Utilizing Metadata Filtering in Amazon Bedrock Knowledge Bases: A Step-by-Step Guide
Amazon Bedrock’s latest feature, metadata filtering, has opened up new possibilities for users looking to enhance the accuracy of their retrieval processes. In this blog post, we will walk you through how to leverage this new feature with Knowledge Bases for Amazon Bedrock, using tabular data as an example.
The key to utilizing metadata filtering effectively lies in the preparation of your data. By creating and ingesting data and metadata into the knowledge base, users can tailor their retrieval process to meet specific criteria. In our example, we use the public dataset Food.com – Recipes and Reviews to showcase how data can be enriched with metadata to provide more accurate responses.
To set up a knowledge base with metadata for each record, we split each row of the tabular data into a single text file, with metadata fields stored separately in JSON files. By following this format and uploading the files to an Amazon S3 bucket, users can then create the knowledge base on the Amazon Bedrock console using the SDK.
Once the knowledge base is ready, users can retrieve data from it using metadata filtering. By setting up metadata filters based on specific criteria, such as preparation time and cholesterol content, users can ensure that the results returned are more accurate and relevant to their query. This approach allows for more precise retrieval of information, enhancing the overall performance of the foundation models (FMs).
In our demonstration, we show how metadata filtering can significantly impact the accuracy of the retrieval process. By comparing results with and without metadata filtering, users can see the difference in the quality of responses retrieved. By utilizing the metadata filters effectively, users can ensure that the FM provides accurate and relevant information in response to their queries.
In conclusion, metadata filtering is a powerful feature that can elevate the performance of Amazon Bedrock’s Knowledge Bases. By enriching data with metadata and setting up filters based on specific criteria, users can achieve more accurate and relevant results. This post serves as a guide for users looking to leverage metadata filtering with Knowledge Bases for Amazon Bedrock, demonstrating the impact it can have on improving the accuracy of retrieval processes.
For those looking to explore the capabilities of Knowledge Bases for Amazon Bedrock further, the resources provided at the end of this post offer valuable insights. As you delve into the world of metadata filtering, remember that accuracy and relevance are key factors in enhancing the performance of your foundation models.
About the Author:
Tanay Chowdhury is a Data Scientist at the Generative AI Innovation Center at Amazon Web Services. With a focus on solving business problems using Generative AI and Machine Learning, Tanay helps customers unlock the full potential of AI technologies.