Allow thresholding on vector and fulltext indexes for Hybrid retrievers #239
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willtai
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alexthomas93
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Jan 3, 2025
| @@ -159,6 +161,8 @@ def get_search_results( | |||
| query_text (str): The text to get the closest neighbors of. | |||
| query_vector (Optional[list[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None. | |||
| top_k (int, optional): The number of neighbors to return. Defaults to 5. | |||
| threshold_vector_index (float, optional): The minimum normalized score from the vector index to include in the top k search. | |||
| threshold_fulltext_index (float, optional): The minimum normalized score from the fulltext index to include in the top k search. | |||
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Minor point but users might not know or understand the normalisation process. It could be worth adding something to the docs with an example which explains a bit about what's going on with these parameters as I think on their own these descriptions might not be enough
alexthomas93
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Jan 3, 2025
| @@ -37,12 +37,14 @@ def test_hybrid_search_basic() -> None: | |||
| "YIELD node, score " | |||
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Could it be worth adding a test to check the threshold process is working as expected? i.e. the correct scores are set to zero, etc.
alexthomas93
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Jan 3, 2025
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Description
Allow thresholding on vector and fulltext indexes for Hybrid retrievers. Two thresholds can be provided by the user during search to determine the importance of the search results from either vector or fulltext index.
Type of Change
Complexity
Complexity: Low
How Has This Been Tested?
Checklist
The following requirements should have been met (depending on the changes in the branch):