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323 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Mateo Wang
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897dda7920
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fix(router)!: stop a chat-mode Mantle deployment from rewriting the shared Responses routing row (#46263)
* fix(router): stop a chat-mode Mantle deployment from rewriting the shared Responses routing row A deployment with an explicit custom_llm_provider and model_info.mode: chat used to overwrite the shared litellm.model_cost row, moving every other deployment and plain SDK calls onto Chat Completions until restart, because the downgrade guard read a double-prefixed key and never fired. The guard now reads the real key, and a deployment's own mode decides its routing in responses_api_bridge_check, except on chatgpt and github_copilot where Chat Completions is dead for responses-mode models. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(responses): keep the bridge-check mode tests in the sharded bridge flag file Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): let a deployment mode pin replace only the shared-row mode so the bridge capability rules still run Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): keep Responses-only backends bridged under a deployment chat pin and satisfy the type gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): give the bridge casts a reason and drop the bare noqa so the type discipline gate passes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): keep a responses row with no supported_endpoints bridged under a deployment chat pin Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(router): read the downgrade guard's mode from the row registration writes A chat-mode openai/<name> deployment merged into the bare <name> row while the guard read openai/<name>, so the guard never fired and the shared responses row turned to chat. An explicit responses/ prefix now also outranks a deployment's chat pin. * fix(router): let an explicit responses/ prefix outrank a registered chat row The router now registers a chat-pinned openai/responses/<model> deployment under its own name, so the lookup found mode chat and the request went to Chat Completions as responses/<model>. * fix(router): keep the shared row's mode for every deployment pin and honor the pin on /v1/messages A deployment's mode pin now never rewrites the shared cost-map row in either direction, including the responses/-stripped twin key, so siblings and SDK calls keep routing by the registered mode. The backend key formula goes back to main's, which keeps capability fields off rows that registration never wrote at base. The /v1/messages adapter and handler read the deployment's model_info, so a chat-pinned deployment gets a plain reasoning effort on chat completions. * fix(router): keep copilot responses opt-in, make unmapped pins order-free, send Mantle auto summary as auto A responses pin on a github_copilot model or a model missing from the cost map still marks the shared row, since that is how those models opt into the Responses API. A chat pin never marks a row, so registration order no longer matters. Mantle's OpenAI path gets summary auto in place of the auto summary detailed. The Azure o3-pro and o3-deep-research rows drop Chat Completions, which Azure does not serve for them. * fix(router): skip a non-string model_info.mode when keeping the registered mode * fix(router): keep chat-pinned Mantle tool calls and summaries on the Responses API A Mantle row that also serves the Responses API now follows the GPT-5 tool and reasoning summary bridge rules, since Mantle Chat Completions rejects function tools on gpt-5.4+ unless reasoning is off and rejects the summary aliases. Mantle chat gets a dict reasoning_effort as its effort level. The opt-in auto summary asks each Responses config for its level, auto on Mantle's OpenAI path, so a client's own detailed summary follows drop_params as at base. No mode replaces a registered responses mode, and chatgpt and github_copilot rows take any pin until they are responses, which restores the Copilot chat opt-out. * fix(responses): ask the target for its auto summary level on the chat bridge and reasoning_effort * test(integration): run the Mantle region-prefixed v1 base case again This PR routes that deployment by its own chat pin, so the case passes and no longer needs the skip * fix(bedrock_mantle): keep web search and reasoning summaries on the Responses API under a chat pin A chat-pinned Mantle GPT-5 deployment now sends web_search_options and a dict reasoning_effort carrying a summary to the Responses API, which serves them. Mantle Chat Completions drops n above 1 and a reasoning summary under drop_params and answers 400 without it. The /v1/messages adapter probes the bridge with only the client's own summary, so an auto-injected one never moves the request * fix(bedrock_mantle): check n and reasoning summaries only on the Chat Completions request * fix(router): keep a non-string deployment mode off the shared routing row A deployment whose model_info.mode was an int, a list, or an object still reached the shared cost-map row, so an unpinned sibling on the same backend stopped routing to the Responses API. The shared registration now drops a mode that is not a string, and the deployment keeps its own raw value. Adds integration cells for per-deployment routing across the OpenAI and Anthropic SDKs, raw httpx, wildcard routes, malformed modes, an upstream outage, config changes under traffic, and a worker kill plus restart. * test(integration): find each routing cell's spend row by the response it logged A bridged call logs its row under the wrapped Responses id, which the caller never sees on a streamed Messages answer, so look the row up by the answer's own ids and the upstream ids for its marker. Int and list modes 500 their own model group on main too, so those cells check only the sibling, and the summary cell sends the auto level Mantle's Responses path accepts * fix(bedrock_mantle): run extra_body fields through the chat checks A chat-pinned Mantle request that nests n or reasoning_effort in extra_body now gets the same 400, drop, and effort-level handling as the top-level fields. The bridge test injects a MockTransport client instead of patching HTTPHandler.post. * test(router): cover nested reasoning_effort routing, messages thinking summaries and per-target auto summary levels * fix(router): probe a pinned deployment's own API on /health and keep a requested summary beside an extra_body effort * fix(router): keep a custom-priced deployment's mode pin off the shared backend row on each request * fix(router): run per-request custom pricing through the shared mode guard so a health probe keeps the row's mode * fix(router): drop the debug log that wrote a request's model name from keep_registered_mode * test(router): cover custom-priced chat pins, their health probes and summary aliases beside an extra_body effort * fix(proxy): pass only the mode pin to a /health probe so a failing probe never cools its deployment down * test(router): cover failing health probes leaving their deployment out of cooldown, with and without background checks * test(router): kill the worker that holds burst calls, so the worker kill cell never depends on how the kernel spread them * fix(router): route a fine-tune the same whichever deployment registers first Main's register_model now seeds an unmapped fine-tune's row from its base model's built-in row, mode included, on the first registration. The mode guard read only the live row, so a responses pin marked the shared row only when it registered before its siblings. The guard now reads the row the registration merges onto, the live row or else the row it would seed. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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ef1d3959a0
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fix(count_tokens)!: answer 400 for Vertex partner models without a count API and OpenAI-shaped provider errors on /v1/responses/input_tokens (#47163)
* fix(count_tokens): answer 400 for Vertex partner models without a count API and OpenAI-shaped provider errors on /v1/responses/input_tokens * fix(count_tokens): answer 400 for Vertex Mistral counts and type the input_tokens error tests Vertex has no count-tokens route for Mistral or codestral models, so they now get the same 400 as other partner models without a count API instead of a 404 that points operators at IAM. * fix(count_tokens): unwrap prefixed provider errors on /v1/responses/input_tokens * fix(count_tokens): say LiteLLM, not Vertex, cannot count a Vertex partner model * fix(count_tokens): count a Vertex partner model with its configured custom_tokenizer and unwrap prefixed flat provider errors * fix(count_tokens): keep the 400 for contents-only Vertex partner counts with a custom_tokenizer * fix(count_tokens): pass through an OpenAI provider error that omits param or code * fix(count_tokens): keep the status-based type for Anthropic count errors on the Responses route * test(count_tokens): cover the Vertex partner count gate and Responses count error shapes on a real proxy * test(count_tokens): add regression tests for the Vertex partner count gate and Responses count error shapes |
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devin-ai-integration[bot]
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46ae68021e
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fix(anthropic): carry safeguard_results and stop_details in synthesized message_delta events (#46096)
* fix(anthropic): carry safeguard_results and stop_details in synthesized message_delta events When the proxy renders a complete Anthropic Messages response as an SSE stream for a client that asked to stream, the message_delta event carried only stop_reason and stop_sequence. A real Anthropic stream puts safeguard_results and stop_details inside message_delta.delta, so a client such as Claude Code auto mode lost the server-side classifier verdict and a refusal lost its stop_details. Both stream builders now copy those keys from the source response through one shared helper when they are present. * fix(guardrails): keep safeguard_results and stop_details when re-emitting a rewritten Anthropic stream A post-call guardrail that rewrites an Anthropic stream (presidio, bedrock, tool permission, model armor) now carries the upstream message_delta safeguard_results and stop_details into the re-emitted stream. The router fallback message_start no longer leaks those final fields early * fix(guardrails): keep stop_details and safeguard_results in rewritten Anthropic streams without explanations A guardrail that rewrites a /v1/messages stream now carries stop_details (type and category) and the dangerous_tool_use verdicts for the tool calls it still delivers, with every explanation dropped so a masked stream never echoes the unmasked text through those fields. * fix(guardrails): copy only upstream stop_details and forward-compatible safeguard_results into rewritten Anthropic streams A rewritten stream now takes stop_details and safeguard_results from the upstream message_delta alone, so a refusal the upstream sent without stop_details is never given one by the OpenAI round trip. Unknown safeguard types, statuses, reasons and extra fields pass through, a verdict follows a tool call only when its input is unchanged, and tool_permission keeps the explanations since it rewrites no text. * fix(guardrails): apply the safeguard_results and stop_details rules to policy pipeline stream rewrites * fix(router): deliver a refused stream built from a finished agentic turn instead of falling back Websearch, MCP and advisor streams replay a complete reply whose tools already ran, so a refusal fallback would run every tool again. Also give the recovered response's message_start a null stop_details as the API sends it, drop safeguard_results a masking rewrite cannot read, and add the audit cells for synthesized streams. * test(router): annotate the finished-turn refusal stream test locals with Final --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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eb5295b20c
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fix(anthropic): add the dangerous-tool-use beta when a /v1/messages request carries safeguards (#43955)
* fix(anthropic): add the dangerous-tool-use beta when a /v1/messages request carries safeguards * test(azure_ai): pin the outgoing Foundry request keeps the dangerous-tool-use beta next to safeguards * test(azure_ai): inject the Foundry client and scope the beta cache reset to the test * test(azure_ai): own and close the mock transport client, drop the docstring entry * test(integration): audit the safeguards beta derivation across anthropic-compatible providers * fix(github_copilot): keep safeguards from deriving the dangerous-tool-use beta * test(integration): kill the chaos victim worker through the harness helper * fix(anthropic): derive the dangerous-tool-use beta only where the beta map supports it and honor additional_drop_params The safeguards beta is now added only for providers whose beta map column accepts it, so github_copilot, Eden AI and the OpenAI-compatible JSON providers keep their base wire. additional_drop_params now runs before the provider headers are built, so a dropped param no longer drives its derived beta. * fix(anthropic): load the beta map off the event loop and pin a dropped context_management The first /v1/messages request that needs the remote beta map now loads it in a worker thread, so a slow fetch no longer stalls every other request on the worker. An integration cell pins that a deployment dropping context_management sends neither the field nor its derived beta. * fix(anthropic): load the beta map only for safeguards requests, once per worker * fix(anthropic): gate safeguards on the bundled beta map and load the remote map on its own thread --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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82ca10cc2d
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feat(bedrock): apply bedrock_request_metadata_fields on the /bedrock pass-through routes (#47040)
* feat(bedrock): apply bedrock_request_metadata_fields on the /bedrock pass-through routes * fix(bedrock): move Invoke body requestMetadata into the signed header and match Bedrock's ASCII whitespace rule * fix(bedrock): keep Invoke body requestMetadata in the body and answer bad metadata as AWS does Invoke requests whose body already carries requestMetadata keep the resolved pairs in the body instead of the 8,500-character signed header. Malformed caller metadata now answers 400 with the ValidationException error type the AWS SDK expects, and drop_params drops bad pairs on both the pass-through routes and /v1/chat/completions. The caller-pair dedupe is linear, and the chat validator treats \s as ASCII whitespace, as Bedrock does * fix(bedrock): match Bedrock's 400s and header limit on pass-through request metadata Invoke metadata too long for the 8,500-character header now goes in the body, Invoke requestMetadata null answers 400 like Bedrock, Converse type errors carry SerializationException, violation messages name the failing key, and the reserved user_api_key_ prefix is matched case-insensitively after trimming whitespace * fix(bedrock): trim the Invoke metadata header to Bedrock's limit and drop bad requestMetadata under drop_params without the setting * fix(bedrock): leave /bedrock pass-through bodies as sent when request metadata forwarding is off Global drop_params no longer rewrites requestMetadata on the pass-through routes unless bedrock_request_metadata_fields is set. Chat keeps dropping bad pairs under drop_params. Adds a tests/integration wire test for the four pass-through actions and removes an unused parameter from the Invoke header helper. * test(bedrock): drive the pass-through metadata wire test with boto3's bearer key env var * fix(bedrock): trim the pass-through invoke metadata header and stop Nova invoke from checking metadata it drops * test(bedrock): cover pass-through metadata header trimming, malformed caller metadata, and chat drop_params on the wire * fix(bedrock): match requestMetadata keys and values against the whole string so a trailing newline cannot pass the 256-character limit |
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yucheng-berri
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9f115be88d
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fix(bedrock)!: scan Claude invoke-with-response-stream passthrough output with post_call guardrails (#46524)
* fix(bedrock): scan Claude invoke-with-response-stream passthrough frames with post_call guardrails (LIT-9618) Invoke streams of Claude models now go through the same frame-by-frame relay as converse-stream. Each chunk frame's Anthropic event is scanned by the /v1/messages stream translation, a block ends the stream with an AWS exception frame, and a held stream gets its masks written back into re-encoded chunk frames. Claude is told by its model id or, behind an application inference profile, by the anthropic_version its request body carries. Streams of other model families stay as they were * test(bedrock): cover invoke stream vendor outage, upstream exception and release on scan (LIT-9618) * fix(bedrock): hold Claude invoke streams for guardrails the relay cannot run and mark each scan as streaming (LIT-9618) A guardrail released on scan or run in parallel left a Claude invoke-with-response-stream unscanned, while converse-stream holds the whole stream and scans it once. Claude invoke streams now take the same buffered path, with the rewrites written back into the chunk frames that carried each text. Each invoke scan also sends stream true and the events relayed so far as responses, as /v1/messages does, so a vendor can tie the scans of one streamed reply together. * refactor(bedrock): mark the request dicts the buffered stream paths hand to post_call hooks (LIT-9618) * fix(bedrock): scan a Claude invoke stream the upstream breaks off as a finished stream (LIT-9618) An invoke stream that ended on an upstream exception, without a message_delta stop_reason, was scanned as still in flight, so its tool input never reached the guardrail and a held stream released it unscanned. The scan and its scan key now close such a stream the way converse-stream treats the end of its upstream. * test(bedrock): pass the converse-stream endpoint where the tool call without input test frames its stream (LIT-9618) * test(bedrock): expect the ValidationException main now returns when a guardrail blocks a Claude invoke stream before its first frame (LIT-9618) * test(bedrock): type the Claude invoke event helpers in the passthrough guardrail tests * test(bedrock): type the remaining parameters of the Claude invoke stream tests * refactor(bedrock): bind the buffered invoke rewrite in its condition --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yucheng-berri
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ef1cb19351
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fix(bedrock)!: stream Converse passthrough frames through post_call guardrails instead of buffering (#46307)
* test(integration): Converse stream post_call guardrail relays frames as they arrive (LIT-9515) * fix(bedrock): stream Converse passthrough frames through post_call guardrails instead of buffering (LIT-9515) A post_call guardrail that scans through apply_guardrail now runs on /bedrock/.../converse-stream the way it runs on /v1/messages: frames reach the caller as the upstream sends them, every fifth frame and the end of the stream are scanned, and a block after the first frame ends the stream with an AWS exception frame. streaming_buffer_until_moderated still holds the stream and applies masks per content block. Guardrails that need the whole response (own async_post_call_success_hook, mask_response_content, native hooks, policy pipelines) keep the buffered path. * fix(bedrock): apply held Converse stream masks when the upstream ends with an exception frame (LIT-9515) * fix(proxy): log a relayed passthrough stream's spend before its mid-stream failure hook (LIT-9515) * fix(bedrock): close and report every way a relayed Converse stream ends (LIT-9515) - The framer ends the stream with an empty item once the upstream closes, so held masks are applied whether the upstream ends with messageStop, an exception or error frame, or a dropped connection - A guardrail HTTPException after the first frame reaches the relay, which logs the stream's spend, runs the failure hook and ends the stream with the exception frame, instead of ending inside the guardrail with no failure row - A failure before the first frame waits for the stream's spend to be logged, as a mid-stream failure does - Closing the response before its body starts closes the guardrail layers and the upstream - Each stacked guardrail layer gets its own copy of every frame, so an earlier layer's end-of-stream rewrite never reaches frames a later layer still holds - Deltas whose text ends in half a surrogate pair are decoded with the standard JSON parser * fix(bedrock): relay a Converse frame whose payload is not UTF-8 instead of ending the stream (LIT-9515) * test(integration): cover Converse stream relay failure, end-of-stream-only, stacked, disconnect and burst cells (LIT-9515) * fix(bedrock): estimate prompt tokens for a passthrough stream cut before Bedrock reports usage (LIT-9515) Bedrock sends usage only in the last frame of a stream, so a stream ended by a guardrail block or a client disconnect logged 0 prompt tokens while AWS still charges for the prompt. The stream collector now estimates them from the request the way chat completions streams do * fix(bedrock): read tool result blocks without recursion in the passthrough prompt estimate (LIT-9515) * fix(passthrough): keep the spend flush running when the wait for it is cancelled (LIT-9515) * fix(bedrock): keep a converse-stream buffered when its guardrail releases a held stream on scan (LIT-9515) A held post_call guardrail with streaming_buffer_release_on_scan releases each window as received once its scan passes, so the relay delivered the original text instead of the guardrail's masks. Such a stream keeps the buffered path, which applies masks per content block as on main. * fix(bedrock): keep a converse-stream buffered when a post_call guardrail runs in parallel (LIT-9515) The relay ran every guardrail in config order, so a run_in_parallel guardrail listed before a masking guardrail scanned the unmasked text. Main's buffered path runs parallel guardrails after the sequential ones and discards their rewrites, which the relay cannot reproduce, so these streams stay buffered * fix(bedrock): mark Noma v2 relay scans like chat and stop reading Bedrock when a held stream's client leaves - Noma v2 scans on the converse-stream relay carry stream, and the last one carries the finished Converse response with its stopReason, as chat sends them, so Noma keeps one record per reply. Other guardrails get the request unchanged. When the last sampled scan already covered the whole answer, the end-of-stream scan still runs for Noma so it gets the finished answer - A held relay stream whose client leaves before the first frame stops reading Bedrock and returns 499, like chat. When Bedrock sent nothing yet, the budget reservation and the parallel-request slot are released - A cancelled wait for the first streamed chunk now stops the upstream read instead of leaving it running - A Noma v2 scan cancelled before Noma answers records not_run instead of success with an empty response * fix(bedrock): send Noma the finished Converse reply's tool calls and reasoning as Converse blocks * fix(bedrock): give Noma the ended scan of a Converse reply that only calls a tool without input * test(bedrock): expect the ValidationException main now returns for a held block before the first frame Main's AWS-shaped passthrough errors (#46522) answer a guardrail block before the first frame with x-amzn-errortype ValidationException, so boto3 reads the code and the guardrail message instead of a bare 400. * fix(bedrock): keep held masks when an earlier sampled guardrail ends the Converse stream with its message A sampled guardrail that blocks with disable_exception_on_block ends the stream with its message as raw event-stream bytes. A held guardrail after it on the same key only read frame objects, so it never saw the messageStop, skipped its write-back and released the held text without its masks. The Converse handler now reads those bytes back into frames, so the held guardrail sees the end of the message and masks the text * fix(proxy): free the parallel slot when a caller leaves a Converse relay holding part of the first frame The relay skipped releasing the slot and budget reservation whenever a spend flush was scheduled, but a flush of a partial first frame builds no response and logs nothing, so the slot stayed held until it expired. The flush now says whether it logged the stream. --------- Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com> |
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yuneng-jiang
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d346d769f0
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fix(caching): keep prompt cache usage on streamed cache hits (#47073)
* fix(caching): keep prompt cache usage on streamed cache hits A stream=true request served from the response cache rebuilt its usage from the prompt, completion and total counts only, so prompt_tokens_details and the cache read and creation counts were dropped on the replayed stream. /v1/messages clients routed to Bedrock Converse saw input_tokens equal to the whole prompt and no cache tokens on a hit. Both replays now build the usage through one helper that also restores prompt_tokens_details and the integer cache read and creation counts, skipping cached values that do not validate. * fix(caching): coerce replayed token counts the way the live usage does The replay accepted only exact ints for the token counts, so an integral float or numeric string in a cached entry became 0 where the previous replay kept it. Counts now go through pydantic int coercion and fall back only when they do not validate. |
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Mateo Wang
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2c5c9b8d84
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fix(bedrock): use native structured output on Haiku 5.5 and Sonnet 5.5, and fix Fable 5 and Sonnet 4.6 schema 400s (#46870)
* fix(bedrock): use native structured output on every Claude model whose Bedrock profiles accept it Bedrock accepts outputConfig structured output on every reachable profile of Claude Opus 4.7, 4.8, 5, 5.5, Sonnet 5, 5.5, Haiku 5.5, and Fable 5.1, so json_schema now goes out natively there instead of as a forced tool, which kept Bedrock from returning adaptive thinking. Fable 5 rejects outputConfig, so its keys go back to the tool path. GovCloud keys keep their flags, and the drift tests let GovCloud lag its commercial base but never lead it. * fix(bedrock): clean Claude schemas for native structured output and keep recursive schemas on the old path Bedrock native output rejects minimum/maximum, minItems, uniqueItems, oneOf, additionalProperties true, and recursive or unresolved $ref, all of which the forced tool accepted. Claude schemas on the Bedrock native paths now go through the cleaner the Anthropic native path already uses, and a schema with a recursive or unresolved $ref keeps the forced tool, inline, or drop path it had. * fix(bedrock): keep older installs on the json tool with a Bedrock-only native output key Older installs fetch the model map from main, so flipping supports_native_structured_output moved them onto native output with their old schema handling. The new supports_bedrock_native_structured_output key is ignored by older code. Schemas native output cannot hold (recursive refs, open objects such as dict fields) keep the json tool on the new key, models on the old key keep their current behavior for those shapes, and the Anthropic API, Vertex AI, and Azure keep their current behavior. * chore(cost-map): regenerate the model prices schema for the Bedrock native output key * fix(bedrock): keep native output to profiles every account accepts and route schemas Bedrock rejects to the json tool Only Opus 5.5, Sonnet 5.5, Haiku 5.5 and Fable 5.1 keep the Bedrock native output key, since Opus 4.8 and Sonnet 5 answer 400 on some accounts and Opus 5 garbles escaped characters. Lookaround patterns and string formats outside Bedrock's list now keep the json tool, objects typed as a union with null are closed, and the cleaner keeps minItems of 0 or 1. * fix(bedrock): keep Opus 5.5 and Fable 5.1 on the json tool, since native output stops their adaptive thinking * fix(bedrock): keep shared $defs in native schemas and send refused schemas to the json tool on every native model Native output now keeps $defs instead of inlining each use, so a submodel reused across a schema counts once against Bedrock's optional and union limits. Schemas native output cannot hold use the json tool on Converse and Invoke chat for both native keys, and minItems is only kept when it is an integer 0 or 1 * fix(bedrock): keep the json tool unless thinking is requested and bound native output schemas The Bedrock native output key now applies only when the call asks for thinking, so calls without thinking keep the json tool as before. Schemas past Bedrock's documented union and optional counts, or past a grammar size estimate fitted to live probes, keep the json tool on both map keys. Open objects keep the json tool on the Anthropic API, Vertex AI and Azure AI, where native output returned them empty. The $ref check no longer expands $defs, so doubling definitions no longer stall the request. * fix(bedrock): resolve root-path $defs refs so nested Pydantic models keep native output on Invoke * test(bedrock): cover native structured output and json tool routing through the proxy Integration cells drive the proxy against a scripted Bedrock peer that enforces Bedrock's structured output schema rules, covering thinking with native output on Converse, Converse streaming, Invoke, /v1/responses and /v1/messages, the deployment opt-in, Fable 5, schema cleaning on every native entry, open objects, and malformed schemas. Vertex, Anthropic and Azure AI cells cover open objects on native-capable models and the cleaner's minItems and nullable object handling. The unit table now covers reasoning_effort none and disabled thinking * fix(bedrock): keep native output for numeric formats and wide old-key schemas, and keep thinking in json mode Check string formats only on nodes that can be strings, apply the 64-field cap only to the Bedrock-only key, carry thinking blocks into the json mode message, and send malformed json tool schemas as object schemas. * fix(anthropic): keep native output for dict fields unless the json tool can be forced * fix(anthropic): keep native output for dict fields when the caller sends tools * test(bedrock): close the dangerous-tool-use test schema, since Bedrock native output requires closed objects * test(anthropic): cover forced and unforced open-object routing, json mode thinking, numeric formats and wide schemas Add proxy cells for open-object schemas that keep native output when the json tool cannot be forced (a model that refuses forced tools on the Anthropic API and Vertex AI, thinking, caller tools), for the thinking block a json mode answer keeps on Vertex AI and Bedrock Invoke, and for integer and number formats and 65-field schemas on each Bedrock map key. Point the forced open-object cells at the Anthropic API and Azure AI only, since no Vertex AI model in the cost map takes both native output and a forced tool * fix(bedrock): keep the native output opt-in on the deployment that sets it, and route effort none open objects to native output * test(bedrock): cover deployment-scoped native output opt-in, register_model and effort none open objects |
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joshua-berri
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6d0d0bb68b
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feat: add Nace Drex decisions provider (#46523)
* feat: add Nace Drex decisions provider * fix: expose Nace in dashboard provider metadata * ci: include Nace tests in the provider shard * fix: complete Nace provider catalog and preserve OpenRouter tests * test: mark Nace test directories as packages * fix: share Drex score parsing across decision providers --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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Kerry Lu
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e8550c1454
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fix(openai): send chat completions through client.post so the SDK's per-message param transform stops holding the event loop (#46967)
* fix(openai): send chat completions through client.post so the SDK's per-message param transform stops holding the event loop create() walks every message, content part and tool of the request through openai._utils._transform before posting, 91 ms at 300k tokens and ~300 ms at 1M tokens on the event loop, and returns the same dict for the plain dicts litellm builds. client.post is what create() calls after that walk, same request, headers, retries, raw response and stream objects, so the chat helpers now call it directly through the helper embeddings already used * fix(openai): type the chat client.post result as LegacyAPIResponse so the type gate sees .headers and .parse * fix(openai): assert the chat client.post result is a LegacyAPIResponse instead of casting it * test(openai): stub client.post with a LegacyAPIResponse spec in the empty-response and no-leak tests * test(core-utils): stub client.post with a LegacyAPIResponse spec in the agentic loop leak test * test: every client.post stub on the OpenAI chat path returns a LegacyAPIResponse-spec'd mock --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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3ef0b35255
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fix(bedrock)!: block realtime replay after an input-first start and reject malformed Nova Sonic session settings (#46936)
* fix(bedrock): block realtime replay after input starts the session and reject non-string instructions and voice * test(bedrock): name the malformed realtime setting test for its string cases * fix(bedrock): replay the opening realtime message until Bedrock accepts the stream and refuse any fallback after commit Also rejects a malformed modalities, input_sample_rate_hertz or tools setting with invalid_type, dropped under drop_params. * fix(bedrock): replay a refused opening message on any fallback and reject OpenAI voices and non-string audio formats * fix(realtime): read the client scope only when the websocket has one and reject any-case OpenAI and empty Bedrock voices * test(bedrock): cover realtime replay, refusal and committed failure through a real proxy Fix the scripted Bedrock peer's port import and the nested tool call cell's stale expected events, so the Nova Sonic integration file collects again |
||
|
jesus-berri
|
7f603da9f1
|
fix(openai_like)!: surface provider response headers on non-streaming chat completions (#46928)
* fix(openai_like): surface provider response headers on non-streaming chat completions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(openai_like): mark the header test locals Final * fix(proxy): leave out response header values HTTP cannot carry Non-streaming OpenAI-like chat now forwards every provider header, so on the httpx transport a provider header value outside latin-1 made the proxy answer 500 after the provider had served the call. get_custom_headers now leaves such a value out and keeps the other headers, which also ends the same 500 on streaming Adds integration cells for the provider request id on non-streaming chat and the Responses bridge, LiteLLM headers an upstream cannot overwrite, spend parity under an upstream cost header, the latin-1 case, and the logging callback payload * fix(cost): ignore an upstream response cost header that is negative or not a number * test(integration): cover invalid upstream cost headers and non-latin-1 headers on every endpoint * fix(prometheus): keep deployment metrics when a provider rate-limit header is not a number * test(prometheus): type the rate-limit header test and drop its clock reads * test(integration): cover deployment metrics under an unparseable provider rate-limit header --------- 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> |
||
|
ryan-crabbe-berri
|
9aab6ec47b
|
fix(azure_ai): count Grok reasoning tokens in completion_tokens (#46969)
* fix(azure_ai): count Grok reasoning tokens in completion_tokens Grok on Azure AI Foundry reports reasoning_tokens outside completion_tokens, like xAI does, so azure_ai/grok-* calls billed only max(visible, reasoning) output tokens. Fold them with XAIChatConfig.fold_reasoning_tokens_into_completion in transform_response and in a new streaming iterator, matching the xai provider. Split out of #46377. * fix(azure_ai): fold reasoning tokens for every deployment name, streaming or not Non-streaming checked the deployment name for grok while streaming checked the chunk model field, so a Grok deployment named without grok was only folded when streaming. The fold already only fires when total_tokens equals prompt plus completion plus reasoning, so it now runs on every azure_ai response. |
||
|
Mateo Wang
|
b39eadcb82
|
feat(openai)!: honor per-deployment workload identity on non-chat routes (#39966)
* feat(anthropic): workload identity federation and pluggable identity sources Backend half of #38818 (internal copy of the fork PR #38013), rebuilt as one commit on top of litellm_internal_staging without the dashboard changes. Deployments on anthropic/ without a static api_key can exchange an OIDC workload assertion for a short-lived sk-ant-oat01 token through a shared RFC 7523 JWT-bearer engine. The assertion comes from a mounted token file, an env token, a LiteLLM-signed issuer, or Keycloak, chosen per deployment, per named credential, or through ANTHROPIC_IDENTITY_SOURCE. The federation fields are server-owned: refused inline in request bodies and on POST /model/new, proxy-admin only on credentials, and the token exchange is pinned to api.anthropic.com unless LITELLM_ANTHROPIC_WIF_ALLOWED_HOSTS adds a host. GET /credentials/{name}/jwks exports the public key set of a LiteLLM-signed credential for the Claude Console. The OpenAI federation trio from #39613 rides along on the backend side with the same server-owned handling. Fixes #28607 Resolves LIT-6107 Co-authored-by: derhornspieler <15236687+derhornspieler@users.noreply.github.com> * fix(anthropic): let batch-result downloads mint from deployment params and accept host:port allowlist entries The files handler enabled workload identity on batch-result downloads but never received the deployment's litellm_params, so a deployment authenticating through a named credential could only mint from process-wide env vars. It now threads litellm_params through to the auth header the way the batch retrieve path already does. LITELLM_ANTHROPIC_WIF_ALLOWED_HOSTS entries written as host:port were read by urlsplit as a scheme, so the allowlist kept the raw entry while the exchange compared bare hostnames and refused the gateway. Entries are now parsed as network locations whether or not they carry a scheme. * fix(types): move the WIF kwargs key sets to a leaf module so the kwargs funnel imports without a cycle * test(anthropic): pin case-insensitive matching of WIF exchange-host allowlist entries * fix(anthropic): end workload identity federation errors without a period so the router suffix reads cleanly * fix(proxy): decrypt stored litellm_params before the WIF write gate * fix(proxy): hide WIF secret references from /health output * fix(proxy): keep the proxy error shape on credential endpoint refusals * fix(proxy): hide identity token file paths from /health output * feat(openai): honor per-deployment workload identity on non-chat routes Image generation, audio speech and transcription, moderation, text completions, files, batches, and fine-tuning built their OpenAI client from the env vars alone, so a keyless deployment carrying openai_identity_provider_id, openai_service_account_id, and openai_identity_token_file failed with the api_key error on every route other than chat, Responses, and embeddings. Those handlers now build their client through the shared workload identity resolver, and the managed batch file-access credential extraction keeps the trio * fix(openai): cache workload identity clients and let deployment identity outrank process-wide keys Non-chat handlers built a fresh SDK client per call, so files, batches, fine-tuning, text completion and sync moderation exchanged the OIDC token on every request. The builders now reuse the cached workload identity client per identity, base, timeout, retries and organization; static-key clients are still built fresh. A process-wide OPENAI_API_KEY (or litellm.api_key / litellm.openai_key) no longer silently overrides a deployment's openai_identity_* params. An explicit distinct api_key on the deployment or call still wins. * fix(openai): keep shared workload identity clients on the process http session The workload identity client is cached and shared across requests, so it must never borrow the transport a single caller passed in. Static-key clients keep using the caller's http client; federated clients always ride the process-wide session. Also makes the http client helpers on BaseOpenAILLM public since the builder now needs them from outside the class. * test(openai): pin partial deployment identity beating the process key * test(openai): type the managed-object invoke callback * fix(anthropic): rename the federation workspace param so Bedrock's anthropic_workspace_id keeps working The Bedrock Claude Platform route already reads anthropic_workspace_id from optional_params, so banning that spelling as a server-owned federation parameter broke a pre-existing client capability. The federation field is now anthropic_federation_workspace_id (env ANTHROPIC_FEDERATION_WORKSPACE_ID), which restores the base branch's behavior for Bedrock callers, drops the Bedrock-specific hint from the refusal message, and deletes the unconditional ban constant that no longer had a reader * fix(auth): share one exchanged token across workers reading the same assertion Anthropic accepts each identity assertion exactly once, so two uvicorn workers reading the same token file both minting from it means the second exchange is denied with jti_reused. Minted tokens now land in a per-user 0700 cache directory guarded by a file lock, so workers on the same host reuse one exchange until the token expires or the assertion rotates. A 401 is only retried when the re-read assertion actually differs, and the denial hint explains jti_reused. LITELLM_TOKEN_EXCHANGE_CACHE_DIR moves the cache and an empty value disables it * feat(openai): honor deployment workload identity on image edits, vector stores, containers, videos, and evals The httpx-based OpenAI handlers built their Authorization header from the deployment api_key or OPENAI_API_KEY alone, so a keyless deployment carrying openai_identity_provider_id, openai_service_account_id, and openai_identity_token_file sent "Bearer None". They now resolve the bearer through resolve_openai_bearer_token, which exchanges the deployment's identity token the same way the SDK-based routes do. A deployment that carries only part of the identity trio now raises a 500 naming the missing field instead of silently falling back to the process key, and the SDK client builders take a factory for the static-key http client so the federated branch never touches litellm.client_session. * fix: keep anthropic federation from being shadowed or leaked An empty or whitespace-only ANTHROPIC_API_KEY counted as set, so a federated deployment sent an empty x-api-key on every call instead of minting a token. Blank values now read as unset, and a real static key on a federated deployment logs once that it outranks federation and nothing is being federated. The exchange-host allowlist matched hostnames only, so a second process on another port of an allowed host was trusted with the workload's identity token. An entry that names a port now trusts that port alone, while a bare host still trusts every port. The shared token store exists so the workers reading one projected token file do not each spend its single-use jti. A source that mints its own assertion per exchange shares nothing with another worker, so it no longer writes a live token to disk for a lookup that can never hit. * feat(openai): resolve per-deployment workload identity for the assistants routes OpenAIAssistantsAPI built its OpenAI and AsyncOpenAI clients from a locals() scrape, so a deployment carrying openai_identity_provider_id, openai_service_account_id and openai_identity_token_file never exchanged a token. Thread litellm_params through the class and build both clients with the shared workload identity builders. * fix: unlink a staged token file a failed write leaves behind The 401 denial hint now also says federation ignores ANTHROPIC_WORKSPACE_ID, which the Bedrock Claude platform provider already reads. * refactor: move anthropic jwks derivation behind a provider-owned tagged union * feat(openai): resolve per-deployment workload identity for image variations Build the image variations client through the shared workload identity builders instead of scraping locals(), and give the async path the async http session it was missing. Let a federation trio serve the model-less batch fallback, and hold the standard LRU size of exchanged tokens. * fix: unlink the staged token file when its write fails at close A buffered write only reaches the disk when the handle closes, so a full disk surfaces at close and left the staging file behind holding a usable token. * fix(anthropic): close the staging descriptor before writing the shared token file * fix(wif): judge federation writes by what they set, not what is stored The admin gate read the stored deployment, so a team admin lost edit, delete and Test Connection on any deployment carrying federation params. It now returns early unless the submitted fields touch the federation surface, and a Test Connection probe that points the deployment at its own api_base is still refused, with the 403 no longer wrapped into a 500 The rest of the same review pass: POST /model/new refuses only a blocking value of `blocked`, so a client that always sends `blocked: false` is not turned away; a request body can no longer pick which federated identity to mint as by naming a stored credential; an advisory refresh the executor refuses disarms the entry instead of wedging the identity until the follower timeout; the static-key shadow warning resolves its env fallback inside the cache instead of once per request; credential writes drop nulls before storing them; the token exchange validates the endpoint URL before reading an assertion and keeps refusing redirects across a client heal; /health hides every server-owned federation field from non-admins; and the async create_file and create_batch paths say which setting is missing when the provider resolves no URL * style(tests): keep two blank lines before the batch_cost_is_final block * test(batches): type the workload identity fallback test fixtures * fix(moderation): stop validating unrelated kwargs on the sync workload identity path * fix(auth): refuse server-owned workload identity keys in array form and inside a client router config * fix(openai): keep a deployment's own key ahead of workload identity on every route Thread the caller's own api_key into the identity resolver for embedding, transcription, moderation, image generation, and assistants so a deployment key equal to the process key still wins. A partial identity trio now falls back to a configured static key, unset max_retries keeps the SDK default, and /v1/decisions plus OpenAI count_tokens resolve the exchanged bearer for identity-only deployments * fix(containers): pass deployment params to container file auth headers Container file routes built their auth headers without the deployment's litellm_params, so a keyless deployment with an OpenAI workload identity sent Bearer None on upload, list, retrieve, delete and content calls * fix(openai): keep process-wide key precedence and move token exchange off the event loop Restore the merge base order where an explicit or process-wide OpenAI key wins over federation, so exporting OPENAI_API_KEY keeps working the way it did before. Run the header-building validate_environment calls (image edit, videos, containers, vector stores, vector store files) and the decisions and System One request preparation in a worker thread, so a slow token exchange no longer stalls the loop. Federate realtime websocket sessions, live sessions, realtime client secrets and transcription sessions, and the OpenAI moderation guardrail when no key is set. * fix(openai): answer 401 for a partial identity trio and federate realtime health checks A deployment with part of the workload identity trio and no static key now fails as an auth error naming the missing params, so the router does not retry it as a server error. The realtime health check sends the exchanged bearer for a keyless federated OpenAI deployment, and API token counting reads the same static keys as every other route before minting. * fix(openai): keep local token counting without an identity trio and federate the moderation guardrail from its own config * fix(openai): skip a partial-trio moderation guardrail and let vault keys win in token counting * test(integration): refuse caller-chosen identity params in user_config and bracketed form keys * fix(openai): send the honored secret manager key from SDK client builders A deployment whose OPENAI_API_KEY lives only in the secret manager now sends that key on image, assistants, files, batches, and other SDK-built routes instead of failing with no api_key, matching what the workload identity resolver already treats as a static key * test(openai): check the token exchange runs off the event loop instead of timing the loop --------- Co-authored-by: derhornspieler <15236687+derhornspieler@users.noreply.github.com> |
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|
ryan-crabbe-berri
|
9cf09c18fe
|
fix(providers): route Azure Responses past dated api versions, count Grok reasoning tokens on azure_ai, refresh provider sets after register_model, fix gpt-6.1-sol Azure rows (#46377)
* fix(azure): add responses_api_version so the Responses route stops using the deployment chat api_version * fix(azure_ai): fold Grok reasoning tokens into completion_tokens like the xai provider * fix(utils): refresh provider model sets after register_model * fix(cost-map): align azure gpt-6.1-sol reasoning effort flags with the openai row * test(e2e): cover Azure Responses on a deployment pinned to a dated chat api_version * fix(utils): keep router deployment ids out of provider model sets register_model now refreshes the provider sets, so the per-request and router registrations keyed by a deployment id put those opaque ids into wildcard model listings. Those callers now pass add_to_provider_model_sets=False. * fix(azure): carry responses_api_version through the chat to Responses bridge completion() dropped responses_api_version from the litellm_params it hands the Responses bridge, so azure/responses/<model> on a dated chat api_version still built the 404ing /openai/responses URL. Also cover the auto-router marker never forwarding the new param. * fix(azure): stop listing a wrong set of reasoning efforts when none is rejected The message said only low, medium and high are supported, which is wrong for models that also take xhigh such as gpt-6.1-sol. * test(azure): use the latest grok and gpt-5.4 rows in the new azure tests * fix(proxy): list each wildcard-expanded model once A provider set can hold a model both bare and provider-prefixed, which now happens after any priced request registers its openai/<model> key, so the openai/* listing showed that model twice. * fix(azure): route Responses past dated GA api_versions by default A deployment pinned to a dated GA api_version such as 2024-10-21 for chat now sends Responses calls (create, get, delete, cancel, input_items and the azure/responses/ bridge) to the default Responses version, because those dated versions have no Responses route on Azure and 404. responses_api_version still overrides it, -preview, v1, latest and preview versions are kept, an api-version in the api_base query still wins, and chat keeps api_version as before * chore(azure_ai): mark the grok chunk_parser dict as an override contract * fix(azure): rewrite only the route's /openai segment when moving a call to v1 The v1 rewrite in the Azure URL builder replaced every "/openai" substring in the path, so a gateway api_base like https://apim.example.com/openai-gw/team turned into /openai/v1-gw/team/openai/v1/responses. Dated GA Responses calls now land on the v1 route by default, which made this reachable for them too, so the rewrite now only touches the last /openai path segment. The Responses version resolver no longer duplicates the builder's default for an unset api_version. Tests cover the gateway api_base case, the env AZURE_API_BASE query-string precedence, and the e2e suite now has one test for the dated GA default and one where only responses_api_version can make the call work * test(integration): cover Azure Responses api_version, Grok reasoning spend, wildcard listing and reasoning_effort none Wire tests pin the Azure route and query for chat, Responses and the azure/responses bridge on a dated GA api_version, and check responses_api_version never reaches the request body. Cost cases check azure_ai Grok folds reasoning tokens into completion_tokens and spend, streaming and not. A config-driven proxy checks /v1/models next to an openai/* wildcard lists each id once and never a deployment id. Azure gpt-6.1-sol rejects reasoning_effort none before reaching upstream and drops it with drop_params * fix(utils): list ai21 chat and vertex code models under their provider wildcard register_model now files ai21 chat models into ai21_chat_models, which models_by_provider never read, so a registered ai21 chat model dropped out of ai21/* listings. models_by_provider now reads ai21_chat_models and the vertex code chat and code text sets too. * fix(main): keep caller-chosen model names out of provider wildcard listings --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> |
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|
yuneng-jiang
|
0403f8953e
|
fix(cost): bill bridged /v1/messages Responses replies like native /v1/responses for reasoning tokens and service tier (#46975)
* fix(cost): price reasoning tokens of /v1/messages bridged onto the Responses API in the cost header and breakdown * fix(cost): record the bridged Responses API result only after the served body is built * fix(cost): keep the served service tier when a bridged Responses reply has no message item |
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|
AxelRay
|
388251a5b8
|
fix(bedrock): drop dangerous-tool-use beta without safeguards (#44471)
* fix(bedrock): drop dangerous beta without safeguards * fix(bedrock): drop the dangerous-tool-use beta only next to structured outputs, keep it on Mantle Bedrock rejects the pair with 'invalid beta flag' on Claude models before Opus 4.7, while requests without structured outputs and Bedrock Mantle accept the beta, so the drop now only applies to Invoke requests with output_config.format or a strict tool and no safeguards. * fix(anthropic): keep the AnthropicThinkingParam import that main now uses * fix(bedrock): drop the client's dangerous-tool-use beta whenever safeguards is absent Bedrock rejects the beta with 'invalid beta flag' on Sonnet 4.5, Opus 4.5, and Opus 4.6 even without structured outputs, so dropping it only next to structured outputs left those beta-only requests failing. The beta does nothing without safeguards, so Invoke now drops it in every such request. Mantle keeps it. The thinking display helper goes back to main's version. * fix(bedrock): drop the client's dangerous-tool-use beta on Invoke chat without safeguards * test(bedrock): wire cells for the dangerous-tool-use beta on Invoke messages, chat and responses * test(bedrock): type the new dangerous-tool-use beta tests * test(bedrock_mantle): type the dangerous-tool-use beta Mantle test --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
Mateo Wang
|
f8217d2a03
|
fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count (#46843)
* fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count /v1/messages runs clear_tool_uses context edits before it sends an Anthropic body to a Gemini or Vertex AI deployment, but /v1/messages/count_tokens counted the body unedited, so a request with older tool results cleared counted about 2.8x what inference bills. The count now runs the same history and context-edit step inference runs, honors additional_drop_params on the deployment, and skips compact edits so a count never makes a paid summary call. * fix(count_tokens): match the Gemini count to inference for compact edits and drop opt-outs Run compact edits in the count without the summary call so trigger checks and history slicing match /v1/messages, honor a request-level additional_drop_params, keep the full body when any deployment in the model group drops context_management, and answer 400 naming a block Gemini cannot count when an edit trigger fails on it * fix(count_tokens): count the unedited body when an edit fails and honor deployment ids in the drop check * fix(count_tokens): log a failed edit in the count at debug, since the send already logs it * fix(count_tokens): resolve a deployment id before wildcard routes in the drop check, as the router does * fix(count_tokens): honor the router-wide additional_drop_params opt-out in the drop check The router merges default_litellm_params between the deployment and the request, so a router-wide context_management opt-out makes /v1/messages send the full history. The count now reads it in the same order. Adds the audit's wire tests for the count's context edits. * fix(count_tokens): read the opt-out of a deployment the router serves by its litellm model name * fix(count_tokens): read the opt-out and drop_params from the deployments the router and completion_model send to * fix(messages): drop an invalid context_management spec under drop_params so the send matches the count * fix(count_tokens): give Claude Code and Codex counts the drop_params default /v1/messages gives them The proxy turns on drop_params for Claude Code and Codex user agents on /v1/messages. The count now applies the same default, so an invalid context_management spec drops on both endpoints instead of the count answering 400 while the send answers 200. The wire test also covers default_fallbacks, model_group_alias, and completion_model routes. * test(count_tokens): cover a coding agent that turns drop_params off and stop two count tests running as Claude Code |
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|
Mateo Wang
|
7ddc9f1c96
|
fix(bedrock)!: keep reasoning, thinking blocks and the json_mode finish_reason on fake streams (#46863)
* fix(bedrock): keep reasoning, thinking blocks and the json_mode finish_reason on fake streams A Bedrock Converse model without native structured output fakes the stream when response_format carries a json_schema. The fake stream transformed the response without json_mode and rebuilt a chunk from text, tool_use and token counts only, so it dropped reasoning_content, the signed thinking_blocks and the reasoning token count, and ended on finish_reason tool_calls. The three fake-stream sites now pass json_mode to _transform_response and stream the resulting ModelResponse through the shared convert_model_response_to_streaming converter, which now numbers each streamed thinking block with its index the way real streams do * fix(bedrock): parse text tool calls on Converse fake streams like the non-stream answer * fix(responses): stream the answer text when one chat chunk carries reasoning and the answer * fix(streaming): keep every field on the OpenAI and openai_like fake streams and split multi-block thinking Azure o-series and openai_like fake streams now use the shared MockResponseIterator, so extra tool calls, reasoning and cached and reasoning tokens survive; the Bedrock-local iterator is deleted. The /v1/responses bridge also splits a chunk whose thinking block is signed or redacted with no reasoning text, and the sync iterator closes the reasoning item before the answer opens, like the async one. The /v1/messages bridge gives each thinking block in a chunk its own content block, starts a new block when the thinking block index changes, and streams redacted thinking as redacted_thinking. Snowflake replays drop the stream index from thinking blocks. * fix(anthropic): stream each thinking block's text once on /v1/messages An unsigned thinking chunk now opens its content block with empty text, since its thinking_delta carries the text. A multi-block chunk keeps each piece's thinking block, so an unsigned block followed by a signed one keeps its own text and index. * docs(anthropic): describe which thinking block start carries its text * fix(streaming): close merged reasoning before the answer when one chunk carries both * test(streaming): cover fake-stream reasoning across the proxy bridges and type the Snowflake replay test |
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|
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> |
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|
Rick
|
f81e657903
|
feat(volcengine): add Doubao-Seedance video generation via the OpenAI /v1/videos surface (#46156)
* feat(volcengine): add Doubao-Seedance video generation via the OpenAI /v1/videos surface
New VolcEngineVideoConfig maps litellm's OpenAI Videos API onto Volcengine
Ark's contents/generations task API: create hits
/api/v3/contents/generations/tasks and returns a cgt-... task id, status and
content poll /api/v3/contents/generations/tasks/{id}, list maps the same
collection (limit -> page_size, Ark's filter.* through extra_query), delete
maps DELETE on the task URL, and edit/extension reuse create with a
reference_video content item since Ark models both as ordinary generation
tasks. Ark's own edit and extension require ratio adaptive (edit also
duration -1), so those are pinned unless the caller overrides them. Remix has
no Ark equivalent and raises.
The body is flat apart from a typed content array: the prompt is a text item
and every image/video/audio input is a url item carrying a role, which is the
only way to express reference-to-video, edit and extension jobs, so a
caller-supplied content array wins outright. input_reference becomes a
first_frame item, base64 encoded into a data URI for file inputs.
OpenAI params translate per Ark's contract: seconds becomes duration and size
yields both a ratio and a named resolution tier, with the ratio snapped onto
the set Ark accepts (854x480 reduces to 427:240, which Ark rejects) and the
tier taken from the shortest side. Emitting only the ratio leaves the request
on the model default and bills at that default's rate.
Billing reads the delivered duration and resolution rather than the requested
ones, because duration -1 and ratio adaptive both let Ark deliver something
else. Ark bills per output token (tokens = width * height * 24 * seconds /
1024), so the cost map carries per-second rates by resolution tier, derived
from the console's CNY per million token list rates (2.5 70, 2.0 46, 2.0-fast
37, 2.0-mini 23 without a video input) at the repo's 7.0 CNY/USD convention.
Requests that do carry a video input bill at the lower rate instead, which
litellm's per-second tiers cannot express, so those are overestimated.
usage.total_tokens rides along for invoice reconciliation.
Ark echoes the delivered geometry only as a resolution tier plus a ratio, and
the 2.0 family's rendered frame does not follow the tier name: its 16:9 "480p"
canvas is 864x496, not the 854x480 the tier implies, and its other ratios
differ from the tier's nominal shortest side too. A size reconstructed from
the tier alone therefore reports pixels the model never produces and scales
the 480p rate against the wrong pixel count, so both the reported size and
the 2.0 480p rates come from the measured frame (a per-family table, sourced
from the Seedance 2.0 Mini model card and re-verified against a live gateway).
The create response reports the requested duration and resolution, since only
the create, edit and remix calls are costed and Ark's create response carries
no usage block, so without it every video logs zero spend. A caller that
leaves seconds off is not free either: Ark runs its own default length, which
is 5s for the 2.0 family, so that is what the create response bills, while
the 2.5 family's default is -1 (Ark picks) and stays unbilled until the
status response. The create response also echoes the requested seconds and
the frame the job will be rendered at.
Editing and extending need the source video as a URL: Ark's task query is a
GET while litellm's edit prefetch hook only POSTs, so a task id cannot be
resolved into one. The model comes from an explicit extra_body.model, else
the encoded video id, else the model the router already resolved, with
litellm's provider prefix dropped.
The credential's api_base is the Anthropic-compat surface
(/api/compatible), so the base is cut at the first /api/ marker rather than
by a /api/v3 suffix before the video paths append their own version.
The cost-map schema test's exception list gains the 2.0 4K tier, whose
$1.28/second rate exceeds its $1 sanity bound, alongside the existing
runwayml/seedance2 entry.
* fix(volcengine): answer 4xx when a Seedance video is downloaded before it finishes
Downloading a task that is still queued or running raised a bare ValueError, which the
video content handler synthesizes into a 500. The proxy then retried the same poll twice
and reported the caller's own early download as a gateway fault, and the router went
looking for a model group fallback for a job that was simply unfinished
The download path now raises VolcEngineVideoError carrying the status the OpenAI videos
API documents for content that is not ready (404), 400 for a task the provider reported
as failed, 404 for an expired task record, and 502 for a succeeded task with no artifact.
None of them are 5xx, so the router stops retrying them
Regression tests drive avideo_content and assert the caller sees NotFoundError or
BadRequestError instead of an internal server error
* fix(volcengine): clear the type-discipline and override findings on the Seedance video path
The module annotated four mutable collections and carried five `# mutable-ok` markers on
lines that suppressed nothing, so the type-discipline gate counted LIT001 x4 and LIT013
x5 against the PR. The content-item and task-dump helpers hand back read-only views of
what they build, so they now say Mapping. VideoObject.error is declared as a dict by the
type it feeds, so that one marker moves onto the annotation it actually suppresses
transform_video_status_retrieve_response also dropped the client keyword the base class
declares, which basedpyright reported as an incompatible override
* fix(volcengine): bill every Seedance video at create and keep its polls on the deployment that made it
Ark bills a task when it finishes while litellm bills it at create, so a request
whose cost is not known then is rejected: Ark-picked length (duration -1), frames,
a video input, and with it edit and extension. An omitted length is sent as 5s,
since the 2.5 family otherwise lets Ark pick it and logged $0.
Video ids now carry the requested and the served model group, read from
litellm_metadata, so polls and downloads go back to the deployment that created
the task. The routing and input_reference helpers move out of the DashScope
config into a shared module both providers use.
A download before the render finishes answers 400, unreadable or unmapped task
bodies answer 502, malformed seconds, size, parameters and Ark fields raise
unless drop_params is set, an unpriced resolution tier is refused, and the
rendered frame table moves into the cost map.
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
* fix(volcengine): reject a text item in content so the guarded prompt is the only text sent
Input guardrails scan data["prompt"], so a caller could pass a harmless prompt
for scanning and a different, unscanned text item in content, which this branch
kept and the guarded prompt was dropped. The prompt now always leads the content
array and a second text item answers 400.
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
* fix(volcengine): route video to VOLCENGINE_API_BASE when the deployment sets no api_base
* fix(volcengine): let a model with no tier prices keep its resolution instead of rejecting every tier
* fix(volcengine): answer 400 for malformed or unknown create fields, send a 720p default tier, and stop the list cursor
* fix(volcengine): map size to the closest tier by pixel count, reject draft, remix and list cursors, map user to safety_identifier, read ARK_API_BASE
* fix(volcengine): drop unsupported create values under drop_params, take OpenAI's object input_reference, default to 4 seconds, price endpoint ids by base_model
* fix(videos): unwrap nested file tuples without recursion
* fix(volcengine): price Seedance tiers at Ark's own per-resolution token rates
Ark prices 1080p and 4k per token above and below the 480p/720p rate, so the
single 46 CNY per million tokens the rows were derived from billed 2.0 1080p
and 2.5 1080p about 10% low and 2.0 4k 77% high. Ark's per-resolution table
and its own 16:9 examples agree: 1080p 2.48 CNY/s, 4k 5.05 CNY/s for 2.0 and
3.74 CNY/s for 2.5 1080p, which is 51, 26 and 77 CNY per million tokens.
Rewrites the tier test to check each rate against the rendered pixels at that
tier's own price instead of assuming every tier scales from the base rate,
and drops the 4k cost exception that only existed for the old rate.
Co-Authored-By: Bytechoreographer <noreply@bytechoreographer.com>
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
* fix(volcengine): unpack Ark parameters sent through extra_body
* fix(volcengine): ignore the extra_body keys litellm maps itself
The shared request builder merges the caller's raw extra_body back over the
mapped params, so a key this adapter maps itself comes back a second time as a
field of its own, and the unknown field check turned the supported request
into a 400.
|
||
|
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> |
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|
Aryan Pardeshi
|
a844dadcd2
|
feat(gemini): allow disabling thought summaries while keeping thinking on (#36460)
* feat(gemini): allow disabling thought summaries while keeping thinking on Both reasoning-effort mappers hardcoded includeThoughts to True whenever thinking was on, so thinking ON with summaries OFF was unreachable even though that is the google SDK's own default. Summaries are the narration rather than the reasoning, and for callers that discard them they are pure added latency. Adds litellm.gemini_include_thoughts_default, defaulting to True so nothing changes for anyone who does not set it. It gates the includeThoughts=True branches in the thinkingBudget mapper, the thinkingLevel mapper, and the anthropic-style thinking param mapper. The disable and none paths still force includeThoughts=False: with thinking off there is nothing to summarise. * test(gemini): assert thought-summary flag through the outbound Gemini request * feat(gemini): let a request's thought summary ask override the gateway default thinking.display (summarized, updates, omitted) and a reasoning_effort object carrying summary now decide includeThoughts for that request; gemini_include_thoughts_default applies only when the request says nothing * feat(gemini): let a thought summary ask on /v1/messages and /v1/responses override the gateway default * fix(gemini): read a text gateway default as a boolean and leave display omitted to the flag Normalize gemini_include_thoughts_default through normalize_drop_params so a YAML or env value written as "false" or "0" turns thought text off and a null or unknown value keeps the default on. thinking.display "omitted" no longer counts as a per-request ask, so the gateway flag decides, matching /v1/messages. Adds proxy integration cells for chat, /v1/messages and /v1/responses that prove the flag, every per-request ask, streaming, and a burst across workers. * test(gemini): match the streamed Responses usage, which omits null token details * feat(gemini): read the AI SDK reasoning summary alias and thinking.summary as chat asks * fix(gemini): keep the logged reasoning_effort unchanged and key the response cache on the summary alias A dict reasoning_effort next to the AI SDK reasoning_summary alias now counts as a summary ask, and the alias is part of the cache key so a plain request and an alias ask never share a reply * fix(caching): key the response cache on the summary alias wherever the request carries it The cache key now reads reasoning_summary and reasoningSummary through the same reader as the summary fold, so an alias inside extra_body gets its own key too * test: type the new summary-ask unit tests and mark their locals Final * test: annotate the remaining summary-ask unit tests the PR touches --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
debanshd
|
1b1ddef1b2
|
fix: preserve non-null anyOf branches when stripping null types for Vertex (#41683)
* fix: preserve non-null anyOf branches when stripping null types for Vertex * refactor: build anyOf branches immutably and drop explanatory comments * style: apply ruff format * fix: annotate anyOf list construction for the LIT002 budget * fix(vertex_ai): send a null-only anyOf as one nullable null instead of raising * test(vertex_ai): pin empty anyOf passthrough * fix(vertex_ai): forward a non-list anyOf unchanged and add wire cells for the null fold * fix(vertex_ai): forward a non-object anyOf branch for Google to judge instead of crashing * fix(vertex_ai): fold null out of type arrays and union branches before sending to Gemini --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
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> |
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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> |
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Mateo Wang
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b21e659c24
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fix(proxy)!: count endpoints return errors in their source API shape instead of silent local counts (#46815)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): reject unconvertible blocks nested inside a tool_result * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system * fix(proxy): count endpoints return missing-key and request errors in their source API shape * fix(proxy): drop imports the count error change left unused * fix(proxy): let Entra-authenticated Azure AI Claude count tokens and keep Google count errors in the Google envelope * fix(proxy): send Azure environment keys as x-api-key on Azure AI Claude counts and log unexpected Google count failures * fix(proxy): count with the global OpenAI and Anthropic keys chat already uses * fix(proxy): resolve the OpenAI count base with the key so a global key stays on its gateway * fix(proxy): keep unexpected Google count failures generic so internal details stay out of the response * fix(proxy): resolve the Azure AI Claude count base from the same settings chat reads * fix(proxy): count OpenAI tokens with the workload identity bearer chat already uses * fix(proxy): mint the OpenAI workload identity bearer inside the count error handling * fix(proxy): report a failed OpenAI workload identity exchange on the count endpoints as a 401 --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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David Bochenski
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f072498304
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fix(bedrock): grant sts:GetWebIdentityToken and sts:TagGetWebIdentityToken in web identity session policy for claude_platform (#46670)
* fix(bedrock): grant sts:GetWebIdentityToken in web identity session policy for claude_platform Claude Platform on AWS calls STS GetWebIdentityToken (outbound identity federation) with the caller's session before serving inference. The session policy passed to AssumeRoleWithWebIdentity is a permission ceiling and never granted it, so every bedrock/claude_platform request using OIDC web identity auth failed with 403 "no session policy allows the sts:GetWebIdentityToken action", even when the role's identity policy allowed it. Add a ClaudePlatformOutboundIdentityLiteLLM statement granting only sts:GetWebIdentityToken, plus tests that it is granted, scoped to its own statement, and that no sts:* wildcard is introduced. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(bedrock): also grant sts:TagGetWebIdentityToken for claude_platform web identity Claude Platform on AWS tags the outbound web identity token it requests, so the caller also needs sts:TagGetWebIdentityToken. Found end-to-end on a deployment: with only GetWebIdentityToken granted, calls failed with "not authorized to perform: sts:TagGetWebIdentityToken on resource: arn:aws:sts::<account>:self"; granting both made /v1/chat/completions and /v1/messages (streamed and not) succeed. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(bedrock): only let Claude Platform use the outbound identity STS grant Scope the ClaudePlatformOutboundIdentityLiteLLM session policy statement with aws:CalledViaLast = aws-external-anthropic.amazonaws.com, so a web identity session can no longer call sts:GetWebIdentityToken or sts:TagGetWebIdentityToken directly. Drop the code comment and fix the stale test messages. * test(bedrock): fail when the Claude Platform caller condition reaches other statements --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Dan Lemon
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c8b73cde84
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feat(soniox): decode proxy form fields for context, translation and flags (#40605)
* feat(soniox): decode proxy form fields for context, translation and flags Multipart form fields reach the proxy as strings, so Soniox received context and translation as JSON strings and booleans as "true". Decode them at the handler entry point and add typed shapes for the context and translation request fields. * fix(soniox): keep bracket-prefixed free-form context and move types under llms Free-form context such as "[inaudible] consultation" is valid Soniox input, so a string that fails JSON parsing is now forwarded unchanged instead of returning a 400. The Soniox request types live next to the provider code. * test(soniox): exercise SDK-typed context and translation shapes through the decoder * refactor(soniox): derive handler options and provider params without mutating a local dict * chore(soniox): drop explanatory comment on the non-forwarded param set * refactor(soniox): return invalid form bool as a value and raise once in the handler * fix(soniox): validate decoded form fields and keep language with hints Validate structured context before sending it as an object, answer 400 for a bad polling interval or flag unless drop_params is on, nest SDK bracketed extra_body fields, keep the OpenAI language at the head of language_hints, and return the translated text for one-way translation without tags. * fix(soniox): keep the source transcript, rebuild SDK list fields, and reject malformed JSON values * fix(soniox): keep cleanup strings whole and drop the unused translation type * fix(soniox): let explicit language_hints win over language and test the proxy form path --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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79ca820933
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fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert (#46772)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * fix(count_tokens): return a 400 naming Anthropic blocks the OpenAI and Gemini count can't convert * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): reject unconvertible blocks nested inside a tool_result * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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Mateo Wang
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dba86bec28
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fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them (#46767)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * fix(count_tokens): count Anthropic-format requests for Gemini the way /v1/messages inference converts them * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): fetch Gemini remote media without blocking the event loop * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * chore(count_tokens): give the Gemini count helpers' list returns a reason * fix(count_tokens): normalize Anthropic messages for Gemini counts the way /v1/messages does * fix(count_tokens): count Gemini bodies after inference's history rewrites and answer converter errors with 400 * test(count_tokens): cover the Gemini count error paths and compaction with a text-block system --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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Emmanuel Acheampong
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de4d28653d
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fix(crusoe): update base URL to current endpoint (#33121)
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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d25b58f744
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fix(count_tokens): count Anthropic-format requests for OpenAI the way /v1/messages inference converts them (#46759)
* fix(count_tokens): convert Anthropic-format messages for the OpenAI counter the way inference does * test(count_tokens): check /v1/messages/count_tokens sends OpenAI the input /v1/messages inference sends * fix(count_tokens): build the Anthropic count request without a cast and sync dashboard API types * test(count_tokens): compare tools by value in the public api test * fix(count_tokens): count Anthropic bodies the way /v1/messages inference sends them to OpenAI Unwrap affinity-encoded reasoning, count the structured-output schema, flatten replayed web search results, join a list system prompt, and answer a message without a role with a 400 before calling OpenAI. messages_format is now internal to the Anthropic count route instead of a public /utils/token_counter field. * test(count_tokens): expect the image detail the adapter now sends after merging main * fix(count_tokens): type normalize_native_messages without the unimported Any On Python 3.10 to 3.13 the annotation is evaluated at import, so the proxy failed to start with NameError * test(count_tokens): expect the converted Responses input in the budgeted-key count integration test --------- Co-authored-by: Mrinal Chanshetty <mrinal@berri.ai> |
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mrinal-berri
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bd60cc8608
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fix(bedrock): forward Nova Sonic realtime tool calls with their arguments (#46214) | ||
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Mateo Wang
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d489c04554
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fix(anthropic): drop unknown legacy thinking keys under drop_params when rebuilding adaptive thinking (#46741)
* fix(anthropic): drop unknown legacy thinking keys under drop_params when rebuilding adaptive thinking * fix(bedrock): drop unknown thinking keys under drop_params on display updates rebuilds Bedrock Invoke copied every caller thinking key when adding display updates, so an unknown key still reached the provider with drop_params on. Filter that copy the same way, and cover each chat call site plus the global litellm.drop_params path in tests * fix(anthropic): drop budget_tokens from rebuilt adaptive thinking under drop_params * test(anthropic): cover drop_params sources for rebuilt adaptive thinking on /v1/messages and Bedrock Invoke |
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Mateo Wang
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7090588bdf
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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 |
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Mateo Wang
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84ff9f84a9
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fix(anthropic): send detail on input_image parts bridged from /v1/messages to Responses (#46720)
* fix(anthropic): send detail on input_image parts bridged from /v1/messages to Responses * fix(ui): send detail on playground Responses image parts and code snippets * test(messages): cover image detail on the Responses bridge wire |
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Fede Kamelhar
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68759a239f
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fix(oci): drop non-function tools under drop_params and gate reasoning_effort on declared levels (#40732)
* fix(oci): skip non-function tools and gate reasoning_effort on models that reject it Fixes #31449. Two OCI GENERIC request-building defects that made Grok-on-OCI unusable for tool-using coding-agent clients (Codex over the Responses API, Claude Code over the Anthropic Messages API): 1. `adapt_tool_definition_to_oci_standard` raised `OCIError("OCI only supports function tools")` for any tool whose type is not "function", aborting the whole request when a client mixed a built-in/server tool in with its function tools. Non-function tools are now skipped with a warning; if none remain, `tools`/`toolChoice` are omitted from the request instead of sending an empty array. 2. The GENERIC param map forwarded `reasoning_effort` to every non-Cohere model, but OCI rejects it for all served xAI Grok models (`Model grok-4.20 does not support parameter reasoningEffort`) and for OpenAI's non-reasoning models (`Unrecognized request argument supplied: reasoning_effort` on gpt-4o / gpt-4.1); gpt-5, the o-series, gpt-oss-* and Meta/Google models accept it. `_model_supports_reasoning_effort` encodes that, `get_supported_openai_params` stops advertising the param for those models, and `map_openai_params` follows the standard unsupported-param contract (dropped under drop_params, clear error otherwise) so `drop_params` / `additional_drop_params` now work as documented instead of the 400 surfacing from the service. Behaviour verified against OCI GenAI in us-chicago-1 (2026-09-11) per model. The two existing reasoning_effort normalisation tests move from a Grok model to openai.gpt-5, which is the case they exercise. 21 new tests; the OCI suite is 406 passed. * fix(oci): drive reasoning_effort gating from the model catalog and drop tool_choice for skipped tools Review follow-ups on #40732: - `_model_supports_reasoning_effort` now reads the `supports_reasoning` flag of the model's `oci/` catalog entry through `get_model_info` instead of hardcoding xAI / gpt-oss branches. Catalogued models without the flag (xAI Grok, Meta Llama) no longer receive `reasoningEffort`; models absent from the catalog keep the historical pass-through. - Catalog: flag `oci/google.gemini-2.5-{flash,pro,flash-lite}` as reasoning models and add `oci/openai.gpt-oss-120b` / `oci/openai.gpt-oss-20b` (pricing from Oracle's public price list, 128K context per OCI docs). - `_model_uses_max_completion_tokens` routes on the `openai.*` prefix only. OCI-hosted Gemini accepts `maxCompletionTokens` but ignores it (the output cap silently disappears) and Llama rejects it with 400, so the catalog's reasoning flag must not drive that switch. - After tool adaptation, a `FUNCTION` tool_choice naming a tool that was skipped (built-in / server tools) is dropped with a warning instead of being sent to OCI, which rejects the unmatched choice. Verified live against OCI GenAI (us-chicago-1): Grok with mixed tools plus a forced built-in now completes and still calls the function tool; Gemini and gpt-oss forward reasoningEffort with maxTokens honoured; Llama drops it under drop_params; gpt-5 keeps maxCompletionTokens. * style(oci): use an immutable set for the tool-name lookup Keeps the type-discipline gate (LIT002, mutable collections) within its budget; the lookup is read-only. * fix(oci): fail closed when a forced tool_choice names a tool OCI will not receive Review follow-ups on #40732: - When tool_choice forces a function that is not among the adapted tools (a skipped built-in or a misspelled name), send `toolChoice: NONE` instead of removing the choice. Falling back to the service default would let the model call any other supplied function even though the caller forced a specific one, which an agent processing untrusted content must not do. - Move the #31449 regression tests into the mapped test module (`test_oci_chat_transformation.py`) instead of a separate file. Verified live against OCI GenAI: Grok and Llama with a forced skipped built-in now complete with no tool call; a forced kept function is still honoured. * test(oci): point reasoning_effort comments at the in-module gating tests The referenced module was folded into this file. * fix(oci): drop non-function tools only under drop_params and gate reasoning_effort on declared levels Non-function tools now follow the unsupported-param contract: a 400 naming drop_params without it, and dropped with a warning under it, with a forced dropped tool turning tool calls off. reasoning_effort is gated on the model's reasoning_effort_levels catalog entry, so OCI Llama keeps forwarding it and the Grok 4.20 models OCI serves, now in the catalog with their Oracle prices, stop sending a param the service rejects * fix(oci): gate reasoning_effort on oci/xai.grok-4.20, which rejects it * fix(oci): disable tool calls when tool_choice forces a dropped custom tool by name * fix(oci): drop tool_choice that forces a dropped tool and keep a kept function's name * fix(oci): keep Gemini 2.5 Flash Lite unflagged for reasoning so released proxies keep its max_tokens cap * fix(oci): answer 400 when a request depends on a dropped tool and leave Gemini max_tokens routing to its own PR Under drop_params, non-function tools still drop when function tools remain and tool_choice does not force a dropped tool. A request whose every tool is dropped, or whose tool_choice forces one, now answers 400 instead of a tool-less reply the model can fill with invented results, or a swap to another tool. The maxTokens routing change for OCI Gemini goes back to base, since it is outside this fix and an open PR carries it. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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da2f5a498f
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feat(agents): support Databricks Agent Platform agents in the agent gateway (#45681)
* feat(agents): support Databricks Agent Platform agents in the agent gateway Add a databricks_agent provider that speaks the MLflow ResponsesAgent wire (Responses-shaped input, output_text items, output_text.delta SSE) to Model Serving endpoints (/invocations or the unified /serving-endpoints/responses) and Databricks Apps (/responses), with a PAT or OAuth M2M client credentials Register it through the agents config block or the Admin UI agent catalog and call it through /a2a/<agent> (message/send, message/stream) or model: a2a/<agent> chat completions. The chat route now runs every completion-bridge agent through its own provider, and attaches the minted Databricks OAuth token for both bridge and URL agents. Consumed OAuth settings never reach the provider call * fix(agents): harden databricks_agent input, output, and OAuth handling Non-text content parts answer 400 unless drop_params is set, a reply without an output list answers 502, flat OAuth fields resolve an os.environ api_base, the bridge debug log no longer echoes the request model, routing tests stub only the HTTP boundary, and the catalog description fits the dropdown * fix(agents): merge agent and client extra_headers on the a2a chat route, and reject an empty databricks_oauth block * fix(agents): bridge only databricks_agent agents on the a2a chat route, keep the card url over a client api_base, and require workspace_url for a Databricks App * refactor(agents): read the a2a chat bridge providers from a constants set instead of branching on a provider name * fix(databricks_agent): let the configured credential win, reject malformed client extra_headers, and keep a missing endpoint name empty * test(agents): drive the flat OAuth a2a send through the real bridge * fix(agents): keep an empty databricks_oauth block ignored on url agents, as before this change * fix(agents): accept bare App hosts, env service principals, and os.environ refs on Databricks agents * fix(databricks_agent): address review caveats on chat bridging, params, and auth Map max_tokens and max_completion_tokens to max_output_tokens and pass temperature, top_p, and user, so /v1/messages works without drop_params. Send A2A message metadata only with string values, answering 400 or dropping the rest under drop_params. Join message items with a blank line in both stream and non-stream replies. Cache the env service principal token until shortly before it expires. Keep databricks_agent replies out of the response cache. On the chat bridge, let request fields win over the agent's stored params while pinning the agent's api_base and api_key, and apply the agent's static_headers between client and agent extra_headers, as /a2a does. Resolve os.environ/ only for databricks_agent api_base and api_key instead of every bridge provider. Ignore an unusable databricks_oauth block on URL agents on chat, as at the merge base, instead of answering 400. * test(internal_context): list the databricks agent token cache as process-local * fix(databricks_agent): keep the agent's endpoint, input, and target out of client control extra_body model and input answer 400, or are dropped under drop_params. A client api_base or base_url never replaces the agent's card URL on the bridge. Chat to a URL agent keeps its merge-base call, and an incomplete databricks_oauth block on a URL agent keeps its merge-base ValueError. * test(databricks): expect the merge-base 400 for a client api_base on agent chat * fix(databricks): keep agent-owned inputs and identity headers out of a chat caller's reach * fix(databricks): bound the agent-over-caller merge to two levels so it needs no recursion --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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ryan-crabbe-berri
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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
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devin-ai-integration[bot]
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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
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ryan-crabbe-berri
|
1f041a3265
|
fix(openai): strip prompt_cache_breakpoint under drop_params for OpenAI models that cannot read it (#46453)
A chat completions request to gpt-4.1 (or any OpenAI model older than 5.6) with drop_params on still sent the marker inside messages and tools. It is now removed there, while drop_params off keeps sending it through and models that read it keep it either way Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
|
2c4ec9c6fa
|
fix(responses): open faked stream items empty so tool arguments and text arrive once (#46533)
* fix(responses): open faked stream items empty so tool arguments and text arrive once * fix(responses): open faked stream items in progress with a fresh empty list, and open mid-stream guardrail blocks empty * fix(responses): open faked refusals, reasoning summaries, custom tool calls and MCP calls empty so each value arrives once * test(integration): cover the empty-open Responses stream items on every fake, replay and block path Integration cells for the faked /v1/responses stream, the chat and messages bridges, cache replay, guardrail block events and the MCP gateway call. Each opened item starts empty and each value arrives once, checked with raw httpx, the OpenAI SDK and the Anthropic SDK, plus upstream errors, 5 KB values, a client drop, a dropped upstream connection and a worker SIGKILL |
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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> |
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|
devin-ai-integration[bot]
|
17f621e116
|
fix(vertex_ai): route Model Garden models through the generateContent adapter (#41836)
* fix(vertex_ai): route Model Garden models through the generateContent adapter Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(vertex_ai): annotate generateContent route test parameters Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(vertex_ai): type litellm_params in get_vertex_ai_model_route * fix(vertex_ai): bill and log Model Garden generateContent calls like chat completions Streamed Model Garden calls through the generateContent adapter now request stream usage where the deployment supports it, keep cached-token details, and look up the provider-relative cost key, so they bill like the non-stream call. The adapter forwards router metadata, passes upstream errors through with their status, countTokens falls back to local counting for non-Gemini routes, and the native Gemini config is limited to the Gemini routes. * fix(google_genai): keep adapter stream usage, admin opt-out, single failure count, and base_model routing Streamed generateContent through the chat adapter now ends with the upstream usage, honors always_include_stream_usage false and the caller's stream_options, counts one failure per failed call, and routes Vertex deployments by their base_model like chat does * fix(google_genai): ask for stream usage on every adapter stream so Claude streams end with usage * test(google_genai): expect the trailing usage chunk on bridged OpenAI streams The OpenAI bridge test now sends the usage chunk OpenAI returns for include_usage and expects it translated into usageMetadata. The response_format support helper goes back to its single-purpose form since its only other caller is gone. * fix(google_genai): end adapter streams with one STOP event carrying usage, and keep upstream usage after the finish chunk * fix(google_genai): send the held STOP event when no usage arrives, keep a caller's stream_options, and route countTokens like generateContent * test(integration): cover the moved Vertex routes, adapter stream usage, the shared iterator and usage settings over the wire * test(integration): add cache-hit twins for the stream usage setting and non-stream cells for every iterator member --------- 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> |
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|
mrinal-berri
|
08a24d391a
|
fix(count_tokens): count system and tools on Vertex Gemini, fall back to Mantle on Bedrock 404 (#46430)
* fix: align token counting with inference context Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: preserve Vertex Gemini count message shapes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: forward Google countTokens tools in shapes every provider counter accepts Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test: add package marker for Vertex count-token unit tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(vertex_ai): ignore deployment system_instruction when counting tokens Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(count_tokens): forward system and tools on Google AI Studio, keep strict on Anthropic tools Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(count_tokens): ruff format gemini count handler Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(count_tokens): return Mapping from count request body builders Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(count_tokens): truncate long Anthropic tool names on Vertex count, update stale strict test expectations Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(count_tokens): simplify Gemini count context and tests Put the Vertex Gemini count branch back in the same shape as main and the partner model branch, keeping only the system and tools context, the provider error mapping and the gemini tokenizer default Share one error response helper between the Vertex and AI Studio callers, drop the unread kind fields and the extra validation of map_function output, and say Gemini instead of Vertex AI in the error that AI Studio users also see Tighten the tests by removing a stray parametrize on a helper, sharing the SDK count args, asserting the exact forwarded system and tools, and folding the Anthropic tool shape tests into one table * fix(count_tokens): reuse inference tool translation for Vertex Gemini count Vertex and AI Studio count now convert tools with the same functions inference uses, translate_anthropic_tools_to_openai and the Responses tool converter, before VertexGeminiConfig.map_function. Unnamed and Anthropic hosted tools now count the same declarations inference sends instead of being dropped Tools that cannot be converted, such as a non list tools value, a non object tool or a schema that is not a JSON object, now return a 400 before any request instead of being counted without them --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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|
Mateo Wang
|
606ecf56de
|
fix(decisions): keep box answers from OpenAI-format providers instead of turning them into refusals (#46633)
* fix(decisions): keep box answers from OpenAI-format providers instead of turning them into refusals Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): prove an OpenAI-format box answer reaches the caller as a box Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): give the box wire test a deployment that advertises supports_box_questions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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|
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>
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