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.

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API Schema

post

Authorizations

Body

modelundefined · enumRequiredPossible values:

inputany ofRequired

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

string · min: 1Optional

or

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.

encoding_formatstringOptional

The format in which to return the embeddings.

Responses

201Success

post

HTTPcURLJavaScriptPython

POST /v1/embeddings HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 164

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

201Success

No content