This project is a search solution using pgvector for an online retail store product catalog. We’ll build a search system that lets customers provide an item description to find similar items.

For more information, check this blog post, Building AI-powered search in PostgreSQL using Amazon SageMaker and pgvector (2023-05-03)

The overall architecture is like this:

1. Deploy the cdk stacks (For more information, see here).

• A SageMaker Studio in a private VPC.
• An Amazon Aurora Postgresql cluster for storing embeddings.
• Aurora Postgresql cluster's access credentials (username and password) stored in AWS Secrets Mananger as a name such as VSPgVectorStackAuroraPostgr-xxxxxxxxxxxx.

2. Open SageMaker Studio and then open a new System terminal.
3. Run the following commands on the terminal to clone the code repository for this project:

   git clone https://github.com/ksmin23/semantic-vector-search-with-sagemaker-pgvector.git
   

4. Open data_ingestion_to_pgvector.ipynb notebook and Run it. (For more information, see here)
5. Run Streamlit application. (For more information, see here)

• Building AI-powered search in PostgreSQL using Amazon SageMaker and pgvector (2023-05-03)
  • aws-samples/rds-postgresql-pgvector
• Extension versions for Amazon Aurora PostgreSQL
• Build Streamlit apps in Amazon SageMaker Studio (2023-04-11)
• Streamlit - A faster way to build and share data apps