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<a href="https://demo.ragflow.io/">
<img src="https://github.com/infiniflow/ragflow/assets/12318111/f034fb27-b3bf-401b-b213-e1dfa7448d2a" width="320" alt="ragflow logo">
</a>
</div>
<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>
<a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
<img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
</a>
</p>
[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.
- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
- Finds "needle in a data haystack" of literally unlimited tokens.
- Intelligent and explainable.
- Plenty of template options to choose from.
- Visualization of text chunking to allow human intervention.
- Quick view of the key references and traceable citations to support grounded answers.
- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
- Streamlined RAG orchestration catered to both personal and large businesses.
- Configurable LLMs as well as embedding models.
- Multiple recall paired with fused re-ranking.
- Intuitive APIs for seamless integration with business.
<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>
- 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 having the server up and running:
```
*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 and port of your server as prompted and log in to RAGFlow.
When it comes to system configurations, you will need to manage the following files:
- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and `MINIO_PASSWORD`.
- [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services.
- [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up.
You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file.
> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service configurations, and it is IMPORTANT to ensure that all environment settings listed in the [./docker/README](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file.
To change the default serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `<YOUR_SERVING_PORT>:80`.
> Updates to all system configurations require a system reboot to take effect:
>
> ```bash
> $ docker-compose up -d
> ```
## 🛠️ 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
```
See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
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.