User configuration
Store and retrieve per-user configuration for your Cognee Cloud tenant.
POST /api/v1/configuration/store_user_configuration — Store a configuration object.
GET /api/v1/configuration/get_user_configuration/{config_id} — Retrieve a specific configuration by ID.
GET /api/v1/configuration/get_user_configuration/ — Retrieve all configurations for the authenticated user.
Ontologies
Ontologies define the structural schema for knowledge graph extraction. They specify which entity types and relationships Cognee should look for during cognify. See Ontologies for the underlying concept.
GET /api/v1/ontologies — List uploaded ontologies.
POST /api/v1/ontologies — Upload an ontology file.
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/ontologies \
-H "X-Api-Key: your-key" \
-F "ontology_key=my_domain_ontology" \
-F "ontology_file=@my_ontology.owl" \
-F "description=Domain ontology for entity extraction"
| Parameter | Type | Required | Description |
|---|
ontology_key | string | yes | User-defined identifier for this ontology |
ontology_file | file | yes | Ontology file (OWL format) |
description | string | no | Human-readable description |
DELETE /api/v1/ontologies/{ontology_key} — Delete an uploaded ontology by key.
curl -X DELETE https://your-tenant.aws.cognee.ai/api/v1/ontologies/my_domain_ontology \
-H "X-Api-Key: your-key"
{"status": "success", "ontology_key": "my_domain_ontology"}
| Status code | Meaning |
|---|
200 | Ontology deleted successfully |
400 | ontology_key not found |
500 | Internal error (filesystem) |
Ontologies are optional. Without one, Cognee uses its default extraction pipeline. Define an ontology when you need domain-specific entity types or relationships.
LLM helpers
The tenant exposes two LLM helper endpoints that power the Graph Model editor and the Prompt editor in the UI. They are also usable directly with an API key.
POST /api/v1/llm/infer-schema — Propose a JSON schema (entity types and relationships) from sample text and/or uploaded files. The request is multipart/form-data:
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/llm/infer-schema \
-H "X-Api-Key: your-key" \
-F "text=Einstein developed general relativity in 1915." \
-F "data=@sample.pdf" \
-F 'parameters={"temperature":0.1}'
| Form field | Type | Required | Description |
|---|
text | string | one of text/data | Inline sample text to analyze |
data | file(s) | one of text/data | One or more sample files (uses the same loader engine as ingestion) |
parameters | JSON string | no | Forwarded to the LLM. Allowed keys: temperature, max_tokens, top_p, seed |
{ "graphSchema": { ... } }, validated against the graph model utility before being returned. The endpoint returns 400 when neither text nor data is supplied, 422 when the LLM output is not valid JSON, and 500 for other inference errors.
POST /api/v1/llm/custom-prompt — Generate a starter cognify prompt for a given graph model. The request is JSON:
curl -X POST https://your-tenant.aws.cognee.ai/api/v1/llm/custom-prompt \
-H "X-Api-Key: your-key" \
-H "Content-Type: application/json" \
-d '{"graph_model": { ... }, "parameters": {}}'
{ "customPrompt": "..." }. The same parameters keys (temperature, max_tokens, top_p, seed) are accepted.
Both endpoints live on the tenant API (the per-tenant service URL shown on the API Keys page), not the platform management API. 
