Implementing Semantic Search using Amazon Bedrock and RDS for PostgreSQL in .NET

This article demonstrates how to implement semantic search in a .NET application using Amazon Bedrock and Amazon RDS for PostgreSQL, leveraging large language models and vector embeddings.

• Uses Amazon Titan Text Embeddings to convert text into numeric vectors
• Employs pgvector extension in PostgreSQL to store and query vector embeddings
• Enables semantic search by finding similar content through cosine distance calculations
• Provides a step-by-step guide to: - Generate embeddings - Create a PostgreSQL database - Store movie descriptions as vectors - Perform semantic search queries
• Demonstrates finding semantically similar movies based on natural language search queries

The solution showcases how developers can integrate advanced AI-powered search capabilities into .NET applications using AWS services, enabling more intelligent and context-aware search experiences.