嵌入

POST /v1/embeddings 通过 CometAPI 使用所选模型生成文本嵌入，用于语义搜索、聚类和检索工作流。

POST /v1/embeddings

Python

Single Text

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<COMETAPI_KEY>",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The food was delicious and the waiter was friendly.",
)

print(response.data[0].embedding[:5])  # First 5 dimensions
print(f"Dimensions: {len(response.data[0].embedding)}")

200

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.0021,
        -0.0491,
        0.0209,
        0.0314,
        -0.0453
      ]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 2,
    "total_tokens": 2
  }
}

​

概述

Embeddings API 会生成能够捕捉语义含义的文本向量表示。这些向量可用于语义搜索、聚类、分类、异常检测和检索增强生成（RAG）。

CometAPI 支持来自多个提供商的嵌入模型。传入一个或多个文本字符串后，会返回数值向量，你可以将其存储到向量数据库中，或直接用于相似度计算。

​

可用模型

模型	维度	最大 Token 数	最适合
text-embedding-3-large	3,072（可调）	8,191	质量最高的嵌入
text-embedding-3-small	1,536（可调）	8,191	性价比高、速度快
text-embedding-ada-002	1,536（固定）	8,191	兼容旧版

有关所有可用嵌入模型及其定价，请参阅模型列表。

​

重要说明

降低维度 — text-embedding-3-* 模型支持 dimensions 参数，允许你在不显著损失准确率的情况下缩短嵌入向量。这最多可将存储成本降低 75%，同时保留大部分语义信息。

批量输入 — 你可以通过向 input 参数传入字符串数组，在一次请求中为多个文本生成嵌入。与为每段文本分别发起请求相比，这样的效率会高得多。

---

授权

Authorization · string · header · 必填

Bearer token authentication. Use your CometAPI key.

请求体

application/json

model · string · 必填

The embedding model to use. See the Models page for current embedding model IDs.

示例:

"text-embedding-3-small"

input · string string[] integer[][] · 必填

The text to embed. Can be a single string, an array of strings, or an array of token arrays. Each input must not exceed the model's maximum token limit (8,191 tokens for text-embedding-3-* models).

encoding_format · enum<string> · 默认值: float

The format of the returned embedding vectors. float returns an array of floating-point numbers. base64 returns a base64-encoded string representation, which can reduce response size for large batches.

可用选项: float, base64

dimensions · integer

The number of dimensions for the output embedding vector. Only supported by text-embedding-3-* models. Reducing dimensions can lower storage costs while maintaining most of the embedding's utility.

必填范围: x >= 1

user · string

A unique identifier for your end-user, which can help monitor and detect abuse.

响应

200 - application/json

A list of embedding vectors for the input text(s).

object · enum<string>

The object type, always list.

可用选项: list

示例:

"list"

data · object[]

An array of embedding objects, one per input text. When multiple inputs are provided, results are returned in the same order as the input.

Show child attributes

model · string

The model used to generate the embeddings.

示例:

"text-embedding-3-small"

usage · object

Token usage statistics for this request.

Show child attributes