Gemini format

POST

/v1/engines/{model}/embeddings

package main

import (
  "fmt"
  "strings"
  "net/http"
  "io"
)

func main() {

  url := "https://api.mirapi.ai/v1/engines//embeddings"

  payload := strings.NewReader("{ \"model\": \"text-embedding-ada-002\", \"input\": [ \"example\" ], \"encoding_format\": \"float\", \"dimensions\": 1 }")

  req, _ := http.NewRequest("POST", url, payload)

  req.Header.Add("Authorization", "Bearer <token>")
  req.Header.Add("Content-Type", "application/json")

  res, _ := http.DefaultClient.Do(req)

  defer res.Body.Close()
  body, _ := io.ReadAll(res.Body)

  fmt.Println(res)
  fmt.Println(string(body))

}

const url = 'https://api.mirapi.ai/v1/engines//embeddings';
const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: '{"model":"text-embedding-ada-002","input":["example"],"encoding_format":"float","dimensions":1}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}

curl --request POST \
  --url https://api.mirapi.ai/v1/engines//embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{ "model": "text-embedding-ada-002", "input": [ "example" ], "encoding_format": "float", "dimensions": 1 }'

Creates embeddings with the given engine/model

Authorizations

BearerAuth

Parameters

Path Parameters

model

required

string

Model/engine ID

Example

Request Body

application/json

object

model

required

string

input

required

One of:

string
Array<string>

string

encoding_format

string

default: float

Allowed values: float base64

dimensions

Output vector dimensions

integer

Examples

Responses

200

Embeddings created

application/json

object

object

string

data

Array<object>

object

object

string

index

integer

embedding

Array<number>

model

string

usage

object

prompt_tokens

integer

total_tokens

integer

Example

{
  "object": "list",
  "data": [
    {
      "object": "embedding"
    }
  ]
}