# Get a list of documents in a Vector Store

{% openapi src="/files/7T1Jx6BU3fZ2U5LsImW1" path="/vector-stores/{vector-store-id}/documents" 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 \
  -H "x-api-key: YOUR_API_KEY"
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key and `vs_abc123` with your Vector Store ID.
{% endhint %}
{% endtab %}

{% tab title="JavaScript" %}

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

getVectorStoreDocuments('vs_abc123');
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key and `vs_abc123` with your Vector Store ID.
{% endhint %}
{% endtab %}

{% tab title="Python" %}

```python
import requests

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

get_vector_store_documents('vs_abc123')
```

{% hint style="info" %}
Replace `YOUR_API_KEY` with your actual Vector Store API key and `vs_abc123` with your Vector Store ID.
{% endhint %}
{% endtab %}
{% endtabs %}

## Path Parameters

| Parameter       | Type   | Description                                                      |
| --------------- | ------ | ---------------------------------------------------------------- |
| vector-store-id | string | **Required.** The ID of the vector store to list documents from. |

## 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"
  },
  {
    "id": 1235,
    "name": "Technical Specifications.docx",
    "type": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
    "vector_store": "vs_abc123",
    "size": 125000,
    "status": "indexed",
    "processing_status": "completed",
    "indexed_on": "2023-06-15T11:45:00Z",
    "status_error_message": null,
    "created": "2023-06-15T11:30:00Z",
    "modified": "2023-06-15T11:45:00Z"
  }
]
```

## 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 not found        |
| 500         | Internal Server Error                     |

This endpoint retrieves a list of all documents stored in the specified vector store. It provides metadata about each document, including the document's processing status, size, and indexed timestamp. This information is useful for monitoring your vector store's contents and checking document processing status.


---

# 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-a-list-of-documents-in-a-vector-store.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.