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How to implement graph-based retrieval augmented generation pipelines Illustration

How to implement graph-based retrieval augmented generation pipelines

Written by Nikhil Badjatya
Direct Summary:

Graph-based RAG (as implemented in Microsoft's open-source GraphRAG project) extracts entities and relationships from your documents into a knowledge graph, then clusters that graph into communities and generates summaries for each one. Instead of retrieving isolated text chunks, queries can reason over these community summaries — which is what lets it answer holistic questions ("what are the main themes across all these reports?") that plain chunk-based RAG structurally can't, since no single chunk contains a corpus-wide answer.

"Progress is impossible without change."

— George Bernard Shaw

Key Insights

  • It solves a different problem than chunk retrieval: standard RAG is strong at "what does document X say about Y" — a local, single-chunk question. GraphRAG targets "what are the themes across this whole corpus" — a question no single chunk can answer.
  • Indexing is expensive up front: extracting entities, relationships, and generating community summaries means running LLM calls over your entire corpus once during indexing — a real cost trade-off against plain vector-store RAG.
  • Global vs. local search are different modes: GraphRAG's global search reasons over community summaries for corpus-wide questions; local search fans out from a specific entity to its neighbors, closer to how traditional RAG behaves.

Ask a standard RAG system "what does section 4.2 say about liability?" and chunk retrieval works fine — that's a localized question with an answer in one place. Ask it "what are the recurring risk themes across these 200 contracts?" and it structurally can't answer well, because no single retrieved chunk contains a corpus-wide synthesis. That's the gap GraphRAG is built to close.

How the pipeline actually works

1. Extract a knowledge graph from the raw text. An LLM pass identifies entities (people, organizations, concepts) and the relationships between them across your documents, building a graph rather than a flat chunk list.

2. Detect communities and summarize each one. The graph gets partitioned into clusters of densely-related entities, and an LLM generates a summary for each community — this is what makes corpus-wide themes queryable later.

3. Query with the mode that matches your question. Global search reasons over community summaries for holistic questions; local search fans out from a specific entity for narrower questions; basic search falls back to standard vector retrieval when that's genuinely what's needed.

graphrag_query.sh 
# Microsoft GraphRAG: index a corpus, then query it in two different modes

# 1. Index the corpus - extracts entities/relationships, builds community summaries
python -m graphrag index --root ./ragtest

# 2. Global search - reasons over community summaries (holistic questions)
python -m graphrag query \
  --root ./ragtest \
  --method global \
  --query "What are the main themes across all documents?"

# 3. Local search - fans out from a specific entity (narrower questions)
python -m graphrag query \
  --root ./ragtest \
  --method local \
  --query "What is Acme Corp's relationship with Beta Industries?"
Query type Best approach
"What does page 12 say about X?" (localized) Standard chunk-based RAG
"What are the recurring themes across the whole corpus?" (holistic) GraphRAG global search over community summaries

The tradeoff is real: GraphRAG's indexing step costs meaningfully more in LLM calls than embedding chunks for vector search. It's worth that cost specifically when your actual use case involves corpus-wide reasoning — for purely localized lookup questions, standard RAG remains the simpler, cheaper choice.

Practical Challenge

Take a small set of related documents (5-10 articles on one topic) and write one "localized" question and one "holistic, corpus-wide" question about them. Identify which one standard chunk-based RAG would answer well, and which one it would likely struggle with.

Concept Check

Why does standard chunk-based RAG struggle with "what are the main themes across this entire corpus" questions?
Correct! Chunk retrieval surfaces individual passages, not corpus-wide synthesis. GraphRAG's community summaries are generated specifically to answer holistic questions that no single chunk can.
Incorrect. Try again! Hint: think about what information a single retrieved chunk can and can't contain.

Sources & Further Reading

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