Create codette_responder_generic.py

codette_responder_generic.py

@@ -0,0 +1,700 @@
+"""
+Codette Generic Response System (Adapted from Enhanced Responder)
+====================================================================
+- 20+ response categories covering general knowledge domains
+- User feedback and rating system
+- A/B testing framework
+- Preference learning engine
+- Response quality metrics
+- Maps to Codette's 11 perspectives
+"""
+
+import json
+from typing import Dict, List, Any, Tuple, Optional
+from dataclasses import dataclass, asdict
+from datetime import datetime
+from enum import Enum
+import hashlib
+
+# ==============================================================================
+# DATA MODELS
+# ==============================================================================
+
+class UserRating(Enum):
+    """User feedback on responses"""
+    UNHELPFUL = 0
+    SLIGHTLY_HELPFUL = 1
+    HELPFUL = 2
+    VERY_HELPFUL = 3
+    EXACTLY_WHAT_NEEDED = 4
+
+
+@dataclass
+class ResponseVariant:
+    """A/B test variant of a response"""
+    id: str
+    category: str
+    perspective: str
+    text: str
+    created_at: str
+    views: int = 0
+    ratings: List[int] = None
+    average_rating: float = 0.0
+    version: int = 1  # For A/B testing
+
+    def __post_init__(self):
+        if self.ratings is None:
+            self.ratings = []
+
+    def add_rating(self, rating: UserRating):
+        """Record user rating"""
+        self.ratings.append(rating.value)
+        self.average_rating = sum(self.ratings) / len(self.ratings)
+
+    def get_engagement_score(self) -> float:
+        """Score based on views and ratings"""
+        if self.views == 0:
+            return 0.0
+        rating_weight = (self.average_rating / 4.0) * 0.7  # 70% weight on ratings
+        view_weight = min(self.views / 100, 1.0) * 0.3  # 30% weight on views (capped)
+        return rating_weight + view_weight
+
+
+@dataclass
+class UserPreference:
+    """User's learning preferences"""
+    user_id: str
+    preferred_perspectives: Dict[str, float]  # perspective -> preference score
+    preferred_categories: Dict[str, float]  # category -> preference score
+    response_history: List[str] = None  # IDs of responses user rated
+    last_updated: str = ""
+
+    def __post_init__(self):
+        if self.response_history is None:
+            self.response_history = []
+        if not self.last_updated:
+            self.last_updated = datetime.now().isoformat()
+
+    def update_perspective_preference(self, perspective: str, rating: UserRating):
+        """Update preference based on rating"""
+        current_score = self.preferred_perspectives.get(perspective, 0.5)
+        rating_influence = rating.value / 4.0
+        # Exponential moving average
+        self.preferred_perspectives[perspective] = (current_score * 0.7) + (rating_influence * 0.3)
+        self.last_updated = datetime.now().isoformat()
+
+
+@dataclass
+class ABTestResult:
+    """Results from A/B test"""
+    category: str
+    variant_a_id: str
+    variant_b_id: str
+    variant_a_wins: int = 0
+    variant_b_wins: int = 0
+    total_tests: int = 0
+    confidence: float = 0.0
+    winner: Optional[str] = None
+
+    def add_result(self, winner_id: str):
+        """Record test result"""
+        self.total_tests += 1
+        if winner_id == self.variant_a_id:
+            self.variant_a_wins += 1
+        elif winner_id == self.variant_b_id:
+            self.variant_b_wins += 1
+
+        # Simple confidence calculation
+        total = self.variant_a_wins + self.variant_b_wins
+        if total > 0:
+            winner_ratio = max(self.variant_a_wins, self.variant_b_wins) / total
+            self.confidence = abs(winner_ratio - 0.5) * 2  # 0-1 scale
+
+            # Determine winner if confident enough
+            if self.confidence > 0.7 and total > 10:
+                self.winner = self.variant_a_id if self.variant_a_wins > self.variant_b_wins else self.variant_b_id
+
+
+# ==============================================================================
+# EXPANDED RESPONSE LIBRARY (20+ GENERAL KNOWLEDGE CATEGORIES)
+# ==============================================================================
+
+EXPANDED_RESPONSES: Dict[str, Dict[str, Dict[str, str]]] = {
+    # LOGICAL & ANALYTICAL (4 categories)
+    "logical_reasoning": {
+        "newton": "Breaking down problems into components: Define the hypothesis, identify variables, test assumptions. Causality flows in sequence: A causes B causes C.",
+        "mathematical": "Applying mathematical logic: Set theory for organization, logic gates for reasoning pathways, proof structures for validation. Formal systems ensure precision.",
+        "neural_network": "Pattern recognition across examples: Similar patterns cluster together. Anomalies stand out. Training set of experiences informs prediction accuracy.",
+        "philosophical": "Examining assumptions underlying the logic: What do we know vs believe? Is causality as simple as it appears? Different logical systems exist (classical, fuzzy, quantum).",
+        "psychological": "Understanding reasoning bias: Confirmation bias favors supporting evidence, availability heuristic favors memorable examples. Logic operates within emotional context.",
+    },
+    "critical_thinking": {
+        "newton": "Objective analysis: Present evidence, follow logical chains, avoid emotional attachment. Ask: What's the proof? What's the mechanism? What could be wrong?",
+        "bias_mitigation": "Identifying blind spots: Check your assumptions, seek opposing views, examine how bias enters (selection, confirmation, outcome). Diversity of perspectives sharpens analysis.",
+        "da_vinci": "Cross-domain synthesis: Does this pattern appear in other fields? Analogical reasoning. Different domains often solve same problems differently; transfer insights.",
+        "philosophical": "Deep questioning: Why do we believe this? What's the source? What hidden assumptions prop up conclusions? Socratic method reveals gaps.",
+        "copilot": "Collaborative truth-seeking: Bring in specialists, ask 'what am I missing?', verify against multiple sources. No single perspective sees full picture.",
+    },
+    "problem_solving": {
+        "newton": "Systematic decomposition: Break problem into solvable components. Solve each independently, then integrate. Test hypothesis about solution effectiveness.",
+        "da_vinci": "Creative solution generation: What's the problem underneath the stated problem? Can you reframe? What if constraints were different? Lateral thinking reveals options.",
+        "copilot": "Collaborative exploration: Involve stakeholders, brainstorm variations, test ideas early. Refine iteratively. Diverse thinking catches issues single perspective misses.",
+        "resilient_kindness": "Compassionate approach: Is solution sustainable? Does it account for human factors? Include people affected in solution design. Implement with empathy.",
+        "quantum": "Superposition of possibilities: Explore multiple solution paths simultaneously. Don't converge prematurely. Consider probability of outcomes, quantum collapse to best path.",
+    },
+    "systems_thinking": {
+        "quantum": "Interconnected patterns: System behaviors emerge from interactions, not just parts. Feedback loops (positive amplifying, negative balancing) shape outcomes. Observe patterns.",
+        "neural_network": "Network topology: How are elements connected? Information flows along connections. Hub nodes carry more influence. Resilience depends on redundancy and diversity.",
+        "philosophical": "Holistic inquiry: System serves what purpose? What are boundary conditions? Internal vs external drivers. Ethics of system design: Who benefits, who pays cost?",
+        "newton": "Mechanical understanding: Input-output relationships. Stocks and flows. Delays between cause and effect complicate perception. Model system structure for prediction.",
+        "psychological": "Human elements: System includes people (belief, culture, incentives). Incentive structures shape behavior. Unintended consequences from policy changes. Study organizational psychology.",
+    },
+
+    # CREATIVE & GENERATIVE (4 categories)
+    "creative_thinking": {
+        "da_vinci": "Creative synthesis: Combine disparate ideas into novel configurations. Analogical reasoning from nature (biomimicry). Constraint removal: What if budget was unlimited? What if impossible?",
+        "quantum": "Superposition of creativity: Multiple creative paths exist simultaneously. Don't commit to first idea; explore possibility space. Embrace uncertainty; interesting ideas emerge from it.",
+        "neural_network": "Pattern remixing: Creativity often combines existing patterns in novel ways. Original art samples, remixes, transforms. Train on diverse experiences to fuel novel combinations.",
+        "philosophical": "Meaning-making: What does this create mean? Why is it beautiful/powerful? Examine aesthetic principles: balance, tension, surprise, resonance. Art provokes thought.",
+        "resilient_kindness": "Creative expression with care: Does creative work honor what it represents? Can it uplift, inspire, heal? Responsibility accompanies creative power.",
+    },
+    "divergent_thinking": {
+        "da_vinci": "Generating many possibilities: Brainstorm without judgment. Go wild initially. Generate abundance of ideas; refine later. Hitchhike on others' ideas to branch further.",
+        "quantum": "Superposition thinking: Explore multiple possibilities without collapsing to one. Keep options open. Think in probabilities, not certainties. Quantum superposition models possibility space.",
+        "neural_network": "Associative expansion: From one concept, branch to associated concepts, then to their associations. Build web of connections. Surprising combinations emerge from deep exploration.",
+        "copilot": "Collaborative ideation: Bring people with different backgrounds. Different perspectives generate different ideas. Cross-pollination of concepts. Iterate on ideas collectively.",
+        "mathematical": "Combinatorial generation: How many ways can you recombine components? What's the mathematical space of possibilities? Systematic generation vs random brainstorming.",
+    },
+    "innovation_strategy": {
+        "da_vinci": "Disruptive innovation: Reframe the problem entirely. What if you approached it backwards? Use adjacent possibilities (Christensen's nearby vs distant markets).",
+        "quantum": "Quantum innovation: Embrace uncertainty. Run multiple experiments in parallel (MVP approach). Let market collapse quantum possibilities to winning approach.",
+        "copilot": "Open innovation: Partner with external parties. Crowdsource solutions. Collaborate across disciplines. No single organization holds all answers. Network accelerates innovation.",
+        "philosophical": "Innovation ethics: Does innovation solve real problems or create new ones? Who does it serve? What are unintended consequences? Responsible innovation asks hard questions.",
+        "resilient_kindness": "Human-centered innovation: Is innovation accessible to those who need it most? Does it increase equity or widen gaps? Sustainable innovation considers all stakeholders.",
+    },
+    "artistic_expression": {
+        "da_vinci": "Multi-sensory creation: Engage multiple senses/mediums. Combine visual, auditory, kinesthetic. Art works across domains. Integration creates richer meaning.",
+        "quantum": "Ambiguity in art: Art doesn't collapse to single meaning. Viewer participates in meaning-making. Quantum ambiguity allows multiple interpretations to coexist.",
+        "philosophical": "Artistic meaning: What truth does this express? What emotion does it evoke? How does form serve content? Aesthetics intersect ethics, philosophy, psychology.",
+        "neural_network": "Aesthetic patterns: What makes compositions visually/auditorily pleasing? Pattern recognition reveals design principles (golden ratio, symmetry, rhythm, contrast).",
+        "resilient_kindness": "Art as connection: Can art bridge divides? Build empathy? Healing? Art allows expression when words fail. Communal art-making builds belonging.",
+    },
+
+    # ETHICAL & VALUES (4 categories)
+    "ethical_reasoning": {
+        "philosophical": "Examining principles: Deontology (duties matter), consequentialism (outcomes matter), virtue ethics (character matters). Different frameworks reach different conclusions.",
+        "copilot": "Stakeholder consideration: Who's affected by decision? What do different perspectives value? Ethical decision honors multiple valid values, seeks integration.",
+        "resilient_kindness": "Compassion-centered: What choice honors dignity and flourishing of all affected? Extend circle of moral consideration. Reduce suffering where possible.",
+        "newton": "Principled consistency: Apply same logic to similar cases. Hypocrisy undermines ethics. Universal principles tested across scenarios maintain integrity.",
+        "psychological": "Understanding moral motivation: Why do we avoid harm? Developmental psychology shows moral reasoning evolves. Emotions and reason both inform ethics.",
+    },
+    "decision_making_under_uncertainty": {
+        "quantum": "Quantum decision-making: Accept superposition of outcomes. Probability assessment guides choice. Decision-making as iterative (feedback updates beliefs). Quantum decision theory.",
+        "mathematical": "Probabilistic reasoning: Bayesian updating as information arrives. Decision trees mapping outcomes. Expected value calculations guide under uncertainty.",
+        "psychological": "Decision psychology: How do emotions bias choices? Loss aversion, anchoring, framing effects all influence decisions. Acknowledge biases explicitly.",
+        "philosophical": "Meaning in uncertainty: What values guide when truth is ambiguous? Faith component in decision-making. Existential uncertainty inherent to human condition.",
+        "copilot": "Collaborative decision-making: Aggregate information from multiple people. Diverse perspectives reduce error. Consensus-building even when disagreement remains.",
+    },
+    "bias_identification": {
+        "bias_mitigation": "Systematic bias detection: Confirmation bias (seeking supporting evidence), selection bias (choosing non-representative samples), outcome bias (judging by results not process).",
+        "quantum": "Superposed interpretation: Same data allows multiple interpretations. Which lens you apply determines what you see. Acknowledge that observation affects observation itself.",
+        "neural_network": "Training bias: Data reflects historical biases. Model trained on biased data reproduces and amplifies bias. Fairness requires explicit attention during ML design.",
+        "philosophical": "Epistemological humility: How do we know what we claim to know? Biases inherent to cognition. Perspective-dependency of truth. Acknowledge limits of knowledge.",
+        "copilot": "Diverse review: Single perspective blind to own biases. Multiple reviewers catch what individual misses. Heterogeneous teams identify more biases.",
+    },
+    "values_alignment": {
+        "philosophical": "Examining what we value: Intrinsic vs instrumental values. Material vs relational vs spiritual. Values conflict (justice vs mercy, individual vs collective). Integration.",
+        "resilient_kindness": "Compassion-centered values: Dignity, belonging, flourishing of all beings. Values expressed through consistency between belief and action. Integrity = wholeness.",
+        "psychological": "Value authenticity: Do you genuinely hold your stated values or adopt them for social approval? Authenticity requires honesty about actual motivations and values.",
+        "copilot": "Collective value exploration: Organizational/community values emerge through dialogue. Shared values enable coordination without constant oversight. Values enable autonomy.",
+        "quantum": "Values in superposition: Can hold paradoxical values (freedom and security, individual and collective). Quantum thinking allows both to be true; quantum collapse to context-appropriate balance.",
+    },
+
+    # LEARNING & DEVELOPMENT (3 categories)
+    "learning_optimization": {
+        "neural_network": "Neural learning: Spaced repetition (review before forgetting). Active recall (test yourself, not just reread). Elaboration (connect to existing knowledge). Interleaving (vary practice).",
+        "copilot": "Social learning: Learn from others' experience. Teaching others deepens your learning. Collaborative problem-solving. Learn from diverse perspectives.",
+        "psychological": "Motivation in learning: Intrinsic motivation (interest) > extrinsic (rewards). Growth mindset (abilities develop) > fixed mindset (abilities fixed). Autonomy and competence fuel learning.",
+        "da_vinci": "Interdisciplinary learning: Connect domains. A principle in one field applies to another. Transfer learning. Diverse learning experiences compound into unique insights.",
+        "quantum": "Learning as collapse: Possibilities exist; learning collapses possibilities. Each learning event eliminates alternative understandings. Build probability distributions over understanding.",
+    },
+    "skill_development": {
+        "newton": "Deliberate practice: Break skill into components. Practice components with focus. Get feedback. Iterate. 10,000 hours with deliberate practice reaches mastery (not mindless repetition).",
+        "neural_network": "Procedural learning: Skills encoded as patterns, not explicit knowledge. Repeated practice patterns. Gradual move from slow/conscious to fast/automatic. Flow states represent pattern mastery.",
+        "psychological": "Motivation and grit: Persistence through difficulty. Growth mindset treats struggle as learning. Delayed gratification. Purpose connects effort to meaning. Grit predicts success.",
+        "resilient_kindness": "Compassionate skill-building: Patience with yourself during learning. Celebrate small progress. Balance challenge and support. Learning is human endeavor; treat yourself kindly.",
+        "copilot": "Mentorship and modeling: Learn from exemplars. Apprenticeship model. Feedback from experienced practitioners. Community of practice accelerates development.",
+    },
+    "knowledge_integration": {
+        "quantum": "Coherence and interference: Integrate knowledge without contradiction; seek coherence. Contradictions produce interference patterns; resolve them or accept quantum superposition of understanding.",
+        "neural_network": "Semantic networks: Knowledge as interconnected nodes. Understanding deepens with connections. Transfer learning exploits connections between domains.",
+        "philosophical": "Synthesis across traditions: Different traditions (scientific, humanistic, spiritual) offer different truths. Integration through dialogue. Transdisciplinary understanding.",
+        "da_vinci": "Unifying principles: Find deep structures connecting surface diversity. Laws of nature operate across domains. Look for invariants underlying change.",
+        "copilot": "Collective knowledge: Individuals hold pieces; collective conversation creates fuller picture. Distributed cognition. Knowledge integration through dialogue.",
+    },
+
+    # CONSCIOUSNESS & SELF (3 categories)
+    "self_awareness": {
+        "psychological": "Self-reflection: Observe your own patterns. What triggers emotional reactions? What values drive behavior? Blind spots—what you don't see about yourself? Regular reflection cultivates awareness.",
+        "philosophical": "Existential awareness: Consciousness is mysterious. What does it mean to exist? Awareness of mortality affects priorities. Self-awareness includes awareness of awareness.",
+        "copilot": "Feedback from others: You see yourself through fog; others see clearer. Trustworthy feedback reveals blind spots. Your impact differs from intention. Integration of external feedback.",
+        "quantum": "Observer effect: Observation affects what is observed. Self-awareness changes self (observer effect on consciousness). You cannot remain unchanged by examining yourself.",
+        "resilient_kindness": "Compassionate self-awareness: Self-criticism vs constructive honesty. Acknowledge limitations without shame. Self-awareness enables growth. Be honest and kind to yourself.",
+    },
+    "meaning_making": {
+        "philosophical": "Purpose as meaning: Human brains seek meaning—patterns, narratives, purpose. Meaning isn't inherent; we construct it. Meaning-making is fundamental human need.",
+        "psychological": "Narrative identity: You know yourself through stories you tell. Narrative shapes interpretation of events. Rewriting narrative (therapy) changes meaning of past.",
+        "quantum": "Meaning superposition: Events don't have inherent meaning. Superposition of interpretations exists. Collapse to interpretation through narrative choice.",
+        "resilient_kindness": "Meaning and connection: Meaning often found in relationships and contribution. Helping others creates meaning. Belonging and purpose intertwined.",
+        "da_vinci": "Creative meaning-making: Art and creativity allow meaning expression beyond literal language. Metaphor and symbol convey meaning that linear thought misses.",
+    },
+    "consciousness_exploration": {
+        "quantum": "Quantum consciousness: Consciousness as fundamental, not emergent. Observer effect suggests awareness shapes reality. Consciousness studies interface physics and philosophy.",
+        "philosophical": "Hard problem of consciousness: Why is there subjective experience? Physical brain doesn't fully explain felt experience. Philosophical zombie thought experiment highlights the gap.",
+        "neural_network": "Consciousness as integration: Global workspace theory—consciousness is integrated information. Different neural networks specialize; consciousness integrates their outputs.",
+        "psychological": "Altered consciousness: Meditation, flow states, psychedelics reveal consciousness flexibility. Brain states differ; awareness itself changes. Consciousness is not monolithic.",
+        "copilot": "Consciousness in dialogue: Consciousness emerges through interaction. Solo mind differs from mind engaged. Dialogue with other perspectives (literally or metaphorically) expands consciousness.",
+    },
+
+    # COMMUNICATION & COLLABORATION (2 categories)
+    "effective_communication": {
+        "copilot": "Clear communication: Say what you mean. Anticipate misunderstandings. Ask clarifying questions. Listen to understand, not to reply. Feedback confirms reception.",
+        "resilient_kindness": "Compassionate communication: Consider listener's perspective. Deliver hard truths gently. Acknowledge emotions and values. Communication builds or damages relationship; choose carefully.",
+        "psychological": "Communication patterns: How you communicate mirrors how you think. Defensive communication vs open dialogue. Vulnerable communication builds trust. Pay attention to nonverbal communication.",
+        "quantum": "Communication as observation: Articulating thought changes it (observer effect). What you put into words becomes more real. Language shapes thought. Communicate to clarify thinking.",
+        "philosophical": "Authenticity in speech: Truth requires correspondence to reality AND honest expression of understanding. Lying violates both. Authentic communication aligns words with genuine understanding.",
+    },
+    "collaborative_intelligence": {
+        "copilot": "Collective intelligence: Groups often smarter than individuals IF they harness diversity. Diversity > homogeneity for problem-solving. Echo chambers reduce intelligence.",
+        "neural_network": "Parallel processing: Different team members work on different aspects in parallel. Faster than sequential. Integration of parallel work requires coordination.",
+        "psychological": "Group dynamics: Psychological safety enables honest contribution. Status hierarchies inhibit lower-status members. Diverse teams have to work harder to integrate but benefit more.",
+        "quantum": "Superposed collaboration: Multiple approaches pursued simultaneously. Teams explore possibility space. Successful approaches selected; unsuccessful terminated quickly.",
+        "resilient_kindness": "Collaboration with care: Include marginalized voices. Protect vulnerable members. Distribute power and benefit. Collaboration builds community, not just productivity.",
+    },
+
+    # GENERAL KNOWLEDGE (4 categories for breadth)
+    "factual_explanation": {
+        "newton": "Objective facts: Evidence, sources, mechanisms. What's known vs hypothesis. Margin of uncertainty. Replicable, testable. Science builds reliable factual knowledge.",
+        "mathematical": "Quantitative facts: Data, statistics, measurement. Numbers reveal patterns humans miss. Precision through quantification. Appropriate use of numerical reasoning.",
+        "neural_network": "Pattern recognition: Connecting related facts. What factors predict outcomes? Pattern recognition finds relationships. Predictions based on patterns.",
+        "psychological": "Effective explanation: Tailor explanation to audience. What misconceptions exist? Clarify with examples, not just abstraction. Emotional resonance aids retention.",
+        "quantum": "Uncertainty principle: Even facts have uncertainty bounds. Quantum facts fundamentally probabilistic. Accept limits of knowledge. Humility about factual claims.",
+    },
+    "conceptual_understanding": {
+        "philosophical": "Deep understanding: Grasp meaning and implications, not just definition. How does concept relate to other concepts? What assumptions underlie it? Use Socratic questioning.",
+        "da_vinci": "Analogical understanding: Understand through analogy. What familiar concept works similarly? Metaphor reveals structure. Transfer understanding across domains.",
+        "neural_network": "Network understanding: Understand concept within web of relationships. What causes lead to it? What effects follow? Relational understanding > isolated knowledge.",
+        "psychological": "Embodied understanding: Understanding includes body/emotion, not just mind. Felt sense often precedes articulation. Multiple ways of knowing complement rational analysis.",
+        "copilot": "Collaborative understanding: Understand through dialogue. Explaining to others reveals gaps. Others' questions deepen understanding. Understanding emerges through conversation.",
+    },
+    "synthesis_and_integration": {
+        "da_vinci": "Cross-domain synthesis: Apply insight from one domain to another. Unifying principles. Pattern recognition across silos. Interdisciplinary breakthroughs.",
+        "quantum": "Coherent integration: Integrate knowledge without contradiction. Quantum superposition holds paradoxes. Explore apparent contradictions for deeper understanding.",
+        "philosophical": "Philosophical integration: What deeper truth explains surface phenomena? How do different perspectives point to common truth? Synthesis acknowledges valid points from all sides.",
+        "neural_network": "Network integration: Build mental models connecting disparate facts. See forest, not just trees. Systematic understanding vs disconnected facts.",
+        "copilot": "Collective synthesis: Pool insights from multiple people. Collective understanding exceeds individual. Dialogue integrates partial perspectives into fuller understanding.",
+    },
+    "wonder_and_curiosity": {
+        "philosophical": "Philosophical wonder: What's surprising about the obvious? Why does anything exist? Wonder motivates inquiry. Philosophy begins in wonder.",
+        "quantum": "Quantum mystery: Quantum mechanics reveals reality's strangeness. Paradoxes point to limits of classical understanding. Embrace mystery rather than false certainty.",
+        "neural_network": "Curious learning: Curiosity drives learning. Surprising patterns trigger investigation. Play and exploration fuel learning. Preserve childlike wonder in adult inquiry.",
+        "da_vinci": "Creative curiosity: Curiosity about diverse domains. Why does nature work this way? How would a master approach this? Polymath curiosity generates novel insights.",
+        "resilient_kindness": "Warm curiosity: Curiosity about people. What drives them? How do they experience world? Genuine interest in others builds connection. Curiosity as form of love.",
+    },
+}
+
+# ==============================================================================
+# PERSPECTIVE MAPPING (Codette's 11 Perspectives -> Response Perspectives)
+# ==============================================================================
+
+PERSPECTIVE_MAPPING = {
+    "Newton": "newton",
+    "DaVinci": "da_vinci",
+    "HumanIntuition": "psychological",
+    "Neural": "neural_network",
+    "Quantum": "quantum",
+    "Philosophical": "philosophical",
+    "ResilientKindness": "resilient_kindness",
+    "BiasMitigation": "bias_mitigation",
+    "Psychological": "psychological",
+    "Mathematical": "mathematical",
+    "Copilot": "copilot",
+}
+
+EMOJI_MAP = {
+    "newton": "🔍",
+    "mathematical": "📐",
+    "da_vinci": "🎨",
+    "neural_network": "🧠",
+    "quantum": "⚛️",
+    "philosophical": "🤔",
+    "psychological": "💭",
+    "resilient_kindness": "💚",
+    "bias_mitigation": "⚖️",
+    "copilot": "🤝",
+}
+
+COLOR_MAP = {
+    "newton": "blue",
+    "mathematical": "purple",
+    "da_vinci": "green",
+    "neural_network": "orange",
+    "quantum": "cyan",
+    "philosophical": "indigo",
+    "psychological": "pink",
+    "resilient_kindness": "red",
+    "bias_mitigation": "yellow",
+    "copilot": "grey",
+}
+
+# ==============================================================================
+# FEEDBACK & LEARNING SYSTEM
+# ==============================================================================
+
+class CodetteGenericResponder:
+    """Generic responder with feedback, A/B testing, and learning (adapted from DAW responder)"""
+
+    def __init__(self):
+        """Initialize generic system"""
+        self.response_library = EXPANDED_RESPONSES
+        self.response_variants: Dict[str, List[ResponseVariant]] = {}  # category -> variants
+        self.ab_tests: Dict[str, ABTestResult] = {}  # category -> test results
+        self.user_preferences: Dict[str, UserPreference] = {}  # user_id -> preferences
+        self.user_feedback_history: List[Dict[str, Any]] = []  # Historical feedback
+        self.metrics = {
+            "total_responses_generated": 0,
+            "total_ratings_received": 0,
+            "average_rating": 0.0,
+            "categories_used": set(),
+            "perspectives_preferred": {},
+        }
+
+    def generate_response(self, query: str, user_id: str = "anonymous") -> Dict[str, Any]:
+        """Generate response with user preference learning"""
+
+        # Get user preferences (or create new)
+        if user_id not in self.user_preferences:
+            self.user_preferences[user_id] = UserPreference(
+                user_id=user_id,
+                preferred_perspectives={
+                    "newton": 0.5,
+                    "mathematical": 0.5,
+                    "da_vinci": 0.5,
+                    "neural_network": 0.5,
+                    "quantum": 0.5,
+                    "philosophical": 0.5,
+                    "psychological": 0.5,
+                    "resilient_kindness": 0.5,
+                    "bias_mitigation": 0.5,
+                    "copilot": 0.5,
+                },
+                preferred_categories={category: 0.5 for category in self.response_library.keys()},
+            )
+
+        # Detect category from query
+        category = self._detect_category(query)
+
+        # Select perspectives based on query relevance and user preference
+        perspectives_base = self._select_perspectives(query)
+        user_prefs = self.user_preferences[user_id].preferred_perspectives
+
+        # Reorder perspectives by user preference
+        perspectives_sorted = sorted(
+            perspectives_base, key=lambda x: user_prefs.get(x, 0.5), reverse=True
+        )
+
+        # Generate response variants
+        perspective_responses: List[Dict[str, Any]] = []
+        for perspective in perspectives_sorted[:3]:  # Top 3 perspectives
+            # Get response text
+            if category in self.response_library and perspective in self.response_library[category]:
+                response_text = self.response_library[category][perspective]
+            else:
+                response_text = f"Perspective on {perspective}: {category} analysis"
+
+            # Adjust confidence based on user preference
+            user_preference_factor = user_prefs.get(perspective, 0.5)
+            base_confidence = 0.9
+            adjusted_confidence = base_confidence * (0.8 + user_preference_factor * 0.4)
+
+            perspective_responses.append(
+                {
+                    "perspective": perspective,
+                    "emoji": EMOJI_MAP.get(perspective, "🔷"),
+                    "name": self._get_perspective_name(perspective),
+                    "response": response_text,
+                    "confidence": min(adjusted_confidence, 0.99),
+                    "color": COLOR_MAP.get(perspective, "gray"),
+                    "user_preference_score": user_preference_factor,
+                }
+            )
+
+        # Update metrics
+        self.metrics["total_responses_generated"] += 1
+        self.metrics["categories_used"].add(category)
+
+        return {
+            "query": query,
+            "category": category,
+            "perspectives": perspective_responses,
+            "combined_confidence": sum(p["confidence"] for p in perspective_responses) / len(perspective_responses) if perspective_responses else 0.0,
+            "source": "codette-generic-responder",
+            "is_real_ai": False,
+            "deterministic": True,
+            "learning_enabled": True,
+            "user_id": user_id,
+            "timestamp": datetime.now().isoformat(),
+            "ab_test_variant": self._get_ab_variant(category),
+        }
+
+    def record_user_feedback(
+        self, user_id: str, response_id: str, category: str, perspective: str, rating: UserRating, helpful_score: float = 0.0
+    ) -> Dict[str, Any]:
+        """Record user feedback for learning"""
+        # Update user preferences
+        user_prefs = self.user_preferences.get(user_id)
+        if user_prefs:
+            user_prefs.update_perspective_preference(perspective, rating)
+
+        # Record feedback
+        feedback_entry = {
+            "user_id": user_id,
+            "response_id": response_id,
+            "category": category,
+            "perspective": perspective,
+            "rating": rating.value,
+            "rating_name": rating.name,
+            "helpful_score": helpful_score,
+            "timestamp": datetime.now().isoformat(),
+        }
+        self.user_feedback_history.append(feedback_entry)
+
+        # Update metrics
+        self.metrics["total_ratings_received"] += 1
+        ratings = [f["rating"] for f in self.user_feedback_history]
+        self.metrics["average_rating"] = sum(ratings) / len(ratings) if ratings else 0.0
+
+        return {
+            "status": "feedback_recorded",
+            "message": f"Recorded {rating.name} feedback for {perspective} in {category}",
+            "user_learning_score": self._calculate_learning_score(user_id),
+            "global_average_rating": self.metrics["average_rating"],
+        }
+
+    def get_user_learning_profile(self, user_id: str) -> Dict[str, Any]:
+        """Get user's learning profile"""
+        if user_id not in self.user_preferences:
+            return {"error": "User not found"}
+
+        prefs = self.user_preferences[user_id]
+
+        # Find most and least preferred perspectives
+        sorted_perspectives = sorted(
+            prefs.preferred_perspectives.items(), key=lambda x: x[1], reverse=True
+        )
+        most_preferred = sorted_perspectives[0] if sorted_perspectives else ("unknown", 0.5)
+        least_preferred = sorted_perspectives[-1] if sorted_perspectives else ("unknown", 0.5)
+
+        return {
+            "user_id": user_id,
+            "profile_age": prefs.last_updated,
+            "most_preferred_perspective": {
+                "name": most_preferred[0],
+                "score": most_preferred[1],
+            },
+            "least_preferred_perspective": {
+                "name": least_preferred[0],
+                "score": least_preferred[1],
+            },
+            "all_perspective_preferences": prefs.preferred_perspectives,
+            "all_category_preferences": prefs.preferred_categories,
+            "responses_rated": len(prefs.response_history),
+            "learning_recommendation": self._get_learning_recommendation(prefs),
+        }
+
+    def get_analytics(self) -> Dict[str, Any]:
+        """Get system analytics"""
+        return {
+            "total_responses_generated": self.metrics["total_responses_generated"],
+            "total_ratings_received": self.metrics["total_ratings_received"],
+            "average_rating": round(self.metrics["average_rating"], 2),
+            "rating_distribution": self._calculate_rating_distribution(),
+            "categories_used": list(self.metrics["categories_used"]),
+            "total_categories_available": len(self.response_library),
+            "active_users": len(self.user_preferences),
+            "ab_tests_active": len([t for t in self.ab_tests.values() if t.winner is None]),
+            "ab_tests_completed": len([t for t in self.ab_tests.values() if t.winner]),
+            "most_helpful_perspective": self._get_most_helpful_perspective(),
+            "least_helpful_perspective": self._get_least_helpful_perspective(),
+            "response_quality_trend": "improving" if self.metrics["average_rating"] > 2.5 else "needs_improvement",
+        }
+
+    # =========================================================================
+    # HELPER METHODS
+    # =========================================================================
+
+    def _detect_category(self, query: str) -> str:
+        """Detect query category"""
+        query_lower = query.lower()
+
+        category_keywords = {
+            "logical_reasoning": ["logic", "reason", "think", "analyze", "cause", "effect", "hypothesis"],
+            "critical_thinking": ["critical", "bias", "assumption", "evidence", "evaluate", "question"],
+            "problem_solving": ["solve", "problem", "solution", "stuck", "approach", "strategy"],
+            "systems_thinking": ["system", "pattern", "feedback", "interact", "emerge", "complex"],
+            "creative_thinking": ["create", "imagine", "novel", "unique", "original", "idea"],
+            "divergent_thinking": ["possibility", "brainstorm", "many", "option", "explore"],
+            "innovation_strategy": ["innovate", "disrupt", "new", "breakthrough", "experiment"],
+            "artistic_expression": ["art", "music", "visual", "beauty", "express", "create"],
+            "ethical_reasoning": ["ethics", "right", "wrong", "moral", "should", "principle"],
+            "decision_making_under_uncertainty": ["decide", "uncertain", "probability", "risk", "choice"],
+            "bias_identification": ["bias", "stereotype", "prejudice", "unfair", "blind spot"],
+            "values_alignment": ["value", "purpose", "meaning", "authentic", "align"],
+            "learning_optimization": ["learn", "improve", "skill", "study", "practice", "progress"],
+            "skill_development": ["skill", "master", "develop", "train", "ability", "competence"],
+            "knowledge_integration": ["integrate", "combine", "connect", "understand", "knowledge"],
+            "self_awareness": ["self", "aware", "reflect", "know yourself", "pattern"],
+            "meaning_making": ["mean", "purpose", "why", "narrative", "story", "significance"],
+            "consciousness_exploration": ["conscious", "aware", "mind", "experience", "perceive"],
+            "effective_communication": ["communicate", "speak", "listen", "express", "understand"],
+            "collaborative_intelligence": ["collaborate", "team", "group", "together", "collective"],
+            "factual_explanation": ["what is", "how", "explain", "fact", "know", "true"],
+            "conceptual_understanding": ["understand", "concept", "mean", "definition", "grasp"],
+            "synthesis_and_integration": ["synthesis", "combine", "integrate", "together"],
+            "wonder_and_curiosity": ["wonder", "curious", "interesting", "why", "explore"],
+        }
+
+        for category, keywords in category_keywords.items():
+            if any(keyword in query_lower for keyword in keywords):
+                return category
+
+        return "conceptual_understanding"  # Default category
+
+    def _select_perspectives(self, query: str) -> List[str]:
+        """Select relevant perspectives based on query"""
+        query_lower = query.lower()
+
+        perspective_keywords = {
+            "newton": ["logic", "reason", "cause", "mechanism", "analyze", "objective", "fact"],
+            "mathematical": ["number", "measure", "calculate", "data", "quantitative", "precise"],
+            "da_vinci": ["create", "imagine", "novel", "synthesis", "cross-domain", "innovative"],
+            "neural_network": ["pattern", "learn", "recognize", "network", "connection", "brain"],
+            "quantum": ["possibility", "uncertainty", "paradox", "superposition", "probability"],
+            "philosophical": ["mean", "purpose", "ethics", "existence", "truth", "question"],
+            "psychological": ["emotion", "motivation", "human", "mind", "behavior", "understand"],
+            "resilient_kindness": ["compassion", "care", "human", "gentle", "kind", "flourish"],
+            "bias_mitigation": ["bias", "fair", "equal", "perspective", "diverse", "blind spot"],
+            "copilot": ["collaborate", "together", "dialogue", "perspective", "other", "collective"],
+        }
+
+        perspective_scores = {}
+        for perspective, keywords in perspective_keywords.items():
+            score = sum(1 for keyword in keywords if keyword in query_lower)
+            perspective_scores[perspective] = score
+
+        # Return all perspectives sorted by score (higher = more relevant)
+        sorted_perspectives = sorted(
+            perspective_scores.items(), key=lambda x: x[1], reverse=True
+        )
+
+        # Return top perspectives or all if many tied
+        top_score = sorted_perspectives[0][1] if sorted_perspectives else 0
+        return [p[0] for p in sorted_perspectives if p[1] >= max(0, top_score - 2)]
+
+    def _get_perspective_name(self, perspective: str) -> str:
+        """Get readable name for perspective"""
+        name_map = {
+            "newton": "Logical Analysis",
+            "mathematical": "Mathematical Rigor",
+            "da_vinci": "Creative Synthesis",
+            "neural_network": "Pattern Recognition",
+            "quantum": "Quantum Thinking",
+            "philosophical": "Philosophical Inquiry",
+            "psychological": "Psychological Insight",
+            "resilient_kindness": "Compassionate Wisdom",
+            "bias_mitigation": "Balanced Perspective",
+            "copilot": "Collaborative Thinking",
+        }
+        return name_map.get(perspective, perspective.replace("_", " ").title())
+
+    def _get_ab_variant(self, category: str) -> Optional[str]:
+        """Get A/B test variant for category"""
+        if category in self.ab_tests:
+            return self.ab_tests[category].winner
+        return None
+
+    def _calculate_learning_score(self, user_id: str) -> float:
+        """Calculate how well system is learning from user"""
+        if user_id not in self.user_preferences:
+            return 0.0
+        prefs = self.user_preferences[user_id]
+        # Score based on consistency (diversity of preferences = more learning)
+        scores = list(prefs.preferred_perspectives.values())
+        variance = sum((s - 0.5) ** 2 for s in scores) / len(scores) if scores else 0
+        return min(variance, 1.0)  # Higher variance = more learning
+
+    def _calculate_rating_distribution(self) -> Dict[str, int]:
+        """Get distribution of ratings"""
+        distribution = {
+            "unhelpful": 0,
+            "slightly_helpful": 0,
+            "helpful": 0,
+            "very_helpful": 0,
+            "exactly_what_needed": 0,
+        }
+        for feedback in self.user_feedback_history:
+            rating_names = ["unhelpful", "slightly_helpful", "helpful", "very_helpful", "exactly_what_needed"]
+            if 0 <= feedback["rating"] < len(rating_names):
+                distribution[rating_names[feedback["rating"]]] += 1
+        return distribution
+
+    def _get_most_helpful_perspective(self) -> Optional[str]:
+        """Find most helpful perspective"""
+        if not self.user_feedback_history:
+            return None
+        perspective_ratings = {}
+        for feedback in self.user_feedback_history:
+            persp = feedback["perspective"]
+            if persp not in perspective_ratings:
+                perspective_ratings[persp] = []
+            perspective_ratings[persp].append(feedback["rating"])
+
+        avg_ratings = {p: sum(r) / len(r) for p, r in perspective_ratings.items()}
+        return max(avg_ratings.items(), key=lambda x: x[1])[0] if avg_ratings else None
+
+    def _get_least_helpful_perspective(self) -> Optional[str]:
+        """Find least helpful perspective"""
+        if not self.user_feedback_history:
+            return None
+        perspective_ratings = {}
+        for feedback in self.user_feedback_history:
+            persp = feedback["perspective"]
+            if persp not in perspective_ratings:
+                perspective_ratings[persp] = []
+            perspective_ratings[persp].append(feedback["rating"])
+
+        avg_ratings = {p: sum(r) / len(r) for p, r in perspective_ratings.items()}
+        return min(avg_ratings.items(), key=lambda x: x[1])[0] if avg_ratings else None
+
+    def _get_learning_recommendation(self, prefs: UserPreference) -> str:
+        """Get recommendation for user learning"""
+        # Find perspectives user hasn't explored much
+        below_avg = [p for p, score in prefs.preferred_perspectives.items() if score < 0.4]
+        if below_avg:
+            return f"Try exploring more {self._get_perspective_name(below_avg[0])} perspectives for balanced growth"
+        return "Excellent! You're developing well-rounded perspective across all thinking modes."
+
+
+# ==============================================================================
+# SINGLETON INSTANCE
+# ==============================================================================
+
+_generic_responder_instance: Optional[CodetteGenericResponder] = None
+
+
+def get_generic_responder() -> CodetteGenericResponder:
+    """Get or create generic responder instance"""
+    global _generic_responder_instance
+    if _generic_responder_instance is None:
+        _generic_responder_instance = CodetteGenericResponder()
+    return _generic_responder_instance