* Fixed issue #8246 (#8250)
* Fixed issue #8246
* Added unit tests for discard() and for remove_callback_from_list_by_object()
* fix(openai.py): support dynamic passing of organization param to openai
handles scenario where client-side org id is passed to openai
---------
Co-authored-by: Erez Hadad <erezh@il.ibm.com>
* fix(caching_routes.py): mask redis password on `/cache/ping` route
* fix(caching_routes.py): fix linting erro
* fix(caching_routes.py): fix linting error on caching routes
* fix: fix test - ignore mask_dict - has a breakpoint
* fix(azure.py): add timeout param + elapsed time in azure timeout error
* fix(http_handler.py): add elapsed time to http timeout request
makes it easier to debug how long request took before failing
* refactor _get_langfuse_input_output_content
* test_langfuse_logging_completion_with_malformed_llm_response
* fix _get_langfuse_input_output_content
* fixes for langfuse linting
* unit testing for get chat/text content for langfuse
* fix _should_raise_content_policy_error
* fix(convert_dict_to_response.py): only convert if response is the response_format tool call passed in
Fixes https://github.com/BerriAI/litellm/issues/8241
* fix(gpt_transformation.py): makes sure response format / tools conversion doesn't remove previous tool calls
* refactor(gpt_transformation.py): refactor out json schema converstion to base config
keeps logic consistent across providers
* fix(o_series_transformation.py): support o3 mini native streaming
Fixes https://github.com/BerriAI/litellm/issues/8274
* fix(gpt_transformation.py): remove unused variables
* test: update test
* refactor(deepseek/): move deepseek to base llm http handler
Fixes https://github.com/BerriAI/litellm/issues/8128#issuecomment-2635430457
* fix(gpt_transformation.py): support stream parsing for gpt-like calls
* test(test_deepseek_completion.py): add async streaming test
* fix(gpt_transformation.py): fix import
* fix(gpt_transformation.py): return full api base and content type
* fix(ui_sso.py): use common `get_user_object` logic across jwt + ui sso auth
Allows finding users by their email, and attaching the sso user id to the user if found
* Improve Team Management flow on UI (#8204)
* build(teams.tsx): refactor teams page to make it easier to add members to a team
make a row in table clickable -> allows user to add users to team they intended
* build(teams.tsx): make it clear user should click on team id to view team details
simplifies team management by putting team details on separate page
* build(team_info.tsx): separately show user id and user email
make it easy for user to understand the information they're seeing
* build(team_info.tsx): add back in 'add member' button
* build(team_info.tsx): working team member update on team_info.tsx
* build(team_info.tsx): enable team member delete on ui
allow user to delete accidental adds
* build(internal_user_endpoints.py): expose new endpoint for ui to allow filtering on user table
allows proxy admin to quickly find user they're looking for
* feat(team_endpoints.py): expose new team filter endpoint for ui
allows proxy admin to easily find team they're looking for
* feat(user_search_modal.tsx): allow admin to filter on users when adding new user to teams
* test: mark flaky test
* test: mark flaky test
* fix(exception_mapping_utils.py): fix anthropic text route error
* fix(ui_sso.py): handle situation when user not in db
* fix(o_series_transformation.py): add 'reasoning_effort' as o series model param
Closes https://github.com/BerriAI/litellm/issues/8182
* fix(main.py): ensure `reasoning_effort` is a mapped openai param
* refactor(azure/): rename o1_[x] files to o_series_[x]
* refactor(base_llm_unit_tests.py): refactor testing for o series reasoning effort
* test(test_azure_o_series.py): have azure o series tests correctly inherit from base o series model tests
* feat(base_utils.py): support translating 'developer' role to 'system' role for non-openai providers
Makes it easy to switch from openai to anthropic
* fix: fix linting errors
* fix(base_llm_unit_tests.py): fix test
* fix(main.py): add missing param
* fix: support azure o3 model family for fake streaming workaround (#8162)
* fix: support azure o3 model family for fake streaming workaround
* refactor: rename helper to is_o_series_model for clarity
* update function calling parameters for o3 models (#8178)
* refactor(o1_transformation.py): refactor o1 config to be o series config, expand o series model check to o3
ensures max_tokens is correctly translated for o3
* feat(openai/): refactor o1 files to be 'o_series' files
expands naming to cover o3
* fix(azure/chat/o1_handler.py): azure openai is an instance of openai - was causing resets
* test(test_azure_o_series.py): assert stream faked for azure o3 mini
Resolves https://github.com/BerriAI/litellm/pull/8162
* fix(o1_transformation.py): fix o1 transformation logic to handle explicit o1_series routing
* docs(azure.md): update doc with `o_series/` model name
---------
Co-authored-by: byrongrogan <47910641+byrongrogan@users.noreply.github.com>
Co-authored-by: Low Jian Sheng <15527690+lowjiansheng@users.noreply.github.com>
* Add O3-Mini for Azure and Remove Vision Support (#8161)
* Azure Released O3-mini at the same time as OAI, so i've added support here. Confirmed to work with Sweden Central.
* [FIX] replace cgi for python 3.13 with email.Message as suggested in PEP 594 (#8160)
* Update model_prices_and_context_window.json (#8120)
codestral2501 pricing on vertex_ai
* Fix/db view names (#8119)
* Fix to case sensitive DB Views name
* Fix to case sensitive DB View names
* Added quotes to check query as well
* Added quotes to create view query
* test: handle server error for flaky test
vertex ai has unstable endpoints
---------
Co-authored-by: Wanis Elabbar <70503629+elabbarw@users.noreply.github.com>
Co-authored-by: Honghua Dong <dhh1995@163.com>
Co-authored-by: superpoussin22 <vincent.nadal@orange.fr>
Co-authored-by: Miguel Armenta <37154380+ma-armenta@users.noreply.github.com>
* Litellm dev 01 29 2025 p4 (#8107)
* fix(key_management_endpoints.py): always get db team
Fixes https://github.com/BerriAI/litellm/issues/7983
* test(test_key_management.py): add unit test enforcing check_db_only is always true on key generate checks
* test: fix test
* test: skip gemini thinking
* Litellm dev 01 29 2025 p3 (#8106)
* fix(__init__.py): reduces size of __init__.py and reduces scope for errors by using correct param
* refactor(__init__.py): refactor init by cleaning up redundant params
* refactor(__init__.py): move more constants into constants.py
cleanup root
* refactor(__init__.py): more cleanup
* feat(__init__.py): expose new 'disable_hf_tokenizer_download' param
enables hf model usage in offline env
* docs(config_settings.md): document new disable_hf_tokenizer_download param
* fix: fix linting error
* fix: fix unsafe comparison
* test: fix test
* docs(public_teams.md): add doc showing how to expose public teams for users to join
* docs: add beta disclaimer on public teams
* test: update tests
* feat(lowest_tpm_rpm_v2.py): fix redis cache check to use >= instead of >
makes it consistent
* test(test_custom_guardrails.py): add more unit testing on default on guardrails
ensure it runs if user sent guardrail list is empty
* docs(quick_start.md): clarify default on guardrails run even if user guardrails list contains other guardrails
* refactor(litellm_logging.py): refactor no-log to helper util
allows for more consistent behavior
* feat(litellm_logging.py): add event hook to verbose logs
* fix(litellm_logging.py): add unit testing to ensure `litellm.disable_no_log_param` is respected
* docs(logging.md): document how to disable 'no-log' param
* test: fix test to handle feb
* test: cleanup old bedrock model
* fix: fix router check
* refactor(factory.py): refactor async bedrock message transformation to use async get request for image url conversion
improve latency of bedrock call
* test(test_bedrock_completion.py): add unit testing to ensure async image url get called for async bedrock call
* refactor(factory.py): refactor bedrock translation to use BedrockImageProcessor
reduces duplicate code
* fix(factory.py): fix bug not allowing pdf's to be processed
* fix(factory.py): fix bedrock converse document understanding with image url
* docs(bedrock.md): clarify all bedrock document types are supported
* refactor: cleanup redundant test + unused imports
* perf: improve perf with reusable clients
* test: fix test
* feat(handle_jwt.py): initial commit adding custom RBAC support on jwt auth
allows admin to define user role field and allowed roles which map to 'internal_user' on litellm
* fix(auth_checks.py): ensure user allowed to access model, when calling via personal keys
Fixes https://github.com/BerriAI/litellm/issues/8029
* feat(handle_jwt.py): support role based access with model permission control on proxy
Allows admin to just grant users roles on IDP (e.g. Azure AD/Keycloak) and user can immediately start calling models
* docs(rbac): add docs on rbac for model access control
make it clear how admin can use roles to control model access on proxy
* fix: fix linting errors
* test(test_user_api_key_auth.py): add unit testing to ensure rbac role is correctly enforced
* test(test_user_api_key_auth.py): add more testing
* test(test_users.py): add unit testing to ensure user model access is always checked for new keys
Resolves https://github.com/BerriAI/litellm/issues/8029
* test: fix unit test
* fix(dot_notation_indexing.py): fix typing to work with python 3.8
* feat(main.py): use asyncio.sleep for mock_Timeout=true on async request
adds unit testing to ensure proxy does not fail if specific Openai requests hang (e.g. recent o1 outage)
* fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming
Fixes https://github.com/BerriAI/litellm/issues/7942
* Revert "fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming"
This reverts commit 7a052a64e3.
* fix(deepseek-r-1): return reasoning_content as a top-level param
ensures compatibility with existing tools that use it
* fix: fix linting error
* fix(utils.py): initial commit fixing custom cost tracking
refactors out provider specific model info from `get_model_info` - this was causing custom costs to be registered incorrectly
* fix(utils.py): cleanup `_supports_factory` to check provider info, if model info is None
some providers support features like vision across all models
* fix(utils.py): refactor to use _supports_factory
* test: update testing
* fix: fix linting errors
* test: fix testing
* test(test_completion_cost.py): add sdk test to ensure base model is used for cost tracking
* test(test_completion_cost.py): add sdk test to ensure custom pricing works
* fix(main.py): add base model cost tracking support for embedding calls
Enables base model cost tracking for embedding calls when base model set as a litellm_param
* fix(litellm_logging.py): update logging object with litellm params - including base model, if given
ensures base model param is always tracked
* fix(main.py): fix linting errors
* fix(bedrock/converse_handler.py): fix bedrock region name on async calls
* fix(utils.py): fix split model handling
Fixes bedrock cost calculation when region name is given
* feat(_health_endpoints.py): support health checking datadog integration
Closes https://github.com/BerriAI/litellm/issues/7921
* fix(types/utils.py): support returning 'reasoning_content' for deepseek models
Fixes https://github.com/BerriAI/litellm/issues/7877#issuecomment-2603813218
* fix(convert_dict_to_response.py): return deepseek response in provider_specific_field
allows for separating openai vs. non-openai params in model response
* fix(utils.py): support 'provider_specific_field' in delta chunk as well
allows deepseek reasoning content chunk to be returned to user from stream as well
Fixes https://github.com/BerriAI/litellm/issues/7877#issuecomment-2603813218
* fix(watsonx/chat/handler.py): fix passing space id to watsonx on chat route
* fix(watsonx/): fix watsonx_text/ route with space id
* fix(watsonx/): qa item - also adds better unit testing for watsonx embedding calls
* fix(utils.py): rename to '..fields'
* fix: fix linting errors
* fix(utils.py): fix typing - don't show provider-specific field if none or empty - prevents default respons
e from being non-oai compatible
* fix: cleanup unused imports
* docs(deepseek.md): add docs for deepseek reasoning model
* fix(initial-test-to-return-api-timeout-value-in-openai-timeout-exception): Makes it easier for user to debug why request timed out
* feat(openai.py): return timeout value + time taken on openai timeout errors
helps debug timeout errors
* fix(utils.py): fix num retries extraction logic when num_retries = 0
* fix(config_settings.md): litellm_logging.py
support printing payload to console if 'LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD' is true
Enables easier debug
* test(test_auth_checks.py'): remove common checks userapikeyauth enforcement check
* fix(litellm_logging.py): fix linting error
* fix(lm_studio/chat/transformation.py): Fix https://github.com/BerriAI/litellm/issues/7811
* fix(router.py): fix mock timeout check
* fix: drop model name from fallback args since it causes a conflict with the model=model that is provided later on. (#7806)
This error happens if you provide multiple fallback models to the completion function with model name defined in each one.
* fix(router.py): remove mock_timeout before sending to request
prevents reuse in fallbacks
* test: update test
* test: revert test change - wrong pr
---------
Co-authored-by: Dudu Lasry <david1542@users.noreply.github.com>
* feat(pass_through_endpoints.py): fix anthropic end user cost tracking
* fix(anthropic/chat/transformation.py): use returned provider model for anthropic
handles anthropic `-latest` tag in request body throwing cost calculation errors
ensures we can be accurate in our model cost tracking
* feat(model_prices_and_context_window.json): add gemini-2.0-flash-thinking-exp pricing
* test: update test to use assumption that user_api_key_dict can get anthropic user id
* test: fix test
* fix: fix test
* fix(anthropic_pass_through.py): uncomment previous anthropic end-user cost tracking code block
can't guarantee user api key dict always has end user id - too many code paths
* fix(user_api_key_auth.py): this allows end user id from request body to always be read and set in auth object
* fix(auth_check.py): fix linting error
* test: fix auth check
* fix(auth_utils.py): fix get end user id to handle metadata = None
* fix(factory.py): fix bedrock document url check
Make check more generic - if starts with 'text' or 'application' assume it's a document and let it go through
Fixes https://github.com/BerriAI/litellm/issues/7746
* feat(key_management_endpoints.py): support writing new key alias to aws secret manager - on key rotation
adds rotation endpoint to aws key management hook - allows for rotated litellm virtual keys with new key alias to be written to it
* feat(key_management_event_hooks.py): support rotating keys and updating secret manager
* refactor(base_secret_manager.py): support rotate secret at the base level
since it's just an abstraction function, it's easy to implement at the base manager level
* style: cleanup unused imports
* use lru cache wrapper
* use lru_cache_wrapper for _cached_get_model_info_helper
* fix _get_traceback_str_for_error
* huggingface/mistralai/Mistral-7B-Instruct-v0.3
* feat(main.py): initial commit for `/image/variations` endpoint support
* refactor(base_llm/): introduce new base llm base config for image variation endpoints
* refactor(openai/image_variations/transformation.py): implement openai image variation transformation handler
* fix: test
* feat(openai/): working openai `/image/variation` endpoint calls via sdk
* feat(topaz/): topaz sync image variation call support
Addresses https://github.com/BerriAI/litellm/issues/7593
'
* fix(topaz/transformation.py): fix linting errors
* fix(openai/image_variations/handler.py): fix passing json data
* fix(main.py): image_variation/
support async image variation route - `aimage_variation`
* fix(test_get_model_info.py): fix test
* fix: cleanup unused imports
* feat(openai/): add async `/image/variations` endpoint support
* feat(topaz/): support async `/image/variations` calls
* fix: test
* fix(utils.py): fix get_model_info_helper for no model info w/ provider config
handles situation where model info is not known but provider config exists
* test(test_router_fallbacks.py): mark flaky test
* fix: fix unused imports
* test: bump otel load test perf threshold - accounts for current load tests hitting same server
* fix(__init__.py): fix init to exclude pricing-only model cost values from real model names
prevents bad health checks on wildcard routes
* fix(get_llm_provider.py): fix to handle calling bedrock_converse models
* feat(langfuse.py): log the used prompt when prompt management used
* test: fix test
* docs(self_serve.md): add doc on restricting personal key creation on ui
* feat(s3.py): support s3 logging with team alias prefixes (if available)
New preview feature
* fix(main.py): remove old if block - simplify to just await if coroutine returned
fixes lm_studio async embedding error
* fix(langfuse.py): handle get prompt check
* test(test_get_model_info.py): add unit test confirming router deployment updates global 'get_model_info'
* fix(get_supported_openai_params.py): fix custom llm provider 'get_supported_openai_params'
Fixes https://github.com/BerriAI/litellm/issues/7668
* docs(azure.md): clarify how azure ad token refresh on proxy works
Closes https://github.com/BerriAI/litellm/issues/7665
* fix(vertex_ai/gemini/transformation.py): handle 'http://' in gemini process url
* refactor(router.py): refactor '_prompt_management_factory' to use logging obj get_chat_completion logic
deduplicates code
* fix(litellm_logging.py): update 'get_chat_completion_prompt' to update logging object messages
* docs(prompt_management.md): update prompt management to be in beta
given feedback - this still needs to be revised (e.g. passing in user message, not ignoring)
* refactor(prompt_management_base.py): introduce base class for prompt management
allows consistent behaviour across prompt management integrations
* feat(prompt_management_base.py): support adding client message to template message + refactor langfuse prompt management to use prompt management base
* fix(litellm_logging.py): log prompt id + prompt variables to langfuse if set
allows tracking what prompt was used for what purpose
* feat(litellm_logging.py): log prompt management metadata in standard logging payload + use in langfuse
allows logging prompt id / prompt variables to langfuse
* test: fix test
* fix(router.py): cleanup unused imports
* fix: fix linting error
* fix: fix trace param typing
* fix: fix linting errors
* fix: fix code qa check
* fix(main.py): fix lm_studio/ embedding routing
adds the mapping + updates docs with example
* docs(self_serve.md): update doc to show how to auto-add sso users to teams
* fix(streaming_handler.py): simplify async iterator check, to just check if streaming response is an async iterable
* fix(streaming_chunk_builder_utils.py): add test for groq tool calling + streaming + combine chunks
Addresses https://github.com/BerriAI/litellm/issues/7621
* fix(streaming_utils.py): fix modelresponseiterator for openai like chunk parser
ensures chunk parser uses the correct tool call id when translating the chunk
Fixes https://github.com/BerriAI/litellm/issues/7621
* build(model_hub.tsx): display cost pricing on model hub
* build(model_hub.tsx): show cost per token pricing + complete model information
* fix(types/utils.py): fix usage object handling
