wehub-resource-sync
95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
import logging
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import os
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from dotenv import load_dotenv
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from livekit.agents import JobContext, JobProcess, WorkerOptions, cli
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from livekit.agents.job import AutoSubscribe
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from livekit.agents.llm import (
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ChatContext,
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)
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from livekit.agents.pipeline import VoicePipelineAgent
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from livekit.plugins import cartesia, silero, llama_index, assemblyai
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load_dotenv()
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logger = logging.getLogger("voice-assistant")
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from llama_index.llms.ollama import Ollama
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from llama_index.core import (
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SimpleDirectoryReader,
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StorageContext,
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VectorStoreIndex,
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load_index_from_storage,
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Settings
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)
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from llama_index.core.chat_engine.types import ChatMode
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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load_dotenv()
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embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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llm=Ollama(model="gemma3", request_timeout=120.0)
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Settings.llm = llm
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Settings.embed_model = embed_model
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# check if storage already exists
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PERSIST_DIR = "./chat-engine-storage"
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if not os.path.exists(PERSIST_DIR):
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# load the documents and create the index
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documents = SimpleDirectoryReader("docs").load_data()
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index = VectorStoreIndex.from_documents(documents)
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# store it for later
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index.storage_context.persist(persist_dir=PERSIST_DIR)
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else:
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# load the existing index
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storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
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index = load_index_from_storage(storage_context)
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def prewarm(proc: JobProcess):
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proc.userdata["vad"] = silero.VAD.load()
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async def entrypoint(ctx: JobContext):
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chat_context = ChatContext().append(
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role="system",
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text=(
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"You are a funny, witty assistant."
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"Respond with short and concise answers. Avoid using unpronouncable punctuation or emojis."
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),
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)
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chat_engine = index.as_chat_engine(chat_mode=ChatMode.CONTEXT)
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logger.info(f"Connecting to room {ctx.room.name}")
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await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
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participant = await ctx.wait_for_participant()
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logger.info(f"Starting voice assistant for participant {participant.identity}")
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stt_impl = assemblyai.STT()
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agent = VoicePipelineAgent(
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vad=ctx.proc.userdata["vad"],
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stt=stt_impl,
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llm=llama_index.LLM(chat_engine=chat_engine),
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tts=cartesia.TTS(
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model="sonic-2",
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voice="794f9389-aac1-45b6-b726-9d9369183238",
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),
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chat_ctx=chat_context,
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)
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agent.start(ctx.room, participant)
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await agent.say(
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"Hey there! How can I help you today?",
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allow_interruptions=True,
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)
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if __name__ == "__main__":
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print("Starting voice agent...")
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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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) |