LangChain RAG Project | Part 1: Extracting YouTube Transcripts for AI | Video #46

Описание: Welcome to Part 1 of our End-to-End RAG Project! 🎥 In Video #46 of our LangChain Full Course, we begin building a real-world application that can "talk" to any YouTube video.

The first and most crucial step in any RAG (Retrieval-Augmented Generation) pipeline is Data Ingestion. Today, I’ll show you how to use the YoutubeLoader and youtube-transcript-api to pull raw text data directly from a video URL. This transcript will serve as the "brain" for our AI assistant.

Practical code showing 'YoutubeLoader.from_youtube_url' and the resulting 'Document' object in the terminal

✅ In this practical session, we cover:

The Project Vision: An overview of the "Talk to YouTube" app we are building.

Installing Dependencies: Setting up youtube-transcript-api and pytube.

Using YoutubeLoader: How to fetch transcripts with just a few lines of Python code.

Handling Metadata: Why capturing the title, author, and video length is vital for a professional RAG app.

Troubleshooting: How to handle videos that don't have captions or are age-restricted.

Why this matters: You can't build an AI assistant without data. By mastering transcript extraction, you open the door to building automated video summarizers, educational Q&A bots, and content research tools that save hours of manual watching.

👉 What's Next? In Video #47, we will take this raw transcript and perform Text Splitting and Embedding to prepare it for our Vector Store. Don't miss it!

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