plexus.scores.nodes.FuzzyMatchExtractor module

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

Bases: BaseNode

A node that performs fuzzy string matching based on a configurable set of targets and logical operators (AND/OR), without using an LLM.

It evaluates the input text against the configured targets structure. The result indicates overall success (match_found) and provides details of all individual successful matches (matches).

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, match_found: bool = False, matches: ~typing.List[~typing.Dict[str, ~typing.Any]] = <factory>, **extra_data: ~typing.Any)

Bases: GraphState

State specific to the FuzzyMatchExtractor node.

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.

match_found: bool

matches: List[Dict[str, Any]]

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', 'ChatOllama'] = 'AzureChatOpenAI', model_name: str | None = None, base_model_name: str | None = None, reasoning_effort: str | None = 'low', verbosity: str | None = 'medium', model_region: str | None = None, temperature: float | None = 0, top_p: float | None = 0.03, max_tokens: int | None = 500, logprobs: bool | None = False, top_logprobs: int | None = None, 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, targets: FuzzyTargetGroup | FuzzyTarget)

Bases: Parameters

Parameters for configuring the FuzzyMatchExtractor.

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.

model_config: ClassVar[ConfigDict] = {'ignored_types': (<class 'plexus.LangChainUser.LangChainUser.Parameters'>,), 'protected_namespaces': ()}

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

targets: FuzzyTargetGroup | FuzzyTarget

__init__(**parameters)

add_core_nodes(workflow: StateGraph) → StateGraph

Adds the core matcher node to the workflow.

get_matcher_node() → Callable

Returns the callable node function for the graph.