GraphRAG is a retrieval-augmented generation architecture that structures unstructured text into a knowledge graph to dramatically improve AI retrieval accuracy, especially for complex multi-hop queries that require synthesizing information from many documents.
Originally proposed by Microsoft Research and rapidly adopted by the open-source community throughout 2025–2026, GraphRAG addresses the core limitation of standard RAG systems: their inability to answer questions that require connecting facts scattered across different document chunks.
Why It Matters
Standard RAG (Retrieval-Augmented Generation) retrieves text chunks based on vector similarity and feeds them to a language model. This works well for simple factual lookups but fails when the answer requires reasoning over relationships between entities—for example, "Which clients in sector X had projects delayed due to supply-chain issues mentioned in our Q4 reports?" GraphRAG solves this by pre-building an explicit knowledge graph of entities and their relationships, enabling the model to traverse connections rather than relying on embedding proximity alone.
How It Works
GraphRAG operates in two phases. Indexing: the system ingests documents, uses an LLM to extract entities (people, organizations, concepts, events) and their relationships, then constructs a knowledge graph with nodes and edges. It also generates "community summaries"—natural-language descriptions of clusters of related entities. Retrieval: when a user asks a question, the system queries both the graph structure (traversing relationships) and the community summaries, producing a richer context for the final LLM answer generation than flat vector search alone.
Example
A legal team needs to find all contracts where Company A is both a supplier and a subcontractor to different divisions of Company B. Standard RAG would retrieve individual contract chunks that mention either company but fail to connect the dual relationship. GraphRAG's knowledge graph explicitly stores the supplier and subcontractor edges, allowing it to traverse the graph and surface both linkages in a single query.
Related Concepts
- RAG (Retrieval-Augmented Generation)
- Embedding
- Large Language Model (LLM)