> ## 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.

# ingest_files

> Batch ingest multiple files into Morphik

## Usage

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

    db = Morphik()

    # Batch ingest files with shared metadata
    result = db.ingest_files(
        files=["document1.pdf", "document2.docx", "image.png"], 
        metadata={"category": "reports"},
        use_colpali=True,
        parallel=True
    )

    # Process the results
    for doc in result["documents"]:
        print(f"Successfully ingested: {doc.filename} (ID: {doc.external_id})")

    # Check for errors
    for error in result["errors"]:
        print(f"Error ingesting {error.get('filename')}: {error.get('error')}")
    ```
  </Tab>

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

    async with AsyncMorphik() as db:
        # Batch ingest files with shared metadata
        result = await db.ingest_files(
            files=["document1.pdf", "document2.docx", "image.png"], 
            metadata={"category": "reports"},
            use_colpali=True,
            parallel=True
        )
        
        # Process the results
        for doc in result["documents"]:
            print(f"Successfully ingested: {doc.filename} (ID: {doc.external_id})")
        
        # Check for errors
        for error in result["errors"]:
            print(f"Error ingesting {error.get('filename')}: {error.get('error')}")
    ```
  </Tab>
</Tabs>

## Parameters

* `files` (List\[Union\[str, bytes, BinaryIO, Path]]): List of files to ingest (path strings, bytes, file objects, or Path objects)
* `metadata` (Dict\[str, Any] | List\[Dict\[str, Any]], optional): Metadata to apply to the files. Can be either:
  * A single dict to apply to all files
  * A list of dicts, one per file (must match the length of `files`)
* `use_colpali` (bool, optional): Whether to use ColPali-style embedding model. Defaults to True.
* `parallel` (bool, optional): Whether to process files in parallel. Defaults to True.

### Typed Metadata

When specifying `metadata`, you can include Python `datetime`, `date`, `Decimal`, or numeric types. The SDK normalizes them, sends the accompanying `metadata_types`, and unlocks the advanced queries outlined in [Metadata Filtering](/concepts/metadata-filtering).

## Returns

An object containing:

* `documents`: List of successfully ingested [Document](/python-sdk/document) objects
* `errors`: List of errors encountered during ingestion (each error is a dict with 'filename' and 'error' keys)

## Advanced Examples

### Per-File Metadata

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    # Ingest files with different metadata for each file
    files = ["report.pdf", "data.csv", "presentation.pptx"]

    # Metadata must match the length of files list
    metadata_list = [
        {"category": "reports", "author": "Alice"},
        {"category": "data", "source": "database"},
        {"category": "presentations", "department": "marketing"}
    ]

    result = db.ingest_files(
        files=files,
        metadata=metadata_list
    )
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    # Ingest files with different metadata for each file
    files = ["report.pdf", "data.csv", "presentation.pptx"]

    # Metadata must match the length of files list
    metadata_list = [
        {"category": "reports", "author": "Alice"},
        {"category": "data", "source": "database"},
        {"category": "presentations", "department": "marketing"}
    ]

    result = await db.ingest_files(
        files=files,
        metadata=metadata_list
    )
    ```
  </Tab>
</Tabs>

### Using Different File Input Types

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    import io
    from pathlib import Path

    # Mixing different file input types
    file1 = "document.pdf"                       # Path string
    file2 = Path("image.png")                    # Path object
    file3 = open("data.csv", "rb")               # File object
    file4 = b"Hello, world!"                     # Bytes (requires filename)
    file5 = io.BytesIO(b"Some in-memory data")   # BytesIO (requires filename)

    result = db.ingest_files(
        files=[file1, file2, file3, file4, file5],
        metadata=[
            {"type": "document"},
            {"type": "image"},
            {"type": "data"},
            {"type": "text", "filename": "hello.txt"},
            {"type": "text", "filename": "memory-data.txt"}
        ]
    )

    # Don't forget to close file objects
    file3.close()
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    import io
    from pathlib import Path

    # Mixing different file input types
    file1 = "document.pdf"                       # Path string
    file2 = Path("image.png")                    # Path object
    file3 = open("data.csv", "rb")               # File object
    file4 = b"Hello, world!"                     # Bytes (requires filename)
    file5 = io.BytesIO(b"Some in-memory data")   # BytesIO (requires filename)

    result = await db.ingest_files(
        files=[file1, file2, file3, file4, file5],
        metadata=[
            {"type": "document"},
            {"type": "image"},
            {"type": "data"},
            {"type": "text", "filename": "hello.txt"},
            {"type": "text", "filename": "memory-data.txt"}
        ]
    )

    # Don't forget to close file objects
    file3.close()
    ```
  </Tab>
</Tabs>