OpenAI

1. Overview

OpenAI's text embeddings measure the relevance between text strings and are commonly used in the following scenarios:

• Search: Rank search results based on relevance to a query.

• Clustering: Group similar text strings together.

• Recommendations: Suggest items with related text content.

• Anomaly Detection: Identify outlier text with low correlation to other data.

• Diversity Measurement: Analyze the distribution of text similarity.

• Classification: Categorize text based on the most similar labels.

Available model list:

• text-embedding-3-small

• text-embedding-3-large

• text-embedding-ada-002

2. Request Description

• Request method: POST

• Request address: https://gateway.theturbo.ai/v1/audio/speech

3. Input Parameters

3.1 Header Parameters

Parameter Name	Type	Required	Description	Example Value
Content-Type	string	Yes	Set the request header type, which must be application/json	application/json
Accept	string	Yes	Set the response type, which is recommended to be unified as application/json	application/json
Authorization	string	Yes	API_KEY required for authentication. Format: Bearer $YOUR_API_KEY	Bearer $YOUR_API_KEY

3.2 Body Parameters (application/json)

Parameter Name	Type	Required	Description	Example (Default Value)
model	string	Yes	Model ID to use. See available models listed in the Overview for details, such as text-embedding-ada-002.	text-embedding-ada-002
input	string/array	Yes	Input content. The array dimension must not exceed 2048, and the input token count must not exceed 8192.	Hello, please tell me a joke.
encoding_format	string	No	Return vector format: supports float and base64.	float

4. Request Example

HTTP

POST /v1/embeddings
Content-Type: application/json
Accept: application/json
Authorization: Bearer $YOUR_API_KEY

{
	"model": "text-embedding-ada-002",
	"input": "How is the whether today?"
}

Shell

curl https://gateway.theturbo.ai/v1/embeddings \
	-H "Content-Type: application/json" \
	-H "Accept: application/json" \
	-H "Authorization: Bearer $YOUR_API_KEY" \
	-d "{
	\"model\": \"text-embedding-ada-002\",
	\"input\": \"How is the whether today?\"
}"

Go

package main

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

const (
	YOUR_API_KEY    = "sk-123456789012345678901234567890123456789012345678"
	REQUEST_PAYLOAD = `{
	"model": "text-embedding-ada-002",
	"input": "How is the whether today?"
}`
)

func main() {

	requestURL := "https://gateway.theturbo.ai/v1/embeddings"
	requestMethod := "POST"
	requestPayload := strings.NewReader(REQUEST_PAYLOAD)

	req, err := http.NewRequest(requestMethod, requestURL, requestPayload)
	if err != nil {
		fmt.Println("Create request failed, err: ", err)
		return
	}

	req.Header.Add("Content-Type", "application/json")
	req.Header.Add("Accept", "application/json")
	req.Header.Add("Authorization", "Bearer "+YOUR_API_KEY)

	client := &http.Client{}

	resp, err := client.Do(req)
	if err != nil {
		fmt.Println("Do request failed, err: ", err)
		return
	}
	defer resp.Body.Close()

	respBodyBytes, err := ioutil.ReadAll(resp.Body)
	if err != nil {
		fmt.Println("Read response body failed, err: ", err)
		return
	}
	fmt.Println(string(respBodyBytes))
}

5. 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
  }
}