​

cognee.update()

async def update(
    data_id: UUID,
    data: Union[BinaryIO, list[BinaryIO], str, list[str]],
    dataset_id: UUID,
    user: User = None,
    node_set: Optional[List[str]] = None,
    vector_db_config: dict = None,
    graph_db_config: dict = None,
    preferred_loaders: dict[str, dict[str, Any]] = None,
    incremental_loading: bool = True,
) -> Union[Dict[str, PipelineRunInfo], List[PipelineRunInfo]]

​

Description

• Text strings: Direct text content (str) - any string not starting with ”/” or “file://”
• File paths: Local file paths as strings in these formats:
  • Absolute paths: “/path/to/document.pdf”
  • File URLs: “file:///path/to/document.pdf” or “file://relative/path.txt”
  • S3 paths: “s3://bucket-name/path/to/file.pdf”
• Binary file objects: File handles/streams (BinaryIO)
• Lists: Multiple files or text strings in a single call

• Text files (.txt, .md, .csv)
• PDFs (.pdf)
• Images (.png, .jpg, .jpeg) - extracted via OCR/vision models
• Audio files (.mp3, .wav) - transcribed to text
• Code files (.py, .js, .ts, etc.) - parsed for structure and content
• Office documents (.docx, .pptx) Workflow:

1. Data Resolution: Resolves file paths and validates accessibility
2. Content Extraction: Extracts text content from various file formats
3. Dataset Storage: Stores processed content in the specified dataset
4. Metadata Tracking: Records file metadata, timestamps, and user permissions
5. Permission Assignment: Grants user read/write/delete/share permissions on dataset

• Single text string: “Your text content here”
• Absolute file path: “/path/to/document.pdf”
• File URL: “file:///absolute/path/to/document.pdf” or “file://relative/path.txt”
• S3 path: “s3://my-bucket/documents/file.pdf”
• List of mixed types: [“text content”, “/path/file.pdf”, “file://doc.txt”, file_handle]
• Binary file object: open(“file.txt”, “rb”) dataset_name: Name of the dataset to store data in. Defaults to “main_dataset”. Create separate datasets to organize different knowledge domains. user: User object for authentication and permissions. Uses default user if None. Default user: “default_user@example.com” (created automatically on first use). Users can only access datasets they have permissions for. node_set: Optional list of node identifiers for graph organization and access control. Used for grouping related data points in the knowledge graph. vector_db_config: Optional configuration for vector database (for custom setups). graph_db_config: Optional configuration for graph database (for custom setups). dataset_id: Optional specific dataset UUID to use instead of dataset_name.

• Pipeline run ID for tracking
• Dataset ID where data was stored
• Processing status and any errors
• Execution timestamps and metadata

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Parameters

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Returns

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How it works

1. Delete — removes the old data item’s graph nodes, edges, and vector embeddings. Entities that are also referenced by other documents in the same dataset are preserved (shared nodes are not deleted).
2. Add — ingests the new version of the data into the dataset.
3. Cognify — re-runs the knowledge graph construction pipeline on the dataset, extracting entities and relationships from the updated content.

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Examples

import cognee

# Update a data item with new content
await cognee.update(
    data_id=uuid_of_item,
    data="Updated content for this document.",
    dataset_id=uuid_of_dataset,
)

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Further details

# Force re-processing of every document in the dataset
await cognee.update(
    data_id=uuid_of_item,
    data="Updated content.",
    dataset_id=uuid_of_dataset,
    incremental_loading=False,
)

import cognee
from cognee import SearchType

# Query the graph after updating
results = await cognee.search(
    "What entities are related to the updated topic?",
    query_type=SearchType.GRAPH_COMPLETION,
)
print(results)