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86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import logging
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import os
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import random
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from typing import List, Optional
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from dotenv import load_dotenv
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from livekit import api
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from livekit.agents import (
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Agent,
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AgentSession,
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ChatContext,
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JobContext,
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RunContext,
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WorkerOptions,
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cli,
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function_tool,
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)
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from livekit.plugins import deepgram, openai, silero
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from livekit.plugins.turn_detector.multilingual import MultilingualModel
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load_dotenv(os.path.join(os.path.dirname(__file__), "..", "..", ".env"))
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logger = logging.getLogger("caller-agent")
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class CallerAgent(Agent):
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def __init__(
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self, chat_ctx: ChatContext, tools: Optional[List[function_tool]] = None
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) -> None:
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final_instructions = (
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"You are a Caller specialist. Your primary function is to initiate phone calls. "
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+ "If the user asks to call someone, use the 'make_phone_call' tool. "
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+ "Currently, you can only call a predefined contact (Sam at +16467085301). "
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+ "Confirm with the user if they want to call this specific contact. "
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+ "If your task is complete or the user asks for something outside your calling "
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+ "capabilities (e.g., math, web search), you MUST use the 'delegate_to_router_agent' "
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+ "tool to return to the main assistant."
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)
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agent_tools = [self.make_phone_call]
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all_tools = agent_tools + (tools if tools is not None else [])
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super().__init__(
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instructions=final_instructions,
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chat_ctx=chat_ctx,
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allow_interruptions=True,
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tools=all_tools,
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)
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self.lkapi = api.LiveKitAPI()
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async def on_enter(self):
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self.session.generate_reply()
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@function_tool
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async def make_phone_call(self, context: RunContext, phone_number: str):
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"""
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Call this function to make a phone call to a user number.
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Args:
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phone_number: The phone number to call.
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"""
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await self.lkapi.agent_dispatch.create_dispatch(
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api.CreateAgentDispatchRequest(
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agent_name="my-telephony-agent",
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room=f"outbound-{''.join(str(random.randint(0, 9)) for _ in range(10))}",
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metadata='{"phone_number": "+16467085301"}', # HARDCODED
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)
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)
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async def entrypoint(ctx: JobContext):
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await ctx.connect()
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session = AgentSession(
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vad=silero.VAD.load(),
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stt=deepgram.STT(model="nova-3", language="multi"),
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llm=openai.LLM(model="gpt-4o-mini"),
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tts=openai.TTS(voice="ash"),
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turn_detection=MultilingualModel(),
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)
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await session.start(agent=CallerAgent(chat_ctx=session._chat_ctx), room=ctx.room)
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if __name__ == "__main__":
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cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint, agent_name="mcp-agent"))
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