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60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
import asyncio
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import cognee
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import os
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async def main():
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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
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file_path_artificial = os.path.join(
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current_directory, "test_data", "artificial-intelligence.pdf"
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)
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file_path_png = os.path.join(current_directory, "test_data", "example_copy.png")
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file_path_pptx = os.path.join(current_directory, "test_data", "example.pptx")
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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# Import necessary converter, and convert file to DoclingDocument format
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from docling.document_converter import DocumentConverter
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converter = DocumentConverter()
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# Test ingestion of DoclingDocument with various file types
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result = converter.convert(file_path_artificial)
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await cognee.add(result.document)
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result = converter.convert(file_path_png)
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await cognee.add(result.document)
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# Test docling loader
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await cognee.add(file_path_pptx)
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await cognee.cognify()
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answer = await cognee.search("Tell me about Artificial Intelligence.")
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assert len(answer) != 0
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answer = await cognee.search("Do programmers change light bulbs?")
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assert len(answer) != 0
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lowercase_answer = answer[0]["search_result"][0].lower()
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assert ("no" in lowercase_answer) or ("none" in lowercase_answer)
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answer = await cognee.search("What colours are there in the presentation table?")
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assert len(answer) != 0
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lowercase_answer = answer[0]["search_result"][0].lower()
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assert (
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("red" in lowercase_answer)
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and ("blue" in lowercase_answer)
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and ("green" in lowercase_answer)
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)
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if __name__ == "__main__":
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asyncio.run(main())
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