Returning Top-K with Scores

top-k selection and result formatting

Part of: Semantic Search Engine

A ranked list of every document isn't a search result. It's a wall of text. Real search returns the best few matches, the top-k , each with a score so the caller can judge how confident the match is. Slicing off the top Once rank documents has produced a fully sorted list, the top-k is a slice: The min(k, len(ranked)) guard matters. If a caller asks for the top 10 results from a 3-document corpus, you don't want an index error or a list padded with missing entries. You want all 3 documents. Clamping k to the corpus size does that. Putting the full pipeline together This is the shape of a real semantic search function: embed the query, score and rank the whole corpus, slice to the requested size, and hand back something a caller (a UI, an API response, another program) can consume directly. The query gets embedded once per search regardless of k or corpus size, so a search costs one embedding call plus cheap arithmetic. Why return the score at all A bare list of matching documents throws away useful information. A score of 0.91 means "this is almost certainly what the user meant." A score of 0.31 on the last item in a top-5 list means "weak match, included only because the other fou

Challenge: Top-K Search Results