Gemini Embedding 2

模型信息

模型 ID	`gemini-embedding-2`
提供方	`google`
上下文长度	`2,048 tokens`
能力	`embedding`
权限等级	`PAID`
免费试用	`否`
调用端点	`POST /v1/embeddings`
输入模态	`TEXT`
输出模态	`EMBEDDING`
价格	`text in $0.24 / 1M`
计费维度	`TEXT_INPUT_TOKEN`

调用示例

curl

curl https://api.mafdet.ai/v1/chat/completions \
  -H "Authorization: Bearer sk-mafdet-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-embedding-2",
    "messages": [{ "role": "user", "content": "Hello" }]
  }'

Node.js

const res = await fetch("https://api.mafdet.ai/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.MAFDET_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gemini-embedding-2",
    messages: [{ role: "user", content: "Hello" }],
  }),
});
const data = await res.json();
console.log(data.choices[0].message.content);

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.mafdet.ai/v1",
    api_key="sk-mafdet-xxxxxxxxxxxxxxxx",
)
resp = client.chat.completions.create(
    model="gemini-embedding-2",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

常见错误

查看完整错误码与排障 →