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18 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Ayush Shekhar
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7930c0e434
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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> |
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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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devin-ai-integration[bot]
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17f621e116
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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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Kerry Lu
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0d741a9816
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fix: index streamed thinking_blocks so the OpenAI SDK .stream() accumulator accepts them (#46611)
* fix: index streamed thinking_blocks so the OpenAI SDK .stream() accumulator accepts them * fix: satisfy the type discipline and basedpyright gates for the thinking block index * chore: sync the OpenAPI snapshot and schema.d.ts with the thinking block index field --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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c0663d5202
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fix(anthropic)!: translate the OpenAI tool_search tool on /v1/chat/completions for Claude models (#46389)
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models
* fix(responses): define the tool_search_call output item locally for older openai SDKs
* fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields
Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500
* fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out
A replayed Claude tool search call that the caller answered with a tool message kept its
tool_result in the user turn while its tool_use was dropped from the assistant turn, so
Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when
no tool message answers it.
The Responses bridge now strips defer_loading from every tool whenever no tool search tool is
sent, whether the caller sent none or the route cannot run it, since a deferred tool with no
search tool can never be loaded. Namespace members had started carrying the flag to every
bridge provider, which providers that reject unknown tool keys answered 422.
The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit.
* fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact
A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried
* fix(anthropic): translate the OpenAI tool_search tool on /v1/chat/completions for Claude models
* fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups
With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does
A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result
The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator
* fix(bedrock): answer 400 for the OpenAI tool_search tool on Converse unless drop_params is set
* fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic
A function tool whose allowed_callers is not a list of strings now answers 400
instead of 500. The guardrail scan reads tools without malformed flags, so the
route decides between a 400 and a drop under drop_params. When the caller sends
a native Anthropic search tool beside tool_search, only the native one goes out.
Databricks Claude no longer counts as an Anthropic request route.
A previous_response_id follow-up off Anthropic also drops a search call the
client replayed as a function_call with a srvtoolu_ id, and its output. The
answered tool id scan in the Anthropic message factory runs once per assistant
run instead of once per message. A null created_by on tool_search_call and
tool_search_output output items is left out of the serialized response, so a
cache hit returns the same keys as the first call.
* fix(anthropic): send one search tool and judge tool search on the real model for chat tool_search
Several OpenAI tool_search items now collapse into one tool_search_tool_regex
tool, since Anthropic answers 400 when two tools share a name. A native
Anthropic search tool the caller already sent wins over the OpenAI item.
Bedrock invoke and Vertex AI now decide tool search support on the real model
before tool-based response_format swaps in a stub model name, so a Claude 5.5
request with response_format no longer gets a 400 on a route that runs tool
search.
* fix(anthropic): name the proxy drop_params setting in the tool_search 400
* fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan
The scan translated tool_search into Anthropic's regex search tool on every
route, so a guardrail returning only function tools removed tool_search from
native OpenAI requests and OpenAI answered 400 for the deferred tools
* fix(anthropic): run tool search on azure_ai, gate it on Databricks Claude, and strip deferral without recursion
Anthropic's Foundry docs list no tool search exclusion for either hosting
option, so the claude-tool-search rule now fills azure_ai. Databricks Claude
models route their tools through the same unsupported-tool-search gate, so a
tool_search tool answers 400 or drops under drop_params instead of a 500.
_without_deferral strips two fixed levels instead of walking recursively.
* fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai
* fix(anthropic): keep answered tool search replays paired with their answers
* fix(anthropic): drop Claude tool search calls from history sent to other routes, name the fix in the 400
A second turn that carries Claude's srvtoolu_ search call and its result now has both removed
before completion() sends it to a route that does not build an Anthropic request, including the
responses bridge. The tool search 400 now names model_info supports_tool_search for a model the
cost map does not know, and bedrock/invoke for a Converse model that runs tool search there.
* fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails
* fix(chat): keep answered Claude tool search calls in history sent to other providers
Only a srvtoolu_ search call with no tool result is dropped before a
non-Anthropic route, so an answered pair keeps the caller's shape where
OpenAI and Gemini take it. The history check no longer copies the list
through a TypeAdapter.
* fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object
* fix(chat): drop unanswered Claude tool search calls only on routes that reject them
OpenAI, Azure OpenAI and Bedrock Converse answer 400 for a srvtoolu_ call
with no tool result, while Gemini answers 200. Keep the history as sent
everywhere else so routes that already work see what the caller sent.
* fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent
* test(integration): cover chat tool_search routing, history drop and load on the wire
Audit round 1 of 3 at
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mrinal-berri
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1aee1a21ed
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fix(databricks): send a single plain text block as a string for non-Claude models (#45686)
* fix(databricks): send a single plain text block as a string for non-Claude models Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): simplify single-text-block helper Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * docs(databricks): clarify json_object validation wording Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): integration cells for single text block content shape Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): rename list-content cells to stays_list Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(responses): expect plain string content for bridged Databricks messages Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): cover the customer's Unity Catalog model from #45617 and #45618 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(databricks): drop casts from _collapse_single_text_block Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): assert the whole outgoing messages list in the collapse unit test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): cover system joins, fallback, SDK clients, and outage burst for one-block content --------- 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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devin-ai-integration[bot]
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bfb4f4b2c3
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fix(databricks): accept service_tier on requests and carry the served tier on responses (#46111)
* fix(databricks): accept service_tier on requests and carry the served tier on responses Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): mark the in-place service_tier store as rebind-ok Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(databricks): wrap the service_tier store to the line limit Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): keep the rebind-ok suppression on the service_tier line Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): only set service_tier on responses when the upstream returned one Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): name the B010 suppression on the service_tier setattr Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): shorten the service_tier suppression to the line limit Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): read service_tier from the Responses request only Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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e0398455d0
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test: migrate tests/llm_translation into unit, integration and e2e tiers (#45759)
* test: migrate selected llm translation contracts * test: migrate anthropic completion translation tests * test: migrate bedrock and cohere translation tests * test: tighten bedrock translation migration * test: migrate azure and openai model translation tests * test: migrate openai and small-provider translation tests * test: migrate gemini and vertex translation tests * test: migrate optional params and rerank translation tests * test: migrate huggingface, json and openrouter translation tests * test: fix openrouter integration expectations * test: migrate databricks, xai, cometapi and fireworks translation tests * test: migrate ovhcloud, prompt and small provider translation tests * test: split reasoning effort grid into unit and e2e * test: migrate router and realtime guardrail translation tests * test: migrate google interactions translation tests * test: migrate a2a, evals, skills and hosted vllm translation tests * test: remove remaining llm translation suite * test: tighten migrated llm translation unit tests and land missing rows Restores rows the first pass dropped or weakened (bedrock agentcore and openwebui thinking stream, anthropic prefix, citations and tool_choice, azure agents handler, huggingface bare api_base, optional params num_retries, alternating roles, rerank response type), replaces truthiness and call-count checks with whole-value assertions, and moves tests to the mirror file of the code they cover * test: move cohere, databricks, fireworks and gemini rows to mirror files over mocked HTTP Restores three TestCohereV2Transform methods the first pass nested out of collection, replaces direct transform calls with respx-backed completion calls, and drops the databricks and fireworks files that sat outside their mirror paths * test: close lost-intent gaps found in review of the llm translation migration Points the hosted-mock guard at the triton test's new integration path, restores exact rerank result assertions for infinity, drops the reasoning e2e fallback that passed for any multi-line answer, and adds the mixtral invoke system prompt variants, eval deletion, and async pdf url download that the legacy suite exercised * test: assert provider acceptance in the reasoning effort e2e The live half of the split grid proves each route accepts reasoning_effort. claude-fable-5-1 on the direct route answers without surfacing reasoning content, so a reasoning-presence check either fails on head or needs a fallback that passes for any answer. Wire shape stays covered by the unit grid * test: cover async and stream-end paths the legacy translation suite exercised Adds async variants for bedrock converse file and thinking content, bedrock embedding timeout and region resolution, interaction stream termination and success logging, xai reasoning token folding, and bytes chunks with a prefix before data:. Also scopes the vertex safety settings global in test_vertex_safety_settings with monkeypatch so it stops leaking into gemini request bodies on the same worker * test: type the new llm translation e2e calls and mark moved fixture dirs as packages * test: name the reasoning effort e2e models as module constants * test: assert reasoning content, register the new gemini coverage ids and give the guardrail config e2e a unique name * test: block the prompt the shared guardrail denies and drop the reasoning presence check * test: drop the duplicate litellm import left by the main sync --------- Co-authored-by: yuneng <yuneng@berri.ai> |
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devin-ai-integration[bot]
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ca0ff24726
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fix(databricks): call the ai_decide AI Function route for decisions and the auto-router (#45722)
* fix(databricks): call the ai_decide AI Function route for decisions and the auto-router Databricks serves ai_decide at POST <host>/api/2.0/ai-functions/ai-decide, not on a serving endpoint's /invocations route. The decisions provider and the auto-router classifier now accept only the ai_decide model, post to that route without a model field, unwrap the response envelope, and map a noul answer's probability to the System One noul field. * test(databricks): bound the megabyte model name test by scaling, not wall time * test(databricks): assert the whole OpenAI-format decision and floor the liveliness bound * test(decisions): move the Databricks OpenAI-format wire row to the ai_decide route * fix(databricks): keep serving endpoint decisions beside the ai_decide route * test(databricks): assert the whole classifier request body in the test routing case * test(databricks): correct System One spend call type 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> Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mrinal-berri
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fa7ad80a62
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fix(databricks): keep #/$defs refs in json_schema response_format (#45659)
* fix(databricks): keep #/$defs refs in json_schema response_format for non-Claude models Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): type the json_schema response_format override Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): integration cells for json_schema $defs refs across endpoints Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): split stacked loop in streaming json_schema cell Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): mark BaseConfig dict contract on json_schema override Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(databricks): cover json_schema refs on every endpoint, edge refs, sad paths and an outage burst --------- 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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Mateo Wang
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883e3210ee
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feat(decisions): add Databricks ai_decide as a /v1/decisions provider and auto-router decider (#45200)
* feat(decisions): add Databricks ai_decide as a /v1/decisions provider and auto-router decider * fix(decisions): reject Databricks endpoint names carrying URL separators and keep the provider wiring under llms/ * fix(decisions): reject dot-segment Databricks endpoint names and add Databricks to the dashboard's OSS classifier editor * fix(ui): name every OSS classifier provider in the classifier radio copy * feat(decisions): register the Databricks OpenJev decider as an evaluation-mode cost-map entry * fix(cost-map): declare zero cache rates on the databricks-openjev-qwen35-4b entry * fix(proxy): reconcile every DB deployment when a registry miss lands on an auto router A /model/new for an auto router lands on one replica. A request routed to that auto router on a sibling replica missed it, the model read-through loaded only the auto router's own row, and the pre-routing hook then picked a tier the sibling had not loaded, so the caller got a 400 "no healthy deployments for <tier>" until the next periodic reload. An auto router miss now runs the full add_deployment reconcile, tiers included. Found by the audit's two-worker rig on this PR's routing cells; CircleCI's one-worker proxy never opens the window. * test(integration): audit cells for Databricks decisions and the Databricks auto-router classifier Wire cells for /v1/decisions with databricks/<endpoint> deployments, routing cells for the complexity router's databricks classifier on all three endpoints with the Postgres rows read back, management cells for the classifier's spend writes and for the evaluation-mode health check of a Databricks serving endpoint. |
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devin-ai-integration[bot]
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fa2c8984ba
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test: move the unit half of 126 mixed legacy files into tests/unit (#45090)
* test: move the unit half of 126 mixed legacy files into tests/unit * test: restore litellm globals that moved tests set * test: finalize migration test cleanup * test: restore original bodies of moved legacy tests The move into tests/unit had rewritten 612 test bodies, and some of the rewrites dropped assertions. Each moved test now carries its original body from the legacy file, with only the imports, helpers, fake provider credentials and monkeypatched env it needs to run under tests/unit test_timeout_streaming goes back to tests/local_testing because it needs the fake OpenAI endpoint server. The image payload fixture moves with its only user, and two tests that leaked global state (a registered model cost entry and queued logging tasks) are now isolated * test: drop module imports shadowed by restored local imports * test: assert on LiteLLM output in no-assertion moved tests and isolate leaks Twenty no-assertion candidates get one assertion on the value LiteLLM returns, with the original lines unchanged. Four tests go back to their legacy files because they only check types or imports, write into the working directory, or cannot assert without a body change Two moved tests leaked globals into later tests in the same worker, so monkeypatch fixtures now restore the retry-after header parser and the end user cost tracking flags * test: drain queued logging tasks before the Phoenix span test The moved Phoenix test counted spans from logging tasks that earlier tests had queued, so the drain fixture moves to tests/unit/conftest.py and both it and the Datadog batch test use it. test_factory_function goes back to its legacy file because its returned wrapper calls the real Assistants API and cannot be asserted on without a body change --------- Co-authored-by: yuneng <yuneng@berri.ai> |
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devin-ai-integration[bot]
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e2971e0af4
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refactor(llms): expose public names for private provider helpers (#45037)
* refactor(litellm): migrate private usage in llms Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(litellm): preserve Bedrock batch signature marker Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(litellm): migrate llms private usage symbols Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(litellm): preserve UUID Watsonx project IDs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(litellm): retarget llms mocks to public names Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(litellm): limit llms changes to renames and forwarders Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(litellm): keep GCS mock client patching private Vertex auth methods Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(litellm): assert forwarder arguments and type forwarder signatures Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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e814532033
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fix(streaming): keep the served service_tier on streamed chunks and spend rows (#42870)
* fix(streaming): keep the provider's served service_tier on streamed chunks and spend rows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): satisfy type-discipline and strict ruff budgets Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): stamp the served service_tier on every Responses bridge chunk Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic-adapter): expose streamed chunks so disconnects bill partial spend Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(service-tier): cover anthropic and responses served-tier billing paths Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic-adapter): return a chunks-exposing stream so disconnects bill partial spend Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(service-tier): bill disconnects through the router's anthropic stream wrapper Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style: apply ruff format to the anthropic stream changes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(coverage): ignore delegating properties the ast scan cannot see Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style: keep the cast-ok reasons on the cast call line Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): cover served service_tier billing for streamed chat and messages, complete and disconnected Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(anthropic-cache): delegate chunks/messages/model through the messages stream cache writer Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(streaming): keep service_tier on OpenAI-compatible parsed chunks Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(streaming): parameterize delegated chunks and messages types Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tests): follow the anthropic pass_through rename after merging main Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(anthropic): drain the logging worker between response cache tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(spend): cover azure, databricks, responses bridge and gemini served tiers in the stream billing integration test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): keep the served service_tier on streamed chunks and bill it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(databricks): type the served service_tier chunk without a loose kwargs dict Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(cost): bill the served service_tier over the requested one Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(cost): drop explanatory comment from the tier resolution 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> Co-authored-by: kerry <kerry@berri.ai> |
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devin-ai-integration[bot]
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1f77fa65c8
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fix(cost-map): registry audit 2026-09-26, MAI-Image-2.5-Flash price, Databricks Claude Opus 5.5, Azure Foundry retirement dates (#43254) | ||
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yuneng-jiang
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5e6dc89ba1
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test: move tests/test_litellm/llms into tests/unit/llms (#43191)
* ci: run the unit_selection.sh shard files on every event instead of only fork pull requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci: rename fork-flag to unit-flag now that it applies on every event * test: move tests/test_litellm root and small trees into tests/unit Pure renames, no content changes. Follow-up commits in this PR fix references, merge the three files that already existed in tests/unit, keep live-provider tests in tests/test_litellm and wire CI. * test: carry tests/test_litellm conftest isolation into tests/unit Callback lists, routing fallbacks, cached HTTP clients, logger state, AWS, proxy-URL and keychain env, and session-end client cleanup now reset for unit tests too. The environment isolation owns its MonkeyPatch so a test's own monkeypatch is undone before the model-cost teardown runs. * test: merge, split and prune the moved root and small-tree tests Merge batches/test_batch_utils.py and the chat_completions and messages dispatch tests into the files that already existed in tests/unit. Keep the live Gemini interactions tests, the async image-fetch format test and the OpenAI embedding scorer test in tests/test_litellm since they need real network or keys. Put test_router.py under tests/unit/test_router so the existing package no longer shadows it. Delete eight tests the audit found superseded by stronger ones kept in this move. * ci: run the moved root and small-tree tests under their legacy flags Add the misc and responses-caching-types flags to unit_selection.sh and CircleCI, extend enterprise-routing and mcp-integration, and point the legacy GHA shards, Makefile, redis-compat workflow, merge smoke manifest and change classifier at the new paths. * test: make the new tests/unit directories packages tests/unit/test_package_layout.py requires every directory to carry an __init__.py, and without one the moved and retained test_litellm_responses_bridge.py modules collide on import. * test: scope the unit socket block to tests/unit in shared sessions The GHA shards collect the legacy test-path and the unit selection in one pytest session. The unit conftest's loopback-only block leaked into legacy modules that reach the network at import. The legacy conftest now lifts the restriction at collect and setup time, and the unit conftest re-applies it when collecting its own modules. * test: move tests/test_litellm/llms into tests/unit/llms Rename-only. Moves the provider tests and the fine-tuning fixtures they load, mirroring the old paths. Follow-up commits merge, split and wire them. * test: merge, split and prune the moved llms tests Merges the Databricks chat transformation tests into the existing unit file, keeps the tests that need real keys or the network in tests/test_litellm, deletes the audited tests a stronger unit test already covers, and points imports at tests.unit.llms. * ci: run the moved llms tests under their legacy flags The Vertex AI and All Other Providers shards keep their legacy test-path for the retained files and add the llm-vertex-ai and llm-other-providers unit selections. CircleCI gets matching unit jobs. * test: make the tests/unit/llms directories packages Adds __init__.py to the moved dirs and drops the legacy ones whose directories no longer hold tests. * test: drop script runners and path hacks the llms split left dangling The __main__ runners in the split openai_like files and the Databricks e2e runner called tests that now live in the other half of the split or were deleted. The retained legacy halves also no longer need sys.path edits. * test: give the shard-script tests their own GITHUB_OUTPUT They only passed where the runner set it. The CircleCI unit job's env allowlist drops it, so the script's redirect failed there. * test: point the router and module-deletion checks at tests/unit router_code_coverage and code_qa_check_tests only searched tests/test_litellm, so the moved router tests no longer counted. The two silent-experiment tests the audit deleted were the only direct callers of those methods; they are replaced with tests that assert the forwarded shadow request and the recursion guard. * test: keep the Databricks manual e2e runner and fix the SageMaker Nova run path The Databricks e2e file is a manual script whose main() calls the tests that were pruned, so pruning them broke the documented run. It is back to its main version. The SageMaker Nova docstring now points at the file's real location in tests/local_testing. * test: keep the job's UNIT_FLAG out of the shard-script tests --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng
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e4a58ef91a |
test(unit): make every tests/unit directory a package so pytest collection is unique
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng
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e1d2789d29 |
test(llms): migrate phase 6 provider unit tests to tests/unit
Migrate 18 provider test files from tests/test_litellm/llms to tests/unit/llms. 194 kept tests move as-is after mutation testing; 1 test deleted (test_completion_datarobot_with_environment_variables, env-gated no-assert); the fixture-only fal_ai cost calculator file is removed. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |