Langchain Text Splitters (Chunking) for Beginners | 6 Examples!

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 In this langchain video, we will go over how you can implement chunking through 6 different text splitters. This ranges from recursive text splitters through markdown, 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist: https://www.youtube.com/watch?v=5qP6u-WGSPk&list=PLcQVY5V2UY4Kat6vxC7ESzIIzHWdwlnak&ab_channel=RyanNolanData Document Loaders: https://www.youtube.com/watch?v=75uBcITe0gU&feature=youtu.be Chat with a CSV: https://www.youtube.com/watch?v=VVdzQs-FeHE&feature=youtu.be Langchain Chains: https://www.youtube.com/watch?v=gQnJEjiaHFw&ab_channel=RyanNolanData In this comprehensive LangChain tutorial, I walk you through six essential text chunking methods to handle large documents that exceed your model's token limits. Whether you're working with a 400-page college textbook or extensive codebases, understanding chunking is critical for effective AI implementation. I start with character-based splitting and progress through recursive character splitting, which is the recommended approach for most use cases. You'll see exactly why recursive splitting produces cleaner results by respecting paragraph, sentence, and word boundaries instead of cutting mid-sentence. I then demonstrate token-based chunking using tiktoken, explaining the crucial 4-to-1 ratio between characters and tokens that directly impacts your OpenAI API costs. The video continues with specialized splitters for markdown and HTML content, where I show you how to split based on header hierarchies. Finally, I cover code splitting across multiple programming languages including Python, JavaScript, Go, Java, and more. For each method, I provide hands-on examples in Google Colab, showing you the actual output, chunk sizes, and how overlap works in practice. By the end of this tutorial, you'll understand chunk_size and chunk_overlap parameters, know when to use each splitting method, and be able to confidently prepare documents for your LangChain applications. All code examples are demonstrated live so you can follow along and implement these techniques immediately in your own projects. TIMESTAMPS 00:00 Introduction to Chunking Text 01:20 Installing Dependencies 02:00 Character Text Splitter Setup 05:00 Creating and Viewing Chunks 06:20 Recursive Character Text Splitter 08:40 Token-Based Splitting 13:00 Token vs Character Comparison 15:30 Markdown Text Splitting 18:30 HTML Header Text Splitter 21:30 Supported Programming Languages 23:00 Python Code Splitting 25:30 Recap and Conclusion OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.