Add content to the knowledge graph with automatic entity and relationship extraction.

Ingest Knowledge

Ingest text content into Cerebe's Knowledge Graph. The system automatically extracts entities and relationships using Graphiti, building a temporal knowledge graph that evolves over time.

Endpoint

POST /api/v1/knowledge/ingest

Request Body

Parameter	Type	Required	Description
`content`	`string`	Yes	Text content to ingest into the knowledge graph
`entity_id`	`string`	No	Entity to associate the knowledge with
`source`	`string`	No	Source identifier (e.g., `onboarding_notes`, `lesson_plan`)
`metadata`	`object`	No	Additional metadata for the ingestion

Examples

from cerebe import AsyncCerebe

client = AsyncCerebe(api_key="ck_live_...")

result = await client.knowledge.ingest(
    content="Alice is a senior engineer at Acme Corp. She mentors Bob, who joined the team in January.",
    entity_id="team_alpha",
    source="onboarding_notes",
)

print(result)
# → {"entities_created": 3, "relationships_created": 2, "status": "completed"}

import Cerebe from '@cerebe/sdk'

const client = new Cerebe({ apiKey: 'ck_live_...' })

const result = await client.knowledge.ingest({
  content: 'Alice is a senior engineer at Acme Corp. She mentors Bob, who joined the team in January.',
  entityId: 'team_alpha',
  source: 'onboarding_notes',
})

console.log(result)
// → { entitiesCreated: 3, relationshipsCreated: 2, status: "completed" }

curl -X POST https://api.cerebe.ai/api/v1/knowledge/ingest \
  -H "X-API-Key: ck_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Alice is a senior engineer at Acme Corp. She mentors Bob, who joined the team in January.",
    "entity_id": "team_alpha",
    "source": "onboarding_notes"
  }'

Response

{
  "entities_created": 3,
  "relationships_created": 2,
  "status": "completed"
}

Status Values

Status	Description
`completed`	Ingestion succeeded
`unavailable`	Knowledge graph service is not available

How It Works

Content Analysis — The text is parsed to identify entities (people, organizations, concepts) and their relationships.
Entity Extraction — Graphiti extracts structured entities with types and properties.
Relationship Mapping — Connections between entities are identified and stored with temporal metadata.
Temporal Tracking — All facts include timestamps, enabling point-in-time queries (e.g., "Who was Bob's mentor in March?").

Error Responses

Status	Description
`401`	Missing or invalid API key
`503`	Knowledge graph service unavailable

Next Steps

Query Knowledge — Search entities and relationships
Knowledge Overview — Full guide to the Knowledge Graph

Ingest Knowledge

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