73db1a5a53
<!-- .github/pull_request_template.md --> ## Description Introducing scructlog. ## 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
76 lines
3.5 KiB
Python
76 lines
3.5 KiB
Python
import asyncio
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import cognee
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from cognee.shared.logging_utils import get_logger, ERROR
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from cognee.api.v1.search import SearchType
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# Prerequisites:
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# 1. Copy `.env.template` and rename it to `.env`.
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# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
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# LLM_API_KEY = "your_key_here"
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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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print("Adding text to cognee:")
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print(text.strip())
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# Add the text, and make it available for cognify
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await cognee.add(text)
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print("Text added successfully.\n")
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print("Running cognify to create knowledge graph...\n")
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print("Cognify process steps:")
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print("1. Classifying the document: Determining the type and category of the input text.")
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print(
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"2. Checking permissions: Ensuring the user has the necessary rights to process the text."
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)
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print(
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"3. Extracting text chunks: Breaking down the text into sentences or phrases for analysis."
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)
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print("4. Adding data points: Storing the extracted chunks for processing.")
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print(
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"5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph."
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)
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print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
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# Use LLMs and cognee to create knowledge graph
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await cognee.cognify()
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print("Cognify process complete.\n")
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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(query_type=SearchType.INSIGHTS, query_text=query_text)
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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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# Example output:
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# ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
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# (...)
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# It represents nodes and relationships in the knowledge graph:
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# - The first element is the source node (e.g., 'natural language processing').
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# - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
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# - The third element is the target node (e.g., 'computer science').
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if __name__ == "__main__":
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logger = get_logger(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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