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📕 Table of Contents

- 💡 [What is RAGFlow?](#-what-is-ragflow) - 🎮 [Demo](#-demo) - 📌 [Latest Updates](#-latest-updates) - 🌟 [Key Features](#-key-features) - 🔎 [System Architecture](#-system-architecture) - 🎬 [Get Started](#-get-started) - 🔧 [Configurations](#-configurations) - 📚 [Documentation](#-documentation) - 📜 [Roadmap](#-roadmap) - 🏄 [Community](#-community) - 🙌 [Contributing](#-contributing)

## 💡 What is RAGFlow? [RAGFlow](https://ragflow.io/) 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. ## 🎮 Demo Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).

## 🔥 Latest Updates - 2024-09-13 Adds search mode for knowledge base Q&A. - 2024-09-09 Adds a medical consultant agent template. - 2024-08-22 Support text to SQL statements through RAG. - 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) and mind map. - 2024-07-23 Supports audio file parsing. - 2024-07-08 Supports workflow based on [Graph](./agent/README.md). - 2024-06-27 Supports Markdown and Docx in the Q&A parsing method, extracting images from Docx files, extracting tables from Markdown files. - 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval. ## 🌟 Key Features ### 🍭 **"Quality in, quality out"** - [Deep document understanding](./deepdoc/README.md)-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), see [Install Docker Engine](https://docs.docker.com/engine/install/). ### 🚀 Start up the server 1. Ensure `vm.max_map_count` >= 262144: > To check the value of `vm.max_map_count`: > > ```bash > $ sysctl vm.max_map_count > ``` > > Reset `vm.max_map_count` to a value at least 262144 if it is not. > > ```bash > # 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: > > ```bash > vm.max_map_count=262144 > ``` 2. Clone the repo: ```bash $ 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=v0.11.0`, before running the following commands. ```bash $ cd ragflow/docker $ 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: ```bash $ docker logs -f ragflow-server ``` _The following output confirms a successful launch of the system:_ ```bash ____ ______ __ / __ \ ____ _ ____ _ / ____// /____ _ __ / /_/ // __ `// __ `// /_ / // __ \| | /| / / / _, _// /_/ // /_/ // __/ / // /_/ /| |/ |/ / /_/ |_| \__,_/ \__, //_/ /_/ \____/ |__/|__/ /____/ * 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 abnormal` 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 `http://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](./docker/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](https://ragflow.io/docs/dev/llm_api_key_setup) for more information. _The show is on!_ ## 🔧 Configurations When it comes to system configurations, you will need to manage the following files: - [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and `MINIO_PASSWORD`. - [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services. - [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up. You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file. > The [./docker/README](./docker/README.md) 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](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file. To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `:80`. Updates to the above configurations require a reboot of all containers to take effect: > ```bash > $ docker-compose -f docker/docker-compose.yml up -d > ``` ## 📚 Documentation - [Quickstart](https://ragflow.io/docs/dev/) - [User guide](https://ragflow.io/docs/dev/category/user-guides) - [References](https://ragflow.io/docs/dev/category/references) - [FAQ](https://ragflow.io/docs/dev/faq) ## 📜 Roadmap See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162) ## 🏄 Community - [Discord](https://discord.gg/4XxujFgUN7) - [Twitter](https://twitter.com/infiniflowai) - [GitHub Discussions](https://github.com/orgs/infiniflow/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 our [Contribution Guidelines](./CONTRIBUTING.md) first.