## 💡 What is RAGFlow? [RAGFlow](https://demo.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. ## 🌟 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. ## 📌 Latest Features - 2024-04-19 Support conversation API ([detail](./docs/conversation_api.md)). - 2024-04-16 Add an embedding model 'bce-embedding-base_v1' from [BCEmbedding](https://github.com/netease-youdao/BCEmbedding). - 2024-04-16 Add [FastEmbed](https://github.com/qdrant/fastembed), which is designed specifically for light and speedy embedding. - 2024-04-11 Support [Xinference](./docs/xinference.md) for local LLM deployment. - 2024-04-10 Add a new layout recognization model for analyzing Laws documentation. - 2024-04-08 Support [Ollama](./docs/ollama.md) for local LLM deployment. - 2024-04-07 Support Chinese UI. ## 🔎 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 ([more](./docs/max_map_count.md)): > 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: ```bash $ 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: ```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 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 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 [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information. _The show is now 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 all system configurations require a system reboot to take effect: > > ```bash > $ docker-compose up -d > ``` ## 🛠️ Build from source To build the Docker images from source: ```bash $ git clone https://github.com/infiniflow/ragflow.git $ cd ragflow/ $ docker build -t infiniflow/ragflow:v0.3.1 . $ cd ragflow/docker $ chmod +x ./entrypoint.sh $ docker compose up -d ``` ## 📚 Documentation - [FAQ](./docs/faq.md) ## 📜 Roadmap See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162) ## 🏄 Community - [Discord](https://discord.gg/4XxujFgUN7) - [Twitter](https://twitter.com/infiniflowai) ## 🙌 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](https://github.com/infiniflow/ragflow/blob/main/docs/CONTRIBUTING.md) first.