🔗 Building a RAG server with PostgreSQL - part 1: loading your content

Retrieval-Augmented Generation (RAG) has become one of the most practical ways to give Large Language Models (LLMs) access to your own data. Rather than fine-tuning a model or hoping it somehow knows about your documentation, RAG lets you retrieve relevant content from your own sources and provide it as context to the LLM at query time. The result is accurate, grounded responses based on your actual content.

In this three-part series, I'll walk through building a complete RAG server using PostgreSQL as the foundation. We'll cover:

• Part 1 (this post): Creating a schema and loading your documents
• Part 2: Chunking documents and generating embeddings with pgEdge Vectorizer
• Part 3: Deploying a RAG API server for your applications

By the end of the series, you'll have a working RAG system that can answer questions using your own documentation or knowledge base.