Businesses can automate the processing of vast quantities of various document formats using intelligent document processing (IDP) solutions powered by AI. These solutions categorize and extract insights from documents, reducing costs and errors and allowing for scalability. A significant aspect of IDP systems is document categorization, which guides the next steps based on the document type. However, conventional methods often have limitations on document format types and support for new document classes.
Amazon Titan Multimodal Embeddings, a recently-introduced feature of Amazon Bedrock, provides an enhanced solution. This model generates vector representations of documents, including images, thereby enabling the creation of document embeddings. These numerical vectors encapsulate both textual and visual components of documents, making them suitable for rapid indexing, contextual search, and accurate classification.
To utilize this feature, businesses must first convert a selection of documents into embeddings using the Amazon Titan model. These embeddings are stored in a vector database alongside predefined labels. If a new document type is introduced, the Amazon Bedrock API can vectorize it and append it to the IDP system to improve document classification capabilities.
This solution leverages embeddings, vector databases, and semantic search to turn documents into numerical representations and find similar vectors based on distance metrics. When a new document requires classification, the same embedded model is utilized, using semantic similarity search on the vector database.
The process has been simplified into a Python Jupyter notebook available on GitHub, which contains a detailed step-by-step tutorial, including creating a vector database, calling Amazon Bedrock using the Boto3 library, adding embeddings to the database, and conducting a search.
Apart from operational steps, the document also discusses essential considerations including data privacy and security, system integration, cost estimation, and AWS shared responsibility model.
Overall, the tool provides an affordable, scalable solution for document classification in the IDP workflow. As new document templates and types emerge, developers can dynamically vectorize them and improve their IDP systems’ document classification abilities.
