Retrieval Augmented Generation (RAG), undefined

Hierarchical Chunking

Hierarchical chunking maintains multiple levels of segmentation simultaneously, creating a nested structure that enables multi-level retrieval. This approach preserves both document organization and detailed content:

Core Mechanism: The document is segmented at multiple granularity levels (document, section, paragraph, sentence) with appropriate metadata connecting the levels. Retrieval can then happen at different levels of specificity based on the query needs.

Advantages:

Context Preservation: Maintains relationships between segments at different levels
Flexible Retrieval: Enables retrieving both specific details and broader context
Structural Awareness: Preserves document organization in the retrieval system

Disadvantages:

Implementation Complexity: Requires sophisticated data structures and retrieval logic
Storage Overhead: Creates multiple representations of the same content
Query Complexity: Needs logic to determine appropriate retrieval level

Best Use Cases: Hierarchical chunking is ideal for complex, structured documents like technical manuals, educational content, and legal documents. It's particularly valuable when queries might require different levels of context—from specific details to broad overviews—or when the relationship between document sections is important for understanding the content.

# Hierarchical Chunking Example: A Python implementation using LangChain's RecursiveCharacterTextSplitter to create a multi-level structure that captures document, section, and paragraph relationships.
from langchain.text_splitter import RecursiveCharacterTextSplitter
import re

def hierarchical_chunking(document):
    # First level: Split by sections (assuming markdown-style headers)
    section_pattern = r'(?=# .*\n)'
    sections = re.split(section_pattern, document)[1:] if re.search(section_pattern, document) else [document]
    
    # Second level: Split sections into paragraphs
    paragraph_splitter = RecursiveCharacterTextSplitter(
        separators=["\n\n"],
        chunk_size=2000,
        chunk_overlap=0
    )
    
    # Third level: Split paragraphs into smaller chunks
    chunk_splitter = RecursiveCharacterTextSplitter(
        separators=["\n", ". ", "? ", "! ", ",", " "],
        chunk_size=500,
        chunk_overlap=50
    )
    
    hierarchical_chunks = {
        'document': document,
        'sections': []
    }
    
    for section in sections:
        section_title = re.match(r'# (.*)', section).group(1) if re.match(r'# (.*)', section) else 'Untitled Section'
        paragraphs = paragraph_splitter.split_text(section)
        
        section_data = {
            'title': section_title,
            'content': section,
            'paragraphs': []
        }
        
        for paragraph in paragraphs:
            chunks = chunk_splitter.split_text(paragraph)
            section_data['paragraphs'].append({
                'content': paragraph,
                'chunks': chunks
            })
        
        hierarchical_chunks['sections'].append(section_data)
    
    return hierarchical_chunks