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Agentic AIintermediate

Real-Time Voice AI Agent with RAG

Develop a production-ready Real-Time Voice AI Agent that combines Retrieval Augmented Generation (RAG) with ultra-low latency voice processing. This system enables natural, spoken conversations with an AI assistant, accessing custom knowledge bases stored in vector databases for instant information retrieval.

35 lectures

What You Will Learn

Implementing a real-time voice processing pipeline using STT/TTS APIs and WebSocket streaming
Mastering RAG architecture with MongoDB Atlas Vector Search and document embedding generation
Orchestrating AI pipelines using Pipecat for LLM function calling and context management
Building full-stack applications with FastAPI, React, TypeScript, and MongoDB
Deploying production-ready applications on AWS using CloudFormation and ECS Fargate
Performing document processing and knowledge extraction from PDFs and text files
Implementing CI/CD pipelines using GitHub Actions for automated deployment

System Architecture

Real-Time Voice AI Agent with RAG Architecture Diagram

High-level architecture overview of the Real-Time Voice AI Agent with RAG .

What You'll Build

  • A containerized FastAPI backend for real-time voice I/O orchestration
  • A React-based web interface with live audio visualization and chat history
  • A RAG service that performs vector similarity search across uploaded documents
  • A document upload API for processing PDFs/text files and generating embeddings
  • Complete AWS infrastructure for deploying the application
Real-Time Voice AI Agent with RAG
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