Building a YoutubeGPT with LangChain, Gradio, and Vector Database

Author(s): Yanli Liu

Originally published on Towards AI.

Discovering the GenAI Development Stack through a Practical Guide
Photo by Tech Daily on Unsplash

The world of Generative AI (GenAI) is evolving rapidly, making it easier and quicker than ever to develop AI-powered applications.

In this article, we’ll discuss the GenAI Application Development Stack, a key to creating customized AI solutions. We’ll explore key components like LangChain, Gradio, and Vector Database. Through a practical, step-by-step guide, we’ll build a YouTubeGPT, showing how these technologies work together to create an AI application.

By the end of this guide, you’ll not only understand how to use this technology stack for building AI applications but also gain insights into their internal workings. Additionally, we’ll build a functioning prototype of YouTubeGPT. This app will enable you to chat with any YouTube video or local video through a simple user interface.

An Overview of the GenAI Application Development Stack1.1. Embeddings1.2. Vector Databases1.3. Langchain1.2. Gradio1.3. LLMs and Prompts1.3. RAG and the Process of Building a Simple GenAI AppDesigning a User-Friendly YouTubeGPTStep-by-Step Walkthrough to Build YoutubeGPTClosing thoughts

Understanding and using the components of the GenAI stack is key to leverage AI for innovative applications. This modular approach allows for customization, scalability, fitting various business needs and goals.

Embeddings or Vectors are numerical representations of words. The embeddding process transform high-dimensional data… Read the full blog for free on Medium.

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Published via Towards AI