textembedding-gecko

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

• textembedding-gecko-multilingual@001

• textembedding-gecko@003

Model Overview

A state-of-the-art text embedding model designed to convert textual data into numerical vector representations. It captures semantic meanings and relationships within the text, facilitating various natural language processing (NLP) tasks.

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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.

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: 144

{
  "model": "textembedding-gecko-multilingual@001",
  "input": "text",
  "dimensions": 1,
  "auto_truncate": true,
  "task_type": "RETRIEVAL_QUERY",
  "title": "text"
}

201Success

No content