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Introduction

This walkthrough is on using AWS Bedrock's Titan Embed Image model to create and compare embeddings from images and text queries. This guide matches customer text queries, like "I'm looking for a red bag" with relevant product images, showcasing AI's power in e-commerce and digital customer experience.

Overview​

The walkthrough is implemented in a Jupyter Notebook and uses AWS Bedrock and Titan Embed Image model to create and compare embeddings from images and text queries.

Steps​

We'll walk through all the steps needed to implement AWS Bedrock and answer a customer query with relevant products from your E-commerce store.

  1. Prerequisites

  2. What is AWS Bedrock?

  3. Request AWS Bedrock

  4. Setting up Jupyter Notebook

  5. Prepare product catalog

  6. What are embeddings?

  7. What is cosine similarity?

  8. Generate product image embeddings

  9. Query handling

  10. Calculate cosine similarity

  11. Display product recommendations

  12. Summary

  13. Next steps