Document chunking is a crucial step in information retrieval and retrieval-augmented generation (RAG) pipelines, where large documents are broken into smaller, manageable segments called “chunks.” This improves retrieval efficiency, contextual understanding, and overall system performance.

Retrieval Augmented Generation (RAG) Pipeline

Key Terminology

Chunk Size

  • The length of a single chunk, typically measured in tokens, words, characters, or sentences.
  • Large chunk sizes retain more context but increase computational cost.
  • Small chunk sizes reduce processing needs but may lose context.

Chunk Overlap

  • The number of tokens/words that overlap between consecutive chunks.
  • Helps preserve context across chunks, especially when key information spans boundaries.
  • Typical chunk overlaps range from 10% to 30% of the chunk size.

Chunking Strategies

1. Fixed-Length Chunking

  • Splits text into equal-sized chunks based on a predefined token/word count.
  • Example: Breaking a document into 512-token chunks with a 50-token overlap.
  • Pros: Simple and computationally efficient.
  • Cons: Can split sentences or paragraphs unnaturally, losing semantic meaning.

2. Sentence-Based Chunking

  • Uses sentence boundaries to create chunks, ensuring chunks do not break in the middle of a sentence.
  • Pros: Preserves readability and coherence.
  • Cons: Chunk sizes may vary, requiring additional preprocessing.

3. Paragraph-Based Chunking

  • Divides documents based on paragraph boundaries.
  • Pros: Retains more semantic meaning than sentence-based chunking.
  • Cons: Chunk sizes can be inconsistent, and long paragraphs may still need splitting.

4. Semantic Chunking

  • Uses AI models to detect topic shifts and create contextually relevant chunks.
  • Example: LlamaIndex’s semantic chunking based on sentence embeddings.
  • Pros: Preserves meaning while keeping chunk sizes optimized.
  • Cons: More computationally expensive than basic methods.

5. Overlapping Sliding Window Chunking

  • Creates chunks with a fixed overlap (e.g., 512 tokens with a 128-token overlap).
  • Pros: Reduces context loss between chunks.
  • Cons: Introduces redundancy, increasing storage and retrieval costs.

Best Practices for Chunking

  1. Optimize Chunk Size: Choose a size that balances context retention and processing efficiency. Common ranges:

    • Small: 200-400 tokens (good for quick retrieval).
    • Medium: 512-1024 tokens (common for general-purpose retrieval).
    • Large: 2048+ tokens (useful for detailed analysis and reasoning).

  2. Use Overlap for Context Preservation: Overlap 10%-30% of chunk size to ensure context flows across chunks.

  3. Align Chunks with Natural Language Boundaries: Avoid breaking sentences or paragraphs unnaturally.

  4. Leverage Semantic Chunking for Advanced Use Cases: Use embedding-based methods for better chunk coherence.

  5. Test and Tune Based on Application Needs: Different tasks (e.g., QA, summarization, search) require different chunking strategies.

For further reading, explore tools like LlamaIndex and LangChain for implementing chunking in NLP pipelines.