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

# extract_document_pages

> Extract specific pages from a document

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    def extract_document_pages(
        document_id: str,
        start_page: int,
        end_page: int,
    ) -> DocumentPagesResponse
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    async def extract_document_pages(
        document_id: str,
        start_page: int,
        end_page: int,
    ) -> DocumentPagesResponse
    ```
  </Tab>
</Tabs>

## Parameters

* `document_id` (str): ID of the document to extract pages from
* `start_page` (int): Starting page number (1-indexed)
* `end_page` (int): Ending page number (1-indexed)

## Returns

* `DocumentPagesResponse`: Object containing extracted pages with metadata

## Examples

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    from morphik import Morphik

    db = Morphik()

    # Extract pages 1-3 from a document
    response = db.extract_document_pages(
        document_id="doc_123abc",
        start_page=1,
        end_page=3,
    )

    print(f"Document ID: {response.document_id}")
    print(f"Extracted pages {response.start_page}-{response.end_page}")
    print(f"Total pages in document: {response.total_pages}")
    print(f"Number of pages extracted: {len(response.pages)}")

    # Pages are base64 encoded
    for i, page_content in enumerate(response.pages):
        print(f"Page {response.start_page + i}: {len(page_content)} chars")
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    from morphik import AsyncMorphik

    async with AsyncMorphik() as db:
        # Extract pages 1-3 from a document
        response = await db.extract_document_pages(
            document_id="doc_123abc",
            start_page=1,
            end_page=3,
        )

        print(f"Document ID: {response.document_id}")
        print(f"Extracted pages {response.start_page}-{response.end_page}")
        print(f"Total pages in document: {response.total_pages}")
        print(f"Number of pages extracted: {len(response.pages)}")

        # Pages are base64 encoded
        for i, page_content in enumerate(response.pages):
            print(f"Page {response.start_page + i}: {len(page_content)} chars")
    ```
  </Tab>
</Tabs>

## DocumentPagesResponse Properties

The `DocumentPagesResponse` object has the following properties:

* `document_id` (str): ID of the document
* `pages` (List\[str]): List of page contents as base64 encoded strings
* `start_page` (int): Start page number (1-indexed)
* `end_page` (int): End page number (1-indexed)
* `total_pages` (int): Total number of pages in the document

## Notes

* Page numbers are 1-indexed (first page is 1, not 0).
* The `pages` list contains base64 encoded representations of each page.
* Useful for extracting specific sections of large documents.