A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System

A tutorial shows how to build pgvector-powered vector search in PostgreSQL using Google Colab, Psycopg, and SentenceTransformers.

It covers loading embeddings into a table and creating vector indexes such as HNSW for faster retrieval.

The walkthrough tests semantic search, filtered search, distance comparison, half-precision storage, binary quantization, and sparse vector search.

It also demonstrates hybrid retrieval and aggregation patterns for RAG-style applications.