> ## Documentation Index
> Fetch the complete documentation index at: https://api-docs.upmore.net/llms.txt
> Use this file to discover all available pages before exploring further.

# Doubao Embedding Vision

> ByteDance multimodal embedding model — text and images in one vector space.

Doubao Embedding Vision is ByteDance's multimodal embedding model, available through Upmore via the standard embeddings API (`/v1/embeddings`). It maps text and images into a single 2048-dimension vector space — ideal for image-text retrieval and semantic search.

## Key capabilities

* **Multimodal input** — Embed text, an image, or a combined text+image input into one vector
* **Unified vector space** — Text and image vectors are directly comparable
* **2048 dimensions** — Fixed output dimension
* **Split billing** — Text input and vision input are billed at separate rates; usage is reported per type

## Quick example

<CodeGroup>
  ```bash cURL theme={null}
  curl https://api.upmore.net/v1/embeddings \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "doubao-embedding-vision",
      "input": [
        {"type": "text", "text": "A ginger cat napping on a piano"},
        {"type": "image_url", "image_url": {"url": "https://example.com/cat.jpg"}}
      ]
    }'
  ```

  ```python Python theme={null}
  import requests

  response = requests.post(
      "https://api.upmore.net/v1/embeddings",
      headers={
          "Authorization": "Bearer YOUR_API_KEY",
          "Content-Type": "application/json",
      },
      json={
          "model": "doubao-embedding-vision",
          "input": [
              {"type": "text", "text": "A ginger cat napping on a piano"},
              {"type": "image_url", "image_url": {"url": "https://example.com/cat.jpg"}},
          ],
      },
  )

  result = response.json()
  embedding = result["data"]["embedding"]  # 2048 floats
  print(len(embedding), result["usage"])
  ```

  ```typescript TypeScript theme={null}
  const response = await fetch('https://api.upmore.net/v1/embeddings', {
    method: 'POST',
    headers: {
      Authorization: `Bearer ${process.env.UPMORE_API_KEY}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'doubao-embedding-vision',
      input: [
        { type: 'text', text: 'A ginger cat napping on a piano' },
        { type: 'image_url', image_url: { url: 'https://example.com/cat.jpg' } },
      ],
    }),
  })

  const result = await response.json()
  console.log(result.data.embedding.length, result.usage)
  ```
</CodeGroup>

<Note>
  Unlike standard embedding models, `input` is an **array of content objects** (Ark multimodal format), not a plain string. Text-only input also uses this format: `[{"type": "text", "text": "..."}]`. One request returns **one fused embedding** for the whole input.
</Note>

## Parameters

| Parameter               | Type   | Required    | Description                                                                            |
| ----------------------- | ------ | ----------- | -------------------------------------------------------------------------------------- |
| `model`                 | string | Yes         | Must be `doubao-embedding-vision`                                                      |
| `input`                 | array  | Yes         | Content objects: `{type: "text", text}` and/or `{type: "image_url", image_url: {url}}` |
| `input[].image_url.url` | string | Image items | Image URL or Base64 data URI                                                           |

## Response

| Field                                      | Description                                       |
| ------------------------------------------ | ------------------------------------------------- |
| `data.embedding`                           | 2048-dimension float vector for the whole input   |
| `usage.prompt_tokens_details.text_tokens`  | Text tokens — billed at the text rate             |
| `usage.prompt_tokens_details.image_tokens` | Image tokens — billed at the vision rate (higher) |

<Card title="API Reference" icon="code" href="/api-reference/model-api/bytedance/embedding-vision">
  View the interactive API playground.
</Card>