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ebe82db4fe
/kind improvement <!-- This is an auto-generated comment: release notes by coderabbit.ai --> - Core invariant: test infrastructure treats insertion granularity as orthogonal to data semantics—bulk generation gen_row_data_by_schema(nb=2000, start=0, random_pk=False) yields the same sequential PKs and vector payloads as prior multi-batch inserts, so tests relying on collection lifecycle, flush, index build, load and search behave identically. - What changed / simplified: added a full HNSW_PQ parameterized test suite (tests/python_client/testcases/indexes/idx_hnsw_pq.py and test_hnsw_pq.py) and simplified HNSW_SQ test insertion by replacing looped per-batch generation+insert with a single bulk gen_row_data_by_schema(...) + insert. The per-batch PK sequencing and repeated vector generation were redundant for correctness and were removed to reduce complexity. - Why this does NOT cause data loss or behavior regression: the post-insert code paths remain unchanged—tests still call client.flush(), create_index(...), util.wait_for_index_ready(), collection.load(), and perform searches that assert describe_index and search outputs. Because start=0 and random_pk=False reproduce identical sequential PKs (0..1999) and the same vectors, index creation and search validation operate on identical data and index parameters, preserving previous assertions and outcomes. - New capability: comprehensive HNSW_PQ coverage (build params: M, efConstruction, m, nbits, refine, refine_type; search params: ef, refine_k) across vector types (FLOAT_VECTOR, FLOAT16_VECTOR, BFLOAT16_VECTOR, INT8_VECTOR) and metrics (L2, IP, COSINE), implemented as data-driven tests to validate success and failure/error messages for boundary, type-mismatch and inter-parameter constraints. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: zilliz <jiaming.li@zilliz.com>