Error Handling¶

ExtractionError¶

When md2pydantic cannot extract valid data, it raises ExtractionError:

from md2pydantic import MDConverter, ExtractionError

try:
    result = MDConverter(MyModel).parse_tables(
        "no tables here"
    )
except ExtractionError as e:
    print(e)         # Human-readable summary
    print(e.errors)  # List of typed error details

ExtractionError is raised when:

No structured data is found in the input
Structured data is found but none validates against the model

The `.errors` Attribute¶

Each entry in e.errors is one of two types:

TransformError¶

Raised during the parsing phase when raw content cannot be converted to a Python dictionary. Contains:

phase -- always "transform"
message -- description of what went wrong
location -- BlockLocation with line numbers
raw_content -- the original block text

ModelValidationError¶

Raised during the validation phase when Pydantic rejects the data. Contains:

phase -- always "validate"
field_errors -- tuple of FieldError objects
location -- BlockLocation or RowLocation
raw_input -- the dict that was passed to Pydantic

Each FieldError has:

field -- the field name that failed
message -- Pydantic's error message
input_value -- the value that was rejected
error_type -- Pydantic's error type string

Exception Hierarchy¶

Exception
  └── MD2PydanticError
        └── ExtractionError

You can catch MD2PydanticError to handle all library exceptions, or ExtractionError for extraction-specific failures.

Human-Readable Output¶

Calling str() on an ExtractionError produces a numbered summary:

No table rows matched the model
  [1] Validation error in table 0, row 0: age: Input
      should be a valid integer; active: Input should
      be a valid boolean