Amazon announced that Knowledge Bases for Amazon Bedrock are now generally available as of November 2023. This feature allows users to maximize Retrieval Augmented Generation (RAG) by integrating their company data into the language model’s generation process. By incorporating unique data sources, organizations can enhance the relevance, accuracy, and contextual awareness of the language model’s outputs.
Metadata filtering in Knowledge Bases for Amazon Bedrock allows users to control the source data used for retrieval, improving relevance and quality while reducing noise from irrelevant data. This feature enables access control based on metadata fields such as user roles, departments, or data sensitivity levels. By filtering data, organizations can maintain privacy and security, comply with regulations, and ensure that responses are tailored to specific user needs.
Practical applications of metadata filtering include scenarios in HR, B2B platforms, and work organization applications. In the healthcare domain, metadata filtering ensures that doctors can only access transcripts from their patient interactions, enhancing privacy and confidentiality.
To implement access control with metadata filtering, users can use Amazon Cognito for user authentication and DynamoDB to store doctor-patient associations. The dataset format includes PDF files with metadata JSON files containing patient identifiers. Creating knowledge bases with metadata filtering and refining search results based on filter conditions allows for personalized and secure generative AI experiences.
Programmatic querying of knowledge bases can be achieved using Boto3 in Python to call the retrieve_and_generate API, incorporating metadata filtering. A Streamlit sample app serves as the user interface to interact with knowledge bases, ensuring that only authorized users can access patient data.
Cleaning up resources after solution deployment is essential to avoid unnecessary costs. The post emphasizes the benefits of metadata filtering in Knowledge Bases for Amazon Bedrock for improved relevancy, performance, and data security. Readers are encouraged to explore Knowledge Bases for Amazon Bedrock and share their feedback.
Article Source
https://aws.amazon.com/blogs/machine-learning/access-control-for-vector-stores-using-metadata-filtering-with-knowledge-bases-for-amazon-bedrock/
