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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to Chunking Strategies for RAG - Made Simple!

Chunking is a crucial step in Retrieval-Augmented Generation (RAG) systems, where large documents are divided into smaller, manageable pieces. This process ensures that text fits the input size of embedding models and enhances the efficiency and accuracy of retrieval. Let’s explore five common chunking strategies and their implementations.

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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Fixed-size Chunking - Made Simple!

Fixed-size chunking splits text into uniform segments of a specified length. While simple, this method may break sentences or ideas mid-stream, potentially distributing important information across multiple chunks.

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✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Source Code for Fixed-size Chunking - Made Simple!

Let’s break this down together! Here’s how we can tackle this:

def fixed_size_chunking(text, chunk_size):
    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

text = "This is a sample text for demonstrating fixed-size chunking. It may break sentences."
chunks = fixed_size_chunking(text, 20)
print(chunks)

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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Results for: Source Code for Fixed-size Chunking - Made Simple!

Here’s a handy trick you’ll love! Here’s how we can tackle this:

['This is a sample te', 'xt for demonstratin', 'g fixed-size chunki', 'ng. It may break se', 'ntences.']

🚀 Semantic Chunking - Made Simple!

Semantic chunking segments documents based on meaningful units like sentences or paragraphs. It creates embeddings for each segment and combines them based on cosine similarity until a significant drop is detected, forming a new chunk.

🚀 Source Code for Semantic Chunking - Made Simple!

Ready for some cool stuff? Here’s how we can tackle this:

import re
from collections import Counter
import math

def cosine_similarity(vec1, vec2):
    intersection = set(vec1.keys()) & set(vec2.keys())
    numerator = sum([vec1[x] * vec2[x] for x in intersection])
    
    sum1 = sum([vec1[x]**2 for x in vec1.keys()])
    sum2 = sum([vec2[x]**2 for x in vec2.keys()])
    denominator = math.sqrt(sum1) * math.sqrt(sum2)
    
    if not denominator:
        return 0.0
    return float(numerator) / denominator

def create_embedding(text):
    words = re.findall(r'\w+', text.lower())
    return Counter(words)

def semantic_chunking(text, similarity_threshold=0.5):
    sentences = re.split(r'(?<=[.!?])\s+', text)
    chunks = []
    current_chunk = sentences[0]
    current_embedding = create_embedding(current_chunk)
    
    for sentence in sentences[1:]:
        sentence_embedding = create_embedding(sentence)
        similarity = cosine_similarity(current_embedding, sentence_embedding)
        
        if similarity >= similarity_threshold:
            current_chunk += " " + sentence
            current_embedding = create_embedding(current_chunk)
        else:
            chunks.append(current_chunk)
            current_chunk = sentence
            current_embedding = sentence_embedding
    
    chunks.append(current_chunk)
    return chunks

text = "This is a sample text. It shows you semantic chunking. We split based on meaning. New topics start new chunks."
chunks = semantic_chunking(text)
print(chunks)

🚀 Results for: Source Code for Semantic Chunking - Made Simple!

Here’s where it gets exciting! Here’s how we can tackle this:

['This is a sample text. It shows you semantic chunking.', 'We split based on meaning.', 'New topics start new chunks.']

🚀 Recursive Chunking - Made Simple!

Recursive chunking first divides text based on inherent separators like paragraphs or sections. If any resulting chunk exceeds a predefined size limit, it’s further split into smaller chunks.

🚀 Source Code for Recursive Chunking - Made Simple!

Let’s make this super clear! Here’s how we can tackle this:

def recursive_chunking(text, max_chunk_size=100, separator='\n\n'):
    chunks = text.split(separator)
    result = []
    
    for chunk in chunks:
        if len(chunk) <= max_chunk_size:
            result.append(chunk)
        else:
            # Recursively split large chunks
            result.extend(recursive_chunking(chunk, max_chunk_size, '. '))
    
    return result

text = """Paragraph 1 is short.

Paragraph 2 is a bit longer and exceeds the maximum chunk size. It will be split into smaller parts based on sentences.

Paragraph 3 is also short."""

chunks = recursive_chunking(text)
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}: {chunk}")

🚀 Results for: Source Code for Recursive Chunking - Made Simple!

Here’s a handy trick you’ll love! Here’s how we can tackle this:

Chunk 1: Paragraph 1 is short.
Chunk 2: Paragraph 2 is a bit longer and exceeds the maximum chunk size.
Chunk 3: It will be split into smaller parts based on sentences.
Chunk 4: Paragraph 3 is also short.

🚀 Document Structure-based Chunking - Made Simple!

This method uses the inherent structure of documents, such as headings, sections, or paragraphs, to define chunk boundaries. It maintains structural integrity by aligning with the document’s logical sections but assumes a clear structure exists.

🚀 Source Code for Document Structure-based Chunking - Made Simple!

Let’s make this super clear! Here’s how we can tackle this:

import re

def structure_based_chunking(text):
    # Define patterns for different structural elements
    patterns = {
        'heading': r'^#+\s+.*$',
        'paragraph': r'^(?!#+\s+).*(?:\n(?!#+\s+).+)*',
    }
    
    chunks = []
    lines = text.split('\n')
    current_chunk = ''
    
    for line in lines:
        if re.match(patterns['heading'], line):
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = line + '\n'
        elif re.match(patterns['paragraph'], line):
            current_chunk += line + '\n'
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = ''
    
    if current_chunk:
        chunks.append(current_chunk.strip())
    
    return chunks

text = """# Introduction
This is the introduction paragraph.

This is the first section's content.
It spans multiple lines.

## 🎯 Section 2 - Let\'s Get Started!
This is the second section's content."""

chunks = structure_based_chunking(text)
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}:\n{chunk}\n")

🚀 Results for: Source Code for Document Structure-based Chunking - Made Simple!

Here’s where it gets exciting! Here’s how we can tackle this:

Chunk 1:
# Introduction
This is the introduction paragraph.

Chunk 2:
## 🎯 Section 1 - Let\'s Get Started!
This is the first section's content.
It spans multiple lines.

Chunk 3:
## 🎯 Section 2 - Let\'s Get Started!
This is the second section's content.

🚀 LLM-based Chunking - Made Simple!

LLM-based chunking uses language models to create semantically isolated and meaningful chunks. While this method ensures high semantic accuracy, it is computationally demanding and may be limited by the LLM’s context window.

🚀 Source Code for LLM-based Chunking - Made Simple!

Let’s make this super clear! Here’s how we can tackle this:

def simulate_llm_chunking(text, max_chunk_size=100):
    # This is a simplified simulation of LLM-based chunking
    # In practice, you would use an actual LLM API
    
    words = text.split()
    chunks = []
    current_chunk = []
    current_size = 0
    
    for word in words:
        if current_size + len(word) + 1 > max_chunk_size:
            chunks.append(' '.join(current_chunk))
            current_chunk = [word]
            current_size = len(word)
        else:
            current_chunk.append(word)
            current_size += len(word) + 1
    
    if current_chunk:
        chunks.append(' '.join(current_chunk))
    
    return chunks

text = "This is a simulation of LLM-based chunking. In reality, an LLM would understand context and create more semantically meaningful chunks. This method is computationally expensive but potentially more accurate."

chunks = simulate_llm_chunking(text)
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}: {chunk}")

🚀 Results for: Source Code for LLM-based Chunking - Made Simple!

Let me walk you through this step by step! Here’s how we can tackle this:

Chunk 1: This is a simulation of LLM-based chunking. In reality, an LLM would understand context and create more
Chunk 2: semantically meaningful chunks. This method is computationally expensive but potentially more accurate.

🚀 Real-life Example: Text Summarization - Made Simple!

Chunking strategies are crucial in text summarization tasks. For instance, when summarizing a long research paper, semantic chunking can be used to divide the paper into coherent sections, allowing for more accurate summarization of each part.

🚀 Real-life Example: Question Answering Systems - Made Simple!

In question answering systems, document structure-based chunking can be employed to break down textbooks or manuals. This allows the system to quickly locate relevant sections when answering specific questions about the content.

🚀 Additional Resources - Made Simple!

For more information on chunking strategies and their applications in natural language processing, refer to the following ArXiv papers:

1. “Efficient Document Retrieval by End-to-End Refining and Chunking” (arXiv:2310.14102)
2. “Retrieval-Augmented Generation for Large Language Models: A Survey” (arXiv:2312.10997)

These papers provide in-depth discussions on various chunking techniques and their impact on retrieval-augmented generation systems.

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀