Semantic search (embedding-based)

Performs semantic similarity search using vector embeddings.

Availability: This endpoint works for both built-in and custom code systems.

When to use: Best for natural language queries where you want to find conceptually related codes, even when different terminology is used. The search understands meaning, not just keywords.

Examples:

• Query "trouble breathing at night" finds codes like "Sleep apnea", "Orthopnea", "Nocturnal dyspnea" — semantically related but no exact keyword matches
• Query "heart problems" finds "Myocardial infarction", "Cardiac arrest", "Arrhythmia"

Trade-offs: Slower than text search (requires embedding generation), but finds conceptually similar results that keyword search would miss.

See also: /search/text for faster keyword-based lookup with typo tolerance.

Usage of CPT is subject to AMA requirements: see PhenoML Terms of Service.