GraphRAG

Problem

Naive retrieval pulls the top-k chunks for each query, which is fine for local lookup but cannot answer questions about the whole corpus. The answer to 'what are the main themes?' does not live in any single chunk; it requires seeing how chunks connect, what entities recur across them, and how communities of related content cluster. Without a representation that captures corpus-level structure — entities, relations, communities — chunk-level retrieval is mismatched to corpus-level questions, and the system returns confidently wrong, partial summaries that the user has no easy way to spot.

Solution

Index time: extract entities and relations from chunks; build a knowledge graph; cluster into hierarchical communities; summarise each community. Query time: classify query as local (entity-specific) or global (corpus-wide). Local queries use entity-anchored retrieval; global queries map-reduce over community summaries.

When to use

• Users ask global, corpus-wide questions that local chunk retrieval cannot answer.
• The corpus has clear entities and relations worth extracting into a graph.
• Index-time cost can be paid up front to enable hierarchical community summaries.