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Thanks for reaching out, mithun. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

• Are you thinking of using a particular vector database or embedding model for the semantic search? -- Qdrant / Milvis • Do you have any preferences or constraints when it comes to latency, scalability, or where you'd like to deploy this? This is a voice conversation - so latency is important - it will be self hosted • Would you prefer the voice agent to remember and handle follow-up questions within the same session, or should it start fresh with each new query? yes within same session. it should have some context of previous message if a follow up question is asked

I'm excited to help you build a LiveKit voice agent tailored for your e-commerce needs! Here’s a friendly guide to the recommended architecture and best practices: • Core Architecture: I suggest using a Retrieval-Augmented Generation (RAG) pattern. This pipeline includes Speech-to-Text (STT), semantic search with a vector database, a large language model (LLM) for generating responses, and Text-to-Speech (TTS) to complete the flow. • Semantic Search: For a smooth, low-latency experience with self-hosted deployment, Annoy is a great choice for vector indexing. If you’re looking for scalability and persistent storage, integrating Qdrant or Milvus through LlamaIndex works wonderfully. • Session Context: To keep conversations natural and handle follow-up questions, it’s important to maintain session state. You can use methods like

on_user_turn_completed

or

llm_node

to dynamically add the retrieved context into the LLM prompt. • Deployment: For full control over latency and privacy, deploying your agent self-hosted using Docker or CLI-based execution is the way to go. This setup is designed to give you quick, context-aware responses and integrate seamlessly with your product database. If you’d like, I’m here to walk you through any part of the implementation or answer any questions you might have! Sources: Voice Assistants | livekit/livekit_composite | DeepWiki | examples/voice_agents/llamaindex-rag/chat_engine.py | rag/main.py | External data and RAG | LiveKit Docs | LiveKit Docs

ok. I will go with embedding the RAG pipeline directly into the agent.. so what is the recommended architecture for the RAG pipeline itself ?

I'm glad you're exploring the architecture for embedding a Retrieval-Augmented Generation (RAG) pipeline directly into a LiveKit voice agent! Here's a friendly breakdown of the key components involved: 1. Speech-to-Text (STT): Start with a real-time STT service like Deepgram or Whisper to transcribe the user's speech into text. This sets the foundation for the rest of the pipeline. 2. Semantic Search: Next, use a vector database such as Annoy, Qdrant, or Milvus to perform semantic search. You'll want to preload your product data into the database using embeddings like OpenAI embeddings. Then, query this database with the user's input to find the most relevant context. 3. LLM Integration: After that, bring in a large language model like OpenAI GPT to generate thoughtful responses. By injecting the context retrieved from the semantic search into the LLM prompt, you help ensure the answers are accurate and context-aware. 4. Text-to-Speech (TTS): Finally, convert the LLM-generated text back into speech using a TTS service such as ElevenLabs or OpenAI TTS. This wraps up the voice interaction loop nicely. Flow: So, the pipeline flows like this: STT → Semantic Search → LLM → TTS. It's also important to maintain the session state to handle any follow-up questions smoothly by storing conversation history and including it in the LLM prompt as needed. If you'd like, I'm here to help with more details on implementing any of these components—just let me know!

can you double click on how semantic search should be implemented for the product data I have ensuring low latency, at the same time able to answer generic questions

I'm glad you're exploring semantic search for your product data! To help you achieve low latency and support generic questions smoothly, here’s a friendly approach you can follow: 1. Vector Search: Consider using a vector database like Annoy, Qdrant, or Milvus. You’ll want to preload your product data with embeddings (for example, OpenAI embeddings). Then, when a user asks a question, convert their query into embeddings and perform similarity searches to find the most relevant product info. 2. Low-Latency Optimization: To keep things speedy, embed the semantic search right into your agent pipeline. Lightweight options like Annoy work great for smaller datasets, while Qdrant or Milvus integrated via LlamaIndex can scale nicely. Running searches during the

on_user_turn_completed

phase helps avoid any unwanted delays. 3. Generic Question Handling: For questions that aren’t product-specific, it’s perfectly fine to let the LLM respond directly using its trained knowledge. But when the question is about a product, bringing in the retrieved context into the LLM prompt ensures your answers are accurate and grounded. 4. End-to-End Flow: Think of the pipeline like this: STT → Semantic Search → LLM → TTS. Keeping track of the session state is key, so you can handle follow-up questions smoothly by storing conversation history and including it in the LLM prompt when needed. This setup will help you deliver fast, precise, and context-aware responses, whether the questions are about your products or more general. If you’d like, I’m here to help with any part of the implementation—just let me know! Sources: External data and RAG | LiveKit Docs | Groq LLM integration guide | LiveKit Docs | Live Conversations with AI using ChatGPT and WebRTC | External data and RAG | LiveKit Docs | rag/main.py

My dataset have 30k products with 20 attributes each including a verbose product description - what do you recommend as the vector db for it

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

Qdrant or Milvus integrated via LlamaIndex can scale nicely. - do you think this is ideal for product search on structured data ?