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Embed textual data in Amazon RDS for SQL Server using Amazon Bedrock
- This post explores how to generate vector embeddings on Wikipedia data stored in a SQL Server database hosted on Amazon Relational Database Service (Amazon RDS) using Amazon Bedrock.
- Before we explore vector embeddings, let's discuss two key Amazon Web Services (AWS) services in this solution: Amazon RDS for SQL Server and Amazon Bedrock.
- Amazon RDS for SQL Server is a fully managed database service that simplifies the setup, operation, and scaling of SQL Server databases in the cloud.
- Amazon Bedrock is a fully managed service that offers a choice of industry leading foundation models (FMs) along with a broad set of capabilities that you need to build generative AI applications.
- The first step is to establish a connection between the RDS for SQL Server instance and Amazon Bedrock.
- Tokenizing and vectorizing content data into vector embeddings can be accomplished through various approaches.
- Prepare the data for insertion and insert the data into the new table by forming an INSERT statement.
- To test the vector similarity search, create a prompt to input a search string to search for the keyword, Warrior.
- Running this solution created a few AWS resources including an RDS for SQL Server database instance and an Amazon SageMaker Notebook instance. If you don't need these resources going forward, delete them to avoid unnecessary charges.
- This post has provided a comprehensive overview of how to generate vector embeddings, from setting up the environment and generating embeddings to exploring their applications and advanced techniques.
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