Ben Cherry
df30553404
* Update avatar provider example from hedra to anam * Trim anam avatar comment to match original style * Move anam avatar comment to after session.start
131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
import logging
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from dotenv import load_dotenv
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from livekit.agents import (
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Agent,
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AgentServer,
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AgentSession,
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JobContext,
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JobProcess,
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cli,
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inference,
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room_io,
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)
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from livekit.plugins import ai_coustics, silero
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from livekit.plugins.turn_detector.multilingual import MultilingualModel
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logger = logging.getLogger("agent")
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load_dotenv(".env.local")
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AGENT_MODEL = "openai/gpt-5.2-chat-latest"
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class Assistant(Agent):
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def __init__(self) -> None:
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super().__init__(
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instructions="""You are a helpful voice AI assistant. The user is interacting with you via voice, even if you perceive the conversation as text.
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You eagerly assist users with their questions by providing information from your extensive knowledge.
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Your responses are concise, to the point, and without any complex formatting or punctuation including emojis, asterisks, or other symbols.
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You are curious, friendly, and have a sense of humor.""",
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)
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# To add tools, use the @function_tool decorator.
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# Here's an example that adds a simple weather tool.
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# You also have to add `from livekit.agents import function_tool, RunContext` to the top of this file
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# @function_tool
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# async def lookup_weather(self, context: RunContext, location: str):
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# """Use this tool to look up current weather information in the given location.
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#
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# If the location is not supported by the weather service, the tool will indicate this. You must tell the user the location's weather is unavailable.
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#
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# Args:
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# location: The location to look up weather information for (e.g. city name)
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# """
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#
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# logger.info(f"Looking up weather for {location}")
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#
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# return "sunny with a temperature of 70 degrees."
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server = AgentServer()
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def prewarm(proc: JobProcess):
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proc.userdata["vad"] = silero.VAD.load()
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server.setup_fnc = prewarm
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@server.rtc_session(agent_name="my-agent")
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async def my_agent(ctx: JobContext):
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# Logging setup
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# Add any other context you want in all log entries here
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ctx.log_context_fields = {
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"room": ctx.room.name,
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}
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# Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
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session = AgentSession(
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# Speech-to-text (STT) is your agent's ears, turning the user's speech into text that the LLM can understand
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# See all available models at https://docs.livekit.io/agents/models/stt/
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stt=inference.STT(model="deepgram/nova-3", language="multi"),
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# A Large Language Model (LLM) is your agent's brain, processing user input and generating a response
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# See all available models at https://docs.livekit.io/agents/models/llm/
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llm=inference.LLM(model=AGENT_MODEL),
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# Text-to-speech (TTS) is your agent's voice, turning the LLM's text into speech that the user can hear
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# See all available models as well as voice selections at https://docs.livekit.io/agents/models/tts/
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tts=inference.TTS(
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model="cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"
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),
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# VAD and turn detection are used to determine when the user is speaking and when the agent should respond
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# See more at https://docs.livekit.io/agents/build/turns
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turn_detection=MultilingualModel(),
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vad=ctx.proc.userdata["vad"],
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# allow the LLM to generate a response while waiting for the end of turn
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# See more at https://docs.livekit.io/agents/build/audio/#preemptive-generation
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preemptive_generation=True,
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)
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# To use a realtime model instead of a voice pipeline, use the following session setup instead.
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# (Note: This is for the OpenAI Realtime API. For other providers, see https://docs.livekit.io/agents/models/realtime/))
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# 1. Install livekit-agents[openai]
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# 2. Set OPENAI_API_KEY in .env.local
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# 3. Add `from livekit.plugins import openai` to the top of this file
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# 4. Use the following session setup instead of the version above
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# session = AgentSession(
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# llm=openai.realtime.RealtimeModel(voice="marin")
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# )
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# Start the session, which initializes the voice pipeline and warms up the models
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await session.start(
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agent=Assistant(),
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room=ctx.room,
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room_options=room_io.RoomOptions(
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audio_input=room_io.AudioInputOptions(
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noise_cancellation=ai_coustics.audio_enhancement(
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model=ai_coustics.EnhancerModel.QUAIL_VF_L
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),
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),
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),
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)
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# # Add a virtual avatar to the session, if desired
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# # For other providers, see https://docs.livekit.io/agents/models/avatar/
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# avatar = anam.AvatarSession(
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# persona_config=anam.PersonaConfig(
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# name="...",
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# avatarId="...", # See https://docs.livekit.io/agents/models/avatar/plugins/anam
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# ),
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# )
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# # Start the avatar and wait for it to join
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# await avatar.start(session, room=ctx.room)
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# Join the room and connect to the user
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await ctx.connect()
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if __name__ == "__main__":
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cli.run_app(server)
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