Livekit

pip install inferedge-moss \
    python-dotenv

# LiveKit Credentials, keep it as it is for local deployment, don't change it.
LIVEKIT_URL=ws://localhost:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=secret

# Moss Credentials
MOSS_PROJECT_ID=your-moss-id
MOSS_PROJECT_KEY=your-moss-key

# AI Provider Keys
OPENAI_API_KEY=sk-...
DEEPGRAM_API_KEY=your-deepgram-key

import asyncio
import os
from dotenv import load_dotenv
from inferedge_moss import MossClient, DocumentInfo

load_dotenv()

async def main():
    # Initialize the Moss Client
    client = MossClient(
        project_id=os.environ["MOSS_PROJECT_ID"],
        project_key=os.environ["MOSS_PROJECT_KEY"]
    )
    
    index_name = os.getenv("MOSS_INDEX_NAME", "product-knowledge")

    # Define documents
    docs = [
        DocumentInfo(
            id="1", 
            text="Our return policy allows returns within 30 days of purchase with a receipt."
        ),
        DocumentInfo(
            id="2", 
            text="Standard shipping takes 3-5 business days. Express shipping takes 1-2 days."
        ),
        DocumentInfo(
            id="3", 
            text="Technical support is available 24/7 via email at [email protected]."
        ),
    ]

    print(f"Creating index '{index_name}'...")
    
    await client.create_index(index_name, docs, model_id="moss-minilm")
    
    print("Index created successfully.")

if __name__ == "__main__":
    asyncio.run(main())

python build_index.py

import asyncio
import logging
import os
from dotenv import load_dotenv
from livekit.plugins import openai, deepgram, silero
from livekit.plugins.turn_detector.english import EnglishModel
from livekit.agents import (
    JobContext,
    WorkerOptions,
    cli,
    ChatContext,
    ChatMessage,
    Agent,
    AgentSession,
)


# Moss Import
from inferedge_moss import MossClient

load_dotenv()

# Configuration
MOSS_PROJECT_ID = os.getenv("MOSS_PROJECT_ID")
MOSS_PROJECT_KEY = os.getenv("MOSS_PROJECT_KEY")
INDEX_NAME = os.getenv("MOSS_INDEX_NAME", "product-knowledge")

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("moss-agent")

class MossSemanticRetrievalAgent(Agent):

    def __init__(self, moss_client: MossClient):
        super().__init__(
            instructions="""
                You are a helpful customer support voice assistant.
                You have access to a knowledge base which will be provided to you as context.
                Always answer the user's question based on the provided context.
                If the context doesn't contain the answer, politely say you don't know.
            """
        )
        self.moss = moss_client

    async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
        """
        Intercept user message -> Search Moss -> Inject Context -> Continue
        """
        user_query = new_message.text_content
        logger.info(f"User asked: {user_query}")

        try:
            # 1. Automatic Search 
            results = await self.moss.query(
                INDEX_NAME, 
                user_query, 
                top_k=5, 
                alpha=0.8
            )
            
            # 2. Context Injection
            if results.docs:
                context_str = "\n".join([f"- {d.text}" for d in results.docs])
                injection = f"Relevant context from knowledge base:\n{context_str}\n\nUse this to answer the user."
                
                # Insert into chat history as a system message
                turn_ctx.add_message(role="system", content=injection)
                logger.info(f"Injected context: {context_str[:100]}...")  # Log first 100 chars
            else:
                logger.info("No relevant context found in Moss index")
                
        except Exception as e:
            logger.error(f"Moss search failed: {e}", exc_info=True)

        # 3. Proceed with standard generation
        await super().on_user_turn_completed(turn_ctx, new_message)


async def entrypoint(ctx: JobContext):
    await ctx.connect()

    # Initialize Moss
    moss_client = MossClient(project_id=MOSS_PROJECT_ID, project_key=MOSS_PROJECT_KEY)
    
    # Pre-load index
    try:
        await moss_client.load_index(INDEX_NAME)
        logger.info(f"Successfully loaded index: {INDEX_NAME}")
    except Exception as e:
        logger.warning(f"Index not found or failed to load: {e}")
        logger.warning("Moss queries will fail until the index is created. Run upload.py first.")

    # Create Session
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4o"),
        tts=openai.TTS(),
        turn_detection=EnglishModel(),
        vad=silero.VAD.load(),
    )

    # Start the session with our custom MossSemanticRetrievalAgent
    await session.start(
        agent=MossSemanticRetrievalAgent(moss_client),
        room=ctx.room,
    )

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

livekit-server --dev

python agent.py download-files
python agent.py console