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This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on Amazon Bedrock to enable the flexibility on the document processing time and cost.
Many companies have large volumes of paper or electronic documents that contain untapped business intelligence. With the advancement of generative AI, various large language models can be used to accurately extract relevant data from these documents. This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on Amazon Bedrock to enable the flexibility on the document processing time and cost. For time-sensitive requests, one can use the on-demand inference option, while the batch inference option is most cost optimized. It also explains how to dynamically specify the large language model and prompts at the document level, enabling you to extract data from multiple types of documents using the same pipelines.