GraphRAG: Microsoft’s Approach to AI-Powered Knowledge Discovery (Code Release)
AI will be the main gateway to knowledge. Yet making sense of vast, unstructured information remains a significant challenge. Enter GraphRAG.
Microsoft Research has just released the code for this groundbreaking approach.
But how does it work? At its core, GraphRAG is an evolution of retrieval-augmented generation (RAG). Traditional RAG enhances large language model (LLM) performance by providing relevant contextual information during query processing. GraphRAG takes this further. It combines LLM-generated knowledge graphs with advanced graph machine learning techniques.
The process is intricate yet powerful.
First, GraphRAG uses an LLM to process the entire dataset, extracting entities and relationships. These form the building blocks of a comprehensive knowledge graph.
Next, it applies community detection algorithms, identifying clusters of closely related entities.
The system then creates embeddings of these entities and relationships, enabling semantic search capabilities.
When a query comes in, GraphRAG can perform two types of searches.
For broad questions, it uses global search, traversing the entire graph to summarize overarching themes and concepts.
For specific queries, it employs local search, extracting relevant subgraphs and combining graph structure with raw…
