wehub-resource-sync
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
"""Tests the execution of a multi-step Agent process using multiple SLIM models."""
|
|
|
|
from llmware.agents import LLMfx
|
|
|
|
def test_multistep_agent_process():
|
|
# Sample customer transcript
|
|
customer_transcript = "My name is Michael Jones, and I am a long-time customer. The Mixco product is not working currently, and it is having a negative impact on my business, as we can not deliver our products while it is down. This is the fourth time that I have called. My account number is 93203, and my user name is mjones. Our company is based in Tampa, Florida."
|
|
|
|
# Create an agent using LLMfx class
|
|
agent = LLMfx()
|
|
|
|
# Load the work
|
|
agent.load_work(customer_transcript)
|
|
|
|
# Load tools individually
|
|
agent.load_tool("sentiment")
|
|
agent.load_tool("ner")
|
|
|
|
# Load multiple tools
|
|
agent.load_tool_list(["emotions", "topics", "intent", "tags", "ratings", "answer"])
|
|
|
|
# Start deploying tools and running various analytics
|
|
# First, conduct three 'soft skills' initial assessment using 3 different models
|
|
agent.sentiment()
|
|
agent.emotions()
|
|
agent.intent()
|
|
|
|
# Alternative way to execute a tool, passing the tool name as a string
|
|
agent.exec_function_call("ratings")
|
|
|
|
# Call multiple tools concurrently
|
|
agent.exec_multitool_function_call(["ner", "topics", "tags"])
|
|
|
|
# The 'answer' tool is a quantized question-answering model - ask an 'inline' question
|
|
# The optional 'key' assigns the output to a dictionary key for easy consolidation
|
|
agent.answer("What is a short summary?", key="summary")
|
|
|
|
# Prompting tool to ask a quick question as part of the analytics
|
|
response = agent.answer("What is the customer's account number and user name?", key="customer_info")
|
|
|
|
# You can 'unload_tool' to release it from memory
|
|
agent.unload_tool("ner")
|
|
agent.unload_tool("topics")
|
|
|
|
# At the end of processing, show the report that was automatically aggregated by key
|
|
report = agent.show_report()
|
|
|
|
# Display a summary of the activity in the process
|
|
activity_summary = agent.activity_summary()
|
|
|
|
# List of the responses gathered
|
|
for i, entries in enumerate(agent.response_list):
|
|
print(f"Update: response analysis {i}: {entries}")
|
|
assert entries is not None
|
|
|
|
assert activity_summary is not None
|
|
assert agent.journal is not None
|
|
assert report is not None
|
|
|
|
output = {
|
|
"report": report,
|
|
"activity_summary": activity_summary,
|
|
"journal": agent.journal
|
|
}
|
|
|
|
return output
|