ragflow_python/README.md

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<p align="center">
  <a href="./README.md">English</a> |
  <a href="./README_zh.md">简体中文</a> 
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    <a href="https://demo.ragflow.io" target="_blank">
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[RagFlow](https://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.
    

<div align="center" style="margin-top:20px;margin-bottom:20px;">
<img src="https://github.com/infiniflow/ragflow/assets/12318111/b24a7a5f-4d1d-4a30-90b1-7b0ec558b79d" width="1000"/>
</div>

## 🌟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.](./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.  

## 🤺RagFlow vs. other RAG applications



## 🎬 Get Started

### 📝Prerequisites
- CPU >= 2 cores
- RAM >= 8 GB

Then, you need to check the following command:
```bash
$ sysctl vm.max_map_count
vm.max_map_count = 262144
```
If **vm.max_map_count** is not greater  than 65535:
```bash
$ sudo sysctl -w vm.max_map_count=262144
```
Note that this change is reset after a system reboot. To render your change permanent, add or update the following line in **/etc/sysctl.conf**:

```bash
vm.max_map_count=262144
```

### Install docker

If you have not installed *Docker* on your local machine, see [Install Docker Engine](https://docs.docker.com/engine/install/)

### Quick Start

> - In [service_conf.yaml](./docker/service_conf.yaml), configuration of *LLM* in **user_default_llm** is strongly recommended. 
> In **user_default_llm** of [service_conf.yaml](./docker/service_conf.yaml), you need to specify LLM factory and your own _API_KEY_.
> If you do not have _API_KEY_ 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](https://platform.openai.com/login?launch), [Tongyi-Qianwen](https://dashscope.console.aliyun.com/model), 
> [ZHIPU-AI](https://open.bigmodel.cn/), [Moonshot](https://platform.moonshot.cn/docs/docs)
```bash
$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/docker
$ docker compose up -d
```
### OR

```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
```
> 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 running up:
```bash
$ docker logs -f  ragflow-server
```
*Hallelujah! The following outputs indicates that you have successfully launched 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

```
Open your browser, enter the IP address of your server, _**Hallelujah**_ again!
> 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'*'.

## 🔎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"/>
</div>

## 🔧 Configurations

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. 
After changing something, please run *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).

## 📜 Roadmap

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

## 🏄Community

- [Discord](https://discord.gg/uqQ4YMDf)
- X
- [GitHub Discussions]()
- YouTube
- WeChat


## 🙌 Contributing

For those who'd like to contribute code, see our [Contribution Guide](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md).