pipelines.extractor.models

Pydantic models for structured information extraction

EntityCollection


def EntityCollection(
    data:Any
)->None:

Collection of entities extracted from a document.

source

Entity


def Entity(
    data:Any
)->None:

A named entity extracted from text.

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DocumentMetadata


def DocumentMetadata(
    data:Any
)->None:

High-level metadata about a document.

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KPPCollection


def KPPCollection(
    data:Any
)->None:

Collection of Key Performance Parameters from acquisition documents.

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KPPValue


def KPPValue(
    data:Any
)->None:

A Key Performance Parameter with threshold and objective values.

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ParamCollection


def ParamCollection(
    data:Any
)->None:

!!! abstract “Usage Documentation” Models

A base class for creating Pydantic models.

Attributes: class_vars: The names of the class variables defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The core schema of the model.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: A dictionary containing metadata about generic Pydantic models.
    The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__]
    and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias],
    and the `parameter` item maps to the `__parameter__` attribute of generic classes.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
    is set to `'allow'`.
__pydantic_fields_set__: The names of fields explicitly set during instantiation.
__pydantic_private__: Values of private attributes set on the model instance.

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SystemParameter


def SystemParameter(
    data:Any
)->None:

!!! abstract “Usage Documentation” Models

A base class for creating Pydantic models.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The core schema of the model.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: A dictionary containing metadata about generic Pydantic models.
    The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__]
    and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias],
    and the `parameter` item maps to the `__parameter__` attribute of generic classes.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
__pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
__pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.

__pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects.
__pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
    is set to `'allow'`.
__pydantic_fields_set__: The names of fields explicitly set during instantiation.
__pydantic_private__: Values of private attributes set on the model instance.

from onprem import LLM
from onprem.pipelines import Extractor

llm = LLM("anthropic/claude-sonnet-4-5", max_tokens=32000, mute_stream=True)

# Initialize
extractor = Extractor(llm)

# Test document
content = """
 Aircraft Specifications:
 - Maximum Speed: 500 mph
 - Cruise Speed (avg): 350 knots
 - Operational Range: 2000 nm
 - Payload Capacity: 5000 lbs
 - Power Output: 1500 W
 - Total Weight: 25000 kg
"""

# Extract with whitelist filtering
whitelist = {'range', 'cruise speed', 'weight', 'power'}

result = extractor.extract_parameters(
     content=content,
     parameter_whitelist=whitelist,
)

# Display results
print(f"Extracted {len(result['params'])} matching parameters:\n")
for p in result['params']:
    print(f"  {p['name']}: {p['value']} {p['unit']}")


# cruise speed: 350 knots -> matches "cruise speed"
# range: 2000 nm          -> matches "operational range"
# power: 1500 W           -> matches "power output"
# weight: 25000 kg        -> matches "total weight"

  Whitelist filtered params: 6 → 4 items
Extracted 4 matching parameters:

  cruise speed: 350 knots
  range: 2000 nm
  power: 1500 W
  weight: 25000 kg