Building a RAG Pipeline on 2M+ Pages: EpsteinFiles-RAG Project

A developer has successfully built a comprehensive RAG (Retrieval-Augmented Generation) pipeline processing over 2 million pages from the Epstein Files dataset, demonstrating advanced techniques for handling massive document collections locally. The project involved extensive data cleaning, chunking optimization, and performance tuning across every layer of the RAG stack.

This implementation showcases the practical challenges and solutions for deploying large-scale document retrieval systems using local infrastructure. The project required sophisticated optimization techniques to handle the massive dataset efficiently, including careful attention to chunking strategies, embedding generation, and retrieval performance at scale.

For local LLM practitioners, this project provides valuable insights into scaling RAG systems beyond typical proof-of-concept implementations. The techniques developed for processing millions of pages locally offer a blueprint for organizations seeking to deploy private, large-scale document intelligence systems without relying on cloud services, maintaining complete control over sensitive data while achieving production-scale performance.

Source: r/LocalLLaMA · Relevance: 7/10