Guide Shows How to Build a Hybrid Search RAG Agent Using PostgreSQL and LangGraph

A technical tutorial published on DEV Community outlines how to build a production-grade Retrieval-Augmented Generation (RAG) agent using hybrid search techniques. The guide argues that standard vector-only search fails in enterprise settings because it struggles to match exact terms like CVE identifiers while also capturing contextual synonyms. The proposed solution combines dense vector search and sparse keyword search (BM25), merging results using a Reciprocal Rank Fusion (RRF) formula to improve retrieval accuracy. PostgreSQL, with its pgvector and tsvector extensions, is used as a single database handling both embedding storage and full-text search within ACID transactions. The retrieval engine is then connected to a LangGraph Supervisor Agent, which verifies facts before generating a final answer.