Personalización de la división de documentos
Implemente técnicas avanzadas de división de texto, incluida la fragmentación semántica y el tratamiento de código o estructuras de datos específicas.
Personalización de la división de documentos es una lección gratuita de LangChain / RAG / Vector DBs en CoddyKit. Esta es la lección 3 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de LangChain / RAG / Vector DBs, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de LangChain / RAG / Vector DBs incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
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.
Preguntas frecuentes
¿La lección «Personalización de la división de documentos» es gratis?
Sí — el texto completo de «Personalización de la división de documentos» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de LangChain / RAG / Vector DBs, actualiza a CoddyKit PRO. El curso de LangChain / RAG / Vector DBs incluye 4 lecciones en total.
¿Qué aprenderé en «Personalización de la división de documentos»?
Implemente técnicas avanzadas de división de texto, incluida la fragmentación semántica y el tratamiento de código o estructuras de datos específicas. Practicas LangChain / RAG / Vector DBs con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar LangChain / RAG / Vector DBs?
No se requiere experiencia previa. LangChain / RAG / Vector DBs en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 3 de 4.
¿Cuánto tiempo toma la lección «Personalización de la división de documentos»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de LangChain / RAG / Vector DBs?
Sí. Cada lección de LangChain / RAG / Vector DBs incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Carga de distintos tipos de documentos
- Comprensión de las estrategias de división de texto
- Personalización de la división de documentos
- Gestión y filtrado de metadatos de documentos