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📕 Table of Contents
- 💡 [What is RAGFlow?](#-what-is-ragflow)
- 🎮 [Demo](#-demo)
- 📌 [Latest Updates](#-latest-updates)
- 🌟 [Key Features](#-key-features)
- 🔎 [System Architecture](#-system-architecture)
- 🎬 [Get Started](#-get-started)
- 🔧 [Configurations](#-configurations)
- 🛠️ [Build from source](#-build-from-source)
- 🛠️ [Launch service from source](#-launch-service-from-source)
- 📚 [Documentation](#-documentation)
- 📜 [Roadmap](#-roadmap)
- 🏄 [Community](#-community)
- 🙌 [Contributing](#-contributing)
## 💡 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.
## 🎮 Demo
Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
## 🔥 Latest Updates
- 2024-09-13 Adds search mode for knowledge base Q&A.
- 2024-09-09 Adds a medical consultant agent template.
- 2024-08-22 Support text to SQL statements through RAG.
- 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) and mind map.
- 2024-07-23 Supports audio file parsing.
- 2024-07-08 Supports workflow based on [Graph](./agent/README.md).
- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method, extracting images from Docx files, extracting tables from Markdown files.
- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
## 🌟 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
## 🎬 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:
> 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=v0.11.0`, 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 abnormal` 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 the 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 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) 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 `: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:
1. Clone the repository:
```bash
$ git clone https://github.com/infiniflow/ragflow.git
$ cd ragflow/
```
2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
```bash
$ conda create -n ragflow python=3.11.0
$ conda activate ragflow
$ pip install -r requirements.txt
```
```bash
# If your CUDA version is higher than 12.0, run the following additional commands:
$ 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
# Get the Python path:
$ which python
# Get the ragflow project path:
$ pwd
```
```bash
$ cp docker/entrypoint.sh .
$ vi entrypoint.sh
```
```bash
# Adjust configurations according to your actual situation (the following two export commands are newly added):
# - Assign the result of `which python` to `PY`.
# - Assign the result of `pwd` to `PYTHONPATH`.
# - Comment out `LD_LIBRARY_PATH`, if it is configured.
# - Optional: Add Hugging Face mirror.
PY=${PY}
export PYTHONPATH=${PYTHONPATH}
export HF_ENDPOINT=https://hf-mirror.com
```
4. Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):
```bash
$ cd docker
$ docker compose -f docker-compose-base.yml up -d
```
5. Check the configuration files, ensuring 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** match the local machine IP and ports exposed by the container.
6. Launch the RAGFlow backend service:
```bash
$ chmod +x ./entrypoint.sh
$ bash ./entrypoint.sh
```
7. Launch the frontend service:
```bash
$ cd web
$ npm install --registry=https://registry.npmmirror.com --force
$ vim .umirc.ts
# Update proxy.target to http://127.0.0.1:9380
$ npm run dev
```
8. Deploy the frontend 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](https://ragflow.io/docs/dev/)
- [User guide](https://ragflow.io/docs/dev/category/user-guides)
- [References](https://ragflow.io/docs/dev/category/references)
- [FAQ](https://ragflow.io/docs/dev/faq)
## 📜 Roadmap
See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
## 🏄 Community
- [Discord](https://discord.gg/4XxujFgUN7)
- [Twitter](https://twitter.com/infiniflowai)
- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
## 🙌 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](./docs/references/CONTRIBUTING.md) first.
