Personnalisation du découpage des documents
Mettez en œuvre des techniques avancées de découpage du texte, notamment le découpage sémantique et la gestion du code ou de structures de données particulières.
Personnalisation du découpage des documents est une leçon LangChain / RAG / Vector DBs gratuite sur CoddyKit. Ceci est la leçon 3 sur 4. Tu peux lire la leçon complète ci-dessous gratuitement — puis la pratiquer en direct dans le navigateur avec un éditeur de code intégré et un tuteur IA 24/7. Elle fait partie du parcours d'apprentissage LangChain / RAG / Vector DBs, et ta progression se synchronise sur le web et l'application CoddyKit. Le cours LangChain / RAG / Vector DBs comprend 4 leçons au total.
Certaines parties de cette leçon n'ont pas encore été traduites et s'affichent en anglais.
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 fromTextSplitterand 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.
Questions Fréquemment Posées
La leçon « Personnalisation du découpage des documents » est-elle gratuite ?
Oui — le texte complet de « Personnalisation du découpage des documents » est gratuit à lire ici sur le web. Pour la pratiquer de manière interactive (un éditeur de code intégré et un tuteur IA 24/7) et déverrouiller le reste du cours LangChain / RAG / Vector DBs, passe à CoddyKit PRO. Le cours LangChain / RAG / Vector DBs comprend 4 leçons au total.
Qu'est-ce que j'apprendrai dans « Personnalisation du découpage des documents » ?
Mettez en œuvre des techniques avancées de découpage du texte, notamment le découpage sémantique et la gestion du code ou de structures de données particulières. Tu pratiques LangChain / RAG / Vector DBs avec du code pratique que tu exécutes directement dans le navigateur, et un tuteur IA 24/7 répond à tes questions au fur et à mesure que tu avances dans la leçon.
Dois-je avoir de l'expérience pour commencer LangChain / RAG / Vector DBs ?
Aucune expérience préalable n'est requise. LangChain / RAG / Vector DBs sur CoddyKit est structuré pour les débutants jusqu'aux apprenants avancés, donc tu peux commencer ici ou depuis le début et avancer à ton rythme. Ceci est la leçon 3 sur 4.
Combien de temps prend la leçon « Personnalisation du découpage des documents » ?
La plupart des leçons CoddyKit prennent environ 5–10 minutes. Chacune est courte et interactive, tu progresses régulièrement et tu repiques exactement où tu t'es arrêté sur le web et l'app.
Peux-tu écrire et exécuter du code dans cette leçon LangChain / RAG / Vector DBs ?
Oui. Chaque leçon LangChain / RAG / Vector DBs inclut un éditeur de code intégré, tu écris et exécutes du vrai code directement dans ton navigateur et tu reçois des retours IA instantanés — aucune configuration locale requise.
Toutes les leçons de ce cours
- Chargement de différents types de documents
- Comprendre les stratégies de découpage du texte
- Personnalisation du découpage des documents
- Gérer les métadonnées des documents et le filtrage