Guide to Building an AI-Powered Customer Support Chatbot Using Swarm

# main.py

import os
from dotenv import load_dotenv
from swarm import Swarm, Agent

# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# Initialize Swarm client
client = Swarm(openai_api_key=openai_api_key)

# Define specialized agents

# Billing Support Agent
billing_agent = Agent(
    name="Billing Support Agent",
    instructions="""
You are a helpful customer support agent specializing in billing issues.
Assist the user with their billing inquiries, such as charges, refunds, and payment methods.
If the query is not related to billing, politely inform the user and suggest contacting the appropriate department.
""",
)

# Technical Support Agent
technical_agent = Agent(
    name="Technical Support Agent",
    instructions="""
You are a helpful customer support agent specializing in technical issues.
Assist the user with technical problems, such as troubleshooting errors, connectivity issues, and software bugs.
If the query is not related to technical support, politely inform the user and suggest contacting the appropriate department.
""",
)

# General Inquiry Agent
general_agent = Agent(
    name="General Inquiry Agent",
    instructions="""
You are a helpful customer support agent handling general inquiries.
Assist the user with questions about account information, product details, and other general topics.
If the query is specialized (billing or technical), politely inform the user and suggest contacting the appropriate department.
""",
)

# Define a function to triage the user's query
def triage_query(context_variables, query: str):
    """
    Analyze the user's query and determine the appropriate agent to handle it.
    """
    if any(keyword in query.lower() for keyword in ["bill", "charge", "payment", "invoice", "refund"]):
        return billing_agent
    elif any(keyword in query.lower() for keyword in ["error", "issue", "bug", "technical", "problem", "troubleshoot"]):
        return technical_agent
    else:
        return general_agent

# Initial Agent (Triage Agent)
triage_agent = Agent(
    name="Triage Agent",
    instructions="""
You are an AI assistant that routes customer inquiries to the appropriate department.
Analyze the user's message and determine which specialized agent should handle it.
Call the function 'triage_query' to perform the routing.
""",
    functions=[triage_query],
)

def main():
    # Start the conversation
    user_message = input("User: ")

    # Prepare the initial messages
    messages = [
        {"role": "user", "content": user_message}
    ]

    # Run the Swarm client with the triage agent
    response = client.run(
        agent=triage_agent,
        messages=messages,
        context_variables={},
        max_turns=5,
        debug=False
    )

    # Get the final response
    final_agent = response.agent
    final_message = response.messages[-1]["content"]

    print(f"{final_agent.name}: {final_message}")

if __name__ == "__main__":
    main()

def main():
    # Initialize context variables
    context_variables = {}

    # Prepare initial messages
    messages = []

    # Conversation loop
    while True:
        user_message = input("User: ")
        if user_message.lower() in ["exit", "quit"]:
            print("Chatbot: Thank you for contacting support. Goodbye!")
            break

        messages.append({"role": "user", "content": user_message})

        # Run the Swarm client
        response = client.run(
            agent=triage_agent,
            messages=messages,
            context_variables=context_variables,
            max_turns=5,
            debug=False
        )

        # Get the latest agent and message
        final_agent = response.agent
        final_message = response.messages[-1]["content"]

        print(f"{final_agent.name}: {final_message}")

        # Update messages and context variables for the next turn
        messages = response.messages
        context_variables = response.context_variables