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  • Sync
  • Async
def ingest_text(
    content: str,
    filename: Optional[str] = None,
    metadata: Optional[Dict[str, Any]] = None,
    use_colpali: bool = True,
) -> Document

​
Parameters

  • content (str): Text content to ingest
  • filename (str, optional): Optional filename for the document
  • metadata (Dict[str, Any], optional): Optional metadata dictionary
  • use_colpali (bool, optional): Whether to use ColPali-style embedding model to ingest the text (slower, but significantly better retrieval accuracy for text and images). Defaults to True.

​
Typed Metadata

Pass native Python types for metadata (e.g., datetime, date, Decimal, bool). The SDK normalizes them, forwards the appropriate metadata_types, and unlocks range queries described in the Metadata Filtering guide. Example: 
from datetime import datetime, date
from decimal import Decimal

doc = db.ingest_text(
    "SOW details …",
    metadata={
        "priority": 42,
        "start_date": datetime.utcnow(),
        "end_date": date(2024, 12, 31),
        "cost": Decimal("1234.56")
    }
)

​
Returns

  • Document: Metadata of the ingested document

​
Examples

  • Sync
  • Async
from morphik import Morphik

db = Morphik()

doc = db.ingest_text(
    "Machine learning is fascinating...",
    metadata={"category": "tech"},
    use_colpali=True,
)