GraphRAG

Why It Matters

Traditional RAG works well for direct questions where the answer lives in a single chunk of text. But what happens when users ask questions like “What are the main themes across all our customer feedback?” or “How does decision X in document A relate to outcome Y mentioned in document B?”

Standard vector search fails here because it finds similar text, not connected concepts. GraphRAG solves this by building a knowledge graph during indexing, extracting entities, relationships, and hierarchical summaries from your documents. This graph structure enables queries that require understanding how pieces of information connect across your entire corpus.

For AI engineers, GraphRAG matters when your use case involves complex, multi-hop reasoning. Enterprise knowledge bases, research literature analysis, and investigative queries all benefit from graph-enhanced retrieval. The trade-off is increased indexing complexity and cost, but for the right problems, the accuracy improvement is substantial.

Traditional RAG vs GraphRAG

Traditional RAG chunks documents, embeds them, and retrieves the most similar chunks to your query. Great for “What is X?” questions where the answer exists verbatim somewhere in your data.

GraphRAG adds an extraction layer that identifies entities (people, concepts, events) and their relationships, then organizes them into community hierarchies. Queries can now traverse these relationships and access summarized views at different levels of abstraction.

The key difference: vector search finds similar content, while graph search finds connected content. When a user asks about patterns, trends, or relationships spanning multiple documents, GraphRAG retrieves structured knowledge rather than hoping the right text chunks happen to be semantically similar to the query.

Implementation Basics

A GraphRAG pipeline extends traditional RAG with three additional steps:

1. Entity and Relationship Extraction An LLM processes your documents to extract entities (named things) and relationships (how entities connect). This creates a knowledge graph representing the semantic structure of your corpus.

2. Community Detection Graph algorithms cluster related entities into communities at multiple hierarchical levels. Each community gets a summary describing its main themes. This enables answering high-level questions about your entire dataset.

3. Query Routing Incoming queries get classified: local queries (specific facts) use traditional vector search, while global queries (themes, summaries, relationships) leverage the graph structure and community summaries.

Start with Microsoft’s open-source GraphRAG implementation to understand the pipeline. The indexing step is computationally expensive (many LLM calls for extraction), so evaluate whether your query patterns actually require graph-enhanced retrieval before committing to the infrastructure complexity.