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09f7d63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from __future__ import annotations
from typing import List, Optional
from pydantic import BaseModel
class EpisodeMemory(BaseModel):
episode_id: str
episode_number: int
script_niche: str
platform: str
dominant_flaw: str
actions_taken: List[str]
what_worked: List[str]
what_didnt: List[str]
final_total_reward: float
key_learning: str
class CreatorHistoryBuffer(BaseModel):
creator_id: str
total_episodes: int
recent_episodes: List[EpisodeMemory] # sliding window of last 5
recurring_weak_points: List[str] # dominant_flaw in >= 3 of last 5
recurring_strong_points: List[str] # reward component >= 0.7 in >= 4 of last 5
most_effective_action: Optional[str] # action_type with highest avg reward delta
voice_stability_score: float # consistency of R3 (0–1)
improvement_trend: str # "improving" | "plateauing" | "declining"
def to_prompt_context(self) -> str:
n = len(self.recent_episodes)
if n == 0:
return "CREATOR HISTORY: No sessions recorded yet."
last = self.recent_episodes[-1]
weak = ", ".join(self.recurring_weak_points) if self.recurring_weak_points else "none"
strong = ", ".join(self.recurring_strong_points) if self.recurring_strong_points else "none"
effective = self.most_effective_action or "unknown"
last_action = last.actions_taken[0] if last.actions_taken else "unknown"
return (
f"CREATOR HISTORY (last {n} session{'s' if n != 1 else ''}):\n"
f"Recurring weak points: {weak}\n"
f"Recurring strengths: {strong}\n"
f"Most effective fix: {effective}\n"
f"Voice stability: {self.voice_stability_score:.0%}\n"
f"Trend: {self.improvement_trend}\n"
f"Last session: fixed {last.dominant_flaw} with {last_action}, "
f"reward {last.final_total_reward:.2f}"
)
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