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.
207 lines
7.2 KiB
Python
207 lines
7.2 KiB
Python
import os
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import pathlib
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import cognee
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from cognee.shared.logging_utils import get_logger
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from cognee.infrastructure.files.storage import get_storage_config
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from cognee.modules.data.models import Data
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from cognee.modules.users.methods import get_default_user
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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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logger = get_logger()
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async def test_local_file_deletion(data_text, file_location):
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from sqlalchemy import select
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import hashlib
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from cognee.infrastructure.databases.relational import get_relational_engine
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engine = get_relational_engine()
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async with engine.get_async_session() as session:
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# Get hash of data contents
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encoded_text = data_text.encode("utf-8")
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data_hash = hashlib.md5(encoded_text).hexdigest()
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# Get data entry from database based on hash contents
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data = (await session.scalars(select(Data).where(Data.content_hash == data_hash))).one()
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assert os.path.isfile(data.raw_data_location.replace("file://", "")), (
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f"Data location doesn't exist: {data.raw_data_location}"
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)
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# Test deletion of data along with local files created by cognee
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await engine.delete_data_entity(data.id)
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assert not os.path.exists(data.raw_data_location.replace("file://", "")), (
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f"Data location still exists after deletion: {data.raw_data_location}"
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)
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async with engine.get_async_session() as session:
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# Get data entry from database based on file path
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data = (
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await session.scalars(select(Data).where(Data.raw_data_location == file_location))
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).one()
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assert os.path.isfile(data.raw_data_location.replace("file://", "")), (
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f"Data location doesn't exist: {data.raw_data_location}"
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)
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# Test local files not created by cognee won't get deleted
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await engine.delete_data_entity(data.id)
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assert os.path.exists(data.raw_data_location.replace("file://", "")), (
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f"Data location doesn't exists: {data.raw_data_location}"
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)
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async def test_getting_of_documents(dataset_name_1):
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# Test getting of documents for search per dataset
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from cognee.modules.users.permissions.methods import get_document_ids_for_user
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user = await get_default_user()
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document_ids = await get_document_ids_for_user(user.id, [dataset_name_1])
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assert len(document_ids) == 1, (
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f"Number of expected documents doesn't match {len(document_ids)} != 1"
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)
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# Test getting of documents for search when no dataset is provided
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user = await get_default_user()
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document_ids = await get_document_ids_for_user(user.id)
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assert len(document_ids) == 2, (
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f"Number of expected documents doesn't match {len(document_ids)} != 2"
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)
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async def test_vector_engine_search_none_limit():
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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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file_path_nlp = os.path.join(
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pathlib.Path(__file__).parent,
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"test_data/Natural_language_processing.txt",
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)
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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 cognee.add(file_path_quantum)
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await cognee.add(file_path_nlp)
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await cognee.cognify()
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query_text = "Tell me about Quantum computers"
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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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collection_name = "Entity_name"
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query_vector = (await vector_engine.embedding_engine.embed_text([query_text]))[0]
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result = await vector_engine.search(
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collection_name=collection_name, query_vector=query_vector, limit=None
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)
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# Check that we did not accidentally use any default value for limit
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# in vector search along the way (like 5, 10, or 15)
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assert len(result) > 15
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async def main():
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cognee.config.set_vector_db_config(
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{
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"vector_db_provider": "lancedb",
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}
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)
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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_lancedb")
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).resolve()
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)
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cognee.config.data_root_directory(data_directory_path)
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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_lancedb")
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).resolve()
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)
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cognee.config.system_root_directory(cognee_directory_path)
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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_1 = "natural_language"
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dataset_name_2 = "quantum"
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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_1)
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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_2)
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await cognee.cognify([dataset_name_2, dataset_name_1])
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from cognee.infrastructure.databases.vector import get_vector_engine
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await test_getting_of_documents(dataset_name_1)
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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, datasets=[dataset_name_2]
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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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graph_completion = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION,
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query_text=random_node_name,
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datasets=[dataset_name_2],
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)
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assert len(graph_completion) != 0, "Completion result is empty."
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print("Completion result is:")
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print(graph_completion)
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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("\n\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) == 8, "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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connection = await vector_engine.get_connection()
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tables_in_database = await connection.table_names()
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assert len(tables_in_database) == 0, "LanceDB database is not empty"
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await test_vector_engine_search_none_limit()
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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