{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://artifacts.apis.io/freqblog-com/json-schema/freqblog-com-embedding-response-schema.json",
  "title": "EmbeddingResponse",
  "description": "Numeric feature vector for a track — convenient for ML / similarity / clustering.",
  "x-generated": "2026-10-30",
  "x-method": "derived",
  "x-generator": "derive-json-schema.py",
  "x-source": "openapi/freqblog-com-openapi.json#/components/schemas/EmbeddingResponse",
  "properties": {
    "itunes_track_id": {
      "type": "string",
      "title": "Itunes Track Id"
    },
    "track_name": {
      "type": "string",
      "title": "Track Name"
    },
    "artist_name": {
      "type": "string",
      "title": "Artist Name"
    },
    "dim": {
      "type": "integer",
      "title": "Dim",
      "description": "Length of the embedding vector."
    },
    "fields": {
      "items": {
        "type": "string"
      },
      "type": "array",
      "title": "Fields",
      "description": "Field name for each position in `embedding`. Always returned in a stable order so callers can rely on positional indexing."
    },
    "embedding": {
      "items": {
        "type": "number"
      },
      "type": "array",
      "title": "Embedding",
      "description": "Numeric feature vector. Missing values are filled with sensible defaults (see `embedding_mask`)."
    },
    "embedding_mask": {
      "items": {
        "type": "boolean"
      },
      "type": "array",
      "title": "Embedding Mask",
      "description": "Per-position truthy when the value came from real analysis, falsy when filled with a default. Use to mask out filler values in similarity calculations."
    }
  },
  "type": "object",
  "required": [
    "itunes_track_id",
    "track_name",
    "artist_name",
    "dim",
    "fields",
    "embedding",
    "embedding_mask"
  ]
}