GraphRAG (Graphs + Retrieval Augmented Generation) is an open-source project developed by Microsoft Research. It is a modular, graph-based retrieval-augmented generation system. The project combines text extraction, network analysis, and large language model prompting and summarization to form an end-to-end system specifically designed for deep understanding of text datasets.
GraphRAG uses large language models (LLMs) to automatically extract rich knowledge graphs from any collection of text documents. One of the most exciting features of this graph-based data index is its ability to report the semantic structure of the data before any user query.
The system not only extracts entities and relationships but also builds a community hierarchy, generates summaries of these communities, and then leverages these structures when performing RAG-based tasks.
By creating a knowledge graph based on the input corpus, GraphRAG greatly improves the "retrieval" part of RAG, filling the context window with more relevant content, resulting in better answers and capturing evidence sources.
The GraphRAG project is a data pipeline and transformation suite specifically designed to leverage the power of large language models to extract meaningful structured data from unstructured text.
The project provides a command-line interface (CLI) and GraphRAG accelerator solutions, simplifying the user experience for developers and users.
GraphRAG creates multiple output products to store the indexed knowledge model, which will be continuously updated and iterated on in future versions.
GraphRAG is particularly suitable for scenarios that require discovering complex patterns and relationships from large amounts of text data, and can answer global questions that traditional RAG systems struggle with.
For private datasets containing rich narrative content, GraphRAG unlocks the discovery capabilities of LLMs on these data.
The system can generate research questions, optimize knowledge bases, improve user prompts, and create tools to enhance the intelligence of AI agents.
The project provides a simple option for installation from PyPI, including a complete end-to-end example demonstrating how to use the system to index text and answer questions about documents using the indexed data.
graphrag init command.env and settings.yaml configuration filesThe system supports input in various text formats and can handle large-scale document collections and establish corresponding knowledge graphs.
As an open-source project, GraphRAG promotes collaboration and development in graph-enhanced RAG technology in academia and industry.
Microsoft GraphRAG represents a significant advancement in retrieval-augmented generation technology. By combining knowledge graphs with large language models, it significantly enhances the capabilities of text understanding and question answering systems. It is not only a technical tool but also an important milestone in promoting the development of AI in the field of complex text analysis.