68 lines
2 KiB
Python
68 lines
2 KiB
Python
import asyncio
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import cognee
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from cognee.shared.logging_utils import setup_logging, ERROR
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from cognee.api.v1.search import SearchType
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async def main():
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# Create a clean slate for cognee -- reset data and system state
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print("Resetting cognee data...")
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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print("Data reset complete.\n")
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# cognee knowledge graph will be created based on this text
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text = """
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Natural language processing (NLP) is an interdisciplinary
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subfield of computer science and information retrieval.
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"""
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from cognee.tasks.ingestion.data_item import DataItem
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test_item = DataItem(text, "test_item")
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# Add the text, and make it available for cognify
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await cognee.add(test_item)
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# Use LLMs and cognee to create knowledge graph
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ret_val = await cognee.cognify()
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query_text = "Tell me about NLP"
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print(f"Searching cognee for insights with query: '{query_text}'")
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# Query cognee for insights on the added text
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search_results = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION, query_text=query_text
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)
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print("Search results:")
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# Display results
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for result_text in search_results:
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print(result_text)
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from cognee.modules.data.methods.get_dataset_data import get_dataset_data
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for pipeline in ret_val.values():
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dataset_id = pipeline.dataset_id
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dataset_data = await get_dataset_data(dataset_id=dataset_id)
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from fastapi.encoders import jsonable_encoder
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data = [
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dict(
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**jsonable_encoder(data),
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dataset_id=dataset_id,
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)
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for data in dataset_data
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]
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# Check if label is properly added and stored
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assert data[0]["label"] == "test_item"
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if __name__ == "__main__":
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logger = setup_logging(log_level=ERROR)
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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loop.run_until_complete(main())
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finally:
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loop.run_until_complete(loop.shutdown_asyncgens())
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