Gemini 3 models require 'includeThoughts: True' in the thinkingConfig to return the actual thought text. Previously, using reasoning_effort set the 'thinkingLevel' but missed the boolean flag, resulting in empty reasoning_content.
This fix:
1. Updates `_map_reasoning_effort_to_thinking_level` to include `includeThoughts: True` for low/medium/high.
2. Adds unit tests to verify the config mapping.
Fixes#16805
When using Gemini models (2.5/3.0) with streaming + tools enabled,
the reasoning_content field was missing from stream chunks, even though
thinking_blocks were present in non-streaming responses.
Changes:
- Convert thinking_blocks to reasoning_content for streaming responses
- Extract "thinking" field from each thinking_block
- Concatenate multiple thinking parts with newlines
- Assign to reasoning_content in chat_completion_message for streaming
Testing:
- Added test_streaming_chunk_with_tool_calls_includes_reasoning_content
- Test verifies reasoning_content appears with tool calls in streaming
- All 39 existing Gemini tests pass
- 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
* Use auth key name if there are no app id in in headers or in extra_data
* use key alias instead of key name
* Fix
* last priority key alias
* Fix
* Add tests
- Implement GithubCopilotResponsesAPIConfig for /responses endpoint
- Add support for models requiring responses API (e.g., gpt-5.1-codex)
- Auto-detect vision requests and set X-Initiator header
- Follow OpenAI Responses API compatibility pattern
- Add comprehensive unit tests (16 tests passing)
Fixes#16820
* 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
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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
