text-multilingual-embedding-002

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

text-multilingual-embedding-002

Model Overview

A state-of-the-art model designed to convert textual data into numerical vector representations, capturing the semantic meaning and context of the input text. It is particularly focused on supporting multiple languages, making it suitable for global applications.

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

AuthorizationstringRequired

Bearer key

Body

modelundefined · enumRequiredPossible values:

inputany ofRequired

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

string · min: 1Optional

string[] · min: 1Optional

dimensionsnumber · nullableOptional

The number of dimensions the resulting output embeddings should have.

auto_truncatebooleanOptional

If enabled, this parameter automatically truncates the input text to fit within the model’s maximum token limit. It helps ensure that longer texts are processed without errors.

Default: true

task_typestring · enumOptional

Optional task type for which the embeddings will be used

Possible values:

titlestringOptional

An optional title for the text. Only applicable when task_type is RETRIEVAL_DOCUMENT.

Note: Specifying a title for RETRIEVAL_DOCUMENT provides better quality embeddings for retrieval.

Responses

201Success

No content

post

/v1/embeddings

POST /v1/embeddings HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer YOUR_SECRET_TOKEN
Content-Type: application/json
Accept: */*
Content-Length: 139

{
  "model": "text-multilingual-embedding-002",
  "input": "text",
  "dimensions": 1,
  "auto_truncate": true,
  "task_type": "RETRIEVAL_QUERY",
  "title": "text"
}

201Success

No content

Code Example

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="text-multilingual-embedding-002"
)

# Print the embedding
print(response)

import OpenAI from "openai";
import util from "util";

// Initialize the API client
const client = new OpenAI({
  // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
  apiKey: "<YOUR_AIMLAPI_KEY>",
  baseURL: "https://api.aimlapi.com/v1",
});

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

const response = await client.embeddings.create({
  input: text,
  model: "text-embedding-ada-002",
});

// Convert embedding to a regular array (not TypedArray)
const pythonLikeResponse = {
  ...response,
  data: response.data.map(item => ({
    ...item,
    embedding: Array.from(item.embedding),
  })),
};

// Python-like print
console.log(
  util.inspect(pythonLikeResponse, {
    depth: null,
    maxArrayLength: null,
    compact: true,
  })
);

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=[0.10541483759880066, -0.007883904501795769, 0.026113376021385193, 0.054472994059324265, 0.0018547746585682034, 0.02877396158874035, -0.00769799156114459, -0.003931521438062191, -0.057099711149930954, -0.020155033096671104, -0.06802556663751602, -0.019316386431455612, -0.06921651214361191, -0.007963858544826508, -0.05464792624115944, 0.012803184799849987, -0.027932647615671158, -0.0076736449263989925, 0.01603902503848076, 0.03274304419755936, -0.013360762037336826, 0.09150521457195282, 0.01921369507908821, -0.013365000486373901, 0.028981221839785576, 0.04328111559152603, 0.005482512526214123, 0.01715942844748497, -0.013837876729667187, 0.06653714925050735, -0.024773558601737022, 0.02344578132033348, -0.029198521748185158, -0.06147294491529465, -0.021522384136915207, -0.025527307763695717, -0.04977520927786827, 0.0844593346118927, 0.056941527873277664, 0.06679201871156693, 0.015225042589008808, -0.01128363236784935, 0.02270553447306156, 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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.

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