- Apply Black formatting to all Bedrock CountTokens files
- Clean up imports and remove unused variables in tests
- Fix indentation and simplify test structure
- Fix pyright type error with type ignore annotation
- All tests continue to pass after cleanup
- Add endpoint integration test in test_proxy_token_counter.py
- Add unit tests for transformation logic in bedrock/count_tokens/
- Test model extraction from request body vs endpoint path
- Test input format detection (converse vs invokeModel)
- Test request transformation from Anthropic to Bedrock format
- All tests follow existing codebase patterns and pass successfully
* fix: iscoroutine removed from hot path
* fix: replace all instances & separate concerns
1. Replaced all instances of iscoroutine with is_async_callable
2. Place the coroutine checker in its own file
* fix: PR comment changes
* fix: missing config setting declaration
* fix: revert non-performance related changes
* fix: revert to initial implementation
* fix: remove dead const
Bedrock Guardrails - support setting bedrock runtime endpoint + Protect `/health/test_connect` to prevent users without model creation permissions from calling it
UI - allow team member to view service account keys they create + Anthropic - include cache creation tokens in prompt token total (separate out during cost tracking)
* fix: ensure /responses/cancel works for non admins
* test: cancel endpoint
* fix responses API cancel endpoint
* test fix
* TestGoogleAIStudioResponsesAPITest
- Add missing provider_config parameter in main.py for proper HTTP handler integration
- Update tests to use correct respx mocking pattern with litellm.disable_aiohttp_transport
- Add get_error_class method to CompactifAI transformation for proper error handling
- Fix authentication error test to expect APIConnectionError instead of AuthenticationError
- All 8 CompactifAI tests now pass successfully
* Add comprehensive tests for Vertex AI Gemini labels provider filtering
- Test Google GenAI endpoints exclude labels even when explicitly provided
- Test Vertex AI endpoints include labels when provided
- Cover provider detection logic for different endpoint URLs
- Verify metadata-to-labels conversion only happens for Vertex AI
- Ensure edge cases are handled properly (null/empty api_base)
* Fix Vertex AI Gemini labels field provider-aware filtering
- Add _is_google_genai_endpoint() function to detect Google GenAI vs Vertex AI endpoints
- Update _transform_request_body() to accept api_base parameter
- Only include labels field for Vertex AI endpoints (not Google GenAI)
- Pass api_base through sync/async transform functions
- Maintain backward compatibility with existing usage
- Fixes issue where Google GenAI requests failed with unsupported labels field
* Refactor labels filtering to use custom_llm_provider instead of URL parsing
Replace URL-based endpoint detection with custom_llm_provider parameter
checking for cleaner, more reliable provider identification.
Changes:
- Remove _is_google_genai_endpoint() helper function
- Update labels condition to use custom_llm_provider != "gemini"
- Remove api_base parameter from _transform_request_body()
- Simplify sync/async transform function signatures
- Update tests to reflect new parameter structure
- Remove obsolete test_provider_detection test
This approach aligns with existing codebase patterns where
custom_llm_provider="gemini" identifies Google AI Studio endpoints
that don't support labels, while vertex_ai/vertex_ai_beta identify
Vertex AI endpoints that do support labels.
* Use LlmProviders.GEMINI constant instead of hardcoded string
