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RagFlow is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.

Key Features

• Custom-build document understanding engine. Our deep learning engine is made according to the needs of analyzing and searching various type of documents in different domain.
  • For documents from different domain for different purpose, the engine applys different analyzing and search strategy.
  • Easily intervene and manipulate the data proccessing procedure when things goes beyond expectation.
  • Multi-media document understanding is supported using OCR and multi-modal LLM.
• State-of-the-art table structure and layout recognition. Precisely extract and understand the document including table content. See README.
  • For PDF files, layout and table structures including row, column and span of them are recognized.
  • Put the table accrossing the pages together.
  • Reconstruct the table structure components into html table.
    
• Querying database dumped data are supported. After uploading tables from any database, you can search any data records just by asking.
  • Instead of using SQL to query a database, every one cat get the wanted data just by asking using natrual language.
  • The record number uploaded is not limited.
  • Some extra description of column headers should be provided.
    
• Reasoned and well-founded answers. The cited document part in LLM’s answer is provided and pointed out in the original document.
  • The answers are based on retrieved result for which we apply vector-keyword hybrids search and rerank.
  • The part of document cited in the answer is presented in the most expressive way.
  • For PDF file, the cited parts in document can be located in the original PDF.
    
    

Release Notification

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Installation

System Requirements

Be aware of the system minimum requirements before starting installation.

• CPU >= 2 cores
• RAM >= 8GB

Then, you need to check the following command:

121:/ragflow# sysctl vm.max_map_count
vm.max_map_count = 262144

If vm.max_map_count is not larger than 65535, please run the following commands:

121:/ragflow# sudo sysctl -w vm.max_map_count=262144

However, this change is not persistent and will be reset after a system reboot. To make the change permanent, you need to update the /etc/sysctl.conf. Add or update the following line in the file:

vm.max_map_count=262144

Install docker

If your machine doesn’t have Docker installed, please refer to Install Docker Engine

Quick Start

If you want to change the basic setups, like port, password .etc., please refer to .env before starting the system.

If you change anything in .env, please check service_conf.yaml which is a configuration of the back-end service and should be consistent with .env.

• In service_conf.yaml, configuration of LLM in user_default_llm is strongly recommended. In user_default_llm of service_conf.yaml, you need to specify LLM factory and your own _APIKEY. It’s O.K if you don’t have _APIKEY at the moment, you can specify it later at the setting part after starting and logging in the system.
• We have supported the flowing LLM factory, and the others is coming soon: OpenAI, 通义千问/QWen, 智谱AI/ZhipuAI bash 121:/# git clone https://github.com/infiniflow/ragflow.git 121:/# cd ragflow/docker 121:/ragflow/docker# docker compose up -d The core image is about 15GB, please be patient for the first time

After pulling all the images and running up, use the following command to check the server status. If you can have the following outputs, Hallelujah! You have successfully launched the system.

121:/ragflow# docker logs -f  ragflow-server

    ____                 ______ __               
   / __ \ ____ _ ____ _ / ____// /____  _      __
  / /_/ // __ `// __ `// /_   / // __ \| | /| / /
 / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ / 
/_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/  
              /____/                             

 * Running on all addresses (0.0.0.0)
 * Running on http://127.0.0.1:9380
 * Running on http://172.22.0.5:9380
INFO:werkzeug:Press CTRL+C to quit

Open your browser, enter the IP address of your server, Hallelujah again!

The default serving port is 80, if you want to change that, please refer to docker-compose.yml, and change the left part of ‘80:80’’.

Configuration

If you need to change the default setting of the system when you deploy it. There several ways to configure it. Please refer to README and manually set the configuration. After changing something, please run docker-compose up -d again.

RoadMap

• File manager.
• Support URLs. Crawl web and extract the main content.

Contributing

For those who’d like to contribute code, see our Contribution Guide.

License

This repository is available under the Ragflow Open Source License, which is essentially Apache 2.0 with a few additional restrictions.