Document Splitting with LangChain

A tutorial about Document Splitting with LangChain

In this tutorial, we will talk about different ways of how to split the loaded documents into smaller chunks using LangChain. This process is tricky since it is possible that the question of one document is in one chunk and the answer in another, which is a problem for the retrieval models. There is a lot of nuance and significance in how you split the chunks to ensure that you group semantically relevant parts. The core principle behind all text splitters in LangChain revolves around dividing the text into chunks of a certain size with some overlap between them.

Chunk size refers to the size of a section of text, which can be measured in various ways, like characters or tokens. Chunk overlap involves a slight overlap between two adjacent sections, ensuring consistency in context. Text splitters in LangChain offer methods to create and split documents, with different interfaces for text and document lists. Various types of splitters exist, differing in how they split chunks and measure chunk length. Some splitters utilize smaller models to identify sentence endings for chunk division. Metadata consistency is crucial in chunk splitting, with certain splitters focusing on this aspect. Chunk splitting methods may vary depending on the document type, particularly evident in code…