# Get the information of a document

{% openapi src="/files/7T1Jx6BU3fZ2U5LsImW1" path="/vector-stores/{vector-store-id}/documents/{document-id}" method="get" %}
[rememberizer\_openapi.yml](https://2952947711-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FyNqpTh7Mh66N0RnO0k24%2Fuploads%2Fgit-blob-77b6137eeb641262ec8e531c78123c02b825b865%2Frememberizer_openapi.yml?alt=media\&token=cbad765b-1613-4222-b591-9ae17a3b7cfa)
{% endopenapi %}

## Example Requests

{% 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" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and `1234` with the document 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" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and `1234` with the document 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" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key, `vs_abc123` with your Vector Store ID, and `1234` with the document ID.
{% endhint %}
{% endtab %}
{% endtabs %}

## Path Parameters

| Parameter       | Type    | Description                                                       |
| --------------- | ------- | ----------------------------------------------------------------- |
| vector-store-id | string  | **Required.** The ID of the vector store containing the document. |
| document-id     | integer | **Required.** The ID of the document to retrieve.                 |

## Response Format

```json
{
  "id": 1234,
  "name": "Product Manual.pdf",
  "type": "application/pdf",
  "vector_store": "vs_abc123",
  "size": 250000,
  "status": "indexed",
  "processing_status": "completed",
  "indexed_on": "2023-06-15T10:30:00Z",
  "status_error_message": null,
  "created": "2023-06-15T10:15:00Z",
  "modified": "2023-06-15T10:30:00Z"
}
```

## Authentication

This endpoint requires authentication using an API key in the `x-api-key` header.

## Error Responses

| Status Code | Description                                    |
| ----------- | ---------------------------------------------- |
| 401         | Unauthorized - Invalid or missing API key      |
| 404         | Not Found - Vector Store or document not found |
| 500         | Internal Server Error                          |

This endpoint retrieves detailed information about a specific document in the vector store. It's useful for checking the processing status of individual documents and retrieving metadata like file type, size, and timestamps. This can be particularly helpful when troubleshooting issues with document processing or when you need to verify that a document was properly indexed.


---

# 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/developer-resources/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.