Visualize vector embeddings stored in Amazon Aurora PostgreSQL and explore semantic similarities

This article discusses how to visualize vector embeddings stored in Amazon Aurora PostgreSQL and explore their semantic similarities. It covers the integration of PostgreSQL's pgvector extension with Amazon Bedrock to generate and visualize vector embeddings.

Specifically, the article covers:

• An overview of the solution to visualize vector embeddings using Principal Component Analysis (PCA)
• Prerequisites for the solution, including an Aurora PostgreSQL cluster, Bedrock access, and Python libraries
• Step-by-step implementation details:
  • Preparing a sample dataset
  • Generating vector embeddings using Amazon Bedrock's Titan Text Embeddings model
  • Storing data and embeddings in an Aurora PostgreSQL table with pgvector
  • Reading data from PostgreSQL and performing PCA for dimensionality reduction
  • Plotting a 3D scatter plot to visualize semantic similarities
• Cleanup steps to delete the resources created
• Conclusion highlighting the benefits of this solution for exploring semantic similarities