* fix: video status/content credential injection for wildcard models
When using wildcard model patterns like `vertex_ai/*`, the video status
and content endpoints failed to resolve the model_name correctly,
causing credential injection to be skipped.
Changes:
- router.py: Added `custom_llm_provider` parameter to
`resolve_model_name_from_model_id` method
- router.py: Added Strategy 2 (provider prefix matching) and
Strategy 4 (wildcard pattern matching)
- endpoints.py: Pass `provider_from_id` to resolver in video_status,
video_content, and video_remix endpoints
This allows video_id like `vertex_ai:veo-3.0-generate-preview:...` to
correctly match `vertex_ai/*` wildcard pattern and inject credentials
from the model config.
Fixes: Video status returns "Your default credentials were not found"
when using Vertex AI video generation with wildcard model patterns.
* pr18845-video기능버그픽스 (vibe-kanban e43e2d2d)
pr코멘트 대응
litellm fork해서 branch만들고 작업후 pull request를 올렸는데 피드백을줬어.
이 내용 파악해서 내가 올린 pr 브랜치에 해당 작업 이어서 해야할거같아.
https://github.com/BerriAI/litellm/pull/18854#discussion\_r2677026995
여기 내용 읽고 현황 파악해서 작업하자.
테스트코드 작성해달라는데 테스트코드작성후 로컬에서 테스트명령어 한번 돌리고 커밋 푸시하려고.
litellm에서 pull request를 위한 문서가 있어.
https://docs.litellm.ai/docs/extras/contributing\_code
CRA서명은 했어. 그다음거부터 양식에 맞게 해야할듯. 지금 버그만 바로 고쳐서 pr했거든.
* fix: resolve mypy type error in resolve_model_name_from_model_id
Rename loop variable to avoid type conflict between DeploymentTypedDict
and Dict[Any, Any] from pattern_router.route() return type.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* fix(router.py): support base model for model group usage
allows model group info to show accurate cost information for azure models
* fix(router.py): fix changes
* test: add unit tests
* build(pyproject.toml): bump openai version requirements
support custom tool from responses api
Closes https://github.com/BerriAI/litellm/issues/13391
* docs(responses_api.md): add verbosity + free-form function calling parameters
* docs(responses_api.md): add cfg + minimal reasoning to docs
Closes https://github.com/BerriAI/litellm/issues/13391
* docs(responses_api.md): add proxy examples to docs
* refactor: fix ruff error
* 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(router.py): add acompletion_streaming_iterator inside router
allows router to catch errors mid-stream for fallbacks
Work for https://github.com/BerriAI/litellm/issues/6532
* fix(router.py): working mid-stream fallbacks
* fix(router.py): more iterations
* fix(router.py): working mid-stream fallbacks with fallbacks set on router
* fix(router.py): pass prior content back in new request as assistant prefix message
* fix(router.py): add a system prompt to help guide non-prefix supporting models to use the continued text correctly
* fix(common_utils.py): support converting `prefix: true` for non-prefix supporting models
* fix: reduce LOC in function
* test(test_router.py): add unit tests for new function
* test: add basic unit test
* fix(router.py): ensure return type of fallback stream is compatible with CustomStreamWrapper
prevent client code from breaking
* fix: cleanup
* test: update test
* fix: fix linting error
* fix(proxy_cli.py): make use_prisma_migrate proxy default
Fixes https://github.com/BerriAI/litellm/issues/13046
Prisma migrate deploy prevents resetting db
* fix(auth_checks.py): resolve team only models while doing auth checks on model access groups
Fixes issue where key had access via an access group, but team only model could not be called
* test(test_router.py): add unit testing
* feat(provider_specific_fields.tsx): add aws sagemaker on UI
* feat: initial commit for forwarding client headers by model group
* fix(router.py): support new forwarclientsideheadersbymodelgroup class
enables headers to be forwarded to backend model, by model group
* fix(proxy_server.py): load in model group settings from config correctly
* refactor(litellm_pre_call_utils.py): litellm_pre_call_utils.py
introduce new 'secret_fields' field
includes raw request headers (not the sanitized ones used for logging) - needed to support forwarding clientside headers to llm api
* feat(router.py): log the deployment model name as well
allows wildcard models to support forward_client_headers_to_llm_api
* test(test_router.py): add more unit testing
* feat(router.py): specify the model group alias in metadata kwargs
allows usage for internal routing logic
* fix: fix ruff check errors
* fix(router.py): refactor to cleanup optional pre-call checks
* fix: fix ruff check
* test: add missing unit test
* fix(auth_checks.py): resolve a model group alias when key has access to underlying model
Fixes LIT-293
* feat(anthropic/): add mock_response to anthropic /v1/messages
makes it easy to test fallback logic
* fix(router.py): support fallbacks on /v1/messages
adds working fallbacks on generic api route
* refactor(router.py): point _ageneric_api_call_with_fallbacks to updated function
* test: add unit test for new helper on router
* fix(router.py): use correct metadata variable name
* fix(router.py): use correct metadata field
* docs(config_settings.md): document new param
* fix security - mcp
* fix(router.py): validate model provider before adding deployment to pattern
prevents routing on pattern match to invalid deployment
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(main.py): handle router custom azure model name for responses api bridge
* fix(responses/handler): ensure azure model name is stripped before sending to provider
Fixes model name error
* fix(google_genai/main.py): handle stream=true being set in kwargs
* docs: cleanup icons from sidebar
* fix(test-litellm.yml): add google-genai to test litellmyml
* fix(main.py): strip 'responses/' from bridge
* fix(main.py): fix linting errors
* fix(types/openai.py): allow item to be none
handle azure streaming response
* fix(base.py): allow extra fields + handle azure item = none value in response output item added event
* fix(main.py): correctly handle removing responses/
* test(test_main.py): add unit tests
* fix(team_endpoints.py): prevent overwriting current list of team models on new model add
* fix(networking.tsx): fix default proxy base url
* fix(proxy_server.py): include team only models when retrieving all deployments on `/v2/model/info` helper util
ensures team only models are shown to user
* fix(router.py): check model name by team public model name when team id given
Fixes issue where team member could not see team only models when clicking into that team on `Models + Endpoints`
* fix(team_member_view.tsx): fix rendering team member budget, when budget is set
* test: update tests
* test: update unit test
* 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
* fix(spend_tracking_utils.py): add user agent tags from standard logging payload, in spend logs payload
* feat(litellm_logging.py): identify user agent tags as `User-Agent: ..` and allow admin to disable storing user agent as tag
* fix(azure_ai/): pass content type header in azure ai request
Fixes https://github.com/BerriAI/litellm/issues/11227
* test: add unit test
* fix(router.py): fix passing dynamic credentials to retrieve batch
Fixes batch retrieval when using router
* test: add more unit tests
Yuta Saito