48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import os
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import asyncio
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import pathlib
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import cognee
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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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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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# cognee knowledge graph will be created based on the text
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# and description of these files
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mp3_file_path = os.path.join(
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pathlib.Path(__file__).parent.parent.parent,
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".data/multimedia/text_to_speech.mp3",
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)
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png_file_path = os.path.join(
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pathlib.Path(__file__).parent.parent.parent,
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".data/multimedia/example.png",
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)
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# Add the files, and make it available for cognify
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await cognee.add([mp3_file_path, png_file_path])
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# Use LLMs and cognee to create knowledge graph
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await cognee.cognify()
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# Query cognee for summaries of the data in the multimedia files
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search_results = await cognee.search(
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SearchType.SUMMARIES,
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query_text="What is in the multimedia files?",
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)
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# Display search results
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for result_text in search_results:
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print(result_text)
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if __name__ == "__main__":
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asyncio.run(main())
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