How to Use PostgreSQL for Retrieval-Augmented Generation (RAG)

How to Use PostgreSQL for Retrieval-Augmented Generation (RAG)

 

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Retrieval-augmented generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) by retrieving relevant documents from a database to generate more context-aware responses. PostgreSQL, an advanced open-source relational database, is a great choice for implementing RAG because it supports full-text search, vector similarity search (via extensions like pgvector), and scalability. This article will guide you through using PostgreSQL for RAG and integrating it with LLMs to create AI-powered applications.