Get detailed explaination of the Symantec multi agent chat code

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<title>Detailed Explanation of Multi-Agent Chat Code</title> <style> body { font-family: Arial, sans-serif; line-height: 1.6; margin: 20px; } pre { background-color: #f9f9f9; padding: 15px; border: 1px solid #ddd; border-radius: 5px; overflow: auto; } code { background-color: #f4f4f4; padding: 2px 4px; border-radius: 3px; } h1, h2, h3 { color: #333; } h1 { text-align: center; } ul { margin: 10px 0; padding: 0 20px; } hr { margin: 20px 0; border: 1px solid #ddd; } </style>

Detailed Explanation of Multi-Agent Chat Code

This document provides a section-by-section explanation of your multi-agent chat code. Each section explains how it works with details about the purpose, key points, parameters, results, and key points to remember.

<hr>

<h2>1. Import Statements</h2>
<pre>

import asyncio
import os
import textwrap
from datetime import datetime
from pathlib import Path
import shutil

from azure.identity.aio import DefaultAzureCredential
from semantic_kernel.agents import AgentGroupChat
from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings
from semantic_kernel.agents.strategies import TerminationStrategy, SequentialSelectionStrategy
from semantic_kernel.contents.chat_message_content import ChatMessageContent
from semantic_kernel.contents.utils.author_role import AuthorRole
from semantic_kernel.functions.kernel_function_decorator import kernel_function

How It Works

• Purpose: Import necessary libraries and modules for the program.


• Key Points:
  
  
  
  • asyncio: Handles asynchronous operations.
  
  
  • os: Interacts with the operating system (e.g., clearing the console).
  
  
  • textwrap: Formats long strings for readability.
  
  
  • datetime: Works with date and time for timestamps.
  
  
  • Path: Simplifies file path handling using object-oriented methods.
  
  
  • shutil: Performs high-level file operations like copying directories.
  
  
  
  


• Parameters: None for imports; these modules are used later in the code.


• Results: All imported modules are made available for use in the script.

Key Points to Remember

• Standard libraries handle file operations, string formatting, and date/time management.


• Azure and Semantic Kernel libraries provide authentication and multi-agent chat functionality.

<hr>

<h2>2. Constants: Agent Instructions</h2>
<pre>

INCIDENT_MANAGER = "INCIDENT_MANAGER"
INCIDENT_MANAGER_INSTRUCTIONS = """
Analyze the given log file or the response from the devops assistant...
"""

DEVOPS_ASSISTANT = "DEVOPS_ASSISTANT"
DEVOPS_ASSISTANT_INSTRUCTIONS = """
Read the instructions from the INCIDENT_MANAGER and apply the appropriate resolution function...
"""

How It Works

• Purpose: Define the roles and behavior of the two agents: Incident Manager and DevOps Assistant.


• Key Points:
  
  
  
  • Each agent has a unique name and a set of instructions.
  
  
  • The instructions guide the agents on how to process inputs and respond.
  
  
  
  


• Parameters: None directly; these constants are used later in the code.


• Results: The constants are available for use when creating and configuring agents.

Key Points to Remember

• Each agent's name and instructions should align with its role in the system.


• Instructions act as the "rules" for how each agent behaves.

<hr>

<h2>3. Main Function</h2>
<pre>

async def main():
# Clear the console
os.system('cls' if os.name=='nt' else 'clear')

How It Works

• Purpose: Define the entry point for the program and set up the environment.


• Key Points:
  
  
  
  • async def main(): Marks the function as asynchronous, allowing non-blocking operations.
  
  
  • os.system(): Clears the console screen for a clean output display.
  
  
  
  


• Parameters: None directly; this function is called later to start the program.


• Results: The console is cleared, and the program is ready to start initializing components.

Key Points to Remember

• The main function is the starting point for the program.


• Clearing the console improves readability for the user.

<hr>

<h2>4. Log File Setup</h2>
<pre>

script_dir = Path(file).parent # Get the directory of the script
src_path = script_dir / "sample_logs"
file_path = script_dir / "logs"
shutil.copytree(src_path, file_path, dirs_exist_ok=True)

How It Works

• Purpose: Prepare log files for processing by copying them to a working directory.


• Key Points:
  
  
  
  • Path(file).parent: Retrieves the directory where the script is located.
  
  
  • src_path and file_path: Define the source and destination paths for log files.
  
  
  • shutil.copytree(): Copies the contents of the source directory to the destination directory.
  
  
  
  


• Parameters:
  
  
  
  • src_path: The directory containing the original log files.
  
  
  • file_path: The destination directory for the copied log files.
  
  
  • dirs_exist_ok=True: Ensures no errors occur if the destination directory already exists.
  
  
  
  


• Results: Log files are copied to the working directory, ready for processing by agents.

Key Points to Remember

• Log files are essential for the agents to analyze and respond effectively.


• The shutil.copytree function simplifies directory copying tasks.

<hr>

<h2>5. Azure AI Agent Initialization</h2>
<pre>

ai_agent_settings = AzureAIAgentSettings()

async with (
DefaultAzureCredential(exclude_environment_credential=True,
exclude_managed_identity_credential=True) as creds,
AzureAIAgent.create_client(credential=creds) as client,
):

How It Works

• Purpose: Set up Azure AI authentication and create a client for interacting with Azure AI services.


• Key Points:
  
  
  
  • AzureAIAgentSettings: Configures settings for Azure AI agents, such as the model to be used.
  
  
  • DefaultAzureCredential: Handles Azure authentication automatically by detecting the best available credentials.
  
  
  • AzureAIAgent.create_client(): Creates a client object for interacting with Azure AI services.
  
  
  • async with: Ensures that resources (e.g., credentials and client) are properly cleaned up after use.
  
  
  
  


• Parameters:
  
  
  
  • exclude_environment_credential=True: Prevents the use of environment variable-based credentials.
  
  
  • exclude_managed_identity_credential=True: Prevents the use of managed identity credentials.
  
  
  • credential=creds: Provides the authenticated credentials to the client.
  
  
  
  


• Results: The program is authenticated with Azure, and a client is ready for use to create and manage agents.

Key Points to Remember

• Authentication is required to access Azure AI services securely.


• The client object is essential for managing agents and interacting with Azure AI models.

<hr>

<h2>6. Agent Creation</h2>
<pre>

incident_agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
name=INCIDENT_MANAGER,
instructions=INCIDENT_MANAGER_INSTRUCTIONS
)

agent_incident = AzureAIAgent(
client=client,
definition=incident_agent_definition,
plugins=[LogFilePlugin()]
)

devops_agent_definition = await client.agents.create_agent(
model=ai_agent_settings.model_deployment_name,
name=DEVOPS_ASSISTANT,
instructions=DEVOPS_ASSISTANT_INSTRUCTIONS,
)

agent_devops = AzureAIAgent(
client=client,
definition=devops_agent_definition,
plugins=[DevopsPlugin()]
)

How It Works

• Purpose: Create two agents (Incident Manager and DevOps Assistant) and wrap them with plugins for enhanced functionality.


• Key Points:
  
  
  
  • create_agent(): Registers an agent with Azure AI, providing its name, model, and instructions.
  
  
  • AzureAIAgent: Wraps the agent to enable additional functionality using plugins.
  
  
  • LogFilePlugin: Allows the Incident Manager to read log files.
  
  
  • DevopsPlugin: Enables the DevOps Assistant to perform operations like restarting services or rolling back transactions.
  
  
  
  


• Parameters:
  
  
  
  • model: Specifies the AI model to use (e.g., GPT-4).
  
  
  • name: The unique name of the agent (e.g., "INCIDENT_MANAGER").
  
  
  • instructions: Detailed guidelines for the agent's behavior.
  
  
  
  


• Results: Two fully configured agents (Incident Manager and DevOps Assistant) are ready for use in the chat system.

Key Points to Remember

• Each agent has a specific role and set of instructions.


• Plugins extend the default functionality of agents.

<hr>

<h2>7. Agent Group Chat</h2>
<pre>

chat = AgentGroupChat(
agents=[agent_incident, agent_devops],
termination_strategy=ApprovalTerminationStrategy(
agents=[agent_incident],
maximum_iterations=10,
automatic_reset=True
),
selection_strategy=SelectionStrategy(agents=[agent_incident, agent_devops]),
)

How It Works

• Purpose: Combine the two agents into a single chat system with defined rules for interaction and termination.


• Key Points:
  
  
  
  • agents: Specifies the agents participating in the chat.
  
  
  • termination_strategy: Defines when the chat should end.
  
  
  • selection_strategy: Determines which agent should respond next.
  
  
  
  


• Parameters:
  
  
  
  • agents: A list of agents (Incident Manager and DevOps Assistant).
  
  
  • termination_strategy: Ends the chat after 10 turns or if no further actions are needed.
  
  
  • selection_strategy: Alternates responses between agents based on the chat context.
  
  
  
  


• Results: A functional chat system with collaboration between multiple agents is established.

Key Points to Remember

• The group chat enables agents to work together to solve problems.


• Strategies control the flow and termination of the chat.

<p>If you need further explanations or additional sections, feel free to ask!</p>

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