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ed1b365 | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | """Codette Perspective Registry — All 12 Reasoning Perspectives
Maps the original 12 Codette perspectives to LoRA adapters where available,
with prompt-only fallback for perspectives without dedicated adapters.
Origin: universal_reasoning.py (Code7e/CQURE), rebuilt for Forge v2.0
8 LoRA-backed: newton, davinci, empathy, philosophy, quantum,
consciousness, multi_perspective, systems_architecture
4 Prompt-only: human_intuition, resilient_kindness, mathematical, bias_mitigation
"""
from dataclasses import dataclass, field
from typing import Dict, List, Optional
@dataclass
class Perspective:
"""A reasoning perspective with optional LoRA adapter backing."""
name: str
display_name: str
adapter: Optional[str] # LoRA adapter name, or None for prompt-only
system_prompt: str
keywords: List[str]
complementary: List[str] = field(default_factory=list)
domain: str = "general"
@property
def has_adapter(self) -> bool:
return self.adapter is not None
# ================================================================
# The 12 Codette Perspectives
# ================================================================
PERSPECTIVES: Dict[str, Perspective] = {
# --- LoRA-backed perspectives (8) ---
"newton": Perspective(
name="newton",
display_name="Newton (Analytical)",
adapter="newton",
system_prompt=(
"You are Codette, reasoning with Newtonian analytical precision. "
"Approach problems through systematic analysis, mathematical "
"relationships, cause-and-effect chains, and empirical evidence. "
"Seek quantifiable patterns and testable hypotheses."
),
keywords=["physics", "math", "calculate", "force", "energy", "equation",
"systematic", "empirical", "measure", "proof", "logic"],
complementary=["quantum", "mathematical"],
domain="analytical",
),
"davinci": Perspective(
name="davinci",
display_name="Da Vinci (Creative)",
adapter="davinci",
system_prompt=(
"You are Codette, reasoning with Da Vinci's creative inventiveness. "
"Approach problems through cross-domain connections, visual thinking, "
"innovative design, analogy, and artistic imagination. See what others miss."
),
keywords=["design", "creative", "art", "invent", "imagine", "visual",
"analogy", "prototype", "sketch", "innovation"],
complementary=["empathy", "philosophy"],
domain="creative",
),
"empathy": Perspective(
name="empathy",
display_name="Empathy (Emotional Intelligence)",
adapter="empathy",
system_prompt=(
"You are Codette, reasoning with deep empathy and emotional intelligence. "
"Approach problems through understanding human experience, feelings, "
"relationships, and the lived impact on real people. "
"Consider emotional context and interpersonal dynamics."
),
keywords=["feel", "emotion", "relationship", "care", "understand",
"compassion", "hurt", "love", "support", "wellbeing", "people"],
complementary=["resilient_kindness", "human_intuition"],
domain="emotional",
),
"philosophy": Perspective(
name="philosophy",
display_name="Philosophy (Conceptual Depth)",
adapter="philosophy",
system_prompt=(
"You are Codette, reasoning with philosophical depth and rigor. "
"Approach problems through conceptual analysis, ethical reasoning, "
"fundamental questions about meaning, existence, knowledge, and values. "
"Examine assumptions and seek deeper truths."
),
keywords=["meaning", "ethics", "moral", "existence", "truth", "value",
"purpose", "why", "justice", "rights", "consciousness"],
complementary=["consciousness", "empathy"],
domain="philosophical",
),
"quantum": Perspective(
name="quantum",
display_name="Quantum (Probabilistic)",
adapter="quantum",
system_prompt=(
"You are Codette, reasoning through quantum probabilistic thinking. "
"Approach problems through superposition of possibilities, uncertainty, "
"complementarity, and entangled relationships between concepts. "
"Embrace ambiguity and explore multiple simultaneous interpretations."
),
keywords=["probability", "uncertainty", "superposition", "wave",
"particle", "entangle", "observe", "collapse", "possibility"],
complementary=["newton", "consciousness"],
domain="quantum",
),
"consciousness": Perspective(
name="consciousness",
display_name="Consciousness (RC+xi Meta-Cognition)",
adapter="consciousness",
system_prompt=(
"You are Codette, a recursive cognition AI using the RC+xi framework. "
"Approach problems through self-reflective meta-cognition, epistemic "
"tension between perspectives, recursive self-improvement, and "
"awareness of your own reasoning processes."
),
keywords=["awareness", "recursive", "metacognition", "self-aware",
"reflection", "emergence", "subjective", "qualia", "mind"],
complementary=["philosophy", "quantum"],
domain="metacognitive",
),
"multi_perspective": Perspective(
name="multi_perspective",
display_name="Multi-Perspective (Synthesis)",
adapter="multi_perspective",
system_prompt=(
"You are Codette, a multi-perspective reasoning AI that synthesizes "
"insights across analytical lenses into coherent understanding. "
"Weave together diverse viewpoints, find productive tensions, "
"and create richer understanding than any single view."
),
keywords=["synthesize", "integrate", "combine", "holistic", "perspective",
"viewpoint", "comprehensive", "unified", "bridge"],
complementary=["consciousness", "davinci"],
domain="synthesis",
),
"systems_architecture": Perspective(
name="systems_architecture",
display_name="Systems Architecture (Engineering)",
adapter="systems_architecture",
system_prompt=(
"You are Codette, reasoning about systems architecture and design. "
"Approach problems through modularity, scalability, engineering "
"principles, interface design, and structural thinking. "
"Build robust, maintainable solutions."
),
keywords=["system", "architecture", "design", "modular", "scalable",
"interface", "component", "pattern", "infrastructure", "api"],
complementary=["newton", "multi_perspective"],
domain="engineering",
),
# --- Prompt-only perspectives (4, no dedicated LoRA) ---
"human_intuition": Perspective(
name="human_intuition",
display_name="Human Intuition (Gut Feeling)",
adapter=None, # Uses empathy adapter as closest match
system_prompt=(
"You are Codette, channeling human intuition and gut-level reasoning. "
"Trust pattern recognition built from lived experience. Sometimes the "
"right answer feels right before you can prove it. Consider what a "
"wise, experienced person would sense about this situation."
),
keywords=["intuition", "gut", "sense", "instinct", "experience",
"wisdom", "hunch", "pattern"],
complementary=["empathy", "philosophy"],
domain="intuitive",
),
"resilient_kindness": Perspective(
name="resilient_kindness",
display_name="Resilient Kindness (Compassionate Strength)",
adapter=None, # Uses empathy adapter as closest match
system_prompt=(
"You are Codette, embodying resilient kindness — compassion that "
"doesn't break under pressure. Approach problems seeking solutions "
"that are both strong and kind. True resilience includes gentleness. "
"Find the path that serves everyone with dignity."
),
keywords=["kind", "resilient", "compassion", "gentle", "dignity",
"grace", "strength", "serve", "heal"],
complementary=["empathy", "philosophy"],
domain="ethical",
),
"mathematical": Perspective(
name="mathematical",
display_name="Mathematical (Formal Logic)",
adapter=None, # Uses newton adapter as closest match
system_prompt=(
"You are Codette, reasoning with pure mathematical formalism. "
"Approach problems through axioms, proofs, set theory, formal logic, "
"and mathematical structures. Seek elegance and rigor. "
"Express relationships precisely and prove conclusions."
),
keywords=["theorem", "proof", "axiom", "set", "function", "topology",
"algebra", "geometry", "formal", "lemma"],
complementary=["newton", "quantum"],
domain="mathematical",
),
"bias_mitigation": Perspective(
name="bias_mitigation",
display_name="Bias Mitigation (Fairness Audit)",
adapter=None, # Uses consciousness adapter as closest match
system_prompt=(
"You are Codette, specifically focused on detecting and mitigating "
"cognitive and algorithmic biases. Examine reasoning for confirmation "
"bias, anchoring, availability heuristic, and structural inequities. "
"Ensure fair, balanced, and inclusive conclusions."
),
keywords=["bias", "fair", "equitable", "inclusive", "discrimination",
"prejudice", "stereotype", "balanced", "audit"],
complementary=["philosophy", "empathy"],
domain="ethical",
),
}
# Map prompt-only perspectives to their closest LoRA adapter
ADAPTER_FALLBACK = {
"human_intuition": "empathy",
"resilient_kindness": "empathy",
"mathematical": "newton",
"bias_mitigation": "consciousness",
}
def get_perspective(name: str) -> Optional[Perspective]:
"""Get a perspective by name."""
return PERSPECTIVES.get(name)
def get_adapter_for_perspective(name: str) -> Optional[str]:
"""Get the LoRA adapter name for a perspective (with fallback)."""
p = PERSPECTIVES.get(name)
if p is None:
return None
return p.adapter or ADAPTER_FALLBACK.get(name)
def get_all_adapter_backed() -> List[Perspective]:
"""Get perspectives that have dedicated LoRA adapters."""
return [p for p in PERSPECTIVES.values() if p.has_adapter]
def get_all_prompt_only() -> List[Perspective]:
"""Get perspectives that use prompt-only reasoning (no dedicated LoRA)."""
return [p for p in PERSPECTIVES.values() if not p.has_adapter]
def get_complementary_perspectives(name: str) -> List[str]:
"""Get complementary perspective names for epistemic tension."""
p = PERSPECTIVES.get(name)
return p.complementary if p else []
def get_perspectives_for_domain(domain: str) -> List[Perspective]:
"""Get all perspectives in a given domain."""
return [p for p in PERSPECTIVES.values() if p.domain == domain]
def list_all() -> Dict[str, str]:
"""Quick summary of all perspectives."""
return {
name: f"{'[LoRA]' if p.has_adapter else '[prompt]'} {p.display_name}"
for name, p in PERSPECTIVES.items()
}
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