Hey Generative Geeks! 🚀
Welcome back to another exciting code-along tutorial. In today's video, we're diving deep into the world of Large Language Models (LLMs) and exploring a crucial aspect of text processing-**chunking large texts**. If you're working with LLMs like GPT-4, you've probably encountered limitations on input size, measured in tokens rather than characters. In this video, I'll show you why chunking text based on tokens is far more efficient than using characters and how you can implement this in your own projects.
Code : colab.research...
What You'll Learn:
🔹 Understanding Tokens vs Characters:** Learn the fundamental differences between tokens and characters and why this distinction matters in LLMs.
🔹 Character-Based Chunking:** We'll start with a simple approach to chunk text by characters and highlight its limitations.
🔹 Token-Based Chunking: Discover how to chunk text by tokens using the `RecursiveCharacterTextSplitter` and why this method is superior for LLMs.
🔹 *Practical Implementation:* Watch step-by-step as we implement token-based chunking in Python, ensuring each chunk stays within token limits.
🔹 *Real-World Examples:* See practical examples of chunking large texts and understand how to optimize your input for better performance with LLMs.
Chapters:
(00:00) Introduction
(00:18) Workflow for Load, Chunk and Ingestion of text
(01:19) Installing dependencies
(01:35) Tiktoken from OpenAI
(03:32) RecursiveCharacterTextSplitter usage
(04:23) Inspecting character splits
(05:40) Defining a custom function for token length
(07:00) RecursiveCharacterTextSplitter with Token Length splits
(10:40) Outro
Why This Matters:
Working with LLMs often requires handling large texts while staying within token limits. Splitting text by characters might seem straightforward, but it can lead to inefficient and problematic chunks. By using token-based chunking, you ensure that your inputs are optimized for the best possible performance, making your applications more robust and efficient.
Tools & Libraries:
- Python: Our go-to language for this tutorial.
- Text Processing Library: Learn how to use the `RecursiveCharacterTextSplitter` for efficient token-based chunking.
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About the Channel:
Generative Geek is your one-stop destination for all things AI, machine learning, and coding tutorials. Hosted by Vaibhav Pandey, our channel aims to simplify complex topics and make learning fun and engaging. Whether you're a beginner or an experienced developer, you'll find valuable content that helps you grow your skills and stay ahead in the AI revolution.
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