* feat: add Vertex AI support for file content retrieval
- Extended `custom_llm_provider` to include "vertex_ai" in `afile_content` function.
- Implemented file content retrieval logic for Vertex AI in `VertexAIFilesHandler`.
- Added helper method to extract bucket and object from URL-encoded file_id.
- Created comprehensive unit and integration tests for Vertex AI file handling.
- Updated transformation logic to ensure compatibility with Vertex AI file responses.
* fix: update Vertex AI file transformation logic
- Modified the transformation logic in `VertexAIFilesConfig` to return a newline-separated JSON string for batch JSONL files instead of a array if JSON strings.
* fix: enhance Vertex AI output handling in transformation logic
- Updated the transformation logic in `VertexAIBatchTransformation` to utilize the new `OutputInfo` TypedDict for retrieving the GCS output directory.
- Added `OutputInfo` class to type definitions for better structure and clarity in Vertex AI responses.
* fix: avoid NoneType AttributeError when extracting tags
I've been running into this error:
```
21:47:08 - LiteLLM:ERROR: litellm_logging.py:2396 - LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging Traceback (most recent call last):
File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/litellm_logging.py", line 2312, in async_success_handler
await callback.async_log_success_event(
...<6 lines>...
)
File "/usr/lib/python3.13/site-packages/litellm/router_strategy/budget_limiter.py", line 396, in async_log_success_event
request_tags = _get_tags_from_request_kwargs(kwargs)
File "/usr/lib/python3.13/site-packages/litellm/router_strategy/tag_based_routing.py", line 144, in _get_tags_from_request_kwargs
return _metadata.get("tags", [])
^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'get'
```
This makes the function more resilient without resorting to try catch.
* add tests
Signed-off-by: Max Falk <gmdfalk@gmail.com>
---------
Signed-off-by: Max Falk <gmdfalk@gmail.com>
Update Bedrock documentation for Titan V2 encoding_format support + Anthropic - account for 1h vs. 5m cache creation token cost difference + UI - add langsmith_sampling_rate as a dynamic param
- Test encoding_format='float' parameter mapping and response handling
- Test encoding_format='base64' parameter mapping to binary format
- Verify parameter transformation and response processing
- Mock AWS API responses for both float and binary formats
- Ensure OpenAI compatibility with new encoding_format support
- Add Asia/Bangkok (UTC+7) to timezone_map in duration_parser.py
- Update documentation to include Bangkok in common timezone values
- Add test case to verify Bangkok timezone functionality
- 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)
Ishaan Jaffer