curl https://api.yelu.ai/v1/embeddings \
-H "Authorization: Bearer $YELU_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"text-embedding-3-small","input":["可靠的 API 返回结构化错误。","优秀的接口会以可预测方式失败。"],"encoding_format":"float"}'
const response = await fetch('https://api.yelu.ai/v1/embeddings', {
method: 'POST',
headers: { Authorization: `Bearer ${process.env.YELU_API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ model: 'text-embedding-3-small', input: ['可靠的 API 返回结构化错误。', '优秀的接口会以可预测方式失败。'], encoding_format: 'float' }),
});
if (!response.ok) throw new Error(await response.text());
console.log((await response.json()).data.map((item) => item.embedding.length));
import os
import requests
response = requests.post(
"https://api.yelu.ai/v1/embeddings",
headers={"Authorization": f"Bearer {os.environ['YELU_API_KEY']}"},
json={"model": "text-embedding-3-small", "input": ["可靠的 API 返回结构化错误。", "优秀的接口会以可预测方式失败。"], "encoding_format": "float"},
timeout=60,
)
response.raise_for_status()
print([len(item["embedding"]) for item in response.json()["data"]])
{"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.01241,-0.00728,0.03155,-0.01902]},{"object":"embedding","index":1,"embedding":[0.01087,-0.00691,0.03088,-0.01794]}],"model":"text-embedding-3-small","usage":{"prompt_tokens":18,"total_tokens":18}}
{"error":{"message":"Input must not be empty.","type":"invalid_request_error","param":"input","code":"invalid_value"}}
创建 Embedding
将文本转换为向量,用于语义搜索、聚类、推荐与检索增强生成。
POST
/
v1
/
embeddings
curl https://api.yelu.ai/v1/embeddings \
-H "Authorization: Bearer $YELU_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"text-embedding-3-small","input":["可靠的 API 返回结构化错误。","优秀的接口会以可预测方式失败。"],"encoding_format":"float"}'
const response = await fetch('https://api.yelu.ai/v1/embeddings', {
method: 'POST',
headers: { Authorization: `Bearer ${process.env.YELU_API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ model: 'text-embedding-3-small', input: ['可靠的 API 返回结构化错误。', '优秀的接口会以可预测方式失败。'], encoding_format: 'float' }),
});
if (!response.ok) throw new Error(await response.text());
console.log((await response.json()).data.map((item) => item.embedding.length));
import os
import requests
response = requests.post(
"https://api.yelu.ai/v1/embeddings",
headers={"Authorization": f"Bearer {os.environ['YELU_API_KEY']}"},
json={"model": "text-embedding-3-small", "input": ["可靠的 API 返回结构化错误。", "优秀的接口会以可预测方式失败。"], "encoding_format": "float"},
timeout=60,
)
response.raise_for_status()
print([len(item["embedding"]) for item in response.json()["data"]])
{"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.01241,-0.00728,0.03155,-0.01902]},{"object":"embedding","index":1,"embedding":[0.01087,-0.00691,0.03088,-0.01794]}],"model":"text-embedding-3-small","usage":{"prompt_tokens":18,"total_tokens":18}}
{"error":{"message":"Input must not be empty.","type":"invalid_request_error","param":"input","code":"invalid_value"}}
创建文本的数值向量表示。同一模型产生的向量可用于语义相似度比较与检索。
端点
POST https://api.yelu.ai/v1/embeddings
请求头
格式为
Bearer $YELU_API_KEY。必须为
application/json。请求体
账号可用的 Embedding 模型 ID。
单段文本或文本数组。空输入和超过模型上下文限制的输入会被拒绝。
float 返回数字数组;兼容模型可用 base64。支持缩短向量的模型可指定输出维度。
稳定且不敏感的用户标识。
响应
固定为
list。与输入顺序一致的向量数组,每项包含
object、index 与 embedding。生成向量的模型。
Token 用量。
curl https://api.yelu.ai/v1/embeddings \
-H "Authorization: Bearer $YELU_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"text-embedding-3-small","input":["可靠的 API 返回结构化错误。","优秀的接口会以可预测方式失败。"],"encoding_format":"float"}'
const response = await fetch('https://api.yelu.ai/v1/embeddings', {
method: 'POST',
headers: { Authorization: `Bearer ${process.env.YELU_API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ model: 'text-embedding-3-small', input: ['可靠的 API 返回结构化错误。', '优秀的接口会以可预测方式失败。'], encoding_format: 'float' }),
});
if (!response.ok) throw new Error(await response.text());
console.log((await response.json()).data.map((item) => item.embedding.length));
import os
import requests
response = requests.post(
"https://api.yelu.ai/v1/embeddings",
headers={"Authorization": f"Bearer {os.environ['YELU_API_KEY']}"},
json={"model": "text-embedding-3-small", "input": ["可靠的 API 返回结构化错误。", "优秀的接口会以可预测方式失败。"], "encoding_format": "float"},
timeout=60,
)
response.raise_for_status()
print([len(item["embedding"]) for item in response.json()["data"]])
{"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.01241,-0.00728,0.03155,-0.01902]},{"object":"embedding","index":1,"embedding":[0.01087,-0.00691,0.03088,-0.01794]}],"model":"text-embedding-3-small","usage":{"prompt_tokens":18,"total_tokens":18}}
{"error":{"message":"Input must not be empty.","type":"invalid_request_error","param":"input","code":"invalid_value"}}
错误码
| 状态码 | 含义 |
|---|---|
400 | 输入为空、过大,或维度/编码不支持 |
401 | Key 缺失或无效 |
403 | Embedding 模型未开启 |
404 | 模型不可用 |
413 | Batch 或请求体过大 |
429 | 达到请求限制 |
500–504 | 网关或上游临时失败 |
不要比较不同模型或不同维度产生的向量。切换配置时应重新生成整个语料库的向量,并在索引元数据中记录版本。
最后修改于 2026年7月13日
⌘I