fa7aa38b8f
<!-- .github/pull_request_template.md --> ## Description <!-- Please provide a clear, human-generated description of the changes in this PR. DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning. --> As per COG-3050: 1. Remove insights search type and clean up any orphaned code 2. Replace callsites with default search type - `GRAPH_COMPLETION` - where applicable ## Type of Change <!-- Please check the relevant option --> - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [x] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) <!-- Add screenshots or videos to help explain your changes --> ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## 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.
113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
import os
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import shutil
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import cognee
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import pathlib
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from cognee.infrastructure.files.storage import get_storage_config
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.search.types import SearchType
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from cognee.modules.search.operations import get_history
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from cognee.modules.users.methods import get_default_user
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logger = get_logger()
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async def main():
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# Clean up test directories before starting
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data_directory_path = str(
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pathlib.Path(
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os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_remote_kuzu")
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).resolve()
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)
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cognee_directory_path = str(
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pathlib.Path(
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os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_remote_kuzu")
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).resolve()
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)
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try:
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# Set Kuzu as the graph database provider
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cognee.config.set_graph_database_provider("kuzu")
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cognee.config.data_root_directory(data_directory_path)
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cognee.config.system_root_directory(cognee_directory_path)
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# Configure remote Kuzu database using environment variables
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os.environ["KUZU_HOST"] = os.getenv("KUZU_HOST", "localhost")
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os.environ["KUZU_PORT"] = os.getenv("KUZU_PORT", "8000")
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os.environ["KUZU_USERNAME"] = os.getenv("KUZU_USERNAME", "kuzu")
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os.environ["KUZU_PASSWORD"] = os.getenv("KUZU_PASSWORD", "kuzu")
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os.environ["KUZU_DATABASE"] = os.getenv("KUZU_DATABASE", "cognee_test")
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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dataset_name = "cs_explanations"
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explanation_file_path_nlp = os.path.join(
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pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt"
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)
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await cognee.add([explanation_file_path_nlp], dataset_name)
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explanation_file_path_quantum = os.path.join(
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pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt"
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)
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await cognee.add([explanation_file_path_quantum], dataset_name)
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await cognee.cognify([dataset_name])
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from cognee.infrastructure.databases.vector import get_vector_engine
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vector_engine = get_vector_engine()
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random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
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random_node_name = random_node.payload["text"]
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search_results = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
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)
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assert len(search_results) != 0, "The search results list is empty."
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print("\n\nExtracted sentences are:\n")
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for result in search_results:
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print(f"{result}\n")
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search_results = await cognee.search(
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query_type=SearchType.CHUNKS, query_text=random_node_name
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)
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assert len(search_results) != 0, "The search results list is empty."
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print("\n\nExtracted chunks are:\n")
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for result in search_results:
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print(f"{result}\n")
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search_results = await cognee.search(
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query_type=SearchType.SUMMARIES, query_text=random_node_name
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)
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assert len(search_results) != 0, "Query related summaries don't exist."
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print("\nExtracted summaries are:\n")
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for result in search_results:
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print(f"{result}\n")
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user = await get_default_user()
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history = await get_history(user.id)
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assert len(history) == 6, "Search history is not correct."
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await cognee.prune.prune_data()
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data_root_directory = get_storage_config()["data_root_directory"]
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assert not os.path.isdir(data_root_directory), "Local data files are not deleted"
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await cognee.prune.prune_system(metadata=True)
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from cognee.infrastructure.databases.graph import get_graph_engine
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graph_engine = await get_graph_engine()
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nodes, edges = await graph_engine.get_graph_data()
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assert len(nodes) == 0 and len(edges) == 0, "Remote Kuzu graph database is not empty"
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finally:
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# Ensure cleanup even if tests fail
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for path in [data_directory_path, cognee_directory_path]:
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if os.path.exists(path):
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shutil.rmtree(path)
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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