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LangChain / RAG / Vector DBs · Lesson

Understanding Text Splitting Strategies

Learn why and how to split large documents into smaller, meaningful chunks to optimize retrieval and context window usage.

Understanding Text Splitting Strategies is a free LangChain / RAG / Vector DBs lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LangChain / RAG / Vector DBs learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Split Documents?

Large Language Models (LLMs) have a 'context window' – a limit on how much text they can process at once. If you feed them a document that's too long, they simply can't handle it all.

Text splitting is the process of breaking down large documents into smaller, manageable chunks. This makes them suitable for LLMs and helps retrieval systems find more precise information.

The Context Window Limit

Imagine an LLM as a very smart person with a short-term memory limit. The context window is like that limit. If you give it too much information, it might forget the beginning or get confused.

  • LLMs can only process a certain number of tokens (words or sub-words).
  • Going over this limit means information is truncated or ignored.
  • Smaller chunks ensure all relevant information fits and is processed effectively.

Basic Splitting: By Character

One of the most straightforward ways to split text is using a CharacterTextSplitter. It simply breaks text based on a specified separator, usually a newline character (\n).

It's like cutting a long rope into smaller pieces at every knot you find. This method is easy to understand but can sometimes break sentences or paragraphs in awkward places.

Code: Simple Character Split

Try running this example. Notice how the CharacterTextSplitter breaks the text primarily at each newline character.

from langchain_text_splitters import CharacterTextSplitter

text = "Hello world.\nThis is a test.\nAnother line here." 

# Initialize the splitter
text_splitter = CharacterTextSplitter(
    separator="\n",
    chunk_size=20, # Max characters per chunk
    chunk_overlap=0, # No overlap for simplicity
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Understanding Chunk Size

The chunk_size parameter determines the maximum length of each piece of text. If a piece of text (before splitting by a separator) exceeds this size, the splitter will try to break it further.

Choosing the right size is crucial:

  • Too small: Context might be lost across multiple chunks, making it harder for the LLM to understand the full picture.
  • Too large: Might still exceed the LLM's context window or contain too much irrelevant information, diluting the focus.

The Role of Overlap

chunk_overlap specifies how many characters (or tokens) each chunk shares with the previous one. This is vital to maintain continuity and prevent loss of context at the boundaries of chunks.

Imagine a sentence that gets split perfectly in half across two chunks. Without overlap, the LLM might miss the connection between the two halves. Overlap ensures that key phrases or ideas aren't cut off abruptly, providing a smoother flow of information.

Recursive Character Splitting

The RecursiveCharacterTextSplitter is often preferred for general-purpose documents. Instead of just one separator, it tries a list of separators in order of preference (e.g., ["\n\n", "\n", " ", ""]).

It first tries to split by the largest, most semantically meaningful separator (like a double newline for paragraphs). If a chunk is still too big, it then tries the next smaller separator (like a single newline), and so on. This creates more semantically coherent chunks.

Code: Recursive Split in Action

This example uses a recursive splitter. Notice how it prioritizes paragraph breaks (double newlines) to keep related sentences together.

from langchain_text_splitters import RecursiveCharacterTextSplitter

text = """
LangChain is a framework for developing applications powered by language models.
It enables applications that are:
1. Data-aware: connect a language model to other sources of data.
2. Agentic: allow a language model to interact with its environment.

This framework provides tools and components to build complex LLM workflows.
"""

# Initialize the recursive splitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=100, # Max characters per chunk
    chunk_overlap=20, # Overlap to maintain context
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Choosing the Right Strategy

When should you use which splitter?

  • CharacterTextSplitter: Good for simple, highly structured text where you know the exact delimiters (e.g., CSV files, specific log formats).
  • RecursiveCharacterTextSplitter: Generally the default and best choice for most general-purpose documents (like articles, reports), as it aims for more logical and semantically coherent breaks.
  • Other splitters: LangChain offers specialized splitters for code, Markdown, and even semantic content. We'll touch on these in future lessons!

Text Splitting Challenge

You have a long article and need to split it into smaller chunks for an LLM. You decide to use a chunk_size of 500 and a chunk_overlap of 50.

Recap: Text Splitting Fundamentals

Great job! You've learned the fundamental concepts behind text splitting, a crucial step for preparing documents for LLMs.

  • We split text due to LLM context window limits and for more effective retrieval.
  • The CharacterTextSplitter provides basic splitting using a single separator.
  • The RecursiveCharacterTextSplitter offers a smarter, hierarchical approach for general text.
  • chunk_size controls the maximum length of your chunks.
  • chunk_overlap preserves context by sharing text between adjacent chunks.

Next, we'll dive deeper into customizing splitting strategies for specific content types!

Frequently asked questions

Is the “Understanding Text Splitting Strategies” lesson free?

Yes — the full text of “Understanding Text Splitting Strategies” is free to read here on the web, and the LangChain / RAG / Vector DBs course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LangChain / RAG / Vector DBs course, upgrade to CoddyKit PRO.

What will I learn in “Understanding Text Splitting Strategies”?

Learn why and how to split large documents into smaller, meaningful chunks to optimize retrieval and context window usage. You practise LangChain / RAG / Vector DBs with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start LangChain / RAG / Vector DBs?

No prior experience is required. LangChain / RAG / Vector DBs on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Understanding Text Splitting Strategies” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this LangChain / RAG / Vector DBs lesson?

Yes. Every LangChain / RAG / Vector DBs lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Loading Diverse Document Types
  2. Understanding Text Splitting Strategies
  3. Customizing Document Splitting
  4. Handling Document Metadata and Filtering
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