Fundamentos de carga de datos y división de texto
Aprenda a cargar datos no estructurados y aplicar estrategias eficaces de división de texto para lograr un rendimiento óptimo en la recuperación.
Fundamentos de carga de datos y división de texto es una lección gratuita de LLM Apps in Production (RAG + Vector DB + Caching) en CoddyKit. Esta es la lección 2 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 LLM Apps in Production (RAG + Vector DB + Caching), y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de LLM Apps in Production (RAG + Vector DB + Caching) incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Loading Data for RAG
Welcome to Lesson 2! In Retrieval Augmented Generation (RAG), the first step is always to get your data ready. This means loading your information and preparing it for the Large Language Model (LLM).
Most real-world data is unstructured, meaning it doesn't fit neatly into rows and columns like a spreadsheet. Think of documents, web pages, or books.
Common Unstructured Data Sources
RAG systems can work with many types of unstructured data. Here are some common examples:
- Text files (.txt): Simple, plain text documents.
- PDFs (.pdf): Often contain text, images, and complex layouts.
- Word Documents (.docx): Rich text with formatting.
- Web Pages (.html): Content from websites.
- Databases/APIs: Text extracted from various fields.
The goal is to extract the raw text content from these sources.
Basic Text File Loading
Let's start with the simplest form: loading a plain text file. In Python, you can easily read the entire content of a file into a string.
This example creates a small sample.txt and then reads its content.
import os
def load_text_file(filepath):
with open(filepath, 'r', encoding='utf-8') as f:
return f.read()
if __name__ == "__main__":
# Create a dummy file for demonstration
file_content = "This is the first line.\nThis is the second line.\nAnd a final line of text."
with open("sample.txt", "w", encoding="utf-8") as f:
f.write(file_content)
# Load and print the content
loaded_data = load_text_file("sample.txt")
print("--- Loaded Content ---")
print(loaded_data)
# Clean up the dummy file
os.remove("sample.txt")Why Text Chunking is Essential
Once you've loaded your data, you can't usually send an entire book or long document directly to an LLM. Why not?
- Context Window Limits: LLMs have a maximum amount of text they can process at once.
- Cost: Sending very long texts is expensive, as you're typically charged per token.
- Relevance: Shorter, focused pieces of text are often more relevant for retrieval.
This is where text chunking comes in.
Understanding the Context Window
The context window is like an LLM's short-term memory. It's the maximum number of tokens (words or sub-words) it can consider when generating a response.
- If your input text is too long, it gets truncated.
- The LLM only 'sees' what's in its context window.
Chunking breaks your big document into smaller, manageable pieces that fit within this window.
Basic Chunking: Fixed Size
The simplest chunking strategy is fixed-size chunking. You define a specific number of characters or tokens, and then split your document into chunks of that exact size.
For example, if you have a 1000-character document and a chunk size of 100, you'll get 10 chunks.
- Pros: Easy to implement.
- Cons: Can cut sentences or paragraphs in half, losing context.
Fixed-Size Chunking in Action
Here's a Python example demonstrating fixed-size chunking. Notice how the text is simply cut at regular intervals, which might sometimes break words or sentences.
def fixed_size_chunker(text, chunk_size):
chunks = []
for i in range(0, len(text), chunk_size):
chunks.append(text[i : i + chunk_size])
return chunks
if __name__ == "__main__":
sample_text = "Large language models are powerful tools for text generation and understanding. However, they have limitations, especially with very long inputs due to their context window size."
chunk_size = 40
chunks = fixed_size_chunker(sample_text, chunk_size)
print(f"Original text length: {len(sample_text)}")
print(f"Chunk size: {chunk_size}")
print("--- Chunks ---")
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1} ({len(chunk)} chars): '{chunk}'")Improving Context with Overlap
Fixed-size chunking can be problematic if important context is split across two chunks. To mitigate this, we use overlapping chunks.
With overlap, each new chunk starts a bit before the previous one ended. This ensures that some text appears in multiple chunks, preserving continuity.
- A common overlap size is 10-20% of the chunk size.
- It helps the LLM connect ideas even if they span chunk boundaries.
Overlapping Chunking Example
See how adding an overlap helps maintain context. The start of each new chunk includes some text from the end of the previous one.
def overlapping_chunker(text, chunk_size, overlap_size):
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunk = text[start:end]
chunks.append(chunk)
start += chunk_size - overlap_size
if start < 0: # Handle cases where overlap > chunk_size initially
start = 0
return chunks
if __name__ == "__main__":
text_data = "The quick brown fox jumps over the lazy dog. Dogs are mammals and often friendly animals."
chunk_size = 30
overlap_size = 10
chunks = overlapping_chunker(text_data, chunk_size, overlap_size)
print(f"Original text length: {len(text_data)}")
print(f"Chunk size: {chunk_size}, Overlap size: {overlap_size}")
print("--- Overlapping Chunks ---")
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1} ({len(chunk)} chars): '{chunk}'")Check Your Understanding
You've learned about loading data and basic chunking strategies. Now, let's test your knowledge!
Recap: Data Loading & Chunking
Great job! In this lesson, we covered the foundational steps of preparing data for RAG applications:
- Data Loading: Extracting raw text from various unstructured sources like text files, PDFs, and web pages.
- Text Chunking: The essential process of breaking down long documents into smaller, manageable pieces.
- Context Window: Understanding the LLM's limitation on input text length (measured in tokens).
- Chunking Strategies: Explored basic fixed-size chunking and the improved method of fixed-size chunking with overlap to preserve context.
Next, we'll see how these chunks are used to build a simple RAG pipeline!
Preguntas frecuentes
¿La lección «Fundamentos de carga de datos y división de texto» es gratis?
Sí — el texto completo de «Fundamentos de carga de datos y división de texto» 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 LLM Apps in Production (RAG + Vector DB + Caching), actualiza a CoddyKit PRO. El curso de LLM Apps in Production (RAG + Vector DB + Caching) incluye 4 lecciones en total.
¿Qué aprenderé en «Fundamentos de carga de datos y división de texto»?
Aprenda a cargar datos no estructurados y aplicar estrategias eficaces de división de texto para lograr un rendimiento óptimo en la recuperación. Practicas LLM Apps in Production (RAG + Vector DB + Caching) 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 LLM Apps in Production (RAG + Vector DB + Caching)?
No se requiere experiencia previa. LLM Apps in Production (RAG + Vector DB + Caching) 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 2 de 4.
¿Cuánto tiempo toma la lección «Fundamentos de carga de datos y división de texto»?
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 LLM Apps in Production (RAG + Vector DB + Caching)?
Sí. Cada lección de LLM Apps in Production (RAG + Vector DB + Caching) 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
- Elegir un proveedor de LLM
- Fundamentos de carga de datos y división de texto
- Crear una canalización RAG sencilla
- Probar y evaluar su aplicación RAG