{
  "encoding_format": "float",
  "input": "string",
  "model": "null",
  "user": "null"
}

{
  "data": [
    {
      "embedding": [
        0.038483415,
        -0.00076982786,
        -0.020039458
        ...
      ],
      "index": 0,
      "object": "embedding"
    },
    {
      "embedding": [
        0.038483415,
        -0.00076982786,
        -0.020039458
        ...
      ],
      "index": 1,
      "object": "embedding"
    },
    ...
  ],
  "model": "thenlper/gte-base",
  "object": "list",
  "usage": {
    "prompt_tokens": 512,
    "total_tokens": 512
  }
}
Generating an embedding from a dense embedding model 
{
  "encoding_format": "float",
  "input": "string",
  "model": "null",
  "user": "null"
}

{
  "data": [
    {
      "embedding": [
        0.038483415,
        -0.00076982786,
        -0.020039458
        ...
      ],
      "index": 0,
      "object": "embedding"
    },
    {
      "embedding": [
        0.038483415,
        -0.00076982786,
        -0.020039458
        ...
      ],
      "index": 1,
      "object": "embedding"
    },
    ...
  ],
  "model": "thenlper/gte-base",
  "object": "list",
  "usage": {
    "prompt_tokens": 512,
    "total_tokens": 512
  }
}
​
inputs
string | string[]
required
Inputs that need to be embedded
​
normalize
boolean
default:"true"
​
prompt_name
string
The name of the prompt that should be used by for encoding. If not set, no prompt will be applied.Must be a key in the sentence-transformers configuration prompts dictionary.For example if prompt_name is “doc” then the sentence “How to get fast inference?” will be encoded as “doc: How to get fast inference?” because the prompt text will be prepended before any text to encode.
​
truncate
boolean
default:"false"
Automatically truncate inputs that are longer than the maximum supported size
​
truncate_direction
"right" | "left"
default:"right"