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61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
import logging
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import os
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from pathlib import Path
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from dotenv import load_dotenv
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from livekit.agents import Agent, AgentSession, JobContext, WorkerOptions, cli, mcp
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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("mcp-agent")
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load_dotenv(dotenv_path=Path(__file__).parent.parent / ".env")
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class MyAgent(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 can retrieve data via the MCP server. The interface is voice-based: "
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"accept spoken user queries and respond with synthesized speech."
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),
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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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mcp_servers=[
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mcp.MCPServerHTTP(
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url=(
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"https://mcp.gumloop.com/gcalendar/"
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"cY3bcaFS1qNdeVBnj0XIhnP4FEp2%3Aae99858e75594251bea9e05f32bb99b3"
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),
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timeout=5,
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client_session_timeout_seconds=5,
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),
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],
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)
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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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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=MyAgent(), 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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