LangChain Text Splitters Explained for Beginners: Split Documents for RAG

📘 Introduction

Long documents are difficult for LLM applications to use directly. A product manual, PDF page, support article, or knowledge-base document may contain thousands of words, while a retrieval system usually needs smaller pieces that can be searched and passed into a model as context.

In this tutorial, you will learn how LangChain text splitters help prepare documents for RAG, semantic search, summarization, and retrieval workflows. We will use RecursiveCharacterTextSplitter to split a long text into chunks, inspect chunk sizes, preserve metadata, and understand how chunk_size and chunk_overlap affect the result.

💡 What are LangChain text splitters?

A LangChain text splitter takes a long text or a list of Document objects and breaks them into smaller chunks. Those chunks can then be embedded, stored in a vector database, searched by a retriever, or sent to a model for summarization.

The most common beginner-friendly option is RecursiveCharacterTextSplitter. It tries to keep natural text structure together by splitting on larger separators first, such as paragraphs, then lines, then spaces, and finally individual characters if needed.

🧠 Why chunking matters for RAG

RAG systems usually search over chunks, not entire documents. When a user asks a question, the retriever looks for the chunks that are most relevant to that question. The model then receives those chunks as context and uses them to generate an answer.

Good chunks make retrieval easier. They should be small enough to be specific, but large enough to keep the meaning intact. That balance is why text splitting deserves its own tutorial instead of being treated as a tiny setup detail.

✅ Prerequisites

Before getting started, make sure you have:

☑️ Python installed
☑️ Basic Python knowledge
☑️ Basic understanding of documents or RAG workflows
☑️ A terminal or command prompt

⚙️1️⃣ Create a project folder

Create a new local project folder for this tutorial:

mkdir langchain-text-splitters
cd langchain-text-splitters

🐍2️⃣ Create a virtual environment

Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate

On Windows, activate it with:

.venv\Scripts\activate

📦3️⃣ Install libraries

Install the text splitter package:

pip install -U langchain-text-splitters langchain-core

📝4️⃣ Create a long sample document

Create a file named sample_article.txt. This sample is short enough for a tutorial but long enough to show how splitting works.

Customer Analytics Guide

Customer analytics helps teams understand how people use a product. Teams often combine event data, account data, and support data to see which features are popular and where users get stuck.

A simple analytics workflow starts by collecting events such as signups, logins, purchases, and feature usage. These events are cleaned, modeled, and stored in tables that analysts can query.

For AI applications, this content can be used in a RAG system. The system first loads the document, splits it into chunks, stores the chunks as embeddings, and retrieves the most relevant chunks for a user question.

Good chunking is important because each chunk should contain enough context to make sense. If chunks are too tiny, the retriever may return incomplete ideas. If chunks are too large, the retriever may return too much unrelated information.

A practical starting point is to use paragraph-aware splitting with a small amount of overlap. The overlap helps preserve meaning when an important sentence sits near the boundary between two chunks.

✂️5️⃣ Split the text into chunks

Now create a file named split_text.py and split the sample article with RecursiveCharacterTextSplitter.

from langchain_text_splitters import RecursiveCharacterTextSplitter

text = open("sample_article.txt", encoding="utf-8").read()

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=250,
    chunk_overlap=50,
    length_function=len,
    is_separator_regex=False,
)

chunks = text_splitter.split_text(text)

print(f"Number of chunks: {len(chunks)}")

for index, chunk in enumerate(chunks, start=1):
    print(f"\n--- Chunk {index} ({len(chunk)} characters) ---")
    print(chunk)

Run the script:

python split_text.py

You should see multiple chunks with their character lengths. The exact number can change if you edit the sample text, but each chunk should stay near the target size while preserving readable text boundaries.

Number of chunks: 5

--- Chunk 1 (241 characters) ---
Customer Analytics Guide

Customer analytics helps teams understand how people use a product...

--- Chunk 2 (226 characters) ---
A simple analytics workflow starts by collecting events such as signups...