AI Vector Database
Store and query embeddings with pgvector on PostgreSQL. Build semantic search, recommendation systems, and power RAG applications.
What You Get
Vector Search Flow
pgvector - Vectors in PostgreSQL
pgvector adds vector similarity search to PostgreSQL. Store embeddings alongside your regular data and query them with SQL. No separate vector database needed.
Supports exact and approximate nearest neighbor search. Index types include IVFFlat and HNSW for fast queries on millions of vectors.
What You Can Build
Semantic Search
Find content by meaning, not just keywords
RAG Applications
Give LLMs context from your own data
Image Similarity
Find visually similar images in your library
Recommendations
Suggest similar products or content
How It Works
Simple SQL Interface
Query vectors using familiar SQL syntax. Find the most similar items with a single query:
-- Find 5 most similar documents
SELECT content, embedding <-> query_embedding AS distance
FROM documents
ORDER BY embedding <-> query_embedding
LIMIT 5;Vector Stack
Postgres Vector (pgvector)
Vector similarity search for PostgreSQL
PostgreSQL
Reliable relational database
OpenAI Assistant
AI integration for embeddings
S3 Sync Vectorstore
Sync documents to vector store
Why pgvector Over Dedicated Vector DBs?
Dedicated vector databases like Pinecone or Weaviate add complexity and cost. With pgvector, your vectors live in the same database as your application data.
Join vectors with relational data in a single query. No data synchronization needed. Use the PostgreSQL you already know and trust.