plexus.scores.nodes.Extractor module

class plexus.scores.nodes.Extractor.Extractor(**parameters)

Bases: BaseNode, LangChainUser

A node that extracts a specific quote from the input text based on the provided prompt.

class ExtractionOutputParser(*args, name: str | None = None, FUZZY_MATCH_SCORE_CUTOFF: int, text: str, use_exact_matching: bool = False, trust_model_output: bool = False)

Bases: BaseOutputParser[dict]

class Config

Bases: object

arbitrary_types_allowed = True

underscore_attrs_are_private = True

FUZZY_MATCH_SCORE_CUTOFF: int

__init__(*args, **kwargs)

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': (), 'underscore_attrs_are_private': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

parse(output: str) → Dict[str, Any]

Parse a single string model output into some structure.

Args:

text: String output of a language model.

Returns:

Structured output.

text: str

tokenize(text: str) → list[str]

trust_model_output: bool

use_exact_matching: bool

class GraphState(*, text: str, metadata: dict | None = None, results: dict | None = None, messages: ~typing.List[~typing.Dict[str, ~typing.Any]] | None = None, is_not_empty: bool | None = None, value: str | None = None, explanation: str | None = None, reasoning: str | None = None, chat_history: ~typing.List[~typing.Any] = <factory>, completion: str | None = None, classification: str | None = None, confidence: float | None = None, retry_count: int | None = 0, at_llm_breakpoint: bool | None = False, good_call: str | None = None, good_call_explanation: str | None = None, non_qualifying_reason: str | None = None, non_qualifying_explanation: str | None = None, extracted_text: str | None = None, **extra_data: ~typing.Any)

Bases: GraphState

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

extracted_text: str | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Parameters(*, model_provider: Literal['ChatOpenAI', 'AzureChatOpenAI', 'BedrockChat', 'ChatVertexAI'] = 'AzureChatOpenAI', model_name: str | None = None, base_model_name: str | None = None, model_region: str | None = None, temperature: float | None = 0, top_p: float | None = 0.03, max_tokens: int | None = 500, input: dict | None = None, output: dict | None = None, system_message: str | None = None, user_message: str | None = None, example_refinement_message: str | None = None, single_line_messages: bool = False, name: str | None = None, fuzzy_match_score_cutoff: int = 50, use_exact_matching: bool = False, trust_model_output: bool = False)

Bases: Parameters

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

fuzzy_match_score_cutoff: int

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

trust_model_output: bool

use_exact_matching: bool

__init__(**parameters)

add_core_nodes(workflow: StateGraph) → StateGraph

Build and return a core LangGraph workflow. The node name is available as self.node_name when needed.

execute(*args, **kwargs)

get_extractor_node() → LambdaType