Embeddings

POST/api/v1/embeddings

Generate vector embeddings for text input. Useful for semantic search, clustering, and retrieval-augmented generation (RAG) pipelines.

Authentication

Pass your Meridian proxy key as a Bearer token:

Header

Authorization: Bearer sk-mrd-your-proxy-key

Request body

Parameter	Type	Required	Description
`model`	string	Yes	`text-embedding-3-small` or `text-embedding-3-large`
`input`	string \| string[]	Yes	Text to embed. Can be a single string or an array of strings for batch processing.

Response

200 OK

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Examples

curl

curl -X POST https://your-meridian.vercel.app/api/v1/embeddings \
  -H "Authorization: Bearer sk-mrd-your-proxy-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Meridian is an open-source LLM gateway."
  }'

JavaScript

JavaScript (fetch)

const response = await fetch(
  "https://your-meridian.vercel.app/api/v1/embeddings",
  {
    method: "POST",
    headers: {
      "Authorization": "Bearer sk-mrd-your-proxy-key",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "text-embedding-3-small",
      input: "Meridian is an open-source LLM gateway.",
    }),
  }
);

const data = await response.json();
const embedding = data.data[0].embedding;
console.log(`Dimensions: ${embedding.length}`);

Python

Python (requests)

import requests

response = requests.post(
    "https://your-meridian.vercel.app/api/v1/embeddings",
    headers={
        "Authorization": "Bearer sk-mrd-your-proxy-key",
        "Content-Type": "application/json",
    },
    json={
        "model": "text-embedding-3-small",
        "input": "Meridian is an open-source LLM gateway.",
    },
)

data = response.json()
embedding = data["data"][0]["embedding"]
print(f"Dimensions: {len(embedding)}")

Batch embedding

curl (batch)

curl -X POST https://your-meridian.vercel.app/api/v1/embeddings \
  -H "Authorization: Bearer sk-mrd-your-proxy-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-large",
    "input": [
      "First document to embed",
      "Second document to embed",
      "Third document to embed"
    ]
  }'

Supported models

Model	Provider	Dimensions	Max tokens
`text-embedding-3-small`	OpenAI	1,536	8,191
`text-embedding-3-large`	OpenAI	3,072	8,191