Graph RAG

What it is

Plain RAG retrieves chunks by semantic similarity. Graph RAG first builds a knowledge graph of entities and relationships, then walks the graph to assemble a focused, structured context. Result: answers that respect the actual relationships in your data.

How Vaaani uses it

• Legal QA across thousands of contracts with cross-references
• Medical research assistants spanning interconnected papers
• Internal knowledge bases where 'who works on what' matters
• Compliance audits requiring traceable, cited answers

Why it makes the cut

When your domain is highly relational — clinical, legal, scientific, enterprise — vector-only RAG misses the point. Graph RAG is the upgrade that makes the LLM actually reason about your domain.

Sample code

from langchain_community.graphs import Neo4jGraph
from langchain.chains import GraphCypherQAChain

graph = Neo4jGraph(url, user, pwd)
chain = GraphCypherQAChain.from_llm(llm, graph=graph)
chain.invoke("Which patients in Trial X also took drug Y?")