Chris Christiansen
9fcb9c354a
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57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
from enum import Enum, auto
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from dataclasses import dataclass, field
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class EmmaState(Enum):
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IDLE = auto() # Venter på input
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OBSERVING = auto() # Analyserer kontekst
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REASONING = auto() # MCoT resonneringsloop
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ACTING = auto() # Utfører verktøykall/svar
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REFLECTING = auto() # Evaluerer eget output
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LEARNING = auto() # Oppdaterer resonans-minne
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class EmmaAction(Enum):
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CLARIFY = auto() # Be om mer info
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RETRIEVE = auto() # RAG-oppslag
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TOOLCALL = auto() # API/shell/fil
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RESPOND = auto() # Generer svar
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COMPRESS = auto() # Forenkle resonneringskjede (MCoT)
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STOREPATTERN = auto() # Lagre vellykket mønster i resonansminne
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@dataclass
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class EmmaContext:
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userinput: str
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history: list
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toolresults: list = field(default_factory=list)
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thoughtchain: list = field(default_factory=list)
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rewardacc: float = 0.0
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stepcount: int = 0
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complexityscore: float = 0.0 # Flynn-tracker
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# Overgangstabell: (state, action) -> neste state
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TRANSITIONS: dict = {
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(EmmaState.IDLE, EmmaAction.RETRIEVE): EmmaState.OBSERVING,
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(EmmaState.OBSERVING, EmmaAction.CLARIFY): EmmaState.IDLE,
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(EmmaState.OBSERVING, EmmaAction.TOOLCALL): EmmaState.ACTING,
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(EmmaState.OBSERVING, EmmaAction.RESPOND): EmmaState.REASONING,
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(EmmaState.REASONING, EmmaAction.COMPRESS): EmmaState.REASONING,
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(EmmaState.REASONING, EmmaAction.RESPOND): EmmaState.ACTING,
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(EmmaState.ACTING, EmmaAction.STOREPATTERN): EmmaState.REFLECTING,
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(EmmaState.REFLECTING, EmmaAction.RETRIEVE): EmmaState.LEARNING,
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(EmmaState.LEARNING, EmmaAction.STOREPATTERN): EmmaState.IDLE,
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}
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def reward(state: EmmaState, action: EmmaAction, outcome: dict) -> float:
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"""Reward-funksjon: w = sim*ln(1+n)*decay^d*R"""
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r = 0.0
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if outcome.get("task_completed"): r += 1.0
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if outcome.get("user_confirmed"): r += 0.5
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if outcome.get("steps_used", 99) < 5: r += 0.3
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if outcome.get("tool_error"): r -= 0.4
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if outcome.get("hallucination_flag"): r -= 0.8
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return r
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