Building an AI-Powered Podcast Search Engine with Amazon Q Developer, Amazon Bedrock and Cohere Rerank 3.5

Introduction

In this blog post, we’ll explore how to build a sophisticated podcast search engine that leverages AI for semantic search and reranking capabilities. Our solution combines AWS services with Cohere’s powerful language models to create an efficient and accurate podcast discovery platform.

Architecture Overview

Our application consists of three main components:

1. A FastAPI backend service that handles search requests
2. A React frontend for user interactions
3. AWS AppRunner for deployment and scaling

Key Technologies Used

• FastAPI: For building our efficient API endpoints
• React: For creating an interactive frontend
• AWS AppRunner: For containerized deployment
• Cohere: For AI-powered reranking
• boto3: For AWS service integration

Implementation Details

Search and Reranking System

The core of our application lies in its sophisticated search and reranking system powered by AWS Bedrock and Cohere’s reranking model. Let’s look at the key components:

1. Initial Setup and Configuration