Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
2026-09-05 00:08Products & Apps🔥 42.2 heat score
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Amazon Cloud Services (AWS) has integrated the Text Extraction feature (Amazon Textract) into its Amazon Bedrock generative AI platform, aiming to solve the problem faced by customer service teams when handling thousands of complex multi-page invoices. Previously, when customers tried to directly input the original invoices into the Retrieval-Augmented Generation (RAG) system, large language models lacked key details, produced hallucinations, and were unable to effectively handle various file formats, including PDF, DOCX, TXT, HTML, PNG, and XLSX, resulting in delayed responses and frequent errors. The new solution combines Amazon Bedrock’s generative AI capabilities with Textract’s high-precision structured content extraction function, enabling organizations to shift from manual document searches to programmed queries. This solution supports the dialogue-based parsing of complex PDF and image-formatted invoices, accurately extracting core information such as account numbers and invoice details, thereby improving interaction speed and accuracy in large-scale scenarios.
Amazon Bedrock integrates with Amazon Textract to help customer service teams handle thousands of complex multi-page invoices. This solution combines Bella’s generative AI capabilities with Textract’s high-precision structured content extraction technology, enabling organizations to shift from manual document searches to programmatic queries. This allows for the extraction of actionable insights from invoices on a large scale, improving efficiency and accuracy. The article demonstrates how to interact with complex PDF and image-formatted invoices, parse their content, analyze information, and mark relevant tables to assist large language models in extracting key data. Previously, when customers tried to directly input original invoices into RAG solutions, the LLMs often lacked critical details, produced hallucinations, and could not handle various formats (PDF, DOCX, TXT, HTML, PNG, XLSX), resulting in delayed responses and frequent errors. Ultimately, the team used Bella combined with Textract to support the above file types and accurately extract core information such as account numbers and invoice details.