Create embeddings

curl -X POST https://api-llm.sunra.ai/v1/embeddings \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-embedding-2",
    "input": [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    "dimensions": 768
  }'

import requests

response = requests.post(
    "https://api-llm.sunra.ai/v1/embeddings",
    headers={
        "Authorization": "Bearer <SUNRA_KEY>",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-embedding-2",
        "input": [
            "The quick brown fox jumps over the lazy dog.",
            "Sunra is a platform for AI models."
        ],
        "dimensions": 768
    }
)
print(response.json())

const response = await fetch("https://api-llm.sunra.ai/v1/embeddings", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <SUNRA_KEY>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "google/gemini-embedding-2",
    input: [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    dimensions: 768
  })
});
const data = await response.json();
console.log(data);

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.011253, -0.020551, 0.049236, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.031744, 0.008122, -0.014307, "..."]
    }
  ],
  "model": "google/gemini-embedding-2",
  "usage": {
    "prompt_tokens": 18,
    "total_tokens": 18
  }
}

POST

embeddings

curl -X POST https://api-llm.sunra.ai/v1/embeddings \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-embedding-2",
    "input": [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    "dimensions": 768
  }'

import requests

response = requests.post(
    "https://api-llm.sunra.ai/v1/embeddings",
    headers={
        "Authorization": "Bearer <SUNRA_KEY>",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-embedding-2",
        "input": [
            "The quick brown fox jumps over the lazy dog.",
            "Sunra is a platform for AI models."
        ],
        "dimensions": 768
    }
)
print(response.json())

const response = await fetch("https://api-llm.sunra.ai/v1/embeddings", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <SUNRA_KEY>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "google/gemini-embedding-2",
    input: [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    dimensions: 768
  })
});
const data = await response.json();
console.log(data);

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.011253, -0.020551, 0.049236, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.031744, 0.008122, -0.014307, "..."]
    }
  ],
  "model": "google/gemini-embedding-2",
  "usage": {
    "prompt_tokens": 18,
    "total_tokens": 18
  }
}

Creates embedding vectors from text or media inputs. Text-only requests return one vector per input, useful for retrieval, semantic search, clustering, classification, and RAG. Requests containing media (images, audio, or video as base64 data URIs) fuse all inputs into a single cross-modal vector — embed each asset in its own request when you need one vector per asset. Compatible with the OpenAI Embeddings API format. Streaming is not supported on this endpoint.

Authentication

Authorization

string

required

Bearer token. Use your API key as the bearer token in the Authorization header.Format: Bearer <SUNRA_KEY>

Request

This endpoint expects an object.

model

string

required

ID of the embedding model to use, e.g. google/gemini-embedding-2. See the models page for available embedding models.

input

string | string[]

required

Inputs to embed, as a single string or an array of strings. Text-only inputs are embedded independently, returning one vector per input in input order. Media inputs are passed as base64 data URIs (data:image/png;base64,…, data:audio/mpeg;base64,…, data:video/mp4;base64,…) and may be mixed with text — but any request containing media returns a single fused vector for all inputs combined. Limits (upstream): at most 6 images per request, video up to 120 seconds, and a combined budget of 8,192 tokens across all modalities (text tokens; images count 258 tokens each, audio 25 tokens/second, video 66 tokens/second).

dimensions

integer

The number of dimensions for the output embeddings. Only supported by models with flexible output dimensions (Matryoshka Representation Learning). For google/gemini-embedding-2 the default is 3072, and values from 128 to 3072 are supported. Smaller values truncate the vector while preserving most semantic quality.

encoding_format

string

The format of the returned embeddings. Either float (default) or base64.

Response

object

string

Always list.

data

object[]

A list of embedding objects, one per input, in input order.

Show embedding object

object

string

Always embedding.

index

integer

The index of the corresponding input.

embedding

number[]

The embedding vector.

model

string

The model used to create the embeddings.

usage

object

Token usage for the request. Embeddings are billed on input tokens only, with modality-specific rates (see the model page for pricing). Media requests include per-modality token counts.

Show usage object

prompt_tokens

integer

Number of text input tokens.

input_image_tokens

integer

Image input tokens (258 per image). Present only when the request contains images.

input_audio_tokens

integer

Audio input tokens (25 per second). Present only when the request contains audio.

input_video_tokens

integer

Video input tokens (66 per second). Present only when the request contains video.

total_tokens

integer

Total input tokens across all modalities (embeddings produce no output tokens).

curl -X POST https://api-llm.sunra.ai/v1/embeddings \
  -H "Authorization: Bearer <SUNRA_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-embedding-2",
    "input": [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    "dimensions": 768
  }'

import requests

response = requests.post(
    "https://api-llm.sunra.ai/v1/embeddings",
    headers={
        "Authorization": "Bearer <SUNRA_KEY>",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-embedding-2",
        "input": [
            "The quick brown fox jumps over the lazy dog.",
            "Sunra is a platform for AI models."
        ],
        "dimensions": 768
    }
)
print(response.json())

const response = await fetch("https://api-llm.sunra.ai/v1/embeddings", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <SUNRA_KEY>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "google/gemini-embedding-2",
    input: [
      "The quick brown fox jumps over the lazy dog.",
      "Sunra is a platform for AI models."
    ],
    dimensions: 768
  })
});
const data = await response.json();
console.log(data);

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.011253, -0.020551, 0.049236, "..."]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.031744, 0.008122, -0.014307, "..."]
    }
  ],
  "model": "google/gemini-embedding-2",
  "usage": {
    "prompt_tokens": 18,
    "total_tokens": 18
  }
}

Create a response JavaScript / TypeScript

Overview

Platform

Multimodal

LLM

Client Libraries

Create embeddings

Authentication

Request

Response

​Authentication

​Request

​Response

Authentication

Request

Response