Ship the Semantic Search Engine

assembling and shipping the engine

Part of: Semantic Search Engine

Every piece is built: batch embedding, cosine similarity, ranking with a tie-break, top-k with clamping, guards against bad vectors, and a cache to hold costs down. This lesson bolts them into one engine and ships it. Finish it and the project lands in your Portfolio . The whole thing, assembled Read it top to bottom and every lesson is there. add documents batches and caches (lessons 2 and 7). cosine is the similarity math with its zero-vector guard (lesson 3). search ranks, breaks ties, and clamps to top-k (lessons 4-5). The dimension check and the empty-index guard are the hardening (lesson 6). What "shipped" means here No deployment needed. Shipped means three things are true: 1. It runs from a clean start: point it at a folder of documents, call add documents, then search. 2. It handles an empty corpus, a repeated document, and a weird query without crashing. 3. Someone else could use it from your instructions alone: engine.add documents(docs), then engine.search("your question", k=5). Your Portfolio Completing this final lesson records the project in your Portfolio with its title and what you built: a working semantic search engine over a real corpus, backed by real embedding

Challenge: Ship Checklist