Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
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The article describes a solution for customer service teams that need to parse and analyze large, complex utility bills in various formats. By integrating Amazon Textract with Amazon Bedrock, the solution preprocesses documents to extract, clean, and label data before feeding it to a RAG-based knowledge base. This approach addresses issues like incomplete data extraction and hallucinations that occur when raw documents are loaded directly into LLMs. The deployment uses a CloudFormation stack that creates Lambda functions, an S3 bucket, an OpenSearch Serverless cluster, and a Bedrock knowledge base. After uploading documents to an S3 folder, automated Lambda processing uses Textract to parse the files and converts them to TXT for the knowledge base. Users can test the system by syncing the knowledge base and asking questions in the Bedrock console with a model like Amazon Nova Micro. The article also recommends enabling Bedrock Guardrails and grounding validation for production reliability. This solution offers a scalable starting point for building intelligent document querying systems on AWS.
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