[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1934":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":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},1934,"graphrag","microsoft\u002Fgraphrag","microsoft","A modular graph-based Retrieval-Augmented Generation (RAG) system","https:\u002F\u002Fmicrosoft.github.io\u002Fgraphrag\u002F",null,"Python",33656,3562,194,38,0,36,207,737,165,120,"MIT License",false,"main",true,[27,28,29,5,30,31,32],"gpt","gpt-4","gpt4","llm","llms","rag","2026-06-12 04:00:12","# GraphRAG\n\n👉 [Microsoft Research Blog Post](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fblog\u002Fgraphrag-unlocking-llm-discovery-on-narrative-private-data\u002F)\u003Cbr\u002F>\n👉 [Read the docs](https:\u002F\u002Fmicrosoft.github.io\u002Fgraphrag)\u003Cbr\u002F>\n👉 [GraphRAG Arxiv](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.16130)\n\n\u003Cdiv align=\"left\">\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fgraphrag\u002F\">\n    \u003Cimg alt=\"PyPI - Version\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fgraphrag\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fgraphrag\u002F\">\n    \u003Cimg alt=\"PyPI - Downloads\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fgraphrag\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgraphrag\u002Fissues\">\n    \u003Cimg alt=\"GitHub Issues\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002Fmicrosoft\u002Fgraphrag\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgraphrag\u002Fdiscussions\">\n    \u003Cimg alt=\"GitHub Discussions\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fdiscussions\u002Fmicrosoft\u002Fgraphrag\">\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## Overview\n\nThe GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.\n\nTo learn more about GraphRAG and how it can be used to enhance your LLM's ability to reason about your private data, please visit the \u003Ca href=\"https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fblog\u002Fgraphrag-unlocking-llm-discovery-on-narrative-private-data\u002F\" target=\"_blank\">Microsoft Research Blog Post.\u003C\u002Fa>\n\n## Quickstart\n\nTo get started with the GraphRAG system we recommend trying the [command line quickstart](https:\u002F\u002Fmicrosoft.github.io\u002Fgraphrag\u002Fget_started\u002F).\n\n## Repository Guidance\n\nThis repository presents a methodology for using knowledge graph memory structures to enhance LLM outputs. Please note that the provided code serves as a demonstration and is not an officially supported Microsoft offering.\n\n⚠️ *Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.*\n\n## Diving Deeper\n\n- To learn about our contribution guidelines, see [CONTRIBUTING.md](.\u002FCONTRIBUTING.md)\n- To start developing _GraphRAG_, see [DEVELOPING.md](.\u002FDEVELOPING.md)\n- Join the conversation and provide feedback in the [GitHub Discussions tab!](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fgraphrag\u002Fdiscussions)\n\n## Prompt Tuning\n\nUsing _GraphRAG_ with your data out of the box may not yield the best possible results.\nWe strongly recommend to fine-tune your prompts following the [Prompt Tuning Guide](https:\u002F\u002Fmicrosoft.github.io\u002Fgraphrag\u002Fprompt_tuning\u002Foverview\u002F) in our documentation.\n\n## Versioning\n\nPlease see the [breaking changes](.\u002Fbreaking-changes.md) document for notes on our approach to versioning the project.\n\n*Always run `graphrag init --root [path] --force` between minor version bumps to ensure you have the latest config format. Run the provided migration notebook between major version bumps if you want to avoid re-indexing prior datasets. Note that this will overwrite your configuration and prompts, so backup if necessary.*\n\n## Responsible AI FAQ\n\nSee [RAI_TRANSPARENCY.md](.\u002FRAI_TRANSPARENCY.md)\n\n- [What is GraphRAG?](.\u002FRAI_TRANSPARENCY.md#what-is-graphrag)\n- [What can GraphRAG do?](.\u002FRAI_TRANSPARENCY.md#what-can-graphrag-do)\n- [What are GraphRAG’s intended use(s)?](.\u002FRAI_TRANSPARENCY.md#what-are-graphrags-intended-uses)\n- [How was GraphRAG evaluated? What metrics are used to measure performance?](.\u002FRAI_TRANSPARENCY.md#how-was-graphrag-evaluated-what-metrics-are-used-to-measure-performance)\n- [What are the limitations of GraphRAG? How can users minimize the impact of GraphRAG’s limitations when using the system?](.\u002FRAI_TRANSPARENCY.md#what-are-the-limitations-of-graphrag-how-can-users-minimize-the-impact-of-graphrags-limitations-when-using-the-system)\n- [What operational factors and settings allow for effective and responsible use of GraphRAG?](.\u002FRAI_TRANSPARENCY.md#what-operational-factors-and-settings-allow-for-effective-and-responsible-use-of-graphrag)\n\n## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft\ntrademarks or logos is subject to and must follow\n[Microsoft's Trademark & Brand Guidelines](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Flegal\u002Fintellectualproperty\u002Ftrademarks\u002Fusage\u002Fgeneral).\nUse of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.\nAny use of third-party trademarks or logos are subject to those third-party's policies.\n\n## Privacy\n\n[Microsoft Privacy Statement](https:\u002F\u002Fprivacy.microsoft.com\u002Fen-us\u002Fprivacystatement)\n","GraphRAG 是一个基于图的检索增强生成系统，旨在通过大型语言模型（LLMs）从非结构化文本中提取有意义的结构化数据。其核心功能包括利用知识图谱记忆结构来增强 LLM 的输出质量，并提供了一套完整的数据处理和转换流程。项目采用 Python 语言编写，具有模块化设计，支持自定义扩展与优化。适用于需要对私有数据进行深度理解和分析的应用场景，如企业内部文档管理、专业领域知识库构建等。需要注意的是，使用该工具可能涉及较高的计算成本，在实际部署前应充分评估相关资源需求。",2,"2026-06-11 02:46:55","top_all"]