miner.py

from __future__ import annotations

import time
from dataclasses import dataclass, asdict
from typing import Any, Dict, List, Optional

@dataclass
class MinedKnowledge:
    term: str
    definition: str
    aliases: List[str]
    related: List[str]
    theorems: List[Dict[str, str]]
    sources: List[str]
    confidence: float

class KnowledgeMiner:
    """
    Phase 2B: Mining Engine (Stub)
    """

    def mine(self, term: str, context: Optional[str] = None) -> MinedKnowledge:
        term = term.strip()
        if not term:
            raise ValueError("term is empty")

        # Simulate LLM latency
        time.sleep(0.5)

        normalized = term.lower()

        # Stub Data
        if normalized in ("euclidean", "ユークリッド的", "ユークリッド"):
            return MinedKnowledge(
                term="euclidean",
                definition="(Kripke frame) R is euclidean iff ∀x∀y∀z ((xRy ∧ xRz) → yRz).",
                aliases=["euclidean", "ユークリッド的", "ユークリッド性"],
                related=["transitive", "symmetric", "serial"],
                theorems=[
                    {
                        "name": "Axiom 5 (◇A → □◇A)",
                        "statement": "Euclidean frames validate ◇p → □◇p.",
                        "note": "Corresponds to negative introspection in epistemic logic.",
                    }
                ],
                sources=["stub_miner"],
                confidence=0.85,
            )
        
        if normalized in ("reflexive", "反射的"):
             return MinedKnowledge(
                term="reflexive",
                definition="R is reflexive iff ∀x (xRx).",
                aliases=["reflexive", "反射的", "反射性"],
                related=["transitive", "symmetric"],
                theorems=[
                    {
                        "name": "Axiom T (□A → A)",
                        "statement": "Reflexive frames validate □p → p.",
                        "note": "Fundamental to knowledge logic (S4, S5).",
                    }
                ],
                sources=["stub_miner"],
                confidence=0.90,
            )

        # Fallback
        return MinedKnowledge(
            term=term,
            definition=f"Definition for '{term}' not found in stub. Please connect to external LLM.",
            aliases=[term],
            related=[],
            theorems=[],
            sources=["stub"],
            confidence=0.1,
        )

    def to_dict(self, mk: MinedKnowledge) -> Dict[str, Any]:
        return asdict(mk)