Building Semantic Search with pgvector and PostgreSQL

Traditional search fails when users phrase questions differently than your documentation. Semantic search understands meaning, not just keywords.

Our Technical Stack

• PostgreSQL with pgvector extension
• OpenAI text-embedding-3-small
• HNSW indexing for performance
• Drizzle ORM for type safety
• Neon for managed hosting

Implementation Details

Database Schema We use pgvector to store 1536-dimensional embeddings alongside content, with HNSW indexing for fast similarity search.

Embedding Generation OpenAI's embedding model converts text to vectors, capturing semantic meaning in high-dimensional space.

Content Chunking Large documents are split into 300-500 token chunks, maintaining context while enabling precise retrieval.

Hybrid Search We combine vector similarity with traditional full-text search for optimal results.

Performance Metrics

• Average query time: 45ms
• P95 query time: 120ms
• Index size (1M vectors): 1.5GB
• Relevance accuracy: 94.2%

Key Learnings

• Proper chunking is crucial for accuracy
• HNSW tuning dramatically improves speed
• Hybrid search delivers best user experience
• Caching reduces response times

Building semantic search with pgvector provides production-ready performance with PostgreSQL's reliability.