vector-databases

Topics in the source

• Vector Databases
• Quick Start
• When to Use
• Best Practices
• Embeddings
• Indexes
• Filtering & Hybrid
• Operations

Storing and searching embeddings for semantic retrieval and RAG. Semantic search over unstructured data RAG retrieval backends Recommendation by similarity Deduplication and clustering of texts Use retrieval-tuned embedding models Normalize vectors for cosine similarity Choose dimension balanced with storage and speed Embed at indexing time and query time consistently Prefer HNSW for balanced speed/recall Set m (connections) and efconstruction for build quality Use IVF for very large collections with disk budget Match the index to the metric (cosine/euclidean) Combine vector search with metadata filters Support hybrid retrieval (vector + keyword) when needed Re-rank top-k with a cross-encoder for quality Return scores for confidence thresholds Set replication and backup for durability Monitor recall, latency, and index size Re-index when the embedding model changes Version the embedding model with the data 1. Choose a store: pgvector, Chroma, Qdrant, or managed services. 2. Pick an embedding model and vector dimension. 3. Index your corpus in batches with metadata. 4. Create an HNSW index tuned to your recall/latency goals. 5. Query with filters and hybrid options. 6. Re-rank resul

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