In an Aiven blog post, Peter Kim explains how to build a semantic search engine using Aiven for OpenSearch and Vertex AI. The tutorial highlights the use of Vertex AI to generate vector embeddings, enabling OpenSearch to produce highly relevant search results by considering the user’s intent and context, rather than relying solely on keyword matches. Aiven for OpenSearch, a fully-managed service on Google Cloud, offers seamless integration with Google services and supports semantic search applications with native vector search capabilities. Vertex AI, a unified AI development platform, facilitates the creation of vector embeddings using advanced models like Google's Gemini Embedding 001. The tutorial demonstrates how to index sample data by converting text descriptions into vector embeddings, creating an OpenSearch index mapping to handle these vectors, and querying the data using vector representations to perform a k-nearest neighbor search. This approach allows for more meaningful search results, as illustrated by examples where context is understood even without direct keyword matches, such as identifying "tottenham" with soccer-related products.