pgvector

• 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.

• 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.

• 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.

• 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.

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