Term
pgvector
pgvector is an open-source PostgreSQL extension for vector search — adding embedding columns, distance functions, and ANN indexes (HNSW, IVFFlat) directly to an existing Postgres database.
pgvector — explained
Instead of running a separate dedicated vector database, pgvector extends PostgreSQL with a vector data type, distance operators (<-> euclidean, <#> dot product, <=> cosine), and the index types HNSW (fast, low tuning) and IVFFlat (smaller, slower on lookup). Embeddings can sit in the same table as your metadata — joins, WHERE filters, transactions, and the familiar Postgres ecosystem all included.
Example / Practical context
A RAG setup for an existing SaaS app: documents are already in Postgres, pgvector is enabled via CREATE EXTENSION vector, an embedding vector(1536) column is added, and an HNSW index is built. Retrieval runs via SELECT … ORDER BY embedding <=> $1 LIMIT 10. Hosted on Supabase, Neon, AWS RDS, Azure, and many other providers — no extra service required.
Distinction from related terms
Dedicated vector DBs like Pinecone or Qdrant scale into the billions of vectors and often offer lower p99 latency, but require additional infrastructure. pgvector is the pragmatic choice when Postgres is already in the stack and vector counts are in the millions. With pg_trgm or Postgres full-text, hybrid search can be built directly inside the same database.
Entdecke mehr
Saving Tokens with Claude: 6 Principles That Make Experts Twice as Fast
How I turned my CLAUDE.md from a style guide into a token budget — 6 principles for lower cost, less waiting, and more honest reporting.
GlossarChroma
Chroma (ChromaDB) is an open-source vector database for storing and searching embeddings. It is widely used in RAG systems to retrieve relevant text passages via similarity search and feed them to language models.
LexikonVector Databases Compared
What vector databases do, when you need one, and how Chroma, Weaviate, Milvus, Qdrant and pgvector stack up against each other.