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111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
import cognee
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from cognee import SearchType
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from cognee.modules.engine.operations.setup import setup
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from cognee.modules.users.methods import create_user, get_user
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from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
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from cognee.modules.users.roles.methods import add_user_to_role, create_role
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from cognee.modules.users.tenants.methods import add_user_to_tenant, create_tenant, select_tenant
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from cognee.shared.logging_utils import CRITICAL, get_logger, setup_logging
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logger = get_logger()
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text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena.
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At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages
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this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the
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preparation and manipulation of quantum states.
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"""
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def get_dataset_id(remember_result):
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"""Extract dataset_id from remember output."""
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from uuid import UUID
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return UUID(remember_result.dataset_id)
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async def tenant_and_role_setup_example():
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# NOTE: When a document is remembered in Cognee with permissions enabled only the owner of the document has permissions
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# to work with the document initially.
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# Create user_1 before remembering data under the CogneeLab tenant.
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print("\nCreating user_1: user_1@example.com")
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user_1 = await create_user("user_1@example.com", "example")
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# Users can also be added to Roles and Tenants and then permission can be assigned on a Role/Tenant level as well
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# To create a Role a user first must be an owner of a Tenant
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print("User 1 is creating CogneeLab tenant/organization")
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tenant_id = await create_tenant("CogneeLab", user_1.id)
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print("User 1 is selecting CogneeLab tenant/organization as active tenant")
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await select_tenant(user_id=user_1.id, tenant_id=tenant_id)
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print("\nUser 1 is creating Researcher role")
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role_id = await create_role(role_name="Researcher", owner_id=user_1.id)
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print("\nCreating user_2: user_2@example.com")
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user_2 = await create_user("user_2@example.com", "example")
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# To add a user to a role he must be part of the same tenant/organization
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print("\nOperation started as user_1 to add user_2 to CogneeLab tenant/organization")
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await add_user_to_tenant(user_id=user_2.id, tenant_id=tenant_id, owner_id=user_1.id)
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print(
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"\nOperation started by user_1, as tenant owner, to add user_2 to Researcher role inside the tenant/organization"
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)
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await add_user_to_role(user_id=user_2.id, role_id=role_id, owner_id=user_1.id)
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print("\nOperation as user_2 to select CogneeLab tenant/organization as active tenant")
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await select_tenant(user_id=user_2.id, tenant_id=tenant_id)
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# Note: We need to update user_1 from the database to refresh its tenant context changes
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user_1 = await get_user(user_1.id)
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quantum_cognee_lab_remember_result = await cognee.remember(
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[text],
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dataset_name="QUANTUM_COGNEE_LAB",
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user=user_1,
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self_improvement=False,
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)
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quantum_cognee_lab_dataset_id = get_dataset_id(quantum_cognee_lab_remember_result)
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print(
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"\nOperation started as user_1, with CogneeLab as its active tenant, to give read permission to Researcher role for the dataset QUANTUM owned by the CogneeLab tenant"
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)
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await authorized_give_permission_on_datasets(
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role_id,
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[quantum_cognee_lab_dataset_id],
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"read",
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user_1.id,
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)
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# Now user_2 can read from QUANTUM dataset as part of the Researcher role after proper permissions have been assigned by the QUANTUM dataset owner, user_1.
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print("\nRecall result as user_2 on the QUANTUM dataset owned by the CogneeLab organization:")
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recall_results = await cognee.recall(
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query_type=SearchType.GRAPH_COMPLETION,
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query_text="What is in the document?",
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user=user_2,
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dataset_ids=[quantum_cognee_lab_dataset_id],
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)
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for result in recall_results:
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print(f"{result}\n")
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async def main():
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# Create a clean slate for cognee -- reset data and system state and
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# set up the necessary databases and tables for user management.
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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await setup()
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await tenant_and_role_setup_example()
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# Note: All of these function calls and permission system is available through our backend endpoints as well
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# Please set ENABLE_BACKEND_ACCESS_CONTROL=True in .env file
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# Note: When ENABLE_BACKEND_ACCESS_CONTROL is enabled, vector provider is automatically set to use LanceDB.
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# The default graph provider is Ladybug (can be overridden via GRAPH_DATABASE_PROVIDER env var).
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if __name__ == "__main__":
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import asyncio
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logger = setup_logging(log_level=CRITICAL)
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asyncio.run(main())
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