- route_llm_request.py: add acancel_batch and afile_delete to route_type Literal
- router.py: add SearchToolInfoTypedDict and search_tool_info to SearchToolTypedDict
- gemini/files/transformation.py: fix validate_environment signature to match base class
- responses transformation.py: fix Dict type annotations to use int instead of Optional[int]
- vector_stores/endpoints.py: add team_id and user_id to LiteLLM_ManagedVectorStoresTable constructor
Co-authored-by: shin-bot-litellm <shin-bot-litellm@users.noreply.github.com>
The error message for DISABLE_ADMIN_ENDPOINTS incorrectly said
"DISABLING LLM API ENDPOINTS is an Enterprise feature" instead of
"DISABLING ADMIN ENDPOINTS is an Enterprise feature".
This was a copy-paste bug from the is_llm_api_route_disabled() function.
Added regression tests to verify both error messages are correct.
* fix(support-model-specific-tpm/rpm-limits): Allows setting rate limits by tpm/rpm for models by team
* fix(key_management_endpoints.py): enforce guaranteed throughput with key-level model tpm/rpm limits, when team-level tpm/rpm limits are set
* test: add unit testing
* feat(schema.prisma): add metadata to litellm budget table
* feat(proxy/utils.py): add org limits to user api key auth
allows org level tpm/rpm limiting to work
* feat: add org level tpm/rpm limits + inherit org id in key from team
enables org level tpm/rpm limits
* feat: validated working org tpm/rpm limits
* feat: support updating org level, model specific tpm/rpm limits
* fix: working key validation for org level tpm/rpm limits
* fix: working validation for orgs when giving tpm/rpm to teams
* fix(key_management_endpoints.py): fix tpm/rpm limits on orgs
* fix(key_management_endpoints.py): support limits
* refactor: remove duplicate var
* fix: refactor to avoid ruff errors
* fix: fix typign
* fix: fix linting error
* fix: fix testing
* fix(key_management_endpoints.py): document params
* fix(managed_files.py): don't raise error if managed object is not found
* feat(vector_stores): add azure ai search vector store support
Enables direct querying a vector store on azure
* fix(azure/vector_stores): working azure ai search api vector stores
allows azure direct querying on vector stores
* test: update env vars
* docs(docs/): document new azure ai vector store search
* docs(azure_ai_vector_stores.md): add table
* docs: clarify support for 'create' vector stores
* fix(vector_stores/endpoints.py): Fixes https://github.com/BerriAI/litellm/issues/14606
* fix: fix linting errors
* Addd v2/chat support for cohere
* fix streaming
* Use v2_transformation for logging passthrough:
* Use v2_transformation for logging passthrough:
* Add test for checking if document and citation_options is getting passed
* Update the cohere model
* Add cost tracking for vertex ai passthrough batch jobs
* Add full passthrough support
* refactor code according to the comments
* Add passthrough handler
* remove invalid params
* Updated documentation
* Updated documentation
* Updated documentation
* Correct the import
* Add openai videos generation and retrieval support
* add retrieval endpoint
* Add docs
* Add imports
* remove orjson
* remove double import
* fix openai videos format
* remove mock code
* remove not required comments
* Add tests
* Add tests
* Add other video endpoints
* Fix cost calculation and transformation
* Fixed mypy tests
* remove not used imports
* fix documentation for get batch req (#15742)
* Add grounding info to responses API (#15737)
* Add grounding info to responses API
* fix lint errors
* Use typed objects for annotations
* Use typed objects for annotations
* fix mypy error
* Litellm fix json serialize alreting 2 (#15741)
* fix json serializable error for alerts
* Add test
* fix mypt errors
* fix mypt errors
* Add Qwen3 imported model support for AWS Bedrock (#15783)
* Add qwen imported model support
* fix mypy errors
* fix empty user message error (#15784)
* fix typed dict for list
* Add azure supported videos endpoint
* fix mapped tests
* add azure sora models to model map
* Add OpenAI video generation and content retrieval support (#15745)
* Add openai videos generation and retrieval support
* add retrieval endpoint
* Add docs
* Add imports
* remove orjson
* remove double import
* fix openai videos format
* remove mock code
* remove not required comments
* Add tests
* Add tests
* Add other video endpoints
* Fix cost calculation and transformation
* Fixed mypy tests
* remove not used imports
* fix typed dict for list
* fix mypy errors
* move directory
* make v2 chat default
* Fix mypy tests
* Fix mypy tests
* Fix mypy tests
* Fix mypy tests
* Revert "Add Azure Video Generation Support with Sora Integration"
* refactor videos repo
* add test
* Add azure openai videos support
* Add azure openai videos support
* Add router endpoint support for videos
* fix mypy error
* add azure models
* fix mapped test
* fix mypy error
* Add proxy router test
* Add proxy router test
* remove deprecated model name from tests
* fix import error
* fix import error
* Add gaurdrail integration in videos endpoint
* Add logging support for videos endpoint
* Add final documentation supporting videos integration
* fix model name and document input
* Update literals to avoid mypy errors
* Remove unused imports and print statements
* revert guardrail support for video generation and video remix
* revert guardrail support for video generation and video remix
* Fix failing mapped and llm translation tests
* fix: use fastuuid helper across the codebase
First batch of changes, simple drop in replacement.
* second batch of changes
* fixed: script mistake on helper file
* fix: cli auth with SSO okta
* fix: add LITTELM_CLI_SERVICE_ACCOUNT_NAME
* fix: get_litellm_cli_user_api_key_auth
* use existing_key CLI
* fix: use existing key
* test auth commands
* test_cli_sso_callback_regenerate_vs_create_flow
* feat: add CLI Token Utilities
* fix: get_stored_api_key
* move file
* fix: get_valid_models
* fix config.yaml
* TestCLITokenUtils
* TestGetValidModelsWithCLI
* fix: tie user id to keys created through CLI
* fix: add teams interface to CLI
* add /keys/update to the list client commands
* fix /sso/cli/poll to return the user_id
* fix: working TeamsManagementClient
* fix CLI Login command
* fixes for auth
* Potential fix for code scanning alert no. 3400: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* ruff fix
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix(route_checks.py): ensure disable llm api endpoints is correctly set
* fix(route_checks.py): raise httpexception
raise expected exceptions
* fix(router.py): handle team only wildcard models
fixes issue where team only wildcard models were not considered during auth checks
* fix(router.py): handle team only wildcard models
fixes issue where team only wildcard models were not considered during auth checks
* fix(enterprise/litellm_enterprise/proxy/auth/user_api_key_auth.py): bubble up exception if type is ProxyException
* docs(custom_auth.md): doc on bubbling up custom exceptions
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
* feat(proxy_server.py): support batch polling interval
allows admin to control batch polling interval (default is 3600s)
easier debugging
* fix(proxy_settings_endpoint.py): ensure value is actually set before updating env var
* (#11794) use upsert for managed object table rather than create to avoid UniqueViolationError
* (#11794) use upsert for managed object table rather than create to avoid UniqueViolationError
* feat(route_checks.py): allow admin to disable proxy management endpoints on instance
useful for preventing multiple instances from doing admin actions
* docs(scaling_multiple_instances.md): add architecture doc on scaling multiple litellm instances
provide guidance on scaling proxy
* docs(scaling_multiple_instances.md): add doc on scaling across multiple regions for litellm
* fix(route_checks.py): allow disabling llm api endpoints on an instance
allows pure admin instance to exist
* refactor(enterprise/route_checks.py): refactor env var checks
* refactor: finish refactoring
* docs(control_plane_and_data_plane.md): refactor docs
* test: update tests
* feat(check_batch_cost.py): emit spend log on successful request
ensures cost tracked for batch requests
* feat(proxy_server.py): add background job to poll completed batch jobs
used for calculating cost for batch jobs
* fix(proxy_server.py): run batch cost tracking job every hour
batch jobs take time to complete, no need to run every few seconds
* feat(proxy_server.py): run batch cost tracking job every hour
