Graph RAG

Overview

• Graph RAG retrieves through entities and relationships, such as customer → policy → endorsement → covered asset.
• It can combine graph traversal, document retrieval, and generated summaries.
• A graph represents connected facts; it is separate from LangGraph's execution graph.

Why this matters

Some questions depend on relationships across many records rather than local text similarity.

An everyday example

A family tree helps answer relationship questions that a pile of biographies makes difficult.

A technical example

Traverse Policy P1 → modifiedBy E7, then retrieve E7's original text for the answer.

How it works

1. Define entities/relations
2. Ingest verified or reviewed links
3. Query/traverse relevant neighborhoods
4. Retrieve source evidence
5. Explain with provenance.

Architecture and flow

Verified records/documents → entity graph → traversal + text evidence → answer.

The insurance assistant example

Find products affected by a policy amendment and connect each result to the source amendment.

When to use it

Relationship-heavy knowledge, cross-document analysis, and verified entity reasoning.

When to avoid it

A small FAQ collection rarely justifies entity extraction, graph maintenance, and another database.

Interview answer

“Graph RAG uses relationships to retrieve connected evidence. It is useful when entity links matter and ordinary text retrieval cannot answer reliably.”

Benefits, trade-offs, and production considerations

• Model-extracted relationships can be wrong.
• Store evidence per edge, validity dates, and ACLs; do not convert a guessed relationship into an authoritative business fact.
• SQL joins may solve structured relationships more simply.