Building a RAG pipeline with LangChain and pgvector
Retrieval-augmented generation transforms a generic LLM into a domain expert by grounding its answers in your own data. This guide walks through building a production-ready RAG pipeline using LangChain, PostgreSQL with pgvector, and the Anthropic Claude API — covering chunking strategies, embedding storage, retrieval tuning, hybrid search, evaluation, and the operational concerns that separate a demo from a deployed system.
raglangchainpgvectorpostgresqlembeddingsaillm
16 July 2026