Storing OpenAI Embeddings In PostgreSQL With Pg_vector
Effective Vector Storage & Querying in PostgreSQL using pg_vector: Store OpenAI text-embeddings, index with IVFFlat, and query with cosine distance operator for semantic search & similarity matching.
In this guide, we'll explore how to effectively store, index, and query embeddings generated from OpenAI's text-embedding-3-small model using PostgreSQL's pg_vector extension. This approach is particularly powerful for building semantic search and similarity matching systems. Why PostgreSQL with pg_vector? Native Vector Operations: pg_vector allows PostgreSQL to handle vector operations natively Efficient Indexing: Supports fast similarity searches using IVFFlat indexes Production Ready: Scales well with large datasets Cost-Effective: Cheaper than specialized vector databases...