wehub-resource-sync
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
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""" Fast Start Example #8 - Agents
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This example shows how to build locally-running Agents deploying multiple small specialized function-calling
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models as tools with an integrated work management, process and journaling capability using the LLMfx class.
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This example shows a workflow receiving a customer transcript, and having an agent run through a series of
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analytical and classification steps.
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1. Create an agent using the LLMfx class.
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2. Load multiple specialized tools for the agent.
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3. Execute a series of function-calls.
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4. Generate a consolidated automatic dictionary report.
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"""
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from llmware.agents import LLMfx
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def create_multistep_report(customer_transcript):
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""" Creating a multi-step, multi-model agent workflow """
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# create an agent using LLMfx class
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agent = LLMfx()
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agent.load_work(customer_transcript)
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# load tools individually
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agent.load_tool("sentiment")
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agent.load_tool("ner")
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# load multiple tools
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agent.load_tool_list(["emotions", "topics", "intent", "tags", "ratings", "answer"])
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# start deploying tools and running various analytics
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# first conduct three 'soft skills' initial assessment using 3 different models
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agent.sentiment()
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agent.emotions()
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agent.intent()
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# alternative way to execute a tool, passing the tool name as a string
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agent.exec_function_call("ratings")
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# call multiple tools concurrently
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agent.exec_multitool_function_call(["ner","topics","tags"])
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# the 'answer' tool is a quantized question-answering model - ask an 'inline' question
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# the optional 'key' assigns the output to a dictionary key for easy consolidation
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agent.answer("What is a short summary?",key="summary")
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# prompting tool to ask a quick question as part of the analytics
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response = agent.answer("What is the customer's account number and user name?", key="customer_info")
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# you can 'unload_tool' to release it from memory
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agent.unload_tool("ner")
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agent.unload_tool("topics")
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# at end of processing, show the report that was automatically aggregated by key
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report = agent.show_report()
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# displays a summary of the activity in the process
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activity_summary = agent.activity_summary()
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# list of the responses gathered
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for i, entries in enumerate(agent.response_list):
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print("update: response analysis: ", i, entries)
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output = {"report": report, "activity_summary": activity_summary, "journal": agent.journal}
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return output
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if __name__ == "__main__":
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# sample customer transcript
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customer_transcript = "My name is Michael Jones, and I am a long-time customer. " \
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"The Mixco product is not working currently, and it is having a negative impact " \
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"on my business, as we can not deliver our products while it is down. " \
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"This is the fourth time that I have called. My account number is 93203, and " \
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"my user name is mjones. Our company is based in Tampa, Florida."
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output = create_multistep_report(customer_transcript)
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