Amazon Bedrock’s Knowledge Bases feature is aimed at securely linking foundation models with company data using Retrieval Augmented Generation (RAG). This boosts the RAG workflow, eliminating the need for custom data source integrations and data flow management. Guardrails for Amazon Bedrock enables organizations to implement safeguards tailored to their use cases and responsible AI policies. The integration of guardrails into the Knowledge Bases feature provides more control over content generated, enhancing compliance and safety.
RAG applications can typically access the entire vector database, possibly generating inappropriate or sensitive content in violation of guidelines or policies. Guardrails provide a mechanism to control and monitor the generated output, confirming adherence to predefined rules and regulations. This is achieved by filtering and checking content for denied topics, removing any harmful material before it is sent to the InvokeModel API which generates a response free of undesirable content.
Guardrails are useful for a variety of applications. For example, in the legal sector, it can help professionals search through case files without revealing confidential client information. For financial advisors, it can help retrieve data without violating regulatory compliances. In customer support for an e-commerce platform, guardrails can prevent sensitive customer data from being exposed.
The Knowledge Bases for Amazon Bedrock requires data preparation, which involves uploading a dataset to an Amazon S3 bucket. Creating a guardrail involves following a few simple steps, such as providing a name and description for the guardrail, specifying denied topics, and adding sensitive information filters. The Knowledge Bases feature provides side-by-side comparisons of querying a knowledge base without and with guardrails.
Users can make queries via the Amazon Bedrock console or employ some provided sample code for querying with guardrails using the AWS SDK for Python (Boto3). Also, suggestions are given for the appropriate clean-up of resources after concluding the necessary tasks.
The integration between guardrails and Knowledge Bases improves the overall security and compliance of foundation models. This offers better control and confidence in AI-driven applications, aligning with the unique requirements of the application and responsible AI practices. The post concludes with links to relevant resources, namely, Amazon Bedrock Pricing and Create a knowledge base.
