If you want to learn:
How to build a RAG-powered AI voice customer support agent that can answer questions about your business or products?
What are the key steps to integrate ElevenLabs voice agents with Supabase database and n8n workflow automation?
How does retrieval-augmented generation (RAG) work to give AI agents access to your knowledge base and provide accurate answers?
How to create an AI voice assistant that can handle real-time conversational queries using vector embeddings and database integration?
What tools and workflow configurations are needed to build AI voice agents with document retrieval capabilities?
Then this lecture is for you!
This lecture completes the RAG-powered AI voice agent project by building the question-answering component and integrating ElevenLabs voice capabilities. You'll learn how to create an n8n workflow that uses an AI agent to handle conversational queries by retrieving relevant information from your Supabase vector database. The lecture covers the complete RAG system architecture, including how vector embeddings work with OpenAI Embedding Small model to transform queries into 1,536-dimensional vectors for database lookup. You'll configure the AI agent with a chat trigger, integrate a Gemini chat model (or your preferred LLM), implement memory for natural conversation flow, and add RAG tools that enable the agent to search your knowledge base. The session demonstrates how to connect all components—the conversational AI assistant, the vector database with your indexed documents, and the voice interface through ElevenLabs—to create a production-ready AI voice customer support solution. You'll understand both phases of RAG implementation: the data ingest pipeline (extracting, transforming, chunking, and vectorizing documents) and the real-time query response system that provides context-aware, accurate answers to user questions.