Build Your Own AI Voice Assistant with RAG & Python (Step-by-Step)
https://github.com/schogini/voice-ai-...
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In this video, I’ll show you how to build a fully functional AI Agentic Voice Assistant from scratch! 🚀 We are going to build a Python backend using FastAPI, connect it to a Qdrant Vector Database for RAG (Retrieval Augmented Generation), and create a custom embeddable chat widget that supports real-time Voice-to-Text and Text-to-Speech.
By the end of this tutorial, you’ll have your own self-hosted chatbot that you can add to any website using Docker.
👇 Download the Project Files:
https://github.com/schogini/voice-ai-...
🔥 What We'll Build:
A high-performance Python Backend (FastAPI).
A Knowledge Base using Qdrant Vector Database.
A beautiful Chat Widget with a "Voice Mode" toggle.
Full Docker Compose setup for one-click deployment.
🛠️ Tech Stack:
Language: Python 3.11
Framework: FastAPI
AI/RAG: LangChain & FastEmbed
Database: Qdrant (Vector DB)
Frontend: HTML5, CSS3, Vanilla JavaScript (Web Speech API)
Deployment: Docker & Docker Compose
⏱️ Timestamps:
0:00 - Intro & Demo
1:30 - Project Architecture Explained
3:15 - Setting up the Python Backend (FastAPI)
8:45 - Integrating Qdrant Vector Database
12:20 - Implementing RAG (Retrieval Augmented Generation)
16:50 - Building the Voice-Enabled Chat Widget
22:10 - Docker Compose Setup
25:00 - Final Testing & Ingesting Data
27:30 - How to Embed on Your Website
💻 Commands Used:
Start the project:
docker-compose up --build -d
Ingest Data (Add knowledge):
curl -X POST "http://localhost:8000/ingest" -H "Content-Type: application/json" -d '{"text": "Your knowledge here...", "metadata": {"source": "manual"}}'
#Python #AI #RAG #Chatbot #Docker #FastAPI #CodingTutorial #Qdrant #VoiceAI