📕 Table of Contents
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.
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2024-08-02 Supports GraphRAG inspired by graphrag , and mind map.
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2024-07-23 Supports audio file parsing.
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2024-07-21 Supports more LLMs (LocalAI, OpenRouter, StepFun, and Nvidia).
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2024-07-18 Adds more components (Wikipedia, PubMed, Baidu, and Duckduckgo) to the graph.
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2024-07-08 Supports workflow based on Graph.
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2024-06-27 Supports Markdown and Docx in the Q&A parsing method.
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2024-06-27 Supports extracting images from Docx files.
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2024-06-27 Supports extracting tables from Markdown files.
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2024-06-06 Supports Self-RAG, which is enabled by default in dialog settings.
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2024-05-23 Supports RAPTOR for better text retrieval.
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2024-05-15 Integrates OpenAI GPT-4o.
- 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.
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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
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Clone the repo:
$ git clone https://github.com/infiniflow/ragflow.git
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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 exampleRAGFLOW_VERSION=v0.8.0
, 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.
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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 a
network anomaly
error because, at that moment, your RAGFlow may not be fully initialized. -
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 port80
can be omitted when using the default configurations. -
In service_conf.yaml, select the desired LLM factory in
user_default_llm
and update theAPI_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
, andMINIO_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:
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Clone the repository:
$ git clone https://github.com/infiniflow/ragflow.git $ cd ragflow/
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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/
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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
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Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):
$ cd docker $ docker compose -f docker-compose-base.yml up -d
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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.
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Launch the RAGFlow backend service:
$ chmod x ./entrypoint.sh $ bash ./entrypoint.sh
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Launch the frontend service:
$ 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
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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
See the RAGFlow Roadmap 2024
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.