wehub-resource-sync
58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
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"""This example demonstrates the use of LLM function calls to perform document clustering and
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automated classification of different parts of a document. """
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from llmware.parsers import Parser
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from llmware.agents import LLMfx
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from llmware.setup import Setup
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import os
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def document_clustering_example ():
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samples_fp = Setup().load_sample_files(over_write=True)
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agreements_fp = os.path.join(samples_fp, "Agreements")
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agreement_files = os.listdir(agreements_fp)
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if len(agreement_files) == 0:
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print("something went wrong")
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return -1
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# parsing the first file (could be random) found in the os.listdir in the Agreements sample folder
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contract_chunks = Parser().parse_one_pdf(agreements_fp,agreement_files[0])
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# create a LLMfx object
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agent = LLMfx()
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# there are ~65-70 contract_chunks in ~15 page contract - feel free to slice (faster demo), or the whole thing
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agent.load_work(contract_chunks[0:5])
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agent.load_tool_list(["topics","tags", "ner"])
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while True:
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agent.exec_multitool_function_call(["topics", "tags","ner"])
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if not agent.increment_work_iteration():
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break
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agent.show_report()
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agent.activity_summary()
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# uncomment this to see a full view of all of the responses
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"""
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for i, entries in enumerate(agent.response_list):
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print("response_list: ", i, entries)
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"""
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return agent.response_list
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if __name__ == "__main__":
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analysis= document_clustering_example()
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