Best Techniques for Chunking for a RAG Application

Unveiling the Power of RAG Pipeline: Strategies, Tactics, and Trade-offs

In the realm of Natural Language Processing (NLP), the RAG pipeline has emerged as a powerful framework for information retrieval and generation. At its core lies the challenge of handling large documents efficiently

Chunking Strategies:
1. Naive Chunking:
Naive chunking involves the straightforward division of text into smaller chunks based on a fixed character count. It’s a quick and efficient method, but it lacks sophistication in understanding the underlying document structure. While suitable for rapid applications it may not be the most context-aware choice

2. Smart Approaches:
Smart chunking involves leveraging natural language processing frameworks such as NLTK and SpaCy. These tools provide advanced sentence segmentation, taking into account linguistic structures. Recursive character text splitting a variant of this approach, combines character-based splitting with recursive logic improving the handling of document structures

3. Structural Chunking:
For structured documents like HTML or Markdown, structural chunking becomes essential. This strategy involves defining the document schema and using it to guide the chunking process. Metadata, specifying the headers and subsections of each chunk allows for better tracking and organization of information.

4. Summarization Techniques:
Summarization plays a crucial role in handling large documents. The “Chain Type” summarization methods, such as “Stuff,” “Map Reduce,” and “Refine,” offer different approaches. “Stuff” directly summarizes smaller documents, “Map Reduce” iteratively summarizes chunks for larger documents, and “Refine” refines the summary as more chunks are processed. These techniques balance between retaining key information and managing computational costs.

Chunk Decoupling

Chunk decoupling refers to the concept of treating retrieval and generation differently. The “Document Summary” approach involves embedding summaries for retrieval and passing the entire document for generation. On the other hand, the “Sentence Text Windows” approach embeds relevant sentences for retrieval but provides additional context by passing a larger window of text for generation. This approach aims to balance the efficiency of retrieval with the comprehensiveness of generation.

A slightly more advanced way of chunking where tables and images are present inside our document, for that we use multi-model

Multimodal Documents:
Handling documents with diverse content, including text, tables, and images, adds another layer of complexity. Tools like Layout PDF Reader and Tesseract aid in extracting entities. Metadata addition, such as titles and descriptions, enhances the understanding of tables and images. Two retrieval strategies are explored: using a “Text Embedding Model” that embeds text and summaries together, and a “Multimodal Embedding Model” that directly embeds images and tables along with text for a comprehensive similarity search.

Conclusion:

In conclusion, the choice of chunking strategy depends on the specific use case, considering factors such as document size and the desired trade-off between efficiency and context retention. The RAG pipeline offers a versatile framework and understanding these strategies equips NLP practitioners with the tools to navigate the complexities of large document processing.

going to test these strategies on www.asktopdf.com