Python Tutorial: Create AI Podcasts with Agentic RAG (Llama Index + OpenAI + Eleven Labs)

This tutorial demonstrates how to create an AI podcast generator with Python code. Inspired by Google's NotebookLM podcasts, I walk through the process of how you can programmatically make customized podcasts using large language model tools and models, such as Llama Index, OpenAI, and ElevenLabs.

We’ll cover the following:

Upload a PDF and create vector embeddings with Llama Index.
Generate a podcast script using GPT-4o-mini and Retrieval-Augmented Generation (RAG) Agents.
Convert the script to lifelike audio using Eleven Labs’ text-to-speech API.
Stitch MP3 audio clips into a cohesive podcast with PyDub.

Whether you’re an AI Engineer, Machine Learning Engineer, Data Scientist, Developer, Content Creator, or Student, this tutorial provides a practical framework to automate text summarization and podcast production with AI.

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*Full Code*
https://deepcharts.substack.com/p/cre...

*Additional Resources*
OpenAI Developer Platform for API: https://platform.openai.com/docs/over...
ElevenLabs Pricing: https://elevenlabs.io/pricing
Pydub Github Page: https://github.com/jiaaro/pydub

*Chapters*
0:00 - Project Overview, Tools Used, and How to Customize the Starter Code
1:28 - API Key Acquisition, Python Environment Setup, and Initial Configuration
3:33 - PDF Processing, Embeddings, and Agentic Retrieval-Augmented Generation (RAG) using Llama Index and OpenAI LLM models
7:00 - Text-To-Speech Model API with ElevenLabs
8:17 - Stitch Audio Files Together with PyDub
8:31 - Short Example AI Podcast Output