LangChain Text Splitter Explained | Covered Chunking, Overlap & Embeddings for Vector Databases

Hello Everyone,

Welcome back to my YouTube channel Summarized AI !

In this video, we are going to talk about Text Splitters in LangChain — a critical concept when building RAG (Retrieval-Augmented Generation) and document-aware AI applications.

Why Do We Need a Text Splitter?

Imagine you have a huge document like a PDF, Word file, HTML page, JSON, or plain text and you want an AI to answer questions from it.

But there’s a challenge, LLMs have a token limit, so we can’t send the entire document at once.

This is where LangChain Text Splitters come into the picture.

What Does a Text Splitter Do ???

A text splitter:
1. Breaks long documents into smaller chunks
2. Adds overlap between chunks to preserve context
3. Makes the text ready for embeddings and vector storage

For example, if one chunk ends with
“The API integrates with…”

The next chunk repeats part of that sentence so the meaning is not lost.

Text splitters work with:
1. PDF files
2. Word documents
3. HTML pages
4. JSON files
5. TXT and plain text

In this video, we cover:
1. CharacterTextSplitter
2. RecursiveCharacterTextSplitter

GitHub Code Reference:
https://github.com/toimrank/summarizedai/blob/main/langchain/splitter.py

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Видео LangChain Text Splitter Explained | Covered Chunking, Overlap & Embeddings for Vector Databases канала SummarizedAI