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RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep documentunderstanding. 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 formatteddata.

Try our demo athttps://demo.ragflow.io.

• 2025-05-23 Adds a Python/JavaScript code executor component to Agent.
• 2025-05-05 Supports cross-language query.
• 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
• 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
• 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
• 2024-08-22 Support text to SQL statements through RAG.

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• Deep document understanding-based knowledge extraction from unstructured data with complicatedformats.
• 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
• gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.

1. Ensurevm.max_map_count >= 262144:

   To check the value ofvm.max_map_count:

   $ sysctl vm.max_map_count

   Resetvm.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 thevm.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. Start up the server using the pre-built Docker images:

The command below downloads thev0.19.1-slim edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different fromv0.19.1-slim, update theRAGFLOW_IMAGE variable accordingly indocker/.env before usingdocker compose to start the server. For example: setRAGFLOW_IMAGE=infiniflow/ragflow:v0.19.1 for the full editionv0.19.1.

$cd ragflow/docker# Use CPU for embedding and DeepDoc tasks:$ docker compose -f docker-compose.yml up -d# To use GPU to accelerate embedding and DeepDoc tasks:# docker compose -f docker-compose-gpu.yml up -d

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)

   If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt anetwork anormalerror 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 (sans port number) as the defaultHTTP serving port80 can be omitted when using the default configurations.

6. Inservice_conf.yaml.template, select the desired LLM factory inuser_default_llm and updatetheAPI_KEY field with the corresponding API key.

   Seellm_api_key_setup for more information.

   The show is on!

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.template: Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
• docker-compose.yml: The system relies ondocker-compose.yml to start up.

The./docker/README file provides a detailed description of the environment settings and serviceconfigurations which can be used as${ENV_VARS} in theservice_conf.yaml.template file.

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

Updates to the above configurations require a reboot of all containers to take effect:

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

RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch toInfinity, follow these steps:

1. Stop all running containers:

   $ docker compose -f docker/docker-compose.yml down -v

2. SetDOC_ENGINE indocker/.env toinfinity.

3. Start the containers:

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

This image is approximately 2 GB in size and relies on external LLM and embedding services.

git clone https://github.com/infiniflow/ragflow.gitcd ragflow/docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim.

This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.

git clone https://github.com/infiniflow/ragflow.gitcd ragflow/docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly.

1. Install uv, or skip this step if it is already installed:

   pipx install uv pre-commit

2. Clone the source code and install Python dependencies:

   git clone https://github.com/infiniflow/ragflow.gitcd ragflow/uv sync --python 3.10 --all-extras# install RAGFlow dependent python modulesuv run download_deps.pypre-commit install

3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:

   docker compose -f docker/docker-compose-base.yml up -d

   Add the following line to/etc/hosts to resolve all hosts specified indocker/.env to127.0.0.1:

   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager

4. If you cannot access HuggingFace, set theHF_ENDPOINT environment variable to use a mirror site:

   export HF_ENDPOINT=https://hf-mirror.com

5. If your operating system does not have jemalloc, please install it as follows:

   # ubuntusudo apt-get install libjemalloc-dev# centossudo yum install jemalloc

6. Launch backend service:

   source .venv/bin/activateexport PYTHONPATH=$(pwd)bash docker/launch_backend_service.sh

7. Install frontend dependencies:

   cd webnpm install

8. Launch frontend service:

   npm run dev

   The following output confirms a successful launch of the system:

   

9. Stop RAGFlow front-end and back-end service after development is complete:

   pkill -f"ragflow_server.py|task_executor.py"

• Quickstart
• Configuration
• Release notes
• User guides
• Developer guides
• References
• FAQs

See theRAGFlow Roadmap 2025

• Discord
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• 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 ourContribution Guidelines first.