​

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]]

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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

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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,
)