How do you implement vector databases and similarity search for AI applications?
Vector databases store and efficiently search high-dimensional embedding vectors for AI applications. Use cases: semantic search (find documents similar in meaning), recommendation engines (find similar users or products), image search (find visually similar images), and RAG (retrieve relevant context for LLM generation). Database options: pgvector (PostgreSQL extension, integrates with existing PostgreSQL infrastructure, ideal for Supabase), Pinecone (fully managed, purpose-built for vectors), Weaviate (open source, supports hybrid search), Qdrant (open source, high performance), and ChromaDB (embedded, great for development). pgvector implementation: install the vector extension, add a vector column to your table (embedding vector(1536) for OpenAI embeddings, vector(384) for smaller models), create an index for fast similarity search (CREATE INDEX ON items USING ivfflat (embedding vectorcosineops) WITH (lists = 100) for approximate search, or HNSW for better recall), and query using similarity operators ( for cosine distance, for L2 distance). Indexing: IVFFlat (faster to build, good for static datasets, tune lists parameter based on dataset size), and HNSW (better recall, handles updates well, tune m and efconstruction parameters). For datasets under 100K vectors, exact search (no index) may be fast enough. Embedding generation: choose model based on use case (text-embedding-3-small for cost efficiency, text-embedding-3-large for quality, open source alternatives like BGE-large-en for offline), batch embed during ingestion, and cache embeddings for frequently queried content. Query optimization: implement metadata filtering before vector search (reduces candidate set), use hybrid search combining vector similarity with keyword matching for best results, implement reranking with cross-encoder models for top results, and cache frequent query results. Monitoring: track query latency, recall quality (using a benchmark set), index build time, and storage usage.