In November 2023, Amazon announced the general availability of Knowledge Bases for Amazon Bedrock. Knowledge bases enable users to incorporate their company data into the Retrieval Augmented Generation (RAG) process, enhancing the relevance, accuracy, and contextual awareness of the language model’s outputs. This tool helps organizations make better use of large language models by ensuring the responses generated are uniquely tailored to their domain knowledge, regulations, and business requirements.
With the introduction of metadata filtering in Knowledge Bases for Amazon Bedrock, users can define and use metadata fields to filter the source data used for retrieving relevant context during RAG. This improves the quality of retrieved context and reduces noise from irrelevant data, providing better control over RAG for results suited to specific use case needs.
Metadata filtering in knowledge bases enables the control of data access. By specifying metadata fields based on user roles, departments, or data sensitivity levels, organizations can control the information fetched and used only by authorized users or applications, ensuring data privacy and security.
The post also discussed how to implement metadata filtering, exploring practical examples across different domains, including HR chatbots, business-friendly platforms, and work organization applications. A particular highlight was the discussion about access control with metadata filtering in the healthcare domain, showing how a data search application could apply access control to ensure that doctors only accessed their patient interactions and not those of others.
The solution architecture followed included user authentication with Amazon Cognito and doctor-patient association in DynamoDB. The post also outlined how the correct dataset format ensures effective metadata filtering.
The knowledge base creation process in Amazon Bedrock is straightforward, with user-friendly steps guided by the AWS Management Console. Programmatic ways to query the knowledge base using AWS SDKs were also explored. A visual interface can display results and a sample Streamlit application serves as a frontend for users to initiate conversations and interact with the knowledge base.
In conclusion, metadata filtering within Knowledge Bases for Amazon Bedrock provides powerful capabilities by implementing access control and ensuring data privacy as well as security in RAG applications. This tool allows organizations to precisely control the subset of data accessible to different users or applications, improves the relevancy of searches, and enhances their performance.
