* 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] Containers API - Allow routing to regional endpoints (#19118)
* fix get_complete_url
* fix url resolution containers API
* TestContainerRegionalApiBase
* feat(proxy): add keepalive_timeout support for Gunicorn server
Add configurable keepalive timeout parameter for Gunicorn workers to
match existing Uvicorn functionality. This allows users to tune the
keep-alive connection timeout based on their deployment requirements.
Changes:
- Add keepalive_timeout parameter to _run_gunicorn_server method
- Configure Gunicorn's keepalive setting (defaults to 90s if not specified)
- Update --keepalive_timeout CLI help text to document both Uvicorn and Gunicorn behavior
- Pass keepalive_timeout from run_server to _run_gunicorn_server
Tests:
- Add test to verify keepalive_timeout flag is properly passed to Gunicorn
- Add test to verify default 90s timeout when flag is not specified
Co-Authored-By: lizhen921 <294474470@qq.com>
Signed-off-by: Kris Xia <xiajiayi0506@gmail.com>
---------
Signed-off-by: Kris Xia <xiajiayi0506@gmail.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: lizhen921 <294474470@qq.com>
Remove orphaned test files that are not referenced in any tests or code:
- flux2_test_image.png
- test_generic_guardrail_config.yaml
- test_image_edit.png (root only, tests/image_gen_tests/ copy preserved)
- document.txt
- batch_small.jsonl (root and tests/batches_tests/)
* Added ability to customize logfire base url through env var
* Added test to check if env var is used correctly for logfire
* Document the env var
* Documented env var in config_settings.md
* test_should_not_fail_with_forwarded_headers_bedrock_invoke_messages
* use common get_request_headers for BaseAWS
* fix get_request_headers
* test_should_not_fail_with_forwarded_headers_bedrock_invoke_messages
Tools with no parameters (like EnterPlanMode from Anthropic Agents SDK)
send schemas with only $schema and no type field. Gemini rejects these
with "functionDeclaration parameters schema should be of type OBJECT".
Adds type: object when schema has no type and no anyOf/oneOf/allOf.
Extract and preserve provider-specific headers (llm_provider-*) when
handling error responses from LLM providers. This ensures that useful
debugging information from providers is available even when requests
fail with BadRequestError or similar exceptions.
* fix(vertex_ai): improve passthrough endpoint url parsing and construction (#17402)
* test(proxy): add test for vertex passthrough load balancing
Add a test that verifies _base_vertex_proxy_route uses
get_available_deployment for proper load balancing instead of
get_model_list. This ensures the correct deployment is selected
from the router and vertex credentials are properly fetched.
Also refactor the implementation to:
- Use get_available_deployment instead of get_model_list
- Add error handling for deployment retrieval
- Improve code structure with try-except block
* feat(proxy): add pass-through deployment filtering methods
Add dedicated methods to filter and select deployments for pass-through endpoints:
- Implement get_available_deployment_for_pass_through() to ensure only deployments with use_in_pass_through=True are considered
- Implement async_get_available_deployment_for_pass_through() for async operations
- Add _filter_pass_through_deployments() helper method to filter by use_in_pass_through flag
- Update vertex pass-through route to use the new dedicated method
This ensures pass-through endpoints respect the use_in_pass_through configuration and apply proper load balancing strategy only to configured deployments.
Add comprehensive tests to verify filtering and load balancing behavior.
