Newer
Older
<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>
## 💡 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>
- 🍭**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 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)
- 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.
| Feature | RagFlow | Langchain-Chatchat | Assistants API | QAnythig | LangChain |
|---------|:---------:|:-----------:|:-----------:|:-----------:|:-----------:|
| **Well-Founded Answer** | :white_check_mark: | :x: | :x: | :x: | :x: |
| **Trackable Chunking** | :white_check_mark: | :x: | :x: | :x: | :x: |
| **Chunking Method** | Rich Variety | Naive | Naive | Naive | Naive |
| **Table Structure Recognition** | :white_check_mark: | :x: | :x: | :x: | :x: |
| **Structured Data Lookup** | :white_check_mark: | :x: | :x: | :x: | :x: | :x: |
| **Programming Approach** | API-oriented | API-oriented | API-oriented | API-oriented | Python Code-oriented |
| **RAG Engine** | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: |
| **Prompt IDE** | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: |
| **Supported LLMs** | Rich Variety | Rich Variety | OpenAI-only | QwenLLM | Rich Variety |
| **Local Deployment** | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: |
| **Ecosystem Strategy** | Open Source | Open Source | Close Source | Open Source | Open Source |
<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>
> 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
> ```
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_.
> 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 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 having Docker up and running:
*The following output confirms the successful launch of 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
```
In your browser, enter the IP address of your server.
> 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.
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.
> 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)
- [Twitter](https://twitter.com/infiniflowai)
- GitHub Discussions
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.