In this video, we're going to focus on preparing our text using LangChain data loaders, tokenization using the tiktoken tokenizers, chunking with LangChain text splitters, and storing data with Hugging Face datasets. Naturally, the focus here is on OpenAI embedding and completion models, but we can apply the same logic to other LLMs like those available via Hugging Face, Cohere, and so on. 🔗 Notebook link: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/xx-langchain-chunking.ipynb 🎙️ Support me on Patreon: https://patreon.com/JamesBriggs 🎨 AI Art: https://www.etsy.com/uk/shop/IntelligentArtEU 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Data preparation for LLMs 00:45 Downloading the LangChain docs 03:29 Using LangChain document loaders 05:54 How much text can we fit in LLMs? 11:57 Using tiktoken tokenizer to find length of text 16:02 Initializing the recursive text splitter in Langchain 17:25 Why we use chunk overlap 20:23 Chunking with RecursiveCharacterTextSplitter 21:37 Creating the dataset 24:50 Saving and loading with JSONL file 28:40 Data prep is important