Vector Database
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How to Choose a Vector Database for Production
The guide compares pgvector, Qdrant, Milvus, Weaviate, Pinecone, and Elasticsearch by recall, filters, latency, memory, cost, and operational fit.
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Hybrid Search: BM25, Vector Retrieval, and Score Fusion
This guide explains how BM25 and vector retrieval complement each other, why raw scores cannot be combined, and how RRF and alpha fusion work.
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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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Qdrant vs Weaviate vs Milvus: Recall, RAM, and Scale
This comparison covers filtered recall, memory and quantization options, cluster design, multi-tenancy, and scaling across the three engines.
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HNSW vs IVF: Vector Index Tradeoffs Compared
HNSW vs IVF vector index tradeoffs: how graph and inverted-file designs compare on memory, build cost, recall tuning, updates, and filtering.
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Low Vector Search Recall: Causes and Fixes
Why an approximate index returns the wrong neighbours: candidate lists too small, metric mismatch, filters, tombstones, and how to measure recall properly.
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Vector Database Memory Sizing: RAM, Graph and Overhead
Size a vector index before you build it: bytes per embedding, HNSW graph overhead, what quantization actually saves, and what has to fit in RAM.
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Vector Search Fundamentals: Embeddings, ANN and Recall
What an approximate nearest neighbor index does, how graph and cluster based indexes differ, and how quantization trades memory against recall.