Embeddings

LLMGateway exposes an OpenAI-compatible /v1/embeddings endpoint for generating vector representations of text — useful for semantic search, clustering, recommendations, and RAG.

Browse available embedding models on the models page.

Supported providers

• OpenAI — text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
• Google AI Studio — gemini-embedding-2 (recommended), gemini-embedding-001 (legacy)

The gateway translates between provider-native request/response shapes (e.g. Google's :embedContent / :batchEmbedContents) and the OpenAI-compatible payload, so you can swap models without changing your client code.

cURL

curl -X POST "https://api.llmgateway.io/v1/embeddings" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog."
  }'

OpenAI JS SDK

import OpenAI from "openai";

const client = new OpenAI({
	apiKey: process.env.LLM_GATEWAY_API_KEY,
	baseURL: "https://api.llmgateway.io/v1",
});

const response = await client.embeddings.create({
	model: "text-embedding-3-small",
	input: "The quick brown fox jumps over the lazy dog.",
});

console.log(response.data[0].embedding);