router = _router_with_two_pass_through_deployments([True, True]) + with pytest.raises(litellm.ServiceUnavailableError): + router.get_available_deployment_for_pass_through( + model="pt-0", request_kwargs={} + ) + + +def test_get_deployment_credentials_returns_none_for_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + assert router.get_deployment_credentials(model_id="dep-0") is None + assert router.get_deployment_credentials(model_id="dep-1") is not None + + +def test_get_deployment_credentials_with_provider_returns_none_for_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + assert router.get_deployment_credentials_with_provider(model_id="dep-0") is None + assert router.get_deployment_credentials_with_provider(model_id="dep-1") is not None + + +def test_is_deployment_blocked_static_helper_reflects_blocked_flag(): + """ + Exercises Router._is_deployment_blocked so router_code_coverage.py (AST call graph) + marks the helper as covered by router-named tests. + """ + import types + + import litellm + + router = _router_with_two_deployments([True, False]) + blocked_dep = router.get_deployment("dep-0") + unblocked_dep = router.get_deployment("dep-1") + assert blocked_dep is not None and unblocked_dep is not None + assert litellm.Router._is_deployment_blocked(blocked_dep) is True + assert litellm.Router._is_deployment_blocked(unblocked_dep) is False + + # No model_info on deployment object → treated as not blocked + assert litellm.Router._is_deployment_blocked(object()) is False + missing_blocked = types.SimpleNamespace() + assert litellm.Router._is_deployment_blocked(types.SimpleNamespace(model_info=missing_blocked)) is False + assert litellm.Router._is_deployment_blocked( + types.SimpleNamespace(model_info=types.SimpleNamespace(blocked=True)) + ) is True diff --git a/tests/unified_google_tests/conftest.py b/tests/unified_google_tests/conftest.py index d28f89a77b0..5b4f57b8036 100644 --- a/tests/unified_google_tests/conftest.py +++ b/tests/unified_google_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -84,6 +87,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -110,3 +114,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/ui/Dockerfile b/ui/Dockerfile new file mode 100644 index 00000000000..b75c4d0a0c6 --- /dev/null +++ b/ui/Dockerfile @@ -0,0 +1,42 @@ +# syntax=docker/dockerfile:1.7 + +# UI container — Next.js static export served by nginx. + +ARG NODE_VERSION=20.18-alpine3.20 +ARG NGINX_VERSION=1.27-alpine + +# ---------- builder ---------- +FROM node:${NODE_VERSION} AS builder + +ENV NEXT_TELEMETRY_DISABLED=1 \ + npm_config_fund=false \ + npm_config_audit=false + +WORKDIR /app + +# Layer the lockfile-only install above the source copy so source-only +# edits don't bust the install cache. +COPY ui/litellm-dashboard/package.json ui/litellm-dashboard/package-lock.json ./ +RUN --mount=type=cache,target=/root/.npm \ + npm ci --prefer-offline + +COPY ui/litellm-dashboard/ ./ +RUN npm run build + +# ---------- runtime ---------- +FROM nginx:${NGINX_VERSION} AS runtime + +# Drop the upstream default :80 server; we own the config. +RUN rm -f /etc/nginx/conf.d/default.conf + +# Static export → web root. +COPY --from=builder /app/out /usr/share/nginx/html + +# Routing rules — see ui/nginx.conf for the full description. +COPY ui/nginx.conf /etc/nginx/nginx.conf + +EXPOSE 3000/tcp + +# nginx as PID 1 in foreground; respects SIGTERM out of the box, so +# no tini/dumb-init wrapper needed. +CMD ["nginx", "-g", "daemon off;"] diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx index 50dd5e78f37..41dfdb21d1b 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx +++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx @@ -654,12 +654,7 @@ const UsagePage: React.FC = ({ teams, organizations }) => { Input Tokens - {Math.max( - 0, - (userSpendData.metadata?.total_prompt_tokens || 0) - - (userSpendData.metadata?.total_cache_read_input_tokens || 0) - - (userSpendData.metadata?.total_cache_creation_input_tokens || 0) - ).toLocaleString()} + {(userSpendData.metadata?.total_prompt_tokens || 0).toLocaleString()} diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx index 8ae90c1cbbc..554fe86bf66 100644 --- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx +++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx @@ -52,6 +52,8 @@ const AdvancedSettings: React.FC = ({ form.setFieldsValue({ input_cost_per_token: undefined, output_cost_per_token: undefined, + cache_read_input_token_cost: undefined, + cache_creation_input_token_cost: undefined, input_cost_per_second: undefined, }); } @@ -211,6 +213,24 @@ const AdvancedSettings: React.FC = ({ > + + + + + + ) : ( , ac if (formValues.output_cost_per_token !== undefined && formValues.output_cost_per_token !== null && formValues.output_cost_per_token !== "") { formValues.output_cost_per_token = Number(formValues.output_cost_per_token) / 1000000; } + + // Cache Read Cost: if blank, default to Input Cost (already token-unit converted above) + if ( + formValues.cache_read_input_token_cost !== undefined && + formValues.cache_read_input_token_cost !== null && + formValues.cache_read_input_token_cost !== "" + ) { + formValues.cache_read_input_token_cost = + Number(formValues.cache_read_input_token_cost) / 1000000; + } else if ( + formValues.input_cost_per_token !== undefined && + formValues.input_cost_per_token !== null && + formValues.input_cost_per_token !== "" + ) { + formValues.cache_read_input_token_cost = Number(formValues.input_cost_per_token); + } else { + delete formValues.cache_read_input_token_cost; + } + + // Cache Write Cost: explicit value if provided, else leave unset so the + // backend keeps the model-level default (per-second pricing, model_prices + // entries, etc.). Sending 0 here would overwrite that default. + // The backend falls back to input_cost_per_token when this key is absent. + if ( + formValues.cache_creation_input_token_cost !== undefined && + formValues.cache_creation_input_token_cost !== null && + formValues.cache_creation_input_token_cost !== "" + ) { + formValues.cache_creation_input_token_cost = + Number(formValues.cache_creation_input_token_cost) / 1000000; + } else { + delete formValues.cache_creation_input_token_cost; + } // Keep input_cost_per_second as is, no conversion needed // Iterate through the key-value pairs in formValues @@ -119,7 +152,13 @@ export const prepareModelAddRequest = async (formValues: Record, ac } // Handle the pricing fields - else if (key === "input_cost_per_token" || key === "output_cost_per_token" || key === "input_cost_per_second") { + else if ( + key === "input_cost_per_token" || + key === "output_cost_per_token" || + key === "input_cost_per_second" || + key === "cache_read_input_token_cost" || + key === "cache_creation_input_token_cost" + ) { if (value !== undefined && value !== null && value !== "") { litellmParamsObj[key] = Number(value); } diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx index 95a43862de5..5ed4c0468b8 100644 --- a/ui/litellm-dashboard/src/components/model_info_view.tsx +++ b/ui/litellm-dashboard/src/components/model_info_view.tsx @@ -263,6 +263,34 @@ export default function ModelInfoView({ updatedLitellmParams.output_cost_per_token = Number(values.output_cost) / 1_000_000; } + // Cache Read Cost: explicit value if provided, else fall back to input cost (when input cost touched). + if (form.isFieldTouched("cache_read_cost") || form.isFieldTouched("input_cost")) { + if ( + values.cache_read_cost !== undefined && + values.cache_read_cost !== null && + values.cache_read_cost !== "" + ) { + updatedLitellmParams.cache_read_input_token_cost = Number(values.cache_read_cost) / 1_000_000; + } else if (updatedLitellmParams.input_cost_per_token !== undefined) { + updatedLitellmParams.cache_read_input_token_cost = updatedLitellmParams.input_cost_per_token; + } + } + + // Cache Write Cost: explicit value if provided, else clear the override + // so the backend falls back to the model-level default. Sending 0 here + // would persist a zero rate even when the user intended to unset it. + if (form.isFieldTouched("cache_write_cost")) { + if ( + values.cache_write_cost !== undefined && + values.cache_write_cost !== null && + values.cache_write_cost !== "" + ) { + updatedLitellmParams.cache_creation_input_token_cost = Number(values.cache_write_cost) / 1_000_000; + } else { + delete updatedLitellmParams.cache_creation_input_token_cost; + } + } + if (values.litellm_credential_name) { updatedLitellmParams.litellm_credential_name = values.litellm_credential_name; } else { @@ -638,6 +666,22 @@ export default function ModelInfoView({ output_cost: localModelData.litellm_params?.output_cost_per_token ? localModelData.litellm_params.output_cost_per_token * 1_000_000 : localModelData.model_info?.output_cost_per_token * 1_000_000 || null, + cache_read_cost: + localModelData.litellm_params?.cache_read_input_token_cost !== undefined && + localModelData.litellm_params?.cache_read_input_token_cost !== null + ? localModelData.litellm_params.cache_read_input_token_cost * 1_000_000 + : localModelData.model_info?.cache_read_input_token_cost !== undefined && + localModelData.model_info?.cache_read_input_token_cost !== null + ? localModelData.model_info.cache_read_input_token_cost * 1_000_000 + : null, + cache_write_cost: + localModelData.litellm_params?.cache_creation_input_token_cost !== undefined && + localModelData.litellm_params?.cache_creation_input_token_cost !== null + ? localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000 + : localModelData.model_info?.cache_creation_input_token_cost !== undefined && + localModelData.model_info?.cache_creation_input_token_cost !== null + ? localModelData.model_info.cache_creation_input_token_cost * 1_000_000 + : null, cache_control: localModelData.litellm_params?.cache_control_injection_points ? true : false, cache_control_injection_points: localModelData.litellm_params?.cache_control_injection_points || [], model_access_group: Array.isArray(localModelData.model_info?.access_groups) @@ -725,6 +769,52 @@ export default function ModelInfoView({ )} +
+ Cache Read Cost (per 1M tokens) + {isEditing ? ( + + + + ) : ( +
+ {localModelData?.litellm_params?.cache_read_input_token_cost !== undefined && + localModelData?.litellm_params?.cache_read_input_token_cost !== null + ? (localModelData.litellm_params.cache_read_input_token_cost * 1_000_000).toFixed(4) + : localModelData?.model_info?.cache_read_input_token_cost !== undefined && + localModelData?.model_info?.cache_read_input_token_cost !== null + ? (localModelData.model_info.cache_read_input_token_cost * 1_000_000).toFixed(4) + : "Not Set"} +
+ )} +
+ +
+ Cache Write Cost (per 1M tokens) + {isEditing ? ( + + + + ) : ( +
+ {localModelData?.litellm_params?.cache_creation_input_token_cost !== undefined && + localModelData?.litellm_params?.cache_creation_input_token_cost !== null + ? (localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000).toFixed(4) + : localModelData?.model_info?.cache_creation_input_token_cost !== undefined && + localModelData?.model_info?.cache_creation_input_token_cost !== null + ? (localModelData.model_info.cache_creation_input_token_cost * 1_000_000).toFixed(4) + : "Not Set"} +
+ )} +
+
API Base {isEditing ? ( diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx index 06a09ca2c37..a80cf935c4b 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx @@ -49,6 +49,7 @@ import { fetchAvailableModels, ModelGroup } from "../llm_calls/fetch_models"; import { makeOpenAIImageEditsRequest } from "../llm_calls/image_edits"; import { makeOpenAIImageGenerationRequest } from "../llm_calls/image_generation"; import { makeOpenAIResponsesRequest } from "../llm_calls/responses_api"; +import { makeInteractionsRequest } from "../llm_calls/interactions_api"; import A2AMetrics from "./A2AMetrics"; import AdditionalModelSettings from "./AdditionalModelSettings"; import AudioRenderer from "./AudioRenderer"; @@ -649,6 +650,7 @@ const ChatUI: React.FC = ({ EndpointType.ANTHROPIC_MESSAGES, EndpointType.EMBEDDINGS, EndpointType.TRANSCRIPTION, + EndpointType.INTERACTIONS, ]; if (modelRequiredEndpoints.includes(endpointType as EndpointType) && !selectedModel) { @@ -914,6 +916,16 @@ const ChatUI: React.FC = ({ customProxyBaseUrl || undefined, ); } + } else if (endpointType === EndpointType.INTERACTIONS) { + await makeInteractionsRequest( + inputMessage, + (text, model) => updateTextUI("assistant", text, model), + selectedModel, + effectiveApiKey, + selectedTags, + signal, + customProxyBaseUrl || undefined, + ); } } @@ -1241,10 +1253,11 @@ const ChatUI: React.FC = ({ return true; } const optionEndpoint = getEndpointType(option.mode); - // Show chat models for responses/anthropic_messages endpoints as they are compatible + // Show chat models for responses/anthropic_messages/interactions endpoints as they are compatible if ( endpointType === EndpointType.RESPONSES || - endpointType === EndpointType.ANTHROPIC_MESSAGES + endpointType === EndpointType.ANTHROPIC_MESSAGES || + endpointType === EndpointType.INTERACTIONS ) { return optionEndpoint === endpointType || optionEndpoint === EndpointType.CHAT; } @@ -2089,7 +2102,8 @@ const ChatUI: React.FC = ({ endpointType === EndpointType.CHAT || endpointType === EndpointType.EMBEDDINGS || endpointType === EndpointType.RESPONSES || - endpointType === EndpointType.ANTHROPIC_MESSAGES + endpointType === EndpointType.ANTHROPIC_MESSAGES || + endpointType === EndpointType.INTERACTIONS ? "Type your message... (Shift+Enter for new line)" : endpointType === EndpointType.A2A_AGENTS ? "Send a message to the A2A agent..." diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts index 919e3bc1c65..9592a521250 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts @@ -45,4 +45,5 @@ export const ENDPOINT_OPTIONS = [ { value: EndpointType.A2A_AGENTS, label: "/v1/a2a/message/send" }, { value: EndpointType.MCP, label: "/mcp-rest/tools/call" }, { value: EndpointType.REALTIME, label: "/v1/realtime" }, + { value: EndpointType.INTERACTIONS, label: "/v1beta/interactions" }, ]; diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx index f354efe641e..3b2a5e0fd60 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx @@ -28,6 +28,7 @@ export enum EndpointType { A2A_AGENTS = "a2a_agents", MCP = "mcp", REALTIME = "realtime", + INTERACTIONS = "interactions", } // Create a mapping between the model mode and the corresponding endpoint type diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx new file mode 100644 index 00000000000..6a6e4a2cfa0 --- /dev/null +++ b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx @@ -0,0 +1,124 @@ +import NotificationManager from "@/components/molecules/notifications_manager"; +import { getGlobalLitellmHeaderName, getProxyBaseUrl } from "@/components/networking"; + +export async function makeInteractionsRequest( + input: string, + updateUI: (text: string, model?: string) => void, + selectedModel: string, + accessToken: string, + tags?: string[], + signal?: AbortSignal, + customBaseUrl?: string, + previousInteractionId?: string, +): Promise { + if (!accessToken) { + throw new Error("Virtual Key is required"); + } + + const isLocal = process.env.NODE_ENV === "development"; + if (isLocal !== true) { + console.log = function () {}; + } + + const proxyBaseUrl = customBaseUrl || getProxyBaseUrl(); + const normalizedBaseUrl = proxyBaseUrl.endsWith("/") ? proxyBaseUrl.slice(0, -1) : proxyBaseUrl; + const requestUrl = `${normalizedBaseUrl}/v1beta/interactions`; + + const headers: Record = { + "Content-Type": "application/json", + [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`, + }; + if (tags && tags.length > 0) { + headers["x-litellm-tags"] = tags.join(","); + } + + const body: Record = { + model: selectedModel, + input, + stream: true, + }; + if (previousInteractionId) { + body.previous_interaction_id = previousInteractionId; + } + + try { + const response = await fetch(requestUrl, { + method: "POST", + headers, + body: JSON.stringify(body), + signal, + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(errorText || `Request failed with status ${response.status}`); + } + + if (!response.body) { + throw new Error("No response body received"); + } + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let responseModel: string | undefined; + let buffer = ""; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + buffer += decoder.decode(value, { stream: true }); + + // SSE lines are separated by double newlines; split on single newlines and + // look for "data: " prefixed lines. + const lines = buffer.split("\n"); + // Keep the last (potentially incomplete) line in the buffer + buffer = lines.pop() ?? ""; + + for (const line of lines) { + const trimmed = line.trim(); + if (!trimmed.startsWith("data:")) continue; + + const jsonStr = trimmed.slice("data:".length).trim(); + if (!jsonStr || jsonStr === "[DONE]") continue; + + let event: Record; + try { + event = JSON.parse(jsonStr); + } catch { + continue; + } + + const eventType = event.event_type as string | undefined; + + if (eventType === "interaction.start" || eventType === "interaction.complete") { + // Capture model from either the native Gemini shape (nested under + // `interaction`) or the bridge shape (top-level `model` field). + const interaction = event.interaction as Record | undefined; + if (typeof interaction?.model === "string" && interaction.model) { + responseModel = interaction.model; + } else if (typeof event.model === "string" && event.model) { + responseModel = event.model; + } + } else if (eventType === "content.delta" || eventType === "content.start") { + const delta = event.delta as Record | undefined; + // Accept both native Gemini format {"type":"text","text":"..."} and bridge + // format {"text":"..."} (no type discriminator) + if (typeof delta?.text === "string" && delta.text) { + updateUI(delta.text, responseModel ?? selectedModel); + } + } + // content.start, content.stop, interaction.status_update — no UI action needed + } + } + } catch (error: unknown) { + if (signal?.aborted) { + console.log("Interactions request was cancelled"); + throw error; + } + NotificationManager.fromBackend( + `Error occurred while making Interactions API request. Error: ${error}`, + ); + throw error; + } +} diff --git a/ui/nginx.conf b/ui/nginx.conf new file mode 100644 index 00000000000..adc394a28aa --- /dev/null +++ b/ui/nginx.conf @@ -0,0 +1,99 @@ +worker_processes auto; +events { worker_connections 1024; } + +http { + include /etc/nginx/mime.types; + default_type application/octet-stream; + sendfile on; + tcp_nopush on; + keepalive_timeout 65; + + gzip on; + gzip_comp_level 4; + gzip_min_length 1024; + gzip_proxied any; + gzip_types + application/javascript + application/json + text/css + text/html + image/svg+xml + font/woff + font/woff2; + + server { + listen 3000 default_server; + server_name _; + root /usr/share/nginx/html; + + # next.config.mjs sets assetPrefix=/litellm-asset-prefix, which makes + # the built HTML reference /litellm-asset-prefix/_next/... — but the + # static export only emits files under /_next/. Map the prefix to + # the real tree at request time instead of duplicating the directory + # at build time. NB: alias rewrites the location prefix, so + # /litellm-asset-prefix/_next/foo.js → /usr/share/nginx/html/_next/foo.js. + location /litellm-asset-prefix/_next/ { + alias /usr/share/nginx/html/_next/; + expires 1y; + add_header Cache-Control "public, immutable"; + } + + # Content-hashed asset bundles — cache forever. + location /_next/ { + try_files $uri =404; + expires 1y; + add_header Cache-Control "public, immutable"; + } + location /assets/ { + try_files $uri =404; + expires 1y; + add_header Cache-Control "public, immutable"; + } + location = /favicon.ico { + try_files $uri =404; + expires 1d; + } + + # Probe target — doesn't depend on disk. + location = /healthz { default_type text/plain; return 200 "ok\n"; } + + # Next.js App Router (output: "export") emits an RSC/flight payload + # as .txt next to .html, plus __next.*.txt segment + # data. The client router fetches these on soft navigation/prefetch + # (?_rsc=) — the query string is irrelevant, files resolve by + # $uri. These MUST be served from the export: if they fall through + # to the catch-all 404 below, client-side navigation never settles + # and the login flow spins in an infinite redirect loop + # (/ ⇄ /ui/login). Keep this BEFORE the /ui/ regex — ^/ui/(.+)$ is + # also a regex and nginx takes the first matching one, so a stray + # /ui/.txt would otherwise be rewritten to HTML and break RSC + # for nested routes. A genuinely missing payload must 404 (the + # router degrades to a hard navigation); never fall back to HTML. + location ~ \.txt$ { + try_files $uri =404; + } + + # /ui[/] — the dashboard's JS hardcodes URLs under this prefix + # (router.replace("/ui"), buildLoginUrlWithReturn("/ui/login"), ...). + # Mirror what FastAPI StaticFiles(mount="/ui") did in the monolithic + # proxy_server: serve /ui/ from out/.html, with App + # Router-aware fallback (out//index.html) and a final SPA + # fallback to out/index.html for client-side routes. + location = /ui { try_files /index.html =404; } + location = /ui/ { try_files /index.html =404; } + location ~ ^/ui/(.+)$ { + try_files /$1.html /$1/index.html /index.html =404; + } + + # `/` is handy for direct-debug port-forwards. + location = / { try_files /index.html =404; } + + # Anything else (API calls etc.) returns 404 from the UI's + # perspective. A reverse proxy in front of this image routes the + # API surface (/v1, /key, /.well-known/litellm-ui-config, ...) to + # gateway/backend before requests get here; if something slips + # through, fall through to a 404 instead of accidentally serving + # HTML and confusing a JSON-expecting caller. + location / { return 404; } + } +} diff --git a/uv.lock b/uv.lock index ef129187fe4..f3eaf6ca88c 100644 --- a/uv.lock +++ b/uv.lock @@ -67,7 +67,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.13.4" +version = "3.13.5" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -79,76 +79,76 @@ dependencies = [ { name = "propcache" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/45/4a/064321452809dae953c1ed6e017504e72551a26b6f5708a5a80e4bf556ff/aiohttp-3.13.4.tar.gz", hash = "sha256:d97a6d09c66087890c2ab5d49069e1e570583f7ac0314ecf98294c1b6aaebd38", size = 7859748, upload-time = "2026-03-28T17:19:40.6Z" } 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