Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
AWS Machine Learning Blog · 人工智能
For customer service teams handling thousands of utility bills each month, accurately parsing and analyzing complex, multi-page documents is a persistent challenge. Inconsistent formats, dense tables, and varied layouts make it difficult to extract the right information quickly. This leads to delayed responses, billing errors, and frustrated customers. As document volumes grow, these inefficiencies compound, leaving organizations unable to act on the data already in their hands. Amazon Bedrock, integrated with Amazon Textract , provides the retrieval and generation capabilities to solve this. By combining the high-accuracy extraction of structured and unstructured content from Amazon Textract with the generative AI capabilities of Amazon Bedrock, organizations can move from manually searching through documents to programmatically querying them. This unlocks actionable insights from utility bills at scale and delivers faster, more accurate customer interactions. In this post, we demonstrate how to chat with utility bills in complex PDF and image formats, parse them, analyze the content, and tag the relevant tables to help large language models (LLMs) extract the most useful information. You can find the code for this post on GitHub . Use cases overview A customer service support team receives a multitude of queries regarding utility bills, spanning across various domains such as billing, usage, payment, and customer service. The team struggles to efficiently parse and analyze these queries, which come in various formats including PDF, DOCX, TXT, HTML, and XLSX. The manual process of extracting relevant information from these documents is time-consuming and prone to errors, leading to delays in response times and customer dissatisfaction. To solve the problem statement, the customer initially attempted to implement a Retrieval Augmented Generation (RAG) solution using the utility bills directly. However, they quickly encountered significant issues. The large language