Using Vectorize to build an unreasonably good search engine in 160 lines of code

Vector databases and embedding models enable semantic search with minimal code. By converting text into embedding vectors and storing them in a vector database like Cloudflare's Vectorize, you can build search engines that understand meaning rather than just keywords. The tutorial demonstrates building a complete search engine with indexing and querying capabilities using PartyKit's integrations with Vectorize and Workers AI, requiring only 162 lines of code. The approach involves converting documents to embeddings, storing them in the vector database, then querying by finding nearest neighbor vectors to the search query's embedding.