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Dismiss alert {{ message }} infiniflow / ragflow Public * Notifications * Fork 22 * Star 389 * RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. ragflow.io License Apache-2.0 license 389 stars 22 forks Branches Tags Activity Star Notifications * Code * Issues 2 * Pull requests 0 * Actions * Projects 0 * Security * Insights Additional navigation options * Code * Issues * Pull requests * Actions * Projects * Security * Insights infiniflow/ragflow This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main BranchesTags Go to file Code Folders and files Name Name Last commit Last commit message date Latest commit History 204 Commits .github .github api api conf conf deepdoc deepdoc docker docker docs docs rag rag web web .gitignore .gitignore Dockerfile Dockerfile Dockerfile.cuda Dockerfile.cuda LICENSE LICENSE README.md README.md README_zh.md README_zh.md requirements.txt requirements.txt View all files Repository files navigation * README * Apache-2.0 license ragflow logo English | Jian Ti Zhong Wen Static Badge docker pull ragflow:v1.0 license What is RAGFlow? 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 fomatted data. Key Features "Quality in, quality out" * Deep document understanding-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 [317212466-d6ac5664-c237-4200-a7c2-a4a00691b485] Get Started Prerequisites * CPU >= 2 cores * RAM >= 8 GB * Docker If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine. Start up the server 1. Ensure vm.max_map_count > 65535: To check the value of vm.max_map_count: $ sysctl vm.max_map_count Reset vm.max_map_count to a value greater than 65535 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 2. Clone the repo: $ git clone https://github.com/infiniflow/ragflow.git 3. Build the pre-built Docker images and start up the server: $ cd ragflow/docker $ docker compose up -d The core image is about 15 GB in size and may take a while to load. 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) * Running on http://127.0.0.1:9380 * Running on http://172.22.0.5:9380 INFO:werkzeug:Press CTRL+C to quit 5. In your web browser, enter the IP address of your server as prompted and log in to RAGFlow. In the given scenario, you only need to enter http:// 172.22.0.5 (sans port number) as the default HTTP serving port 80 can be omitted when using the default configurations. 6. In 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 for more information. The show is now on! Configurations 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, and MINIO_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 :80. Updates to all system configurations require a system reboot to take effect: $ docker-compose up -d [?] Build from source To build the Docker images from source: $ git clone https://github.com/infiniflow/ragflow.git $ cd ragflow/ $ docker build -t infiniflow/ragflow:v1.0 . $ cd ragflow/docker $ docker compose up -d Roadmap See the RAGFlow Roadmap 2024 Community * Discord * Twitter 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 first. About RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. ragflow.io Topics nlp machine-learning information-retrieval ocr deep-learning orchestration preprocessing pdf-to-text data-pipelines document-parser rag document-understanding table-structure-recognition llm llmops retrieval-augmented-generation Resources Readme License Apache-2.0 license Activity Custom properties Stars 389 stars Watchers 5 watching Forks 22 forks Report repository Releases No releases published Packages 0 No packages published Contributors 8 * @KevinHuSh * @cike8899 * @writinwaters * @yangqianjuan * @KKould * @JinHai-CN * @yingfeng * @blly5 Languages * Python 65.3% * TypeScript 32.0% * Less 2.5% * Other 0.2% Footer (c) 2024 GitHub, Inc. Footer navigation * Terms * Privacy * Security * Status * Docs * Contact * Manage cookies * Do not share my personal information You can't perform that action at this time.