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
* fix(spend-logs): trim logged response strings
- route spend-log responses through the existing string sanitizer so oversized base64/text fields are truncated before persistence
- add unit tests covering the truncation path and the feature flag
Note: embeddings-specific truncation (numeric vectors) is still pending and will be handled separately.
* remove unnecessary comment
* add: sanitization unit test for embeddings
* fix: simplify sanatization logic
I overcomplicated a simple change for lack of understanding, fixed.
Cost tracking was failing for Responses API when using custom deployment names
with base_model configuration. The issue occurred because:
- Chat Completions API stores model_info in 'metadata'
- Responses API stores model_info in 'litellm_metadata'
- Cost calculator only checked 'metadata', missing Responses API costs
Changes:
- Updated _get_base_model_from_metadata() to check both metadata locations
- Added comprehensive unit tests covering all scenarios
- Maintains backward compatibility (metadata takes precedence)
Fixes#16772
* litellm_proxy_unit_testing_part1
* test proxy unit test
* litellm_proxy_unit_testing_key_generation
* test_async_call_with_key_over_model_budget
* test_aasync_call_with_key_over_model_budget
* Add support for vector store files endpoints (#16490)
* Add base code for vector store integration
* fix azure related tests and linting error
* fix mypy errors
* Add vector store files documentation
* fix mapped tests
* Add bytedance and ideogram support in fal ai (#16636)
* Add fal ai flux pro v1.1 support (#16578)
* Add fal ai flux pro v1.1 support
* Add tests and docs
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
OpenAI's GPT-5 model family supports a verbosity parameter to control
the length and detail of responses. This parameter accepts three values:
'low', 'medium', or 'high'.
Changes:
- Added verbosity parameter to completion() and acompletion() signatures
- Added verbosity to DEFAULT_CHAT_COMPLETION_PARAM_VALUES in constants.py
- Added verbosity to get_optional_params() in utils.py
- Added verbosity to GPT-5 supported params list
- Updated OpenAI docs with verbosity usage examples
- Added comprehensive test for verbosity parameter
Supported models: gpt-5, gpt-5.1, gpt-5-mini, gpt-5-nano, gpt-5-codex, gpt-5-pro
