Add more eval examples + filtering examples by language + fix git concurrent usage (#28719)

Release Notes: - N/A --------- Co-authored-by: michael <michael@zed.dev> Co-authored-by: agus <agus@zed.dev>
2025-04-14 22:05:46 +00:00
parent a8b1ef3531
commit d74f0735c2
76 changed files with 365 additions and 8 deletions
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+url = "https://github.com/redis/redis-vl-python.git"
+revision = "494e5e2f8cf800b90c7383385095c2e503404bc5"
+language_extension = "py"
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+1. The changes involve renaming the `TestData` class to `LabeledData` across multiple files. This includes updating the import statements in `__init__.py`, `cache.py`, `router.py`, `schema.py`, and `utils.py` to reflect this new class name. The `__all__` list in `__init__.py` is also updated to export `LabeledData` instead of `TestData`. This appears to be a conceptual renaming to better reflect the purpose of the data structure.
+2. The modifications update all function signatures and type hints that previously used `TestData` to now use `LabeledData`. This affects several functions in `cache.py` including `_generate_run_cache`, `_eval_cache`, and `_grid_search_opt_cache`, as well as functions in `router.py` like `_generate_run_router` and `_eval_router`. The utility functions in `utils.py` are also updated to work with `LabeledData` instead of `TestData`.
+3. The changes introduce a new `search_step` parameter in the router optimization logic within `router.py`, with a default value of 0.10. This parameter is passed through to the `_router_random_search` function and is used in the optimization process. The test file `test_threshold_optimizer.py` is updated to explicitly set this parameter to 0.5 when calling the optimize method, demonstrating how it can be configured for different search granularities during threshold optimization.
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+I need to refactor our codebase to improve the clarity and consistency of our data model, particularly around how we handle labeled evaluation data for our threshold optimization system. Currently, the naming and structure might imply that this data is only used for testing, when in reality it represents labeled examples that power both training and evaluation. The changes should better reflect that these are curated data points with known outcomes, not just test cases. Focus on updating the core data model and ensuring all dependent components—like the cache optimizer, router, and evaluation utilities—properly reference this updated concept. The implementation should maintain all existing functionality while making the naming more semantically accurate. Where relevant, consider adding parameters to fine-tune optimization behavior, like allowing control over the granularity of threshold searches.
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				I need to refactor our codebase to improve the clarity and consistency of our data model, particularly around how we handle labeled evaluation data for our threshold optimization system. Currently, the naming and structure might imply that this data is only used for testing, when in reality it represents labeled examples that power both training and evaluation. The changes should better reflect that these are curated data points with known outcomes, not just test cases. Focus on updating the core data model and ensuring all dependent components—like the cache optimizer, router, and evaluation utilities—properly reference this updated concept. The implementation should maintain all existing functionality while making the naming more semantically accurate. Where relevant, consider adding parameters to fine-tune optimization behavior, like allowing control over the granularity of threshold searches.