wehub-resource-sync
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
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""" This example shows a complex multi-part research analysis. In this example, we will:
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1. Build a "research" library.
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2. Query the research library to identify topics of interest.
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3. Create an agent with several analytical tools: sentiment, emotions, topic, entities analysis
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4. Pass the results of our query to the agent to conduct multifaceted analysis.
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5. Apply a top-level filter ('sentiment') on the results from the query
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6. For any of the passages with negative sentiment, we will run a follow-up set of analyses.
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7. Finally, we will assemble the follow-up analysis into a list of detailed reports.
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"""
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import os
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import shutil
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from llmware.agents import LLMfx
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from llmware.library import Library
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from llmware.retrieval import Query
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from llmware.configs import LLMWareConfig
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from llmware.setup import Setup
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def multistep_analysis():
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""" In this example, our objective is to research Microsoft history and rivalry in the 1980s with IBM. """
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# step 1 - assemble source documents and create library
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print("update: Starting example - agent-multistep-analysis")
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# note: lines 38-49 attempt to automatically pull sample document into local path
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# depending upon permissions in your environment, you may need to set up directly
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# if you pull down the samples files with Setup().load_sample_files(), in the Books folder,
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# you will find the source: "Bill-Gates-Biography.pdf"
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# if you have pulled sample documents in the past, then to update to latest: set over_write=True
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print("update: Loading sample files")
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sample_files_path = Setup().load_sample_files(over_write=False)
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bill_gates_bio = "Bill-Gates-Biography.pdf"
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path_to_bill_gates_bio = os.path.join(sample_files_path, "Books", bill_gates_bio)
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microsoft_folder = os.path.join(LLMWareConfig().get_tmp_path(), "example_microsoft")
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print("update: attempting to create source input folder at path: ", microsoft_folder)
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if not os.path.exists(microsoft_folder):
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os.mkdir(microsoft_folder)
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os.chmod(microsoft_folder, 0o777)
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shutil.copy(path_to_bill_gates_bio,os.path.join(microsoft_folder, bill_gates_bio))
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# create library
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print("update: creating library and parsing source document")
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LLMWareConfig().set_active_db("sqlite")
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my_lib = Library().create_new_library("microsoft_history_0210_1")
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my_lib.add_files(microsoft_folder)
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# run our first query - "ibm"
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query = "ibm"
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search_results = Query(my_lib).text_query(query)
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print(f"update: executing query to filter to key passages - {query} - results found - {len(search_results)}")
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# create an agent and load several tools that we will be using
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agent = LLMfx()
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agent.load_tool_list(["sentiment", "emotions", "topic", "tags", "ner", "answer"])
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# load the search results into the agent's work queue
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agent.load_work(search_results)
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while True:
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agent.sentiment()
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if not agent.increment_work_iteration():
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break
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# analyze sections where the sentiment on ibm was negative
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follow_up_list = agent.follow_up_list(key="sentiment", value="negative")
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for job_index in follow_up_list:
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# follow-up 'deep dive' on selected text that references ibm negatively
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agent.set_work_iteration(job_index)
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agent.exec_multitool_function_call(["tags", "emotions", "topics", "ner"])
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agent.answer("What is a brief summary?", key="summary")
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my_report = agent.show_report(follow_up_list)
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activity_summary = agent.activity_summary()
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for entries in my_report:
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print("my report entries: ", entries)
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return my_report
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if __name__ == "__main__":
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multistep_analysis()
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