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Usage

  • Sync
  • Async
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')}")

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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.

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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.

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Returns

An object containing:
  • documents: List of successfully ingested Document objects
  • errors: List of errors encountered during ingestion (each error is a dict with ‘filename’ and ‘error’ keys)

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Advanced Examples

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Per-File Metadata

  • Sync
  • Async
# 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
)

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Using Different File Input Types

  • Sync
  • Async
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()