Update test_agent_llmfx_process.py

Consider adding try-except blocks to handle any potential errors that might occur during the execution of the various tools and functions
Implement a logging mechanism to track the progress of the agent process and any important events or errors that occur. This can be useful for debugging and monitoring purposes.
Add the ability to save and load the state of the agent process, allowing users to pick up where they left off or share their work with others
 Design the agent process to be easily extensible, so that new tools or functionality can be added without requiring major changes to the existing codebase.
Investigate ways to optimize the performance of the agent process, such as caching results or parallelizing the execution of certain tools.

Author: Yash-2707

tests/models/test_agent_llmfx_process.py

@@ -1,74 +1,66 @@
-
-""" Tests the execution of a multi-step Agent process using multiple SLIM models. """
+"""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."
 
-    # 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
+    # Create an agent using LLMfx class
     agent = LLMfx()
 
+    # Load the work
     agent.load_work(customer_transcript)
 
-    #   load tools individually
+    # Load tools individually
     agent.load_tool("sentiment")
     agent.load_tool("ner")
 
-    #   load multiple tools
+    # 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
+    # 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
+    # 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"])
+    # 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")
+    # 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
+    # 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
+    # You can 'unload_tool' to release it from memory
     agent.unload_tool("ner")
     agent.unload_tool("topics")
 
-    #   at end of processing, show the report that was automatically aggregated by key
+    # At the end of processing, show the report that was automatically aggregated by key
     report = agent.show_report()
 
-    #   displays a summary of the activity in the process
+    # Display a summary of the activity in the process
     activity_summary = agent.activity_summary()
 
-    #   list of the responses gathered
+    # List of the responses gathered
     for i, entries in enumerate(agent.response_list):
-        print("update: response analysis: ", i, entries)
-
+        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}
+    output = {
+        "report": report,
+        "activity_summary": activity_summary,
+        "journal": agent.journal
+    }
 
     return output
-
-
-