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📕 Table of Contents

• 💡What is RAGFlow?
• 🎮Demo
• 📌Latest Updates
• 🌟Key Features
• 🔎System Architecture
• 🎬Get Started
• 🔧Configurations
• 🛠️Build from source
• 🛠️Launch service from source
• 📚Documentation
• 📜Roadmap
• 🏄Community
• 🙌Contributing

💡 What is RAGFlow?

RAGFlowis 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 athttps://demo.ragflow.io.

🔥 Latest Updates

• 2024-08-02 Supports GraphRAG inspired bygraphrag,and mind map.

• 2024-07-23 Supports audio file parsing.

• 2024-07-21 Supports more LLMs (LocalAI, OpenRouter, StepFun, and Nvidia).

• 2024-07-18 Adds more components (Wikipedia, PubMed, Baidu, and Duckduckgo) to the graph.

• 2024-07-08 Supports workflow based onGraph.

• 2024-06-27 Supports Markdown and Docx in the Q&A parsing method.

• 2024-06-27 Supports extracting images from Docx files.

• 2024-06-27 Supports extracting tables from Markdown files.

• 2024-06-06 SupportsSelf-RAG,which is enabled by default in dialog settings.

• 2024-05-30 IntegratesBCEandBGEreranker models.

• 2024-05-23 SupportsRAPTORfor better text retrieval.

• 2024-05-15 Integrates OpenAI GPT-4o.

🌟 Key Features

🍭"Quality in, quality out"

• Deep document understanding-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), seeInstall Docker Engine.

🚀 Start up the server

1. Ensurevm.max_map_count>= 262144:

   To check the value ofvm.max_map_count:

   $ sysctl vm.max_map_count

   Resetvm.max_map_countto a value at least 262144 if it is not.

   #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 thevm.max_map_countvalue in/etc/sysctl.confaccordingly:

   vm.max_map_count=262144

2. Clone the repo:

   $ 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 thedevversion RAGFlow Docker image. To download and run a specified Docker version, updateRAGFLOW_VERSIONindocker/.envto the intended version, for exampleRAGFLOW_VERSION=v0.8.0,before running the following commands.

   $cdragflow/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:

   $ docker logs -f ragflow-server

   The following output confirms a successful launch of the system:

   ____ ______ __
   / __\____ _ ____ _ / ____// /____ _ __
   / /_/ // __`// __`// /_ / // __\||/|/ /
   / _, _// /_/ // /_/ // __/ / // /_/ /||/|/ /
   /_/|_|\__,_/\__, //_/ /_/\____/|__/|__/
   /____/
   
   *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 anetwork anomalyerror 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 enterhttp://IP_OF_YOUR_MACHINE(sansport number) as the default HTTP serving port80can be omitted when using the default configurations.

6. Inservice_conf.yaml,select the desired LLM factory inuser_default_llmand update theAPI_KEYfield with the corresponding API key.

   Seellm_api_key_setupfor more information.

   The show is now on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

• .env:Keeps the fundamental setups for the system, such asSVR_HTTP_PORT,MYSQL_PASSWORD,andMINIO_PASSWORD.
• service_conf.yaml:Configures the back-end services.
• docker-compose.yml:The system relies ondocker-compose.ymlto start up.

You must ensure that changes to the.envfile are in line with what are in theservice_conf.yamlfile.

The./docker/READMEfile 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/READMEfile are aligned with the corresponding configurations in theservice_conf.yamlfile.

To update the default HTTP serving port (80), go todocker-compose.ymland change80:80to<YOUR_SERVING_PORT>:80.

Updates to all system configurations require a system reboot to take effect:

$ docker-compose up -d

🛠️ Build from source

To build the Docker images from source:

$ git clone https://github.com/infiniflow/ragflow.git
$cdragflow/
$ docker build -t infiniflow/ragflow:dev.
$cdragflow/docker
$ chmod +x./entrypoint.sh
$ docker compose up -d

🛠️ Launch service from source

To launch the service from source:

1. Clone the repository:

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

2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:

   $ conda create -n ragflow python=3.11.0
   $ conda activate ragflow
   $ pip install -r requirements.txt

   #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:

   #Get the Python path:
   $ which python
   #Get the ragflow project path:
   $pwd

   $ cp docker/entrypoint.sh.
   $ vi entrypoint.sh

   #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=${PY}
   exportPYTHONPATH=${PYTHONPATH}
   exportHF_ENDPOINT=https://hf-mirror.com

4. Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):

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

5. Check the configuration files, ensuring that:

   • The settings indocker/.envmatch those inconf/service_conf.yaml.
   • The IP addresses and ports for related services inservice_conf.yamlmatch the local machine IP and ports exposed by the container.

6. Launch the RAGFlow backend service:

   $ chmod +x./entrypoint.sh
   $ bash./entrypoint.sh

7. Launch the frontend service:

   $cdweb
   $ 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:

   $cdweb
   $ 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
• User guide
• References
• FAQ

📜 Roadmap

See theRAGFlow Roadmap 2024

🏄 Community

• Discord
• Twitter
• GitHub 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 ourContribution Guidelinesfirst.