wehub-resource-sync
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import logging
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import os
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from livekit.agents import JobContext, JobProcess, WorkerOptions, cli
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from livekit.agents.telemetry import set_tracer_provider
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from livekit.agents.voice import Agent, AgentSession
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from livekit.plugins import openai, silero
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from opentelemetry import trace
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.resources import SERVICE_NAME, Resource
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("voice-agent")
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def configure_mlflow_tracing():
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"""Configure OpenTelemetry to send traces to MLflow."""
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if not os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT"):
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logger.warning("OTEL_EXPORTER_OTLP_ENDPOINT not set, tracing disabled")
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return None
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service_name = os.environ.get("OTEL_SERVICE_NAME", "livekit-voice-agent")
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resource = Resource.create({SERVICE_NAME: service_name})
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provider = TracerProvider(resource=resource)
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provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
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trace.set_tracer_provider(provider)
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set_tracer_provider(provider)
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logger.info("MLflow tracing configured successfully!")
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return provider
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async def entrypoint(ctx: JobContext) -> None:
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"""Main entrypoint for the agent."""
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logger.info(f"Agent starting for room: {ctx.room.name}")
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# Connect to the room
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await ctx.connect()
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# Create the voice agent with all components
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agent = Agent(
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instructions="""You are a helpful voice assistant. Keep your responses
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concise and conversational since you're speaking out loud.
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Be friendly and helpful.""",
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vad=silero.VAD.load(), # Voice Activity Detection
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stt=openai.STT(), # Speech-to-Text (Whisper)
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llm=openai.LLM(model="gpt-4o-mini"), # Language Model
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tts=openai.TTS(voice="alloy"), # Text-to-Speech
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)
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# Create and start the agent session
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session = AgentSession()
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await session.start(agent, room=ctx.room)
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logger.info("Agent session started! Ready for conversation.")
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def prewarm(proc: JobProcess) -> None:
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"""Prewarm function to load models before handling requests."""
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# Configure tracing before anything else
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configure_mlflow_tracing()
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# Preload Silero VAD model for faster startup
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proc.userdata["vad"] = silero.VAD.load()
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logger.info("Prewarmed VAD model")
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if __name__ == "__main__":
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cli.run_app(
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WorkerOptions(
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entrypoint_fnc=entrypoint,
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prewarm_fnc=prewarm,
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)
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)
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