* test(test_router.py): initial unit test confirming router.afile_content uses dynamic api key / api base
* fix(managed_files.py): filter deployments for only those within file id mapping
ensure call works - only route to models where the file was written
* fix(proxy_server.py): fix loading in model ids from config, if config id is int
* fix(router.py): return all model file id mappings on create_file
if multiple deployments - this ensures all the file id mappings are bubbled up
Fixes issue when trying to use loadbalanced deployments - only 1 file id mapping was being stored
* feat(router_utils/common_utils.py): filter models by team id when selecting for routing
Prevents team only models from being used by other teams
* fix(common_utils.py): additional fixes around filtering team-based models
* fix(batches_endpoints/endpoints): support list batches with target model names specified
* fix(common_utils.py): more testing for team deployment filters
* test(test_router.py): initial unit test confirming router.afile_content uses dynamic api key / api base
* fix(managed_files.py): filter deployments for only those within file id mapping
ensure call works - only route to models where the file was written
* fix(proxy_server.py): fix loading in model ids from config, if config id is int
* fix(router.py): return all model file id mappings on create_file
if multiple deployments - this ensures all the file id mappings are bubbled up
Fixes issue when trying to use loadbalanced deployments - only 1 file id mapping was being stored
* init litellm google gen ai methods
* feat init structure of functions for generate content
* add init
* add BaseGoogleGenAIGenerateContentConfig
* add generate_content_handler
* add get_provider_google_genai_generate_content_config
* fixes for generate content
* add get_vertex_ai_project etc to base
* use VertexBase
* fixes for BaseGoogleGenAIGenerateContentConfig
* working validate env for google gemini
* feat - add transform google response
* fixes for transform_generate_content_request
* fix get_supported_generate_content_optional_params
* add BaseGoogleGenAITest
* working e2e test
* fixes init config
* use correct types
* fix test for google gen ai
* fix types
* add sync_get_auth_token_and_url
* fixes for transform
* add llm http handler for google
* working non-streaming google endpoints
* add BaseGoogleGenAIGenerateContentStreamingIterator
* add GoogleGenAIGenerateContentStreamingIterator
* fix working sync stream
* fixes for litellm logging obj
* working async streaming
* add google gen ai types
* fix - required imports
* fix readme
* fix deps
* fix deps
* fix ruff code QA checks
* fix linting
* fixes TYPE_CHECKING
* fixes for typing
* add google gemini methods to litellm router
* [Feat] Add initial endpoints for using Gemini SDK (gemini-cli) with LiteLLM (#12040)
* init with google endpoints
* add Depends
* feat - add gemini endpoints
* google_generate_content
* fix init
* fixes import
* fixes for streaming
* fixes for sync/async
* working streaming with google gemini cli
* add google endpoints to llm api routes
* add VertexAIGoogleGenAIConfig
* use aiter_bytes
* use common request for streaming data
* re-use logic for anthropic streaming
* add GoogleAIStudioDataGenerator
* init litellm google gen ai methods
* feat init structure of functions for generate content
* add init
* add BaseGoogleGenAIGenerateContentConfig
* add generate_content_handler
* add get_provider_google_genai_generate_content_config
* fixes for generate content
* add get_vertex_ai_project etc to base
* use VertexBase
* fixes for BaseGoogleGenAIGenerateContentConfig
* working validate env for google gemini
* feat - add transform google response
* fixes for transform_generate_content_request
* fix get_supported_generate_content_optional_params
* add BaseGoogleGenAITest
* working e2e test
* fixes init config
* use correct types
* fix test for google gen ai
* fix types
* add sync_get_auth_token_and_url
* fixes for transform
* add llm http handler for google
* working non-streaming google endpoints
* add BaseGoogleGenAIGenerateContentStreamingIterator
* add GoogleGenAIGenerateContentStreamingIterator
* fix working sync stream
* fixes for litellm logging obj
* working async streaming
* add google gen ai types
* fix - required imports
* fix readme
* fix deps
* fix deps
* fix ruff code QA checks
* fix linting
* fixes TYPE_CHECKING
* fixes for typing
* docs: add Elasticsearch logging tutorial and update sidebar
* docs: update Elasticsearch logging tutorial to include OpenTelemetry setup and configuration
* docs: remove sections from Elasticsearch logging tutorial
* docs: remove analytics examples from Elasticsearch logging tutorial
* Update Elasticsearch version and logging exporter configuration in the Elasticsearch logging tutorial
* Add visualization instructions for LLM telemetry data in Kibana to Elasticsearch logging tutorial
* Add Elasticsearch demo image to documentation
* Move demo image for Elasticsearch logging tutorial
* fix(handler.py): support routing custom llm's to chat completion handler
Adds custom llm support for anthropic
* test(test_anthropic_experimental_pass_through_messages_handler.py): add unit test confirming custom llm respected
* docs(custom_llm_server.md): document anthropic custom llm translation
* test(volcengine.py): map thinking in extra body
Fixes https://github.com/BerriAI/litellm/issues/11879
* feat(main.py): support `azure/responses/<deployment-name>` model string
this allows us to route the model correctly
Closes https://github.com/BerriAI/litellm/issues/11879
* docs(azure_responses.md): document calling azure responses api models via chat completions bridge
Closes https://github.com/BerriAI/litellm/issues/11917
* fix: fix custom provider check
* test: update tests
* fix(team_endpoints.py): support setting default key expiry
allows admin to set key expiry on all team member keys
makes it easier to setup default team for experimentation
* feat(key_management_endpoints.py): allows admin to set duration for keys created by team members
* feat(team_endpoints.py): support team_member_key_duration on `/team/update`
allows setting max time team member keys are valid for
* fix(team_info.tsx): ui component to update team member key duration
* fix(team_info.tsx): support updating team member key duration, if set
* feat(teams.tsx): add team member key duration param ui component
allow admin to set this on UI
* feat(ui_sso.py): support restricting ui access by sso group
allows controlling who can/can't access the UI
* feat(ssomodals.tsx): add initial commit adding sso group access to admin ui
* feat(proxy_server.py): support reading + writing ui_access_mode from db
allows admin to configure allowed sso groups from UI
* feat(ui_sso.py): support enforcing all teams on sso jwt handler
if ui access mode set via ui, support reading the value and enforcing it
* feat(ui/): ui component for controlling sso access group
allow admin to only allow users within specific sso group to log into UI
* fix(uiaccesscontrolform.tsx): fix field names
* feat(ui_sso.py): return received sso response in the clientside error message - enables easier debugging
* test: add unit tests
* fix: minor fixes
* Refactor unpack_defs to use iterative approach instead of recursion
- Replace recursive depth-first traversal with iterative queue-based approach
- Add collections.deque import for efficient queue operations
- Avoid potential stack overflow issues with deeply nested schemas
- Maintain same functionality while improving performance and safety
* Remove unused import of Set in common_utils.py
* Enhance type hinting for queue in unpack_defs function in common_utils.py
* Enhance unpack_defs function to handle key validation for parent structures in common_utils.py
- Added checks to ensure that the parent is a dictionary or list and that the key is of the appropriate type (string for dicts, integer for lists) before assigning the resolved schema.
- This improves the robustness of the unpack_defs function when dealing with various schema structures.
* SCIM fix new user roles
* test_create_user_defaults_to_viewer
* test_create_user_uses_default_internal_user_params_role
* fix default user for SCIM
* fix linting error
* refactor(unpack_defs): enhance handling of schema properties and anyOf structures
- Improved the unpack_defs function to handle top-level properties and nested structures more effectively.
- Added recursion for items in schemas and refined the handling of anyOf branches to ensure proper unpacking of references.
- Streamlined the logic for resolving $ref keys and managing nested schemas.
* test(unpack_defs): add test for resolving nested $ref in anyOf items
- Introduced a new test to verify that unpack_defs correctly resolves references within items of anyOf structures, addressing a specific bug scenario (Issue #11372).
- The test includes a minimal schema to ensure proper unpacking and validation of the resolved items schema.
* refactor(unpack_defs): implement a generic resolver for $ref entries
- Redesigned the unpack_defs function to provide a more robust and dependency-free implementation for resolving all $ref entries in JSON schemas.
- Introduced a depth-first traversal method that efficiently handles nested structures, including anyOf, allOf, and items, while avoiding infinite recursion.
- Enhanced memory management by resolving nodes in-place without creating a full dereferenced copy, improving performance and reducing overhead.
* Remove test for unpack_defs resolving nested references in anyOf items from test_utils.py
* Add test for unpack_defs resolving nested references in anyOf items
This commit introduces a new test to ensure that the unpack_defs function correctly resolves $ref references within items of anyOf schemas, addressing issue #11372. The test verifies that the unpacked schema contains the expected properties and structure.
* fix(litellm_logging.py): fix using router model id for logging calls
Fixes https://github.com/BerriAI/litellm/issues/11975#issuecomment-2995882238
* test(test_litellm_logging.py): add unit test for custom price tracking
* fix(vertex_ai/): don't send invalid format parameter to vertex
causes calls to fail
* fix(vertex_ai_context_caching.py): if cached content present and tools in message, cache tools as well
gemini throws errors if tools passed in alongside cached content
* test: add unit tests
* fix: fix linting errors
* test: test_vertex_ai_common_utils.py
update test
* fix(streaming_handler.py): unset response cost when creating model response
