Knowledge Bases for Amazon Bedrock is a fully managed capability that helps securely connect base models (FM) in Amazon Bedrock to your company’s data through Retrieve and Generate (RAG) enhanced generation. This feature optimizes the entire RAG workflow, from ingest to retrieval and increased requests, eliminating the need for custom data source integrations and data flow management.
The recent announcement of Guardrails for Amazon Bedrock allows you to implement safeguards in your generative artificial intelligence (AI) custom applications for your use cases and responsible AI policies. You can create multiple security barriers tailored to various use cases and apply them across various FMs, standardizing security controls in generative AI applications.
Today’s release of Guardrails in Knowledge Bases for Amazon Bedrock brings enhanced security and compliance to your generative AI RAG applications. This new feature offers industry-leading security measures that filter harmful content and protect confidential information in your documents, enhancing user experience and aligning with organizational standards.
Knowledge Bases for Amazon Bedrock enables you to configure your RAG applications to query your knowledge base through Retrieve and Generate APIs generating responses from the retrieved information. By default, knowledge bases allow RAG applications to query the entire vector database, access all records, and retrieve relevant results. Integrating guardrails with your knowledge base provides a mechanism to filter and control the generated output, complying with predefined rules and regulations.
Common use cases for integrating guardrails with knowledge bases include internal knowledge management in a law firm, conversational search for financial services, and customer support for an e-commerce platform. Guardrails ensure the security and filtering of confidential information, inappropriate language, and compliance regulations in the responses generated by AI applications.
To prepare a dataset for Knowledge Bases for Amazon Bedrock, you can use a sample dataset containing various fictional emergency room reports. By storing the dataset in an Amazon S3 bucket and creating a knowledge base with guardrails, you can effectively query the data and ensure secure and compliant responses.
In conclusion, the integration of guardrails with Knowledge Bases for Amazon Bedrock enhances overall security, compliance, and responsible AI usage. This integration provides a customizable security framework aligned with your application’s unique requirements and responsible AI practices, offering greater control and confidence in AI-driven applications. By following the provided steps, you can efficiently set up and test knowledge bases with guardrails for enhanced security and compliance in generative AI applications.
Article Source
https://aws.amazon.com/blogs/machine-learning/introducing-guardrails-in-knowledge-bases-for-amazon-bedrock/