How to Build GraphRAG with Vector Search and Knowledge Graphs

Vector search is useful, but it is not enough for every retrieval problem.

If a user asks, "Which products from suppliers audited last year had severe quality complaints?", a vector-only RAG system can find text that sounds related to "quality complaints." What it cannot reliably enforce is the chain of relationships: complaint to product, product to supplier, supplier to audit year, and then severity ranking.

That is the problem GraphRAG solves. It combines semantic recall with graph structure, so the answer is not just similar. It is connected, filtered, and explainable.

What You Will Build

This tutorial shows how to build a GraphRAG pattern that can:

Find semantically similar records with vector search.
Traverse relationships across suppliers, products, and complaints.
Apply structured filters like audit year or region.
Score the surviving records with an LLM function.
Return an answer from one graph query.

The example uses supply-chain quality complaints, but the same pattern applies to cybersecurity investigations, contract analysis, patient similarity, fraud rings, customer 360 systems, and research knowledge graphs.

Why Vector-Only RAG Hits a Wall

Classic RAG usually follows this pattern:

1. Embed the question. 2. Retrieve similar chunks from a vector database. 3. Send chunks to an LLM. 4. Ask the LLM to produce an answer.

That works well when the answer is contained in one or two text passages. It becomes fragile when the question depends on relationships.

For example:

"Find customers connected to the same suspicious payment device."
"Which vendors have contracts with risky termination clauses?"
"Which research papers cite methods similar to this abstract?"
"Which products have complaints connected to suppliers audited in 2025?"

These are not just text retrieval questions. They are relationship questions.

The GraphRAG Pattern

GraphRAG adds a graph traversal step to retrieval. Instead of stopping at "these records are semantically similar," it asks:

What is this record connected to?
Which paths matter?
Which metadata constraints must be true?
Which results should be scored or ranked?

In SynapCores, this can happen directly in the database using vector-aware graph queries and AI scoring.

Step 1: Model the Business Graph

Start by representing the real-world entities as graph nodes:

Supplier
Product
Complaint

Then connect them with relationships:

Supplier supplies Product.
Complaint is about Product.
Complaint can be semantically similar to another Complaint.

In Cypher, the setup looks like this:

MERGE (s:Supplier {
  id: "SUP-201",
  name: "Pacifica Components",
  audited_year: 2025,
  region: "APAC"
})

MERGE (p:Product {
  sku: "PRD-7700",
  name: "Quad-port USB-C hub",
  embedding: [0.11, 0.32, -0.04, 0.27, 0.18]
})

MERGE (c:Complaint {
  id: "CX-9001",
  text: "Hub stops charging after 30 minutes of heavy load",
  embedding: [0.12, 0.31, -0.03, 0.26, 0.19]
})

MERGE (s)-[:SUPPLIES]->(p)
MERGE (c)-[:ABOUT]->(p);

This is the core advantage of graph modeling: the database understands the shape of the business problem, not only the text.

Step 2: Use Vector Similarity as a Graph Hop

The semantic part of GraphRAG finds complaints similar to a seed complaint.

MATCH (seed:Complaint {id: "CX-9001"})-[:SIMILAR_TO > 0.85]->(c:Complaint)
RETURN seed.text AS source_complaint, c.text AS related_complaint;

This query is not doing a detached vector lookup in a separate service. The semantic similarity behaves like a graph relationship that can be used inside the traversal.

That means semantic recall becomes one step in a larger reasoning path.

Step 3: Enforce Business Structure

Now combine the semantic hop with real relationships.

MATCH (seed:Complaint {id: "CX-9001"})-[:SIMILAR_TO > 0.85]->(c:Complaint)
MATCH (c)-[:ABOUT]->(p:Product)<-[:SUPPLIES]-(s:Supplier)
WHERE s.audited_year = 2025
RETURN
  s.name AS supplier,
  p.name AS product,
  c.text AS complaint;

This query does three things at once:

Finds complaints similar to the seed complaint.
Walks from complaint to product to supplier.
Filters to suppliers audited in 2025.

A vector-only RAG system would usually need to retrieve candidates first, pass IDs into another data store, run joins or graph lookups, and then re-rank in application code.

Step 4: Add LLM Scoring

Some questions need judgment. For quality complaints, a user may care about severity, not just similarity.

MATCH (seed:Complaint {id: "CX-9001"})-[:SIMILAR_TO > 0.85]->(c:Complaint)
MATCH (c)-[:ABOUT]->(p:Product)<-[:SUPPLIES]-(s:Supplier)
WHERE s.audited_year = 2025
WITH s, p, c,
     llm_score(
       "rate severity of this product complaint from 0 to 1",
       c
     ) AS severity
WHERE severity > 0.5
RETURN
  s.name AS supplier,
  p.name AS product,
  c.text AS complaint,
  severity
ORDER BY severity DESC;

The useful part is not only that an LLM scores the complaint. It is that scoring happens after graph constraints have narrowed the candidate set. You reduce noise before invoking judgment.

When to Use GraphRAG

GraphRAG is a strong fit when your questions include both meaning and relationships.

Use it for:

Supply-chain risk analysis.
Threat intelligence correlation.
Fraud ring detection.
Contract obligation mapping.
Patient similarity and clinical history.
Knowledge graph question answering.
Product recommendation systems.
Research and citation discovery.

If the answer depends on "what is connected to what," plain vector search is usually not enough.

Production Considerations

Before production, think through:

Graph schema: model relationships that matter to actual user questions.
Permission boundaries: graph traversal must respect access control.
Similarity thresholds: tune thresholds by use case, not by demo data.
LLM scoring cost: score after filtering, not before.
Explainability: return the path used to reach the answer when users need trust.

Where SynapCores Fits

SynapCores is designed for workloads where AI, graph structure, vectors, and operational data belong together. GraphRAG is a strong example: semantic recall, relationship traversal, filtering, and AI scoring can run in one database workflow.

Download or request access to SynapCores at https://synapcores.com and try the full GraphRAG recipe.

FAQ

What is GraphRAG?

GraphRAG is a retrieval-augmented generation pattern that combines vector search with graph relationships. It helps answer questions that depend on both semantic similarity and connected data.

How is GraphRAG different from normal RAG?

Normal RAG retrieves similar text chunks. GraphRAG retrieves relevant entities or documents and then uses graph structure to follow relationships, apply constraints, and explain how results are connected.

Do I need a graph database for GraphRAG?

You need a way to represent and query relationships. SynapCores supports graph-style querying with AI-native operations, which reduces the need to split the workflow across separate systems.

What are the best use cases for GraphRAG?

GraphRAG works best for compliance, investigations, recommendations, threat intelligence, supply-chain analysis, research graphs, and any domain where relationships determine the answer.