Effective chunking strategies for multimodal documents involve breaking down content into meaningful segments while preserving relationships between text, images, tables, and other data types. The goal is to create manageable pieces that retain context and enable efficient processing, such as search, analysis, or machine learning. Three key approaches include hierarchical chunking, modality-specific chunking, and context-aware grouping. Each method balances granularity with coherence, ensuring chunks are neither too fragmented nor too large to handle.
Hierarchical chunking organizes content based on its natural structure, such as sections, paragraphs, or subsections. For example, a PDF document might be split into chapters using headings, then into subheadings, and finally into individual paragraphs or bullet points. This works well for structured formats like research papers or reports. When dealing with mixed content like images embedded in text, you might group an image with its adjacent caption and explanatory text. Tools like PyMuPDF or Apache PDFBox can help extract structural metadata (e.g., font sizes or bounding boxes) to automate this. However, unstructured formats like scanned documents may require optical character recognition (OCR) to infer hierarchy, which adds complexity.
Modality-specific chunking treats different data types separately. For instance, text could be split into sentences or paragraphs using natural language processing (NLP) libraries like spaCy, while images are segmented into regions of interest using computer vision tools like OpenCV. Tables might be extracted as structured data with libraries like Camelot or Tabula. The challenge lies in maintaining cross-modal references—for example, linking a chart in a slide deck to its explanatory text. One solution is to tag chunks with metadata (e.g., positional coordinates in a PDF or slide timestamps in a video) to reconstruct relationships later. JSON or XML formats can store these associations, enabling downstream tasks like retrieving all content related to a specific diagram.
Finally, context-aware grouping focuses on preserving logical connections. For example, in a technical manual, a troubleshooting step might include a screenshot, a code snippet, and a warning note. Instead of splitting these into isolated chunks, group them into a single unit. This requires analyzing layout and semantic cues, such as proximity or repeated keywords. Rule-based systems (e.g., regex patterns for “Figure X:”) or machine learning models (e.g., layout detection in Document AI tools) can automate this. Testing is critical: validate chunk sizes by checking if search results or ML model performance degrades when chunks are too small (losing context) or too large (introducing noise). Iterate by adjusting thresholds for splitting, such as sentence counts or visual breaks, until the balance feels right for your use case.