GraphRAG uses LLMs to extract structured data from unstructured text and applies knowledge graph memory structures to support retrieval-augmented generation over private data. It is offered as a Python package with a command line workflow, and its provided code serves as a demonstration rather than an officially supported Microsoft offering.
Project overview
The project uses knowledge graph memory structures to enhance LLM outputs over private data. It requires LLMs to transform unstructured text into structured data, a process associated with high costs during indexing operations.
Project type
RAG · Data Processing
Use cases
Knowledge Q&A
Deployment
Refer to project documentation
License
MIT
Best for
Developers, data teams, and AI engineers building retrieval-augmented generation applications who need to process unstructured text into structured data using LLMs.
Key capabilities
Extracts meaningful, structured data from unstructured text using the power of LLMs.
Uses knowledge graph memory structures to enhance LLM outputs.
Supports fine-tuning prompts to yield better results for specific data.
Provides a command line quickstart and CLI commands for initialization.
Limitations and risks
GraphRAG indexing can be an expensive operation, resulting in high costs associated with indexing operations.
The provided code serves as a demonstration and is not an officially supported Microsoft offering.
Using GraphRAG with your data out of the box may not yield the optimal results without fine-tuning prompts.
Getting started
Setup involves reading documentation to understand costs, installing the graphrag package via PyPI, running 'graphrag init --root [path] --force', and following the command line quickstart. Prompt tuning is required to yield better results for specific data.
Evidence and sources
GitHub project description: A modular graph-based Retrieval-Augmented Generation (RAG) system
README: 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.
README: 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…
README: ⚠️ *Warning: GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.*
README: 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](https:/…