Chunking Strategy

Question

Hi Team,

I am working with a set of documents, including .pdf, .docx, and .doc formats, containing a combination of images, project-related textual information, and tables. I would greatly appreciate your expertise in identifying the most effective chunking strategies that can be implemented using Azure’s suite of services.

Additionally, if you have any custom chunking methods or innovative suggestions for processing and structuring the content within these documents, I would be eager to hear your recommendations.

Thank you in advance for your support and insights.

Answer 1

@Keerthana, D When working with a diverse set of documents, such as PDFs, DOCX, and DOC files that contain a mix of images, textual information, and tables, it's important to employ an effective chunking strategy to process and analyze the content efficiently. Azure provides several services that can facilitate this task. To start, you can use Azure Cognitive Services, specifically the Form Recognizer, to extract structured data from your documents. This service is adept at handling complex documents with mixed content types, including tables and text, and can even process some images. For documents with significant visual content, the Computer Vision API is useful for extracting text from images through OCR and providing insights about the images themselves.

For the chunking strategy, you should consider text segmentation by breaking down the text content into smaller, meaningful chunks, such as paragraphs, headers, or bullet points. Images and tables should be extracted as separate entities, using metadata tagging to associate them with their corresponding text chunks. This can be effectively managed using the outputs from Form Recognizer or Computer Vision. If your documents have a specific structure, such as repeating patterns in project reports, developing custom logic to handle these structures can guide your chunking process.

Once the content is extracted, structuring and storing it efficiently is crucial. Azure Blob Storage is ideal for storing the extracted chunks, images, and tables, using structured formats like JSON or CSV for text and metadata, while keeping images in their native formats. For more complex data relationships or structured querying, consider using Azure Cosmos DB or Azure SQL Database. To enable powerful search capabilities, index the structured content using Azure Cognitive Search. This service allows you to index text chunks and leverage AI enrichments to add semantic understanding, facilitating searches over images and tables as well.

For deeper insights and analytics, Azure Synapse Analytics can be used to analyze the structured data, run queries, and generate comprehensive reports. Additionally, if your documents exhibit specific patterns or require specialized processing, training custom machine learning models with Azure Machine Learning can enhance extraction and chunking. Integrating with Power BI allows for visualization and exploration of the structured data, helping to derive insights and present them in a user-friendly manner. Implementing these strategies should effectively help you process and structure the content within your documents using Azure's suite of services.