# Retrieve document contents

{% openapi src="<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>" path="/documents/{document\_id}/contents/" 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/documents/12345/contents/?start_chunk=0&end_chunk=20" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN"
```

{% hint style="info" %}
Replace `YOUR_JWT_TOKEN` with your actual JWT token and `12345` with an actual document ID.
{% endhint %}
{% endtab %}

{% tab title="JavaScript" %}

```javascript
const getDocumentContents = async (documentId, startChunk = 0, endChunk = 20) => {
  const url = new URL(`https://api.rememberizer.ai/api/v1/documents/${documentId}/contents/`);
  url.searchParams.append('start_chunk', startChunk);
  url.searchParams.append('end_chunk', endChunk);
  
  const response = await fetch(url.toString(), {
    method: 'GET',
    headers: {
      'Authorization': 'Bearer YOUR_JWT_TOKEN'
    }
  });
  
  const data = await response.json();
  console.log(data);
  
  // If there are more chunks, you can fetch them
  if (data.end_chunk < totalChunks) {
    // Fetch the next set of chunks
    await getDocumentContents(documentId, data.end_chunk, data.end_chunk + 20);
  }
};

getDocumentContents(12345);
```

{% hint style="info" %}
Replace `YOUR_JWT_TOKEN` with your actual JWT token and `12345` with an actual document ID.
{% endhint %}
{% endtab %}

{% tab title="Python" %}

```python
import requests

def get_document_contents(document_id, start_chunk=0, end_chunk=20):
    headers = {
        "Authorization": "Bearer YOUR_JWT_TOKEN"
    }
    
    params = {
        "start_chunk": start_chunk,
        "end_chunk": end_chunk
    }
    
    response = requests.get(
        f"https://api.rememberizer.ai/api/v1/documents/{document_id}/contents/",
        headers=headers,
        params=params
    )
    
    data = response.json()
    print(data)
    
    # If there are more chunks, you can fetch them
    # This is a simplistic example - you might want to implement a proper recursion check
    if 'end_chunk' in data and data['end_chunk'] < total_chunks:
        get_document_contents(document_id, data['end_chunk'], data['end_chunk'] + 20)

get_document_contents(12345)
```

{% hint style="info" %}
Replace `YOUR_JWT_TOKEN` with your actual JWT token and `12345` with an actual document ID.
{% endhint %}
{% endtab %}
{% endtabs %}

## Path Parameters

| Parameter    | Type    | Description                                                    |
| ------------ | ------- | -------------------------------------------------------------- |
| document\_id | integer | **Required.** The ID of the document to retrieve contents for. |

## Query Parameters

| Parameter    | Type    | Description                                           |
| ------------ | ------- | ----------------------------------------------------- |
| start\_chunk | integer | The starting chunk index. Default is 0.               |
| end\_chunk   | integer | The ending chunk index. Default is start\_chunk + 20. |

## Response Format

```json
{
  "content": "The full text content of the document chunks...",
  "end_chunk": 20
}
```

## Error Responses

| Status Code | Description           |
| ----------- | --------------------- |
| 404         | Document not found    |
| 500         | Internal server error |

## Pagination for Large Documents

For large documents, the content is split into chunks. You can retrieve the full document by making multiple requests:

1. Make an initial request with `start_chunk=0`
2. Use the returned `end_chunk` value as the `start_chunk` for the next request
3. Continue until you have retrieved all chunks

This endpoint returns the raw text content of a document, allowing you to access the full information for detailed processing or analysis.