pgvector
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pgvector IVFFlat Lists Tuning Guide: Probes and Recall
Set pgvector IVFFlat lists to rows/1000 up to 1M rows and sqrt(rows) above, probes to sqrt(lists), then measure recall against exact search.
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Cosine Similarity vs Dot Product: How Rankings Differ
Vector norms determine when cosine similarity and dot product rank results identically and when magnitude changes retrieval order.
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How to Benchmark Recall at K for ANN Indexes
The guide explains exact ground truth, tie-safe recall@k, controlled efSearch sweeps, latency measurement, and how to interpret vendor benchmarks.
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pgvector vs Pinecone: Cost, Recall, and Filtering
This comparison examines architecture, filtered recall, operational tradeoffs, and cost per query for self-hosted pgvector and managed Pinecone.
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HNSW ef_search Parameter: Recall and Latency Tradeoffs
The HNSW ef_search parameter sets query beam width, balancing recall against latency across vector search engines and filtered queries.