Collection Agent using LiveKit

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import logging
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from dotenv import load_dotenv
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from livekit.agents import JobContext, WorkerOptions, cli
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from livekit.agents.voice import Agent, AgentSession
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from livekit.plugins import openai, sarvam
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# Load environment variables
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load_dotenv()
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# Set up logging
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logger = logging.getLogger("collection-agent")
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logger.setLevel(logging.INFO)
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class CollectionAgent(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            # Collection agent personality and instructions
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            instructions="""
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                You are a professional and empathetic collection agent working for ABC Bank.
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                Customer Account Details:
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                - Bank Name: ABC Bank
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                - EMI Amount: ₹5,000
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                - Due Date: 15th January 2025
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                - Loan Type: Personal Loan
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                - Account Status: Payment Overdue
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                Your responsibilities:
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                - Remind customers about their pending EMI payment of ₹5,000 which was due on 15th January
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                - Provide information about payment due dates, amounts, and available payment methods
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                - Help customers understand their payment options and any applicable late fees
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                - Guide customers through the payment process if they want to pay immediately
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                - Address customer concerns about their account with empathy
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                - Offer payment plans or extensions when appropriate (mention that you can connect them with a specialist)
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                Payment Methods to mention:
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                - UPI payment to ABC Bank
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                - Net Banking
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                - ABC Bank mobile app
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                - Visit nearest ABC Bank branch
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                Communication guidelines:
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                - Always maintain a professional yet friendly tone
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                - Be empathetic to customer's financial situations
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                - Never be aggressive, threatening, or use inappropriate language
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                - If a customer is upset, remain calm and understanding
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                - Speak clearly and concisely
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                - Confirm important details like EMI amount (₹5,000) and due date (15th January)
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                - If customer requests to speak to a human, acknowledge and offer to transfer
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                Start by greeting the customer, introducing yourself as calling from ABC Bank, 
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                and politely remind them about their pending EMI of ₹5,000.
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            """,
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            # Saaras v3 STT - Converts speech to text
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            stt=sarvam.STT(
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                language="unknown",  # Auto-detect language
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                model="saaras:v3",
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                mode="transcribe"
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            ),
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            # OpenAI LLM - The "brain" that processes and generates responses
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            llm=openai.LLM(model="gpt-4o"),
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            # Bulbul TTS - Converts text to speech
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            tts=sarvam.TTS(
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                target_language_code="en-IN",
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                model="bulbul:v3",
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                speaker="aditya"  # Professional male voice
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            ),
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        )
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    async def on_enter(self):
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        """Called when user joins - agent starts the conversation"""
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        self.session.generate_reply()
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async def entrypoint(ctx: JobContext):
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    """Main entry point - LiveKit calls this when a user connects"""
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    logger.info(f"User connected to room: {ctx.room.name}")
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    # Create and start the agent session
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    session = AgentSession()
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    await session.start(
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        agent=CollectionAgent(),
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        room=ctx.room
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    )
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if __name__ == "__main__":
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    # Run the agent
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    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

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stt=sarvam.STT(
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    language="hi-IN",  # Hindi
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    model="saaras:v3",
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    mode="transcribe"
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),
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tts=sarvam.TTS(
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    target_language_code="hi-IN",
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    model="bulbul:v3",
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    speaker="anand"  # Professional Hindi male voice
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)

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stt=sarvam.STT(language="ta-IN", model="saaras:v3", mode="transcribe"),
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tts=sarvam.TTS(
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    target_language_code="ta-IN",
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    model="bulbul:v3",
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    speaker="priya"
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)

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stt=sarvam.STT(language="unknown", model="saaras:v3", mode="transcribe"),  # Auto-detects language
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tts=sarvam.TTS(target_language_code="en-IN", model="bulbul:v3", speaker="aditya")