获取向量存储的信息
get
Retrieve information about the vector store associated with the API key.
Header parameters
x-api-keystringRequired
The API key for authentication.
Responses
200
Vector store information retrieved successfully.
application/json
get
/vector-stores/meGET /api/v1/vector-stores/me HTTP/1.1
Host: api.rememberizer.ai
x-api-key: text
Accept: */*
200
Vector store information retrieved successfully.
{
"id": "text",
"name": "text",
"description": "text",
"embedding_model": "text",
"indexing_algorithm": "text",
"vector_dimension": 1,
"search_metric": "text",
"created": "2025-11-15T01:53:44.491Z",
"modified": "2025-11-15T01:53:44.491Z"
}示例请求
curl -X GET \
https://api.rememberizer.ai/api/v1/vector-stores/me \
-H "x-api-key: YOUR_API_KEY"const getVectorStoreInfo = async () => {
const response = await fetch('https://api.rememberizer.ai/api/v1/vector-stores/me', {
method: 'GET',
headers: {
'x-api-key': 'YOUR_API_KEY'
}
});
const data = await response.json();
console.log(data);
};
getVectorStoreInfo();import requests
def get_vector_store_info():
headers = {
"x-api-key": "YOUR_API_KEY"
}
response = requests.get(
"https://api.rememberizer.ai/api/v1/vector-stores/me",
headers=headers
)
data = response.json()
print(data)
get_vector_store_info()响应格式
{
"id": "vs_abc123",
"name": "我的向量存储",
"description": "用于产品文档的向量存储",
"embedding_model": "sentence-transformers/all-mpnet-base-v2",
"indexing_algorithm": "ivfflat",
"vector_dimension": 128,
"search_metric": "余弦距离",
"created": "2023-06-01T10:30:00Z",
"modified": "2023-06-15T14:45:00Z"
}认证
此端点需要使用 x-api-key 头中的 API 密钥进行认证。
错误响应
状态码
描述
401
未授权 - 无效或缺失的 API 密钥
404
未找到 - 找不到向量存储
500
内部服务器错误
此端点检索与提供的 API 密钥关联的向量存储的信息。它对于检查配置细节非常有用，包括使用的嵌入模型、维度和搜索指标。这些信息对于优化搜索查询和理解向量存储的能力非常有价值。
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