wehub-resource-sync
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
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""" This example shows an end-to-end processing of Master Services Agreements (MSAs) - including the parsing and
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text chunking of the documents with document filtering to rapidly identify the "MSA" agreements from a large
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batch of contract documents, using queries to extract source materials, using a locally-running GPU to review and
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answer the key questions, with evidence checking, and output for final human review.
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The example uses a quantized 6B parameter model running on a local machine.
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Note: this example tracks the example #6 in the Fast Start.
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"""
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import os
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from llmware.setup import Setup
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from llmware.library import Library
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from llmware.prompts import Prompt, HumanInTheLoop
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from llmware.retrieval import Query
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from llmware.configs import LLMWareConfig
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def msa_processing():
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local_path = Setup().load_sample_files()
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agreements_path = os.path.join(local_path, "AgreementsLarge")
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# create a library with all of the Agreements (~80 contracts)
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msa_lib = Library().create_new_library("msa_lib503_635")
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msa_lib.add_files(agreements_path)
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# find the "master service agreements" (MSA)
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q = Query(msa_lib)
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query = "master services agreement"
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results = q.text_search_by_page(query, page_num=1, results_only=False)
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# results_only = False will return a dictionary with 4 keys: {"query", "results", "doc_ID", "file_source"}
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msa_docs = results["file_source"]
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# load prompt/llm locally
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model_name = "llmware/dragon-yi-6b-gguf"
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prompter = Prompt().load_model(model_name)
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# analyze each MSA - "query" & "llm prompt"
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for i, docs in enumerate(msa_docs):
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print("\n")
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print (i+1, "Reviewing MSA - ", docs)
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# look for the termination provisions in each document
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doc_filter = {"file_source": [docs]}
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termination_provisions = q.text_query_with_document_filter("termination", doc_filter)
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# package the provisions as a source to a prompt
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sources = prompter.add_source_query_results(termination_provisions)
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print("update: sources - ", sources)
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# call the LLM and ask our question
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response = prompter.prompt_with_source("What is the notice for termination for convenience?")
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# post processing fact checking
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stats = prompter.evidence_comparison_stats(response)
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ev_source = prompter.evidence_check_sources(response)
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for i, resp in enumerate(response):
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print("update: llm response - ", resp)
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print("update: compare with evidence- ", stats[i]["comparison_stats"])
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print("update: sources - ", ev_source[i]["source_review"])
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prompter.clear_source_materials()
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# Save jsonl report with full transaction history to /prompt_history folder
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print("\nupdate: prompt state saved at: ", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))
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prompter.save_state()
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# Generate CSV report for easy Human review in Excel
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csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()
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print("\nupdate: csv output for human review - ", csv_output)
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return 0
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if __name__ == "__main__":
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m = msa_processing()
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