- Fix blank function name in completions response when using native function calling
- Fix Enum name being used instead of Enum value for comparison in chunk conversion
- Added additional tests to cover changes
Thanks to @mcowger for the invaluable assitance with figuring this issue out!
Fixed#16863
Fixes#16810
## Problem
When using completion() with models that have mode: "responses" (like o3-pro,
gpt-5-codex), the response_format parameter with JSON schemas was being ignored
or incorrectly handled, causing:
- Large schemas (>512 chars) to fail with "metadata.schema_dict_json: string too long" error
- Structured outputs to be silently dropped
- Users' code to break unexpectedly
## Root Cause
The completion -> responses bridge in
litellm/completion_extras/litellm_responses_transformation/transformation.py
was missing the conversion of response_format (Chat Completion format) to
text.format (Responses API format).
The inverse bridge (responses -> completion) already had this conversion
implemented in commit 29f0ed223a, but the completion -> responses direction
was incomplete.
## Solution
Added _transform_response_format_to_text_format() method that converts:
- response_format with json_schema → text.format with json_schema
- response_format with json_object → text.format with json_object
- response_format with text → text.format with text
Updated transform_request() to detect and convert response_format parameter
before sending to litellm.responses().
## Changes
- Added _transform_response_format_to_text_format() method (lines 592-647)
- Modified transform_request() to handle response_format (lines 199-203)
- Added comprehensive tests to validate the conversion
## Testing
- 5 new unit tests covering all conversion scenarios
- Real API test with OpenAI confirming large schemas (>512 chars) work
- No more metadata.schema_dict_json errors
## Impact
Users can now use completion() with models that have mode: "responses" and:
- Use large JSON schemas without hitting metadata 512 char limit
- Get proper structured outputs
- Have their existing code continue working
This aligns the proxy experience with other models that think
automatically (e.g. Deepseek R1 and grok3). It does so by setting
the necessary request input to return thinking, but not specifying
a budget or effort (thus defaulting to the internal automatic level).
* fix: handle reasoning parameters and response in responses bridge
Updates the OpenAI completions/responses bridge to map
reasoning_effort to reasoning parameters, and the chunk parser
to return reasoning_content.
ref: 12432
* fix: using type checked objects in responses bridge transform
ref: 12432
Sameer Kankute