{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://artifacts.apis.io/cloudmersive/json-schema/cloudmersive-vector-embedding-result-schema.json",
  "title": "VectorEmbeddingResult",
  "description": "Result of generating a vector embedding",
  "x-generated": "2026-11-05",
  "x-method": "derived",
  "x-generator": "derive-json-schema.py",
  "x-source": "openapi/cloudmersive-vectorembedding-api-openapi.yml#/components/schemas/VectorEmbeddingResult",
  "type": "object",
  "properties": {
    "Successful": {
      "description": "True if an embedding vector was produced, false otherwise",
      "type": "boolean"
    },
    "Embedding": {
      "description": "The embedding vector - an array of numbers representing the semantic meaning of the input.  The vector is normalized to unit length, so a dot product between two vectors gives their cosine similarity.",
      "type": "array",
      "items": {
        "format": "float",
        "type": "number"
      }
    },
    "Dimension": {
      "format": "int32",
      "description": "The length of the Embedding vector (for example, 1024).  Use this to size your vector store.",
      "type": "integer"
    },
    "TextCharacterCount": {
      "format": "int32",
      "description": "The number of characters of text that were extracted from the document and embedded; populated for the document endpoint",
      "type": "integer"
    },
    "Truncated": {
      "description": "True if the input exceeded the maximum length supported and was truncated before embedding",
      "type": "boolean"
    }
  },
  "additionalProperties": false
}