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<p align="center">
<a href="./README.md">English</a> |
<a href="./README_zh.md">简体中文</a>
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<a href="https://demo.ragflow.io" target="_blank">
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alt="docker pull ragflow:v1.0"></a>
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[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.
- 🍭**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.
<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 pulling all images and having Docker up and running:
```bash
```
*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.
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-compose.yaml): The system relies on [docker-compose.yml](./docker-compose.yaml) to start up.
You must ensure that changes in [.env](./docker/.env) 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 should be aligned with the corresponding settings in the [service_conf.yaml](./docker/service_conf.yaml) file.
To change the default serving port (80), go to [docker-compose.yml](./docker-compose.yaml) 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.