This blog post explores the implementation of semantic search using vector databases, specifically Pinecone. We'll cover the basics of vector embeddings, how to create and query a Pinecone index, and best practices for optimizing search results. Through practical examples, we'll demonstrate how to build a powerful semantic search engine that can understand context and meaning beyond simple keyword matching.