3850e9c7a1
<!-- .github/pull_request_template.md --> ## Description Notebook and python example for cognee simple example ## 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 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced an interactive demo showcasing asynchronous document processing and querying for key insights from a sample text. - **Documentation** - Added an in-depth, step-by-step guide in a Jupyter Notebook that walks users through setup, configuration, querying, and visualizing processed data. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
34 lines
960 B
Python
34 lines
960 B
Python
import asyncio
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import cognee
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import os
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# By default cognee uses OpenAI's gpt-4o-mini LLM model
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# Provide your OpenAI LLM API KEY
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os.environ["LLM_API_KEY"] = ""
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async def cognee_demo():
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# Get file path to document to process
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from pathlib import Path
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current_directory = Path(__file__).resolve().parent.parent
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file_path = os.path.join(current_directory, "data", "alice_in_wonderland.txt")
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# Call Cognee to process document
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await cognee.add(file_path)
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await cognee.cognify()
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# Query Cognee for information from provided document
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answer = await cognee.search("List me all the important characters in Alice in Wonderland.")
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print(answer)
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answer = await cognee.search("How did Alice end up in Wonderland?")
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print(answer)
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answer = await cognee.search("Tell me about Alice's personality.")
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print(answer)
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# Cognee is an async library, it has to be called in an async context
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asyncio.run(cognee_demo())
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