wehub-resource-sync
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
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""" This Example shows a packaged 'document_summarizer' prompt using the slim-summary-tool. It shows a variety of
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techniques to summarize documents generally larger than a LLM context window, and how to assemble multiple source
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batches from the document, as well as using a 'query' and 'topic' to focus on specific segments of the document. """
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import os
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from llmware.prompts import Prompt
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from llmware.setup import Setup
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def test_summarize_document(example="jd salinger"):
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# pull a sample document (or substitute a file_path and file_name of your own)
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sample_files_path = Setup().load_sample_files(over_write=False)
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topic = None
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query = None
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fp = None
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fn = None
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if example not in ["jd salinger", "employment terms", "just the comp", "un resolutions"]:
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print ("not found example")
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return []
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if example == "jd salinger":
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fp = os.path.join(sample_files_path, "SmallLibrary")
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fn = "Jd-Salinger-Biography.docx"
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topic = "jd salinger"
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query = None
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if example == "employment terms":
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fp = os.path.join(sample_files_path, "Agreements")
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fn = "Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
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topic = "executive compensation terms"
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query = None
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if example == "just the comp":
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fp = os.path.join(sample_files_path, "Agreements")
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fn = "Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
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topic = "executive compensation terms"
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query = "base salary"
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if example == "un resolutions":
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fp = os.path.join(sample_files_path, "SmallLibrary")
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fn = "N2126108.pdf"
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# fn = "N2137825.pdf"
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topic = "key points"
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query = None
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# optional parameters: 'query' - will select among blocks with the query term
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# 'topic' - will pass a topic/issue as the parameter to the model to 'focus' the summary
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# 'max_batch_cap' - caps the number of batches sent to the model
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# 'text_only' - returns just the summary text aggregated
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kp = Prompt().summarize_document_fc(fp, fn, topic=topic, query=query, text_only=True, max_batch_cap=15)
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print(f"\nDocument summary completed - {len(kp)} Points")
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for i, points in enumerate(kp):
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print(i, points)
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return 0
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if __name__ == "__main__":
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print(f"\nExample: Summarize Documents\n")
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# 4 examples - ["jd salinger", "employment terms", "just the comp", "un resolutions"]
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# -- "jd salinger" - summarizes key points about jd salinger from short biography document
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# -- "employment terms" - summarizes the executive compensation terms across 15 page document
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# -- "just the comp" - queries to find subset of document and then summarizes the key terms
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# -- "un resolutions" - summarizes the un resolutions document
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summary_direct = test_summarize_document(example="employment terms")
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