Document Chunking Strategies - Algorithms and Approaches

What is Document Chunking?

Document chunking is the process of splitting large documents into smaller, coherent pieces that can be:

• Efficiently embedded as vectors
• Retrieved independently based on relevance
• Fit within LLM context windows
• Maintain semantic meaning and context

The goal is to create chunks that are semantically coherent while being optimally sized for retrieval and processing.

1. Fixed-Size Chunking

Algorithm Overview

The simplest approach that splits text based on fixed parameters.

Algorithm: Fixed-Size Chunking
Input: text, chunk_size, overlap_size
Output: list of chunks

1. Initialize: position = 0, chunks = []
2. While position < length(text):
   a. Extract chunk from position to (position + chunk_size)
   b. If chunk ends mid-word, backtrack to last word boundary
   c. Add chunk to chunks list
   d. Advance position by (chunk_size - overlap_size)
3. Return chunks

Parameters

• Chunk Size: 200-1000 tokens (typically 500-800)
• Overlap: 10-20% of chunk size (maintains context continuity)
• Boundary Respect: Word, sentence, or paragraph boundaries

Pros & Cons