Building an AI-Powered Product Chat with Amazon S3 Vectors and Nova Lite

#awsbedrock #vectordatabase #serverless

Vector search is quickly becoming the foundation of modern AI applications. Whether it’s product recommendations, semantic search, or conversational assistants, vectors enable systems to understand meaning rather than just keywords.

But building vector-powered systems hasn’t been easy:

Managed vector databases can cost thousands per month
Self-hosting open-source solutions means managing clusters, scaling, and performance
Scaling to millions of vectors introduces serious operational complexity

With Amazon S3 Vectors, AWS changes this equation by bringing vector search directly into Amazon S3 — no clusters, no capacity planning, no new infrastructure.

In this post, I’ll walk through how I built a serverless AI-powered product chat application using Amazon S3 Vectors, Amazon Bedrock (Nova Lite + Titan Embed), and AWS Lambda, following a clean RAG (Retrieval Augmented Generation) architecture.

What Is Amazon S3 Vectors?

Amazon S3 Vectors allows you to:

Store vector embeddings natively in S3
Perform high-performance similarity search (cosine similarity)
Scale automatically without managing infrastructure

GitHub Repository

Source code for the full implementation is available here:

https://github.com/debadatta30/awss3vector-productchat.git

Architecture Overview

This application is fully serverless and consists of:

Frontend: React app hosted on S3 static website
Backend: AWS Lambda
Vector Store: Amazon S3 Vectors
Embeddings: Titan Embed (Amazon Bedrock)
LLM: Nova Lite (Amazon Bedrock)
API Layer: Amazon API Gateway

Amazon S3 Vectors fundamentally simplifies how we build vector-based AI systems. When combined with Amazon Bedrock and serverless AWS services, you can build production-ready RAG applications that are: