TextEmbedding

An embedding is a vector (list) of floating-point numbers that represents the semantic meaning of text. Embeddings can be used to compare text for similarity, classify text, or cluster text. Text with similar meaning will have similar embedding vectors.

Fields
values[] number

The embedding vector. The size of the vector is fixed and determined by the model used for embedding generation.

statistics object (Statistics)

Statistics about the input text.

JSON representation 
{
  "values": [
    number
  ],
  "statistics": {
    object (Statistics)
  }
}

Statistics

Statistics about the input text.

Fields
tokenCount integer

The number of tokens in the input text.

truncated boolean

Whether the input text was truncated. If true, the embedding was generated from a truncated version of the input text. This can happen if the input text was longer than the model's input token limit.

JSON representation 
{
  "tokenCount": integer,
  "truncated": boolean
}