Text Splitting in LangChain: A Component of the RAG System

Author(s): Mdabdullahalhasib

Originally published on Towards AI.

Understand the importance of text splitter, explore different techniques & implement each technique in LangChain.

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Photo by Clark Tibbs on Unsplash

RAG(Retrieval Augmented Generation) is an efficient way to create an LLM-based application. It helps to generate accurate answers to user queries. To create an RAG-based application, we have to perform some operations such as document loading, splitting large documents into multiple small chunks, embedding, indexing embedding, and storing them in a vector database. Then depending on the user queries, the system extracts the relevant context from the vector database and pass to the prompt as well as user queries. Then LLM takes the User Queries as well as the content and generates appropriate responses to the user. This is the overall procedure of the RAG system.

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Source: Image By Authod

Text Splitter in LangChain helps to break down large documents into smaller chunks. In large documents or texts, it is hard to find the relevant context based on the user queries. Then we can’t pass the large document to the LLM model. Every LLM model can take limited tokens as input and process to generate… Read the full blog for free on Medium.

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Published via Towards AI