- Updated the `_add_reasoning_system_prompt_if_needed` method to maintain the original format of list content when prepending the reasoning prompt.
- Adjusted tests to verify that both string and list content types are correctly handled, ensuring the reasoning prompt is added without altering the content structure.
- Updated the `_add_reasoning_system_prompt_if_needed` method to convert list content to strings before prepending the reasoning prompt.
- Adjusted tests to verify that system messages with list content are correctly transformed into strings, ensuring original content is preserved.
- Revised the reasoning support indicators in the Mistral model documentation for clarity.
- Improved the `_add_reasoning_system_prompt_if_needed` method to handle both string and list content types for system messages, ensuring the reasoning prompt is correctly prepended.
- Added a new test case to verify the functionality of adding the reasoning system prompt when the existing content is a list.
* fix(utils.py): convert stringified numbers to numbers
Closes https://github.com/BerriAI/litellm/issues/11266
* fix(convert_dict_to_model_response_object/): bubble up azure content_filter_results
* fix: fix linting error
* fix: fix linting errors
* fix(types/utils.py): ensure choices is correctly set
* fix: delete field if not set
* fix: expand scope of choicelogprobs value
* refactor(responses/): refactor to move responses_to_completion in separate folder
future work to support completion_to_responses bridge
allow calling codex mini via chat completions (and other endpoints)
* Revert "refactor(responses/): refactor to move responses_to_completion in separate folder"
This reverts commit ff87cb8958.
* feat: initial responses api bridge
write it like a custom llm - requires lesser 'new' components
* style: add __init__'s and bubble up the responses api bridge
* feat(responses/transformation): working sync completion -> responses and back bridge (non-streaming)
* feat(responses/): working async (non-streaming) completion <-> responses bridge
Allows calling codex mini via proxy
* feat(responses/): working sync + async streaming for base model response iterator
* fix: reduce function size
maintain <50 LOC
* fix(main.py): safely handle responses api model check
* fix: fix linting errors
* feat(parallel_request_limiter_v3.py): allows admin to enforce token rate limit based on just output tokens
Useful when trying to rate limit for primarily self hosted model use-cases
* test(test_parallel_request_limiter_v3.py): add unit test for token rate limit type
* feat(parallel_request_limiter_v3.py): return remaining token limits in header
* feat: return rate limit headers in response
* feat(parallel_request_limiter_v3.py): working rate limit response headers
* feat(parallel_request_limiter_v3.py): fix rate limit tracking for tpm when rpm also set
* feat(parallel_request_limiter_v3.py): show headers for key/user/team
* feat(parallel_request_limiter_v3.py): decrement max parallel request limiter on failure event
* feat(parallel_request_limiter_v3.py): add in-memory cache implementation of parallel request rate limiter
allows rate limiter to work even without redis cache setup
Work for GA of parallel request limiter v3
* refactor(proxy/hooks/__init__.py): replace with new parallel request handler
* test: update testing
* fix: fix ruff check
* fix: revert ga of multi instance rate limiting - needs more work to pass testing
* Added type casting for messages to enhance type safety.
* Updated the handling of system messages to ensure correct content formatting.
* Cleaned up code for better readability and maintainability.
* Added support for reasoning parameters in magistral models, including "reasoning_effort" and "thinking".
* Updated the MistralConfig class to handle reasoning system prompts.
* Implemented tests to verify reasoning functionality and ensure correct parameter mapping for magistral models.
* Enhanced the model prices JSON to reflect new reasoning capabilities.
* Checkpoint before follow-up message
* Add comprehensive tests for Deepgram transcription functionality
* clean up transform
* just use 1 test
* test cleanup
* test fix get_complete_url
* test rename file
* refactor deepgram URL construction
* add logging_obj.pre_call
* fix unused imports
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* Add Deepgram provider to supported providers list and mappings
* add logo
* Add deepgram to model cost map
* ui - require api key for deepgram
* fix logo path
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix: passing litellm_params_dict instead of litellm_params which is a list of strings
* updated custom llm embedding params and litellm_params_dict
---------
Co-authored-by: Ganesh Jagadeesan <cjagadeesan@dataminr.com>
* fix(internal_user_endpoints.py): support user with `+` in email on user info
ensures user is correctly parsed from input
* fix(factory.py): support vertex function call args as None
handles empty string in args for vertex gemini calls
* docs(langfuse_integration.md): pin langfuse sdk version on docs
* fix(vertex_ai/): return empty dict, instead of none when empty string given
* refactor: reduce function size
* fix: fix linting errors
* fix: revert check
* fix(internal_user_endpoints.py): fix check
* test: update tests
* test: update tests
* fix(vertex_ai/anthropic): extract vertex credentials from correct dict
Fixes passing credentials for vertex anthropic via `/v1/messages`
* fix(vertex_ai/anthropic): working e2e vertex ai anthropic messages via `/v1/messages`
Ensures vertex anthropic models can be called via /v1/messages
* fix(vertex_passthrough_logging_handler.py): fix vertex anthropic logging to emit the model name on spend logs
enables observability of passthrough route
* fix(vertex_passthrough_logging_handler.py): don't redefine variable
* fix(vertex_passthrough_logging_handler.py): log cost + token usage for anthropic passthrough requests on vertex ai
* fix(vertex_ai/anthropic): extract vertex credentials from correct dict
Fixes passing credentials for vertex anthropic via `/v1/messages`
* fix(vertex_ai/anthropic): working e2e vertex ai anthropic messages via `/v1/messages`
Ensures vertex anthropic models can be called via /v1/messages
* fix(vertex_passthrough_logging_handler.py): fix vertex anthropic logging to emit the model name on spend logs
enables observability of passthrough route
* fix(vertex_passthrough_logging_handler.py): don't redefine variable
* build(model_prices_and_context_window.json): add o3-pro pricing
* build(model_prices_and_context_window.json): add updated o3 model pricing
* build(model_prices_and_context_window.json): add new o3-pro model version
