README.md

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## 💡 What is RAGFlow?

[RAGFlow](http://demo.ragflow.io) 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.](./deepdoc/README.md)
  - 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.  

## 🔎 System Architecture

<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
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## 🎬 Get Started

### 📝 Prerequisites

- CPU >= 2 cores
- RAM >= 8 GB
- Docker: If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).

### Start up the server

1. Ensure `vm.max_map_count` > 65535: 

   > To check the value of `vm.max_map_count`:
   >
   > ```bash 

   > $ sysctl vm.max_map_count

   > ```
   >
   > Reset `vm.max_map_count` to a value greater than 65535 if it is not.
   >
   > ```bash

   > # In this case, we set it to 262144:

   > $ 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 `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
   >
   > ```bash

   > vm.max_map_count=262144

   > ```

2. Clone the repo:

   ```bash

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

   ```


3. Build the pre-built Docker images and start up the server: 

   ```bash

   $ cd ragflow/docker

   $ docker compose up -d

   ```

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

4. Check the server status after pulling all images and having Docker up and running:
   ```bash

   $ docker logs -f ragflow-server

   ```
   *The following output confirms a successful launch of the system:*

```bash

    ____                 ______ __               

   / __ \ ____ _ ____ _ / ____// /____  _      __

  / /_/ // __ `// __ `// /_   / // __ \| | /| / /

 / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ / 

/_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/  

              /____/                             



 * 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

```

5. In your web browser, enter the IP address of your server as prompted.
   *The show is on!*


## 🔧 Configurations

> The default serving port is 80, if you want to change that, refer to the [docker-compose.yml](./docker-compose.yaml) 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](./docker/README.md) 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](./docker/.env) before starting up the system.

> If you change anything in [.env](./docker/.env), please check [service_conf.yaml](./docker/service_conf.yaml) which is a configuration of the back-end service and should be consistent with [.env](./docker/.env).

## 🛠️ Build from source

To build the Docker images from source:

```bash

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

$ cd ragflow/

$ docker build -t infiniflow/ragflow:v1.0 .

$ cd ragflow/docker

$ docker compose up -d

```

## 📜 Roadmap

See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)

## 🏄 Community

- [Discord](https://discord.gg/uqQ4YMDf)
- [Twitter](https://twitter.com/infiniflowai)

## 🙌 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](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md) first.