> ## Documentation Index > Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt > Use this file to discover all available pages before exploring further. # Cognify > Transform datasets into structured knowledge graphs through cognitive processing. This endpoint is the core of Cognee's intelligence layer, responsible for converting raw text, documents, and data added through the add endpoint into semantic knowledge graphs. It performs deep analysis to extract entities, relationships, and insights from ingested content. ## Processing Pipeline 1. Document classification and permission validation 2. Text chunking and semantic segmentation 3. Entity extraction using LLM-powered analysis 4. Relationship detection and graph construction 5. Vector embeddings generation for semantic search 6. Content summarization and indexing ## Request Parameters - **datasets** (Optional[List[str]]): List of dataset names to process. Dataset names are resolved to datasets owned by the authenticated user. - **dataset_ids** (Optional[List[UUID]]): List of existing dataset UUIDs to process. UUIDs allow processing of datasets not owned by the user (if permitted). - **run_in_background** (Optional[bool]): Whether to execute processing asynchronously. Defaults to False (blocking). - **custom_prompt** (Optional[str]): Custom prompt for entity extraction and graph generation. If provided, this prompt will be used instead of the default prompts for knowledge graph extraction. - **ontology_key** (Optional[List[str]]): Reference to one or more previously uploaded ontology files to use for knowledge graph construction. ## Response - **Blocking execution**: Complete pipeline run information with entity counts, processing duration, and success/failure status - **Background execution**: Pipeline run metadata including pipeline_run_id for status monitoring via WebSocket subscription ## Error Codes - **400 Bad Request**: When neither datasets nor dataset_ids are provided, or when specified datasets don't exist - **409 Conflict**: When processing fails due to system errors, missing LLM API keys, database connection failures, or corrupted content ## Example Request ```json { "datasets": ["research_papers", "documentation"], "run_in_background": false, "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections.", "ontology_key": ["medical_ontology_v1"] } ``` ## Notes To cognify data in datasets not owned by the user and for which the current user has write permission, the dataset_id must be used (when ENABLE_BACKEND_ACCESS_CONTROL is set to True). ## Next Steps After successful processing, use the search endpoints to query the generated knowledge graph for insights, relationships, and semantic search. ## OpenAPI ````yaml /cognee_openapi_spec.json post /api/v1/cognify openapi: 3.1.0 info: title: Cognee API description: Cognee API with Bearer token and Cookie auth version: 1.0.0 servers: - url: https://api.cognee.ai description: Production server (full functionality) - url: http://localhost:8000 description: Local development server (requires local setup) security: - BearerAuth: [] - ApiKeyAuth: [] tags: - name: activity description: '' - name: add description: Data ingestion endpoints for adding text, files, and structured data. - name: auth description: >- Authentication endpoints for user registration, login, and token management. - name: checks description: '' - name: cognify description: >- Knowledge processing endpoints to transform raw data into knowledge graphs. - name: configuration description: '' - name: datasets description: Dataset management endpoints for listing, creating, and deleting datasets. - name: delete description: Data deletion endpoints (deprecated — use datasets endpoints instead). - name: forget description: '' - name: health description: '' - name: improve description: '' - name: llm description: '' - name: memify description: '' - name: notebooks description: '' - name: ontologies description: '' - name: permissions description: Permission management for multi-user access control. - name: recall description: '' - name: remember description: '' - name: responses description: Response generation endpoints using the knowledge graph. - name: search description: Search endpoints for querying the knowledge graph. - name: sessions description: '' - name: settings description: Configuration endpoints for managing Cognee settings. - name: sync description: '' - name: update description: '' - name: users description: User management endpoints. - name: visualize description: Graph visualization endpoints. paths: /api/v1/cognify: post: tags: - cognify summary: Cognify description: >- Transform datasets into structured knowledge graphs through cognitive processing. This endpoint is the core of Cognee's intelligence layer, responsible for converting raw text, documents, and data added through the add endpoint into semantic knowledge graphs. It performs deep analysis to extract entities, relationships, and insights from ingested content. ## Processing Pipeline 1. Document classification and permission validation 2. Text chunking and semantic segmentation 3. Entity extraction using LLM-powered analysis 4. Relationship detection and graph construction 5. Vector embeddings generation for semantic search 6. Content summarization and indexing ## Request Parameters - **datasets** (Optional[List[str]]): List of dataset names to process. Dataset names are resolved to datasets owned by the authenticated user. - **dataset_ids** (Optional[List[UUID]]): List of existing dataset UUIDs to process. UUIDs allow processing of datasets not owned by the user (if permitted). - **run_in_background** (Optional[bool]): Whether to execute processing asynchronously. Defaults to False (blocking). - **custom_prompt** (Optional[str]): Custom prompt for entity extraction and graph generation. If provided, this prompt will be used instead of the default prompts for knowledge graph extraction. - **ontology_key** (Optional[List[str]]): Reference to one or more previously uploaded ontology files to use for knowledge graph construction. ## Response - **Blocking execution**: Complete pipeline run information with entity counts, processing duration, and success/failure status - **Background execution**: Pipeline run metadata including pipeline_run_id for status monitoring via WebSocket subscription ## Error Codes - **400 Bad Request**: When neither datasets nor dataset_ids are provided, or when specified datasets don't exist - **409 Conflict**: When processing fails due to system errors, missing LLM API keys, database connection failures, or corrupted content ## Example Request ```json { "datasets": ["research_papers", "documentation"], "run_in_background": false, "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections.", "ontology_key": ["medical_ontology_v1"] } ``` ## Notes To cognify data in datasets not owned by the user and for which the current user has write permission, the dataset_id must be used (when ENABLE_BACKEND_ACCESS_CONTROL is set to True). ## Next Steps After successful processing, use the search endpoints to query the generated knowledge graph for insights, relationships, and semantic search. operationId: cognify_api_v1_cognify_post requestBody: content: application/json: schema: $ref: '#/components/schemas/CognifyPayloadDTO' example: datasets: - main_dataset required: true responses: '200': description: Successful Response content: application/json: schema: additionalProperties: true type: object title: Response Cognify Api V1 Cognify Post '400': description: Bad Request content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '403': content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: Forbidden '422': content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: Unprocessable Entity '500': content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' description: Internal Server Error security: - BearerAuth: [] - ApiKeyAuth: [] components: schemas: CognifyPayloadDTO: properties: datasets: anyOf: - items: type: string type: array - type: 'null' title: Datasets datasetIds: anyOf: - items: type: string format: uuid type: array - type: 'null' title: Datasetids examples: - [] runInBackground: anyOf: - type: boolean - type: 'null' title: Runinbackground default: false graphModel: anyOf: - additionalProperties: true type: object - type: 'null' title: Graphmodel examples: - {} customPrompt: anyOf: - type: string - type: 'null' title: Customprompt description: Custom prompt for entity extraction and graph generation default: '' ontologyKey: anyOf: - items: type: string type: array - type: 'null' title: Ontologykey description: Reference to one or more previously uploaded ontologies examples: - [] chunksPerBatch: anyOf: - type: integer - type: 'null' title: Chunksperbatch description: >- Number of chunks to process per task batch in Cognify (overrides default). examples: - 10 - 20 - 50 - 100 type: object title: CognifyPayloadDTO ErrorResponse: properties: error: type: string title: Error detail: anyOf: - type: string - type: 'null' title: Detail type: object required: - error title: ErrorResponse securitySchemes: BearerAuth: type: http scheme: bearer bearerFormat: JWT ApiKeyAuth: type: apiKey in: header name: X-Api-Key ````