Create Embeddings

POST https://www.kkiai.com/v1/embeddings

Get a vector representation of the given input that machine learning models and algorithms can easily use.

Related guide: Embeddings

Create an embedding vector that represents the input text.

Request Parameters

Authorization

Add the Authorization parameter in the Header with the value of Token concatenated after Bearer

Example: Authorization: Bearer ********************

Header Parameters

Parameter Name	Type	Required	Description	Example
Authorization	string	Optional		Bearer {{YOUR_API_KEY}}

Body Parameters (application/json)

Parameter Name	Type	Required	Description
model	string	Required	The ID of the model to use. You can use the List models API to see all available models, or see our Models overview to learn about their descriptions.
input	string	Required	Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs in a single request, pass an array of strings or array of token arrays. The length of each input must not exceed 8192 tokens.

Request Example

{
  "model": "text-embedding-3-large",
  "input": "Artificial intelligence helps developers build smarter applications."
}

cURL Example

curl --location --request POST 'https://www.kkiai.com/v1/embeddings' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
  "model": "text-embedding-3-large",
  "input": "Artificial intelligence helps developers build smarter applications."
}'

Response

🟢200 Create embeddings

Response Body

Parameter Name	Type	Required	Description
object	string	Required
data	array[object]	Required
└ object	string	Optional
└ embedding	array[number]	Optional
└ index	integer	Optional
model	string	Required
usage	object	Required
└ prompt_tokens	integer	Required
└ total_tokens	integer	Required

Response Example

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}