Introducing AutoRAG: fully managed Retrieval-Augmented Generation on Cloudflare

This post introduces AutoRAG, a fully managed Retrieval-Augmented Generation (RAG) pipeline on Cloudflare. It details the end-to-end process of building a RAG system, from data ingestion and automatic chunking/embedding to storing vectors in Vectorize, performing semantic retrieval, and generating responses using Workers AI. The indexing process involves file ingestion from data sources (initially R2), Markdown conversion using Workers AI, chunking, embedding with Workers AI's embedding model, and storage in Vectorize. The querying process includes receiving queries, optional query rewriting, embedding the query, vector search in Vectorize, metadata and content retrieval, and response generation using a Workers AI text-generation model. A tutorial demonstrates using a Cloudflare Worker with Puppeteer and Browser Rendering API to fetch webpages, render them, and upload the HTML content to an R2 bucket for AutoRAG processing.