Create an embedding

Creates an embedding vector representing the input text.

Request

POST

https://ai.api.cloud.yandex.net/v1/embeddings

Body

application/json
{
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "emb://<folder_id>/text-embeddings-v2-doc/",
  "encoding_format": "float",
  "dimensions": 1,
  "user": "example"
}

Name

Description

input
One of: string or array

  • string

    Type: string

    The string that will be turned into an embedding.

    Default: ``

    Example: This is a test.

  • array

    Type: string[]

    The array of strings that will be turned into an embedding.

    Min items: 1

    Max items: 1

    Example
    [
  "['This is a test.']"
]

Input text to embed, encoded as a string or 1 element array. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models). Cannot be an empty string, and any array must be 1 dimension or less.

Example: The quick brown fox jumped over the lazy dog

model
Any of 1 type

  • Type: string

    Example: example

ID of the model to use. You can use the List models API to see all of your available models.

Example: emb://<folder_id>/text-embeddings-v2-doc/

dimensions

Type: integer

The number of dimensions the resulting output embeddings should have.

Min value: 1

encoding_format

Type: string

The format to return the embeddings in. Can be float only.

Default: float

Const: float

user

Type: string

A unique identifier representing your end-user, which can help Yandex to monitor and detect abuse.

Example: example

Responses

200 OK

OK

Body

application/json
{
  "data": [
    {
      "index": 0,
      "embedding": [
        0.5
      ],
      "object": "embedding"
    }
  ],
  "model": "example",
  "object": "list",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

Name

Description

data

Type: Embedding[]

The list of embeddings generated by the model.

Example
[
  {
    "index": 0,
    "embedding": [
      0.5
    ],
    "object": "embedding"
  }
]

model

Type: string

The name of the model used to generate the embedding.

Example: example

object

Type: string

The object type, which is always "list".

Const: list

Example: example

usage
Type: object

prompt_tokens

Type: integer

The number of tokens used by the prompt.

total_tokens

Type: integer

The total number of tokens used by the request.

The usage information for the request.

Example
{
  "prompt_tokens": 0,
  "total_tokens": 0
}

Embedding

Represents an embedding vector returned by embedding endpoint.

Name

Description

embedding

Type: number[]

The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding concepts.

Example
[
  0.5
]

index

Type: integer

The index of the embedding in the list of embeddings.

object

Type: string

The object type, which is always "embedding".

Const: embedding

Example: example
Example
{
  "index": 0,
  "embedding": [
    0.5
  ],
  "object": "embedding"
}
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