README.md

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## 💡 What is RAGFlow?

[RAGFlow](http://demo.ragflow.io) is an open-source, Retrieval-Augmented Generation engine built on large language models (LLM), deep document understanding, and multiple recall. It offers a streamlined RAG workflow for businesses of any scale, providing truthful responses with solid citations through a generative AI knowledge management platform.

## 🌟 Key Features
 
### 🍭 **"Quality in, quality out"**
  - Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
  - Finds "needle in a data haystack" of literally unlimited tokens.

### 🍱 **Template-based chunking**
  - Intelligent and explainable.
  - Plenty of template options to choose from.

### 🌱 **Grounded citations with reduced hallucinations**
  - Visualization of text chunking to allow human intervention.
  - Quick view of the key references and traceable citations to support grounded answers.

### 🍔 **Compatibility with heterogeneous data sources**
  - Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.

### 🛀 **Automated and effortless RAG workflow**
  - 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.

## 🔎 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 having the server 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 and log in to RAGFlow.
6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
   > See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.
   
   *The show is now on!*


## 🔧 Configurations

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 you are REQUIRED 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 update 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

```

## 📜 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.