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@ -20,16 +20,17 @@
<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](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.
## 💡 What is RagFlow?
[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
## 🌟 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.
- For documents from different domain for different purpose, the engine applies 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)
@ -46,34 +47,52 @@
## 🤺RagFlow vs. other RAG applications
## 🔎 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
### 📝 Prerequisites
- CPU >= 2 cores
- RAM >= 8 GB
- Docker
- `vm.max_map_count` > 65535
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**:
> 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. In this case, we set it to 262144:
>
> ```bash
> $ 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 following line in **/etc/sysctl.conf** accordingly:
>
> ```bash
> vm.max_map_count=262144
> ```
```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/)
### Start up the RagFlow server
### Quick Start
1. Clone the repo
```bash
$ git clone https://github.com/infiniflow/ragflow.git
```
2.
> - 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_.
@ -83,7 +102,7 @@ Settings the next time you log in to the system.
> [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
```
@ -98,11 +117,11 @@ $ 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:
Check the server status after pulling all images and having Docker up and running:
```bash
$ docker logs -f ragflow-server
```
*Hallelujah! The following outputs indicates that you have successfully launched the system:*
*The following output confirms the successful launch of the system:*
```bash
____ ______ __
@ -118,20 +137,17 @@ $ docker logs -f ragflow-server
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'*'.
In your browser, enter the IP address of your server.
## 🔎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
> 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`, say `66:80`.
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.
Updates to system configurations require a system reboot to take effect *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.
@ -141,15 +157,12 @@ After changing something, please run *docker-compose up -d* again.
See the [RagFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
## 🏄Community
## 🏄 Community
- [Discord](https://discord.gg/uqQ4YMDf)
- X
- [GitHub Discussions]()
- YouTube
- WeChat
- [Twitter](https://twitter.com/infiniflowai)
- GitHub Discussions
## 🙌 Contributing
For those who'd like to contribute code, see our [Contribution Guide](https://github.com/infiniflow/ragflow/blob/main/CONTRIBUTING.md).
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.