LangChain / RAG / Vector DBs · Lesson

Customizing Document Splitting

Implement advanced text splitting techniques, including semantic chunking and handling code or specific data structures.

Customizing Document Splitting is a free LangChain / RAG / Vector DBs lesson on CoddyKit — lesson 3 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 Customize Text Splitting?

When preparing documents for Retrieval Augmented Generation (RAG), how you split them into chunks is crucial. Basic text splitters are a good start, but they often fall short for complex or highly structured content.

Customizing your text splitting strategy allows you to maintain better contextual integrity, leading to more accurate retrievals and better LLM responses.

Tailoring Character Splitters

LangChain's CharacterTextSplitter is simple but powerful. You can customize it by providing specific separator characters. This is useful when your documents have unique delimiters you want to respect, like a specific tag or a unique line break pattern.

By defining your own separators, you can ensure logical breaks rather than arbitrary character counts.

from langchain.text_splitter import CharacterTextSplitter

class Main:
    def run(self):
        text = "Chapter 1: Intro.Section 1.1: Basics.Section 1.2: Advanced."
        # Custom separator is "."
        splitter = CharacterTextSplitter(
            separator=".",
            chunk_size=20,
            chunk_overlap=0
        )
        chunks = splitter.split_text(text)
        for i, chunk in enumerate(chunks):
            print(f"Chunk {i+1}: {chunk}")

if __name__ == "__main__":
    Main().run()

Refining Recursive Splitters

The RecursiveCharacterTextSplitter attempts to split text using a list of separators in order, trying to keep chunks as large as possible. You can customize this list to match your document's inherent structure.

For example, you might prioritize splitting by double newlines, then single newlines, then spaces, and finally characters.

from langchain.text_splitter import RecursiveCharacterTextSplitter

class Main:
    def run(self):
        text = "Hello there!\n\nThis is a paragraph.\nAnd this is another sentence."
        # Custom list of separators
        splitter = RecursiveCharacterTextSplitter(
            separators=["\n\n", "\n", " ", ""],
            chunk_size=40,
            chunk_overlap=0
        )
        chunks = splitter.split_text(text)
        for i, chunk in enumerate(chunks):
            print(f"Chunk {i+1}: {chunk}")

if __name__ == "__main__":
    Main().run()

Intro to Semantic Chunking

Instead of relying solely on character counts or delimiters, what if we could split text based on its meaning?

Semantic chunking aims to create chunks that represent complete, coherent ideas or topics. This method helps prevent important concepts from being arbitrarily split across different chunks, which often happens with fixed-size or simple character splitters.

How Semantic Chunking Works

Semantic chunking typically involves a few steps:

Embed Sentences: Each sentence or a small unit of text is converted into a vector embedding.
Measure Similarity: The semantic similarity between adjacent sentences or units is measured using their embeddings.
Identify Breakpoints: Chunks are formed where semantic similarity drops significantly, indicating a topic change or a shift in discussion.

While LangChain doesn't have a single 'semantic splitter' out-of-the-box, it's a pattern you can build using embedding models and custom logic.

Specialized Code Splitters

Code has a unique structure, with functions, classes, comments, and specific syntax. Generic text splitters often break code in awkward places, making the resulting chunks hard to understand or use as context for an LLM.

LangChain provides specialized splitters for different programming languages. These splitters understand the syntax of a language and ensure that chunks are syntactically meaningful, like keeping a whole function or class together.

Python Code Splitter Demo

The RecursiveCharacterTextSplitter.from_language method allows you to specify a programming language. It then uses language-specific separators (like class definitions, function definitions, etc.) to create more intelligent chunks.

This ensures that code snippets passed to an LLM are more coherent.

from langchain.text_splitter import RecursiveCharacterTextSplitter, Language

class Main:
    def run(self):
        python_code = """
def calculate_sum(a, b):
    # This function adds two numbers
    return a + b

class MyCalculator:
    def __init__(self):
        self.result = 0

    def add(self, num):
        self.result += num

if __name__ == "__main__":
    total = calculate_sum(5, 3)
    print(f"Sum: {total}")
    calc = MyCalculator()
    calc.add(10)
    print(f"Calc result: {calc.result}")
"""

        # Initialize splitter for Python code
        python_splitter = RecursiveCharacterTextSplitter.from_language(
            language=Language.PYTHON,
            chunk_size=100, # Adjust chunk size to see more splits
            chunk_overlap=0
        )

        docs = python_splitter.create_documents([python_code])

        for i, doc in enumerate(docs):
            print(f"--- Chunk {i+1} ---")
            print(doc.page_content)

if __name__ == "__main__":
    Main().run()

Beyond Code: Other Structures

LangChain also offers specialized splitters for other structured formats, not just code:

MarkdownTextSplitter: Understands Markdown syntax (headings, code blocks, lists) to create logically grouped chunks.
LatexTextSplitter: Recognizes LaTeX sections, chapters, and environments, preserving the document's academic structure.

These specialized splitters are invaluable for processing documents where the formatting itself conveys important structural information.

Creating Custom Splitter Logic

For truly unique document structures or proprietary data formats, you might need to implement your own splitting logic. LangChain allows you to:

Subclass TextSplitter: Create a new class that inherits from TextSplitter and overrides its methods to define custom splitting rules.
Write a custom function: Develop a function that takes your text and returns a list of chunks based on your specific parsing requirements.

This approach offers maximum flexibility to handle complex regex patterns, custom delimiters, or nested structures unique to your dataset.

Check Your Understanding

You've learned about various ways to customize text splitting for different document types. Let's test your knowledge.

Custom Splitting Recap

In this lesson, we explored how to go beyond basic text splitting to handle diverse and complex document types more effectively:

We customized Character and Recursive Character splitters with specific lists of separators.
We introduced the concept of Semantic Chunking for meaning-based splits.
We learned about Language-specific splitters for code (e.g., Python, Java) and other structured formats like Markdown and LaTeX.
Finally, we discussed the power and flexibility of creating entirely custom splitting logic for unique data.

Mastering customized splitting strategies is a critical step in building accurate and robust RAG applications.

Frequently asked questions

Is the “Customizing Document Splitting” lesson free?

Yes — the full text of “Customizing Document Splitting” 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 “Customizing Document Splitting”?

Implement advanced text splitting techniques, including semantic chunking and handling code or specific data structures. 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 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Customizing Document Splitting” 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

Loading Diverse Document Types
Understanding Text Splitting Strategies
Customizing Document Splitting
Handling Document Metadata and Filtering

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