Add support for function calling (tools) with Snowflake Cortex models that support it (e.g., Claude 3.5 Sonnet).
Changes:
- Add 'tools' and 'tool_choice' to supported OpenAI parameters
- Implement request transformation: OpenAI function format → Snowflake tool_spec format
- Implement response transformation: Snowflake content_list with tool_use → OpenAI tool_calls
- Add tool_choice transformation: OpenAI nested format → Snowflake array format
Request transformation:
- Transform tools from nested {"type": "function", "function": {...}} to Snowflake's {"tool_spec": {"type": "generic", "name": "...", "input_schema": {...}}}
- Transform tool_choice from {"type": "function", "function": {"name": "..."}} to {"type": "tool", "name": ["..."]}
Response transformation:
- Parse Snowflake's content_list array containing tool_use objects
- Extract tool calls with tool_use_id, name, and input
- Convert to OpenAI's tool_calls format with proper JSON serialization
Testing:
- Add 7 unit tests covering request/response transformations
- Add integration test for Responses API with tool calling
- All tests passing
Fixes issue #15218🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: use fastuuid helper across the codebase
First batch of changes, simple drop in replacement.
* second batch of changes
* fixed: script mistake on helper file
* 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.
Allow passing aiohttp.ClientSession to acompletion() calls for better
performance and resource management. Includes debug logging, tests,
and documentation. Backward compatible.
- 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
- Change parameter from request_metadata to requestMetadata to match camelCase convention
- Consistent with guardrailConfig and performanceConfig naming pattern
- Update all references in transformation code and error messages
- Update tests and documentation to use correct parameter name
- Fix type checking for parameter validation
- Test requestMetadata parameter support in get_supported_openai_params
- Test transformation to top-level field in Bedrock API request
- Test validation of AWS constraints: max 16 items, key/value length limits
- Test character set validation for keys and values
- Cover edge cases including empty values and special characters
- Ensure compatibility with existing test patterns
- 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
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)
