feat(ibm_db): support all 6 Db2 vector distance metrics - #4045
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GeetikaChughIBM wants to merge 3 commits into
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GeetikaChughIBM wants to merge 3 commits into
GeetikaChughIBM wants to merge 3 commits into
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Add two parametrized integration tests inside the existing TestDocumentStore class covering all 6 supported distance metrics (COSINE, DOT, EUCLIDEAN, EUCLIDEAN_SQUARED, HAMMING, MANHATTAN): - test_all_distance_metrics_return_ranked_results: verifies correct ranking, finite scores, and ORDER BY ASC ordering for each metric - test_all_distance_metrics_respect_filters: verifies metadata filters work correctly alongside each distance metric Each test creates an isolated table (embedding_dim=4) via the connection_config fixture, runs retrieval assertions, then drops the table. All 12 tests verified passing against a real Db2 instance.
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- Move DuplicatePolicy import to correct sorted position (I001) - Remove unused noqa BLE001 directives (RUF100) - Collapse multi-line f-strings and remove alignment spaces to satisfy ruff formatter
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Summary
Extends the IBM Db2 document store to support all 6 distance metrics
available in Db2's
VECTOR_DISTANCEfunction, up from the previous 3.Changes
document_store.pydistance_metrictype annotation fromLiteral["EUCLIDEAN", "COSINE", "MANHATTAN"]to include all 6:COSINE,DOT,EUCLIDEAN,EUCLIDEAN_SQUARED,HAMMING,MANHATTANtest_document_store.pyTestIBMDb2DocumentStoreUnit) verifying:to_dict/from_dict_embedding_retrievalemits the correctVECTOR_DISTANCE(..., <METRIC>)SQL fragment for each metricTestDocumentStore) verifying against a real Db2 instance:test_all_distance_metrics_return_ranked_results— correct ranking and score ordering for each metrictest_all_distance_metrics_respect_filters— metadata filters work correctly alongside each metricTesting
All 12 integration tests verified passing against a real Db2 instance
Closes #4046