Vector databases
Definition
Vector databases store high-dimensional vectors (embeddings) and support fast similarity search (e.g. k-NN, approximate nearest neighbor). They are the backbone of retrieval in RAG.
They sit between embeddings (which produce the vectors) and the RAG retriever (which needs the top-k chunks). Unlike keyword search, they support semantic similarity: “customer support” can match “help desk” if the embedding model maps them close together. See RAG architecture for how the index fits into the full pipeline.
How it works
Documents are embedded and their vectors are written to an index (e.g. HNSW, IVF, or flat for small datasets). At query time, the query vector is compared against the index via k-NN (or approximate k-NN for scale); the index returns top-k ids (and optionally the vectors or stored metadata). You then fetch the corresponding chunks and pass them to the LLM. Options include Pinecone, Weaviate, Chroma, pgvector, and others; choice depends on scale, latency, and whether you need metadata filtering.
Use cases
Vector stores are used whenever you need fast similarity search over many embeddings (RAG, recommendations, dedup).
- Storing and querying document embeddings for RAG
- Real-time similarity search at scale (e.g. recommendations, dedup)
- Combining vector search with metadata filters (e.g. by date, category)
External documentation
- Chroma – Get started
- Pinecone – Vector database docs
- pgvector — Vector similarity search in PostgreSQL