microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
What it solves
GraphRAG leverages knowledge graphs to help LLMs reason better about private, unstructured narrative data. It addresses the problem of extracting structured meaning from complex text to provide a more targeted context for question answering.
How it works
It functions as a data pipeline and transformation suite. It uses LLMs to extract structured data from unstructured text, creating a knowledge graph memory structure that the LLM can then use to enhance its outputs.
Who it’s for
Developers and researchers who need to enhance an LLM's ability to perform discovery and reasoning over their own private datasets.
Highlights
- own private data
- knowledge graph memory structures
- data pipeline and transformation suite
- prompt tuning capabilities
Related
- Project
rahulnyk/knowledge_graphA tool that converts text corpora into interactive knowledge graphs of concepts using local LLMs, enabling advanced text analysis and Graph RAG.
- Project
InternScience/GraphGenA framework for generating knowledge-driven synthetic data using knowledge graphs to fill knowledge gaps in LLMs for improved supervised fine-tuning.
- Project
HKUDS/LightRAGLightRAG is an open‑source Python framework that blends vector embeddings with a knowledge‑graph index for fast, scalable Retrieval‑Augmented Generation. It supports multiple chunking strategies, multimodal document parsing, incremental updates, and a variety of storage back‑ends, and ships with a REST API and Web UI.
- Project
LHRLAB/HyperGraphRAGA Retrieval-Augmented Generation framework that uses hypergraph-structured knowledge representation to better capture complex relationships between entities.
- Project
DEEP-PolyU/LinearRAGLinearRAG is a relation-free graph construction method for GraphRAG that eliminates LLM token costs during graph building to enable efficient, scalable multi-hop reasoning on large corpora.