Summary and improvements
Summary
In this guide, we walked through using AWS Bedrock and its Titan model to match customer queries with product images.
We started by setting up our tools and introducing AWS Bedrock. We then showed how to turn text and images into numerical vectors, called embeddings, which help in comparing different products.
Next, we explained how to measure the similarity between customer queries and product images using a method called cosine similarity. This helps us find the best product matches for customer searches. We covered how to create embeddings for both products and customer queries, and how to calculate and display these similarities in a clear way.
To sum up, this guide helps anyone looking to use AWS Bedrock and Titan to build a smart system, like a chatbot or agent, that understands and matches customer searches with the right products, making online shopping easier and more efficient.
Excited to dive deeper and implement this AI-driven image-matching system in your own project?
Check the next section.
Improvements
Here are some thoughts on how you could improve this system:
1. Expanding Data Sources
Consider integrating additional data sources to enrich your product embeddings. This could include customer reviews, social media mentions, or detailed product specifications.
2. Feedback Loops
Implement a system to collect user feedback on the accuracy and relevance of the recommendations. Use this feedback to analyze and improve product recommendation over time.
3. Scalability
As your product catalog grows, ensure your system is scalable. Look into optimizing your embeddings and similarity calculations for larger datasets to maintain performance.