Embeddings

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.embeddings.create(
    model="<embedding-model-id>",
    input="Extract a vector representation for this text.",
)

print(len(response.data[0].embedding))

curl https://gateway.vlm.run/v1/openai/embeddings \
  -X POST \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<embedding-model-id>",
    "input": "Extract a vector representation for this text."
  }'

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.vlm.run/v1/openai",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.embeddings.create({
  model: "<embedding-model-id>",
  input: "Extract a vector representation for this text.",
});

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

{
  "model": "<string>",
  "data": [
    {
      "index": 123,
      "embedding": [
        123
      ],
      "object": "embedding"
    }
  ],
  "id": "<string>",
  "object": "list",
  "created": 123,
  "served_model_id": "<string>",
  "backend": "<string>",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

POST

openai

embeddings

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.embeddings.create(
    model="<embedding-model-id>",
    input="Extract a vector representation for this text.",
)

print(len(response.data[0].embedding))

curl https://gateway.vlm.run/v1/openai/embeddings \
  -X POST \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<embedding-model-id>",
    "input": "Extract a vector representation for this text."
  }'

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.vlm.run/v1/openai",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.embeddings.create({
  model: "<embedding-model-id>",
  input: "Extract a vector representation for this text.",
});

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

{
  "model": "<string>",
  "data": [
    {
      "index": 123,
      "embedding": [
        123
      ],
      "object": "embedding"
    }
  ],
  "id": "<string>",
  "object": "list",
  "created": 123,
  "served_model_id": "<string>",
  "backend": "<string>",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

Authentication is optional. See Rate Limits for per-tier quotas. For available embedding models, see Models.

Request parameters

Field	Type	Default	Description
`model`	`string`	required	Embedding model id, e.g. `qwen/qwen3-vl-embedding-2b`.
`input`	`string`, array of strings, or array of content parts	required	Text, or `text` / `image_url` / `video_url` content parts for multimodal embeddings. See below.
`encoding_format`	`string`	`float`	`float` or `base64`.
`dimensions`	`integer`	model default	Truncate the output vector to fewer dimensions, if the model supports it.
`truncate_prompt_tokens`	`integer`	`null`	Truncate long inputs to this many tokens instead of erroring.
`user`	`string`	`null`	Opaque end-user identifier, accepted for OpenAI SDK compatibility.

Text input

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.embeddings.create(
    model="<embedding-model-id>",
    input="Extract a vector representation for this text.",
)

print(len(response.data[0].embedding))

curl https://gateway.vlm.run/v1/openai/embeddings \
  -X POST \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<embedding-model-id>",
    "input": "Extract a vector representation for this text."
  }'

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.vlm.run/v1/openai",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.embeddings.create({
  model: "<embedding-model-id>",
  input: "Extract a vector representation for this text.",
});

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

Multimodal input

Vision-language embedding models such as qwen/qwen3-vl-embedding-2b also accept image_url and video_url content parts in input, so you can embed images or sampled video frames into the same vector space as text, for example to build visual search or image-to-text retrieval. Confirm accepted input types in a model’s capabilities.supported_input_types on GET /v1/openai/models before relying on a modality, since not every embedding model on the catalog accepts images or video.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.embeddings.create(
    model="qwen/qwen3-vl-embedding-2b",
    input=[
        {
            "type": "image_url",
            "image_url": {
                "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"
            },
        }
    ],
)

print(len(response.data[0].embedding))

curl https://gateway.vlm.run/v1/openai/embeddings \
  -X POST \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-vl-embedding-2b",
    "input": [
      {
        "type": "image_url",
        "image_url": {
          "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg"
        }
      }
    ]
  }'

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.vlm.run/v1/openai",
  apiKey: process.env.VLMRUN_API_KEY,
});

const response = await client.embeddings.create({
  model: "qwen/qwen3-vl-embedding-2b",
  input: [
    {
      type: "image_url",
      image_url: {
        url: "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/car.jpg",
      },
    },
  ],
});

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

Models

Available embedding models.

Multimodal Inputs

Content part types shared across chat and embeddings.

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

OpenAI-compatible embeddings request.

The input field is intentionally polymorphic to match both OpenAI's API (string / list-of-strings / list-of-token-ids) and vLLM's multimodal extension (list of content parts). The router normalises everything to a list of items before handing it to the backend, so individual backends only see one shape.

model

string

required

input

required

encoding_format

enum<string>

default:float

Available options:

float,

base64

dimensions

integer | null

user

string | null

truncate_prompt_tokens

integer | null

Response

Successful Response

OpenAI-compatible embeddings response with audit extras (served_model_id and backend).

model

string

required

data

EmbeddingData · object[]

required

Hide child attributes

data.index

integer

required

data.embedding

required

data.object

string

default:embedding

Allowed value: "embedding"

string

object

string

default:list

Allowed value: "list"

created

integer

served_model_id

string | null

backend

string | null

usage

EmbeddingUsage · object

Hide child attributes

usage.prompt_tokens

integer

default:0

usage.total_tokens

integer

default:0

Get Model Audio Transcriptions

Overview

Guides

API Reference

Misc

Request parameters

Text input

Multimodal input

Models

Multimodal Inputs

Authorizations

Body

Response

​Request parameters

​Text input

​Multimodal input

​Related

Models

Multimodal Inputs

Authorizations

Body

Response

Request parameters

Text input

Multimodal input

Related