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LangChain / RAG / Vector DBs · Ders

Belge Bölmeyi Özelleştirme

Anlamsal parçalara ayırma ve kod ya da belirli veri yapılarıyla çalışma dahil gelişmiş metin bölme tekniklerini uygulayın.

Belge Bölmeyi Özelleştirme, CoddyKit'te ücretsiz bir LangChain / RAG / Vector DBs dersidir. Bu, 4 dersinin 3. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, LangChain / RAG / Vector DBs öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. LangChain / RAG / Vector DBs kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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.

Sıkça Sorulan Sorular

“Belge Bölmeyi Özelleştirme” dersi ücretsiz mi?

Evet — “Belge Bölmeyi Özelleştirme” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve LangChain / RAG / Vector DBs kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. LangChain / RAG / Vector DBs kursu toplamda 4 dersten oluşur.

“Belge Bölmeyi Özelleştirme” dersinde ne öğreneceğim?

Anlamsal parçalara ayırma ve kod ya da belirli veri yapılarıyla çalışma dahil gelişmiş metin bölme tekniklerini uygulayın. LangChain / RAG / Vector DBs ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

LangChain / RAG / Vector DBs öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te LangChain / RAG / Vector DBs, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 3. dersidir.

“Belge Bölmeyi Özelleştirme” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu LangChain / RAG / Vector DBs dersinde kod yazıp çalıştırabilir miyim?

Evet. Her LangChain / RAG / Vector DBs dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

  1. Çeşitli Belge Türlerini Yükleme
  2. Metin Bölme Stratejilerini Anlama
  3. Belge Bölmeyi Özelleştirme
  4. Belge Üst Verilerini İşleme ve Filtreleme
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