100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
from uuid import UUID
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from typing import Union, BinaryIO, List, Optional
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from cognee.modules.users.models import User
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from cognee.api.v1.delete import delete
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from cognee.api.v1.add import add
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from cognee.api.v1.cognify import cognify
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async def update(
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data_id: UUID,
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data: Union[BinaryIO, list[BinaryIO], str, list[str]],
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dataset_id: UUID,
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user: User = None,
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node_set: Optional[List[str]] = None,
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vector_db_config: dict = None,
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graph_db_config: dict = None,
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preferred_loaders: List[str] = None,
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incremental_loading: bool = True,
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):
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"""
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Update existing data in Cognee.
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Supported Input Types:
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- **Text strings**: Direct text content (str) - any string not starting with "/" or "file://"
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- **File paths**: Local file paths as strings in these formats:
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* Absolute paths: "/path/to/document.pdf"
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* File URLs: "file:///path/to/document.pdf" or "file://relative/path.txt"
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* S3 paths: "s3://bucket-name/path/to/file.pdf"
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- **Binary file objects**: File handles/streams (BinaryIO)
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- **Lists**: Multiple files or text strings in a single call
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Supported File Formats:
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- Text files (.txt, .md, .csv)
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- PDFs (.pdf)
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- Images (.png, .jpg, .jpeg) - extracted via OCR/vision models
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- Audio files (.mp3, .wav) - transcribed to text
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- Code files (.py, .js, .ts, etc.) - parsed for structure and content
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- Office documents (.docx, .pptx)
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Workflow:
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1. **Data Resolution**: Resolves file paths and validates accessibility
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2. **Content Extraction**: Extracts text content from various file formats
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3. **Dataset Storage**: Stores processed content in the specified dataset
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4. **Metadata Tracking**: Records file metadata, timestamps, and user permissions
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5. **Permission Assignment**: Grants user read/write/delete/share permissions on dataset
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Args:
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data_id: UUID of existing data to update
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data: The latest version of the data. Can be:
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- Single text string: "Your text content here"
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- Absolute file path: "/path/to/document.pdf"
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- File URL: "file:///absolute/path/to/document.pdf" or "file://relative/path.txt"
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- S3 path: "s3://my-bucket/documents/file.pdf"
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- List of mixed types: ["text content", "/path/file.pdf", "file://doc.txt", file_handle]
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- Binary file object: open("file.txt", "rb")
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dataset_name: Name of the dataset to store data in. Defaults to "main_dataset".
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Create separate datasets to organize different knowledge domains.
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user: User object for authentication and permissions. Uses default user if None.
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Default user: "default_user@example.com" (created automatically on first use).
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Users can only access datasets they have permissions for.
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node_set: Optional list of node identifiers for graph organization and access control.
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Used for grouping related data points in the knowledge graph.
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vector_db_config: Optional configuration for vector database (for custom setups).
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graph_db_config: Optional configuration for graph database (for custom setups).
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dataset_id: Optional specific dataset UUID to use instead of dataset_name.
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Returns:
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PipelineRunInfo: Information about the ingestion pipeline execution including:
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- Pipeline run ID for tracking
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- Dataset ID where data was stored
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- Processing status and any errors
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- Execution timestamps and metadata
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"""
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await delete(
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data_id=data_id,
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dataset_id=dataset_id,
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user=user,
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)
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await add(
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data=data,
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dataset_id=dataset_id,
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user=user,
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node_set=node_set,
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vector_db_config=vector_db_config,
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graph_db_config=graph_db_config,
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preferred_loaders=preferred_loaders,
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incremental_loading=incremental_loading,
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)
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cognify_run = await cognify(
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datasets=[dataset_id],
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user=user,
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vector_db_config=vector_db_config,
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graph_db_config=graph_db_config,
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incremental_loading=incremental_loading,
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)
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return cognify_run
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