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20834 commits
| Author | SHA1 | Message | Date | |
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JingHao-Leon
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cc3afe1b76 |
fix(mistral): keep every content-list chunk instead of the last text block
When Mistral returns message.content as a list (a citation between two text chunks becomes a reference chunk in the list), the non-streaming conversion overwrote the flattened string per text block, so only the last chunk survived — an answer closed by a citation reached the client as "." while usage.completion_tokens reported the full answer. The streaming path concatenated correctly but silently dropped reference chunks. The non-streaming conversion now joins every text chunk in order, and both paths expose the original block list under provider_specific_fields["mistral_content_blocks"] when citation chunks are present, so references are reachable instead of dropped. Fixes #45378 |
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yuneng-jiang
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d7b28840eb
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test(integration): read stream stall spend rows from the proxy that served them (#47410)
The cooldown cases in test_hosted_vllm_stream_stall_wire.py boot an owned proxy, which writes to a private database since #47213. The file was written on a base without #47213 and read its rows from the worker database through read_rows, so every cooldown case that waits for a failure row timed out on main with State did not converge: [] Every row read now goes through Gateway.rows on the gateway that served the request |
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Kerry Lu
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09e9193514
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fix(cost): bill Gemini audio/image/video prompt tokens at the resolved input rate when no modality rate exists (#47373)
* fix(cost): bill Gemini audio/image/video prompt tokens at the resolved input rate when no modality rate exists Co-authored-by: shivam <shivam@berri.ai> * style(cost): ruff format the modality cost calls --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: shivam <shivam@berri.ai> |
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yuneng-jiang
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1ef59fab8b
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test(ui-e2e): read the virtual key from its own pre in the Save your Key dialog (#47408)
#47199 added a Gateway URL pre to the created-key dialog, so the bare pre locator matched two elements and failed strict mode in four specs |
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yuneng-jiang
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77bd7d6de8
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test(integration): expect the requested model in streamed Responses events on the arize v2 rig (#47407)
#46662 made streamed /v1/responses lifecycle events report the requested model in the nested response.model, matching the non-streamed body. The arize v2 caller-stream helper still expected the upstream deployment name there, so f1 and a6 failed on every main run since
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yuneng-jiang
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b110c8cfa7
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test(integration): check SSO sign-in removal without reaching the public internet (#47409)
The SSO removal test configured Google sign-in, which fetches its discovery document from accounts.google.com before redirecting. CircleCI blocks that egress, so the test failed on every main run since #46709 added it. Microsoft sign-in builds its endpoints locally and goes through the same SSO environment variable handling, so the test now uses it |
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Mateo Wang
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cf9ebeda07
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fix(proxy): stop billing every OpenAI pass-through /responses sub-route as a Responses call (#47171)
* test(proxy): cover OpenAI pass-through billing for each /responses sub-route * fix(proxy): bill only the OpenAI pass-through Responses calls OpenAI bills * fix(proxy): fail closed on unknown /responses sub-routes and label unbilled spend rows Bill every OpenAI pass-through /responses call except input_tokens, input_items, delete, and a non-background retrieve or cancel, so an encoded or new sub-route is never free. Unbilled calls keep the response's model on their spend row, and a resumed background stream takes its model from the stream's final event. * fix(proxy): give billed background Responses reads their own spend log row A billed retrieve or cancel of a finished background response reused the create's resp_ id, so the spend log insert dropped the row that carried the cost. It now takes the native path's _background_cost suffix. Adds Azure path coverage and types the test recorder. * fix(proxy): bill a background Responses read only once it carries usage An in-progress poll took the _background_cost spend log id with $0, so the finished read's row that carried the cost was dropped again. Polls with no usage are now unbilled like any other read. * fix(proxy): keep OpenAI's response id for callbacks on a billed background read * fix(openai): give a streamed background create the settlement spend row id * fix(proxy): keep a request body from giving its spend row the background settlement id * test(proxy): give the forged settlement marker test a fixed time * fix(passthrough): move the background settlement marker into the pass-through types module A test that reloads spend_tracking_utils gave it a new enum class, so the handler's marker no longer matched by identity and the settlement row lost its _background_cost id. The types module is never reloaded. |
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yucheng-berri
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6162184b1c
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fix(managed_files): send provider file ids when creating a fine-tuning job from managed files (#46552)
* fix(managed_files): send provider file ids when creating a fine-tuning job from managed files Fine-tuning job creation with a managed training file sent the gateway's managed id and the provider-prefixed deployment model to OpenAI, which rejected the job. The pre-call hook now builds the file id mapping for training_file and validation_file, the deployment hook swaps both, the SDK strips the provider prefix from the model, and the create response returns the caller's own file ids * fix(managed_files): route managed fine-tuning creates only to deployments holding every file Fine-tuning creates now narrow routing the same way managed batches do, so a deployment added after the upload is never picked. Adds a two-worker chaos integration test and runs tests/unit/fine_tuning in the endpoints shard. * fix(managed_files): keep fine-tuning creates on the model group when no deployment holds the files A managed file used on a model group it was never uploaded for kept failing with the provider's 400 before the routing filter. The filter turned that into a 429 no-deployments error, so fine-tuning now falls back to the whole group in that case while batch input files keep routing only to deployments that hold them. * test(fine_tuning): cover the provider prefix strip for OpenAI and Azure creates through the proxy * test(fine_tuning): start each multi-deployment case from an empty upstream log * test(fine_tuning): boot both proxies of the late deployment cell on one scratch database --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Kerry Lu
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62ce515d15
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feat(router): let a deployment set a timeout per service_tier (#47406)
* feat(router): let a deployment set a timeout per service_tier Adds timeout_by_service_tier and stream_timeout_by_service_tier to a deployment's litellm_params. A request whose service_tier matches a key uses that value in place of the deployment's timeout or stream_timeout. A timeout the client sends still wins and requests without a matching tier are unchanged Co-authored-by: adc77 <amandogra2016@gmail.com> * style(router): sort the request_timeout_resolver import * fix(router): read the service tier maps without the untyped params get * fix(router): look the service tier timeout up through a typed get --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: adc77 <amandogra2016@gmail.com> |
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Kerry Lu
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53821f6654
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fix(decisions): log every request field the caller sent on /v1/systemone and /v1/decisions (#47183)
* fix(decisions): log every request field the caller sent on /v1/systemone and /v1/decisions * fix(decisions): serialize the logged request with model_dump_json so no mutable dict is built * refactor(decisions): drop the state-only messages pick in function_setup, prepare_call logs the request * fix(decisions): return the single logged message so no mutable list type is annotated * fix(decisions): log the caller's raw request fields before validation so a rejected request is still recorded * fix(decisions): serialize the raw request fields with pydantic_core so question objects and mapping proxies log as JSON * fix(decisions): log the caller request at the SDK entry so requests rejected before prepare_call are recorded too * test(decisions): assert the exact BadRequestError for the rejected-questions case --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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b51b9b7d43
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fix(anthropic)!: answer 400 for a malformed tool_result text on /v1/messages and count_tokens (#47347)
* fix(anthropic): answer 400 for a malformed tool_result text on the Responses bridge and count_tokens A tool_result text block whose text is null, a number, or missing made /v1/messages and /v1/messages/count_tokens answer 500 on OpenAI and Azure models. Both routes now answer 400 naming the block, and drop the block when drop_params is on. * fix(anthropic): keep a missing tool_result text as empty text, bound the error, honor body drop_params on count_tokens * fix(anthropic): apply the tool_result text check on the chat/completions bridge and every count route The chat/completions bridge used for Azure, Gemini and other providers now answers the same 400 for a null or numeric tool_result text and drops the block under drop_params, so a model group that mixes OpenAI with another provider answers the same way on every deployment. Gemini and Vertex count_tokens apply the same check, count_tokens turns on drop_params for Claude Code and Codex the way /v1/messages does, and a tool_result without a tool_use_id is checked too * fix(proxy): answer 400 for a malformed tool_result text on count_tokens models without a count API Providers with no token counting API fall back to the local tokenizer, which counted a null or numeric tool_result text as fine while /v1/messages on the same model answers 400. The fallback now runs the same check, unless drop_params applies to the deployment or the request * fix(proxy): honor request drop_params on the local count fallback when no deployment resolves * test(integration): cover non-string tool_result text on both /v1/messages bridges and count routes * fix(anthropic): keep a dropped tool_result on Mistral and reject it before a guardrail rewrite Under drop_params a tool_result whose only text block was null or a number became an empty tool message, and Mistral's empty-message filter removed it, so Mistral answered 400 for a missing tool reply. The filter now only removes empty assistant messages, which is what Mistral rejects. A guardrail that rewrites structured_messages wrote the dropped text back as "", so a null or numeric text was silently blanked even without drop_params. The rewrite path now answers the same 400 as the bridges unless the request or global drop_params is set. * test(integration): cover the Mistral empty tool reply and the guardrail rewrite tool_result text check * fix(anthropic): keep a malformed tool_result text through a guardrail rewrite and keep Mistral's null-message drop A guardrail that rewrites structured_messages now writes the caller's null or numeric tool_result text back in place, so the route's bridge and the deployment's drop_params decide after routing, the same as with no guardrail. The rewrite path no longer answers its own 400 before routing. Mistral drops a null-content system or user message again, as main does, and only a tool reply is exempt from that filter so a dropped tool_result keeps its tool call. * test(mistral): expect empty system and user turns dropped and the empty tool reply kept * perf(anthropic): restore kept tool_result text parts in one linear pass * test(integration): cover the tool_result text restore across tool_results, the chat bridge, and a batch upload behind a rewriting guardrail * fix(anthropic): let guardrails scan malformed tool_result text and honor drop_params on native Anthropic A rewriting guardrail now sees a non-string tool_result text as its JSON text and the write-back restores the caller's value by the sanitized tool_use_id when the guardrail left it unchanged. Native Anthropic drops the bad block under drop_params on /v1/messages and count_tokens, and Mistral keeps an empty tool reply only when it answers a tool call, sent as empty text. * fix(anthropic): compare a kept tool_result text to its scanned form once and align the wire tests with drop_params * test(integration): cover native Anthropic drop_params resolution, guardrail scanning of numeric tool_result text, and the Mistral orphan tool message * fix(count_tokens): honor the router's default drop_params when counting a malformed tool_result text * test(count_tokens): send the tool_result count tests through a real request body instead of a mocked parser * test(count_tokens): send the native Claude count test through a real request body * fix(anthropic): keep each non-string tool_result text when tool_use_ids sanitize to the same id * fix(anthropic): keep non-ASCII characters in the guardrail's view of a non-string tool_result text * test(gemini): mock the count_tokens upstream with respx instead of patching the client factory * fix(mistral): keep an empty tool reply only for an open call, and forward non-object message items on the native drop An empty tool reply re-answering a call the conversation already moved past made Mistral reject the request, and a tool call id that is not a string crashed the filter. Native Anthropic under drop_params assumed every message item was an object. * fix(mistral): find answering tool replies in one pass instead of copying the open call set per reply * fix(mistral): ignore dropped empty placeholders when tracking open tool calls * fix(mistral): drop an empty tool reply when another reply to the same call carries the result * refactor(anthropic): share tool_result text helpers to shrink the diff * fix(anthropic): answer 400 for a non-string tool_result text on the OpenAI Responses bridge |
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yucheng-berri
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365d7342c0
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fix(prometheus): label deployment failures with the deployment's provider instead of None (#47384)
* fix(prometheus): label deployment failures with the deployment's provider instead of None * test(prometheus): drive every deployment failure surface through the provider label Streaming chat, responses and messages, dropped and stalled upstreams, JSON-configured, custom-provider-map, prompt-management and unprefixed deployments, forged request metadata, an unauthenticated request, the OpenAI and Anthropic SDKs sync and async, and a concurrent burst counted exactly once per deployment --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Kerry Lu
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1212260ab1
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fix(logging): report the cost-map entry billing used in model_map_information (#47182)
* fix(logging): report the cost-map entry billing used in model_map_information Billing resolved the deployment's own cost-map entry and logging ran a second, name-only lookup, so callbacks and spend logs showed the catalog rates for a custom-priced deployment. completion_cost now stores the entry it billed from on the logging object and the standard logging payload reads it, falling back to the old lookup only when billing never ran. * fix(logging): pass the deployment id into the logging cost-map lookup Drop the logging-object plumbing and keep the lookup where it was: get_standard_logging_object_payload now hands the deployment id to get_model_cost_information as router_model_id, the same id billing uses, so the deployment's own cost-map entry is reported instead of the catalog entry. * test(logging): mirror the proxy metadata in the deployment-price regression test * fix(logging): keep the catalog model_map_key, report only the deployment's own rates --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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c0d88b46c9
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fix(azure): cache the remaining per-request Entra credentials and keep token refresh off the event loop (#47348)
* fix(azure): cache the remaining per-request Entra credentials and keep token refresh off the event loop Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(realtime): await the now async azure health-check auth header helper Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(azure): await async azure_ad_token_provider results in the threaded wrapper * fix(azure): keep transport params when validating async Azure client params on pydantic 2.11 * test(azure): assert the token and validation thread hops by thread identity instead of sleeps * fix(azure): give the OIDC token exchange its own short-lived HTTP client The exchange now runs from worker threads, so it no longer shares litellm.module_level_client's connection pool with other threads * test(azure): check the OIDC exchange closes its client through an in-process transport * refactor(responses): merge caller extra_headers into a new dict instead of updating validated headers in place --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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litellm-stampbot
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9bafafc2df
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feat(search): add DeepSeek as a /v1/search provider (#47401)
* feat(search): add DeepSeek as a /v1/search provider Adds DeepSeekSearchConfig for POST https://api.deepseek.com/v1/tools/search, registers it under search_provider=deepseek, prices deepseek/search at 0.00075 per query and marks search support in provider_endpoints_support.json * fix(search): reject DeepSeek queries over the 128 character limit before the call * test(search): escape regex metacharacters in DeepSeek query limit match --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yucheng-berri
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89a194bbd0
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test(integration): hold the upstream until the disconnect is settled before reading its spend row (#47303)
test_client_disconnect_before_any_content_sends_no_response_scan released the gated upstream right after
the client closed, so the upstream's next chunk raced the disconnect into the proxy. When the chunk won,
the proxy had " tail" on record for the client and the cut-short scan recorded a post_call success on
the spend row, which the test asserts is absent (CircleCI integration-extensions job 2263560 on
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yucheng-berri
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3e18a4b55d
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fix(router): record cooled deployments on the all-cooled failure row (#47120)
* fix(router): record cooled deployments on the all-cooled failure row A RouterRateLimitError now carries the cooled deployments of the route the request resolves to for the caller's team (model group, alias, wildcard or team route), each with its masked api_base. get_error_information copies them into error_information.error_cooldown_deployments so the SpendLogs failure row and every logging callback can tell which deployments were cooling * fix(router): name cooled deployments from the router's own resolved route Use the deployments the router resolved for the request, before health and cooldown filtering, instead of re-deriving the route. Alias chains, the provider-model fallback, wildcards and team filtering now match routing by construction. A routing-strategy refusal records null. * fix(router): name only the cooled deployments tag and region routing allow The refusal's cooled-deployment list now leaves out deployments the request's tags or the end user's allowed region keep it from, so a tenant's refusal row no longer shows another tenant's api_base * fix(router): keep failure logging working for refusal subclasses that skip init A RouterRateLimitError subclass that never calls its parent init now logs a null cooled-deployment list instead of breaking the failure payload * test(router): call the refusal routing helpers directly The router coverage check needs each router function called by name from a unit test --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yucheng-berri
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2e427c6247
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chore(deps): require anyio>=4.14.0 to rule out the lock-waiter deadlock (#47378)
anyio below 4.14.0 can leave a cancelled waiter queued on an asyncio Lock or Semaphore, so the next task to acquire waits forever on a lock nobody holds (agronholm/anyio#1145, fixed in 4.14.0). httpcore takes these locks for every httpx request, including the prisma query-engine client litellm imports anyio directly but never declared it, so pip and uv kept an already installed anyio 4.13.0 when installing litellm and accepted an explicit anyio==4.13.0 next to it. uv.lock already resolves 4.14.2, so Docker and CI installs do not change. Both the litellm and litellm-core manifests get the floor, and the lock only records the new direct dependency Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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Paul El Khoury
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d7620c77a1
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fix(openai): drop unsupported reasoning effort for gpt-6.1-sol (#43935)
* fix(openai): drop unsupported reasoning effort for gpt-6.1-sol gpt-6.1-sol accepts low, medium, high, xhigh, and max. none and minimal were still forwarded, because chat completions ignored an explicit supports_none_reasoning_effort=false and the Responses mapper never applied that contract. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(openai): mark the stripped reasoning object as outgoing JSON Co-authored-by: Cursor <cursoragent@cursor.com> * Fix Azure responses reading the OpenAI effort map gpt-6-astra on Azure accepts reasoning.effort none. The responses gate was checking the OpenAI entry, which rejects it. Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> * fix(openai): gate every reasoning_effort level from the cost map on chat and Responses One gate reads the model's cost-map row for whatever effort level a request asks for, on Chat Completions and the Responses API, for OpenAI and Azure. xhigh stays opt-in on chat (Azure also keeps none opt-in); every other level is refused only when its supports_<level>_reasoning_effort flag is false. A refused level is dropped under drop_params or answered with a local 400, and Responses keeps the rest of the reasoning object when it drops effort. The guards that follow judge temperature by the level the provider will see. Tests move into the mapped test files and derive the expected outcome for each level from the map row, so a new GPT-6 model needs only its row. * test(openai): pin gpt-6.1-sol's refused effort levels and prove the row flags alone drive the gate * fix(cost-map): azure gpt-6-astra refuses none and honors max on Responses, as live calls show * test(reasoning_effort): pin the astra max and none flips through the adapter and Foundry paths * fix(reasoning_effort): keep max out of the route gates and correct the gpt-5.3-codex rows The same model honors max on Responses and refuses it on Chat Completions, so one row flag cannot gate it on either route. The gates now skip max, the Azure gpt-6-astra rows keep max off so the /v1/messages adapter degrades to xhigh and /model_group/info stops short of it as on main, and chat still forwards max for the Responses bridge. The gpt-5.3-codex rows said none and xhigh off and minimal on; the live model answers the other way round (2026-10-01), and an opt-out gate would have dropped the levels Codex CLI sends. * fix(reasoning_effort): bridge function tools to Responses when the chat gate drops none * docs(reasoning_effort): correct the max advertisement note * fix(azure_ai): read the Foundry row on the Responses effort gate and turn none off for Azure gpt-6.1-sol * fix(azure): gate a prefixed Foundry deployment's Responses effort off its row litellm.responses(model="azure_ai/<deployment>", api_base=<Foundry host>) is re-routed to the azure provider with the azure_ai/ prefix intact, so the Azure Responses config saw a prefixed name, skipped it as another provider's pass-through, and forwarded none and minimal to Azure even under drop_params. The Azure gpt-5 resolver also read no row at all for a prefixed name. The Azure Responses gate now detects the family the way the Azure chat gate does, and an azure_ai/ name on the Azure OpenAI configs reads its azure_ai/ row when the map has one, else the azure/ twin, the rule the Foundry chat config already applies to the same deployment. * fix(cost-map): turn none off on azure/eu/gpt-6-astra like its sibling rows * docs(reasoning_effort): say how each gate reads none and shorten the module docstring * fix(azure_ai): call the public effort-level check from the Foundry Responses gate * test(reasoning_effort): fold the Foundry Responses tests into the mapped file and type the row helpers --------- Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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Justin Israel
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40e4ade6a6
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feat(proxy): opt-in self-serve budget edits for key owners on /key/update (#44763)
* feat(proxy): opt-in self-serve budget edits for key owners on /key/update Adds general_settings.self_serve_budget_policy (disabled by default) so a non-admin can change max_budget or budget_limits on a key they created and still own. lower_only allows adding or lowering limits. ceiling also allows raising or removing them when the owner could create that key with the new budget via /key/generate (same member permissions and delegation ceiling). soft_budget and spend stay admin-only * fix(proxy): sync dashboard API types and cover the UI session cap on update * test(proxy): cover team role restriction for owner budget raises Drop two unreachable branches from the non-raising key generation check. * fix(proxy): close self-serve budget bypasses for linked budgets, null caps and retained limits Keys with a linked budget_id need an admin to change max_budget, owners cannot leave a key uncapped when an upperbound max_budget is configured, and the ceiling check now runs on the budget the key would carry after the update. * fix(proxy): keep open key budget windows and the admin path on self-serve budget edits A resubmitted budget_limits window now keeps its stored reset_at while that period is still running, so adding or editing one window no longer restarts the others. Team and org admins answer False on a ceiling loosen so they fall through to _check_key_admin_access as before. The self-serve tests feed the team through the cache and prisma mocks instead of patching get_team_object, and every helper is typed * perf(proxy): group key budget windows by duration in one pass The per-duration minimum used to rescan the whole window list once per distinct duration, so a request with thousands of windows pinned the event loop before any permission check ran. Sort once and take each group's minimum instead * refactor(proxy): take budget windows as a Sequence in the delegation ceiling checks * test(proxy): assert allowed self-serve budget edits return no denial Also drop the unreachable assert_never arm from the exhaustive budget change match. --------- Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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devin-ai-integration[bot]
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2f49e7f1c3
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test(cost): add tier 1 cost tracking cases and wire bridge legs for 2026-10-19 (#47342)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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c82e8244a5
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fix(count_tokens)!: count tools the way DeepSeek V4 and Qwen3.5 chat templates render them (#47173)
* fix(count_tokens): count tools the way DeepSeek V4 and Qwen3.5 chat templates render them * refactor(count_tokens): hold the tool counting wiring for a follow-up commit * refactor(count_tokens): hold the chat template tool sections for a follow-up commit * fix(count_tokens): count tools the way DeepSeek V4 and Qwen3.5 chat templates render them * fix(count_tokens): skip tool_choice and non-function tools for chat template families, leave out Fireworks V4.1 spelling * fix(count_tokens): count Anthropic tools in the shape the messages bridge sends them A missing description is no longer rendered as null and strict is counted when set, matching what DeepSeek V4 and Qwen3.5 deployments receive. Adds tests that pin each template's schema rendering and its system turn. * test(count_tokens): check the prompt caching threshold agrees with token_counter for DeepSeek V4 and Qwen3.5 tools * fix(count_tokens): skip Anthropic web search tools and count unmapped Anthropic tool keys for chat template families * test(token_counter): cover chat-template tool counts across count routes, budgets, ITPM and context edits Integration cells check that DeepSeek V4 and Qwen3.5 tools are counted the way their chat templates render them on /v1/messages/count_tokens, /utils/token_counter, /v1/responses/input_tokens, strict budget reservation, project ITPM on chat, messages and responses, and the clear_tool_uses trigger, with grok controls keeping OpenAI framing |
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devin-ai-integration[bot]
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f8f6a01cae
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feat(ui): show kubernetes pods on the model page (#44666)
Show discovered Kubernetes pods on the model page for admins Resolves LIT-9168 Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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f85926a652
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feat(router): keep a session on one kubernetes pod (#44393)
Requests with an explicit session id stick to one discovered pod via rendezvous hashing Requests without a session id keep round robin Pod routing records are included in logs, spend logs, and traces Resolves LIT-9168 Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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6fc60bc00d
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feat(router): discover kubernetes pods behind a headless service api_base (#44342)
Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Alex Ker
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3eeee3e761
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fix(baseten): list current Model APIs instead of retired 2023 deployment IDs (#46945)
* fix(baseten): list current Model APIs instead of retired 2023 deployment IDs `baseten/*` wildcard expansion and `/v1/models` read a hardcoded set of three 2023 deployment IDs (Falcon 7B / WizardLM / MosaicML) that no longer exist, while the model cost map already carries current Baseten entries. - Populate `baseten_models` from the model cost map, like fireworks_ai. - Add `BasetenConfig.get_models()` against the Model API `/v1/models` endpoint and register it in `ProviderConfigManager.get_provider_model_info`, so `check_provider_endpoint: true` returns the live catalog. - Remove the retired deployment IDs from `constants.py`. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(baseten): mark retired Model APIs deprecated and skip them in listings 15 Baseten entries in the model cost map are no longer servable: the Model API returns 410 "model version ... has been deprecated" (DeepSeek-V3.1 returns 400 deactivated), and none appear in `inference.baseten.co/v1/models`. - Set `deprecation_date` on those entries (kept for spend tracking). - Skip entries past their `deprecation_date` when building `baseten_models`, so `baseten/*` expansion only lists models that still serve. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * test(baseten): assert the custom api_base model listing on the returned models The custom api_base test only inspected the patched client's call args, which trips the TQ002 mock-echo gate. Serve the custom URL with respx and assert on the models that come back instead, so a wrong host or a broken parse both fail. * fix(baseten): drop newly retired models on cost map reload and fake discovery at the HTTP boundary A cost map refresh only ever added to the Baseten set, so a model that gained a past deprecation_date after boot stayed listed under baseten/* until restart. Discard it on reload. Tests now serve the catalog with respx instead of patching our own HTTP client, the isinstance registration test is gone since the get_valid_models test already proves discovery end to end, and new test locals carry Final. --------- Co-authored-by: AlexKer <AlexKer@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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devin-ai-integration[bot]
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c80f1c2ec8
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fix(router)!: count native Responses WebSocket turns toward deployment limits and budgets (#45727)
* fix(responses): write one spend-log row per native WebSocket turn Each response.create turn on a native Responses WebSocket connection now logs its own call as it finishes, with its own usage and the resp_ id the client saw as request_id. A failed turn is booked alone and no longer flips the other turns. * fix(responses): bill the usage a failed native WebSocket turn reports * fix(proxy): pin the limiter slot owner to the proxy call id and refund unsettled reservations on disconnect The v3 limiter stashed whichever litellm_call_id the request body carried as the slot owner, so a client-supplied id on a pass-through body left the proxy's own success and failure events unable to release the slot. The owner is now the proxy's logging call id. A WebSocket connection whose turns log under their own ids never settles the TPM, ITPM, or OTPM estimate the connection reserved, so the disconnect release now refunds any reservation no callback settled, once. * fix(proxy): bill failed WebSocket turns and refund only the reserved window on disconnect A failed turn's reported usage now counts toward the key's TPM and the project ITPM/OTPM scopes the same way a success does, and closing the socket hands back only the estimate still unused in the window that holds it, so a window that already rolled is left alone. An id-less error event is booked against the newest pending turn, the one the backend rejected at parse time, and a turn that nests its input under response logs its user message. A connection's one budget reservation goes to the first turn that settles it, every later turn charging its cost directly. * test(proxy): expect the settling call id on a pass-through budget reservation * fix(proxy): charge successful Responses WebSocket turns to TPM and ITPM/OTPM A native Responses WebSocket turn reaches the rate limiter's success callback with its raw event list as the response and no combined_usage_object, so the turn's tokens never reached the key's TPM window or the project's input and output token limits. Read the usage from the turn's events when the call type is a Responses WebSocket turn. * fix(proxy): release a rejected pass-through's slot and count each websocket turn once toward project quotas The limiter's failure hook looked up the parallel slot by the request body's litellm_call_id, while the slot belongs to the proxy's logging call id, so a pass-through request a pre-call hook rejected held its slot for an hour when its body carried an id. Both now resolve the id the same way. Each response.create frame already charges its estimate to project ITPM/OTPM, so the turn's own usage no longer adds a second count. The cost callback's failure hook read the body's litellm_call_id through a strict model and raised on a non-string value, which dropped the failure row. It now reads a string id or none. * fix(proxy): keep per-turn WebSocket limits, request bodies and collector reservations right Free a rejected request's parallel slot through the request it runs in, and skip native Responses WebSocket connections, whose disconnect frees the slot. Count WebSocket turns toward the connection's tag TPM. In collector mode, claim the shared budget reservation before the spend event is built, so only the first turn settles it. Store each turn's own frame as its request body, and send an id-less error to the oldest waiting turn unless an earlier turn is streaming. * fix(proxy): store a failed WebSocket turn's own request and count tag TPM only for calls that carry the tag * fix(responses): log the client's turn frame, keep the payload id and reservation shape for non-WebSocket calls * fix(proxy): keep the legacy limiter's WebSocket slot until the socket closes * fix(router): count native Responses WebSocket turns toward deployment limits and budgets * fix(router): re-check deployment budget and RPM on each Responses WebSocket turn * test(router): list the WebSocket turn admission method as covered through its partial * fix(responses): give a refused WebSocket turn's error frame a 429 status so Codex ends the turn * fix(router): leave ITPM/OTPM to socket open and record a refused WebSocket turn as the socket's failure * fix(proxy): log the routed model group on a WebSocket turn's failure row * fix(router): count WebSocket turn tokens toward TPM and re-run routing strategy checks on each turn * test(integration): cover per-turn admission on Responses WebSockets Adds integration cells for budget, RPM, TPM, a mid-socket TPM edit, usage-based-routing-v2, the project quota frame, and the open-time failure frame on the native and managed relays, plus HTTP controls for TPM counting and the failure row model_group * fix(responses): count WebSocket turn tokens once and end a socket whose turn check fails A native Responses WebSocket turn is now logged as an async call, so a proxy with a sync success callback such as the response cache no longer runs each CustomLogger's sync success hook on top of the async one. That second run counted every turn's tokens twice toward deployment TPM. A turn whose admission check raises an unexpected error now gets a 500 error frame, the socket closes with 1011, and the error becomes the socket's failure, on both the native and the managed relay, instead of the socket dropping with no frame and no failure row. A refused turn's failure row now carries the request's tags from the router's metadata, the same way it already carries the model group. * test(integration): cover a crashed WebSocket turn check and failure-row tags A turn whose admission check raises gets a 500 server_error frame, the socket closes with 1011 and its one failure row carries the error, on both relays. The refused turn's failure row keeps the caller's tags, and HTTP failure rows on chat, responses and messages keep them too. * fix(responses): keep a WebSocket open after a crashed turn check and keep only string tags on failure rows A turn whose admission check raises an unexpected error still gets a 500 error frame, but the socket now stays open, like a refused turn. Closing it cut any turn still streaming on the same socket, which a client that sends its next response.create early would lose. A refused turn's failure row now takes the router's tags only when every tag is a string, so the fallback never copies a caller's unvalidated tags onto the row. * test(integration): cover a turn still streaming when the next WebSocket turn's check crashes A native turn still streaming when the next turn's admission check raises now has its own cell. It must complete and be billed at the turn's price. The crashed-check cell also keeps the fault set for one more turn, so a relay that stopped checking turns after a crash goes red. * fix(responses): run per-turn guardrails before the deployment's turn check on WebSockets A turn after the first now runs the project quota check, then the proxy's pre_call guardrails, then the deployment's turn admission, on both the native and the managed relay, the order HTTP calls use. A turn a guardrail blocks gets the guardrail's error even past the budget and no longer counts toward the deployment's rpm or runs its pre-call checks. * fix(responses): bill a WebSocket turn's guardrail cost when the deployment refuses it A turn the per-turn guardrail passes and the deployment then refuses now writes its own failed spend row with that turn's guardrail information, on both relays, the way a guardrail-blocked frame and an HTTP refusal already do * fix(responses): bill a refused WebSocket turn on one row only A refused turn whose guardrails recorded a cost is already billed on its own row, so it no longer also becomes the connection's close-time failure row * fix(responses): bill a refused WebSocket turn's guardrail cost on the socket's failure row The turns a deployment refuses after their guardrails passed now add their guardrail records to the socket's close-time failure row instead of a row of their own, so that row's failure hooks, model group and call type cover them * fix(responses): bill a WebSocket's refused turns when the session limit closes it * fix(responses): bill refused WebSocket turns when the session limit cancels the relay * fix(responses): free the WebSocket slot at the session limit without waiting on the failure row * fix(responses): keep the session limit's second cancel so a relay that swallows the first frees the slot * fix(responses): free the WebSocket slot before the close-time failure row and drain pending rows at shutdown * fix(proxy): commit spend before waiting on pending logging tasks at shutdown and again after * fix(responses): start the close-time failure row before the slot release so a late cancel cannot drop it * fix(responses): build the close-time failure row inside its task so a bad guardrail record cannot skip the crash close * fix(proxy): always commit spend again after the shutdown wait for pending logging tasks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Jorik
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492272a051
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fix(azure): reuse the cached Entra token provider on the Responses path (#46421)
get_azure_ad_token() built a new DefaultAzureCredential for every request when enable_azure_ad_token_refresh is on, so each Azure Responses call fetched a fresh Entra token synchronously on the event loop. Use the per-scope cached provider the SDK-client path already uses, so the credential's own token cache does its job. Fixes #46419 |
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Habon Laszlo
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a2773bdcad
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fix(azure): route WebRTC client_secrets and calls to the GA v1 realtime API (#47316) | ||
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alexcastgonz
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6762d65eb8
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fix(vertex_ai): price streamed pass-through calls with the regional uplift (#44795)
* fix(vertex_ai): price streamed pass-through calls with the regional uplift The streamed Vertex pass-through handler builds the response with stream_chunk_builder, which stamps a cost into _hidden_params without the vertex_location. The handler then prices the response again with the location from the URL, but only into kwargs, and the logger prefers the stamped cost. Streamed calls on a regional endpoint were billed at the global rate. Stamp the location-aware cost on the response in the shared generateContent payload builder so the logger records it. Fixes #44755 * style(vertex_ai): keep the response_cost assignment within the 120-char limit * refactor(vertex_ai): set the passthrough response cost with set_hidden_param Writing _hidden_params directly mutated a parameter and needed a pyright ignore, which the type discipline gate counts as LIT004 and LIT011. set_hidden_param is the shared helper for this. * test(integration): check Vertex pass-through spend rows keep the regional uplift when streamed * fix(proxy): price streamed Vertex pass-through spend like the non-streamed call Drop the location-blind cost stream_chunk_builder stores instead of stamping a cost, so the logger prices streamed generateContent and rawPredict calls with the URL's location and the deployment's custom pricing, the same way it prices non-streamed calls * fix(proxy): price custom-priced Vertex pass-through deployments at their own rates The stamped response_cost from the generateContent logging helper is returned before the logger checks custom pricing, so a deployment with custom input/output rates was billed at the cost-map price. The helper now passes custom_pricing and the deployment id into completion_cost, so the stamp holds the deployment rate for streamed and non-streamed calls, and deployments without custom pricing keep the regional uplift. * fix(proxy): bill Vertex /v1beta and interrupted pass-through streams by the deployment location and price * fix(proxy): give the new pyright ignore a reason for the LIT004 gate * test(integration): bill a custom-priced Vertex pass-through stream at the deployment price, cut-off streams included * fix(proxy): let the logger price non-streamed /gemini pass-through calls so both modes bill the same * fix(proxy): keep a client base_model out of pass-through pricing and price gateway Gemini streams as Gemini A top-level base_model in a pass-through body now reaches the logging params only for a key or team with allow_client_pricing_override, so a plain key cannot pick a cheaper model's price once the logger prices these calls. The test that pinned every litellm-owned body name into the logging params now leaves base_model to its own key-gated test. The Vertex handler now classifies a non-Google upstream host as Vertex only for Vertex resource paths, so a /gemini stream through a GEMINI_API_BASE gateway is priced as Gemini without the regional uplift. Adds the audit's integration spend test for Claude on Vertex, /v1beta, the named custom-priced /gemini deployment, and a cut OpenAI stream. * test(integration): price the gateway /gemini stream case as Gemini and cover client base_model and gateway host pricing The cost harness sends /gemini through GEMINI_API_BASE, which the pass-through now logs and bills as gemini, so the streamed case moves from the Vertex cases to the Gemini cases under the Gemini price. New spend tests cover a body base_model repricing only for keys allowed client pricing, a GEMINI_API_BASE gateway billing as Gemini in both modes, and a configured non-Google gateway picking the provider from the path. * fix(proxy): price pass-through Gemini by the route and ignore a non-string base_model The Vertex pass-through handler picked Gemini pricing from the URL path, so a configured pass-through to a Vertex gateway with a short path (no /projects/ segment) was billed as Gemini, and its Claude streams at $0. The provider now comes from the route: a call through /gemini or to generativelanguage.googleapis.com prices as Gemini, and everything else as Vertex, the same rule main used. Matching the GEMINI_API_BASE host was rejected because one gateway can serve both APIs, and the path rule was rejected because short gateway paths look like Gemini paths. A non-string base_model (a number, list or object) made the cost calculation fail and bill $0 once the stamped stream costs were gone. get_base_model_from_metadata now ignores it and falls back to the deployment's base_model, so the call bills the list price. A 400 was the other option, but the logger runs after the upstream answered. * test(integration): bill a non-string body base_model at the list price on every pass-through route * fix(proxy): bill a non-string model_info base_model at the list price and drop a non-object client model_info A key allowed client pricing can send metadata.model_info in a pass-through body. A non-string model_info.base_model made the cost lookup fail, and once the stamped stream and Gemini costs were gone those calls billed $0 on /gemini and on streamed /vertex_ai and /v1beta. The lookup now ignores a base_model that is not a string, the same rule the root base_model got, so the call bills the list price. A model_info that is not an object (a string or a list) broke several logging steps that read it as a mapping, so a non-streamed /gemini call fired no success callback and no pass-through call wrote a spend row. The pass-through route now leaves a non-object model_info out of the logging metadata, so the call bills the list price. A 400 was the other option, but it would turn calls that answer 200 today into errors, and guarding every reader of model_info would spread the same check over many files. * fix: reject a non-object model_info before the provider call again The previous commit made the base_model lookup return None for a model_info that is not an object. That lookup also runs during call setup on every router route and SDK entrypoint, where such a value used to fail before any provider request. With the early return the call went upstream, the provider billed it, and the request then failed in a later reader that still expects an object. On /v1/messages and /v1/responses the key was also charged for a response the client never got. The lookup again reads base_model from any truthy model_info, so a non-object value fails during setup the way it did before this PR. A non-string base_model inside an object still bills the list price, and the pass-through route still drops a non-object client model_info at intake, so pass-through calls keep billing the list price. * fix: match the Gemini API host exactly when picking the pass-through cost provider The provider check accepted any host ending in generativelanguage.googleapis.com, so a Vertex host such as vertex-generativelanguage.googleapis.com was priced as Gemini and lost the regional uplift. It now accepts that host or a subdomain of it. * test: pin that a subdomain of the Gemini API host keeps the Gemini price --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Nate Armstrong
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bcd80b6aa1
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feat(rust): encode LiteLLM response ids on native responses (#46572)
Native Responses now hands back the same managed ids as Python: the resp_ id carries the provider and deployment, encrypted-content items get encitem_ ids and wrapped content under affinity routing, and container ids become cntr_ ids. Encoded encrypted-content items in the input are restored before the provider request. The codec lives in inference-responses with goldens taken from litellm/responses/utils.py |
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Nate Armstrong
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4995d319ed
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test(rust): add native vs Python parity harness for responses (#46567)
Each case runs one aresponses call through Python and through the native route against the same recorded OpenAI response and compares the dispatch, provider request, return value and success log. Known gaps are strict xfails so the PRs that close them must flip the marker |
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vanraj Solanki
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8b90649fb7
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fix(proxy): sanitize unexpected /key/info database failures (#46190) | ||
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yuneng-jiang
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19f69ed6b6
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test(integration): wait for every worker in Scenario.model (#47277) | ||
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joshua-berri
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9c958aff34
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fix(logging): deliver completed batch success callbacks consistently (#47192)
* fix(batches): dispatch configured success callbacks during reconciliation * fix(batches): retain billed job claim when sync dispatch shuts down * fix(batches): finish reconciliation when logging executor is stopped * fix(logging): avoid duplicate custom hooks during async batch dispatch * fix: preserve sync-only custom callback delivery * fix: scope callback deduplication to per-call async recipients * refactor: type per-call async success callbacks * refactor: retain logging constructor callback compatibility * test: support current batch reconciliation metadata * fix(logging): honor per-logger redaction during sync success delivery * fix(logging): centralize completed success callback dispatch * fix(guardrails): avoid duplicate success callback dispatch --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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Mateo Wang
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7e6fb09da3
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fix(streaming)!: map stream read timeouts to litellm.Timeout on chat, messages, and responses (#47198)
* fix(streaming): map stream read timeouts to litellm.Timeout * revert(streaming): move the timeout mapping to a later commit * fix(streaming): map stream read timeouts to litellm.Timeout on chat, messages, and responses * fix(streaming): keep stream fallback and cooldown unchanged for transport timeouts A chat stream served through the Responses bridge now keeps mid-stream fallback on a stall, because the fallback exemption also covers a litellm.Timeout whose cause is an httpx transport timeout. The Responses stream hands the raw transport error to failure callbacks again, so router cooldown sees a stall as it did before. The 408 deployment info now lists stream_timeout. * test(integration): cover a stalled hosted_vllm stream on chat, Responses, and Messages with its spend log row * fix(router): keep a fallback deployment whose stream stalled out of cooldown * test(integration): cover bridged chat, fallback and cooldown for stalled hosted_vllm streams |
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Mateo Wang
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5012083b72
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feat(vertex_ai): let operators drop chosen anthropic-beta values on the Vertex AI pass-through routes (#47185)
* feat(vertex_ai): let operators drop chosen anthropic-beta values on the Vertex AI pass-through routes * fix(vertex_ai): drop configured betas sent through x-pass-anthropic-beta and only fail beta requests on a malformed setting The drop list now runs on the final upstream headers, after x-pass- expansion, through an upstream_headers_filter on pass_through_request, so x-pass-anthropic-beta cannot carry a dropped value to Vertex. A malformed setting now answers 500 only for requests that carry anthropic-beta or x-pass-anthropic-beta, so Gemini and discovery calls keep working. * test(vertex_ai): accept the new upstream_headers_filter argument in the existing Vertex route tests * fix(vertex_ai): refuse a malformed drop-betas list at boot and match list entries written with spaces * fix(vertex_ai): split drop-betas list entries on commas like the header |
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yuneng-jiang
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eb114e638d
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test(integration): look up owned cache keys and clean up sweep models (#47255)
Three integration tests diffed the whole shared Redis keyspace (scan_iter or dbsize before and after), so a sibling test's cache write could satisfy or break them. Each now finds its own entry by a value only it wrote: the Gemini reasoning test by a per-run Gemini responseId, the max_messages test by a per-test answer text, and the S3 cache-hit test by its request marker The sweep auth tests created models through raw /model/new on the shared gateway and never deleted them. They now create them through Scenario.model, which deletes each model on exit, including when setup fails |
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yuneng-jiang
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44d99475a5
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test(integration): stop GET /__observations from draining the upstream log (#47256)
* test(integration): stop GET /__observations from draining the upstream log GET /__observations used to empty the queue, so a second read in a test saw nothing and a "never reached upstream" check could pass whether or not the request was forwarded. GET now returns every request since the last reset and DELETE /__observations is the reset. An autouse fixture resets the worker upstream before each test, which also covers tests that read without the gateway fixture. Discarded reads used as resets become DELETEs, helpers that accumulated drained reads now read one snapshot, and tests with several phases reset between them. The three vacuous negative checks (rag allowlist, guardrail opt-out chaos, xAI batch output) now assert the absence on the same read that shows a request which did reach the upstream. * style(integration): space the observations unit test like its neighbours |
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Mrunmayee Daware
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e979bd0ecf
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fix(proxy): authorize the public name of a team-scoped model on batch retrieve/cancel (#47067)
* fix(proxy): authorize the public name of a team-scoped model on batch retrieve/cancel
A unified batch id resolves to the deployment's internal model_name
(model_name_{team_id}_{uuid}), which is in no key or team allowlist, so
authorize_model_for_key always denied and team keys got 403 retrieving or
cancelling their own batches. Authorize against model_info
team_public_model_name when the resolved deployment is team-scoped, falling
back to the resolved name otherwise.
Fixes #46958
* fix(proxy): authorize team-scoped batch models by their public name on both auth paths
* fix(proxy): accept the owning team's public name for its own team deployment on every auth path
Managed batch, file, fine-tuning, vector store, character and video ids resolve to the
team deployment's internal name again, so teams that granted the internal name keep access.
The model access check now also accepts the owning team's public name and its legacy team
aliases for that internal name, and only when the caller's team owns the deployment.
* fix(proxy): match a team deployment's public and alias names against the caller's own grants
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
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Kerry Lu
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8d3b38fffc
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test(perf): align read queries with current dashboard calls (#47243)
* test(perf): align read queries with current dashboard calls * test(perf): remove tiny-fixture read API cases * test(perf): focus UI queries on larger matching datasets * test(perf): report all-request percentiles over ten samples --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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moyai-berriai[bot]
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f370235bb4
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fix(guardrails)!: preserve Bedrock Converse redactions and honor tool-only scans (#45874)
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* fix(guardrails): preserve Bedrock Converse redactions and honor tool-only scans * Preserve main's per-block streaming fixes and regression coverage * fix(bedrock): apply Converse guardrail rewrites texts-first and keep main's write-backs Map tool results onto their text and json parts only, accept a structured row the flat texts already reproduce, let a flat-text mask win over a structured leaf, ignore texts that do not line up with the request when structured rows come back, keep tool call arguments out of the rows under skip_tool_message_in_guardrail and send no tool definitions to tool-only guardrails * fix(bedrock): accept blank content on tool-call-only Converse turns A vendor that returns "" or [] where an assistant row carried only tool calls no longer fails the request closed. Also type the leaf lookup without Any and restore the original layout of the existing unit tests. * fix(bedrock): scan additionalModelRequestFields in Converse guardrail rows and split the handler Guardrails that read structured_messages instead of texts (Akto, Prompt Security, CrowdStrike AIDR and others) stopped seeing strings in additionalModelRequestFields, which Bedrock can forward into the model prompt. Those strings now go to the guardrail as one user row placed before the conversation, and a row-only edit to them is written back. The Converse to OpenAI row conversion moves to litellm/llms/bedrock/chat/converse_to_openai.py and the returned-row diffing to structured_edits.py, so the handler keeps only the glue. Rows record the Converse path of every text part, scope flags travel as one value, and path lookups return a value instead of raising. Unit tests use a real CustomGuardrail subclass instead of mocks, and the integration vendor modes get descriptive names. * fix(bedrock): keep new Converse helpers inside the type discipline rules * test(bedrock): pin Converse row edits that change shape, role or placement * fix(bedrock): type the Converse body once so its edits keep their reference * fix(bedrock): list request-field texts where their Converse row sits * fix(bedrock): send a reshaped Converse tool result as one text block A vendor that compresses or summarises a tool result, such as Headroom collapsing a JSON list into a table string, returned a 500 because the row could not be split back into the original text and JSON parts. The tool result now carries the vendor's string as one text block where its parts were, keeping images and documents in place. It still fails closed when the flat texts masked that tool result or the row changed its tool call id. * fix(bedrock): apply guardrail tool edits on Converse and keep compression guardrails off it A guardrail that masks a Converse tool description or schema now has that mask written into toolConfig, a tool it removes is dropped, and a tool it adds, renames or reorders is refused before Bedrock. Headroom, Compresr and TypeSafe get only the flat texts on Converse again, as on main, because the pass-through route has no loop to answer their retrieve tool * test(bedrock): refuse a guardrail that changes a Converse tool's kind * fix(bedrock): keep partial-row and row-rebuilding guardrails on flat Converse texts OpenAI moderation, the LLM judge, CrowdStrike AIDR, Prompt Security, PANW Prisma AIRS and HiddenLayer scan only part of the rows or rebuild their returned texts from them, so on Converse they get the flat texts only, as on main. When a vendor returns changed rows, its texts are applied only if they keep the flat layout and change only rows the vendor also changed, so row-shaped texts of the same length no longer overwrite unrelated Converse fields. * test(bedrock): pin how flat texts sit next to rewritten Converse rows * fix(bedrock): keep the Bedrock guardrail on flat Converse texts With experimental_use_latest_role_message_only, the Bedrock guardrail places its mask by counting text parts per row, and Converse rows do not line up with the flat texts, so the mask could land in tool call arguments. Give it the flat texts only, as on main. * fix(bedrock): keep Akto on flat Converse texts Akto maps its masked payload back onto the flat texts by value, and a Converse row joins or serialises tool call arguments and JSON tool results, so a masked card there could not be placed and the request was blocked. Give Akto the flat texts only, as on main, where the mask is applied. * fix(bedrock): list request-field texts last again, as on main Flat texts listed additionalModelRequestFields strings right after the system prompt, so flat-text guardrails got a different order than on main. Akto's live verdict on a benign request depends on that order and started blocking it. Rows already keep their own order and texts that do not follow rows are dropped, so the flat texts go back to main's order. * fix(bedrock): list request-field texts next to their row for row readers Row readers that rebuild texts from rows could swap a masked request field with a masked user turn when the field strings came last. Row readers now get those strings right after the system prompt, where their row sits. Flat-text guardrails keep the end-of-list order. * fix(bedrock): list nested request-field texts in their row's order for row readers Row readers that rebuild texts from rows could write a masked request field onto a sibling key when the field held nested objects, because the texts walked the fields depth first and the row breadth first. Row readers now get those strings in the row's own order. * fix(bedrock): walk request fields breadth first instead of resolving each path Resolving every request-field string by its path revalidated the parent object per string, so a wide additionalModelRequestFields took seconds. The string walk now goes breadth first for row readers, the same order the row lists them, in one pass. A unit test pins that texts and rows map the same way when the vendor masks them differently. * fix(bedrock): fail closed when a vendor's rows drop a mask its texts carry When a vendor returns changed rows whose texts do not line up with them, the rows win. A text the vendor masked on a field its rows left untouched (a tool call argument, say) then reached Bedrock unmasked. Refuse that rewrite instead, unless the edited body holds the changed text elsewhere, as texts rebuilt from the rows do. * fix(bedrock): read same-length texts by position when rows drop a mask Matching a dropped text against any string of the edited body let a row mask on another field with the same token cancel it, so the raw tool call argument still went out. Texts of the length that was sent are now checked position by position, and only texts of another length, which cannot be placed, are matched by value. * test(bedrock): count each rebuilt text against the rows' edits * fix(bedrock): keep PromptGuard on flat texts and refuse texts that cannot be placed PromptGuard rebuilds its returned texts from the user rows, and a vendor that does so while masking only row content let a card in a tool call argument reach Bedrock, which main masked. PromptGuard now gets the flat texts as on main, and a reply whose rows changed next to texts of another length fails closed, since what those texts mask cannot be checked. * test(bedrock): let the structured checks decide the unsafe tool call and missing row cases The replies in these tests carried texts that no longer matched the sent texts, so the new refusal for texts that cannot be placed answered first and the tool call and row count checks went unexercised. * fix(bedrock): send request field strings to row readers after the conversation, where main puts them Row readers got the additionalModelRequestFields row before the conversation. Singulr judges only the latest user message, so an injection in a request field that main blocked reached Bedrock. The row and its texts now come last, as main sends those strings in texts * test(bedrock): pin nested request field order for flat readers and the texts view Both request field tests now use nested fields whose depth-first and breadth-first orders differ, so a flat reader getting the row order or a texts view written in main's order fails * refactor(bedrock): ask each guardrail whether Converse should hand it rows Converse picked the guardrails that get only the flat texts from a list of provider names. CustomGuardrail now has supports_structured_messages, True by default, and the ten classes on that list override it to return False, so the handler asks the guardrail instead of the list. Every guardrail class gets the same answer as before * fix(bedrock): keep Singulr on flat Converse texts as on main Singulr wraps each flat text as a user message and its backend judges only the latest user message, so on main a Converse tool result was the turn it judged. Handed rows, the tool result arrives with role tool and the latest user row is the original question, so an injection in a tool result reached Bedrock. Singulr now returns False from supports_structured_messages * fix(bedrock): keep Qualifire on flat Converse texts as on main Qualifire builds its evaluation input from the last user row only when that row's content is a string, so Converse rows (content-block lists) gave an evaluation_id policy an empty input. Opt it out of structured messages so it gets main's flat texts again. * fix(bedrock): keep Conduct on flat Converse texts as on main The conduct-litellm-guard plugin scans only user rows, so with Converse rows a system prompt, assistant text or tool result left its scan. Opt it out of structured messages so it gets main's flat texts again, all sent as user turns. * fix(bedrock): refuse tool removals that leave a toolConfig Bedrock rejects A guardrail that removed every tool, or the tool toolChoice names, produced an empty tools list or a dangling toolChoice that Bedrock answers with an opaque ValidationException. Refuse those rewrites before Bedrock, like other tool changes this route cannot carry. * fix(bedrock): list request-field strings in main's order for every guardrail The request-field row walked additionalModelRequestFields breadth first, and guardrails that get rows had their texts follow that order. With two or more strings the last text changed, so Ovalix, which blocks only when the last text is flagged, rewrote an injection main blocked. The row now walks the fields in the order main lists them in the texts, and the texts use main's order for every guardrail. * test(bedrock): pin a container's own request-field strings before its nested ones * test(bedrock): pin tool_policy and MCP end-user filtering on Converse tools * fix(bedrock): rebuild Converse tools the guardrail changed and drop blanked text like chat A guardrail that adds, renames, reorders or retypes a tool now gets its list sent to Bedrock, converted the way chat completions converts tools, with cache points kept behind their tool. When every tool is removed, toolConfig is dropped and earlier tool calls and results become text, as chat does. Text blocks a guardrail blanks are dropped, and a turn left empty fails closed. The body copies used to place edits are JSON copies, so deeply nested tool JSON no longer hits the recursion limit. * test(bedrock): keep the client tool schema on Converse masks and refuse deep JSON text drops cleanly * fix(guardrails): keep Airia on flat texts for Bedrock Converse so its redactions still apply * fix(guardrails): a pipeline step whose rewrite the endpoint cannot apply follows on_fail on_error: allow sent the original request on when the endpoint refused a guardrail rewrite it could not apply, so a Converse request the guardrail masked reached Bedrock raw. The refusal now counts as a fail on every endpoint. * fix(guardrails): a pipeline step blocks when its rewrite cannot be applied, whatever on_fail and on_error say A rewrite the endpoint cannot apply is neither a content verdict nor a technical error, so on_fail next or allow and on_error allow must not send the original request on * test(bedrock): trim the Converse guardrail tests Drops the chaos and per-vendor integration cells and the duplicate unit cases, and leaves the existing handler tests as they are on main * fix(bedrock): put Converse request-field strings in the latest user message for row readers A guardrail that judges only the latest user message, like Noma v2, read the additionalModelRequestFields row placed after the conversation, so a request with extended thinking or anthropic_beta sent the user's card to Bedrock and let an injection through. Those strings now follow the latest user message's text in its row, and get a user row of their own only when no user message has text. * fix(bedrock): create toolConfig when a guardrail adds the first Converse tool A guardrail that returned a tool for a Converse request with no toolConfig got a 500, since the tool write-back only rebuilt an existing list. It now creates toolConfig, as chat completions does. Guardrails that get only flat texts have their returned tools ignored, as on main. * test(guardrails): check Converse flat-text opt-outs without patching apply_guardrail Each built-in opt-out is checked through its own supports_structured_messages(), the handler's flat path through an injected scripted guardrail, and Headroom through the real class, which would call its unreachable service if it got rows * fix(guardrails): keep Converse tool matching and write-back linear in request width Matching each returned tool rescanned the whole tool list, and every write-back step revalidated the list or object it passed through, so a request with thousands of tools or request fields held the event loop for seconds. Returned tools are now looked up by name in one index, and path steps read the child directly. * fix(guardrails): let later Converse guardrails scan guardContent text When a guardrail removes every tool on a Converse request that carries guardrailConfig, tool results are rewritten as guardContent blocks. The flat text extractor read only top-level text, so a later masker in the same request never saw them and a card in a tool result reached Bedrock unmasked. Chat completions and /v1/messages keep tool results visible to later guardrails after the tools are removed. The extractor now reads guardContent text the same way the structured rows already do, so flat-text guardrails also scan guardContent text a caller sends. * fix(guardrails): keep caller guardContent out of Converse flat texts and send tool history as plain text Flat-text guardrails read only top-level text blocks again, as on main. A guardContent block after the caller's text no longer becomes the last text, so guardrails that judge the last text (HiddenLayer, Qualifire) check what the caller wrote When a guardrail removes every tool, earlier tool calls and results are now written as plain text blocks instead of guardContent. Later guardrails still scan and mask them, and Bedrock's own guardrail keeps checking every message, since a single guardContent block makes it skip all plain text * fix(guardrails): split a newline-joined Converse message back into its parts A vendor that returns a multi-part message as one newline-joined string now has its masks written back to each part, guardContent included, when no part sent already holds a newline. * fix(bedrock): keep Converse tool history as tool messages until send and match chat on emptied turns When a guardrail removes every tool, the tool calls and results stay tool messages for the guardrails after it, so a scan_only_tool_results guardrail still masks them. The toolConfig with no tool is dropped and the history turned into text only when the request is signed, as chat completions does at request build. A caller's toolConfig with no tool gets the same treatment, where Bedrock returned 400 Request-field strings join the latest user message that has text when a tool-result-only turn follows it, so a guardrail judging only that message reads the caller's text with them A turn the vendor empties entirely is sent as written, as on main and chat completions, instead of a 500 Messages go to the guardrail through a JSON round trip instead of a deep copy, and a reply that changes nothing returns early, so a long Converse conversation guards about as fast as on /v1/messages --------- Co-authored-by: moyai-devin-berriai[bot] <336287033+moyai-devin-berriai[bot]@users.noreply.github.com> Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yuneng-jiang
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fc27dfdc7d
|
test(integration): give each owned proxy a private database and Redis by default (#47213)
* test(integration): give each owned proxy a private database and Redis by default owned_proxy and owned_proxy_process now boot on a scratch database and a per-proxy redis-server unless the caller opts into the worker's state with state=OwnedState.SHARED_WITH_WORKER, passes its own DATABASE_URL, or strips DATABASE_URL. Under the replica profile the private database gets the writer and reader roles so the reader path stays covered. Gateway carries the database_url its proxy writes to, and the Scenario cleanup checks and delete_key_if_present read from it, so they keep checking something on a private database. The 189 test files with owned proxy sites that depend on the worker's database or Redis opt in with a shares_worker_state module marker, which a module-scoped conftest fixture turns into the shared state. The 7 helper modules with such sites opt in per call, since conftest fixtures and other test files reach them. * test(integration): reach session fixtures with the shared marker and keep the worker retry default The module-scoped fixture that applied shares_worker_state ran after session-scoped fixtures, so proxies those booted went private in marked files. The marker is now applied around each marked item's setup, call and teardown, which covers fixtures of every scope. Owned proxies on the worker database inherited the worker gateway's router_settings.num_retries of 0 from it. A private proxy now gets the same default in its config copy unless the config sets num_retries itself. Five providers files read the owned proxy's spend rows or response cache through the worker's database or Redis and opt into sharing. |
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devin-ai-integration[bot]
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4bebcdf757
|
fix(databricks): use the native Anthropic route for Claude /v1/messages (#46091)
* fix(databricks): use the native Anthropic route for Claude /v1/messages Claude models on a Databricks workspace were bridged through chat completions on /v1/messages, which drops the safeguards field and never returns safeguard_results. They now call the workspace's /serving-endpoints/anthropic/v1/messages, the proxy forwards the client's anthropic-beta and anthropic-version headers to Databricks deployments, and the beta header map marks dangerous-tool-use-2026-09-03 as supported there. Any route whose platform does not take that beta drops safeguards together with it instead of answering 400. Non-Claude Databricks models keep the chat completions bridge * fix(databricks): derive the dangerous-tool-use beta from safeguards instead of forwarding client betas A Databricks Claude serving endpoint answers 400 to betas Claude Code sends on every turn, prompt-caching-scope-2026-01-05 among them, so the proxy no longer forwards the client's anthropic-beta header to Databricks deployments. The beta the classifier needs is derived from the safeguards body field, the same way the Azure AI route derives it. The Rust beta-header mirror stops rejecting the Databricks dangerous-tool-use cell so its config-cell test matches the JSON again * fix(databricks): keep the billing block, build the messages route once, and pass the partner user agent * fix(databricks): route Unity Catalog Claude names to the gateway messages route and keep env credentials Unity Catalog model-service names now reach the AI Gateway's native Anthropic route instead of a serving-endpoints path that answers 404 for them, and the native route resolves the key and base through the same order the chat route uses, so environment-only deployments keep working. The native route is limited to Databricks foundation-model Claude names, an unlisted provider forwards safeguards and the beta as sent, and the ten betas the workspace rejects are marked unsupported for Databricks in the beta map and its Rust mirror. * test(rust): expect Databricks to drop the advanced-tool-use beta * fix(databricks): keep passthrough safeguards, partner user agent, and classifier fallback Only the OpenAI-compatible JSON providers drop safeguards now, so a deployment opted into /v1/messages passthrough forwards the field and the beta as sent. When a route drops safeguards and the proxy has a classifier model, the proxy answers the verdict itself, as the chat completions bridge did. The Databricks Messages route also reads the databricks_user_agent param like the chat route. * fix(databricks): drop rejected server tools under drop_params, short-circuit web search, route UC names from a full Messages URL * fix(databricks): drop container on the native Messages route, as the chat completions bridge did * fix(databricks): keep every api_base on a route the workspace serves and gate the derived beta on the map Gate the dangerous-tool-use beta derivation on the beta map the way #43955 does, so GitHub Copilot, DeepSeek, and opt-in passthroughs keep the merge base wire. Recognize /serving-endpoints/anthropic api_bases, fall back to the merge base routing for an api_base path the workspace trim does not know, strip Bedrock invocation metrics from the streamed message_stop, forward client betas to Databricks filtered by the map, and keep tool_choice when a dropped server tool has no name * test(databricks): cover the native Messages route, derived beta and JSON provider safeguards drop on the wire * test(anthropic): expect the dangerous-tool-use beta derived from safeguards next to the client's betas * fix(databricks): send client anthropic headers only on the native Messages route and keep SDK auth Databricks chat completions, called directly or through the Messages bridge, no longer receive the client's anthropic-beta and anthropic-version, as at the merge base. The native Messages route still forwards them, filtered by the beta map, without counting them as caller credentials, so a deployment with no api_key keeps falling back to Databricks SDK auth. A pathless api_base goes to the native route only for Unity Catalog names, matching the chat route. * test(databricks): cover SDK auth with client anthropic headers and a Unity Catalog workspace root on the native route * fix(databricks): keep client anthropic headers off an opted-in passthrough api_base A databricks Claude deployment whose api_base is no workspace route and that opts into /v1/messages with supported_endpoints falls back to the OpenAI-like passthrough. That config does not filter betas through the beta map, yet it received the native-only Databricks header entry, so Claude Code's prompt-caching-scope beta reached the upstream raw and got a 400 where the merge base answered 200. Each Messages config now says whether it is the provider's native route, the OpenAI-like passthrough says no, and the handler passes that answer when it reads provider-specific headers, so native-only entries reach only native configs. * test(databricks): cover client anthropic betas on an opted-in passthrough api_base A databricks Claude deployment whose api_base is no workspace route and whose model_info.supported_endpoints opts into /v1/messages takes the OpenAI-like passthrough. The cell sends Claude Code's prompt-caching-scope and context-1m betas through the proxy and asserts the caller gets the upstream's message and the gateway receives no anthropic-beta, as on the merge base. The scripted gateway answers 400 on the prompt-caching-scope beta, as the real one does * fix(anthropic): keep beta map loads off shared threads and build the safeguards evaluator only when the proxy classifies Unfiltered configs such as first-party Anthropic never read the beta map to decide safeguards support. Filtered configs await the map on its dedicated loader before reading it, on both the native and the MCP paths. The native handler decides after the upstream call and builds the evaluator only when the proxy classifies, so malformed messages keep the upstream 400. The Databricks dispatch tests assert the dispatched route instead of the config class, and the sail and Eden AI wire rows expect safeguards dropped, as JSON providers do. * test(databricks): cover a loaded beta map that marks the safeguards beta unsupported A remote beta map that nulls dangerous-tool-use for databricks while the bundled map allows it must send neither safeguards nor the beta to the serving endpoint, with or without a client beta, and the caller gets a 200 with no verdict. * test(anthropic): annotate the MCP beta map recordings as Final * fix(anthropic): drop safeguards on routes without the classifier only under drop_params A route whose upstream does not answer safeguards (a JSON provider, or a map that marks the dangerous-tool-use beta unsupported) now forwards the field and the beta as the merge base did unless drop_params is set or the proxy runs its own classifier, matching how litellm treats any unsupported param. Databricks keeps dropping both, since its merge base bridge never forwarded the field. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
Kerry Lu
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a6245c1bbb
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test(perf): measure SpendLog-backed UI query latency (#46955)
* test(perf): measure SpendLog-backed UI query latency * test(perf): validate every numeric row and both counter snapshots * test(perf): isolate read API cases and report request metrics --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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68a8b12ab3
|
fix(proxy)!: end a chat completions stream with an error event when a guardrail blocks mid-stream (#46953)
* fix(proxy): end a chat completions stream with an error event when a guardrail blocks mid-stream A streaming guardrail that raised an HTTPException after the first chunk re-raised it inside the SSE generator, so the 200 body was cut off with no finish_reason, no error event and no [DONE]. Build the error frame through sse_error_payload instead, keeping the status, message and structured detail, and encode values JSON cannot represent as strings so the frame always goes out. * fix(proxy): keep early guardrail blocks on their HTTP status and end mid-stream errors with [DONE] A guardrail block before the first chunk is raised again, so its response stays the 400 event-stream body it is on main. A mid-stream error event now carries the failure hook's replacement error when one is returned, is followed by data: [DONE] on OpenAI-format streams, and leaves the Gemini route's raise unchanged. Error details JSON cannot encode are written as strings instead of breaking the event. * fix(proxy): keep a per-chunk guardrail block on the first chunk on its event-stream 400 * fix(proxy): treat a keepalive ping as a started stream so a later guardrail block ends in-band * fix(proxy): encode non-JSON guardrail details in early stream error frames An early guardrail block whose detail holds a value JSON cannot encode sent 400 headers and then cut the body. The error frame now writes such a value as its string form, the same as the mid-stream event. The keepalive test now runs on a virtual clock instead of real sleeps. * fix(proxy): show an in-band stream error event as an error in the CLI, Python client, and dashboard playgrounds ChatClient.completions_stream raises StreamError on a data frame carrying an error, so litellm-proxy chat prints the block reason and keeps the partial reply out of history. The prompt playground and the Interactions playground read the same frame through a shared streamErrorMessage helper. Adds the audit's integration cells for chat stream error events, and makes the endpoint tests' failure-hook mocks return None so they check the real error payload. * fix(proxy): print stream error text verbatim in the CLI and read split error events in the prompt playground The CLI printed the server's error message as Rich markup, so a closing tag like [/pii] in a guardrail message crashed lite chat. The prompt playground now buffers partial lines across reads, ChatClient raises only on a non-empty error, StreamError redacts keys like the other client exceptions, and StreamError is exported from litellm.proxy.client * fix(proxy): print CLI stream errors without Rich highlighting or emoji codes * test(proxy): cover the CLI's plain-text 400 error print * test(proxy): annotate the new client stream test locals as Final * fix(proxy): send the failure hook replacement on Responses streams and before the first chunk * fix(proxy): end a Responses stream with response.failed when it fails on a built-in exception * fix(proxy): keep a failure hook replacement before the first chunk in-band so bare stream routes still answer it A non-HTTP failure whose hook returns an HTTP error was raised before the first chunk, which create_response turned into the hook's status but which a bare StreamingResponse route such as /queue/chat/completions answered as a 200 with zero bytes. Only the original HTTP errors raise now. The replacement goes out as the stream's error event, which create_response still answers with the hook's status as a JSON error. Adds audit round 2's integration cells for failure hook replacements on chat and Responses streams, built-in exceptions on Responses streams, and the Python client. * test(proxy): cover a queue stream whose failure hook replaces a failure before the first chunk The audit cell posts to /queue/chat/completions with a callback that raises before the first chunk and a failure hook that answers 403. It checks for a 200 event stream whose only event is the replacement, then one [DONE], with the callback's own message absent |
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ryan-crabbe-berri
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52023601a8
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test(baseten): annotate test locals with Final (#47205)
Follow-up to #40152. Greptile flagged the two new reasoning_effort tests for assigning config and result without Final. Annotates every local in the file so it passes the LIT010 check cleanly |
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Yousef Joe
|
13335266f5
|
fix(baseten): forward reasoning_effort to Baseten Model API (#40152)
* fix(baseten): forward reasoning_effort to Baseten Model API Baseten's Model API accepts a top-level reasoning_effort parameter and validates it server-side (https://docs.baseten.co/inference/model-apis/reasoning), but BasetenConfig.get_supported_openai_params did not list it, so LiteLLM silently dropped it when drop_params=True and raised UnsupportedParamsError otherwise. Add reasoning_effort to the supported params so map_openai_params forwards it verbatim, mirroring the hosted_vllm and deepseek providers. Baseten returns a clear 400 for values outside a model's supported set and ignores the parameter on non-reasoning models, so verbatim passthrough is safe for the whole catalog. [Claude_Code] * fix(baseten): forward reasoning_effort to Baseten Model API Baseten's Model API accepts a top-level reasoning_effort parameter and validates it server-side (https://docs.baseten.co/inference/model-apis/reasoning), but BasetenConfig.get_supported_openai_params did not list it, so LiteLLM silently dropped it when drop_params=True and raised UnsupportedParamsError otherwise. Add reasoning_effort to the supported params so map_openai_params forwards it verbatim, mirroring the hosted_vllm and deepseek providers. Baseten returns a clear 400 for values outside a model's supported set and ignores the parameter on non-reasoning models, so verbatim passthrough is safe for the whole catalog. --------- Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> |
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Mateo Wang
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75faaeab64
|
feat(count_tokens)!: configurable token_count_framing and token_count_model, count source in the response (#46777)
* feat(count_tokens): configurable token_count_framing and token_count_fallback_model, count source in the response
Count endpoints now say where each number came from in litellm_count_source
(provider, fallback_model, local_estimate) and name the fallback model in
litellm_count_fallback_model. When the provider answers 400 or 404 for a model
it cannot count, the proxy counts the same request with token_count_fallback_model.
token_count_framing adds a per-model constant to local estimates. Both settings
ship in the cost map for Bedrock Claude Sonnet/Haiku 5.5, Bedrock Kimi K3, and
Databricks Kimi K3 and DeepSeek V4 Flash, and can be overridden in model_info.
* test(count_tokens): classify the new cost map keys in the schema generator and expect count_source in exact-shape tests
* test(count_tokens): e2e matrix against real inference and integration wire tests for count source, fallback and framing
* fix(count_tokens): keep the provider error when local counting is disabled, name the fallback error, keep settings in cooldown
Moves the cost map lookup off the event loop
* ci(cost map): bound token_count_framing to non-negative and token_count_fallback_model to non-empty
* fix(count_tokens): keep a failed fallback's own status, ship fallbacks only where they count, add system.ai framing
A fallback model that answers 404 returns the requested model's error unchanged, and any other fallback
failure answers with its own status and message. The eu, apac, au and jp Sonnet and Haiku 5.5 entries
no longer ship the Opus 5.5 fallback, which AWS serves only in us-east-1. A configured empty
token_count_fallback_model turns the shipped fallback off. Config framing accepts whole-number floats and
rejects negatives. The Databricks system.ai Kimi K3 and DeepSeek V4 Flash names carry the same framing as
their serving endpoint names, and the e2e matrix runs on Bedrock Haiku 5.5 too.
* test(databricks): list the system.ai Kimi K3 and DeepSeek V4 Flash names with their published DBU rates
* fix(count_tokens): skip a token count fallback model for an hour after it answers 404 on that deployment
A us. or global. Sonnet 5.5 deployment outside us-east-1 paid two extra Bedrock
calls and about 200 ms on every count, since Opus 5.5 cannot count there either.
The Kimi K3 e2e also runs the large system prompt shape now.
* test(count_tokens): use the shared integration port allocator in the fallback wire test
* fix(cost map): keep the Databricks system.ai entries to framing only, so their spend stays as today
* test(count_tokens): cover the fallback 404 skip, inference profile base_model, shipped framing, and deployment-scoped token count settings
Adds integration cells for a fallback model that answers 404, an application inference profile ARN deployment whose base_model carries the shipped fallback, and the shipped Kimi K3 framing with token_count_framing 0 turning it off. Adds unit tests that keep token count settings off the shared backend cost map key and that store a token count setting on PATCH only when the operator changed it
* fix(count_tokens): keep the counter's own 400 and the provider's error when a framed estimate cannot read the body
A 400 the OpenAI or Gemini counter returns before calling the provider is the caller's answer, so it no longer tries the fallback model or a framed estimate. When the framed local estimate cannot read a content block, the client gets the provider's error instead of a 500. Adds /audit round 2's integration tests for the fallback leg's converted body, the Anthropic envelope on fallback failures, and both framing cases.
* fix(count_tokens): answer the provider's error whenever a framed estimate raises
A tool schema the local estimate cannot format, such as a boolean subschema or an array with no items, raised AttributeError or KeyError and answered a 500 on a framed deployment whose provider had already answered 400. Any estimate failure now answers the provider's own error.
* fix(count_tokens): load the custom tokenizer inside the guarded framed estimate
A framed deployment whose custom_tokenizer could not load answered a 500 or 503 where the provider had already answered 400, because the tokenizer was selected outside the estimate's error handling. Selecting it inside answers the provider's error, logs a warning with the estimate's error, and still raises the Rust extension's fork-guard errors.
* fix(count_tokens): look up framing under the deployment's custom_llm_provider
* fix(count_tokens): share one tokenizer download per worker and bound the wait after a provider error
A framed deployment whose custom_tokenizer is not cached locally made every
count wait out the Hugging Face hub's whole retry cycle when the hub could not
be reached, and a burst of those loads held the default thread limiter that
every count's cost map lookup also needs. Counts on a worker now share one
download per minute, a failed download is reused for the rest of that minute,
and after a provider error a count waits at most 5 s before answering the
provider's error. The swallowed-estimate warning logs only the error type.
* fix(count_tokens): keep one tokenizer download per key running and load the local route per request
* test(count_tokens): type the scripted counter and wait on events instead of wall time where possible
* fix(count_tokens): run framed tokenizer loads on their own capped threads and answer a tokenizer panic with the provider error
Framed loads used anyio's default 40 threads, so a hub that drops packets let about 40 hanging loads stall every count on the worker. Loads now run on a per-worker pool of 4, and a count that finds 4 loads running answers the provider's error at once. Any finished load is reused for a minute after it finishes, so a load that fell back to tiktoken is not retried on every count. A pyo3 PanicException from the tokenizer, while loading or counting, now answers the provider's error instead of a plain 500
* feat(proxy): count tokens with token_count_model in its pinned region
token_count_model {model, region} replaces token_count_fallback_model. Bedrock Sonnet and Haiku 5.5 count with Opus 5.5 in us-east-1, or Melbourne for au. profiles, and a failure names the region to enable
* fix(count_tokens): use the renamed count model constant in the e2e test and list token_count_model with the object keys
* fix(proxy): keep the upstream error envelope when adding the region hint
* test(proxy): annotate the token count setting echo test
* ci(cost map): require non-blank token_count_model strings and test the region rules
* fix(count_tokens): answer the token_count_model's error even when framing is set
* fix(count_tokens): keep the provider error for Google contents instead of a framed estimate
* fix(count_tokens): estimate Google contents the route translated to messages
* fix(count_tokens): keep the provider error for Google contents with non-text parts
* test(proxy): drive tokenizer wait and cost map tests by events and a skippable loop clock
* fix(count_tokens): check Google text-only parts without a KeysView comparison basedpyright flags
* feat(proxy): count Bedrock Kimi K3 tokens exactly with its pinned tokenizer
* test(bedrock): keep the Kimi K3 image check in-process
* fix(count_tokens): join the region hint to an upstream message that already ends with a period
Also pins that a blank token_count_model in config falls through to the cost map default
* fix(count_tokens): count Gemini tool-call turns and Responses tools, and stop tokenizer retries from stalling counts
The Google countTokens route estimates contents with functionCall, functionResponse and thoughtSignature parts again, and still answers the provider error for media, thought and unknown parts
The Kimi K3 exact count renders Responses API and Anthropic tools through the chat tool translation the Vertex count already used, now shared from the prompt factory, and falls back to the estimate when the Converse body cannot carry a tool
A failed Kimi K3 tokenizer load is retried in the background without making counts wait, and a load still running after the wait logs a warning
* fix(count_tokens): count Kimi K3 tools the way Bedrock inference sends them
A flat Responses API function or custom tool becomes the chat tool the
Responses route sends to inference, and every other tool goes to the
Converse body as is, so hosted tools Bedrock drops are left out of the
count instead of being counted or turning it into an estimate.
* test(count_tokens): annotate new token counter test locals with Final
* fix(count_tokens): keep shipped count regions inside the deployment's geography
A plain Bedrock Sonnet or Haiku 5.5 deployment in a non-US region no longer sends its count prompt to us-east-1. Global count models and configured token_count_model keep their pinned region. Also expect litellm_count_source in the Vertex Llama count test main added
* fix(count_tokens): resolve count settings off the event loop
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joshua-berri
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3fb95c8fe3
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fix(batches): preserve spend across malformed output and metadata failures (#47184)
* fix(batches): preserve recoverable spend and terminal reconciliation state * fix(batches): narrow validated envelope types * fix(batches): serialize stored metadata when releasing claims * fix(batches): sanitize metadata before claiming completed batches * refactor(batches): expose existing metadata serializer for shared use --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |