Line-by-Line Semantic Search with TypeSafe AI

ArticleTutorial

A cookbook guide from TypeSafe AI demonstrates how to build semantic line-level document search without an external vector database. A single API request scores candidate lines and simultaneously verifies whether an answer is present in the text.

Key points

  1. Each line of text is prefixed with a concise ID (e.g. L052) so the model can cite specific lines.
  2. A Choice question ranks line IDs by relevance; because its probabilities always sum to 1, a line is ranked first even when no true answer exists.
  3. A concurrent Noul question checks independently whether the text addresses the query, guarding against false positives.
  4. Both queries are evaluated simultaneously in a single client.system_one call using the jev-1.12 model.
  5. Thresholds applied to the existence probability classify outcomes as answered, partially addressed, or absent.
  6. Choice questions support up to 255 options in a single call, requiring a two-pass windowing strategy for longer documents.

Why it matters

Standard embedding-based search and generative retrieval often highlight irrelevant passages when an answer is missing. Pairing relative ranking with an independent existence check yields verified citations while explicitly detecting out-of-scope queries.

Analysis

This method provides a structured approach for auditing compliance texts and contracts. However, the limit of 255 options per Choice question restricts single-pass execution to shorter documents. Larger corpora require hierarchical or windowed searches to scale effectively.

Mentioned links

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Summary generated by AI (Gemini 3.8 Flash, 29 September 2026). It may contain errors – the original source is authoritative.

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