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RAGFlow 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.

Try our demo at https://demo.ragflow.io.

• 2024-06-14 Supports PDF in the Q&A parsing method.

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

• 2024-05-30 Integrates BCE and BGE reranker models.

• 2024-05-28 Supports LLM Baichuan and VolcanoArk.

• 2024-05-23 Supports RAPTOR for better text retrieval.

• 2024-05-21 Supports streaming output and text chunk retrieval API.

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

• 2024-05-08 Integrates LLM DeepSeek-V2.

• Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
• Finds "needle in a data haystack" of literally unlimited tokens.

• Intelligent and explainable.
• Plenty of template options to choose from.

• Visualization of text chunking to allow human intervention.
• Quick view of the key references and traceable citations to support grounded answers.

• Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.

• 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.

• 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.

1. Ensure vm.max_map_count >= 262144:

   To check the value of vm.max_map_count:

   $ sysctl vm.max_map_count

   Reset vm.max_map_count to 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 the vm.max_map_count value in /etc/sysctl.conf accordingly:

   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 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=v0.7.0, before running the following commands.

   $ 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:

   $ 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 https://127.0.0.1:9380
    * Running on https://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 anomaly 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 https://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, 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 for more information.

   The show is now on!

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

• .env: Keeps the fundamental setups for the system, such as SVR_HTTP_PORT, MYSQL_PASSWORD, and MINIO_PASSWORD.
• service_conf.yaml: Configures the back-end services.
• docker-compose.yml: The system relies on docker-compose.yml to start up.

You must ensure that changes to the .env file are in line with what are in the service_conf.yaml file.

The ./docker/README 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 file are aligned with the corresponding configurations in the service_conf.yaml file.

To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80 to <YOUR_SERVING_PORT>:80.

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

$ docker-compose up -d

To build the Docker images from source:

$ 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

To launch the service from source:

1. Clone the repository:

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

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=${PY}
   export PYTHONPATH=${PYTHONPATH}
   export HF_ENDPOINT=https://hf-mirror.com

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

   $ 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:

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

7. Launch the frontend service:

   $ cd web
   $ npm install --registry=https://registry.npmmirror.com --force
   $ vim .umirc.ts
   # Update proxy.target to https://127.0.0.1:9380
   $ npm run dev 

8. Deploy the frontend service:

   $ 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

• Quickstart
• User guide
• References
• FAQ

See the RAGFlow Roadmap 2024

• Discord
• Twitter
• 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 first.