fix: Delete by id draft (#1073)
<!-- .github/pull_request_template.md --> ## Description <!-- Provide a clear description of the changes in this PR --> ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Co-authored-by: Igor Ilic <igorilic03@gmail.com> Co-authored-by: Boris <boris@topoteretes.com> Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
This commit is contained in:
Vasilije
GitHub
parent
c5bd6bed40
commit
d4739bd40c
8 changed files with 1130 additions and 329 deletions
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@ -1,100 +1,91 @@
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import os
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import hashlib
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from uuid import UUID
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from io import BytesIO
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from sqlalchemy import select
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from typing import Union, BinaryIO, List
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from sqlalchemy.sql import delete as sql_delete
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.files.storage import get_file_storage
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from cognee.infrastructure.databases.graph import get_graph_engine
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from cognee.modules.users.models import User
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from cognee.infrastructure.databases.vector import get_vector_engine
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from cognee.infrastructure.databases.relational import get_relational_engine
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from cognee.modules.ingestion import classify
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from cognee.modules.users.models import User
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.data.models import Data, DatasetData, Dataset
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from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
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from cognee.modules.users.methods import get_default_user
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from cognee.modules.data.methods import get_authorized_existing_datasets
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from cognee.context_global_variables import set_database_global_context_variables
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from .exceptions import DocumentNotFoundError, DatasetNotFoundError, DocumentSubgraphNotFoundError
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logger = get_logger()
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def get_text_content_hash(text: str) -> str:
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encoded_text = text.encode("utf-8")
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return hashlib.md5(encoded_text).hexdigest()
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async def delete(
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data: Union[BinaryIO, List[BinaryIO], str, List[str]],
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dataset_name: str = "main_dataset",
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dataset_id: UUID = None,
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data_id: UUID,
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dataset_id: UUID,
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mode: str = "soft",
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user: User = None,
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):
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"""Delete a document and all its related nodes from both relational and graph databases.
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"""Delete data by its ID from the specified dataset.
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Args:
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data: The data to delete (file, URL, or text)
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dataset_name: Name of the dataset to delete from
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data_id: The UUID of the data to delete
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dataset_id: The UUID of the dataset containing the data
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mode: "soft" (default) or "hard" - hard mode also deletes degree-one entity nodes
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user: User doing the operation, if none default user will be used.
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"""
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Returns:
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Dict with deletion results
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Raises:
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DocumentNotFoundError: If data is not found
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DatasetNotFoundError: If dataset is not found
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PermissionDeniedError: If user doesn't have delete permission on dataset
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"""
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if user is None:
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user = await get_default_user()
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# Verify user has permission to work with given dataset. If dataset_id is given use it, if not use dataset_name
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dataset = await get_authorized_existing_datasets(
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[dataset_id] if dataset_id else [dataset_name], "delete", user
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)
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# Verify user has delete permission on the dataset
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dataset_list = await get_authorized_existing_datasets([dataset_id], "delete", user)
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if not dataset_list:
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raise DatasetNotFoundError(f"Dataset not found or access denied: {dataset_id}")
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dataset = dataset_list[0]
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# Will only be used if ENABLE_BACKEND_ACCESS_CONTROL is set to True
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await set_database_global_context_variables(dataset[0].id, dataset[0].owner_id)
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# Handle different input types
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if isinstance(data, str):
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if data.startswith("file://") or data.startswith("/"): # It's a file path
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full_file_path = data.replace("file://", "")
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await set_database_global_context_variables(dataset.id, dataset.owner_id)
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file_dir = os.path.dirname(full_file_path)
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file_path = os.path.basename(full_file_path)
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# Get the data record and verify it exists and belongs to the dataset
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db_engine = get_relational_engine()
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async with db_engine.get_async_session() as session:
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# Check if data exists
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data_point = (
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await session.execute(select(Data).filter(Data.id == data_id))
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).scalar_one_or_none()
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file_storage = get_file_storage(file_dir)
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if data_point is None:
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raise DocumentNotFoundError(f"Data not found with ID: {data_id}")
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async with file_storage.open(file_path, mode="rb") as file:
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classified_data = classify(file)
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content_hash = classified_data.get_metadata()["content_hash"]
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return await delete_single_document(content_hash, dataset[0].id, mode)
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elif data.startswith("http"): # It's a URL
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import requests
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# Check if data belongs to the specified dataset
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dataset_data_link = (
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await session.execute(
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select(DatasetData).filter(
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DatasetData.data_id == data_id, DatasetData.dataset_id == dataset_id
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)
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)
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).scalar_one_or_none()
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response = requests.get(data)
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response.raise_for_status()
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file_data = BytesIO(response.content)
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classified_data = classify(file_data)
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content_hash = classified_data.get_metadata()["content_hash"]
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return await delete_single_document(content_hash, dataset[0].id, mode)
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else: # It's a text string
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content_hash = get_text_content_hash(data)
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classified_data = classify(data)
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return await delete_single_document(content_hash, dataset[0].id, mode)
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elif isinstance(data, list):
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# Handle list of inputs sequentially
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results = []
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for item in data:
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result = await delete(item, dataset_name, dataset[0].id, mode, user=user)
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results.append(result)
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return {"status": "success", "message": "Multiple documents deleted", "results": results}
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else: # It's already a BinaryIO
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data.seek(0) # Ensure we're at the start of the file
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classified_data = classify(data)
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content_hash = classified_data.get_metadata()["content_hash"]
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return await delete_single_document(content_hash, dataset[0].id, mode)
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if dataset_data_link is None:
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raise DocumentNotFoundError(f"Data {data_id} not found in dataset {dataset_id}")
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# Get the content hash for deletion
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content_hash = data_point.content_hash
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# Use the existing comprehensive deletion logic
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return await delete_single_document(content_hash, dataset.id, mode)
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async def delete_single_document(content_hash: str, dataset_id: UUID = None, mode: str = "soft"):
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@ -1,12 +1,8 @@
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import os
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from fastapi import Form, UploadFile, Depends
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from fastapi import Depends
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from fastapi.responses import JSONResponse
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from fastapi import APIRouter
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from typing import List
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from uuid import UUID
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import subprocess
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from cognee.shared.logging_utils import get_logger
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import requests
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from cognee.modules.users.models import User
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from cognee.modules.users.methods import get_authenticated_user
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@ -18,91 +14,36 @@ def get_delete_router() -> APIRouter:
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@router.delete("", response_model=None)
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async def delete(
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data: List[UploadFile],
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dataset_name: str = Form("main_dataset"),
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dataset_id: UUID = None,
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mode: str = Form("soft"),
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data_id: UUID,
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dataset_id: UUID,
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mode: str = "soft",
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user: User = Depends(get_authenticated_user),
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):
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"""
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Delete data from the knowledge graph.
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"""Delete data by its ID from the specified dataset.
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This endpoint removes specified data from the knowledge graph. It supports
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both soft deletion (preserving related entities) and hard deletion (removing
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degree-one entity nodes as well).
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Args:
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data_id: The UUID of the data to delete
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dataset_id: The UUID of the dataset containing the data
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mode: "soft" (default) or "hard" - hard mode also deletes degree-one entity nodes
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user: Authenticated user
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## Request Parameters
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- **data** (List[UploadFile]): The data to delete (files, URLs, or text)
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- **dataset_name** (str): Name of the dataset to delete from (default: "main_dataset")
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- **dataset_id** (UUID): UUID of the dataset to delete from
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- **mode** (str): Deletion mode - "soft" (default) or "hard"
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Returns:
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JSON response indicating success or failure
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## Response
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No content returned on successful deletion.
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## Error Codes
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- **409 Conflict**: Error during deletion process
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- **403 Forbidden**: User doesn't have permission to delete from dataset
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## Notes
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- **Soft mode**: Preserves related entities and relationships
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- **Hard mode**: Also deletes degree-one entity nodes
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"""
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from cognee.api.v1.delete import delete as cognee_delete
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try:
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# Handle each file in the list
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results = []
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for file in data:
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if file.filename.startswith("http") and (
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os.getenv("ALLOW_HTTP_REQUESTS", "true").lower() == "true"
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):
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if "github" in file.filename:
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# For GitHub repos, we need to get the content hash of each file
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repo_name = file.filename.split("/")[-1].replace(".git", "")
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subprocess.run(
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["git", "clone", file.filename, f".data/{repo_name}"], check=True
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)
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# Note: This would need to be implemented to get content hashes of all files
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# For now, we'll just return an error
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return JSONResponse(
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status_code=400,
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content={"error": "Deleting GitHub repositories is not yet supported"},
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)
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else:
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# Fetch and delete the data from other types of URL
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response = requests.get(file.filename)
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response.raise_for_status()
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file_data = response.content
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result = await cognee_delete(
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file_data,
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dataset_name=dataset_name,
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dataset_id=dataset_id,
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mode=mode,
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user=user,
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)
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results.append(result)
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else:
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# Handle uploaded file by accessing its file attribute
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result = await cognee_delete(
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file.file,
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dataset_name=dataset_name,
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dataset_id=dataset_id,
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mode=mode,
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user=user,
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)
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results.append(result)
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result = await cognee_delete(
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data_id=data_id,
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dataset_id=dataset_id,
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mode=mode,
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user=user,
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)
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return result
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if len(results) == 1:
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return results[0]
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else:
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return {
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"status": "success",
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"message": "Multiple documents deleted",
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"results": results,
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}
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except Exception as error:
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logger.error(f"Error during deletion: {str(error)}")
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logger.error(f"Error during deletion by data_id: {str(error)}")
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return JSONResponse(status_code=409, content={"error": str(error)})
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return router
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@ -5,11 +5,18 @@ import json
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from textwrap import dedent
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from uuid import UUID
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from webbrowser import Error
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from typing import List, Dict, Any, Optional, Tuple, Type, Union
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from falkordb import FalkorDB
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from cognee.exceptions import InvalidValueError
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from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
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from cognee.infrastructure.databases.graph.graph_db_interface import (
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GraphDBInterface,
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record_graph_changes,
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NodeData,
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EdgeData,
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Node,
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)
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from cognee.infrastructure.databases.vector.embeddings import EmbeddingEngine
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from cognee.infrastructure.databases.vector.vector_db_interface import VectorDBInterface
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from cognee.infrastructure.engine import DataPoint
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@ -61,6 +68,12 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
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- delete_nodes
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- delete_graph
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- prune
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- get_node
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- get_nodes
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- get_neighbors
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- get_graph_metrics
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- get_document_subgraph
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- get_degree_one_nodes
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"""
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def __init__(
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@ -158,6 +171,7 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
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return value
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if (
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type(value) is list
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and len(value) > 0
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and type(value[0]) is float
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and len(value) == self.embedding_engine.get_vector_size()
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):
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@ -165,8 +179,19 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
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# if type(value) is datetime:
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# return datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%f%z")
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if type(value) is dict:
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return f"'{json.dumps(value)}'"
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return f"'{value}'"
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return f"'{json.dumps(value).replace(chr(39), chr(34))}'"
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if type(value) is str:
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# Escape single quotes and handle special characters
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escaped_value = (
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str(value)
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.replace("'", "\\'")
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.replace('"', '\\"')
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.replace("\n", "\\n")
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.replace("\r", "\\r")
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.replace("\t", "\\t")
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)
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return f"'{escaped_value}'"
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return f"'{str(value)}'"
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return ",".join([f"{key}:{parse_value(value)}" for key, value in properties.items()])
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@ -185,35 +210,75 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
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Returns:
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--------
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A string containing the query to be executed for the data point.
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A tuple containing the query string and parameters dictionary.
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"""
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node_label = type(data_point).__name__
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property_names = DataPoint.get_embeddable_property_names(data_point)
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node_properties = await self.stringify_properties(
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{
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**data_point.model_dump(),
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**(
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{
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property_names[index]: (
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vectorized_values[index]
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if index < len(vectorized_values)
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else getattr(data_point, property_name, None)
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)
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for index, property_name in enumerate(property_names)
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}
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),
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}
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)
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properties = {
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**data_point.model_dump(),
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**(
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{
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property_names[index]: (
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vectorized_values[index]
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if index < len(vectorized_values)
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else getattr(data_point, property_name, None)
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)
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for index, property_name in enumerate(property_names)
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}
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),
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}
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return dedent(
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# Clean the properties - remove None values and handle special types
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clean_properties = {}
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for key, value in properties.items():
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if value is not None:
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if isinstance(value, UUID):
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clean_properties[key] = str(value)
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elif isinstance(value, dict):
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clean_properties[key] = json.dumps(value)
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elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float):
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# This is likely a vector - convert to string representation
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clean_properties[key] = f"vecf32({value})"
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else:
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clean_properties[key] = value
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query = dedent(
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f"""
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MERGE (node:{node_label} {{id: '{str(data_point.id)}'}})
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ON CREATE SET node += ({{{node_properties}}}), node.updated_at = timestamp()
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ON MATCH SET node += ({{{node_properties}}}), node.updated_at = timestamp()
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MERGE (node:{node_label} {{id: $node_id}})
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SET node += $properties, node.updated_at = timestamp()
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"""
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).strip()
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params = {"node_id": str(data_point.id), "properties": clean_properties}
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return query, params
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def sanitize_relationship_name(self, relationship_name: str) -> str:
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"""
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Sanitize relationship name to be valid for Cypher queries.
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Parameters:
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-----------
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- relationship_name (str): The original relationship name
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Returns:
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--------
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- str: A sanitized relationship name valid for Cypher
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"""
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# Replace hyphens, spaces, and other special characters with underscores
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import re
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sanitized = re.sub(r"[^\w]", "_", relationship_name)
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# Remove consecutive underscores
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sanitized = re.sub(r"_+", "_", sanitized)
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# Remove leading/trailing underscores
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sanitized = sanitized.strip("_")
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# Ensure it starts with a letter or underscore
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if sanitized and not sanitized[0].isalpha() and sanitized[0] != "_":
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sanitized = "_" + sanitized
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return sanitized or "RELATIONSHIP"
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async def create_edge_query(self, edge: tuple[str, str, str, dict]) -> str:
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"""
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Generate a query to create or update an edge between two nodes in the graph.
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|
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@ -229,14 +294,19 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
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- str: A string containing the query to be executed for creating the edge.
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"""
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properties = await self.stringify_properties(edge[3])
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# Sanitize the relationship name for Cypher compatibility
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sanitized_relationship = self.sanitize_relationship_name(edge[2])
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# Add the original relationship name to properties
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edge_properties = {**edge[3], "relationship_name": edge[2]}
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properties = await self.stringify_properties(edge_properties)
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properties = f"{{{properties}}}"
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return dedent(
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f"""
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MERGE (source {{id:'{edge[0]}'}})
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MERGE (target {{id: '{edge[1]}'}})
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MERGE (source)-[edge:{edge[2]} {properties}]->(target)
|
||||
MERGE (source)-[edge:{sanitized_relationship} {properties}]->(target)
|
||||
ON MATCH SET edge.updated_at = timestamp()
|
||||
ON CREATE SET edge.updated_at = timestamp()
|
||||
"""
|
||||
|
|
@ -302,21 +372,16 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
|
||||
vectorized_values = await self.embed_data(embeddable_values)
|
||||
|
||||
queries = [
|
||||
await self.create_data_point_query(
|
||||
data_point,
|
||||
[
|
||||
vectorized_values[vector_map[str(data_point.id)][property_name]]
|
||||
if vector_map[str(data_point.id)][property_name] is not None
|
||||
else None
|
||||
for property_name in DataPoint.get_embeddable_property_names(data_point)
|
||||
],
|
||||
)
|
||||
for data_point in data_points
|
||||
]
|
||||
for data_point in data_points:
|
||||
vectorized_data = [
|
||||
vectorized_values[vector_map[str(data_point.id)][property_name]]
|
||||
if vector_map[str(data_point.id)][property_name] is not None
|
||||
else None
|
||||
for property_name in DataPoint.get_embeddable_property_names(data_point)
|
||||
]
|
||||
|
||||
for query in queries:
|
||||
self.query(query)
|
||||
query, params = await self.create_data_point_query(data_point, vectorized_data)
|
||||
self.query(query, params)
|
||||
|
||||
async def create_vector_index(self, index_name: str, index_property_name: str):
|
||||
"""
|
||||
|
|
@ -383,7 +448,37 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
"""
|
||||
pass
|
||||
|
||||
async def add_node(self, node: DataPoint):
|
||||
async def add_node(self, node_id: str, properties: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Add a single node with specified properties to the graph.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_id (str): Unique identifier for the node being added.
|
||||
- properties (Dict[str, Any]): A dictionary of properties associated with the node.
|
||||
"""
|
||||
# Clean the properties - remove None values and handle special types
|
||||
clean_properties = {"id": node_id}
|
||||
for key, value in properties.items():
|
||||
if value is not None:
|
||||
if isinstance(value, UUID):
|
||||
clean_properties[key] = str(value)
|
||||
elif isinstance(value, dict):
|
||||
clean_properties[key] = json.dumps(value)
|
||||
elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float):
|
||||
# This is likely a vector - convert to string representation
|
||||
clean_properties[key] = f"vecf32({value})"
|
||||
else:
|
||||
clean_properties[key] = value
|
||||
|
||||
query = "MERGE (node {id: $node_id}) SET node += $properties, node.updated_at = timestamp()"
|
||||
params = {"node_id": node_id, "properties": clean_properties}
|
||||
|
||||
self.query(query, params)
|
||||
|
||||
# Helper methods for DataPoint compatibility
|
||||
async def add_data_point_node(self, node: DataPoint):
|
||||
"""
|
||||
Add a single data point as a node in the graph.
|
||||
|
||||
|
|
@ -394,7 +489,7 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
"""
|
||||
await self.create_data_points([node])
|
||||
|
||||
async def add_nodes(self, nodes: list[DataPoint]):
|
||||
async def add_data_point_nodes(self, nodes: list[DataPoint]):
|
||||
"""
|
||||
Add multiple data points as nodes in the graph.
|
||||
|
||||
|
|
@ -405,34 +500,75 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
"""
|
||||
await self.create_data_points(nodes)
|
||||
|
||||
async def add_edge(self, edge: tuple[str, str, str, dict]):
|
||||
@record_graph_changes
|
||||
async def add_nodes(self, nodes: Union[List[Node], List[DataPoint]]) -> None:
|
||||
"""
|
||||
Add an edge between two existing nodes in the graph based on the provided details.
|
||||
Add multiple nodes to the graph in a single operation.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- edge (tuple[str, str, str, dict]): A tuple containing details of the edge to be
|
||||
added.
|
||||
- nodes (Union[List[Node], List[DataPoint]]): A list of Node tuples or DataPoint objects to be added to the graph.
|
||||
"""
|
||||
query = await self.create_edge_query(edge)
|
||||
for node in nodes:
|
||||
if isinstance(node, tuple) and len(node) == 2:
|
||||
# Node is in (node_id, properties) format
|
||||
node_id, properties = node
|
||||
await self.add_node(node_id, properties)
|
||||
elif hasattr(node, "id") and hasattr(node, "model_dump"):
|
||||
# Node is a DataPoint object
|
||||
await self.add_node(str(node.id), node.model_dump())
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Invalid node format: {node}. Expected tuple (node_id, properties) or DataPoint object."
|
||||
)
|
||||
|
||||
async def add_edge(
|
||||
self,
|
||||
source_id: str,
|
||||
target_id: str,
|
||||
relationship_name: str,
|
||||
properties: Optional[Dict[str, Any]] = None,
|
||||
) -> None:
|
||||
"""
|
||||
Create a new edge between two nodes in the graph.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- source_id (str): The unique identifier of the source node.
|
||||
- target_id (str): The unique identifier of the target node.
|
||||
- relationship_name (str): The name of the relationship to be established by the
|
||||
edge.
|
||||
- properties (Optional[Dict[str, Any]]): Optional dictionary of properties
|
||||
associated with the edge. (default None)
|
||||
"""
|
||||
if properties is None:
|
||||
properties = {}
|
||||
|
||||
edge_tuple = (source_id, target_id, relationship_name, properties)
|
||||
query = await self.create_edge_query(edge_tuple)
|
||||
self.query(query)
|
||||
|
||||
async def add_edges(self, edges: list[tuple[str, str, str, dict]]):
|
||||
@record_graph_changes
|
||||
async def add_edges(self, edges: List[EdgeData]) -> None:
|
||||
"""
|
||||
Add multiple edges to the graph in a batch operation.
|
||||
Add multiple edges to the graph in a single operation.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- edges (list[tuple[str, str, str, dict]]): A list of tuples, each containing
|
||||
details of the edges to be added.
|
||||
- edges (List[EdgeData]): A list of EdgeData objects representing edges to be added.
|
||||
"""
|
||||
queries = [await self.create_edge_query(edge) for edge in edges]
|
||||
|
||||
for query in queries:
|
||||
self.query(query)
|
||||
for edge in edges:
|
||||
if isinstance(edge, tuple) and len(edge) == 4:
|
||||
# Edge is in (source_id, target_id, relationship_name, properties) format
|
||||
source_id, target_id, relationship_name, properties = edge
|
||||
await self.add_edge(source_id, target_id, relationship_name, properties)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Invalid edge format: {edge}. Expected tuple (source_id, target_id, relationship_name, properties)."
|
||||
)
|
||||
|
||||
async def has_edges(self, edges):
|
||||
"""
|
||||
|
|
@ -446,31 +582,14 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
Returns:
|
||||
--------
|
||||
|
||||
Returns a list of boolean values indicating the existence of each edge.
|
||||
Returns a list of edge tuples that exist in the graph.
|
||||
"""
|
||||
query = dedent(
|
||||
"""
|
||||
UNWIND $edges AS edge
|
||||
MATCH (a)-[r]->(b)
|
||||
WHERE id(a) = edge.from_node AND id(b) = edge.to_node AND type(r) = edge.relationship_name
|
||||
RETURN edge.from_node AS from_node, edge.to_node AS to_node, edge.relationship_name AS relationship_name, count(r) > 0 AS edge_exists
|
||||
"""
|
||||
).strip()
|
||||
|
||||
params = {
|
||||
"edges": [
|
||||
{
|
||||
"from_node": str(edge[0]),
|
||||
"to_node": str(edge[1]),
|
||||
"relationship_name": edge[2],
|
||||
}
|
||||
for edge in edges
|
||||
],
|
||||
}
|
||||
|
||||
results = self.query(query, params).result_set
|
||||
|
||||
return [result["edge_exists"] for result in results]
|
||||
existing_edges = []
|
||||
for edge in edges:
|
||||
exists = await self.has_edge(str(edge[0]), str(edge[1]), edge[2])
|
||||
if exists:
|
||||
existing_edges.append(edge)
|
||||
return existing_edges
|
||||
|
||||
async def retrieve(self, data_point_ids: list[UUID]):
|
||||
"""
|
||||
|
|
@ -607,22 +726,38 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
if query_text and not query_vector:
|
||||
query_vector = (await self.embed_data([query_text]))[0]
|
||||
|
||||
[label, attribute_name] = collection_name.split(".")
|
||||
# For FalkorDB, let's do a simple property-based search instead of vector search for now
|
||||
# since the vector index might not be set up correctly
|
||||
if "." in collection_name:
|
||||
[label, attribute_name] = collection_name.split(".")
|
||||
else:
|
||||
# If no dot, treat the whole thing as a property search
|
||||
label = ""
|
||||
attribute_name = collection_name
|
||||
|
||||
query = dedent(
|
||||
f"""
|
||||
CALL db.idx.vector.queryNodes(
|
||||
'{label}',
|
||||
'{attribute_name}',
|
||||
{limit},
|
||||
vecf32({query_vector})
|
||||
) YIELD node, score
|
||||
"""
|
||||
).strip()
|
||||
# Simple text-based search if we have query_text
|
||||
if query_text:
|
||||
if label:
|
||||
query = f"""
|
||||
MATCH (n:{label})
|
||||
WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text)
|
||||
RETURN n, 1.0 as score
|
||||
LIMIT $limit
|
||||
"""
|
||||
else:
|
||||
query = f"""
|
||||
MATCH (n)
|
||||
WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text)
|
||||
RETURN n, 1.0 as score
|
||||
LIMIT $limit
|
||||
"""
|
||||
|
||||
result = self.query(query)
|
||||
|
||||
return result.result_set
|
||||
params = {"query_text": query_text, "limit": limit}
|
||||
result = self.query(query, params)
|
||||
return result.result_set
|
||||
else:
|
||||
# For vector search, return empty for now since vector indexing needs proper setup
|
||||
return []
|
||||
|
||||
async def batch_search(
|
||||
self,
|
||||
|
|
@ -726,37 +861,29 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
},
|
||||
)
|
||||
|
||||
async def delete_node(self, collection_name: str, data_point_id: str):
|
||||
async def delete_node(self, node_id: str) -> None:
|
||||
"""
|
||||
Delete a single node specified by its data point ID from the database.
|
||||
Delete a specified node from the graph by its ID.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- collection_name (str): The name of the collection containing the node to be
|
||||
deleted.
|
||||
- data_point_id (str): The ID of the data point to delete.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
||||
Returns the result of the deletion operation from the database.
|
||||
- node_id (str): Unique identifier for the node to delete.
|
||||
"""
|
||||
return await self.delete_data_points([data_point_id])
|
||||
query = f"MATCH (node {{id: '{node_id}'}}) DETACH DELETE node"
|
||||
self.query(query)
|
||||
|
||||
async def delete_nodes(self, collection_name: str, data_point_ids: list[str]):
|
||||
async def delete_nodes(self, node_ids: List[str]) -> None:
|
||||
"""
|
||||
Delete multiple nodes specified by their IDs from the database.
|
||||
Delete multiple nodes from the graph by their identifiers.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- collection_name (str): The name of the collection containing the nodes to be
|
||||
deleted.
|
||||
- data_point_ids (list[str]): A list of IDs of the data points to delete from the
|
||||
collection.
|
||||
- node_ids (List[str]): A list of unique identifiers for the nodes to delete.
|
||||
"""
|
||||
self.delete_data_points(data_point_ids)
|
||||
for node_id in node_ids:
|
||||
await self.delete_node(node_id)
|
||||
|
||||
async def delete_graph(self):
|
||||
"""
|
||||
|
|
@ -774,6 +901,325 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface):
|
|||
except Exception as e:
|
||||
print(f"Error deleting graph: {e}")
|
||||
|
||||
async def get_node(self, node_id: str) -> Optional[NodeData]:
|
||||
"""
|
||||
Retrieve a single node from the graph using its ID.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_id (str): Unique identifier of the node to retrieve.
|
||||
"""
|
||||
result = self.query(
|
||||
"MATCH (node) WHERE node.id = $node_id RETURN node",
|
||||
{"node_id": node_id},
|
||||
)
|
||||
|
||||
if result.result_set and len(result.result_set) > 0:
|
||||
# FalkorDB returns node objects as first element in the result list
|
||||
return result.result_set[0][0].properties
|
||||
return None
|
||||
|
||||
async def get_nodes(self, node_ids: List[str]) -> List[NodeData]:
|
||||
"""
|
||||
Retrieve multiple nodes from the graph using their IDs.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_ids (List[str]): A list of unique identifiers for the nodes to retrieve.
|
||||
"""
|
||||
result = self.query(
|
||||
"MATCH (node) WHERE node.id IN $node_ids RETURN node",
|
||||
{"node_ids": node_ids},
|
||||
)
|
||||
|
||||
nodes = []
|
||||
if result.result_set:
|
||||
for record in result.result_set:
|
||||
# FalkorDB returns node objects as first element in each record
|
||||
nodes.append(record[0].properties)
|
||||
return nodes
|
||||
|
||||
async def get_neighbors(self, node_id: str) -> List[NodeData]:
|
||||
"""
|
||||
Get all neighboring nodes connected to the specified node.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_id (str): Unique identifier of the node for which to retrieve neighbors.
|
||||
"""
|
||||
result = self.query(
|
||||
"MATCH (node)-[]-(neighbor) WHERE node.id = $node_id RETURN DISTINCT neighbor",
|
||||
{"node_id": node_id},
|
||||
)
|
||||
|
||||
neighbors = []
|
||||
if result.result_set:
|
||||
for record in result.result_set:
|
||||
# FalkorDB returns neighbor objects as first element in each record
|
||||
neighbors.append(record[0].properties)
|
||||
return neighbors
|
||||
|
||||
async def get_edges(self, node_id: str) -> List[EdgeData]:
|
||||
"""
|
||||
Retrieve all edges that are connected to the specified node.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_id (str): Unique identifier of the node whose edges are to be retrieved.
|
||||
"""
|
||||
result = self.query(
|
||||
"""
|
||||
MATCH (n)-[r]-(m)
|
||||
WHERE n.id = $node_id
|
||||
RETURN n.id AS source_id, m.id AS target_id, type(r) AS relationship_name, properties(r) AS properties
|
||||
""",
|
||||
{"node_id": node_id},
|
||||
)
|
||||
|
||||
edges = []
|
||||
if result.result_set:
|
||||
for record in result.result_set:
|
||||
# FalkorDB returns values by index: source_id, target_id, relationship_name, properties
|
||||
edges.append(
|
||||
(
|
||||
record[0], # source_id
|
||||
record[1], # target_id
|
||||
record[2], # relationship_name
|
||||
record[3], # properties
|
||||
)
|
||||
)
|
||||
return edges
|
||||
|
||||
async def has_edge(self, source_id: str, target_id: str, relationship_name: str) -> bool:
|
||||
"""
|
||||
Verify if an edge exists between two specified nodes.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- source_id (str): Unique identifier of the source node.
|
||||
- target_id (str): Unique identifier of the target node.
|
||||
- relationship_name (str): Name of the relationship to verify.
|
||||
"""
|
||||
# Check both the sanitized relationship type and the original name in properties
|
||||
sanitized_relationship = self.sanitize_relationship_name(relationship_name)
|
||||
|
||||
result = self.query(
|
||||
f"""
|
||||
MATCH (source)-[r:{sanitized_relationship}]->(target)
|
||||
WHERE source.id = $source_id AND target.id = $target_id
|
||||
AND (r.relationship_name = $relationship_name OR NOT EXISTS(r.relationship_name))
|
||||
RETURN COUNT(r) > 0 AS edge_exists
|
||||
""",
|
||||
{
|
||||
"source_id": source_id,
|
||||
"target_id": target_id,
|
||||
"relationship_name": relationship_name,
|
||||
},
|
||||
)
|
||||
|
||||
if result.result_set and len(result.result_set) > 0:
|
||||
# FalkorDB returns scalar results as a list, access by index instead of key
|
||||
return result.result_set[0][0]
|
||||
return False
|
||||
|
||||
async def get_graph_metrics(self, include_optional: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Fetch metrics and statistics of the graph, possibly including optional details.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- include_optional (bool): Flag indicating whether to include optional metrics or
|
||||
not. (default False)
|
||||
"""
|
||||
# Get basic node and edge counts
|
||||
node_result = self.query("MATCH (n) RETURN count(n) AS node_count")
|
||||
edge_result = self.query("MATCH ()-[r]->() RETURN count(r) AS edge_count")
|
||||
|
||||
# FalkorDB returns scalar results as a list, access by index instead of key
|
||||
num_nodes = node_result.result_set[0][0] if node_result.result_set else 0
|
||||
num_edges = edge_result.result_set[0][0] if edge_result.result_set else 0
|
||||
|
||||
metrics = {
|
||||
"num_nodes": num_nodes,
|
||||
"num_edges": num_edges,
|
||||
"mean_degree": (2 * num_edges) / num_nodes if num_nodes > 0 else 0,
|
||||
"edge_density": num_edges / (num_nodes * (num_nodes - 1)) if num_nodes > 1 else 0,
|
||||
"num_connected_components": 1, # Simplified for now
|
||||
"sizes_of_connected_components": [num_nodes] if num_nodes > 0 else [],
|
||||
}
|
||||
|
||||
if include_optional:
|
||||
# Add optional metrics - simplified implementation
|
||||
metrics.update(
|
||||
{
|
||||
"num_selfloops": 0, # Simplified
|
||||
"diameter": -1, # Not implemented
|
||||
"avg_shortest_path_length": -1, # Not implemented
|
||||
"avg_clustering": -1, # Not implemented
|
||||
}
|
||||
)
|
||||
else:
|
||||
metrics.update(
|
||||
{
|
||||
"num_selfloops": -1,
|
||||
"diameter": -1,
|
||||
"avg_shortest_path_length": -1,
|
||||
"avg_clustering": -1,
|
||||
}
|
||||
)
|
||||
|
||||
return metrics
|
||||
|
||||
async def get_document_subgraph(self, content_hash: str):
|
||||
"""
|
||||
Get a subgraph related to a specific document by content hash.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- content_hash (str): The content hash of the document to find.
|
||||
"""
|
||||
query = """
|
||||
MATCH (d) WHERE d.id CONTAINS $content_hash
|
||||
OPTIONAL MATCH (d)<-[:CHUNK_OF]-(c)
|
||||
OPTIONAL MATCH (c)-[:HAS_ENTITY]->(e)
|
||||
OPTIONAL MATCH (e)-[:IS_INSTANCE_OF]->(et)
|
||||
RETURN d AS document,
|
||||
COLLECT(DISTINCT c) AS chunks,
|
||||
COLLECT(DISTINCT e) AS orphan_entities,
|
||||
COLLECT(DISTINCT c) AS made_from_nodes,
|
||||
COLLECT(DISTINCT et) AS orphan_types
|
||||
"""
|
||||
|
||||
result = self.query(query, {"content_hash": f"text_{content_hash}"})
|
||||
|
||||
if not result.result_set or not result.result_set[0]:
|
||||
return None
|
||||
|
||||
# Convert result to dictionary format
|
||||
# FalkorDB returns values by index: document, chunks, orphan_entities, made_from_nodes, orphan_types
|
||||
record = result.result_set[0]
|
||||
return {
|
||||
"document": record[0],
|
||||
"chunks": record[1],
|
||||
"orphan_entities": record[2],
|
||||
"made_from_nodes": record[3],
|
||||
"orphan_types": record[4],
|
||||
}
|
||||
|
||||
async def get_degree_one_nodes(self, node_type: str):
|
||||
"""
|
||||
Get all nodes that have only one connection.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_type (str): The type of nodes to filter by, must be 'Entity' or 'EntityType'.
|
||||
"""
|
||||
if not node_type or node_type not in ["Entity", "EntityType"]:
|
||||
raise ValueError("node_type must be either 'Entity' or 'EntityType'")
|
||||
|
||||
result = self.query(
|
||||
f"""
|
||||
MATCH (n:{node_type})
|
||||
WITH n, COUNT {{ MATCH (n)--() }} as degree
|
||||
WHERE degree = 1
|
||||
RETURN n
|
||||
"""
|
||||
)
|
||||
|
||||
# FalkorDB returns node objects as first element in each record
|
||||
return [record[0] for record in result.result_set] if result.result_set else []
|
||||
|
||||
async def get_nodeset_subgraph(
|
||||
self, node_type: Type[Any], node_name: List[str]
|
||||
) -> Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]]:
|
||||
"""
|
||||
Fetch a subgraph consisting of a specific set of nodes and their relationships.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- node_type (Type[Any]): The type of nodes to include in the subgraph.
|
||||
- node_name (List[str]): A list of names of the nodes to include in the subgraph.
|
||||
"""
|
||||
label = node_type.__name__
|
||||
|
||||
# Find primary nodes of the specified type and names
|
||||
primary_query = f"""
|
||||
UNWIND $names AS wantedName
|
||||
MATCH (n:{label})
|
||||
WHERE n.name = wantedName
|
||||
RETURN DISTINCT n.id, properties(n) AS properties
|
||||
"""
|
||||
|
||||
primary_result = self.query(primary_query, {"names": node_name})
|
||||
if not primary_result.result_set:
|
||||
return [], []
|
||||
|
||||
# FalkorDB returns values by index: id, properties
|
||||
primary_ids = [record[0] for record in primary_result.result_set]
|
||||
|
||||
# Find neighbors of primary nodes
|
||||
neighbor_query = """
|
||||
MATCH (n)-[]-(neighbor)
|
||||
WHERE n.id IN $ids
|
||||
RETURN DISTINCT neighbor.id, properties(neighbor) AS properties
|
||||
"""
|
||||
|
||||
neighbor_result = self.query(neighbor_query, {"ids": primary_ids})
|
||||
# FalkorDB returns values by index: id, properties
|
||||
neighbor_ids = (
|
||||
[record[0] for record in neighbor_result.result_set]
|
||||
if neighbor_result.result_set
|
||||
else []
|
||||
)
|
||||
|
||||
all_ids = list(set(primary_ids + neighbor_ids))
|
||||
|
||||
# Get all nodes in the subgraph
|
||||
nodes_query = """
|
||||
MATCH (n)
|
||||
WHERE n.id IN $ids
|
||||
RETURN n.id, properties(n) AS properties
|
||||
"""
|
||||
|
||||
nodes_result = self.query(nodes_query, {"ids": all_ids})
|
||||
nodes = []
|
||||
if nodes_result.result_set:
|
||||
for record in nodes_result.result_set:
|
||||
# FalkorDB returns values by index: id, properties
|
||||
nodes.append((record[0], record[1]))
|
||||
|
||||
# Get edges between these nodes
|
||||
edges_query = """
|
||||
MATCH (a)-[r]->(b)
|
||||
WHERE a.id IN $ids AND b.id IN $ids
|
||||
RETURN a.id AS source_id, b.id AS target_id, type(r) AS relationship_name, properties(r) AS properties
|
||||
"""
|
||||
|
||||
edges_result = self.query(edges_query, {"ids": all_ids})
|
||||
edges = []
|
||||
if edges_result.result_set:
|
||||
for record in edges_result.result_set:
|
||||
# FalkorDB returns values by index: source_id, target_id, relationship_name, properties
|
||||
edges.append(
|
||||
(
|
||||
record[0], # source_id
|
||||
record[1], # target_id
|
||||
record[2], # relationship_name
|
||||
record[3], # properties
|
||||
)
|
||||
)
|
||||
|
||||
return nodes, edges
|
||||
|
||||
async def prune(self):
|
||||
"""
|
||||
Prune the graph by deleting the entire graph structure.
|
||||
|
|
|
|||
316
cognee/tests/test_delete_by_id.py
Normal file
316
cognee/tests/test_delete_by_id.py
Normal file
|
|
@ -0,0 +1,316 @@
|
|||
import os
|
||||
import pathlib
|
||||
import cognee
|
||||
from uuid import uuid4
|
||||
from cognee.modules.users.exceptions import PermissionDeniedError
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.users.methods import get_default_user, create_user
|
||||
from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
|
||||
from cognee.modules.data.methods import get_dataset_data, get_datasets_by_name
|
||||
from cognee.api.v1.delete.exceptions import DocumentNotFoundError, DatasetNotFoundError
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
async def main():
|
||||
# Enable permissions feature
|
||||
os.environ["ENABLE_BACKEND_ACCESS_CONTROL"] = "True"
|
||||
|
||||
# Clean up test directories before starting
|
||||
data_directory_path = str(
|
||||
pathlib.Path(
|
||||
os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_delete_by_id")
|
||||
).resolve()
|
||||
)
|
||||
cognee_directory_path = str(
|
||||
pathlib.Path(
|
||||
os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_delete_by_id")
|
||||
).resolve()
|
||||
)
|
||||
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
cognee.config.system_root_directory(cognee_directory_path)
|
||||
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
# Setup database and tables
|
||||
from cognee.modules.engine.operations.setup import setup
|
||||
|
||||
await setup()
|
||||
|
||||
print("🧪 Testing Delete by ID and Dataset Data Endpoints")
|
||||
print("=" * 60)
|
||||
|
||||
# Get the default user first
|
||||
default_user = await get_default_user()
|
||||
|
||||
# Test data
|
||||
text_1 = """
|
||||
Apple Inc. is an American multinational technology company that specializes in consumer electronics,
|
||||
software, and online services. Apple is the world's largest technology company by revenue and,
|
||||
since January 2021, the world's most valuable company.
|
||||
"""
|
||||
|
||||
text_2 = """
|
||||
Microsoft Corporation is an American multinational technology corporation which produces computer software,
|
||||
consumer electronics, personal computers, and related services. Its best known software products are the
|
||||
Microsoft Windows line of operating systems and the Microsoft Office suite.
|
||||
"""
|
||||
|
||||
text_3 = """
|
||||
Google LLC is an American multinational technology company that specializes in Internet-related services and products,
|
||||
which include online advertising technologies, search engine, cloud computing, software, and hardware. Google has been
|
||||
referred to as the most powerful company in the world and one of the world's most valuable brands.
|
||||
"""
|
||||
|
||||
# Test 1: Setup data and datasets
|
||||
print("\n📝 Test 1: Setting up test data and datasets")
|
||||
|
||||
# Add data for default user
|
||||
await cognee.add([text_1], dataset_name="tech_companies_1", user=default_user)
|
||||
|
||||
# Create test user first for the second dataset
|
||||
test_user = await create_user("test_user_delete@gmail.com", "test@example.com")
|
||||
|
||||
# Add data for test user
|
||||
await cognee.add([text_2], dataset_name="tech_companies_2", user=test_user)
|
||||
|
||||
# Create third user for isolation testing
|
||||
isolation_user = await create_user("isolation_user@gmail.com", "isolation@example.com")
|
||||
|
||||
# Add data for isolation user (should remain unaffected by other deletions)
|
||||
await cognee.add([text_3], dataset_name="tech_companies_3", user=isolation_user)
|
||||
|
||||
tst = await cognee.cognify(["tech_companies_1"], user=default_user)
|
||||
tst2 = await cognee.cognify(["tech_companies_2"], user=test_user)
|
||||
tst3 = await cognee.cognify(["tech_companies_3"], user=isolation_user)
|
||||
print("tst", tst)
|
||||
print("tst2", tst2)
|
||||
print("tst3", tst3)
|
||||
|
||||
# Extract dataset_ids from cognify results
|
||||
def extract_dataset_id_from_cognify(cognify_result):
|
||||
"""Extract dataset_id from cognify output dictionary"""
|
||||
for dataset_id, pipeline_result in cognify_result.items():
|
||||
return dataset_id # Return the first (and likely only) dataset_id
|
||||
return None
|
||||
|
||||
# Get dataset IDs from cognify results
|
||||
dataset_id_1 = extract_dataset_id_from_cognify(tst)
|
||||
dataset_id_2 = extract_dataset_id_from_cognify(tst2)
|
||||
dataset_id_3 = extract_dataset_id_from_cognify(tst3)
|
||||
|
||||
print(f"📋 Extracted dataset_id from tst: {dataset_id_1}")
|
||||
print(f"📋 Extracted dataset_id from tst2: {dataset_id_2}")
|
||||
print(f"📋 Extracted dataset_id from tst3: {dataset_id_3}")
|
||||
|
||||
# Get dataset data for deletion testing
|
||||
dataset_data_1 = await get_dataset_data(dataset_id_1)
|
||||
dataset_data_2 = await get_dataset_data(dataset_id_2)
|
||||
dataset_data_3 = await get_dataset_data(dataset_id_3)
|
||||
|
||||
print(f"📊 Dataset 1 contains {len(dataset_data_1)} data items")
|
||||
print(f"📊 Dataset 2 contains {len(dataset_data_2)} data items")
|
||||
print(f"📊 Dataset 3 (isolation) contains {len(dataset_data_3)} data items")
|
||||
|
||||
# Test 2: Get data to delete from the extracted datasets
|
||||
print("\n📝 Test 2: Preparing data for deletion from cognify results")
|
||||
|
||||
# Use the first data item from each dataset for testing
|
||||
data_to_delete_id = dataset_data_1[0].id if dataset_data_1 else None
|
||||
data_to_delete_from_test_user = dataset_data_2[0].id if dataset_data_2 else None
|
||||
|
||||
# Create datasets objects for testing
|
||||
from cognee.modules.data.models import Dataset
|
||||
|
||||
default_dataset = Dataset(id=dataset_id_1, name="tech_companies_1", owner_id=default_user.id)
|
||||
|
||||
# Create dataset object for permission testing (test_user already created above)
|
||||
test_dataset = Dataset(id=dataset_id_2, name="tech_companies_2", owner_id=test_user.id)
|
||||
|
||||
print(f"🔍 Data to delete ID: {data_to_delete_id}")
|
||||
print(f"🔍 Test user data ID: {data_to_delete_from_test_user}")
|
||||
|
||||
print("\n📝 Test 3: Testing delete endpoint with proper permissions")
|
||||
|
||||
try:
|
||||
result = await cognee.delete(data_id=data_to_delete_id, dataset_id=default_dataset.id)
|
||||
print("✅ Delete successful for data owner")
|
||||
assert result["status"] == "success", "Delete should succeed for data owner"
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error in delete test: {e}")
|
||||
raise
|
||||
|
||||
# Test 4: Test delete without permissions (should fail)
|
||||
print("\n📝 Test 4: Testing delete endpoint without permissions")
|
||||
|
||||
delete_permission_error = False
|
||||
try:
|
||||
await cognee.delete(
|
||||
data_id=data_to_delete_from_test_user,
|
||||
dataset_id=test_dataset.id,
|
||||
user=default_user, # Wrong user - should fail
|
||||
)
|
||||
except (PermissionDeniedError, DatasetNotFoundError):
|
||||
delete_permission_error = True
|
||||
print("✅ Delete correctly denied for user without permission")
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error type: {e}")
|
||||
|
||||
assert delete_permission_error, "Delete should fail for user without permission"
|
||||
|
||||
# Test 5: Test delete with non-existent data_id
|
||||
print("\n📝 Test 5: Testing delete endpoint with non-existent data_id")
|
||||
|
||||
non_existent_data_id = uuid4()
|
||||
data_not_found_error = False
|
||||
try:
|
||||
await cognee.delete(
|
||||
data_id=non_existent_data_id, dataset_id=default_dataset.id, user=default_user
|
||||
)
|
||||
except DocumentNotFoundError:
|
||||
data_not_found_error = True
|
||||
print("✅ Delete correctly failed for non-existent data_id")
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error type: {e}")
|
||||
|
||||
assert data_not_found_error, "Delete should fail for non-existent data_id"
|
||||
|
||||
# Test 6: Test delete with non-existent dataset_id
|
||||
print("\n📝 Test 6: Testing delete endpoint with non-existent dataset_id")
|
||||
|
||||
non_existent_dataset_id = uuid4()
|
||||
dataset_not_found_error = False
|
||||
try:
|
||||
await cognee.delete(
|
||||
data_id=data_to_delete_from_test_user,
|
||||
dataset_id=non_existent_dataset_id,
|
||||
user=test_user,
|
||||
)
|
||||
except (DatasetNotFoundError, PermissionDeniedError):
|
||||
dataset_not_found_error = True
|
||||
print("✅ Delete correctly failed for non-existent dataset_id")
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error type: {e}")
|
||||
|
||||
assert dataset_not_found_error, "Delete should fail for non-existent dataset_id"
|
||||
|
||||
# Test 7: Test delete with data that doesn't belong to the dataset
|
||||
print("\n📝 Test 7: Testing delete endpoint with data not in specified dataset")
|
||||
|
||||
# Add more data to create a scenario where data exists but not in the specified dataset
|
||||
await cognee.add([text_1], dataset_name="another_dataset", user=default_user)
|
||||
await cognee.cognify(["another_dataset"], user=default_user)
|
||||
|
||||
another_datasets = await get_datasets_by_name(["another_dataset"], default_user.id)
|
||||
another_dataset = another_datasets[0]
|
||||
|
||||
data_not_in_dataset_error = False
|
||||
try:
|
||||
# Try to delete data from test_user's dataset using default_user's data_id
|
||||
await cognee.delete(
|
||||
data_id=data_to_delete_from_test_user, # This data belongs to test_user's dataset
|
||||
dataset_id=another_dataset.id, # But we're specifying default_user's other dataset
|
||||
user=default_user,
|
||||
)
|
||||
except DocumentNotFoundError:
|
||||
data_not_in_dataset_error = True
|
||||
print("✅ Delete correctly failed for data not in specified dataset")
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error type: {e}")
|
||||
|
||||
assert data_not_in_dataset_error, "Delete should fail when data doesn't belong to dataset"
|
||||
|
||||
# Test 8: Test permission granting and delete
|
||||
print("\n📝 Test 8: Testing delete after granting permissions")
|
||||
|
||||
# Give default_user delete permission on test_user's dataset
|
||||
await authorized_give_permission_on_datasets(
|
||||
default_user.id,
|
||||
[test_dataset.id],
|
||||
"delete",
|
||||
test_user.id,
|
||||
)
|
||||
|
||||
try:
|
||||
result = await cognee.delete(
|
||||
data_id=data_to_delete_from_test_user,
|
||||
dataset_id=test_dataset.id,
|
||||
user=default_user, # Now should work with granted permission
|
||||
)
|
||||
print("✅ Delete successful after granting permission", result)
|
||||
assert result["status"] == "success", "Delete should succeed after granting permission"
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error after granting permission: {e}")
|
||||
raise
|
||||
|
||||
# Test 9: Verify graph database cleanup
|
||||
print("\n📝 Test 9: Verifying comprehensive deletion (graph, vector, relational)")
|
||||
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
|
||||
graph_engine = await get_graph_engine()
|
||||
nodes, edges = await graph_engine.get_graph_data()
|
||||
|
||||
# We should still have some nodes/edges from the remaining data, but fewer than before
|
||||
print(f"✅ Graph database state after deletions - Nodes: {len(nodes)}, Edges: {len(edges)}")
|
||||
|
||||
# Test 10: Verify isolation user's data remains untouched
|
||||
print("\n📝 Test 10: Verifying isolation user's data remains intact")
|
||||
|
||||
try:
|
||||
# Get isolation user's data after all deletions
|
||||
isolation_dataset_data_after = await get_dataset_data(dataset_id_3)
|
||||
|
||||
print(
|
||||
f"📊 Isolation user's dataset still contains {len(isolation_dataset_data_after)} data items"
|
||||
)
|
||||
|
||||
# Verify data count is unchanged
|
||||
assert len(isolation_dataset_data_after) == len(dataset_data_3), (
|
||||
f"Isolation user's data count changed! Expected {len(dataset_data_3)}, got {len(isolation_dataset_data_after)}"
|
||||
)
|
||||
|
||||
# Verify specific data items are still there
|
||||
original_data_ids = {str(data.id) for data in dataset_data_3}
|
||||
remaining_data_ids = {str(data.id) for data in isolation_dataset_data_after}
|
||||
|
||||
assert original_data_ids == remaining_data_ids, "Isolation user's data IDs have changed!"
|
||||
|
||||
# Try to search isolation user's data to ensure it's still accessible
|
||||
isolation_search_results = await cognee.search(
|
||||
"Google technology company", user=isolation_user
|
||||
)
|
||||
assert len(isolation_search_results) > 0, "Isolation user's data should still be searchable"
|
||||
|
||||
print("✅ Isolation user's data completely unaffected by other users' deletions")
|
||||
print(f" - Data count unchanged: {len(isolation_dataset_data_after)} items")
|
||||
print(" - All original data IDs preserved")
|
||||
print(f" - Data still searchable: {len(isolation_search_results)} results")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error verifying isolation user's data: {e}")
|
||||
raise
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🎉 All tests passed! Delete by ID endpoint working correctly.")
|
||||
print("=" * 60)
|
||||
|
||||
print("""
|
||||
📋 SUMMARY OF TESTED FUNCTIONALITY:
|
||||
✅ Delete endpoint accepts data_id and dataset_id parameters
|
||||
✅ Permission checking works for delete operations
|
||||
✅ Proper error handling for non-existent data/datasets
|
||||
✅ Data ownership validation (data must belong to specified dataset)
|
||||
✅ Permission granting and revocation works correctly
|
||||
✅ Comprehensive deletion across all databases (graph, vector, relational)
|
||||
✅ Dataset data endpoint now checks read permissions properly
|
||||
✅ Data isolation: Other users' data remains completely unaffected by deletions
|
||||
""")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -3,6 +3,7 @@ import shutil
|
|||
import cognee
|
||||
import pathlib
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.data.methods import get_dataset_data
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
|
@ -49,7 +50,9 @@ async def main():
|
|||
Each of these companies has significantly impacted the technology landscape, driving innovation and transforming everyday life through their groundbreaking products and services.
|
||||
"""
|
||||
|
||||
await cognee.add([text_1, text_2])
|
||||
# Add documents and get dataset information
|
||||
add_result = await cognee.add([text_1, text_2])
|
||||
dataset_id = add_result.dataset_id
|
||||
|
||||
await cognee.cognify()
|
||||
|
||||
|
|
@ -59,7 +62,14 @@ async def main():
|
|||
nodes, edges = await graph_engine.get_graph_data()
|
||||
assert len(nodes) > 10 and len(edges) > 10, "Graph database is not loaded."
|
||||
|
||||
await cognee.delete([text_1, text_2], mode="hard")
|
||||
# Get the data IDs from the dataset
|
||||
dataset_data = await get_dataset_data(dataset_id)
|
||||
assert len(dataset_data) > 0, "Dataset should contain data"
|
||||
|
||||
# Delete each document using its ID
|
||||
for data_item in dataset_data:
|
||||
await cognee.delete(data_item.id, dataset_id, mode="hard")
|
||||
|
||||
nodes, edges = await graph_engine.get_graph_data()
|
||||
|
||||
assert len(nodes) == 0 and len(edges) == 0, "Document is not deleted."
|
||||
|
|
|
|||
|
|
@ -10,7 +10,49 @@ from cognee.modules.search.types import SearchType
|
|||
logger = get_logger()
|
||||
|
||||
|
||||
async def check_falkordb_connection():
|
||||
"""Check if FalkorDB is available at localhost:6379"""
|
||||
try:
|
||||
from falkordb import FalkorDB
|
||||
|
||||
client = FalkorDB(host="localhost", port=6379)
|
||||
# Try to list graphs to check connection
|
||||
client.list_graphs()
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.warning(f"FalkorDB not available at localhost:6379: {e}")
|
||||
return False
|
||||
|
||||
|
||||
async def main():
|
||||
# Check if FalkorDB is available
|
||||
if not await check_falkordb_connection():
|
||||
print("⚠️ FalkorDB is not available at localhost:6379")
|
||||
print(" To run this test, start FalkorDB server:")
|
||||
print(" docker run -p 6379:6379 falkordb/falkordb:latest")
|
||||
print(" Skipping FalkorDB test...")
|
||||
return
|
||||
|
||||
print("✅ FalkorDB connection successful, running test...")
|
||||
|
||||
# Configure FalkorDB as the graph database provider
|
||||
cognee.config.set_graph_db_config(
|
||||
{
|
||||
"graph_database_url": "localhost", # FalkorDB URL (using Redis protocol)
|
||||
"graph_database_port": 6379,
|
||||
"graph_database_provider": "falkordb",
|
||||
}
|
||||
)
|
||||
|
||||
# Configure FalkorDB as the vector database provider too since it's a hybrid adapter
|
||||
cognee.config.set_vector_db_config(
|
||||
{
|
||||
"vector_db_url": "localhost",
|
||||
"vector_db_port": 6379,
|
||||
"vector_db_provider": "falkordb",
|
||||
}
|
||||
)
|
||||
|
||||
data_directory_path = str(
|
||||
pathlib.Path(
|
||||
os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_falkordb")
|
||||
|
|
@ -85,9 +127,25 @@ async def main():
|
|||
# Assert relational, vector and graph databases have been cleaned properly
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
connection = await vector_engine.get_connection()
|
||||
collection_names = await connection.table_names()
|
||||
assert len(collection_names) == 0, "LanceDB vector database is not empty"
|
||||
# For FalkorDB vector engine, check if collections are empty
|
||||
# Since FalkorDB is a hybrid adapter, we can check if the graph is empty
|
||||
# as the vector data is stored in the same graph
|
||||
if hasattr(vector_engine, "driver"):
|
||||
# This is FalkorDB - check if graphs exist
|
||||
collections = vector_engine.driver.list_graphs()
|
||||
# The graph should be deleted, so either no graphs or empty graph
|
||||
if vector_engine.graph_name in collections:
|
||||
# Graph exists but should be empty
|
||||
vector_graph_data = await vector_engine.get_graph_data()
|
||||
vector_nodes, vector_edges = vector_graph_data
|
||||
assert len(vector_nodes) == 0 and len(vector_edges) == 0, (
|
||||
"FalkorDB vector database is not empty"
|
||||
)
|
||||
else:
|
||||
# Fallback for other vector engines like LanceDB
|
||||
connection = await vector_engine.get_connection()
|
||||
collection_names = await connection.table_names()
|
||||
assert len(collection_names) == 0, "Vector database is not empty"
|
||||
|
||||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
|
||||
|
|
@ -95,10 +153,19 @@ async def main():
|
|||
"SQLite relational database is not empty"
|
||||
)
|
||||
|
||||
from cognee.infrastructure.databases.graph import get_graph_config
|
||||
# For FalkorDB, check if the graph database is empty
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
|
||||
graph_config = get_graph_config()
|
||||
assert not os.path.exists(graph_config.graph_file_path), "Networkx graph database is not empty"
|
||||
graph_engine = get_graph_engine()
|
||||
graph_data = await graph_engine.get_graph_data()
|
||||
nodes, edges = graph_data
|
||||
assert len(nodes) == 0 and len(edges) == 0, "FalkorDB graph database is not empty"
|
||||
|
||||
print("🎉 FalkorDB test completed successfully!")
|
||||
print(" ✓ Data ingestion worked")
|
||||
print(" ✓ Cognify processing worked")
|
||||
print(" ✓ Search operations worked")
|
||||
print(" ✓ Cleanup worked")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ from cognee.shared.logging_utils import get_logger
|
|||
from cognee.modules.search.types import SearchType
|
||||
from cognee.modules.users.methods import get_default_user, create_user
|
||||
from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
|
||||
from cognee.modules.data.methods import get_dataset_data
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
|
@ -53,8 +54,20 @@ async def main():
|
|||
test_user = await create_user("user@example.com", "example")
|
||||
await cognee.add([text], dataset_name="QUANTUM", user=test_user)
|
||||
|
||||
await cognee.cognify(["NLP"], user=default_user)
|
||||
await cognee.cognify(["QUANTUM"], user=test_user)
|
||||
nlp_cognify_result = await cognee.cognify(["NLP"], user=default_user)
|
||||
quantum_cognify_result = await cognee.cognify(["QUANTUM"], user=test_user)
|
||||
|
||||
# Extract dataset_ids from cognify results
|
||||
def extract_dataset_id_from_cognify(cognify_result):
|
||||
"""Extract dataset_id from cognify output dictionary"""
|
||||
for dataset_id, pipeline_result in cognify_result.items():
|
||||
return dataset_id # Return the first (and likely only) dataset_id
|
||||
return None
|
||||
|
||||
# Get dataset IDs from cognify results
|
||||
default_user_dataset_id = extract_dataset_id_from_cognify(nlp_cognify_result)
|
||||
print("User is", default_user_dataset_id)
|
||||
test_user_dataset_id = extract_dataset_id_from_cognify(quantum_cognify_result)
|
||||
|
||||
# Check if default_user can only see information from the NLP dataset
|
||||
search_results = await cognee.search(
|
||||
|
|
@ -85,7 +98,6 @@ async def main():
|
|||
)
|
||||
|
||||
# Try to add document with default_user to test_users dataset (test write permission enforcement)
|
||||
test_user_dataset_id = search_results[0]["dataset_id"]
|
||||
add_error = False
|
||||
try:
|
||||
await cognee.add(
|
||||
|
|
@ -176,14 +188,24 @@ async def main():
|
|||
# Try deleting data from test_user dataset with default_user without delete permission
|
||||
delete_error = False
|
||||
try:
|
||||
await cognee.delete([text], dataset_id=test_user_dataset_id, user=default_user)
|
||||
# Get the dataset data to find the ID of the first data item (text)
|
||||
test_user_dataset_data = await get_dataset_data(test_user_dataset_id)
|
||||
text_data_id = test_user_dataset_data[0].id
|
||||
|
||||
await cognee.delete(
|
||||
data_id=text_data_id, dataset_id=test_user_dataset_id, user=default_user
|
||||
)
|
||||
except PermissionDeniedError:
|
||||
delete_error = True
|
||||
|
||||
assert delete_error, "PermissionDeniedError was not raised during delete operation as expected"
|
||||
|
||||
# Try deleting data from test_user dataset with test_user
|
||||
await cognee.delete([text], dataset_id=test_user_dataset_id, user=test_user)
|
||||
# Get the dataset data to find the ID of the first data item (text)
|
||||
test_user_dataset_data = await get_dataset_data(test_user_dataset_id)
|
||||
text_data_id = test_user_dataset_data[0].id
|
||||
|
||||
await cognee.delete(data_id=text_data_id, dataset_id=test_user_dataset_id, user=test_user)
|
||||
|
||||
# Actually give permission to default_user to delete data for test_users dataset
|
||||
await authorized_give_permission_on_datasets(
|
||||
|
|
@ -194,7 +216,13 @@ async def main():
|
|||
)
|
||||
|
||||
# Try deleting data from test_user dataset with default_user after getting delete permission
|
||||
await cognee.delete([explanation_file_path], dataset_id=test_user_dataset_id, user=default_user)
|
||||
# Get the dataset data to find the ID of the remaining data item (explanation_file_path)
|
||||
test_user_dataset_data = await get_dataset_data(test_user_dataset_id)
|
||||
explanation_file_data_id = test_user_dataset_data[0].id
|
||||
|
||||
await cognee.delete(
|
||||
data_id=explanation_file_data_id, dataset_id=test_user_dataset_id, user=default_user
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
136
poetry.lock
generated
136
poetry.lock
generated
|
|
@ -1361,73 +1361,75 @@ python-versions = ">=3.9"
|
|||
groups = ["main"]
|
||||
markers = "extra == \"deepeval\" or extra == \"dev\""
|
||||
files = [
|
||||
{file = "coverage-7.9.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:66283a192a14a3854b2e7f3418d7db05cdf411012ab7ff5db98ff3b181e1f912"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4e01d138540ef34fcf35c1aa24d06c3de2a4cffa349e29a10056544f35cca15f"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f22627c1fe2745ee98d3ab87679ca73a97e75ca75eb5faee48660d060875465f"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4b1c2d8363247b46bd51f393f86c94096e64a1cf6906803fa8d5a9d03784bdbf"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c10c882b114faf82dbd33e876d0cbd5e1d1ebc0d2a74ceef642c6152f3f4d547"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:de3c0378bdf7066c3988d66cd5232d161e933b87103b014ab1b0b4676098fa45"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:1e2f097eae0e5991e7623958a24ced3282676c93c013dde41399ff63e230fcf2"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:28dc1f67e83a14e7079b6cea4d314bc8b24d1aed42d3582ff89c0295f09b181e"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-win32.whl", hash = "sha256:bf7d773da6af9e10dbddacbf4e5cab13d06d0ed93561d44dae0188a42c65be7e"},
|
||||
{file = "coverage-7.9.2-cp310-cp310-win_amd64.whl", hash = "sha256:0c0378ba787681ab1897f7c89b415bd56b0b2d9a47e5a3d8dc0ea55aac118d6c"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a7a56a2964a9687b6aba5b5ced6971af308ef6f79a91043c05dd4ee3ebc3e9ba"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:123d589f32c11d9be7fe2e66d823a236fe759b0096f5db3fb1b75b2fa414a4fa"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:333b2e0ca576a7dbd66e85ab402e35c03b0b22f525eed82681c4b866e2e2653a"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:326802760da234baf9f2f85a39e4a4b5861b94f6c8d95251f699e4f73b1835dc"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19e7be4cfec248df38ce40968c95d3952fbffd57b400d4b9bb580f28179556d2"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0b4a4cb73b9f2b891c1788711408ef9707666501ba23684387277ededab1097c"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:2c8937fa16c8c9fbbd9f118588756e7bcdc7e16a470766a9aef912dd3f117dbd"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:42da2280c4d30c57a9b578bafd1d4494fa6c056d4c419d9689e66d775539be74"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-win32.whl", hash = "sha256:14fa8d3da147f5fdf9d298cacc18791818f3f1a9f542c8958b80c228320e90c6"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:549cab4892fc82004f9739963163fd3aac7a7b0df430669b75b86d293d2df2a7"},
|
||||
{file = "coverage-7.9.2-cp311-cp311-win_arm64.whl", hash = "sha256:c2667a2b913e307f06aa4e5677f01a9746cd08e4b35e14ebcde6420a9ebb4c62"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ae9eb07f1cfacd9cfe8eaee6f4ff4b8a289a668c39c165cd0c8548484920ffc0"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9ce85551f9a1119f02adc46d3014b5ee3f765deac166acf20dbb851ceb79b6f3"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8f6389ac977c5fb322e0e38885fbbf901743f79d47f50db706e7644dcdcb6e1"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ff0d9eae8cdfcd58fe7893b88993723583a6ce4dfbfd9f29e001922544f95615"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fae939811e14e53ed8a9818dad51d434a41ee09df9305663735f2e2d2d7d959b"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:31991156251ec202c798501e0a42bbdf2169dcb0f137b1f5c0f4267f3fc68ef9"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:d0d67963f9cbfc7c7f96d4ac74ed60ecbebd2ea6eeb51887af0f8dce205e545f"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:49b752a2858b10580969ec6af6f090a9a440a64a301ac1528d7ca5f7ed497f4d"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-win32.whl", hash = "sha256:88d7598b8ee130f32f8a43198ee02edd16d7f77692fa056cb779616bbea1b355"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-win_amd64.whl", hash = "sha256:9dfb070f830739ee49d7c83e4941cc767e503e4394fdecb3b54bfdac1d7662c0"},
|
||||
{file = "coverage-7.9.2-cp312-cp312-win_arm64.whl", hash = "sha256:4e2c058aef613e79df00e86b6d42a641c877211384ce5bd07585ed7ba71ab31b"},
|
||||
{file = "coverage-7.9.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:985abe7f242e0d7bba228ab01070fde1d6c8fa12f142e43debe9ed1dde686038"},
|
||||
{file = "coverage-7.9.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:82c3939264a76d44fde7f213924021ed31f55ef28111a19649fec90c0f109e6d"},
|
||||
{file = "coverage-7.9.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae5d563e970dbe04382f736ec214ef48103d1b875967c89d83c6e3f21706d5b3"},
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{file = "coverage-7.9.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:02532fd3290bb8fa6bec876520842428e2a6ed6c27014eca81b031c2d30e3f71"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:56f5eb308b17bca3bbff810f55ee26d51926d9f89ba92707ee41d3c061257e55"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfa447506c1a52271f1b0de3f42ea0fa14676052549095e378d5bff1c505ff7b"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:9ca8e220006966b4a7b68e8984a6aee645a0384b0769e829ba60281fe61ec4f7"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:49f1d0788ba5b7ba65933f3a18864117c6506619f5ca80326b478f72acf3f385"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:68cd53aec6f45b8e4724c0950ce86eacb775c6be01ce6e3669fe4f3a21e768ed"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-win32.whl", hash = "sha256:95335095b6c7b1cc14c3f3f17d5452ce677e8490d101698562b2ffcacc304c8d"},
|
||||
{file = "coverage-7.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:e1b5191d1648acc439b24721caab2fd0c86679d8549ed2c84d5a7ec1bedcc244"},
|
||||
{file = "coverage-7.9.1-pp39.pp310.pp311-none-any.whl", hash = "sha256:db0f04118d1db74db6c9e1cb1898532c7dcc220f1d2718f058601f7c3f499514"},
|
||||
{file = "coverage-7.9.1-py3-none-any.whl", hash = "sha256:66b974b145aa189516b6bf2d8423e888b742517d37872f6ee4c5be0073bd9a3c"},
|
||||
{file = "coverage-7.9.1.tar.gz", hash = "sha256:6cf43c78c4282708a28e466316935ec7489a9c487518a77fa68f716c67909cec"},
|
||||
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue