๐ New Feature
๐ Bug Fix
๐งน Refactoring
๐ Documentation
๐ Infrastructure
โ Test
Changes
[REQUIRED] Testing - Attach a screenshot of any new tests passing locall
If UI changes, send a screenshot/GIF of working UI fixes
Description by Korbit AI
What change is being made?
Enhance JSON response handling by updating JSON schema structure and adding "supports_response_schema" support to various models, remove a redundant model, and introduce new unit tests for validation of JSON response formats.
Why are these changes being made?
The changes are aimed at improving the compatibility and robustness of JSON response formats by encapsulating schema properties within a "values" object, thereby maintaining consistency across different models. The redundant model "anthropic/claude-3-5-sonnet-20241022" was removed to avoid duplication. New unit tests have been added to ensure that models properly support response schema and handle potential errors gracefully, which are crucial for reliable model performance and testing of edge cases like Internal Server Errors.
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Stable release for Nov 21st
Fixes https://github.com/BerriAI/litellm/issues/6766
Title
Relevant issues
Type
๐ New Feature ๐ Bug Fix ๐งน Refactoring ๐ Documentation ๐ Infrastructure โ Test
Changes
[REQUIRED] Testing - Attach a screenshot of any new tests passing locall
If UI changes, send a screenshot/GIF of working UI fixes
Description by Korbit AI
What change is being made?
Enhance JSON response handling by updating JSON schema structure and adding "supports_response_schema" support to various models, remove a redundant model, and introduce new unit tests for validation of JSON response formats.
Why are these changes being made?
The changes are aimed at improving the compatibility and robustness of JSON response formats by encapsulating schema properties within a "values" object, thereby maintaining consistency across different models. The redundant model "anthropic/claude-3-5-sonnet-20241022" was removed to avoid duplication. New unit tests have been added to ensure that models properly support response schema and handle potential errors gracefully, which are crucial for reliable model performance and testing of edge cases like Internal Server Errors.