microsoft / graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
AI Architecture Analysis
This repository is indexed by RepoMind. By analyzing microsoft/graphrag in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.
Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.
Embed this Badge
Showcase RepoMind's analysis directly in your repository's README.
Repository Summary (README)
PreviewGraphRAG
👉 Microsoft Research Blog Post<br/> 👉 Read the docs<br/> 👉 GraphRAG Arxiv
<div align="left"> <a href="https://pypi.org/project/graphrag/"> <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/graphrag"> </a> <a href="https://pypi.org/project/graphrag/"> <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/graphrag"> </a> <a href="https://github.com/microsoft/graphrag/issues"> <img alt="GitHub Issues" src="https://img.shields.io/github/issues/microsoft/graphrag"> </a> <a href="https://github.com/microsoft/graphrag/discussions"> <img alt="GitHub Discussions" src="https://img.shields.io/github/discussions/microsoft/graphrag"> </a> </div>Overview
The 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.
To 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 <a href="https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/" target="_blank">Microsoft Research Blog Post.</a>
Quickstart
To get started with the GraphRAG system we recommend trying the command line quickstart.
Repository Guidance
This 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.
⚠️ Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.
Diving Deeper
- To learn about our contribution guidelines, see CONTRIBUTING.md
- To start developing GraphRAG, see DEVELOPING.md
- Join the conversation and provide feedback in the GitHub Discussions tab!
Prompt Tuning
Using GraphRAG with your data out of the box may not yield the best possible results. We strongly recommend to fine-tune your prompts following the Prompt Tuning Guide in our documentation.
Versioning
Please see the breaking changes document for notes on our approach to versioning the project.
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.
Responsible AI FAQ
- What is GraphRAG?
- What can GraphRAG do?
- What are GraphRAG’s intended use(s)?
- How was GraphRAG evaluated? What metrics are used to measure performance?
- What are the limitations of GraphRAG? How can users minimize the impact of GraphRAG’s limitations when using the system?
- What operational factors and settings allow for effective and responsible use of GraphRAG?
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.