test_vertex_passthrough_with_default_credentials and
test_view_spend_logs_with_date_range_summarized fail intermittently when a
prior xdist worker sets master_key — auth then rejects the unauthenticated
test requests before the code under test is reached.
- mock user_api_key_auth in test_vertex_passthrough_with_default_credentials
(same pattern used for test_vertex_passthrough_with_no_default_credentials
in #21810)
- wrap test_view_spend_logs_with_date_range_summarized in
app.dependency_overrides[ps.user_api_key_auth] with try/finally cleanup
(same pattern used for the other spend log tests in #21810)
* fix(tests): add app.dependency_overrides for auth in spend logs tests
test_ui_view_spend_logs_with_status, test_ui_view_spend_logs_with_model,
test_ui_view_spend_logs_with_model_id, and test_view_spend_logs_summarize_parameter
all send Bearer sk-test without mocking user_api_key_auth. When a prior test
in the same xdist worker sets master_key, the auth check fails for sk-test
and the test fails intermittently.
Fix: use app.dependency_overrides[ps.user_api_key_auth] to bypass auth,
same pattern as other tests in the same file.
* fix(tests): mock user_api_key_auth in test_vertex_passthrough_with_no_default_credentials
vertex_proxy_route calls user_api_key_auth internally. When a prior test in the
same xdist worker sets master_key, the auth check fails for the test request
and create_pass_through_route is never called, causing assert_called_once_with to fail.
Fix: patch user_api_key_auth as an AsyncMock in the with mock.patch() block.
* fix: add custom_body parameter to endpoint_func in create_pass_through_route
The bedrock_proxy_route calls `endpoint_func(custom_body=data)` to
pass a pre-parsed, SigV4-signed request body. However, the
`endpoint_func` closure created by `create_pass_through_route` does
not accept a `custom_body` keyword argument, causing:
TypeError: endpoint_func() got an unexpected keyword argument 'custom_body'
Add `custom_body: Optional[dict] = None` to both `endpoint_func`
definitions (adapter-based and URL-based). In the URL-based path,
when `custom_body` is provided by the caller, use it instead of
re-parsing the body from the raw request.
Fixes#16999
* Add tests for custom_body handling in create_pass_through_route
Address reviewer feedback on PR #20849:
- Document why the adapter-based endpoint_func accepts custom_body
for signature compatibility but does not forward it (the underlying
chat_completion_pass_through_endpoint does not support it).
- Add test_create_pass_through_route_custom_body_url_target: verifies
that when a caller (e.g. bedrock_proxy_route) supplies custom_body,
it takes precedence over the body parsed from the raw request.
- Add test_create_pass_through_route_no_custom_body_falls_back:
verifies that the default path (no custom_body) correctly uses the
request-parsed body, preserving existing behavior.
Both tests are fully mocked following the project's CONTRIBUTING.md
guidelines and the patterns established in the existing test file.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: themavik <themavik@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(vertex_ai): replace custom model names with actual Vertex AI model names in passthrough URLs (#19948)
When the passthrough URL already contains project and location, the code
was skipping the deployment lookup and forwarding the URL as-is to Vertex AI.
For custom model names like gcp/google/gemini-2.5-flash, Vertex AI returned
404 because it only knows the actual model name (gemini-2.5-flash).
The fix makes the deployment lookup always run, so the custom model name
gets replaced with the actual Vertex AI model name before forwarding.
* add _resolve_vertex_model_from_router
* fix: get_llm_provider
* Potential fix for code scanning alert no. 4020: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* 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.
Fixes#17477
Guardrails couldn't access request headers (like User-Agent) on Bedrock
pass-through endpoints because headers were only stored in
data["proxy_server_request"]["headers"] but not in data["metadata"]["headers"]
where guardrails typically look for them.
This fix adds headers to metadata in add_litellm_data_to_request() so
guardrails can access User-Agent, API keys, and other header-based checks
on all endpoints including Bedrock pass-through.
Test added to verify headers are available in metadata for guardrails.
extract model id from vertex ai passthrough routes that follow the pattern:
/vertex_ai/*/models/{model_id}:*
the model extraction now handles vertex ai routes by regex matching the model
segment from the url path, which allows proper model identification for
authentication and authorization in proxy pass-through endpoints.
adds comprehensive test coverage for vertex ai model extraction including:
- various vertex api versions (v1, v1beta1)
- different locations (us-central1, asia-southeast1)
- model names with special suffixes (gemini-1.5-pro, gemini-2.0-flash)
- precedence verification (request body model over url)
- non-vertex route isolation
* fix(unified_guardrails.py): send all chunks on completion of final stream
* feat(generic_guardrail_api.py): handle tool call response on streaming LLM responses
* fix(anthropic/chat/guardrail_translation): initial commit adding anthropic tool response streaming guardrails
enables guardrail checks on tool response from llm's to work via `/v1/messages`
* feat(anthropic/): working guardrail checks on tool response from LLMs
ensures guardrail checks on anthropic /v1/messages works as expected
* feat(responses/guardrail_translation): support tool call response guardrails on streaming for /v1/responses
ensures complete coverage of tool call responses
* refactor(openai.py): refactor to use consistent pydantic model for responses api tool response on streaming
enables non-openai model tool call response to work correctly with guardrail checks on /v1/responses
* test: update tests
* fix: fix linting error
* fix: fix failing tests
* fix: fix import errors
* fix(openai/chat/guardrail_transformation): fix final chunk returned on streaming
* refactor: remove api-key conversion logic for Azure Anthropic
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* fix(passthrough): pass custom_llm_provider to completion_cost for Azure AI Anthropic
The passthrough logging for Anthropic was failing when using Azure AI Anthropic
because the completion_cost function was not receiving the custom_llm_provider
parameter, causing it to fail with "LLM Provider NOT provided" error.
This fix:
- Retrieves custom_llm_provider from logging_obj.model_call_details
- Prepends provider prefix to model name for cost calculation
- Passes both formatted model and custom_llm_provider to completion_cost
- Centralizes provider prefix logic in _create_anthropic_response_logging_payload
This ensures cost calculation works correctly for Azure AI Anthropic requests
with models like azure_ai/claude-sonnet-4-5_gb_20250929.
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* test: add unit tests for Azure AI Anthropic fixes
- Add tests for custom_llm_provider cost calculation in passthrough logging
- Add tests for ProviderConfigManager returning AzureAnthropicMessagesConfig
- Update existing tests to reflect removal of api-key to x-api-key conversion
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
---------
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* feat(llm_passthrough_endpoints.py): support milvus passthrough api
* fix(llm_passthrough_endpoints.py): move streaming request value to the top of the function
* docs: document new milvus vector store passthrough flow
* feat(milvus/): initial commit adding milvus vector store support to LiteLLM
allows querying milvus vector store through litellm
* feat(bedrock/vector_stores): support translating openai filters param to aws kb
adds filtering to aws kb
* feat(milvus/): add milvus vector store unified search support
allows calling milvus vector store in through chat completions
* docs(milvus_vector_stores.md): document new milvus vector search integration
* feat(pass_through_endpoints.py): support passing form data through to a passthrough endpoint
Closes LIT-1147
* 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
Ephrim Stanley