Are you ready to enhance your document understanding capabilities? Dive into the world of RAGFlow, an open-source RAG (Retrieval-Augmented Generation) engine tailored for effective question-answering. Whether you’re a seasoned developer or a curious newbie, this guide breaks down the steps you need to take to get RAGFlow up and running—along with troubleshooting tips!

What is RAGFlow?

RAGFlow is designed to streamline the RAG workflow for businesses of any scale. It cleverly integrates Large Language Models (LLMs) to provide accurate question-answering mechanisms, enriched by trustworthy citations from intricately formatted data sources.

Demo

Check out our live demo at demo.ragflow.io.

Latest Updates

• 2024-09-13: Adds search mode for knowledge base QA.
• 2024-09-09: Introduces a medical consultant agent template.
• 2024-08-22: Supports text to SQL statements through RAG.
• 2024-08-02: Introduces GraphRAG inspired by graphrag.
• 2024-07-23: Adds audio file parsing support.
• 2024-07-08: Incorporates workflow based on Graph.
• 2024-06-27: Expanded support for Markdown and Docx parsing methods.
• 2024-05-23: Enhanced text retrieval with RAPTOR.

Getting Started

Follow these quick steps to set up RAGFlow.

Prerequisites

• CPU: 4 cores
• RAM: 16 GB
• Disk: 50 GB
• Docker: 24.0.0
• Docker Compose: v2.26.1

If you haven’t installed Docker, refer to Install Docker Engine.

Starting Up the Server

1. Check and set the value of `vm.max_map_count`:

bash
$ sysctl vm.max_map_count
$ sudo sysctl -w vm.max_map_count=262144

2. Clone the RAGFlow repository:

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

3. Build and start the Docker images:

bash
$ cd ragflow/docker
$ chmod +x .entrypoint.sh
$ docker compose up -d

4. Check the server status:

bash
$ docker logs -f ragflow-server

5. Access RAGFlow through your web browser at http://IP_OF_YOUR_MACHINE.

Configurations

You’ll need to manage several configuration files for the system:

• .env: Contains basic setups like ports and passwords.
• service_conf.yaml: Configures back-end services.
• docker-compose.yml: Key for starting up the system.

Troubleshooting

If you encounter issues, consider the following:

• Make sure all services in service_conf.yaml align with your local machine settings.
• Check if Docker is running and your machine is not running out of memory.
• Ensure that you followed the startup command steps correctly; skipping may lead to launch errors.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Documentation & Community

For a comprehensive guide, refer to:

• Quickstart
• User Guide
• References
• FAQ

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

By following this guide, you’ll unlock the potential of RAGFlow for your business solutions. Happy coding, and may your data always be insightful!