OpenAI Realtime API Tutorial: Build a Voice-to-Voice RAG Chatbot in 2026

TL;DR

Build a realtime voice-to-voice AI chatbot using OpenAI’s Realtime API and a retrieval-augmented generation (RAG) pipeline in about 60–90 minutes.

In this tutorial, you’ll create a voice-enabled chatbot that:

• Streams microphone input to OpenAI’s Realtime API for low-latency responses
• Retrieves relevant information from your own documents using RAG
• Generates grounded answers and converts them back into natural-sounding speech
• Can be adapted into a website chat widget or a production voice support system

By the end, you’ll have a working voice chatbot that listens, retrieves knowledge from your data, and responds in real time—the core building block behind modern AI-powered customer support systems.

Prerequisites

Before building your realtime voice-to-voice AI chatbot, make sure your development environment is properly set up.

You’ll need the following:

• Node.js 18+ (LTS recommended)
• npm 9+ or pnpm 8+
• An OpenAI API key with access to the Realtime API
• A modern browser (Chrome recommended) with microphone access
• Basic knowledge of:
  • JavaScript (async/await, WebSockets)
  • REST APIs
  • Vector embeddings and RAG fundamentals
• A small set of documents (PDF, TXT, or Markdown) to use as your knowledge base

This tutorial assumes you’re comfortable running a local Node.js server and working with client-side JavaScript. If not, consider reviewing those basics before proceeding.

Estimated completion time: 60–90 minutes if you follow along and copy the code examples.

What We’re Building

By the end of this tutorial, you’ll have a realtime voice-to-voice AI chatbot that listens through your microphone, retrieves answers from your own knowledge base, and responds back with natural speech—instantly.

This tutorial focuses on building the core architecture behind a voice-enabled chat widget or an AI chat widget for a website—the same general system design used in modern AI customer support platforms.

Here’s what your system will do:

• Capture live microphone audio in the browser
• Stream audio to OpenAI’s Realtime API over WebSockets
• Transcribe and interpret user intent in milliseconds
• Retrieve relevant documents using a RAG pipeline
• Generate grounded responses from your knowledge base
• Convert responses into natural-sounding speech
• Stream synthesized audio back to the user in real time

The result is a fully functional customer service chatbot that can later be extended into a website chat widget, a voice support assistant, or a broader AI-powered support system.

In this guide, we’ll focus on building the underlying realtime voice and retrieval architecture step by step, using production-oriented design patterns that you can adapt for real-world deployments.

Key Points

1. Build a realtime voice-to-voice AI chatbot using OpenAI’s Realtime API
2. Stream microphone audio to the model and receive synthesized speech responses
3. Ground answers using a Retrieval-Augmented Generation (RAG) pipeline
4. Structure the system so it can power a voice-enabled chat widget or website AI chat experience
5. Follow an architecture suitable for production-grade AI support systems