Semantic Search with TypeScript: Using embed() and embedMany() for Vector Search

Semantic Search with TypeScript: Using embed() and embedMany() for Vector Search In the age of information overload, keyword-based search often falls short. Users aren't just looking for exact matches; they're looking for meaning. This is where semantic search shines, allowing systems to understand the intent behind a query and retrieve results that are conceptually similar, even if they don't contain the exact keywords. At the heart of semantic search lies the concept of embeddings – dense numerical representations of text that capture its meaning. NeuroLink, the universal AI SDK for TypeScript, simplifies the process of generating and utilizing these embeddings, making it straightforward to build powerful semantic search capabilities into your applications. This article will guide you through generating embeddings with NeuroLink's embed() and embedMany() methods, performing similarity search, and integrating with vector databases to build a complete semantic search engine. What are E