qwen-text-embedding-v3

This documentation is valid for the following list of our models:

• alibaba/qwen-text-embedding-v3

Try in Playground

Model Overview

A compact language model supporting over 100 languages. It features a 4B parameter architecture, a context length of up to 32K tokens, and outputs embeddings with up to 2560 dimensions.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.

API Schema

post

Authorizations

access-token

AuthorizationstringRequired

Bearer key

Body

application/json

modelundefined · enumRequiredPossible values:

inputany ofRequired

Input text to embed, encoded as a string or array of tokens.

string · min: 1Optional

or

string[] · min: 1Optional

dimensionsinteger · min: 64 · max: 2048Optional

The number of dimensions for the embedding. Default is 1024.

Default: 1024

Responses

200Success

application/json

post

/v1/embeddings

HTTPcURLJavaScriptPython

POST /v1/embeddings HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer YOUR_SECRET_TOKEN
Content-Type: application/json
Accept: */*
Content-Length: 75

{
  "model": "alibaba/qwen-text-embedding-v3",
  "input": "text",
  "dimensions": 1024
}

200Success

{
  "object": "object",
  "data": [
    {
      "object": "embedding",
      "index": 1,
      "embedding": [
        1
      ]
    }
  ],
  "model": "text",
  "usage": {
    "total_tokens": 1
  }
}

Code Example

Python

import openai

# Initialize the API client
client = openai.OpenAI(
    # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
    api_key="<YOUR_AIMLAPI_KEY>",
    base_url="https://api.aimlapi.com/v1",
)

# Define the text for which to generate an embedding
text = "Laura is a DJ."

# Request the embedding
response = client.embeddings.create(
    input=text,
    model="alibaba/qwen-text-embedding-v3"
)

# Print the embedding
print(response)

JS

This example shows how to set up an API client, send text to the embedding API, and print the response with the embedding vector. See how large a vector response the model generates from just a single short input phrase.

Response

CreateEmbeddingResponse(data=[Embedding(embedding=[-0.1248791441321373, 0.022519821301102638, -0.06440284103155136, -0.0016516941832378507, -0.07079307734966278, 0.06540372222661972, -0.012751608155667782, 0.06559620052576065, -0.02534923516213894, -0.006650084163993597, 0.060668785125017166, 0.03968878090381622, -0.04049718379974365, -0.021557435393333435, 0.03533879667520523, -0.014830361120402813, 0.041382577270269394, 0.017428802326321602, -0.01000880915671587, -0.0008950185729190707, -0.01976739801466465, 0.0008144187740981579, -0.05716570094227791, -0.005095831584185362, 0.00028510671108961105, 0.033548761159181595, -0.06294001638889313, -0.03601246699690819, -0.0015169602120295167, -0.02221185900270939, -0.013598507270216942, -0.001029151026159525, -0.03345252200961113, -0.052122801542282104, -0.009003116749227047, -0.02221185900270939, -0.026638831943273544, -0.006346932612359524, -0.06702052801847458, -0.048042286187410355, 0.024271363392472267, 0.055895350873470306, 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model='text-embedding-v3', object='list', usage=Usage(prompt_tokens=4, total_tokens=4), id='8ef7c577-75e9-9056-ba09-dc4331f81f6d', meta={'usage': {'credits_used': 1}})

You can find a more advanced example of using embedding vectors in our article Find Relevant Answers: Semantic Search with Text Embeddings in the Use Cases section.