Last Update: 2025/3/26

Qwen Embedding API

The Qwen Embedding API allows you to generate vector representations of text using OpenAI's SDK. This document provides an overview of the API endpoints, request parameters, and response structure.

Endpoint

POST https://platform.llmprovider.ai/v1/embeddings

Request Headers

Header	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Request Body

The request body should be a JSON object with the following parameters:

Parameter	Type	Description
model	string	The model to use (e.g., text-embedding-v3).
input	string	The input text to embed.
encoding_format	string	(Optional) The format to return the embeddings in. Can be either float or base64.
dimensions	integer	(Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
user	string	(Optional) A unique identifier representing the end-user.

Example Request

{
  "model": "text-embedding-v3",
  "input": "The quick brown fox jumps over the lazy dog.",
  "user": "user-1234"
}

Response Body

The response body will be a JSON object containing the generated embeddings and other metadata.

Field	Type	Description
object	string	The type of object returned, usually embedding.
data	array	A list of embedding objects.
model	string	The model used for the embedding.
usage	object	Token usage statistics for the request.

Embedding Object

Field	Type	Description
index	integer	The index of the embedding in the list of embeddings.
embedding	array	The embedding vector.
object	string	The object type, which is always "embedding".

Example Response

{
  "object": "embedding",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        ...
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-v3",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

Example Requests

Shell

curl -X POST https://platform.llmprovider.ai/v1/embeddings \
-H "Authorization: Bearer $YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-v3",
    "encoding_format": "float"
}'

Node.js

const axios = require('axios');
const apiKey = 'YOUR_API_KEY';
const url = 'https://platform.llmprovider.ai/v1/embeddings';

const data = {
    model: 'text-embedding-v3',
    input: 'The food was delicious and the waiter...',
    encoding_format: 'float'
};

const headers = {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
};

axios.post(url, data, {headers})
    .then(response => {
        console.log('Response:', response.data);
    })
    .catch(error => {
        console.error('Error:', error);
    });

Python

import requests
import json

api_key = 'YOUR_API_KEY'
url = 'https://platform.llmprovider.ai/v1/embeddings'

headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

data = {
    'model': 'text-embedding-v3',
    'input': 'The food was delicious and the waiter...',
    'encoding_format': 50
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    print('Response:', response.json())
else:
    print('Error:', response.status_code, response.text)

For any questions or further assistance, please contact us at [email protected].