vector-search
Product Quantization Explained for Vector Search
Learn how product quantization compresses embeddings, how IVF-PQ changes recall, and how to evaluate a Faiss …
VectorOpsReport — Vector Database HNSW Index & ANN Performance Report
Vector Database HNSW Index & ANN Performance Report
VectorOpsReport
Independent · 2026
Independent · 2026
Featured guide
What Is Reranking in Vector Search? A Practical Guide
Learn how reranking improves vector search relevance, measure nDCG and candidate recall, and instrument a PyTorch reranker without hiding latency costs.
Read the article →Latest articles
Indexing
HNSW M Parameter Tuning: Recall, Memory, and Latency
The guide explains how M affects graph connectivity and memory, maps engine-specific settings, and shows a sw…
Guide
How to Choose a Vector Database for Production
The guide compares pgvector, Qdrant, Milvus, Weaviate, Pinecone, and Elasticsearch by recall, filters, latenc…
Comparison
Qdrant vs Weaviate vs Milvus: Recall, RAM, and Scale
This comparison covers filtered recall, memory and quantization options, cluster design, multi-tenancy, and s…
Fundamentals
Cosine Similarity vs Dot Product: How Rankings Differ
Vector norms determine when cosine similarity and dot product rank results identically and when magnitude cha…
Operations
How to Benchmark Recall at K for ANN Indexes
The guide explains exact ground truth, tie-safe recall@k, controlled efSearch sweeps, latency measurement, an…
The field guide
Vector search breaks in a handful of predictable ways: an index that will not fit in memory, recall that quietly drops, a distance metric that does not match the embedding model, or a database chosen before the constraints were known. VectorOpsReport works those problems from the engine documentation up, with the arithmetic to size an index and the harness to measure it. New to the topic? Start with the vector search fundamentals, then size a build with the HNSW index RAM calculator.