Building a RAG System in Rails — Retrieval-Augmented Generation from Scratch

This article explains how to build a Retrieval-Augmented Generation (RAG) pipeline in Ruby on Rails. The pipeline includes ingesting documents, chunking them, generating embeddings, and using OpenAI to retrieve relevant chunks and generate grounded answers.

Details

The article outlines the architecture of the RAG pipeline, which consists of three main steps: 1) Ingest documents, chunk them, and generate embeddings to store in a Postgres database; 2) When a user asks a question, retrieve the most relevant chunks via vector search; 3) Send the retrieved chunks and the question to OpenAI to generate a grounded answer. The author provides the code for the chunking logic and the embedding generation using the OpenAI API. RAG is a powerful pattern for building 'chat with your docs' products, where the language model is augmented with relevant context from the user's own data, rather than relying solely on the model's memorized knowledge.