Embeddings

Convert text into numerical vectors for semantic search and similarity matching.

API endpoint

Generate embeddings for text input using GreenPT models.

POST /v1/embeddings

Creates an embedding vector representing the input text.

Code example

import OpenAI from 'openai';
const openai = new OpenAI();

const embedding = await openai.embeddings.create({
  model: 'green-embedding',
  input: 'Your text string goes here',
  encoding_format: 'float',
});

Parameters

Response format

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        -4.547132266452536e-05,
        -0.024047505110502243
      ]
    }
  ],
  "model": "green-embedding",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

Use cases

• Semantic search: find similar documents or text passages.
• Clustering: group similar content together.
• Recommendations: surface related content based on similarity.
• Classification: classify text into categories using embedding similarity.
• Anomaly detection: identify outliers in text data.

Models

green-embedding is backed by Qwen3-Embedding-4B: a multilingual (100+ languages) embedding model with a 32k token context and Matryoshka Representation Learning, so output dimensions are configurable from 32 up to 2560.

See the full list of available models on the Models page.