Introduction
GraphRAG is a Microsoft Research open-source project that provides a pipeline and transformation suite to extract structured entities, relations, and events from unstructured text, enabling knowledge-graph-backed RAG applications and precise temporal queries.
Key Features
- Pipelines to convert text into structured knowledge (entities, relations, events).
- Support for prompt tuning and configurable indexing strategies, with comprehensive docs and quickstart examples.
- Integrations with multiple backends and retrieval tools to support extensibility.
Use Cases
- Extracting queryable knowledge from enterprise documents, logs, or narrative data to enrich retrieval.
- Building agent memories and systems that require temporal reasoning over historical events.
- Research and experimental RAG workflows for exploring knowledge-graph-enhanced retrieval.
Technical Highlights
- Includes CLI quickstart examples and documentation; relies on LLMs for structured extraction.
- Intended as a demonstration and research toolkit (not an officially supported commercial product); designed for configurability and portability.
- See repository docs for contribution guidelines and responsible AI notes; indexing can be resource-intensive—review cost and operational guidance before large-scale use.