A RAG system built on ordinary vector similarity search answers narrow, fact-lookup questions about a large private document collection well, but performs poorly on a broad question like 'what are the main recurring themes across this entire collection,' because no single retrieved passage or small handful of passages contains a synthesis of the whole corpus. GraphRAG (Microsoft Research, 2024) was designed to address this class of question. What does it do differently from standard vector-similarity RAG?
- It increases the number of passages retrieved for every query to the maximum the context window allows, on the theory that more raw passages will eventually contain the needed synthesis
- It replaces the embedding model with a larger one that has more parameters, so that individual passage embeddings become more informative on their own
- It fine-tunes the language model directly on the entire document collection so that broad thematic questions can be answered from the model's own updated parameters instead of from any retrieval step at all
- It first uses a language model to extract entities and relationships from the corpus into a knowledge graph, partitions that graph into communities of closely related entities, and pre-generates a summary for each community, so a broad question can be answered from these higher-level community summaries instead of depending on any single retrieved passage to contain the whole synthesis
Why D? And why not the others?
Correct answer: D. It first uses a language model to extract entities and relationships from the corpus into a knowledge graph, partitions that graph into communities of closely related entities, and pre-generates a summary for each community, so a broad question can be answered from these higher-level community summaries instead of depending on any single retrieved passage to contain the whole synthesis
GraphRAG targets exactly this gap between narrow fact-lookup and broad, corpus-wide sensemaking questions by building a knowledge graph of entities and relationships extracted from the corpus with an LLM, then partitioning that graph into communities of closely related entities and pre-generating a summary for each community; a broad question about overall themes can then be answered using these pre-built, higher-level community summaries, which already synthesize information spread across many source passages, rather than hoping the answer happens to sit inside whichever handful of passages a similarity search returns. Simply retrieving more raw passages does not solve the underlying problem, since the answer to a genuinely corpus-wide question is not contained in any single passage or a larger pile of individually retrieved passages to begin with, no matter how many are pulled in. Swapping in a larger embedding model improves how well individual passages are represented but does not create the cross-document synthesis a broad thematic question needs, since that synthesis does not exist inside any one passage's embedding. Fine-tuning the language model on the whole collection is a fundamentally different approach from GraphRAG, which deliberately keeps retrieval-based grounding rather than baking the corpus into the model's weights, and fine-tuning does not build the graph structure or community summaries that GraphRAG's method is centered on.
Source: Edge et al., 'From Local to Global: A Graph RAG Approach to Query-Focused Summarization' (Microsoft Research, 2024), arXiv:2404.16130