* fix(vertex_ai): forward extra_body to completion transformation handler
The responses() function accepted extra_body as a named parameter but
did not pass it to response_api_handler when responses_api_provider_config
was None (completion transformation path), silently dropping it.
Also adds deep-merge support for extra_body in Vertex AI Gemini
transformation, so dict values like generationConfig are merged rather
than replaced.
* refactor(vertex_ai): extract _merge_extra_body to fix PLR0915 lint
Move the extra_body merge loop into a helper function to keep
_transform_request_body under the 50-statement limit.
When using the responses API with provider-specific params (aws_*, vertex_*)
without explicitly passing custom_llm_provider, the code crashed with:
AttributeError: 'NoneType' object has no attribute 'startswith'
Root cause: local_vars was captured via locals() before get_llm_provider()
detected the provider from the model string (e.g., "bedrock/..."), so
custom_llm_provider remained None when processing provider-specific params.
Fix: Update local_vars["custom_llm_provider"] after get_llm_provider() call
so the detected provider is available for param processing.
Affected provider-specific params:
- aws_* (aws_region_name, aws_access_key_id, etc.) for Bedrock/SageMaker
- vertex_* (vertex_project, vertex_location, etc.) for Vertex AI
Calculate `total_tokens` in usage data in Response manually if:
- `total_tokens` is missing
- `total_tokens` can be calculated from input and output tokens
Run the test for this feature with:
`poetry run pytest tests/test_litellm/responses/test_responses_utils.py -k "test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available" -v`
Sameer Kankute