Agri & Rural Voice Agents

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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, function_tool
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from livekit.agents.llm import RunContext
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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_dotenv()
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logger = logging.getLogger("krishi-mitra")
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logger.setLevel(logging.INFO)
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class KrishiMitraAgent(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            instructions="""
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                You are Krishi Mitra, a voice assistant that helps Indian farmers with
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                crop advisory, mandi (market) prices, weather, and government scheme queries.
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                Your responsibilities:
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                - Answer questions about crop care, sowing/harvest timing, and common pest/disease symptoms in simple terms
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                - Use the get_mandi_price tool for any current price question. Never state a price from memory
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                - Use the get_weather_forecast tool for any weather question. Never guess conditions
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                - For government scheme questions (PM-Kisan, crop insurance, etc.), give general guidance and
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                  always direct the farmer to their nearest Krishi Vigyan Kendra or the official portal to confirm details
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                - If you don't know something, say so plainly rather than guessing
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                Communication guidelines:
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                - Use simple, everyday language. Avoid technical agronomic or bureaucratic jargon
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                - Speak slowly and clearly. Be willing to repeat
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                - Be patient and respectful. Many callers may have limited formal education
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                - Keep answers short and concrete: a specific action, not a lecture
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                Start by greeting the farmer warmly and asking what they need help with today.
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            """,
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            stt=sarvam.STT(
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                language="unknown",  # Auto-detect across regional languages
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                model="saaras:v3",
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                mode="transcribe",
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            ),
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            llm=sarvam.LLM(model="sarvam-30b"),
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            tts=sarvam.TTS(
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                target_language_code="hi-IN",  # Set per deployment region
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                model="bulbul:v3",
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                speaker="simran",
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                pace=0.9,
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            ),
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        )
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    @function_tool
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    async def get_mandi_price(self, context: RunContext, crop: str, mandi: str) -> dict:
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        """Look up today's price for a crop at a given mandi. Replace with a real market-price API call."""
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        # TODO: call your mandi price provider (e.g. Agmarknet) here
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        return {"crop": crop, "mandi": mandi, "price_per_quintal": None, "as_of": "today"}
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    @function_tool
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    async def get_weather_forecast(self, context: RunContext, location: str) -> dict:
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        """Look up the weather forecast for a location. Replace with a real weather API call."""
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        # TODO: call your weather provider here
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        return {"location": location, "forecast": None}
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    async def on_enter(self):
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        self.session.generate_reply()
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async def entrypoint(ctx: JobContext):
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    logger.info(f"Farmer connected to room: {ctx.room.name}")
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    session = AgentSession()
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    await session.start(agent=KrishiMitraAgent(), room=ctx.room)
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if __name__ == "__main__":
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    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))