mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-18 04:44:37 +00:00
135 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
devin-ai-integration[bot]
|
859529ccbc
|
fix(realtime)!: keep turn_detection.type when a transcript guardrail disables auto-response (#44939)
* fix(realtime): keep turn_detection.type when a transcript guardrail disables auto-response * test(realtime): type the GA session.update helper and mark its locals Final * test(realtime): expect typed turn_detection on the voice guardrail session updates * test(realtime): accept either gate path for the client's first voice session.update * fix(gemini): drop VAD tuning when the guardrail turns auto-response off * fix(gemini): check create_response without a redundant isinstance * fix(vertex_ai): keep the client's VAD tuning on Vertex when auto-response is off * fix(realtime): keep the transcript-guardrail gate typeless on xAI so it does not auto-answer * fix(realtime): replace any client turn_detection with the untyped gate on xAI xAI answers on its own under a typed server_vad even with create_response off, so a client's typed turn_detection must not reach xAI while the transcript guardrail gates the turn. * fix(realtime): keep a client's early turn_detection and hide the guardrail cancel's error A session.update sent before session.created reached OpenAI ahead of the proxy's own gate, so the gate replaced the client's turn_detection. The proxy now re-sends the client's gated turn_detection after the gate when the two differ. The guardrail's response.cancel now carries a litellm_guardrail_ event_id, and the error OpenAI returns for it (response_cancel_not_active when nothing is answering) no longer reaches the client. * fix(realtime): send the guardrail gate before a client's early session.update and let Muse and Chirp fall back to server_vad under drop_params * fix(realtime): hide only the cancel errors that echo the guardrail's own response.cancel ids Track the event ids of the guardrail cancels this session sent and drop a backend error only when its error.event_id is one of them, so a client's own event id with the same prefix still gets its error. Cover the scripted response.cancel reply in the integration upstream, update the stale list-mode test to the typed gate, and mark the xAI cancel error cell as an expected failure, since xAI replies with its own event id. * test(integration support): deliver the disconnect a real server sends after the client goes away The scripted upstream now keeps reading frames after a reply hits a closed socket, so the unit test's fake ASGI receive has to end with the websocket.disconnect message a real server delivers. * fix(realtime): delete the blocked transcript item before the guardrail's refusal turn OpenAI GA answered the blocked question in some refusal turns because the blocked user item stayed in the conversation. The raw OpenAI-protocol path now sends conversation.item.delete for it right after the cancel. Provider config backends have no such event, so that path no longer passes an item id. Also bound the guardrail integration file by the shared graceful-stop timeout and make the xAI cancel-error xfail require an AssertionError. * fix(realtime): keep the blocked item on xAI, which cannot delete an audio input item * test(realtime): assert the xAI voice block sends no conversation.item.delete upstream --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Ayush Shekhar
|
7930c0e434
|
feat(router): mid-stream fallback continuation for chat completions (#41127)
* feat(router): continue chat streams on fallback after a mid-stream break A chat-completions stream that breaks after content has been delivered was re-raised and the fallback deployment never ran (#40404), while the Responses-API path already continues via a prefilled assistant turn. Add the same for chat completions behind `enable_mid_stream_fallback_continuation` (opt-in, async-only), so a post-content break re-enters the fallback chain with the partial text as an assistant prefill instead of surfacing the error. Continuation is only attempted when it is safe: the stream emitted plain assistant text (no tool/function calls, thinking blocks, reasoning items, audio or images), the request is not constrained output (response_format or a forced tool_choice) and not merge-reasoning mode, and the fallback target's model supports assistant prefill. The target check runs as a deployment pre-call filter, so a chain with no prefill-capable deployment empties and the original error is surfaced rather than a duplicated or rejected request being sent. Plain reasoning_content is treated as out-of-band and does not block a continuation, matching the Responses-API path; Anthropic thinking is excluded because its signed thinking blocks cannot ride a text-only prefill. CustomStreamWrapper now latches whether a disqualifying delta was streamed and carries it on MidStreamFallbackError so the router can decide without re-scanning chunks. Default behavior is unchanged. * fix(router): harden mid-stream continuation eligibility and prefill folding Address review of the mid-stream continuation: - keep response_format={"type": "text"} eligible; only json_object / json_schema and other structured formats decline, since "text" is the unconstrained default - fold a new partial into an existing trailing assistant prefill instead of appending a second one, so a nested mid-stream break stays a single prefill turn even on providers that do not merge consecutive assistant messages - split the deployment prefill-capability lookup into two readable steps - cover the merge_reasoning decline branch, the text response_format pass, and the prefill-folding path with tests * fix(router): address review of mid-stream continuation - register the continuation deployment filter as a process-global singleton instead of tracking it per router, so discarding one router no longer removes the type-deduplicated filter that other live routers still depend on - disqualify reasoning_content: it reaches the caller as visible reasoning that a text-only prefill cannot carry, so a reasoning-capable fallback would re-derive it and produce an incoherent response - move MID_STREAM_CONTINUATION_KWARG to constants and lazy-import the filter class in the router, breaking the module-level import cycle CodeQL flagged - trim the added docstrings to the repository comment policy * fix(router): keep mid-stream continuation within the type-discipline budget The LIT* type-discipline gate counts mutable-collection use the ruff pass does not see. Annotate the reads with read-only Sequence/Mapping views, compare response_format against a frozen MappingProxyType sentinel, and mark the genuinely mutable litellm message and deployment-list boundaries with mutable-ok reasons, so LIT001 and LIT002 stay under their limits. * fix(router): clear basedpyright operator error and the import-cycle alert - narrow the accumulated stream delta content to str before appending, so the shared helper no longer trips reportOperatorIssue (str += object) - keep the continuation marker in the pre-call-check module and lazy-import it at the two router call sites instead of routing it through constants, so the router has no module-level dependency that closes an import cycle * fix(router): make the mid-stream continuation marker unforgeable The deployment filter is process-global and read the continuation marker as a truthy value from request_kwargs. Since the proxy can forward arbitrary request-body fields into the router, a client could send `"_mid_stream_continuation": true` on a normal request to filter a mixed model group down to prefill-capable deployments and steer their prompt onto that provider. Set a private sentinel object internally and check it by type instead of truthiness: a JSON request body cannot construct one, so a forged flag is ignored. Adds a regression test that forged client values (true, "true", 1, a dict) leave the deployment list untouched. * feat(router): honor per-deployment model_info for continuation prefill capability The deployment filter only read supports_assistant_prefill from the cost map, so a model configured with `model_info: {supports_assistant_prefill: true}` on the deployment (the usual way to declare a capability for a model that is not in the cost map, or to override it) was wrongly skipped. Check the deployment's model_info first (True or False both authoritative), then fall back to the cost map. Adds tests for the override in both directions. * fix(router): bound mid-stream continuation to the caller's output allowance A continuation handed the fallback the full max_tokens / max_completion_tokens even though part of the answer had already streamed, so the total could exceed the caller's limit and repeated interruptions could re-grant the allowance on every hop, amplifying spend. Reduce each configured ceiling by the tokens already emitted before re-entry, and decline the continuation (surface the original error) when the allowance is exhausted. The reductions compound across hops because the trimmed ceiling is what the next hop sees. Also add the missing explanatory comment on the model_info fall-through except. * refactor(router): extract mid-stream decline to stay within the complexity budget The output-ceiling branch pushed stream_with_fallbacks past the C901 limit. Both decline paths (ineligible after content, and exhausted allowance) re-raised the original provider error the same way, so pull that into _raise_original_mid_stream_error. It removes the duplicated branching, drops the function back under the complexity ceiling, and reads clearer. * fix(cost-map): mark Claude Sonnet 4.6 as not supporting assistant prefill The Anthropic API and Bedrock both reject an assistant-last message on claude-sonnet-4-6 with "This model does not support assistant message prefill", so the continuation filter must not route a mid-stream continuation to it. Flip the flag on every claude-sonnet-4-6 entry and on databricks' opus-4-6 entry, whose anthropic sibling already says false. The prefill check tests now register synthetic cost-map entries instead of pinning real models, which broke when claude-3-opus left the map. * refactor(router): type the streaming fallback kwargs and drop narration comments Removes an unused mutable-ok marker (LIT013) and the explanatory comments Greptile flagged, and types initial_kwargs on both streaming fallback iterators as dict[str, Any] like their Responses siblings so the new continuation helpers take a typed mapping and the basedpyright budget stays at or below main. * fix(router): run post_call_rules over the joined text of a continued stream Each provider stream wrapper validates only the fragment it produced, so a rule that trips on the primary's prefix plus the continuation's text never fired. The router now feeds litellm.post_call_rules the emitted text plus every continuation chunk before yielding it. Also drops a narrating test-helper docstring. * refactor(router): read a deployment's declared prefill support without an empty except * test(router): cover _raise_original_mid_stream_error directly for the router coverage gate * fix(router): fold the continuation prefill into a trailing assistant turn A request that already ends in an assistant message took the partial output as a second assistant turn, which prefill providers reject. The partial now extends that turn in place, with prefix set, so the continuation request never ends in two assistant messages. * fix(router): keep content blocks on a trailing assistant turn when extending it with the partial output A trailing assistant message whose content is a list of blocks was stringified before the partial output was appended, so the fallback received the Python repr of the blocks as its prefill. The partial now lands as one more text block, and a content shape that cannot be extended surfaces the original stream error instead of a garbled continuation. * fix(router): continue the trailing text block in place when extending a prefill with content blocks Anthropic strips trailing whitespace per content block, so appending the partial output as a new text block after an assistant turn that ends in "Sure, " would send the prefill as "Sure,here". The continuation now extends the last text block itself and only opens a new text block when the trailing block is not text (an image, say), so the prefill reads exactly as the text the caller already received * test(router): type the prefill check's deployment helper * fix(router): keep a continued stream on the primary's identity and widen what can continue Continuation chunks now keep the primary stream's id and created time and drop the fallback's second role delta. Requests with n > 1 no longer continue. Prefill support reads model_info.base_model and litellm_params.base_model before the deployment model. enable_mid_stream_fallback_continuation is runtime-updatable through update_settings and registers the prefill filter. The bridge rebuild of MidStreamFallbackError keeps the inner stream's disqualifier. A fallback that repeats the prefill is not doubled in the continuation input. * fix(router): decline mid-stream continuation after a content filter, merged reasoning, or a plain trailing assistant turn Also count the partial stream's completion tokens in the continued stream's usage, accept string values for enable_mid_stream_fallback_continuation, and list the new router helpers in the router coverage check * fix(router): drop re-emitted whitespace at the continuation join and sum both legs' usage consistently * fix(router): keep the full prefill so a nested break keeps the separator * fix(router): bill both calls of a continued stream and fix Bedrock Converse, Databricks, and Vertex DeepSeek prefill routes * test(router): integration cells for mid-stream fallback continuation Covers the continued stream the caller gets, the prefill turn and reduced max_tokens the fallback receives, the prefill-capability filter, the runtime toggle, repeated requests through the response cache, and the spend row of a continued stream * fix(router): log a continued stream under the caller's id, price each cut on its own prompt, and keep prefill flags per deployment * fix(router): price and log each cut leg before its continuation reuses the logging object Price the cut stream's partial and let its failure log finish (bounded) before the fallback overwrites a shared proxy logging object, and skip continuation when an output guardrail is registered or a request fallback carries its own messages. * test(router): integration cells for the output-guardrail and request-fallback declines and the cut call's failure log Cover a registered post_call guardrail and a request fallback carrying its own messages keeping the original error, an input-only guardrail leaving continuation on, and the cut call's failure event naming its own deployment and price. * fix(router): skip mid-stream continuation when a request fallback overrides request params A request-body fallback entry that sets anything besides model (stream, messages, max_tokens) changes the request the prefill continuation relies on, so a stream: false entry ended the answer with no error. * fix(router): skip mid-stream continuation for a fallback group target that overrides request params The override check read only top-level fallback entries, so a target inside a group's list, such as {"primary": [{"model": "backup", "stream": false}]} in the request, the config, or a key's router settings, still ended the answer with no error. * fix(router): check only the request's own fallback chain for request param overrides Scanning every group's chain turned continuation off for all requests when one unrelated group's target overrode a param. Resolve the chain the router will use for this request, the same way the fallback path does. * fix(router): keep the original error when request fallbacks are malformed The request-param override check resolved a standard-shape chain outside the router's own error handling, so an empty entry or a non-list chain raised StopIteration or TypeError and replaced the original mid-stream error. Skip empty entries and treat a non-list chain as no chain. * fix(router): read deployment-configured constraints, decline deployment-id fallbacks without prefill, strip only the last Bedrock text block * fix(router): read a deployment's tool_choice ahead of router defaults in the continuation check, and restore the renamed prefill test names * fix(router): filter continuation deployments on every routing strategy, read extra_body and fallback deployments' own params The deployment filter now also runs on the sync selection path that usage-based-routing v1 and custom strategies use, one predicate reads n, response_format, tool_choice and merge_reasoning_content_in_choices with extra_body laid over them, fallback deployments whose own config constrains the output are skipped, and continuation declines when the cut deployment's id is not in the router (per-request credential deployments) * fix(router): decline a cut mid-echo, reduce extra_body output limits, and check post_call_rules once on nested continuations --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Marcus
|
642a2ad934
|
fix(prompt-templates): treat initial_prompt_value and final_prompt_value as optional (#39762)
* feat(dd_span_tagger): emit litellm_user_email span tag for JWT-authenticated requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(dd_span_tagger): use dotted litellm.user_email tag for consistency Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(model_hub): surface model_info.description in model group info and Model Hub UI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): aggregate model group description without in-place mutation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(prompt-templates): treat initial_prompt_value and final_prompt_value as optional completion() only stores a custom prompt template key when its value is truthy, and custom_prompt() already defaults both prompt values to "". Seven call sites read them back with direct indexing, so a template that sets only roles raises KeyError instead of rendering, on ollama, petals, vllm, predibase, codestral, anthropic text completion, and response_schema_prompt. Five other providers already read defensively, so the same config works on sagemaker and 500s on ollama. Read both keys with .get(key, ""), matching those five providers and the defaults custom_prompt() already declares. Templates that set all three keys render exactly as before. * test(prompt-templates): drop the explanatory docstrings from the optional-keys regression test * fix(health): skip background health check DB writes when the latest-row read fails Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(prompt-templates): read every custom prompt template field through one typed helper Templates that omit roles, initial_prompt_value, or final_prompt_value now render with empty defaults across every provider that reads them, instead of failing with KeyError or TypeError. A field with the wrong type fails with a validation error naming it. * fix(prompt-templates): keep templates that rendered on main working and fail bad fields with a 400 Only the roles a request uses are checked, null fields mean unset, and extra role keys are ignored, so every template main rendered renders the same. A wrong-type field now raises a BadRequestError naming it. initial_prompt_value is declared litellm-owned so it stays out of provider request bodies, like final_prompt_value. The template tests move into the mapped factory test file, and the predibase test asserts the real rendered prompt * fix(prompt-templates): keep bad-template failover and render non-mapping role entries like main A wrong-type template field now raises a 500 InternalServerError naming the field, so the router still retries and cools down the bad deployment. A roles value or role entry that is not a mapping renders unwrapped as it did on main, and role keys are only checked for roles a request uses. * fix(prompt-templates): warn when a custom prompt template roles value is not a mapping * fix(prompt-templates): keep deployments in rotation on a bad template and reject a template with nothing to render * test(integration): audit custom prompt templates across endpoints, providers, and chaos --------- Co-authored-by: milan <milan@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
4e49394ade
|
fix(anthropic): keep trailing user messages with None or empty list content (#41397)
* fix(anthropic): keep user messages with None or empty list content so requests do not end on an assistant turn Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): avoid mutable empty-list construction in sanitizer Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): avoid rebinding sanitizer input Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic): keep a trailing user turn whose content key was dropped litellm.completion strips None fields from every message before the provider transform, so a user message sent with content null reaches the Anthropic converter with no content key and raised KeyError. Read the content with get so that turn gets the same placeholder as an empty list. * fix(anthropic): only fill user turns with empty content and keep their cache_control * fix(anthropic): fill each empty user message in place like an empty string * fix(anthropic): add the placeholder only for an empty turn or a cache breakpoint, in linear time * test(anthropic): cover the tool-result guard with cache_control and widen the linear-time bound * test(anthropic): cover empty user turns through the proxy on chat, responses and messages * test(anthropic): type the empty user turn tests and move the linear-time check to a benchmark --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
moyai-devin-berriai[bot]
|
a3251a3bfd
|
fix(responses): preserve provider request IDs in streaming failure logs (#45453)
* fix(responses): preserve provider request IDs in streaming failure logs * fix(bedrock): log the provider request id for in-stream errors on bridged Bedrock streams Bedrock models that run through the chat-completions bridge (Claude, converse, invoke) raise in-stream failures from the event-stream decoder as a BedrockError without headers, so their failure logs kept error_provider_request_id null. The decoder now passes the stream's request-id headers to those errors. Both the native Responses iterator and the decoder pass only the PROVIDER_REQUEST_ID_HEADERS subset, so error responses and failure logs pick up the request id and none of the 200 response's other headers. MidStreamFallbackError gets its response at construction instead of mutating it afterwards. --------- 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> |
||
|
devin-ai-integration[bot]
|
91ae7d1b7d
|
fix(cost): bill Responses API image generation and usage reported web search as tool usage cost (#41877)
* fix(cost): bill Responses API image_generation_call tool usage Price completed image_generation_call output items from tool_usage.image_gen token counts when present, otherwise per image from the quality and size on the item, with the image model taken from tools[].model (default gpt-image-1). Add the result into tool_usage_cost alongside web search and file search, and read tool_usage from the top level or usage.tool_usage. Squashed and rebased onto main * style(cost): annotate read-only views and mark casts for the type discipline gate * fix(cost): price Responses image tool from tools[].model only, no default model or provider * refactor(cost): drop redundant status check, unread usage fields and unused output_type literal * test(cost): integration case for Responses image_generation tool token billing --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
Kerry Lu
|
5ed3629242
|
fix(responses): write one spend-log row per native WebSocket turn (#46768)
* fix(responses): write one spend-log row per native WebSocket turn Each response.create turn on a native Responses WebSocket connection logs and bills as its own call when its terminal event arrives, keyed by the resp_ id the client saw, instead of one row at socket close that sums every turn * test(responses): mock the websocket slot release and mark the per-turn start time naive * style(responses): keep the per-turn start time line within ruff format * refactor(responses): type websocket turn events as read-only mappings * fix(responses): settle the websocket budget reservation on the first turn only Every per-turn logging copy carried the connection's admission reservation, so two turns whose cost callbacks overlapped could both reconcile it. The first turn keeps the reservation and later turns charge their cost directly * fix(proxy): keep the legacy limiter's websocket slot until disconnect A websocket turn callback no longer decrements current_requests in the legacy limiter. The socket's slot is released once from arelease_max_parallel_requests_on_disconnect, which now dispatches to the legacy handler as well --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
38f3f8a1a9
|
fix(proxy): cooldown errors carry 429, Sentry starts at boot without request bodies, tracing failures log at ERROR, exporter counters exported (#46344)
* fix(router): carry a 429 status on cooldown and no-deployment errors RouterRateLimitError and RouterRateLimitErrorBasic had no status_code, so the proxy answered an all-deployments-cooled-down request as a generic 500 class error and logged the bug report notice for every cooldown. Both now carry status_code 429, which the proxy maps to a rate_limit_error and which the retry policy already treats as retryable * fix(proxy): start Sentry at config load and never attach request bodies Sentry only came up inside set_callbacks on the first LLM call, so boot and background errors were never reported, and general_settings.alerting: [sentry] always raised Missing SENTRY_DSN because the alerting path read a dead litellm.utils pin instead of the live SDK. The proxy now initializes Sentry once while loading the config whenever sentry is a configured callback or alerting target, the alerting and failure handlers read the live instance, and max_request_body_size is pinned to never so prompts and completions stay out of Sentry events * fix(tracing): log Lens failures at ERROR and export row counters to Prometheus A tracing store that fails to start, or a Lens service that rejects a batch, was only logged at WARNING, so operators running at the default ERROR level never saw tracing silently go dark. Both now log at ERROR and name the lens store. The exporter's rows_written and rows_dropped tallies were never exported, so the two counts are now Prometheus counters litellm_lens_export_rows_written_total and litellm_lens_export_rows_dropped_total, registered lazily on the default registry like the other proxy-level counters and charted on the all-metrics Grafana dashboard * test(proxy): drop the litellm import the alerting tests no longer use * fix(proxy): start Sentry once a DSN is set, from any config source Boot-time init used to lock Sentry in even with no SENTRY_DSN, so a DSN loaded later from DB environment_variables was never picked up. initialize_sentry now skips when no DSN is set and re-inits when the DSN changes. alerting_handler calls it too, so alerting: [sentry] set through the DB works. Drops the litellm_settings.callbacks check since that config fails to load with sentry in it * fix(sentry): keep stack frame locals out of events unless PII is opted in Frame locals carried prompts (messages, request bodies, provider options) into every error event. include_local_variables now follows SENTRY_SEND_DEFAULT_PII * test(router): drop the test that only pinned the 429 class attribute * fix(proxy): start Sentry at boot when it is only an input callback * fix(proxy): keep Sentry init and alerting within the strict ruff and basedpyright ceilings --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> |
||
|
devin-ai-integration[bot]
|
67bfb7a148
|
fix(ollama): prevent reused response ids from dropping spend logs (#39227)
* fix(spend_tracking): key completion spend logs off the per-call id so duplicate provider response ids cannot drop rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(spend_tracking): narrow the provider response id with isinstance instead of cast Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(gcs_bucket): store request payloads under the spend log request id so the logs viewer can find them Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(spend_tracking): validate callback fields for current type gates * test(gcs_pubsub): assert provider response id in spend metadata * fix(ollama): normalize reused response ids automatically --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
Mateo Wang
|
7090588bdf
|
feat(gemini)!: auto-continue gemini-4-argon long decodes with continuationToken (#46240)
* feat(gemini): continue MAX_TOKENS decodes with continuationToken across chat, streaming, and interactions * fix(gemini): continue long decode on CONTINUATION and cancel interactions at /cancel * feat(gemini): add public gemini-4-argon cost-map rows for AI Studio and Vertex AI Add gemini/gemini-4-argon and vertex_ai/gemini-4-argon alongside the existing early-access row, carrying the same limits and capabilities. The AI Studio row mirrors the early-access entry; the Vertex row differs only in provider and pricing source. Prices follow once Google publishes them. Extend the mapped metadata test to cover all three ids, that each routes to its provider, that its backup copy matches, and that the public rows carry the same limits as the early-access row. * feat(gemini): raise gemini-4-argon to the 1M long decode cap and report initial_prompt_tokens across hops * chore(prices): build on the merged gemini-4-argon rows and only raise the long decode cap * fix(gemini): follow continuation hops in a loop instead of recursion The unary hop collectors and the stream iterator called themselves once per continuation request, which the recursion check in CI rejects and which would hit the interpreter's call depth on a very long output. They now loop, and a test follows more hops than the interpreter allows nested calls * fix(gemini): bill Google Search grounding from every continuation request Google sends search grounding once, on the request that ran the search, and does not repeat it on a continuation request. The merged response kept only the last request's grounding, so usage.web_search_requests and the per-search fee dropped every earlier search. Unary merges and the stream iterator now carry the grounding of every request in the chain * fix(gemini): continue long gemini-4-argon answers on streams and Vertex AI A continued stream stopped after the first chunk of its second request. The stream wrapper asks the iterator for a fresh start before every chunk, and that reset it to the first request's finished stream. The iterator now reads from the newest request's stream On Vertex AI the continuation request went to the v1 API version, which rejects continuationToken with a 400. A request that carries the token now goes to v1beta1, which accepts it, including a token that v1 issued On a stream, initial_prompt_tokens came from a usage chunk in the middle of the stream, which reports a smaller prompt count than the last chunk. It is now the count the first request's stream ends with, the same number a unary call reports continuation_token is no longer forwarded on the Interactions API, which answers 400 for the field. It is dropped there as it was before this branch * chore(gemini): name the type errors the dual-mode stream iterator trips The stream iterator holds a sync or an async line iterator in one untyped attribute. Pointing it at a continuation request's stream gave the type checker a concrete union for it, so six existing lines that only ever run in one of the two modes started to report errors This adds a rule-named suppression with a reason on each of those lines and changes no code. Splitting the class into a sync and an async iterator would remove them and is left for a follow-up * fix(gemini): continue only when Google ends a request with CONTINUATION, and stop streaming mid-answer tokens A long decode now sends the next request only when Google's finish reason is CONTINUATION, so an answer cut at the caller's own max_tokens returns in one request with its continuation_token instead of drawing a rejected extra request A request for more than one choice is never continued, since Google rejects a continuation with more than one candidate Chat-format streams no longer carry the tokens Google sends mid-answer. Only a stream that ends cut off carries a continuation_token, on its last content chunk The response cache key now includes continuation_token, so a resend with a token is not served the earlier segment * fix(gemini): keep one response id across a continued answer A continued chat stream changed its chunk id each time the next request to Google started, and a unary answer came back under the last request's id. Every chunk and the unary answer now carry the first request's id, including the chunk that ends a cut-off stream A request whose n or max_tokens arrives as a string is sent once and never continued, where it used to fail with a 500 on the comparison. An empty continuation_token no longer changes the cache key * test(gemini): add integration cells for long decode continuation Covers continued answers on chat, /v1/messages and /v1/responses, streamed and not, on the Gemini API and Vertex AI against a scripted Google, plus concurrency, outage and worker restart runs Also moves the Google stand-in cancel route to /cancel, the URL this PR now calls for Interactions cancel, and checks the cancel test posts there exactly once * fix(vertex_ai): fetch the access token again before each continuation request A long Gemini 4 answer on Vertex AI can run for over half an hour of follow-up requests, and every one of them reused the bearer token fetched before the first request. Once that token expired Google answered 401 and the caller got a cut-off answer with finish_reason length Each Google request in a chain now asks for the token again, which is a cache hit until the token nears expiry, on the streamed and unary paths, sync and async. A refresh that fails counts as a failed follow-up, so the caller still gets the answer so far with its continuation token. A caller's own Authorization in extra_headers still wins on every request, and the Gemini API key path is unchanged * fix(vertex_ai): start each continuation stream with an empty parser buffer When a Google stream ended on an unfinished frame, its leftover text stayed in the stream parser and every frame of the next continuation stream was appended to it. None of them could be decoded, so the whole continuation was billed and dropped without an error Each continuation stream now starts with a fresh buffer, and a test covers a stream that ends on an unfinished frame, sync and async * fix(vertex_ai): treat a continuation stream with no readable frame as a failed follow-up A continuation stream that answered 200 and then sent Google's error frame first, sent a body that is not JSON, or dropped before its first frame ended the caller's stream with `stop` or an error and no token, so a cut answer looked complete or was lost. It now ends the way a refused follow-up does, with the answer so far, `finish_reason` length and that request's token Every continuation stream request now also gets the caller's per-request timeout, which #46349 added to the first one, so a follow-up that never answers ends at that timeout the same way * fix(vertex_ai): keep every follow-up's citations and URL context in a continued answer A non-streamed answer that took several Google requests kept citationMetadata and urlContextMetadata from the last request only, and left every later request's offsets counting from that request's own text. So a URL read on the first request was missing from the answer, citations from earlier requests were dropped, and a later request's citations and search supports pointed at the wrong part of the joined answer The joined answer now keeps every request's citations (Vertex AI's `citations` and AI Studio's `citationSources`) and URL context, and shifts each later request's citation and search support offsets by the UTF-8 bytes of the answer text sent before it, thought parts excluded, since Google counts these offsets in bytes * fix(vertex_ai): answer when a candidate sends null citationMetadata or urlContextMetadata A Gemini candidate whose citationMetadata or urlContextMetadata was JSON null made the call fail on every Gemini model, non-streamed with a 422 or 500 and streamed with an error event, because the per-request metadata helper handed the null back as is and the caller tried to extend a list with it. A continued answer also failed when a follow-up's citation or search support came with a null startIndex A null field is now kept as one entry on a single request, as it was before this branch, a follow-up's null URL context adds nothing to a continued answer, and a null startIndex counts from the start of that request's text. Tests cover missing, null and empty fields on one request and on a continued answer, streamed and not * fix(vertex_ai): pass through spans with no end index and point a continued stream's citations into the whole answer A later request's citation or search support with no endIndex made the whole non-streamed answer fail with a 500, so a span Google sends without an end index now comes back as Google sent it, the way one request for the whole answer returns it. A span with an end index but no start index still counts from 0 A continued stream handed callers each later request's citation and search support offsets counted from that request's own text. The stream now adds the answer bytes of every earlier request, so the offsets point into the whole answer like the non-streamed path and one request for the whole answer Adds the audit's wire tests for both, which fail 24 cells before this change * test(vertex_ai): cover a later request whose cited span starts at its first byte with no start index Google leaves out a zero startIndex, so the second request of a continued answer can cite its own opening with only an endIndex. These wire cells check that span lands where one request for the whole answer puts it, on Gemini and Vertex AI, chat and Responses, unary and streamed * fix(vertex_ai): log a continued answer's raw response as one object A non-streamed answer joined from several Google requests logged its raw response as a JSON list of every request's body. The OpenTelemetry logger reads that raw response as one Gemini body to set its `llm.gemini.*` attributes, so on a continued answer it hit a validation error and set none of them It now logs the merged body, which carries only the last request's token, and a single request still logs Google's body unchanged. The new unit test fails with the change reverted Also adds integration cells for a cut chat stream's token chunk, an answer joined from 41 requests, a caller that gave up on a non-streamed call, counts and caps that are not numbers or are sent twice, `n` sent as true, a partial answer cached after a refused continuation, the token in another provider's cache key, and the sync SDK path with its Vertex token refetch * test(integration): check a continued answer logs one merged raw response A non-streamed Gemini answer that took several Google requests now hands logging one merged response, so the OpenTelemetry span carries the llm.gemini attributes. These 12 cells fail on the merge base and on the commit before that fix, and pass on the branch * test(vertex_ai): cover a follow-up tool call and its thought signature in long decode * test(gemini): pin that a one-request answer logs Google's body unchanged |
||
|
Mateo Wang
|
62af39aaf8
|
fix(anthropic): count a replayed server tool call's cache mark the way modify_params sends it (#46591)
* fix(anthropic): count a replayed server tool call's cache mark the way modify_params sends it With modify_params on, a trailing tool search turn whose call has a kept result got a placeholder answer from the factory and went out as a marked tool_use, but the cache control hook counted marks before that placeholder existed and skipped the mark, so a request at the cap reached Anthropic with 5 marks and failed with a 400. The hook now finds client answers on the sanitized messages, the same list the factory reads. Also make the replay checks cheaper (prefix check before validation, early return without provider_specific_fields, and look up client answers only when a tool call carries cache_control), drop the two audit cells that cover old gaps outside the tool search fix, and derive the pass-through test timeout from the graceful stop budget. * fix(anthropic): keep the duplicate tool result warning out of the cache control hook The hook finds client answers with only the step of the modify_params sanitize pass that pairs tool calls with their results, so a request with a duplicate tool result logs the warning once again instead of once per mark count. Also restore the Snowflake non-stream and cached-stream tool search cells as known bugs with pytest.skip instead of deleting them, and put the Anthropic pass-through test timeout back to 240 seconds. * fix(anthropic): count cache marks next to a message the pairing step cannot read With modify_params on and a marked tool call, the cache control hook paired tool calls with their results before counting. A message without a get method (a stray string or null in extra_body.messages, or an openai SDK ChatCompletionMessage) made that pairing raise AttributeError before any provider call, where main counted the marks and let the request through. The hook now pairs only when every message can be read the way the pairing step reads it, and otherwise counts on the messages as sent, like main did. * fix(anthropic): count cache marks after placing a later system message the way the request builder does * test(anthropic): cover a leading system prompt and a system message before the answer in the cache mark census * test(anthropic): align the tool search replay cells with main and cover a system message after the search turn The malformed kept result cells now expect the unanswered search call to be dropped, as main does since #45882. The stream cut cell checks streamed tool call names, since the kept search result names get_weather as a tool reference. The streamed response cache hit cell runs again because #46104 fixed it. The modify_params cap cell expects the four client marks, since the placeholder answer sends the search call as a plain tool_use with its mark, and a new cell covers a system message after the trailing search turn in both stream modes * fix(anthropic): count marks on the raw messages when they cannot be placed, and find client answers once per request * test(anthropic): cover marked tool calls next to a malformed tool id and a later system message * perf(anthropic): look up client tool answers only when a marked tool call is a server tool call Answered ids only decide whether a marked srvtoolu_ call is rebuilt as server_tool_use, so a request whose marked tool calls are all client calls no longer places system messages or runs the modify_params pairing step to count marks * test(anthropic): reach the client answer lookup with a marked search call in the malformed tool id cell * perf(anthropic): place mid-conversation system messages in linear time for models that keep them * fix(anthropic): count cache marks with the model's own system message placement and pair tool results in linear time * test(anthropic): cover modify_params dropping a tool answer that answers no call of the latest assistant turn * fix(anthropic): count marks with in-place system placement when the model lookup cannot confirm the flag * test(anthropic): cover a bare model with custom_llm_provider filling the cache mark budget through litellm.completion and Router * fix(anthropic): give unmapped models the request builder's converting placement and survive any model lookup failure * test(anthropic): cover an unmapped model name counting cache marks with the request builder's converting placement * fix(anthropic): count server tool call cache marks the request builder's way only on routes that build Anthropic Messages requests Databricks, OpenRouter and Bedrock Converse send every marked tool call as is, so the hook keeps the census main uses there. The seeding step carries custom_llm_provider on the injection points when a server tool call is marked, so a bare model name still resolves its route. * test(anthropic): cover two answered marked server tool calls before a system message on Databricks, OpenRouter and Bedrock Converse * fix(anthropic): keep main's cache mark count on GitHub Copilot and ChatGPT routes without resolving them, so a sync call with no stored login never starts the device flow * test(anthropic): cover a marked server tool call reaching the GitHub Copilot and ChatGPT login hosts like an unmarked one |
||
|
mgeorgaklis
|
b3c9d4c1f7
|
fix(prompt_templates): do not mutate messages in-place in function_call_prompt (#41676)
* fix(prompt_templates): do not mutate messages in-place in function_call_prompt * refactor(test): avoid local list mutation and satisfy type discipline gate * style(prompt_templates): keep line length under 120 chars for ruff format * style(prompt_templates): type _with_function_prompt with AllMessageValues for ANN401 * style(prompt_templates): eliminate basedpyright errors in function_call_prompt * fix(prompt_templates): keep tuple and None system content when adding the function prompt Missing system content becomes the function prompt alone, a tuple keeps its parts, and the old test that pinned the caller-side mutation now asserts the caller's messages stay unchanged * fix(prompt_templates): reject unreadable system content without echoing it Only list and tuple content is read as parts. Any other non-string content raises a TypeError that names the type, so error logs never carry the system prompt * fix(prompt_templates): leave non-list system content failing as before None and tuple content fail the same way they did before this change, so the only behavior change is that the caller's messages are no longer mutated * fix(prompt_templates): answer 400 for unreadable system content, send the function prompt alone under drop_params * test(prompt_templates): pin the function prompt on every system message * test(integration): cover function prompt fallbacks, retries, cache and drop_params on ollama --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Kerry Lu
|
11e9fcc033
|
fix(cost): price bridged /v1/messages reasoning tokens from the chat completion, not the served Anthropic body (#46726)
* fix(cost): price bridged /v1/messages reasoning tokens from the chat completion, not the served Anthropic body * fix(cost): keep the type gate flat for the bridged /v1/messages billing path --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
f470e418ea
|
fix(anthropic): keep signed empty thinking blocks and honor thinking.block_binding (#46342)
* fix(anthropic): treat adaptive thinking as enabled when mapping response_format
is_thinking_enabled only recognised thinking.type=enabled, so a request that
paired thinking.type=adaptive with response_format took the synthetic JSON
tool path with a forced tool_choice, which Anthropic rejects alongside
thinking. The same check also gates the Bedrock Converse and Databricks
forced tool_choice downgrades, so they now see adaptive thinking too.
* fix(anthropic): keep signed thinking blocks with empty text on chat completions
Models that default to display: omitted return signed thinking blocks whose
text is empty, and Anthropic rejects the next turn when they are not replayed
unchanged. is_unsignable_thinking_block dropped them along with unsigned
blocks, and the inline content path required non-empty text, so a tool-use
turn on Opus 4.7+ or the 5 families lost its thinking blocks and 400ed on the
tool-result turn. Only a missing signature now drops a block, and the
mid-conversation system placement mirrors that rule. The /v1/messages strip
helper is left to #41362.
* fix(anthropic): skip the thinking-strip retry when block_binding asks for an error
The /v1/messages retry loop strips thinking blocks and resends on any
invalid-thinking 400, including the binding mismatch a caller opted into
with thinking.block_binding.prefix_mismatch_behavior: error. The retry
decision now sees the request body and surfaces that 400 instead of
overriding the caller's choice. #42888 covers the litellm_params side.
* fix(bedrock): carry thinking.block_binding through the Invoke thinking rebuilds
Bedrock Invoke rebuilt the thinking param from scratch when display: updates
was requested, on both the /v1/messages and chat completions paths, and the
legacy enabled-to-adaptive translation plus the clear_thinking injection did
the same, so thinking.block_binding never reached Bedrock. Every rebuild now
spreads the caller's thinking and only overrides the keys it owns, and
AnthropicThinkingParam types block_binding. The thinking-binding-controls
beta was already on the Bedrock allowlist.
* fix(anthropic): keep forcing the response_format tool under adaptive thinking
This reverts
|
||
|
devin-ai-integration[bot]
|
e344dbd8db
|
feat(proxy): split Azure PTU deployments across teams with per-team PTU ceilings and PTU-hour reporting (#43043)
* feat(proxy): split Azure PTU deployments across teams by share
A PTU deployment's model_info takes ptu_shares (team_id -> whole PTUs adding up to
ptu_count) in place of a single team_id. With LITELLM_ENABLE_PTU_COST_ATTRIBUTION on,
routing hides the deployment from teams holding no share, the v3 limiter enforces each
share as a per-team TPM ceiling from Azure's per-model TPM-per-PTU sizing, the daily
flat-cost rollup splits the hourly cost by share, and /team/daily/activity plus the Team
Usage page report PTU-hours next to tokens.
* fix(proxy): weight cached input in PTU ceilings and size team-scoped deployments by public name
The PTU flag is read from the process environment on every check instead of
through the secret-manager path, so a proxy with a hosted secret manager does
not pay a round trip per request. A team's ceiling now carries the model's
cached-input ratio and settlement charges cache reads at that ratio, matching
how Azure sizes a PTU. A deployment registered for one team is found by its
team_public_model_name for PTU-hours and the ceiling, since its model_name is
rewritten to a unique internal name. ptu_shares are StrictInt on ModelInfo and
parsed_ptu_shares refuses booleans, strings, and non-string team ids, a
fractional ptu_count reserves nothing, and models_processed counts distinct
deployments rather than holdings.
* fix(proxy): reserve PTU ceilings in normalized tokens, keep share routing flag-independent, and warn only shared or Azure reservations
* fix(proxy): size the uncapped output floor against the PTU share in raw output tokens and escape the exported team id
* fix(ptu): refuse ptu_shares naming a team that does not exist
* refactor(ptu): keep Azure detection under llms/azure and check share teams with one query
* fix(proxy): drop the reverted TeamIdSearchFilter import after merging main
Main reverted the team key search change that added TeamIdSearchFilter, so the PTU share team check types its where clause locally
* fix(proxy): chunk the ptu_shares team lookup under the IN-list bound
* fix(proxy): count a request naming a shared PTU deployment directly against its group's ceiling
* fix(proxy): resolve every name a shared PTU deployment is served under to its group's ceiling
* fix(ptu): refuse ptu_shares declared without ptu_count and cost_per_ptu_per_hour
* fix(ui): seed total_ptu_hours in the empty daily activity metadata
* fix(ptu): size usage rows by the name the request used
* fix(ptu): size a team's usage rows by the deployment it is served from
* fix(ptu): never size a team's usage row by another team's reservation
* fix(ptu): reserve an uncapped request against the cap the proxy writes, not its prompt length
* fix(ptu): read a share map only through the terms registration requires
A ptu_shares map stored without ptu_count, cost_per_ptu_per_hour and
ptu_effective_from, or one that does not add up to ptu_count, is refused at
registration but was enforced once loaded: every other team got the 400 and
the share holder a ceiling while no flat cost accrued. _deployment_shares now
reads the shares through ptu_terms, so such a row reserves nothing, the same
as before the map was read.
* fix(ptu): keep a declared split as the access rule and check a config split at boot with the flag off
|
||
|
moyai-devin-berriai[bot]
|
3151c47960
|
fix(guardrails): resolve decision models in caller team scope (#46085)
* fix(guardrails): resolve decision and judge models in caller team scope * fix(guardrails): scope guardrail model calls to the caller's team only where it owns the deployment Read the team only from the auth object the proxy attached to the request, never from caller JSON, and use it only when the router's route to the guardrail model for that team reaches a deployment a teamless caller cannot. Shared names, aliases, legacy team tags and wildcards keep resolving and billing as before. Decision calls pass the team in litellm_metadata only when it is used, so the logged model group stays intact. Adds integration cells for chat, responses and messages, forged team ids, a team deployment that shares a name with a shared model or wildcard, and billing, plus unit cases for legacy team tags and aliases. * fix(guardrails): route team guardrail calls to the team deployment without charging the team Guardrail calls on a team's own model now go to the deployment the router picks for that team, by its id, with no team on the call. Like every other guardrail call, they write no spend row and add nothing to the team's or the key's spend * fix(guardrails): pin the team deployment only for the judge guardrail, not shadow eval judge calls * fix(guardrails): keep the teamless route when only a team wildcard covers the guardrail model * refactor(guardrails): scope the team model fix to the decision model guardrail only * fix(guardrails): send decision calls on a team's own model with the team, and keep team wildcards out The decision call on a team's own model now carries the caller's team instead of being pinned to a deployment id, so it is charged to that team and goes through the team's normal routing: an access window that reserves the deployment for the team lets the call through, and pre-call checks see the input and skip a deployment too small for it. A team deployment listed under a wildcard public name, such as team_public_model_name openai/*, no longer captures a guardrail model the team never named, whatever model it points at. Adds unit and integration cells for the reserved deployment, mixed context windows under enable_pre_call_checks, a team wildcard name over a fixed model, and the owner team's spend. * fix(guardrails): log the owner team's name on decision calls charged to it The decision call on a team's own model now carries the team alias beside the team id, so the spend row and the Logs page name the team the call was charged to. * fix(decision_model): use the caller's team only for names nothing else serves A team model named like a shared deployment, a shared wildcard or an SDK public name took over the guardrail's decision calls for that team, so a team admin could point a proxy-wide guardrail at their own endpoint. The team is now used only when a caller without a team reaches no model under the name. * refactor(decision_model): drop the team route subset check the served check covers Once nothing serves the name without a team, every deployment a caller without a team can reach is a wildcard, so the subset check only ever rejected an empty team route. Pin team wildcards over a name nothing else serves. * fix(decision_model): use the caller's team only when judge_target finds nothing without a team The served check rebuilt the router's rules and missed a shared wildcard whose provider comes from custom_llm_provider, so a team model under the guardrail's name took over that team's decision calls. Ask judge_target, the same lookup the teamless dispatch makes, instead --------- 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> |
||
|
devin-ai-integration[bot]
|
fbc2e358bc
|
fix(responses): bill a background response once, on the first read or poll that sees it finished (#45703)
* fix(responses): bill a background response once, from the poll job
Reading a completed background response with GET /v1/responses/{id} wrote a
full-cost spend row on every read, on top of the row the background cost poll
writes. Reads of a Responses object (GET, DELETE, cancel) now never bill, and
the poll job is the single biller.
The poll job now bills with the creating request's attribution (key hash, team,
org, user, aliases, tags) read off the managed object row, through a helper it
shares with the batch cost poll. The background create persists the creating
key and request tags so that attribution survives until the poll runs.
* test(proxy): a background response read reports zero cost
* fix(responses): bill a background response once, on the first read or poll that sees it finished
A conditional update moves the stored row out of queued or in_progress so only one worker or pod bills it, through one re-read stamped with the creator's attribution. A failed billing read puts the row back for the next poll
* fix(responses): keep the caller's response id out of the billing failure warning
* fix(responses): keep the creating client's User-Agent tags on the one background charge
* fix(responses): keep managed background reads free and bill each response once
The managed files hook marks reads and cancels of proxy-issued response ids, so only those stay free. SDK and database-less reads of a background response still bill, extra_body background creates are tracked, and the poll's status read no longer cools down a deployment on a 404.
* fix(responses): bill finished background responses from the fetched body and store rows Postgres accepts
* fix(responses): give the background billing event its own spend log row and bill responses the create left untracked
* fix(responses): narrow the managed files hook with a protocol instead of an enterprise import
* fix(responses): leave a read the caller retargets with client-side credentials to the cost poll
* fix(responses): bill background responses on the creator's end user and report the charge on the settling read
The read that settles the bill returns the charge in x-litellm-response-cost, the billed event
carries the creator's end user and the key, team, and user budget fields, and its spend row is
named after the id the client got. Override reads, cancels, and deletes settle from the
configured deployment, a streamed create marks its row billed, stored rows keep no model
output, and model insights counts the billed row's spend without a request.
* test(responses): cover background response billing across proxies, reads and chaos
* fix(responses): settle background billing only for callers allowed to address the response
A delete now settles the bill only after the response id access check passes, a streamed
create is marked billed when it sets background inside extra_body or its completed event
arrives after the last chunk, the billing event keeps the deployment's litellm_params
base_model, a key with no user and no team bills no user instead of its owner scope, and the
enterprise cost check import is optional
* fix(responses): record the bound on the pending status IN filters
* fix(responses): send the billed cost split on the settling read and keep the batch cost event as it was
The read that settles a background response now sends the whole cost header
family from the billed event, so the breakdown headers add up to the charge.
Batch cost events go back to main's attribution (creator id as the user, no
budget fields); only background responses get the end user and budgets.
Integration tests expect the charge on the settling read (or none when the
poll billed first), one charge for a streamed create, and SDK flows that poll
with the id each read returns.
* fix(responses): bill each background response once across upgrades, streams, and failed settles
Rows stored before this billing model are found by the issued id and rekeyed by the
provider id, and the cost poll retires one a read already billed. A streamed background
create is free on its own and bills the finished response through the same managed row
before the client sees response.completed, so a racing read cannot bill it again. A
settle that fails retries in the background, a retargeted read with no row tracks and
bills from the configured deployment, and a delete keeps its billed cost header.
Reverts the unrelated json.dumps narrowing in the completion transformation.
* fix(responses): bill cache-polled background streams and match poll rows by the issued id
* test(responses): cover deletes, failed settles, budgets, base models, and cache-polled background billing
Adds integration cells for the background response billing paths the audit
inventory lists: a DELETE that settles the bill and refuses a stranger, a
settle the database refuses that retries once it recovers, the Prometheus
remaining budget, base_model pricing from model_info and litellm_params, the
daily model usage rollup, the end user, the settling read's cost split, stored
rows without model output, a forged managed marker on the create, streamed
creates with background in extra_body, and the cache-polled create
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
|
||
|
Thijmen Stavenuiter
|
a76e723b26
|
feat(proxy): report when each per-model budget window resets (#45797)
* feat(proxy): report when each per-model budget window resets model_max_budget_usage on /key/info, /user/info and /team/info now carries budget_reset_at per model, the open window's start plus its period, or null when no window is open. The window start is read in the same batched lookup as the spend counter * fix(proxy): count a monthly budget window from its own start when reporting its reset duration_in_seconds measures Nmo from now, so a window opened on January 15 and read on February 10 reported February 12, then null while the counter still refused requests until February 15. duration_in_seconds_from measures the month from the window start, and duration_in_seconds now shares that month arithmetic * fix(proxy): measure months from timezone-aware datetimes and assert whole usage values datetime.fromtimestamp without a tz tripped DTZ006. The month arithmetic now runs on an aware local datetime, which keeps the same wall-clock result. The duration tests move to parametrized functions, since subTest payloads carrying datetimes cannot cross the xdist worker boundary, and the new usage tests compare the whole entry * fix(budgets): end a monthly budget window a calendar month after it opens Each charge recounted the month from the charge date, so a monthly window opened in a month of another length expired days before or after the reset /key/info reports. The window is now measured from its start, by the same helper the usage report uses. A period ending after year 9999 reports no reset instead of failing the read. * fix(budgets): count monthly budget windows in UTC for every router budget Months were counted on the host's local calendar, so replicas with different TZ settings disagreed on when a monthly window ends. Provider, deployment, tag and per-model budgets now all measure a window from its start in UTC, and a test covers the router budgets. A reset past year 9999 is documented as null. * test(budgets): cover monthly tag budgets in the router window test * test(budgets): add integration cells for budget_reset_at and monthly budget windows * test(budgets): find a monthly window in another span on every calendar day --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Taylor Hawkes
|
fd84e97f49
|
feat(providers): add Inference APIs as OpenAI-compatible provider (#41669)
* feat(providers): add Inference APIs as OpenAI-compatible provider Registers https://api.inferenceapis.com/v1 as a JSON-configured provider (chat completions and Responses API). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(providers): list Inference APIs in Add Model and the provider docs index, add request-level tests - provider_create_fields.json entry so the Add Model dropdown test passes - provider_endpoints_support.json (+ backup) entry so the provider docs check passes - tests now capture the outgoing HTTP request (URL, auth header, body) instead of only inspecting registrations - drop a redundant comment Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(providers): inject a recording HTTP session instead of patching httpx.Client Request-level tests now set litellm.client_session to an httpx.Client with a MockTransport, so nothing is monkeypatched on a class. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011qUBY2P8qTTuJCUeMzTCD4 * chore(providers): drop redundant comment on the inferenceapis entry Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011qUBY2P8qTTuJCUeMzTCD4 * feat(inferenceapis): add cost map rows for the 12 chat models and test the responses route Prices and context windows come from https://inferenceapis.com/pricing and GET /v1/models. Moves the provider test to tests/unit and adds the interactions flag the endpoint matrix now carries. * feat(inferenceapis): price the vendor's chat model aliases and mark /v1/messages supported * fix(inferenceapis): serve /v1/messages natively and keep audio routes off the provider * fix(json providers): send provider-specific params in extra_body and report context overflow as ContextWindowExceededError * fix(exception mapping): match the Inference APIs overflow phrase instead of any context_length_exceeded substring * fix(exception mapping): map context overflow to ContextWindowExceededError for Inference APIs only Providers without their own mapper keep reporting an overflow as BadRequestError, since the router raises a ContextWindowExceededError instead of trying generic fallbacks when context_window_fallbacks has no entry for the group. Adds the integration cells for the provider, its fallbacks, and the extra_body and overflow families. * test(inferenceapis): hold upstream replies across the worker kill in the chaos cell A reply the killed worker sent before SIGKILL can lose its queued spend row, which the product never promised to keep. Holding the upstream until after the kill means only the surviving worker answers, so every 200 must log exactly once. * test(inferenceapis): make the chaos cell kill the quieter worker and always release held replies The survivor now must hold at least one in-flight request, so the burst check cannot pass as 0 == 0, and the held vendor is released on exit so a failed assertion cannot stall the wire server teardown --------- Co-authored-by: TaylorHawkes <857858+TaylorHawkes@users.noreply.github.com> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
구동하
|
a9e45a24d3
|
fix(streaming): keep provider response headers on SDK streaming errors (#41528)
* fix(streaming): keep provider headers on MidStreamFallbackError A 429 raised while opening a stream is wrapped in MidStreamFallbackError, which dropped the litellm_response_headers exception_type() attaches to the wrapped error. retry-after and x-request-id reached callers on the non-streaming path but not on the streaming one, so retry backoff, provider request id logging and proxy error headers lost them. * refactor(streaming): drop explanatory comment from MidStreamFallbackError header copy * test(streaming): cover sync and async header carry on MidStreamFallbackError Parametrizes the provider-header test over the sync and async stream wrappers, and drops the hasattr test, which pinned how the attribute is stored rather than what any caller observes * test(streaming): add integration cells for stream-open provider headers Cover the SDK stream openers (gemini, vertex_ai, codestral, predibase and the eager providers), the Router stream retry and fallback paths, and the proxy on both the router and the config-less serving paths, including a mixed burst, a worker SIGKILL and a SIGTERM restart. Add a helper that boots a config-less proxy owned by the test --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
Mateo Wang
|
18831b818c
|
fix: keep model capability flags out of provider requests (#46647)
* fix: keep model capability flags out of provider requests A supports_* flag or another model map field set on a deployment's litellm_params was forwarded to the provider as a request field, so Bedrock answered 400 Extra inputs are not permitted on chat traffic and on the background health check alike. The shared owned-kwarg predicate now treats every ProviderSpecificModelInfo field as litellm-owned. Builds on the health-check-only approach in #40952 by leilei3167. * fix: treat every supports_ model map flag as a capability field and warn when one sits in litellm_params * fix(router): skip declared litellm params in the capability flag warning and warn for runtime-added deployments * fix(router): strip model capability flags from the router's deployments instead of every request * fix(router): keep capability flags out of health probes and read them from the loaded catalog * perf(router): read the catalog's supports_ field set once per cost map load Cache the loaded catalog's field names when the cost map snapshot is taken so the capability-flag check stops walking every catalog entry per deployment, and add integration cells that drive the flagged and control deployments through chat, streaming, messages, responses, embeddings, health and model management against a vendor that rejects the flags |
||
|
Sushit Lal Pakrashy
|
958cba7016
|
fix(vertex_ai): prevent Gemini from misinterpreting $ref in tool results (#38284)
* fix(vertex_ai): prevent Gemini from misinterpreting $ref in tool results
* fix(vertex_ai): use object instead of Any in schema-ref traversal
* fix(vertex_ai): use immutable tuple traversal to satisfy type-discipline gate
* test(vertex_ai): add direct contract test for _contains_json_schema_ref
* fix(vertex_ai): type the $ref walk over JsonValue and drop the private-helper test
* fix(vertex_ai): wrap a tool result only when a $ref holds a non-empty string
Gemini rejects {"$ref": "<name>"} in function_response.response but accepts null, empty,
numeric, boolean, object, and list values, so those tool results stay structured
* perf(vertex_ai): skip the $ref walk when the tool result text cannot spell the key
* test(vertex_ai): cover a $ref on the tool result's root object
* docs(vertex_ai): cite where the Gemini $ref rule was observed
* fix(vertex_ai): keep a $ref under a list of lists structured
Gemini accepts a $ref reached through a list nested directly in a list,
so those tool results keep their structured form. Adds the audit's
integration cells for the tool result wire, chaos and edge shapes
* test(vertex_ai): teach the fake Gemini servers the list of lists rule
The audit's scripted upstreams now skip lists inside a list like Gemini
does, add list of lists cells on every endpoint, and give the 8 MiB
timing calls their own client timeout
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
|
||
|
Kerry Lu
|
efd8274315
|
feat(xai): bill grok voice realtime sessions (#46600)
* feat(xai): bill grok voice realtime sessions * chore: revert unrelated formatting churn in test_cost_calculator.py * refactor(xai): tidy realtime usage meter and tests * fix(xai): read push-to-talk mode from client session.update only * fix(xai): clamp decoded audio bytes and lock push-to-talk before content --------- Co-authored-by: kerry <kerry@berri.ai> |
||
|
Kerry Lu
|
0d741a9816
|
fix: index streamed thinking_blocks so the OpenAI SDK .stream() accumulator accepts them (#46611)
* fix: index streamed thinking_blocks so the OpenAI SDK .stream() accumulator accepts them * fix: satisfy the type discipline and basedpyright gates for the thinking block index * chore: sync the OpenAPI snapshot and schema.d.ts with the thinking block index field --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
Mateo Wang
|
52fc37888b
|
fix(responses bridge): round-trip tool_search_call and tool_search_output items between turns (#46444)
* fix(responses bridge): keep defer_loading on function tools sent to the Responses API Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): give the defer_loading casts a cast-ok reason Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(responses bridge): build defer_loading outside the tool loop so Final stays valid Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): round-trip tool_search_call and tool_search_output items between turns Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(responses bridge): read replayed tool-search and namespace fields through typed helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep tool_search items on a turn with no message or tool call Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep every reasoning item on a search-only turn Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): leave content unset on a search-only turn so replay adds no empty message Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): replay tool search items only with tool_search, count replayed tools in the allowlist, keep namespace off guardrails * fix(responses bridge): honor drop_params for defer_loading and route tool_search to Responses Under drop_params, defer_loading is dropped when no tool_search tool is sent, so the 400 it would cause upstream goes away as before. A non-bool value answers 400 without drop_params and is dropped with it. A null tool-level value falls back to the nested function value. A request carrying a tool_search tool now bridges to Responses on OpenAI and Azure endpoints even with reasoning_effort none, since chat completions rejects the tool_search type. * fix(responses bridge): replay tool search items in output order and count replayed tools in tool policy Store the ordered output prefix through the last tool search item, reasoning included, so a second reasoning item no longer makes OpenAI reject the replay. The shared chat-to-Responses converter now replays tool search items and namespace only when the bridge opts in, so Noma, the complexity router and guardrail payloads keep their shape. The tool policy guardrail and the allowlist count replayed tools only when the request can send them. * fix(responses bridge): forward defer_loading only beside a tool_search tool defer_loading only means something with tool_search, and backends without it reject the key (xAI answers 403). A true value with no tool_search tool now answers 400 naming drop_params and is dropped under drop_params; a false value there is a no-op and is dropped. * fix(tool_loop): keep tool search items and namespace between rounds * fix(responses bridge): check replayed tool search loads when a deployment adds tool_search * fix(chat): drop litellm response fields before strict OpenAI-compatible providers A chat reply from the Responses bridge carries provider_specific_fields on the message and on each tool call, plus reasoning_items. Groq, Fireworks, Mistral and Together reject unknown fields, so sending that reply back to them as history failed with a 400 or 422. Strip them in the shared helper and use it in those providers. * fix(caching): keep reasoning_items and tool search items on response cache hits A cached bridge response lost reasoning_items on a non-streaming hit and lost both reasoning_items and provider_specific_fields (tool_search_items) on a streamed hit, so the next turn could not replay the search or the reasoning around it * test(integration): cover tool search item replay on the chat to Responses bridge Proxy, SDK, authorization and chaos cells for returning and replaying tool_search_call and tool_search_output items and the tool call namespace * test(integration): add response cache twins for the tool search guardrail cells Each guardrail cell now has a twin where the answer is already in the Redis response cache: a key opted out of global guardrails caches a replay that loads a policy-blocked tool, the same replay without guardrails is cached before block mode sees it, rewrite mode is not served the unrewritten conversation's cached answer, and an allowed replay is served from the cache. * fix(responses bridge): forward defer_loading beside a client-executed tool_search The bridge now replays a client tool_search_call and the client's tool_search_output through provider_specific_fields.tool_search_items, so a deferred function beside a client tool_search keeps defer_loading like it does beside a hosted one * test(responses bridge): pin the client tool_search turn and its replay, streamed and not * fix(responses bridge): strip nested namespace for strict providers and keep streamed reasoning_items on text replies Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses bridge): keep dropping defer_loading beside a client-executed tool_search Forwarding defer_loading beside a client tool_search made GPT stop with a search call the chat client has to answer by hand, so a plain question that used to get tool calls got none. Keep main's behavior of dropping it there. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
314f17986a
|
fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models (#45882)
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models * fix(responses): define the tool_search_call output item locally for older openai SDKs * fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500 * fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out A replayed Claude tool search call that the caller answered with a tool message kept its tool_result in the user turn while its tool_use was dropped from the assistant turn, so Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when no tool message answers it. The Responses bridge now strips defer_loading from every tool whenever no tool search tool is sent, whether the caller sent none or the route cannot run it, since a deferred tool with no search tool can never be loaded. Namespace members had started carrying the flag to every bridge provider, which providers that reject unknown tool keys answered 422. The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit. * fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried * fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator * fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic A function tool whose allowed_callers is not a list of strings now answers 400 instead of 500. The guardrail scan reads tools without malformed flags, so the route decides between a 400 and a drop under drop_params. When the caller sends a native Anthropic search tool beside tool_search, only the native one goes out. Databricks Claude no longer counts as an Anthropic request route. A previous_response_id follow-up off Anthropic also drops a search call the client replayed as a function_call with a srvtoolu_ id, and its output. The answered tool id scan in the Anthropic message factory runs once per assistant run instead of once per message. A null created_by on tool_search_call and tool_search_output output items is left out of the serialized response, so a cache hit returns the same keys as the first call. * fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan The scan translated tool_search into Anthropic's regex search tool on every route, so a guardrail returning only function tools removed tool_search from native OpenAI requests and OpenAI answered 400 for the deferred tools * fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai * fix(anthropic): keep answered tool search replays paired with their answers * fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails * fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object * fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent * fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id * fix(responses): answer 400 to a tool_search execution that is not server or client OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed execution is dropped like the other malformed tool flags, which leaves a server search * test(responses): cover the 400 and drop_params paths for a malformed tool_search execution * test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
e889d9d653
|
fix(bedrock)!: replay redacted_thinking on Converse and keep its data as Claude's own string (#46532)
* fix(bedrock): send redacted_thinking back to Converse as reasoningContent.redactedContent Replaying an assistant turn that carried a redacted_thinking block to Bedrock Converse turned it into an empty unsigned reasoningText, which the assistant content builder then dropped as blank text. Bedrock returned 200 so callers never noticed. Map redacted_thinking to redactedContent in both the thinking_blocks and inline content paths of the sync and async builders * fix(bedrock): drop blank redacted_thinking data and keep non-str signatures signed on Converse replay * fix(bedrock): replay redacted_thinking to Converse only when the target is Claude Nova, Llama, DeepSeek and gpt-oss reject reasoningContent.redactedContent with a 400, so a Claude history replayed to them through a Router fallback or model switch keeps dropping the redacted block the way main does. Claude is resolved from the Bedrock base model of the target, and an application inference profile ARN counts as Claude when its deployment's base_model names a Claude model. Adds integration contracts for the replayed Converse request shape, stream and non-stream, with a tool call, and for non-Claude and ARN targets. * test(e2e): cover Bedrock Converse replaying redacted_thinking live through the proxy Turn 1 triggers redacted thinking on Claude Sonnet 4.6, turn 2 replays it, and a live edge between the proxy and Bedrock checks the replayed Converse body carries the same reasoningContent.redactedContent. Covers nonstream, stream and a tool call round trip * fix(bedrock): validate litellm_params before reading the deployment base_model litellm_params arrives as an untyped dict, so passing it straight into the typed helper grew reportUnknownArgumentType. Validating it into Mapping[str, object] first keeps the read typed without growing the type-check gate * fix(bedrock): wrap non-Converse redacted_thinking data and let a named model id win over base_model * test(bedrock): cover redacted_thinking minted outside Converse, model_id and the sync Router base_model path * fix(bedrock): match bare claude- base_model names without reading the untyped cost map * fix(bedrock): keep redacted_thinking data as Claude's own string on every route Converse hands redactedContent back base64-wrapped, and that wrapper used to leak out as data, so a Converse turn replayed to Invoke or the Anthropic API was rejected. Converse replies now decode it, Converse requests wrap it exactly once, and the Anthropic-shaped builders unwrap histories saved with the old wrapping. Responses API encrypted reasoning is no longer sent to Claude on Converse, which rejected it with a 400 |
||
|
devin-ai-integration[bot]
|
47e4ed88c2
|
fix(anthropic): keep refusal stop_reason and stop_details on the logged response (#46104)
* fix(anthropic): keep refusal stop_reason and stop_details on the logged response * fix(anthropic): keep the refusal stop-fields boundary tolerant of a non-dict upstream body * test(anthropic): use Anthropic's documented refusal example as the unit-test fixture * fix(streaming): keep provider fields on async stream cache hits and received_model_id on synthesized finish chunks * fix(caching): replay cached completion_tokens_details on stream cache hits and skip non-object cached provider fields * test(integration): cover Anthropic-family refusal stop_details on the wire, the cache and under chaos * fix(caching): skip a cached completion_tokens_details that fails validation on stream replay * fix(anthropic): keep refusal stop fields on a JSON-mode non-stream response Also adds the round 3 audit cells for JSON-mode refusals, the proxy response serialization, and wrong-typed cached usage details on both stream replays * test(anthropic): expect content_filter on streamed JSON tool refusals after main's stream change --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
493ed616bb
|
fix(config): treat blank env values as unset so the proxy neither crash-loops nor silently drops a guardrail (#46341)
* fix(config): treat blank numeric env values as unset instead of crashing at import
A numeric knob exported as an empty string, which is what a Helm value: "" or an
emptied .env line produces, raised ValueError inside litellm/constants.py and the
proxy crash-looped before serving traffic. Every int(os.getenv(...)) and
float(os.getenv(...)) in constants.py now goes through get_env_int / get_env_float,
which treat blank as unset and fall back to the default with a warning on garbage.
* fix(guardrails): treat blank litellm_params values as unset so the guardrail still loads
A guardrail whose litellm_params read os.environ/X resolves X to "" when the
variable is exported but empty. Pydantic rejected "" for bool and typed fields
such as default_on and fail_on_error, the boot log reported the guardrail as
skipped and the proxy came up without it. A before-validator on BaseLitellmParams
now drops blank string values so each field takes its default, and the
default_on shortcut in LitellmParams treats a blank value as False.
* fix(config): warn once about an unparsable numeric env value so logging cannot recurse
The LiteLLM loggers' filter reads MAX_STRING_LENGTH_STDOUT_LOG and MAX_BASE64_LENGTH_STDOUT_LOG through get_env_int on every record. Warning about a garbage value from inside that read re-entered the filter, so every log call raised RecursionError. The value is now marked as reported before the warning is logged, which also stops per-record readers from repeating it. get_env_int_or_none and the new get_env_float_or_none share the same parser
* fix(config): treat blank values as unset for the remaining import-time numeric knobs
LITELLM_MAX_STREAMING_DURATION_SECONDS, REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES, a whitespace CLI_JWT_EXPIRATION_HOURS, LITELLM_CONFIG_PARAM_CACHE_TTL_SECONDS, the agent health check timeouts and the Prisma reconnect knobs still crashed the proxy at boot when blank. A blank or unparsable REQUEST_TIMEOUT no longer counts as explicitly set, so chat completion() keeps its 600s fallback
* fix(guardrails): map blank litellm_params to the field default so PATCH can still clear a value
Dropping blank keys before validation made PATCH /guardrails/{id} with api_base "" a silent no-op. Blank values of optional fields now resolve to the field default and stay in the set fields, so the PATCH clears the stored value. Required fields and unknown keys keep the value as given
* test: cover proxy numeric knobs in-process and expect no warning for a blank Straiker api_version
The three proxy import-time numeric knobs move into litellm/constants.py so the existing in-process blank-env test covers them, replacing the subprocess test. A blank api_version now means unset, so the Straiker test asserts the key-prefix version with no warning.
* fix(guardrails): keep LitellmParams kwargs typed without adding argument-type errors
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
|
||
|
Kerry Lu
|
6bd5580425
|
fix(streaming): add CustomStreamWrapper.close() and release the connection when a sync stream ends (#46354)
* fix(streaming): close sync streams so the connection goes back to the pool CustomStreamWrapper gets a sync close() (plus context manager support) that closes the wrapped provider stream, and both the sync and async wrappers close the provider stream when the stream reaches its end. The Anthropic stream iterator keeps the httpx response it reads so close()/aclose() can release the socket after message_stop instead of leaving it checked out of the pool Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): keep the basedpyright ceiling flat in CustomStreamWrapper.close() Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): close the response on the shared sync stream path too BaseModelResponseIterator gets a sync close() next to its aclose(), and the sync branch of BaseLLMHTTPHandler hands it the httpx response the way the async branch already does, so providers built on it release the socket on close() instead of waiting for garbage collection Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(streaming): unit tests for CustomStreamWrapper.close() and the shared iterator close() Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): close the provider stream before re-raising a mid-stream error Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com> |
||
|
devin-ai-integration[bot]
|
2f3ccbe7c7
|
feat(decisions)!: send images to Cloudflare Clef through its images field (#45734)
* feat(decisions): send images to Cloudflare Clef OpenAI-format input_image parts and the System One images field now reach Cloudflare clef and clef-flash in its images field, with remote URLs downloaded and inlined first. Providers that cannot take images drop them under drop_params and answer 400 otherwise. * fix(decisions): follow drop_params for images and use the call timeout for image downloads * fix(decisions): drop a malformed images entry on its own under drop_params * fix(decisions): refuse images next to input whatever drop_params says * fix(decisions): bound clef image fetches, honor drop_params for images, and log caller URLs Refuse more than 4 images for Cloudflare clef before any download, keep the caller's timeout as one deadline across image fetch retries, accept images next to input under drop_params, validate image strings and objects, and pass the caller's image URLs (not inlined bytes) to logging callbacks. * fix(decisions): check input_image URLs and clef image sizes * fix(decisions): accept data URL media type parameters and check clef image types * fix(decisions): count clef images after dropping unsupported types and flag clef vision --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
7bd108139f
|
fix(openai): carry cache_control to prompt_cache_breakpoint on /v1/responses to gpt-5.6 (#46105)
* fix(openai): carry cache_control to prompt_cache_breakpoint on /v1/responses to gpt-5.6 Native /v1/responses used to strip every Anthropic cache_control mark, so a gpt-5.6 deployment in explicit mode never wrote a cache. Marks on system, developer and user parts and on function_call_output now become prompt_cache_breakpoint on the same block when the deployment reads breakpoints, and are stripped exactly as before everywhere else * fix(openai): read str providers and base_url in the prompt cache breakpoint gate Responses configs generated from a provider JSON file, and openai_like itself, name their provider as a plain str, so .value crashed every native Responses request through them. The gate also only read api_base, while responses() keeps a caller's base_url as an extra field on the params, so an injected mark on a custom host was rewritten to a breakpoint * test(openai): cover the pass-through branches of the prompt cache breakpoint swap A part type OpenAI does not cache keeps no breakpoint and an item mark skips it, and an item or content of a shape OpenAI will reject reaches the strip untouched instead of failing inside the proxy * fix(prompt_caching): keep a client-written prompt_cache_breakpoint when a cache_control mark lands on the same block |
||
|
devin-ai-integration[bot]
|
74e5dc9747
|
fix(proxy): keep provider refusal, logprobs and system_fingerprint in chat completions (#44788)
* fix(proxy): keep provider refusal, logprobs and system_fingerprint in chat completions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): handle missing chat completion roles Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): restore throwaway Usage skip in ModelResponse Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): handle explicit fields on custom response dumps Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test: update refusal expectations for LIT-9235 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(lint): suppress type discipline violations with reasons Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style: ruff format proxy utils Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): keep basedpyright counts at merge-base for azure dump and ModelResponse Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): keep an explicit null system_fingerprint passed to ModelResponse Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: kerry <kerry@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
yuneng-jiang
|
1587faeeb9
|
test(unit): wait on the work itself instead of fixed sleeps (#46261)
* test(unit): wait on the work itself instead of fixed sleeps in 21 flaky files Replace sleeps that raced background logging, cache writes, refreshes and thread work with either awaiting that work directly (logging worker flush, the batch logger's flush_queue, the tasks the call spawned, thread joins) or an Event the test's own fake sets when it runs. Each wait keeps a 5s hang guard that never decides pass or fail. Tests that need a fake clock, real sockets, or measure latency are left unchanged, and no production code moves * test(unit): await the tasks each completion spawns instead of yielding once The logging tests yielded once with asyncio.sleep(0) so the task that enqueues the success log could run before the worker flush. That relied on the enqueue happening before the task's first await. Snapshot the running tasks around each acompletion call and await the ones it spawned, so the guarantee holds however many steps sit before the enqueue |
||
|
moyai-devin-berriai[bot]
|
0b3fcc197d
|
fix(logging): redact nested callback input and provider response fields (#45923)
* fix(logging): redact nested callback input and provider response fields * fix(logging): keep shared request body for per-callback redaction and drop hidden provider fields The deprecated per-callback message_logging=False path ran perform_redaction on the shared model_call_details, which now also redacted the nested proxy_server_request body. The spend log Request column and previous_response_id replay read that body, so one opted-out callback blanked them for everyone. That path now keeps the shared body and hidden params, and redact_streaming_responses_for_custom_logger hands the opted-out callback its own redacted litellm_params. Redacted litellm_params also clear provider_specific_fields copied into metadata.hidden_params (Anthropic thinking text), in litellm_params and in the nested request body, and the nested body redacts system and instructions. * fix(logging): keep replayed instructions and router usage marker under redaction The shared request body keeps system and instructions again, so spend logs store them and previous_response_id follow-ups replay the real instructions. Per-callback copies still redact them. Provider-specific fields in metadata.hidden_params are cleared in place, so the metadata dict keeps its identity and the router's counted-usage marker stays visible to deployment_callback_on_success (no double tpm/rpm count). * fix(logging): never copy shared metadata dicts during redaction The shared redaction pass now leaves litellm_params metadata dicts as they are, so a hidden_params write from the other success handler between the in-place clear and the copy can no longer move the router usage marker to a stale dict. The key-presence predicate change is reverted as unneeded. * refactor(logging): redact nested request body and provider fields with the existing message rules Run perform_redaction on a copy of proxy_server_request.body, the same call the spend log already makes, and redact reasoning inside provider_specific_fields the way the message's own reasoning_content and thinking_blocks are redacted, instead of clearing every provider field. Opaque fields such as Gemini thought_signatures stay, and the metadata dicts and streaming objects are handled as on main. --------- 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> |
||
|
Kerry Lu
|
61841887f3
|
fix(pricing): drop duplicate and bare gemini-4-argon rows (#46246)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
Kerry Lu
|
59ef56f636
|
feat(pricing): add vertex gemini-4-argon (#46237)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
Kerry Lu
|
24bbf8f0e1
|
fix(cost): bill azure_ai claude web searches under deployment-owned pricing ids (#46135)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
5338edb7e3
|
fix(cost): bill azure_ai claude web searches per server_tool_use request (#46114)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
yujonglee
|
05cf428761
|
feat(rust): settle EdenAI Messages reported cost (#45698)
* feat(edenai): settle native Messages reported cost * test(rust): add live EdenAI Messages contract cases * refactor(rust): settle call cost once as an ExecutionFacts variant The route now decides Cost (Reported, CacheHit or Deferred) from the result source and the provider's reported figure. The legacy callback adapter only projects a Reported amount onto provider_reported_cost and no longer writes response_cost, which the Python calculator derives Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * refactor(rust): keep EdenAI wire shaping independent of Invoke * style(rust): format cost settlement imports * fix(rust): trust provider costs without bypassing configured pricing * fix(rust): clear untrusted response costs before spend settlement --------- Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> |
||
|
devin-ai-integration[bot]
|
c2bc5aecff
|
fix(cost_calculator): apply the anthropic fast multiplier to cache cost breakdown (#46112)
* fix(cost_calculator): apply the anthropic fast multiplier to cache cost breakdown Anthropic fast mode billed totals at provider_specific_entry.fast while the spend log cost_breakdown kept cache_read_cost and cache_creation_cost at 1x. Move the speed lookup into a shared helper, multiply it into the billed token rates used by get_token_type_cost_breakdown, and have the Anthropic calculator read the same helper so totals and breakdown cannot drift apart. Production change carried from #44074. Co-authored-by: kerry <kerry@berri.ai> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cost_calculator): narrow the provider entry instead of defaulting to a dict literal Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: kerry <kerry@berri.ai> |
||
|
devin-ai-integration[bot]
|
4f16aaa32c
|
feat(cost): support provider/model-pattern keys in cost_discount_config (#43335)
cost_discount_config keys may now be <provider>/<model-pattern> with fnmatch globs, so a discount can target a subset of a provider's models. An exact pattern beats a glob, the glob with the longest literal wins, and the bare provider key is the fallback. PATCH /config/cost_discount_config validates the provider half only, and the admin UI cost tracking page gains a Model pattern field and a Models column Resolves LIT-8465 Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
ryan-crabbe-berri
|
c2d0726a08
|
fix(anthropic): carry cache_control to prompt_cache_breakpoint on the /v1/messages to Responses bridge (#46082)
A caller hitting /v1/messages with a cache_control mark and an OpenAI gpt-5.6 or newer deployment behind it lost the mark when the request was rebuilt as a Responses request, so OpenAI never cached at the boundary the caller chose. The bridge now rewrites each marked system, user and tool_result block to prompt_cache_breakpoint on the same block when the deployment reads breakpoints, keeps the system prompt as a developer message so its mark survives, and still strips the mark for deployments that do not read it The hook's gate becomes public so the bridge and the hook share one answer for which deployments read breakpoints, and the one-block swap lives next to the existing breakpoint helper in prompt_templates |
||
|
mayank-affirm
|
56a1e94a31
|
fix(bedrock_mantle): map the OpenAI-shaped context overflow envelope too (#41747)
* feat(dd_span_tagger): emit litellm_user_email span tag for JWT-authenticated requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(dd_span_tagger): use dotted litellm.user_email tag for consistency Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(model_hub): surface model_info.description in model group info and Model Hub UI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(router): aggregate model group description without in-place mutation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(health): skip background health check DB writes when the latest-row read fails Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(bedrock_mantle): map the OpenAI-shaped context overflow envelope too Mantle reports context overflow in two envelopes. #37862 covered the structured `validation_error` one carrying token counts ("prompt tokens (N) exceed model maximum (M)"). Mantle also returns the OpenAI-shaped body: {"error":{"code":"context_length_exceeded","message":"Your input exceeds the context window of this model. ...","param":"input", "type":"invalid_request_error"}} That passes the existing `invalid_request_error` gate but matches neither the token-count pattern nor `is_error_str_context_window_exceeded`'s substring list, so it fell through to a generic BadRequestError. Clients such as Claude Code only reactive-compact on a recognised overflow phrase, so an overflowing request surfaced as an ordinary error with no recovery. Return the same normalised "prompt is too long: ..." message for this envelope when it carries no counts, keeping the numeric wording when the provider supplies them. * fix(bedrock_mantle): map the streamed context overflow and keep its 400 on /v1/messages streams * fix(anthropic_adapter): keep only the context overflow's 400 on streamed /v1/messages so other provider 4xx still fall back * test(anthropic_adapter): wrap a parametrize line over the 120 char limit * test(integration): audit Bedrock Mantle context overflow mapping across routes, clients, and fallbacks * test(integration): move the streamed /v1/messages overflow spend row into a BUG-skipped test * test(integration): keep every chaos cell in the burst and skip only the missing spend row * fix(bedrock_mantle): answer 400 on an in-stream context overflow The Mantle overflow mapper built its ContextWindowExceededError on the original exception's response, so an overflow that Mantle reports inside a 200 stream (the chat-mode token-count envelope, which the OpenAI chunk parser wraps as a 500) kept that 500 and the streamed /v1/messages route answered HTTP 500 api_error instead of 400 invalid_request_error. The mapper now builds the error on a 400 response that keeps the original headers and request, and the mapping test covers an in-stream 500 and 503. The integration suites pin the streamed /v1/messages overflow to the pre-content 400 route under a retry budget and to the message_start plus invalid_request_error event without one, drop the chaos carve-out that expected no spend row, and give the fallbacks file a timeout mark sized to the proxy's graceful stop. * fix(bedrock_mantle): keep the provider's 400 body and match the overflow code only as error.code --------- Co-authored-by: milan <milan@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: ryan-crabbe-berri <ryan@berri.ai> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
7dc7f101d9
|
feat(decisions): bill decisions and systemone from _decisions cost map fields (#45904)
* feat(decisions): bill decisions and systemone from <provider>/decisions/<model> cost map rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(decisions): mark decisions pricing rows with mode decisions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(ui): sync generated API types for decisions mode Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(decisions): update pricing expectations in proxy tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(decisions): price classifier calls from decision rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(health): drop redundant evaluation handler after main merge Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(decisions): inject pricing row for deployed classifier Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(decisions): ship the decisions pricing lookup without cost map rows Restore both cost maps to main. Tests inject their own <provider>/decisions/<model> rows. The Add Model dropdown hides those pricing keys and offers mode decisions instead of evaluation. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(ui): expect the Decisions mode option in the Add Model form Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(decisions): bill decisions and systemone from _decisions cost map fields Replace the provider/decisions/model pricing rows with <field>_decisions fields on the model's own row. cost_per_token overlays them for the decisions, adecisions, systemone and asystemone call types and prices through generic_cost_per_token; chat, responses and batch keep the plain fields. gpt-6-luna gains its Decisions rates (input 1e-07, output 0, cached and cache-write input 1e-07, 2x above 272k tokens). Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(decisions): satisfy type gate, schema sync and test_utils schema for _decisions fields Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(decisions): import cost_calculator module to keep basedpyright unknown-variable count flat Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(decisions): drop stray fireworks import from __init__ and annotate test cost rows for the type gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): cover gpt-6-luna decisions and systemone above the 272k tier Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(decisions): keep the decisions pricing block clear of main's stale azure comment Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore(cost_calculator): drop stale azure models doc comment Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: kerry <kerry@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
a7a3a6b375
|
test: migrate legacy unit dirs and unrun folders into tests/unit, tests/integration and tests/e2e (#45940)
* test: migrate legacy unit dirs and unrun folders into tests/unit, tests/integration and tests/e2e * test: fix disk cache collection, ungate saturation check, drop RSS memory tests * test: assert one delivered log per completion in Datadog and LangSmith checks * test: drop SDK-direct e2e copies and align cost-map path filter cases --------- Co-authored-by: yuneng <yuneng@berri.ai> |
||
|
devin-ai-integration[bot]
|
e0398455d0
|
test: migrate tests/llm_translation into unit, integration and e2e tiers (#45759)
* test: migrate selected llm translation contracts * test: migrate anthropic completion translation tests * test: migrate bedrock and cohere translation tests * test: tighten bedrock translation migration * test: migrate azure and openai model translation tests * test: migrate openai and small-provider translation tests * test: migrate gemini and vertex translation tests * test: migrate optional params and rerank translation tests * test: migrate huggingface, json and openrouter translation tests * test: fix openrouter integration expectations * test: migrate databricks, xai, cometapi and fireworks translation tests * test: migrate ovhcloud, prompt and small provider translation tests * test: split reasoning effort grid into unit and e2e * test: migrate router and realtime guardrail translation tests * test: migrate google interactions translation tests * test: migrate a2a, evals, skills and hosted vllm translation tests * test: remove remaining llm translation suite * test: tighten migrated llm translation unit tests and land missing rows Restores rows the first pass dropped or weakened (bedrock agentcore and openwebui thinking stream, anthropic prefix, citations and tool_choice, azure agents handler, huggingface bare api_base, optional params num_retries, alternating roles, rerank response type), replaces truthiness and call-count checks with whole-value assertions, and moves tests to the mirror file of the code they cover * test: move cohere, databricks, fireworks and gemini rows to mirror files over mocked HTTP Restores three TestCohereV2Transform methods the first pass nested out of collection, replaces direct transform calls with respx-backed completion calls, and drops the databricks and fireworks files that sat outside their mirror paths * test: close lost-intent gaps found in review of the llm translation migration Points the hosted-mock guard at the triton test's new integration path, restores exact rerank result assertions for infinity, drops the reasoning e2e fallback that passed for any multi-line answer, and adds the mixtral invoke system prompt variants, eval deletion, and async pdf url download that the legacy suite exercised * test: assert provider acceptance in the reasoning effort e2e The live half of the split grid proves each route accepts reasoning_effort. claude-fable-5-1 on the direct route answers without surfacing reasoning content, so a reasoning-presence check either fails on head or needs a fallback that passes for any answer. Wire shape stays covered by the unit grid * test: cover async and stream-end paths the legacy translation suite exercised Adds async variants for bedrock converse file and thinking content, bedrock embedding timeout and region resolution, interaction stream termination and success logging, xai reasoning token folding, and bytes chunks with a prefix before data:. Also scopes the vertex safety settings global in test_vertex_safety_settings with monkeypatch so it stops leaking into gemini request bodies on the same worker * test: type the new llm translation e2e calls and mark moved fixture dirs as packages * test: name the reasoning effort e2e models as module constants * test: assert reasoning content, register the new gemini coverage ids and give the guardrail config e2e a unique name * test: block the prompt the shared guardrail denies and drop the reasoning presence check * test: drop the duplicate litellm import left by the main sync --------- Co-authored-by: yuneng <yuneng@berri.ai> |
||
|
devin-ai-integration[bot]
|
4c532a5713
|
feat(decisions): accept decisions as a model mode alongside evaluation (#45474)
* refactor(decisions): rename the evaluation model mode to decisions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(decisions): canonicalize the health check mode without rebinding Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(decisions): replace canonical_model_mode with is_decisions_model_mode Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(decisions): preserve evaluation mode compatibility Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore: drop the rebuilt dashboard bundle from this PR Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(dashboard): format decision mode helper Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: kerry <kerry@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
a2e334febe
|
test: migrate local_testing SDK and provider tests out of build_and_test (#45760)
* test: migrate local_testing SDK and provider tests out of build_and_test * test: cover sagemaker region, hf template and nova paths end to end Region rows now go through litellm.completion on the sagemaker/ handler that the legacy tests exercised, the prompt template test mocks the Hugging Face tokenizer config instead of switching to custom_prompt_dict, streaming tests assert role, finish reason and body, and Nova single turn and streaming run through litellm.completion with a recorded request body * test: tighten migrated integration tests for local OpenAI routes, timeouts and air-gapped boot Split the catch-all providers migration file by behavior. The fake endpoint route tests now hit the owned upstream directly, as the legacy harness tests did. LM Studio asserts the outbound key, path and stripped model. The embedding timeout test asserts the proxy maps a deployment timeout to a 408 and forwards the read timeout to the provider client, instead of timing out the test client The Azure streaming tool-call test also asserts the second identical stream is served from the Redis cache with the same reassembled tool call The Docker network test always skipped in CI because the security job has no litellm/litellm image and no egress to pull one, and it tested a published image rather than the checkout. It now boots the checkout proxy with every outbound HTTP connection refused and asserts readiness, the bundled cost map fallback and that the cost map host is the only egress attempted * test: tighten the migrated e2e rows for local_testing SDK and provider tests Drop the duplicated Anthropic covers marker and the Anthropic cell wrongly claimed by the Bedrock and Vertex cache tests. Keep the file-wide Anthropic model as it is on main. Send the 2023-06-01 version header to Anthropic through the deployment's extra_headers so the header actually reaches the provider, and mark the creation-token case 1h then 5m like the legacy test did Reuse the Bedrock file's own registration for the pydantic response test, swap the hand-built PNGs for the shared cat.jpg fixture, assert the SageMaker Nova image answer names the cat and that top_logprobs=3 is honored, and register the SageMaker Nova opt-in markers the same way as the other opt-in markers * test: restore lost assertions in migrated test_main tests Restore the role alternation check in test_completion_anthropic_hanging, the embedding and streaming response-header checks, the logit_bias drop check and the gemini system and safety settings checks. Add an azure 429 retry-after test for test_exception_with_headers, trigger the router fallback with mock_timeout like the legacy test, and stop the mock timeout tests sleeping 3s * test: restore lost legacy assertions in migrated vertex ai unit tests * test: tighten migrated cost, streaming and embedding tests and scope the hosted mock guard * test: strengthen migrated anthropic, azure, bedrock and cohere provider tests Assert full request bodies and usage instead of presence checks, restore the dummy-tool and custom_llm_provider assertions the legacy tests made, add the async and per-model variants the legacy parametrization covered, split streamed tool arguments across deltas, and move the Azure chat streaming tests to the azure.py mirror file * test: tighten migrated llms provider tests and cover legacy intents lost in the move Strengthen the migrated fireworks, gemini, mistral, together, openai text completion, openrouter, perplexity and openai error tests so they assert what the legacy tests proved (cost, provider tag, usage-only-on-last-chunk, function_call rebuild, reasoning param, sync/async/stream error fields). Replace the huggingface and jina tests that doubled litellm code with respx end-to-end tests. Add the tests the pre-existing mapped tests never covered: cohere 400 mapping, token-array text completion, hosted_vllm/llamafile/lm_studio env api base embeddings, watsonx embedding auth, ollama ssl_verify client and ollama vision prompt * test: cover residual legacy paths for anthropic tool streams, watsonx async stream, ollama http images and caller async embedding clients * test: restore single request assertions in the deep_infra and azure_ai embedding tests * test: drop a no-op streaming cost flag from the openai streaming cost test * test: cover residual legacy-test lines in caching, main, utils, replicate, vertex and bedrock invoke * test: cover streamed audio assembly, choice membership, untranslated responses events and the stream log guard * test: pin cost rates in unit tests, use sdk for cohere e2e, drop vertex llama e2e * test: send the vertex maps e2e request through the openai sdk * test: drop the local_testing jobs that select no tests after the migrations * test: wait for the streamed cache entry and keep custom Mistral rates test-local * test: keep untouched Mistral test lines as they were --------- Co-authored-by: yuneng <yuneng@berri.ai> |
||
|
nate-berri
|
9d559d8579
|
refactor(core): move path_utils from proxy common_utils to litellm_core_utils (#45775)
Part of LIT-9423 |