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<a href="https://demo.ragflow.io/">
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<p align="center">
<a href="./README.md">English</a> |
<a href="./README_zh.md">简体中文</a>
</p>
<a href="https://demo.ragflow.io" target="_blank">
<img alt="Static Badge" src="https://img.shields.io/badge/RAGFLOW-LLM-white?&labelColor=dd0af7"></a>
<a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
<img src="https://img.shields.io/badge/docker_pull-ragflow:v1.0-brightgreen"
alt="docker pull ragflow:v1.0"></a>
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[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.
<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
```bash
121:/ragflow# sysctl vm.max_map_count
vm.max_map_count = 262144
```
If **vm.max_map_count** is not greater than 65535:
```bash
121:/ragflow# 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
```
If you have not installed *Docker* on your local machine, see [Install Docker Engine](https://docs.docker.com/engine/install/)
> - 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)
$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/docker
$ docker compose up -d
$ 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:
```
*Hallelujah! The following outputs indicates that you have successfully launched 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
```
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'*'.
<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"/>
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).
See the [RagFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
- [Discord](https://discord.gg/uqQ4YMDf)
- X
- [GitHub Discussions]()
- YouTube
- WeChat
For those who'd like to contribute code, see our [Contribution Guide](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md).