README.md

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## 💡 What is RAGFlow?

[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.

## 📌 Latest Updates

- 2024-05-15 Integrates OpenAI GPT-4o.
- 2024-05-08 Integrates LLM DeepSeek-V2.
- 2024-04-26 Adds file management.
- 2024-04-19 Supports conversation API ([detail](./docs/conversation_api.md)).
- 2024-04-16 Integrates an embedding model 'bce-embedding-base_v1' from [BCEmbedding](https://github.com/netease-youdao/BCEmbedding), and [FastEmbed](https://github.com/qdrant/fastembed), which is designed specifically for light and speedy embedding.

- 2024-04-11 Supports [Xinference](./docs/xinference.md) for local LLM deployment.

- 2024-04-10 Adds a new layout recognition model for analyzing legal documents.

- 2024-04-08 Supports [Ollama](./docs/ollama.md) for local LLM deployment.

- 2024-04-07 Supports Chinese UI.



## 🌟 Key Features



### 🍭 **"Quality in, quality out"**



- [Deep document understanding](./deepdoc/README.md)-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"/>

</div>



## 🎬 Get Started



### 📝 Prerequisites



- CPU >= 4 cores

- RAM >= 16 GB

- Disk >= 50 GB

- Docker >= 24.0.0 & Docker Compose >= v2.26.1

  > 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` >= 262144 ([more](./docs/max_map_count.md)):



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

   >

   > ```bash

   > $ sysctl vm.max_map_count

   > ```

   >

   > Reset `vm.max_map_count` to a value at least 262144 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:



   > Running the following commands automatically downloads the *dev* version RAGFlow Docker image. To download and run a specified Docker version, update `RAGFLOW_VERSION` in **docker/.env** to the intended version, for example `RAGFLOW_VERSION=v0.5.0`, before running the following commands.



   ```bash

   $ cd ragflow/docker

   $ chmod +x ./entrypoint.sh

   $ docker compose up -d

   ```

   



   > The core image is about 9 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://x.x.x.x:9380

    INFO:werkzeug:Press CTRL+C to quit

   ```
   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anomaly` error because, at that moment, your RAGFlow may not be fully initialized.  

5. In your web browser, enter the IP address of your server and log in to RAGFlow.
   > With default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
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 HTTP 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:dev .

$ cd ragflow/docker

$ chmod +x ./entrypoint.sh

$ docker compose up -d

```

## 🛠️ Launch Service from Source

To launch the service from source, please follow these steps:

1. Clone the repository
```bash

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

$ cd ragflow/

```

2. Create a virtual environment (ensure Anaconda or Miniconda is installed)
```bash

$ conda create -n ragflow python=3.11.0

$ conda activate ragflow

$ pip install -r requirements.txt

```
If CUDA version is greater than 12.0, execute the following additional commands:
```bash

$ pip uninstall -y onnxruntime-gpu

$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

```

3. Copy the entry script and configure environment variables
```bash

$ cp docker/entrypoint.sh .

$ vi entrypoint.sh

```
Use the following commands to obtain the Python path and the ragflow project path:
```bash

$ which python

$ pwd

```

Set the output of `which python` as the value for `PY` and the output of `pwd` as the value for `PYTHONPATH`.

If `LD_LIBRARY_PATH` is already configured, it can be commented out.

```bash

# Adjust configurations according to your actual situation; the two export commands are newly added.

PY=${PY}

export PYTHONPATH=${PYTHONPATH}

# Optional: Add Hugging Face mirror

export HF_ENDPOINT=https://hf-mirror.com

```

4. Start the base services
```bash

$ cd docker

$ docker compose -f docker-compose-base.yml up -d 

```

5. Check the configuration files
Ensure that the settings in **docker/.env** match those in **conf/service_conf.yaml**. The IP addresses and ports for related services in **service_conf.yaml** should be changed to the local machine IP and ports exposed by the container.

6. Launch the service
```bash

$ chmod +x ./entrypoint.sh

$ bash ./entrypoint.sh

```

7. Start the WebUI service
```bash

$ cd web

$ npm install --registry=https://registry.npmmirror.com --force

$ vim .umirc.ts

# Modify proxy.target to 127.0.0.1:9380

$ npm run dev 

```

8. Deploy the WebUI service
```bash

$ cd web

$ npm install --registry=https://registry.npmmirror.com --force

$ umi build

$ mkdir -p /ragflow/web

$ cp -r dist /ragflow/web

$ apt install nginx -y

$ cp ../docker/nginx/proxy.conf /etc/nginx

$ cp ../docker/nginx/nginx.conf /etc/nginx

$ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d

$ systemctl start nginx

```

## 📚 Documentation

- [Quickstart](./docs/quickstart.md)
- [FAQ](./docs/faq.md)

## 📜 Roadmap

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

## 🏄 Community

- [Discord](https://discord.gg/4XxujFgUN7)
- [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/docs/CONTRIBUTING.md) first.