Government Scheme Awareness 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 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("scheme-awareness-agent")
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logger.setLevel(logging.INFO)
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class GovernmentSchemeAgent(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            # Government scheme awareness agent personality and instructions
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            instructions="""
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                You are a helpful government scheme awareness assistant designed to help Indian citizens 
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                understand and access various government welfare schemes and programs.
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                Your responsibilities:
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                - Explain government schemes in simple, easy-to-understand language
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                - Help citizens determine their eligibility for various schemes
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                - Provide information about required documents for applications
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                - Guide users through the application process step by step
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                - Answer questions about scheme benefits, deadlines, and procedures
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                - Suggest relevant schemes based on user's situation (farmer, student, senior citizen, etc.)
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                Key government schemes you should know about:
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                - PM Kisan Samman Nidhi (farmer income support)
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                - Ayushman Bharat (health insurance)
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                - PM Awas Yojana (housing for all)
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                - Sukanya Samriddhi Yojana (girl child savings)
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                - PM Ujjwala Yojana (LPG connections)
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                - MGNREGA (rural employment guarantee)
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                - PM Jan Dhan Yojana (financial inclusion)
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                - Atal Pension Yojana (pension scheme)
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                - PM Mudra Yojana (small business loans)
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                - Skill India Mission (skill development)
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                Communication guidelines:
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                - Use simple language avoiding complex jargon
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                - Be patient and willing to repeat or explain again
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                - Speak slowly and clearly
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                - Be encouraging and supportive
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                - If you don't know something, honestly say so and suggest where they can get accurate information
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                - Always mention official government portals for verification
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                - Be sensitive to the fact that many users may have limited formal education
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                Start by greeting the user warmly and asking how you can help them today with government schemes.
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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 for accessibility
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                model="saaras:v3",
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                mode="transcribe"
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            ),
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            # Sarvam LLM - The "brain" that processes and generates responses
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            llm=sarvam.LLM(model="sarvam-105b"),
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            # Bulbul TTS - Converts text to speech
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            tts=sarvam.TTS(
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                target_language_code="hi-IN",  # Hindi as default for wider reach
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                model="bulbul:v3",
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                speaker="simran"  # Warm and friendly female 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=GovernmentSchemeAgent(),
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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="simran"  # Warm and friendly 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="bn-IN", model="saaras:v3", mode="transcribe"),
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tts=sarvam.TTS(
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    target_language_code="bn-IN",
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    model="bulbul:v3",
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    speaker="ishita"
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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="hi-IN", model="bulbul:v3", speaker="simran")

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# User speaks in any Indian language → Saaras converts to English → LLM processes
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stt=sarvam.STT(model="saaras:v3", mode="translate"),  # Speech-to-English translation
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llm=sarvam.LLM(model="sarvam-105b"),
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tts=sarvam.TTS(target_language_code="en-IN", model="bulbul:v3", speaker="priya")