ragflow/README.md

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💡 What is RagFlow?

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 applies 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.
  • You can now query a database using natural language instead of using SQL.
  • The record number uploaded is not limited.
• 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 re-rank.
  • 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.
    

🤺RagFlow vs. other RAG applications

Feature	RagFlow	Langchain-Chatchat	Assistants API	QAnythig	LangChain
Well-Founded Answer	:white_check_mark:	:x:	:x:	:x:	:x:
Trackable Chunking	:white_check_mark:	:x:	:x:	:x:	:x:
Chunking Method	Rich Variety	Naive	Naive	Naive	Naive
Table Structure Recognition	:white_check_mark:	:x:	:x:	:x:	:x:
Structured Data Lookup	:white_check_mark:	:x:	:x:	:x:	:x:
Programming Approach	API-oriented	API-oriented	API-oriented	API-oriented	Python Code-oriented
RAG Engine	:white_check_mark:	:white_check_mark:	:white_check_mark:	:x:	:x:
Prompt IDE	:white_check_mark:	:white_check_mark:	:white_check_mark:	:x:	:x:
Supported LLMs	Rich Variety	Rich Variety	OpenAI-only	QwenLLM	Rich Variety
Local Deployment	:white_check_mark:	:white_check_mark:	:x:	:x:	:x:
Ecosystem Strategy	Open Source	Open Source	Close Source	Open Source	Open Source

🔎 System Architecture

🎬 Get Started

📝 Prerequisites

• CPU >= 2 cores
• RAM >= 8 GB
• Docker
• vm.max_map_count > 65535

To check the value of vm.max_map_count:

> $ sysctl vm.max_map_count
> ```
>
> Reset `vm.max_map_count` to a value greater than 65535 if it is not. In this case, we set it to 262144:
>
> ```bash
> $ sudo sysctl -w vm.max_map_count=262144
> ```
>
> This change will be reset after a system reboot. To ensure your change remains permanent, add or update the following line in **/etc/sysctl.conf** accordingly:
>
> ```bash
> vm.max_map_count=262144
> ```



### Start up the RagFlow server

1. Clone the repo

   ```bash
   $ git clone https://github.com/infiniflow/ragflow.git

• 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. If you do not have _APIKEY at the moment, you can specify it in Settings the next time you log in to the system.
• RagFlow supports the flowing LLM factory, with more coming in the pipeline: OpenAI, Tongyi-Qianwen, ZHIPU-AI, Moonshot ```bash

$ cd ragflow/docker $ docker compose up -d ```

OR

$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/
$ docker build  -t infiniflow/ragflow:v1.0 .
$ cd ragflow/docker
$ docker compose up -d

The core image is about 15 GB in size and may take a while to load.

Check the server status after pulling all images and having Docker up and running:

$ docker logs -f  ragflow-server

The following output confirms the successful launch of the system:

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

 * 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

In your browser, enter the IP address of your server.

🔧 Configurations

The default serving port is 80, if you want to change that, refer to the docker-compose.yml and change the left part of 80:80, say 66:80.

If you need to change the default setting of the system when you deploy it. There several ways to configure it. Please refer to this README to manually update the configuration. Updates to system configurations require a system reboot to take effect docker-compose up -d again.

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

📜 Roadmap

See the RagFlow Roadmap 2024

🏄 Community

• Discord
• Twitter
• GitHub Discussions

🙌 Contributing

RagFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.