wehub-resource-sync
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
from pathlib import Path
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from dotenv import load_dotenv
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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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)
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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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AutoSubscribe,
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JobContext,
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cli,
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inference,
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llm,
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)
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load_dotenv()
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# check if storage already exists
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THIS_DIR = Path(__file__).parent
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PERSIST_DIR = THIS_DIR / "query-engine-storage"
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if not PERSIST_DIR.exists():
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# load the documents and create the index
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documents = SimpleDirectoryReader(THIS_DIR / "data").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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@llm.function_tool
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async def query_info(query: str) -> str:
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"""Get more information about a specific topic"""
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query_engine = index.as_query_engine(use_async=True)
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res = await query_engine.aquery(query)
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print("Query result:", res)
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return str(res)
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server = AgentServer()
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@server.rtc_session()
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async def entrypoint(ctx: JobContext):
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await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
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agent = Agent(
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instructions=(
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"You are a voice assistant created by LiveKit. Your interface "
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"with users will be voice. You should use short and concise "
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"responses, and avoiding usage of unpronouncable punctuation."
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),
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stt=inference.STT("deepgram/nova-3"),
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llm=inference.LLM("openai/gpt-4.1-mini"),
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tts=inference.TTS("cartesia/sonic-3"),
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tools=[query_info],
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)
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session = AgentSession()
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await session.start(agent=agent, room=ctx.room)
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await session.say("Hey, how can I help you today?", allow_interruptions=False)
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if __name__ == "__main__":
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cli.run_app(server)
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