[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-391":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":36,"readmeContent":37,"aiSummary":38,"trendingCount":16,"starSnapshotCount":16,"syncStatus":39,"lastSyncTime":40,"discoverSource":41},391,"ragflow","infiniflow\u002Fragflow","infiniflow","RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs","https:\u002F\u002Fragflow.io",null,"Python",82940,9578,335,2789,0,115,628,2275,523,120,"Apache License 2.0",false,"main",[26,27,28,29,30,31,32,33,34,35],"agentic-ai","agentic-retrieval","agentic-search","ai","ai-agents","context-engine","context-management","llm-apps","rag","retrieval-augmented-generation","2026-06-17 04:00:03","\u003Cdiv align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fcloud.ragflow.io\u002F\">\n\u003Cimg src=\"web\u002Fsrc\u002Fassets\u002Flogo-with-text.svg\" width=\"520\" alt=\"ragflow logo\">\n\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\".\u002FREADME.md\">\u003Cimg alt=\"README in English\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEnglish-DBEDFA\">\u003C\u002Fa>\n  \u003Ca href=\".\u002FREADME_zh.md\">\u003Cimg alt=\"简体中文版自述文件\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F简体中文-DFE0E5\">\u003C\u002Fa>\n  \u003Ca href=\".\u002FREADME_tzh.md\">\u003Cimg alt=\"繁體版中文自述文件\" 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src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Finfiniflow\u002Fragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square\" alt=\"docker pull infiniflow\u002Fragflow:v0.25.1\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow\u002Freleases\u002Flatest\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fv\u002Frelease\u002Finfiniflow\u002Fragflow?color=blue&label=Latest%20Release\" alt=\"Latest Release\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow\u002Fblob\u002Fmain\u002FLICENSE\">\n        \u003Cimg height=\"21\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4\" alt=\"license\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fdeepwiki.com\u002Finfiniflow\u002Fragflow\">\n        \u003Cimg alt=\"Ask DeepWiki\" src=\"https:\u002F\u002Fdeepwiki.com\u002Fbadge.svg\">\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Ch4 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fcloud.ragflow.io\">Cloud\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002F\">Document\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow\u002Fissues\u002F12241\">Roadmap\u003C\u002Fa> |\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002FNjYzJD3GM3\">Discord\u003C\u002Fa>\n\u003C\u002Fh4>\n\n\u003Cdiv align=\"center\" style=\"margin-top:20px;margin-bottom:20px;\">\n\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Finfiniflow\u002Fragflow-docs\u002Frefs\u002Fheads\u002Fimage\u002Fimage\u002Fragflow-octoverse.png\" width=\"1200\"\u002F>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F9064\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F9064\" alt=\"infiniflow%2Fragflow | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cdetails open>\n\u003Csummary>\u003Cb>📕 Table of Contents\u003C\u002Fb>\u003C\u002Fsummary>\n\n- 💡 [What is RAGFlow?](#-what-is-ragflow)\n- 🎮 [Get Started](#-get-started)\n- 📌 [Latest Updates](#-latest-updates)\n- 🌟 [Key Features](#-key-features)\n- 🔎 [System Architecture](#-system-architecture)\n- 🎬 [Self-Hosting](#-self-hosting)\n- 🔧 [Configurations](#-configurations)\n- 🔧 [Build a Docker image](#-build-a-docker-image)\n- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)\n- 📚 [Documentation](#-documentation)\n- 📜 [Roadmap](#-roadmap)\n- 🏄 [Community](#-community)\n- 🙌 [Contributing](#-contributing)\n\n\u003C\u002Fdetails>\n\n## 💡 What is RAGFlow?\n\n[RAGFlow](https:\u002F\u002Fragflow.io\u002F) is a leading open-source Retrieval-Augmented Generation ([RAG](https:\u002F\u002Fragflow.io\u002Fbasics\u002Fwhat-is-rag)) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged [context engine](https:\u002F\u002Fragflow.io\u002Fbasics\u002Fwhat-is-agent-context-engine) and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.\n\n## 🎮 Get Started\n\nTry our cloud service at [https:\u002F\u002Fcloud.ragflow.io](https:\u002F\u002Fcloud.ragflow.io).\n\n\u003Cdiv align=\"center\" style=\"margin-top:20px;margin-bottom:20px;\">\n\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Finfiniflow\u002Fragflow-docs\u002Frefs\u002Fheads\u002Fimage\u002Fimage\u002Fchunking.gif\" width=\"1200\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Finfiniflow\u002Fragflow-docs\u002Frefs\u002Fheads\u002Fimage\u002Fimage\u002Fagentic-dark.gif\" width=\"1200\"\u002F>\n\u003C\u002Fdiv>\n\n## 🔥 Latest Updates\n\n- 2026-04-24 Supports DeepSeek v4.\n- 2026-03-24 [RAGFlow Skill on OpenClaw](https:\u002F\u002Fclawhub.ai\u002Fyingfeng\u002Fragflow-skill) — Provides an official skill for accessing RAGFlow datasets via OpenClaw.\n- 2025-12-26 Supports 'Memory' for AI agent.\n- 2025-11-19 Supports Gemini 3 Pro.\n- 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.\n- 2025-10-23 Supports MinerU & Docling as document parsing methods.\n- 2025-10-15 Supports orchestrable ingestion pipeline.\n- 2025-08-08 Supports OpenAI's latest GPT-5 series models.\n- 2025-08-01 Supports agentic workflow and MCP.\n- 2025-05-23 Adds a Python\u002FJavaScript code executor component to Agent.\n- 2025-05-05 Supports cross-language query.\n- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.\n\n## 🎉 Stay Tuned\n\n⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new\nreleases! 🌟\n\n\u003Cdiv align=\"center\" style=\"margin-top:20px;margin-bottom:20px;\">\n\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F18c9707e-b8aa-4caf-a154-037089c105ba\" width=\"1200\"\u002F>\n\u003C\u002Fdiv>\n\n## 🌟 Key Features\n\n### 🍭 **\"Quality in, quality out\"**\n\n- [Deep document understanding](.\u002Fdeepdoc\u002FREADME.md)-based knowledge extraction from unstructured data with complicated\n  formats.\n- Finds \"needle in a data haystack\" of literally unlimited tokens.\n\n### 🍱 **Template-based chunking**\n\n- Intelligent and explainable.\n- Plenty of template options to choose from.\n\n### 🌱 **Grounded citations with reduced hallucinations**\n\n- Visualization of text chunking to allow human intervention.\n- Quick view of the key references and traceable citations to support grounded answers.\n\n### 🍔 **Compatibility with heterogeneous data sources**\n\n- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.\n\n### 🛀 **Automated and effortless RAG workflow**\n\n- Streamlined RAG orchestration catered to both personal and large businesses.\n- Configurable LLMs as well as embedding models.\n- Multiple recall paired with fused re-ranking.\n- Intuitive APIs for seamless integration with business.\n\n## 🔎 System Architecture\n\n\u003Cdiv align=\"center\" style=\"margin-top:20px;margin-bottom:20px;\">\n\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F31b0dd6f-ca4f-445a-9457-70cb44a381b2\" width=\"1000\"\u002F>\n\u003C\u002Fdiv>\n\n## 🎬 Self-Hosting\n\n### 📝 Prerequisites\n\n- CPU >= 4 cores\n- RAM >= 16 GB\n- Disk >= 50 GB\n- Docker >= 24.0.0 & Docker Compose >= v2.26.1\n- [gVisor](https:\u002F\u002Fgvisor.dev\u002Fdocs\u002Fuser_guide\u002Finstall\u002F): Required only if you intend to use the code executor (sandbox) feature of RAGFlow.\n\n> [!TIP]\n> If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https:\u002F\u002Fdocs.docker.com\u002Fengine\u002Finstall\u002F).\n\n### 🚀 Start up the server\n\n1. Ensure `vm.max_map_count` >= 262144:\n\n   > To check the value of `vm.max_map_count`:\n   >\n   > ```bash\n   > $ sysctl vm.max_map_count\n   > ```\n   >\n   > Reset `vm.max_map_count` to a value at least 262144 if it is not.\n   >\n   > ```bash\n   > # In this case, we set it to 262144:\n   > $ sudo sysctl -w vm.max_map_count=262144\n   > ```\n   >\n   > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the\n   > `vm.max_map_count` value in **\u002Fetc\u002Fsysctl.conf** accordingly:\n   >\n   > ```bash\n   > vm.max_map_count=262144\n   > ```\n   >\n2. Clone the repo:\n\n   ```bash\n   $ git clone https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow.git\n   ```\n3. Start up the server using the pre-built Docker images:\n\n> [!CAUTION]\n> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.\n> If you are on an ARM64 platform, follow [this guide](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Fbuild_docker_image) to build a Docker image compatible with your system.\n\n> The command below downloads the `v0.25.1` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.25.1`, update the `RAGFLOW_IMAGE` variable accordingly in **docker\u002F.env** before using `docker compose` to start the server.\n\n```bash\n   $ cd ragflow\u002Fdocker\n\n   # git checkout v0.25.1\n   # Optional: use a stable tag (see releases: https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow\u002Freleases)\n   # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.\n\n   # Use CPU for DeepDoc tasks:\n   $ docker compose -f docker-compose.yml up -d\n\n   # To use GPU to accelerate DeepDoc tasks:\n   # sed -i '1i DEVICE=gpu' .env\n   # docker compose -f docker-compose.yml up -d\n```\n\n> Note: Prior to `v0.22.0`, we provided both images with embedding models and slim images without embedding models. Details as follows:\n\n| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?        |\n|-------------------|-----------------|-----------------------|----------------|\n| v0.21.1           | ≈9       | ✔️                    | Stable release |\n| v0.21.1-slim      | ≈2       | ❌                     | Stable release |\n\n> Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.\n\n4. Check the server status after having the server up and running:\n\n   ```bash\n   $ docker logs -f docker-ragflow-cpu-1\n   ```\n\n   _The following output confirms a successful launch of the system:_\n\n   ```bash\n\n         ____   ___    ______ ______ __\n        \u002F __ \\ \u002F   |  \u002F ____\u002F\u002F ____\u002F\u002F \u002F____  _      __\n       \u002F \u002F_\u002F \u002F\u002F \u002F| | \u002F \u002F __ \u002F \u002F_   \u002F \u002F\u002F __ \\| | \u002F| \u002F \u002F\n      \u002F _, _\u002F\u002F ___ |\u002F \u002F_\u002F \u002F\u002F __\u002F  \u002F \u002F\u002F \u002F_\u002F \u002F| |\u002F |\u002F \u002F\n     \u002F_\u002F |_|\u002F_\u002F  |_|\\____\u002F\u002F_\u002F    \u002F_\u002F \\____\u002F |__\u002F|__\u002F\n\n    * Running on all addresses (0.0.0.0)\n   ```\n\n   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network abnormal`\n   > error because, at that moment, your RAGFlow may not be fully initialized.\n   >\n5. In your web browser, enter the IP address of your server and log in to RAGFlow.\n\n   > With the default settings, you only need to enter `http:\u002F\u002FIP_OF_YOUR_MACHINE` (**sans** port number) as the default\n   > HTTP serving port `80` can be omitted when using the default configurations.\n   >\n6. In [service_conf.yaml.template](.\u002Fdocker\u002Fservice_conf.yaml.template), select the desired LLM factory in `user_default_llm` and update\n   the `API_KEY` field with the corresponding API key.\n\n   > See [llm_api_key_setup](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Fllm_api_key_setup) for more information.\n   >\n\n   _The show is on!_\n\n## 🔧 Configurations\n\nWhen it comes to system configurations, you will need to manage the following files:\n\n- [.env](.\u002Fdocker\u002F.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and\n  `MINIO_PASSWORD`.\n- [service_conf.yaml.template](.\u002Fdocker\u002Fservice_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.\n- [docker-compose.yml](.\u002Fdocker\u002Fdocker-compose.yml): The system relies on [docker-compose.yml](.\u002Fdocker\u002Fdocker-compose.yml) to start up.\n\n> The [.\u002Fdocker\u002FREADME](.\u002Fdocker\u002FREADME.md) file provides a detailed description of the environment settings and service\n> configurations which can be used as `${ENV_VARS}` in the [service_conf.yaml.template](.\u002Fdocker\u002Fservice_conf.yaml.template) file.\n\nTo update the default HTTP serving port (80), go to [docker-compose.yml](.\u002Fdocker\u002Fdocker-compose.yml) and change `80:80`\nto `\u003CYOUR_SERVING_PORT>:80`.\n\nUpdates to the above configurations require a reboot of all containers to take effect:\n\n> ```bash\n> $ docker compose -f docker-compose.yml up -d\n> ```\n\n### Switch doc engine from Elasticsearch to Infinity\n\nRAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to [Infinity](https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Finfinity\u002F), follow these steps:\n\n1. Stop all running containers:\n\n   ```bash\n   $ docker compose -f docker\u002Fdocker-compose.yml down -v\n   ```\n\n> [!WARNING]\n> `-v` will delete the docker container volumes, and the existing data will be cleared.\n\n2. Set `DOC_ENGINE` in **docker\u002F.env** to `infinity`.\n3. Start the containers:\n\n   ```bash\n   $ docker compose -f docker-compose.yml up -d\n   ```\n\n> [!WARNING]\n> Switching to Infinity on a Linux\u002Farm64 machine is not yet officially supported.\n\n## 🔧 Build a Docker image\n\nThis image is approximately 2 GB in size and relies on external LLM and embedding services.\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow.git\ncd ragflow\u002F\ndocker build --platform linux\u002Famd64 -f Dockerfile -t infiniflow\u002Fragflow:nightly .\n```\n\nOr if you are behind a proxy, you can pass proxy arguments:\n\n```bash\ndocker build --platform linux\u002Famd64 \\\n  --build-arg http_proxy=http:\u002F\u002FYOUR_PROXY:PORT \\\n  --build-arg https_proxy=http:\u002F\u002FYOUR_PROXY:PORT \\\n  -f Dockerfile -t infiniflow\u002Fragflow:nightly .\n```\n\n## 🔨 Launch service from source for development\n\n1. Install `uv` and `pre-commit`, or skip this step if they are already installed:\n\n   ```bash\n   pipx install uv pre-commit\n   ```\n2. Clone the source code and install Python dependencies:\n\n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow.git\n   cd ragflow\u002F\n   uv sync --python 3.12 # install RAGFlow dependent python modules\n   uv run python3 download_deps.py\n   pre-commit install\n   ```\n3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:\n\n   ```bash\n   docker compose -f docker\u002Fdocker-compose-base.yml up -d\n   ```\n\n   Add the following line to `\u002Fetc\u002Fhosts` to resolve all hosts specified in **docker\u002F.env** to `127.0.0.1`:\n\n   ```\n   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager\n   ```\n4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site:\n\n   ```bash\n   export HF_ENDPOINT=https:\u002F\u002Fhf-mirror.com\n   ```\n5. If your operating system does not have jemalloc, please install it as follows:\n\n   ```bash\n   # Ubuntu\n   sudo apt-get install libjemalloc-dev\n   # CentOS\n   sudo yum install jemalloc\n   # OpenSUSE\n   sudo zypper install jemalloc\n   # macOS\n   sudo brew install jemalloc\n   ```\n6. Launch backend service:\n\n   ```bash\n   source .venv\u002Fbin\u002Factivate\n   export PYTHONPATH=$(pwd)\n   bash docker\u002Flaunch_backend_service.sh\n   ```\n7. Install frontend dependencies:\n\n   ```bash\n   cd web\n   npm install\n   ```\n8. Launch frontend service:\n\n   ```bash\n   npm run dev\n   ```\n\n   _The following output confirms a successful launch of the system:_\n\n   ![](https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F0daf462c-a24d-4496-a66f-92533534e187)\n9. Stop RAGFlow front-end and back-end service after development is complete:\n\n   ```bash\n   pkill -f \"ragflow_server.py|task_executor.py\"\n   ```\n\n## 📚 Documentation\n\n- [Quickstart](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002F)\n- [Configuration](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Fconfigurations)\n- [Release notes](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Frelease_notes)\n- [User guides](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fcategory\u002Fuser-guides)\n- [Developer guides](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fcategory\u002Fdeveloper-guides)\n- [References](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Fcategory\u002Freferences)\n- [FAQs](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Ffaq)\n\n## 📜 Roadmap\n\nSee the [RAGFlow Roadmap 2026](https:\u002F\u002Fgithub.com\u002Finfiniflow\u002Fragflow\u002Fissues\u002F12241)\n\n## 🏄 Community\n\n- [Discord](https:\u002F\u002Fdiscord.gg\u002FNjYzJD3GM3)\n- [Twitter](https:\u002F\u002Ftwitter.com\u002Finfiniflowai)\n- [GitHub Discussions](https:\u002F\u002Fgithub.com\u002Forgs\u002Finfiniflow\u002Fdiscussions)\n\n## 🙌 Contributing\n\nRAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community.\nIf you would like to be a part, review our [Contribution Guidelines](https:\u002F\u002Fragflow.io\u002Fdocs\u002Fdev\u002Fcontributing) first.\n","RAGFlow 是一个领先的开源检索增强生成（RAG）引擎，它结合了先进的RAG技术和代理能力，为大型语言模型（LLMs）提供了一个卓越的上下文层。该项目利用Python开发，能够显著提升AI应用在处理复杂查询和对话时的表现力与准确性。其核心功能包括高效的文档检索、基于上下文的信息生成以及灵活的任务执行框架。这些特点使得RAGFlow特别适用于需要深度理解和生成高质量文本的应用场景，如智能客服、内容创作助手及知识管理系统等。",2,"2026-06-17 02:35:09","top_all"]