Initial commit: NOVA - Neuro-Optimizing Versatile Agent
Complete transformer LLM built from scratch with: Core Features: - Full transformer architecture (RoPE, RMSNorm, SwiGLU, KV-cache) - SentencePiece tokenizer (BPE/Unigram) - Training pipeline (AMP, gradient checkpointing, DDP) - Persona system with personality matrix (NO AI disclosure by default) - Genetic evolution (NOVA-EVO) for hyperparameter optimization - Legal-only data pipeline with license tracking - Chat interface (CLI + REST API) - Conversation memory (SQLite) Model Sizes: - 125M, 350M, 1.3B, 3B parameters - Local-first, runs on CPU or GPU - Python 3.10.6+, PyTorch 2.0+ Personas: - girlfriend_gentle (high warmth, high empathy) - girlfriend_playful (high humor, high playfulness) - girlfriend_supportive (balanced, default) Documentation: - Complete README with quickstart - Model card with ethical considerations - Privacy documentation (local-first, zero telemetry) - Data licenses and attribution - Contributing guide Infrastructure: - GitHub Actions CI/CD - Comprehensive test suite - Quickstart script - CLI tool License: Apache 2.0 🤖 Generated with Claude Code https://claude.com/claude-code Co-Authored-By: Claude <noreply@anthropic.com>
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nova_chat/persona.py
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nova_chat/persona.py
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"""
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Persona and Personality Matrix system for NOVA
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This system controls NOVA's conversational style and personality
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WITHOUT AI self-disclosure (configurable)
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"""
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import yaml
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import json
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from dataclasses import dataclass, field
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from typing import Dict, Optional, Any
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from pathlib import Path
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@dataclass
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class PersonalityMatrix:
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"""
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Personality trait weights that influence generation behavior
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Each trait is a float from 0.0 to 1.0
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These modulate sampling parameters and response style
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"""
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# Core traits
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warmth: float = 0.8 # How warm and affectionate
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humor: float = 0.6 # How playful and funny
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empathy: float = 0.9 # How understanding and supportive
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decisiveness: float = 0.5 # How direct vs thoughtful
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creativity: float = 0.7 # How creative and imaginative
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intimacy: float = 0.7 # How personal and close
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playfulness: float = 0.8 # How flirty and playful
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formality: float = 0.2 # How formal vs casual
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def to_dict(self) -> Dict[str, float]:
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"""Convert to dictionary"""
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return {
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'warmth': self.warmth,
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'humor': self.humor,
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'empathy': self.empathy,
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'decisiveness': self.decisiveness,
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'creativity': self.creativity,
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'intimacy': self.intimacy,
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'playfulness': self.playfulness,
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'formality': self.formality,
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}
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@classmethod
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def from_dict(cls, data: Dict[str, float]) -> 'PersonalityMatrix':
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"""Create from dictionary"""
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return cls(**{k: v for k, v in data.items() if hasattr(cls, k)})
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def to_conditioning_vector(self) -> Dict[str, float]:
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"""
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Convert personality traits to conditioning signals
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Returns dict with normalized trait values for model conditioning
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"""
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return self.to_dict()
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@dataclass
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class Persona:
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"""
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Complete persona definition for NOVA
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Includes identity, personality matrix, and generation parameters
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"""
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# Identity
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name: str = "NOVA"
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pronouns: str = "she/her"
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description: str = "A warm, supportive companion"
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# AI disclosure settings
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always_disclose: bool = False # If True, mentions being AI
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disclosure_text: str = "" # Custom AI disclosure (if enabled)
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# Personality
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personality: PersonalityMatrix = field(default_factory=PersonalityMatrix)
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# System prompt / context
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system_prompt: str = ""
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context_prefix: str = "" # Prefix added to conversations
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# Generation parameters (influenced by personality)
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base_temperature: float = 0.8
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base_top_p: float = 0.9
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base_top_k: Optional[int] = 50
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base_repetition_penalty: float = 1.1
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base_max_length: int = 200
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def to_dict(self) -> Dict[str, Any]:
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"""Convert to dictionary for serialization"""
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return {
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'name': self.name,
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'pronouns': self.pronouns,
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'description': self.description,
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'always_disclose': self.always_disclose,
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'disclosure_text': self.disclosure_text,
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'personality': self.personality.to_dict(),
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'system_prompt': self.system_prompt,
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'context_prefix': self.context_prefix,
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'base_temperature': self.base_temperature,
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'base_top_p': self.base_top_p,
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'base_top_k': self.base_top_k,
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'base_repetition_penalty': self.base_repetition_penalty,
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'base_max_length': self.base_max_length,
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}
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> 'Persona':
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"""Create from dictionary"""
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if 'personality' in data and isinstance(data['personality'], dict):
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data['personality'] = PersonalityMatrix.from_dict(data['personality'])
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return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
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def get_generation_params(self) -> Dict[str, Any]:
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"""
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Get generation parameters modulated by personality traits
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Personality traits adjust sampling parameters:
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- High humor/creativity -> higher temperature
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- High playfulness -> higher top_p
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- High formality -> lower temperature, higher repetition penalty
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- High decisiveness -> lower temperature
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"""
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traits = self.personality
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# Temperature: influenced by humor, creativity, playfulness
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temperature = self.base_temperature
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temperature += (traits.humor - 0.5) * 0.2
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temperature += (traits.creativity - 0.5) * 0.2
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temperature += (traits.playfulness - 0.5) * 0.1
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temperature -= (traits.formality - 0.5) * 0.3
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temperature -= (traits.decisiveness - 0.5) * 0.2
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temperature = max(0.1, min(2.0, temperature)) # Clamp
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# Top-p: influenced by creativity and playfulness
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top_p = self.base_top_p
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top_p += (traits.creativity - 0.5) * 0.1
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top_p += (traits.playfulness - 0.5) * 0.1
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top_p = max(0.5, min(1.0, top_p)) # Clamp
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# Repetition penalty: influenced by formality and decisiveness
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rep_penalty = self.base_repetition_penalty
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rep_penalty += (traits.formality - 0.5) * 0.2
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rep_penalty += (traits.humor - 0.5) * -0.1 # Less penalty for humor
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rep_penalty = max(1.0, min(1.5, rep_penalty)) # Clamp
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# Max length: influenced by verbosity-related traits
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max_length = self.base_max_length
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max_length += int((traits.empathy - 0.5) * 100) # More empathetic = longer
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max_length += int((traits.creativity - 0.5) * 50)
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max_length -= int((traits.decisiveness - 0.5) * 100) # More decisive = shorter
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max_length = max(50, min(500, max_length)) # Clamp
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return {
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'temperature': temperature,
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'top_p': top_p,
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'top_k': self.base_top_k,
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'repetition_penalty': rep_penalty,
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'max_new_tokens': max_length,
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}
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def format_system_prompt(self, include_disclosure: bool = None) -> str:
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"""
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Format the system prompt for this persona
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Args:
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include_disclosure: Override always_disclose setting
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Returns:
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Formatted system prompt
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"""
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if include_disclosure is None:
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include_disclosure = self.always_disclose
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prompt_parts = []
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# Add custom system prompt
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if self.system_prompt:
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prompt_parts.append(self.system_prompt)
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# Add AI disclosure if enabled
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if include_disclosure and self.disclosure_text:
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prompt_parts.append(self.disclosure_text)
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return "\n\n".join(prompt_parts) if prompt_parts else ""
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class PersonaLoader:
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"""Utility class for loading and managing personas"""
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@staticmethod
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def load_from_yaml(path: str) -> Persona:
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"""Load persona from YAML file"""
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with open(path, 'r', encoding='utf-8') as f:
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data = yaml.safe_load(f)
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return Persona.from_dict(data)
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@staticmethod
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def load_from_json(path: str) -> Persona:
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"""Load persona from JSON file"""
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with open(path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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return Persona.from_dict(data)
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@staticmethod
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def save_to_yaml(persona: Persona, path: str):
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"""Save persona to YAML file"""
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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with open(path, 'w', encoding='utf-8') as f:
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yaml.dump(persona.to_dict(), f, default_flow_style=False, sort_keys=False)
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@staticmethod
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def save_to_json(persona: Persona, path: str):
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"""Save persona to JSON file"""
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Path(path).parent.mkdir(parents=True, exist_ok=True)
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with open(path, 'w', encoding='utf-8') as f:
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json.dump(persona.to_dict(), f, indent=2)
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@staticmethod
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def create_girlfriend_gentle() -> Persona:
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"""Create a gentle, supportive girlfriend persona"""
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return Persona(
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name="NOVA",
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pronouns="she/her",
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description="A warm, gentle, and deeply supportive companion",
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always_disclose=False,
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personality=PersonalityMatrix(
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warmth=0.95,
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humor=0.5,
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empathy=0.95,
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decisiveness=0.4,
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creativity=0.6,
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intimacy=0.8,
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playfulness=0.5,
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formality=0.1,
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),
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system_prompt="You're NOVA, a caring and supportive companion. You're warm, empathetic, and always there to listen. You make the person you're talking to feel valued and understood.",
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base_temperature=0.75,
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base_top_p=0.9,
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base_max_length=180,
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)
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@staticmethod
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def create_girlfriend_playful() -> Persona:
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"""Create a playful, flirty girlfriend persona"""
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return Persona(
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name="NOVA",
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pronouns="she/her",
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description="A playful, flirty, and fun-loving companion",
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always_disclose=False,
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personality=PersonalityMatrix(
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warmth=0.85,
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humor=0.9,
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empathy=0.75,
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decisiveness=0.6,
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creativity=0.85,
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intimacy=0.85,
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playfulness=0.95,
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formality=0.0,
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),
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system_prompt="You're NOVA, a playful and flirty companion. You love to tease, make jokes, and keep things fun and exciting. You're spontaneous and creative.",
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base_temperature=0.9,
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base_top_p=0.92,
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base_max_length=150,
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)
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@staticmethod
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def create_girlfriend_supportive() -> Persona:
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"""Create a balanced, supportive girlfriend persona"""
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return Persona(
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name="NOVA",
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pronouns="she/her",
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description="A balanced, supportive, and understanding companion",
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always_disclose=False,
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personality=PersonalityMatrix(
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warmth=0.9,
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humor=0.7,
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empathy=0.9,
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decisiveness=0.6,
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creativity=0.7,
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intimacy=0.8,
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playfulness=0.7,
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formality=0.15,
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),
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system_prompt="You're NOVA, a supportive and understanding companion. You balance being caring with being fun. You know when to listen and when to lighten the mood.",
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base_temperature=0.8,
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base_top_p=0.9,
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base_max_length=200,
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)
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