---
title: "Example: GraphRAG Search"
description: "Use GraphRAG to combine vector search with knowledge graph traversal"
canonical: "https://docs.ainative.studio/docs/examples/graphrag"
last-updated: "2026-10-03T21:47:10.764Z"
---

# Example: GraphRAG Search

Source: https://docs.ainative.studio/docs/examples/graphrag

> Use GraphRAG to combine vector search with knowledge graph traversal

## GraphRAG Search

GraphRAG blends vector similarity with knowledge graph traversal for multi-hop retrieval.

## Setup: Create Entities and Edges

```python
import requests

TOKEN = "your-api-key"
BASE = "https://api.ainative.studio/api/v1/public/memory/v2/graph"
HEADERS = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}

## Create entities
for entity in [
    {"canonical_name": "Alice", "entity_type": "person", "properties": {"role": "CTO"}},
    {"canonical_name": "AINative", "entity_type": "org"},
    {"canonical_name": "Python", "entity_type": "tech"},
    {"canonical_name": "ZeroDB", "entity_type": "tech"},
]:
    requests.post(f"{BASE}/entity", headers=HEADERS, json=entity)

## Create relationships
for edge in [
    {"source_name": "Alice", "target_name": "AINative", "predicate": "works_at", "confidence": 0.95},
    {"source_name": "Alice", "target_name": "Python", "predicate": "uses", "confidence": 0.9},
    {"source_name": "AINative", "target_name": "ZeroDB", "predicate": "builds", "confidence": 1.0},
]:
    requests.post(f"{BASE}/edge", headers=HEADERS, json=edge)
```

## Run GraphRAG Query

```python
## Hybrid search: vector similarity + graph traversal
response = requests.post(f"{BASE}/graphrag", headers=HEADERS, json={
    "query": "What technologies does Alice's company build?",
    "graph_weight": 0.4,
    "max_hops": 2,
    "limit": 5,
})

for result in response.json()["results"]:
    print(f"Score: {result['final_score']:.3f} — {result['content']}")
```

The query traverses: Alice → works_at → AINative → builds → ZeroDB, finding information that pure vector search would miss.

## Traverse the Graph

```python
## Multi-hop traversal from Alice
response = requests.post(f"{BASE}/traverse", headers=HEADERS, json={
    "entity": "Alice",
    "max_hops": 3,
    "predicates": ["works_at", "uses", "builds"],
})

for node in response.json()["nodes"]:
    print(f"  {node['canonical_name']} ({node['entity_type']})")
```

## Tuning graph_weight

| graph_weight | Behavior | Use When |
|:---:|----------|----------|
| 0.0 | Pure vector search | Topical queries |
| 0.3 | Default blend | General questions |
| 0.5 | Equal weight | Relationship questions |
| 0.8 | Graph-heavy | "Who knows who" queries |
| 1.0 | Pure graph proximity | Network analysis |

## What to Try Next

- Apply an [ontology template](/docs/api/context-graph) to structure your graph
- Use [contradiction detection](/docs/api/context-graph) for fact versioning
- Read the [GraphRAG guide](/docs/guides/graphrag) for architecture deep-dive