NexusMind

import datetime from enum import Enum from typing import Annotated, List from pydantic import BaseModel, BeforeValidator, Field, field_validator # Helper for probability distributions (list of floats summing to 1.0) def _validate_probability_distribution(v: List[float]) -> List[float]: if not v: # Empty list is valid if optional, but if provided, must sum to 1 return v if not all(0.0 <= p <= 1.0 for p in v): raise ValueError("All probabilities must be between 0.0 and 1.0") if abs(sum(v) - 1.0) > 1e-6 and sum(v) != 0: # Allow sum to be 0 for uninitialized # For flexibility, we might allow non-normalized lists and normalize them later # For now, strict validation. Could also add a normalization step here. # raise ValueError("Probabilities must sum to 1.0") pass # Relaxing this for now, assume normalization happens elsewhere if critical return v ProbabilityDistribution = Annotated[ List[float], BeforeValidator(_validate_probability_distribution) ] # Confidence Vector based on P1.5 # Using a class for better type hinting and potential methods later class ConfidenceVector(BaseModel): empirical_support: float = Field(default=0.5, ge=0.0, le=1.0) theoretical_basis: float = Field(default=0.5, ge=0.0, le=1.0) methodological_rigor: float = Field(default=0.5, ge=0.0, le=1.0) consensus_alignment: float = Field(default=0.5, ge=0.0, le=1.0) def to_list(self) -> List[float]: return [ self.empirical_support, self.theoretical_basis, self.methodological_rigor, self.consensus_alignment, ] @classmethod def from_list(cls, values: List[float]) -> "ConfidenceVector": if len(values) != 4: raise ValueError("Confidence list must have exactly 4 values.") return cls( empirical_support=values[0], theoretical_basis=values[1], methodological_rigor=values[2], consensus_alignment=values[3], ) @property def average_confidence(self) -> float: return sum(self.to_list()) / 4.0 # Single scalar certainty/confidence if needed CertaintyScore = Annotated[float, Field(ge=0.0, le=1.0)] # Impact Score (P1.28) ImpactScore = Annotated[float, Field(ge=0.0, le=1.0)] # Assuming normalized impact class TimestampedModel(BaseModel): created_at: datetime.datetime = Field(default_factory=datetime.datetime.now) updated_at: datetime.datetime = Field(default_factory=datetime.datetime.now) def touch(self): """Updates the updated_at timestamp.""" self.updated_at = datetime.datetime.now() # Standardized way to represent a probability distribution for discrete outcomes class DiscreteProbabilityDistribution(BaseModel): outcomes: List[str] # The labels for each probability probabilities: ProbabilityDistribution @field_validator("probabilities") def check_probabilities_match_outcomes(cls, v, info): values = info.data if "outcomes" in values and len(v) != len(values["outcomes"]): raise ValueError("Number of probabilities must match number of outcomes") return v class EpistemicStatus(str, Enum): """P1.12: Epistemic status of a node/claim.""" ASSUMPTION = "assumption" HYPOTHESIS = "hypothesis" EVIDENCE_SUPPORTED = "evidence_supported" EVIDENCE_CONTRADICTED = "evidence_contradicted" THEORETICALLY_DERIVED = "theoretically_derived" WIDELY_ACCEPTED = "widely_accepted" DISPUTED = "disputed" UNKNOWN = "unknown" INFERRED = "inferred" SPECULATION = "speculation"