* fix: align max_tokens with max_output_tokens for consistency
Fixed inconsistent max_tokens definitions in model_prices_and_context_window.json.
According to LiteLLM convention, max_tokens should equal max_output_tokens when available.
Models fixed:
- deepseek-chat: 131072 → 8192 (now equals max_output_tokens)
- dashscope/qwen-flash: 1000000 → 32768 (now equals max_output_tokens)
- databricks/databricks-gemma-3-12b: 128000 → 32000 (now equals max_output_tokens)
This ensures consistency across all providers where max_tokens represents
the maximum number of tokens that can be generated in the output.
* fix: align max_tokens with max_output_tokens for 244 models
- Fix 244 models where max_tokens != max_output_tokens
- Add test to validate max_tokens consistency and prevent regressions
According to model_prices_and_context_window.json spec:
- max_tokens is a LEGACY parameter
- Should always equal max_output_tokens when both are present
This ensures consistency across all model definitions.
Fix router embedding methods to properly propagate proxy model
configuration headers to LLM API calls by calling
_update_kwargs_before_fallbacks() just like completion() does.
Previously, router.embedding() and router.aembedding() manually
set num_retries and metadata but didn't call
_update_kwargs_before_fallbacks(), which meant default_litellm_params
(including headers) were not propagated correctly.
Changes:
- Replace manual kwargs setup with _update_kwargs_before_fallbacks()
in _embedding method (litellm/router.py:3318)
- Apply Black formatting to router.py for consistency
- Add comprehensive unit tests for header propagation
- Add integration tests for various router configurations
Tests verify:
- Headers from default_litellm_params are included in embedding calls
- Metadata (model_group) is properly set
- Consistency between completion() and embedding() behavior
- Support for deployment-specific headers, fallbacks, and retries
