Boost Your Knowledge Base with RAGFlow – Open‑Source RAG Engine with 60K Stars

Introduction

RAGFlow is an open‑source Retrieval‑Augmented Generation (RAG) engine that enables large language models (LLMs) to access external knowledge bases before generating answers, improving relevance, accuracy, timeliness and reducing hallucinations. The project has attracted more than 60 000 stars on GitHub.

Key Features

Supports a wide range of document formats including Word, PPT, Excel, PDF (even scanned PDFs), images, web pages and plain‑text files.

Deep document understanding that automatically splits large files into logical “knowledge chunks”, with a UI that allows manual adjustment for better downstream QA.

Provides citations and click‑through traceability so users can see exactly which source text an answer originates from.

Compatible with many LLM providers such as OpenAI GPT‑4o, Baidu Wenxin, Firefly, DeepSeek, Baichuan, etc., and works with various vector stores.

Optimized for very large knowledge bases, delivering fast retrieval even when the index grows without bound.

RAG Workflow

The engine offers an almost fully automated pipeline that starts from document ingestion, proceeds through chunking, embedding, retrieval and finally answer generation with source references.

Deployment Guide

RAGFlow recommends using Docker for deployment. Minimum hardware requirements are 4 CPU cores, 16 GB RAM, 50 GB disk space, Docker ≥ 24.0.0 and Docker‑Compose ≥ 2.26.1.

Step 1 – System Settings

Adjust the kernel parameter vm.max_map_count to at least 262144.

Step 2 – Clone Repository

git clone https://github.com/infiniflow/ragflow.git

Step 3 – Start Services

Enter the docker directory and run the compose file: docker compose -f docker-compose-CN.yml up -d This command pulls the necessary images and launches all required services, including the database and vector store.

Step 4 – Verify Startup

Monitor the containers with docker logs until the logs indicate that the server has started successfully.

Step 5 – Configure Model API

Open a browser to the server’s IP address, log in for the first time, and add the API key of your chosen LLM (e.g., OpenAI) in the configuration file.

Step 6 – Use the System

Upload your documents, then ask questions through the web UI. Answers are generated with citations and direct links to the original document locations, making the output trustworthy and traceable.