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Embeddings

Last updated January 15, 2026

Generate vector embeddings from input text for semantic search, similarity matching, and retrieval-augmented generation (RAG).

Endpoint
POST /embeddings
Example request
embeddings.ts
import OpenAI from 'openai';
 
const apiKey = process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN;
 
const openai = new OpenAI({
  apiKey,
  baseURL: 'https://ai-gateway.vercel.sh/v1',
});
 
const response = await openai.embeddings.create({
  model: 'openai/text-embedding-3-small',
  input: 'Sunny day at the beach',
});
 
console.log(response.data[0].embedding);
embeddings.py
import os
from openai import OpenAI
 
api_key = os.getenv("AI_GATEWAY_API_KEY") or os.getenv("VERCEL_OIDC_TOKEN")
 
client = OpenAI(
    api_key=api_key,
    base_url="https://ai-gateway.vercel.sh/v1",
)
 
response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="Sunny day at the beach",
)
 
print(response.data[0].embedding)
Response format
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [-0.0038, 0.021, ...]
    },
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 6,
    "total_tokens": 6
  },
  "providerMetadata": {
    "gateway": {
      "routing": { ... }, // Detailed routing info
      "cost": "0.00000012"
    }
  }
}
Dimensions parameter

You can set the root-level dimensions field (from the OpenAI Embeddings API spec) and the gateway will auto-map it to each provider's expected field; providerOptions.[provider] still passes through as-is and isn't required for dimensions to work.

embeddings-dimensions.ts
const response = await openai.embeddings.create({
  model: 'openai/text-embedding-3-small',
  input: 'Sunny day at the beach',
  dimensions: 768,
});
embeddings-dimensions.py
response = client.embeddings.create(
    model='openai/text-embedding-3-small',
    input='Sunny day at the beach',
    dimensions=768,
)

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