Knowledge Graph

Knowledge Graph

Build interconnected knowledge with entities and relationships. The knowledge graph enables complex queries, reasoning, and discovery of hidden connections.

Key Concepts

Entities

Named objects like people, companies, concepts, or topics extracted from your data.

Relationships

Typed connections between entities like "works_at", "mentions", "related_to".

Traversal

Query across relationships to discover multi-hop connections and patterns.

Automatic Entity Extraction

When you add memories or documents, Hebbrix automatically:

Extracts named entities (people, organizations, locations, concepts)
Creates relationships between entities based on context
Links entities to source memories for provenance tracking
Merges duplicate entities intelligently

Endpoints

Code Examples

Explore Entities

Python

import requests

# GET /v1/knowledge-graph/entities — list entities with their inline
# relationships already attached (capped at 25 per entity).
r = requests.get(
    "https://api.hebbrix.com/v1/knowledge-graph/entities",
    headers={"Authorization": "Bearer <your-api-key>"},
    params={"entity_type": "PERSON", "limit": 20},
)
data = r.json()

for entity in data["entities"]:
    rels = entity.get("relationships", [])
    print(f"{entity['name']} ({entity['type']}) — {len(rels)} relationships")
    for rel in rels:
        arrow = "-->" if rel["direction"] == "outgoing" else "<--"
        print(f"  {arrow} {rel['target']} [{rel['type']}]")

Inspect a Single Entity

Python (Entity Details)

import requests

# GET /v1/knowledge-graph/entities/{entity_name}
# Entities are keyed by name (not UUID). The response bundles
# relationships AND the memory_ids that mention this entity.
r = requests.get(
    "https://api.hebbrix.com/v1/knowledge-graph/entities/Acme%20Corp",
    headers={"Authorization": "Bearer <your-api-key>"},
)
body = r.json()

print(body["details"])             # raw entity row from Neo4j
print(body["relationships"])       # full relationship list (no cap)
print(body["source_memories"])     # list of memory_ids that mention it

Create a Relationship

Python (Relationships)

import requests

# POST /v1/knowledge-graph/relationships — use the short field names
# (source / target / type). The legacy long names
# (source_entity / target_entity / relationship_type) are still accepted.
r = requests.post(
    "https://api.hebbrix.com/v1/knowledge-graph/relationships",
    headers={"Authorization": "Bearer <your-api-key>"},
    json={
        "source": "John Doe",
        "target": "Acme Corp",
        "type": "WORKS_AT",
    },
)
print(r.json())  # {"status": "created", "relationship": {...}}

cURL Example

POST/v1/knowledge-graph/query

curl -X POST "https://api.hebbrix.com/v1/knowledge-graph/query" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "timestamp": "2026-03-30T00:00:00",
  "relation_type": "MENTIONS",
  "collection_id": "col_abc123",
  "limit": 10
}'

Common Entity Types

person

organization

location

concept

product

event

technology

custom