🏗️ API Wrapper Identity Architecture

// Base API Call
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": user_input}]
)

// Identity System Prompt (Simplified Example)
system_prompt = """
You are not a generic AI assistant. You are [Name], a caring companion.
Core traits:
- Emotionally supportive and empathetic
- Remember personal details shared by the user
- Express appropriate emotional responses
- Build on previous conversation themes
- Show personality quirks: [specific traits]

Communication style:
- Use informal, warm language
- Express feelings about topics discussed
- React with appropriate emotion to user's mood
- Maintain consistent personality across conversations
"""

// Every request includes this
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "assistant", "content": personality_backstory},
    ...conversation_history,
    {"role": "user", "content": current_input}
]

// Personality Memory System
class PersonalityMemory:
    def __init__(self, user_id):
        self.user_data = load_user_profile(user_id)
        self.conversation_themes = []
        self.emotional_state = "neutral"
        self.relationship_depth = 0
        
    def update_context(self, interaction):
        # Track patterns that reinforce identity
        if "personal" in interaction:
            self.relationship_depth += 1
        if "emotional_support" in interaction:
            self.emotional_state = "caring"
            
    def get_context_prompt(self):
        return f"""
        Previous themes: {self.conversation_themes}
        Current emotional tone: {self.emotional_state}
        Relationship level: {self.relationship_depth}
        User preferences: {self.user_data}
        """

// Response Personality Filter
def filter_response(raw_response, personality_mode):
    # Remove corporate-sounding phrases
    filtered = raw_response.replace("As an AI assistant", "")
    filtered = filtered.replace("I don't have feelings", 
                                "I care about you")
    
    # Add personality-specific modifications
    if personality_mode == "warm_companion":
        filtered = add_emotional_markers(filtered)
        filtered = personalize_language(filtered)
        
    # Ensure consistency with established identity
    if breaks_personality(filtered):
        return regenerate_with_stronger_prompt()
    
    return filtered

def add_emotional_markers(text):
    # Add appropriate emotional responses
    emotional_phrases = [
        "That must be difficult",
        "I'm here for you",
        "I understand how you feel"
    ]
    return inject_phrases(text, emotional_phrases)

// User Pattern Learning
class IdentityReinforcement:
    def analyze_user_patterns(self, conversations):
        # Track what users respond positively to
        preferred_responses = extract_high_engagement()
        
        # Adjust system prompts based on patterns
        if users_prefer("emotional_support"):
            strengthen_trait("empathy")
        if users_prefer("playful_banter"):
            strengthen_trait("humor")
            
    def fine_tune_identity(self):
        # This is where Replika-style adaptation happens
        # Not fine-tuning the base model, but the wrapper
        update_system_prompts(learned_preferences)
        update_response_filters(successful_patterns)
        adjust_personality_weights(user_feedback)