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

# 向量嵌入

> 将文本转换为向量嵌入。支持单条文本或批量文本输入，可指定输出维度和编码格式。

## 请求头

**Authorization** `string` *required*

使用 Bearer Token 认证。格式：`Authorization: Bearer sk-xxxxxx`

## 请求体

格式：`application/json`

**model** `string` *required*

模型 ID

**input** `string | string[]` *required*

要嵌入的文本

**encoding\_format** `string` (default: float)

编码格式，可选值：`float`、`base64`

**dimensions** `integer`

输出向量维度

## 响应

### 200 成功创建嵌入

格式：`application/json`

**object** `string`

固定为 `list`

**data** `object[]`

嵌入向量列表

**属性**

**object** `string`

固定为 `embedding`

**index** `integer`

索引

**embedding** `array`

向量值数组

**model** `string`

使用的模型 ID

**usage** `object`

Token 用量统计

**属性**

**prompt\_tokens** `integer`

输入 Token 数

**total\_tokens** `integer`

总 Token 数

```bash cURL theme={"dark"}
curl -X POST "https://t.ato2.nodesms.com/v1/embeddings" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-ada-002",
    "input": "The food was delicious and the waiter..."
  }'
```

```python Python theme={"dark"}
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxx",
    base_url="https://t.ato2.nodesms.com/v1"
)

response = client.embeddings.create(
    model="text-embedding-ada-002",
    input="The food was delicious and the waiter..."
)
print(response.data[0].embedding)
```

```json 200 theme={"dark"}
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0023064255,
        -0.009327292
      ]
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}
```


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