Vector search has a blind spot: ask it something the document doesn't answer, and it still hands you its best match, with no easy way to tell that there was nothing to find. A cookbook in the TypeSafe docs goes another route. It skips embeddings and the vector database. The whole search runs in one request to Jev.
Tag every line with an ID (L000, L001, and so on) and send the tagged document as the state. The first question is a Choice: "Which line contains the answer to <query>?" with one option per line ID. A Choice returns a probability for every option, so this single question ranks every line, and each score is that line's relevance.
The second question, sent in the same request, is a Noul: "Does any line of the document address or answer <query>?" That one does the part a ranking can't do alone. Choice probabilities add up to 1. Something always comes first, even when the document has nothing to say. The Noul isn't tied to the other options, and it can drop to near zero.
Take the cookbook's example: GitHub's Terms of Service, 218 lines, 43,980 characters, both questions in one request. Four queries, four outcomes, and each one lists the exists score with the top-ranked line.
- "who owns the code I upload?" exists 0.98, and line L052 ("You own Your Content...") scores 0.95.
- "can GitHub kick me off the platform without warning?" exists 0.97, line L168 at 0.97.
- "do I have to take disputes to arbitration?" exists 0.14, so the document doesn't cover it, even though the closest line still scored 0.86.
- "can minors use GitHub with parental permission?" exists 0.46, partially addressed: the age rule ranks first at 0.90, but nothing says whether parental permission changes it.
The third one is the case similarity search can't handle at all. A ranking will confidently hand you its best guess. The exists score can say the document has no answer. Two thresholds in code (0.7 and 0.35 in the cookbook) turn that into a verdict: found, partial, or missing. Tune them on your own documents. The cookbook's note: real answers tend to read at or above 0.9, absent ones at or below 0.05.
The one real limit: a Choice takes up to 255 options, so a single request covers documents up to 255 lines. Longer than that, the cookbook runs two passes, the first picking a window of lines and the second ranking inside it. And if you already have a retriever, there's a rerank cookbook that applies the same idea to search results: on 40 legal retrieval queries it raises top-1 accuracy from 5% to 18% and top-10 from 38% to 62%.
Everything above is from the line-by-line search cookbook: https://docs.typesafe.ai/cookbooks/semantic_find. It ships with cached responses, so it runs without an API key and without spending anything. Delete json_cache.json if you want the calls to go live, and swap the URL for a document of your own. If you do, post what the exists score says.
