---
title: "Get indexed document"
slug: "rag-get-indexed"
updated: 2026-09-17T09:35:51Z
published: 2026-09-17T09:35:51Z
canonical: "api.ncloud-docs.com/rag-get-indexed"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://api.ncloud-docs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Get indexed document

Available in VPC

Get an indexed document in the service. You can check out the details and content of the document.

## Request

This section describes the request format. The method and URI are as follows:

| Method | URI |
| --- | --- |
| GET | /api/v1/svc/{serviceId}/doc/{docId} |

### Request headers

For information about the headers common to all RAG APIs, see [RAG request headers](/docs/en/rag-overview#%EC%9A%94%EC%B2%AD%ED%97%A4%EB%8D%94).

### Request path parameters

You can use the following path parameters with your request:

| Field | Type | Required | Description |
| --- | --- | --- | --- |
| `serviceId` | String | Required | Service's unique identifier |
| `docId` | String | Required | Document's unique identifier |

### Request example

The request example is as follows:

```
curl --location --request GET 'https://kr-pub-gateway.rag.naverncp.com/api/v1/svc/{serviceId}/doc/12345' \
--header 'Authorization: Bearer {apiKey}' \
--header 'Content-Type: application/json'
```

## Response

This section describes the response format.

### Response body

The response body includes the following data:

| Field | Type | Required | Description |
| --- | --- | --- | --- |
| `status` | Object | - | API processing result |
| `status.code` | Integer | - | [API response status code](/docs/en/rag-overview#%EC%9D%91%EB%8B%B5%EC%83%81%ED%83%9C%EC%BD%94%EB%93%9C) |
| `status.message` | String | - | [API response status message](/docs/en/rag-overview#%EC%9D%91%EB%8B%B5%EC%83%81%ED%83%9C%EC%BD%94%EB%93%9C) |
| `result` | Object | - | Document query result |
| `result.has_next` | Boolean | - | Whether the next page exists - `true` \| `false` - `true`: next page present - `false`: last page |
| `result.next_url` | String | - | Next page URL - Empty string if there is no next page |
| `result.documents` | Array | - | Document details: [documents](/docs/en/rag-get-indexed#documents) |

#### `documents`

The following describes `documents`.

| Field | Type | Required | Description |
| --- | --- | --- | --- |
| `id` | String | - | Document's unique identifier - English letters, numbers, and special characters "-" and "_" are allowed. |
| `orgid` | String | - | Original document's unique identifier |
| `title` | String | - | Document title |
| `file_name` | String | - | File name |
| `body` | String | - | Document body |
| `url` | String | - | Website URL |
| `created_at` | String | - | Creation date and time - ISO 8601 format |
| `updated_at` | String | - | Modification date and time - ISO 8601 format - The system automatically adds the indexed date and time. |
| `extra` | Object | - | Additional information - The user can add arbitrary fields under `extra` when sending documents. - `extra.bucket`, `extra.key`, `extra.last_modified`, `extra.size`, `extra.{userField}` - Field types are automatically determined upon initial data entry. - Use for categorizing, filtering, etc. |
| `salt` | Integer | - | Number automatically added by the system for distributed processing |

### Response status codes

For information about the HTTP status codes common to all RAG APIs, see [RAG response status codes](/docs/en/rag-overview#%EC%9D%91%EB%8B%B5%EC%83%81%ED%83%9C%EC%BD%94%EB%93%9C).

### Response example

The response example is as follows:

```
{
    "status": {
        "code": 200,
        "message": ""
    },
    "result": {
        "has_next": false,
        "next_url": "",
        "documents": [
            {
                "id": "12345",
                "orgid": "naver:media-ai-understanding.txt",
                "title": "Media AI Understanding",
                "file_name": "media-ai-understanding.txt",
                "body": "Where's that scene? Media AI, your video editing savior\n\nIf you've ever edited a video, you've probably experienced it: wasting time looking for a specific scene among dozens of hours of footage. NAVER Cloud's Media AI improves the efficiency of your video production process. Simply upload your footage and the AI will automatically analyze the people and actions in the scene, and even organize the script.\n\nSay, \"Find all the shots of the main character running\", and it will find all the shots of a specific person running in a flash, so you can take a break from editing and focus on your creative work. You can also associate meta information with your editing tools to complete your video editing tasks efficiently.",
                "url": "www.naver.com",
                "created_at": "2025-07-09T19:45:11.000000+00:00",
                "updated_at": "2025-07-09T10:58:39.723905+00:00",
                "extra": {
                    "bucket": "naver",
                    "size": 1024,
                    "last_modified": "2025-07-09T19:45:11+00:00",
                    "key": "media-ai-understanding.txt"
                },
                "salt": 33404
            }
        ]
    }
}
```