Experimenting with Different Chunking Strategies via LangChain

This tutorial explores the impact of different chunking strategies on retrieval augmented generation applications using LangChain. Chunking is the process of dividing text into smaller parts, and the choice of strategy can significantly affect the output quality. The code for this post can be found in a GitHub repo on LLM experimentation. The tutorial covers setting up the environment, importing necessary tools, and creating a function that takes parameters for document ingestion and chunking experimentation. It then tests five different chunking strategies with varying lengths and overlaps. The results show that finding an ideal chunking size is challenging and depends on the desired output format. Future tutorials may cover testing overlaps and using other libraries to refine chunking strategies further.

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