Organizations in various industries are seeking ways to categorize and extract insights from large volumes of documents in different formats. Manual processing of these documents for classification and information extraction is costly, error-prone, and not easily scalable. The advancement of generative artificial intelligence (AI) has led to the development of intelligent document processing (IDP) solutions that can automate document classification, creating a cost-effective layer capable of handling diverse enterprise documents.
Document categorization is a crucial initial step in IDP systems to determine the appropriate actions based on the type of document. Traditional rule engines or machine learning-based classification methods may reach limitations in document format support and the addition of new document classes. Amazon Comprehend offers a document classifier with layout support for improved accuracy.
Amazon recently introduced the Titan Multimodal Embeddings model in Amazon Bedrock, enabling the creation of embeddings for images and text to be used in document classification workflows. This model generates optimized vector representations of documents scanned as images, combining visual and textual elements into unified numerical vectors for rapid indexing, contextual search, and accurate document classification.
To implement document classification using the Amazon Titan Multimodal Embeddings model, a solution is outlined with key components such as embeddings, vector databases, and semantic search. The solution involves converting input documents into embeddings and storing them in a vector database for semantic search and classification.
The architecture diagram illustrates the workflow of using the Amazon Titan Multimodal Embeddings model with documents stored in an Amazon S3 bucket for image gallery creation. The process involves uploading sample document images, converting them into embeddings, and performing semantic search for classification.
To test the solution with custom documents, an example Python Jupyter notebook is available on GitHub, requiring an AWS account with appropriate permissions to call Amazon Bedrock. The implementation involves creating a vector database, generating embeddings for sample documents, and conducting semantic search for document classification.
Additional considerations for effective use of the solution include data privacy and security, integration with existing systems, and cost estimates. Amazon Bedrock follows the AWS shared responsibility model for data protection, ensuring customer control over content hosted on the infrastructure. The Amazon Titan Multimodal Embeddings model is trained with the Euclidean L2 algorithm, enabling efficient document classification with vector databases.
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