👉 Use the GraphRAG Accelerator solution
👉 Microsoft Research Blog Post
👉 Read the docs
👉 GraphRAG Arxiv
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.
New: GraphRAG now supports additional model types, including AIME API Server chat models (aime_chat) and BGE embedding models (bge_embedding). See the configuration docs for details and examples.
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 Microsoft Research Blog Post.
To get started with the GraphRAG system we recommend trying the Solution Accelerator package. This provides a user-friendly end-to-end experience with Azure resources.
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.
- 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!
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.
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.
- 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?
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.
- New Model Support: Added support for AIME API Server chat models (
aime_chat) and BGE embedding models (bge_embedding). See the model configuration docs for details. - Enhanced Modularity Metrics: Introduced new modularity calculation options for community detection:
- Graph-wide modularity
- Largest connected component modularity
- Weighted component modularity See the configuration docs for details.