# 獲取文件的信息

{% openapi src="/files/WF90zszaISusKGEBimez" path="/vector-stores/{vector-store-id}/documents/{document-id}" method="get" %}
[rememberizer\_openapi.yml](https://2492455604-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FTVKmtXKPeA8gAZJsuGLA%2Fuploads%2Fgit-blob-77b6137eeb641262ec8e531c78123c02b825b865%2Frememberizer_openapi.yml?alt=media\&token=3b4a9db2-4dd7-440f-b670-9555703d351d)
{% endopenapi %}

## 示例請求

{% tabs %}
{% tab title="cURL" %}

```bash
curl -X GET \
  https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/1234 \
  -H "x-api-key: YOUR_API_KEY"
```

{% hint style="info" %}
將 `YOUR_API_KEY` 替換為您的實際向量存儲 API 金鑰，`vs_abc123` 替換為您的向量存儲 ID，`1234` 替換為文件 ID。
{% endhint %}
{% endtab %}

{% tab title="JavaScript" %}

```javascript
const getDocumentInfo = async (vectorStoreId, documentId) => {
  const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/${documentId}`, {
    method: 'GET',
    headers: {
      'x-api-key': 'YOUR_API_KEY'
    }
  });
  
  const data = await response.json();
  console.log(data);
};

getDocumentInfo('vs_abc123', 1234);
```

{% hint style="info" %}
將 `YOUR_API_KEY` 替換為您的實際向量存儲 API 金鑰，`vs_abc123` 替換為您的向量存儲 ID，`1234` 替換為文件 ID。
{% endhint %}
{% endtab %}

{% tab title="Python" %}

```python
import requests

def get_document_info(vector_store_id, document_id):
    headers = {
        "x-api-key": "YOUR_API_KEY"
    }
    
    response = requests.get(
        f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/{document_id}",
        headers=headers
    )
    
    data = response.json()
    print(data)

get_document_info('vs_abc123', 1234)
```

{% hint style="info" %}
將 `YOUR_API_KEY` 替換為您的實際向量存儲 API 金鑰，`vs_abc123` 替換為您的向量存儲 ID，`1234` 替換為文件 ID。
{% endhint %}
{% endtab %}
{% endtabs %}

## 路徑參數

| 參數              | 類型 | 描述                     |
| --------------- | -- | ---------------------- |
| vector-store-id | 字串 | **必填。** 包含文件的向量儲存的 ID。 |
| document-id     | 整數 | **必填。** 要檢索的文件的 ID。    |

## 回應格式

```json
{
  "id": 1234,
  "name": "產品手冊.pdf",
  "type": "application/pdf",
  "vector_store": "vs_abc123",
  "size": 250000,
  "status": "已編入索引",
  "processing_status": "已完成",
  "indexed_on": "2023-06-15T10:30:00Z",
  "status_error_message": null,
  "created": "2023-06-15T10:15:00Z",
  "modified": "2023-06-15T10:30:00Z"
}
```

## 認證

此端點需要使用 `x-api-key` 標頭中的 API 金鑰進行認證。

## 錯誤回應

| 狀態碼 | 描述                  |
| --- | ------------------- |
| 401 | 未授權 - 無效或缺失的 API 金鑰 |
| 404 | 找不到 - 向量儲存或文件未找到    |
| 500 | 內部伺服器錯誤             |

此端點檢索有關向量儲存中特定文件的詳細資訊。它對於檢查單個文件的處理狀態以及檢索元數據（如文件類型、大小和時間戳）非常有用。在排除文件處理問題或需要驗證文件是否正確編入索引時，這特別有幫助。


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.rememberizer.ai/zh-hk/kai-fa-zhe-zi-yuan/api-docs/vector-store/get-the-information-of-a-document.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.