File size: 21,508 Bytes
74f2af5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 | #!/usr/bin/env python3
"""Codette Phase 6 Inference Bridge — ForgeEngine integration for web server
This module provides a bridge between codette_server.py and ForgeEngine,
enabling Phase 6 capabilities (query complexity routing, semantic tension,
specialization tracking, pre-flight prediction) without breaking the web UI.
Usage:
from codette_forge_bridge import CodetteForgeBridge
bridge = CodetteForgeBridge(orchestrator=orch, use_phase6=True)
result = bridge.generate(query, adapter=None, max_adapters=2)
The bridge falls back to lightweight orchestrator if Phase 6 disabled or heavy.
"""
import re
import sys
import time
from pathlib import Path
from typing import Dict, Optional
# Substrate-aware cognition
try:
from substrate_awareness import SubstrateMonitor, HealthAwareRouter, CocoonStateEnricher
SUBSTRATE_AVAILABLE = True
except ImportError:
SUBSTRATE_AVAILABLE = False
# Add repo to path
sys.path.insert(0, str(Path(__file__).parent.parent))
try:
from reasoning_forge.forge_engine import ForgeEngine
from reasoning_forge.query_classifier import QueryClassifier, QueryComplexity
from reasoning_forge.executive_controller import ExecutiveController, ComponentDecision
PHASE6_AVAILABLE = True
PHASE7_AVAILABLE = True
except ImportError as e:
PHASE6_AVAILABLE = False
PHASE7_AVAILABLE = False
print(f"[WARNING] ForgeEngine not available - Phase 6/7 disabled: {e}")
class CodetteForgeBridge:
"""Bridge between web server (lightweight) and ForgeEngine (Phase 6)."""
def __init__(self, orchestrator, use_phase6: bool = True, use_phase7: bool = True, verbose: bool = False, health_check_fn=None):
"""
Args:
orchestrator: CodetteOrchestrator instance for fallback
use_phase6: Enable Phase 6 (requires ForgeEngine)
use_phase7: Enable Phase 7 (Executive Controller routing)
verbose: Log decisions
health_check_fn: Callable that returns real system health dict
"""
self.orchestrator = orchestrator
self.verbose = verbose
self._health_check_fn = health_check_fn
self.use_phase6 = use_phase6 and PHASE6_AVAILABLE
self.use_phase7 = use_phase7 and PHASE7_AVAILABLE
# Substrate-aware cognition
self.substrate_monitor = None
self.health_router = None
self.cocoon_enricher = None
if SUBSTRATE_AVAILABLE:
try:
self.substrate_monitor = SubstrateMonitor()
self.health_router = HealthAwareRouter(self.substrate_monitor)
self.cocoon_enricher = CocoonStateEnricher(self.substrate_monitor)
if self.verbose:
print("[SUBSTRATE] Substrate-aware cognition initialized")
except Exception as e:
print(f"[WARNING] Substrate awareness init failed: {e}")
self.forge = None
self.classifier = None
self.executive_controller = None
if self.use_phase6:
try:
self._init_phase6()
except Exception as e:
print(f"[WARNING] Phase 6 initialization failed: {e}")
self.use_phase6 = False
if self.use_phase7 and self.use_phase6:
try:
self.executive_controller = ExecutiveController(verbose=verbose)
if self.verbose:
print("[PHASE7] Executive Controller initialized - intelligent routing enabled")
except Exception as e:
print(f"[WARNING] Phase 7 initialization failed: {e}")
self.use_phase7 = False
def _init_phase6(self):
"""Initialize ForgeEngine with Phase 6 components."""
if self.verbose:
print("[PHASE6] Initializing ForgeEngine...")
self.forge = ForgeEngine(orchestrator=self.orchestrator)
self.classifier = QueryClassifier()
# Wire cocoon memories into orchestrator so they enrich LLM prompts
if hasattr(self.forge, 'memory_kernel') and self.forge.memory_kernel:
self.orchestrator.set_memory_kernel(self.forge.memory_kernel)
if self.verbose:
print(f"[PHASE6] Memory kernel wired to orchestrator ({len(self.forge.memory_kernel)} cocoon memories)")
if self.verbose:
print(f"[PHASE6] ForgeEngine ready with {len(self.forge.analysis_agents)} agents")
def generate(self, query: str, adapter: Optional[str] = None,
max_adapters: int = 2, memory_budget: int = 3,
max_response_tokens: int = 512) -> Dict:
"""Generate response with optional Phase 6 routing.
Args:
query: User query
adapter: Force specific adapter (bypasses routing)
max_adapters: Max adapters for multi-perspective
memory_budget: Max cocoons for forge memory injection (from BehaviorGovernor)
max_response_tokens: Response length budget (from BehaviorGovernor)
Returns:
{
"response": str,
"adapter": str or list,
"phase6_used": bool,
"complexity": str, # if Phase 6
"conflicts_prevented": int, # if Phase 6
"reasoning": str,
...rest from orchestrator...
}
"""
start_time = time.time()
# Self-diagnostic: intercept health check queries before LLM
_health_patterns = [
r'self[\s-]*(?:system|health|diagnostic|check)',
r'system[\s-]*health[\s-]*check',
r'run[\s-]*(?:a\s+)?diagnostic',
r'check\s+(?:your|all)\s+systems',
r'health[\s-]*report',
r'are\s+(?:all\s+)?(?:your\s+)?systems?\s+(?:ok|working|online|running)',
]
if any(re.search(p, query, re.I) for p in _health_patterns) and self._health_check_fn:
try:
health = self._health_check_fn()
# Format as a natural response with real data
lines = [f"**Self-System Health Check — {health['overall']}** ({health['score']} checks passed)\n"]
for sys_name, sys_data in health.get("systems", {}).items():
status = sys_data.get("status", "?") if isinstance(sys_data, dict) else str(sys_data)
icon = "✅" if status == "OK" else ("⚠️" if status in ("DISABLED", "MISSING", "DEGRADED") else "❌")
label = sys_name.replace("_", " ").title()
lines.append(f"{icon} **{label}**: {status}")
# Add sub-details for key systems
if isinstance(sys_data, dict):
if "adapters_loaded" in sys_data:
lines.append(f" └ {sys_data['adapters_loaded']} adapters: {', '.join(sys_data.get('adapters', []))}")
if "components" in sys_data:
for comp, cdata in sys_data["components"].items():
cstatus = cdata.get("status", "?") if isinstance(cdata, dict) else str(cdata)
cicon = "✅" if cstatus == "OK" else "❌"
comp_label = comp.replace("_", " ").title()
detail = ""
if isinstance(cdata, dict):
if "memories" in cdata:
detail = f" ({cdata['memories']} memories)"
elif "audit_entries" in cdata:
detail = f" ({cdata['audit_entries']} audit entries)"
elif "stored_cocoons" in cdata:
detail = f" ({cdata['stored_cocoons']} cocoons)"
lines.append(f" {cicon} {comp_label}{detail}")
if "subsystems" in sys_data:
for sub, sstatus in sys_data["subsystems"].items():
sicon = "✅" if sstatus == "OK" else "❌"
lines.append(f" {sicon} {sub}")
if "spiderweb_metrics" in sys_data:
sm = sys_data["spiderweb_metrics"]
lines.append(f" └ Coherence: {sm.get('phase_coherence', 0):.4f}, Entropy: {sm.get('entropy', 0):.4f}, Nodes: {sm.get('node_count', 0)}, Attractors: {sm.get('attractor_count', 0)}, Glyphs: {sm.get('glyph_count', 0)}")
if "behavior_lessons" in sys_data:
lines.append(f" └ {sys_data['behavior_lessons']} learned behaviors, {sys_data['permanent_locks']} permanent locks")
if "alive" in sys_data:
lines.append(f" └ {sys_data['alive']}/{sys_data['total']} alive, {sys_data.get('pending_requests', 0)} pending")
if health.get("warnings"):
lines.append(f"\n⚠️ **Warnings**: {', '.join(health['warnings'])}")
if health.get("errors"):
lines.append(f"\n❌ **Errors**: {', '.join(health['errors'])}")
return {
"response": "\n".join(lines),
"adapter": "self_diagnostic",
"tokens": 0,
"time": round(time.time() - start_time, 2),
"phase6_used": True,
"reasoning": "Real self-diagnostic (not LLM-generated)",
"health": health,
}
except Exception as e:
pass # Fall through to normal LLM generation
# Ethical query validation (from original framework)
if self.forge and hasattr(self.forge, 'ethical_governance') and self.forge.ethical_governance:
try:
qv = self.forge.ethical_governance.validate_query(query)
if not qv["valid"]:
return {
"response": "I can't help with that request. " + "; ".join(qv.get("suggestions", [])),
"adapter": "ethical_block",
"tokens": 0,
"phase6_used": True,
"reasoning": "Blocked by EthicalAIGovernance",
}
except Exception:
pass # Non-critical, continue
# If adapter forced or Phase 6 disabled, use orchestrator directly
if adapter or not self.use_phase6:
result = self.orchestrator.route_and_generate(
query,
max_adapters=max_adapters,
strategy="keyword",
force_adapter=adapter,
)
result["phase6_used"] = False
return result
# Store governor budgets for forge access
self._memory_budget = memory_budget
self._max_response_tokens = max_response_tokens
# Try Phase 6 route first
try:
return self._generate_with_phase6(query, max_adapters)
except Exception as e:
if self.verbose:
print(f"[PHASE6] Error: {e} - falling back to orchestrator")
# Fallback to orchestrator
result = self.orchestrator.route_and_generate(
query,
max_adapters=max_adapters,
strategy="keyword",
force_adapter=None,
)
result["phase6_used"] = False
result["phase6_fallback_reason"] = str(e)
return result
def _generate_with_phase6(self, query: str, max_adapters: int) -> Dict:
"""Generate using orchestrator LLM with Phase 6/7 routing and classification.
All complexity levels use the orchestrator for actual LLM inference.
Phase 6 adds query classification and domain routing.
Phase 7 adds executive routing metadata.
"""
start_time = time.time()
# 1. Classify query complexity (Phase 6)
complexity = self.classifier.classify(query)
if self.verbose:
print(f"[PHASE6] Query complexity: {complexity}", flush=True)
# 2. Route with Phase 7 Executive Controller
route_decision = None
if self.use_phase7 and self.executive_controller:
route_decision = self.executive_controller.route_query(query, complexity)
if self.verbose:
print(f"[PHASE7] Route: {','.join([k for k, v in route_decision.component_activation.items() if v])}", flush=True)
# 3. Domain classification for adapter routing
domain = self._classify_domain(query)
# 4. Determine adapter count based on complexity
if complexity == QueryComplexity.SIMPLE:
effective_max_adapters = 1
elif complexity == QueryComplexity.MEDIUM:
effective_max_adapters = min(max_adapters, 2)
else:
effective_max_adapters = max_adapters
# 4.5 SUBSTRATE-AWARE ROUTING — adjust based on system pressure
substrate_adjustments = []
if self.health_router:
original_complexity = complexity
original_max = effective_max_adapters
complexity, effective_max_adapters, substrate_adjustments = \
self.health_router.adjust_routing(complexity, effective_max_adapters)
if substrate_adjustments:
for adj in substrate_adjustments:
print(f" [SUBSTRATE] {adj}", flush=True)
if self.verbose:
print(f"[PHASE6] Domain: {domain}, max_adapters: {effective_max_adapters}", flush=True)
# 5. Generate via orchestrator (actual LLM inference)
result = self.orchestrator.route_and_generate(
query,
max_adapters=effective_max_adapters,
strategy="keyword",
force_adapter=None,
)
elapsed = time.time() - start_time
# 6. Add Phase 6/7 metadata
result["phase6_used"] = True
result["phase7_used"] = self.use_phase7 and self.executive_controller is not None
result["complexity"] = str(complexity)
result["domain"] = domain
if route_decision:
try:
route_metadata = ExecutiveController.create_route_metadata(
route_decision,
actual_latency_ms=elapsed * 1000,
actual_conflicts=0,
gamma=0.95
)
result.update(route_metadata)
except Exception as e:
if self.verbose:
print(f"[PHASE7] Metadata error: {e}", flush=True)
result["reasoning"] = f"Phase 6: {complexity.name} complexity, {domain} domain"
# EMPTY RESPONSE FALLBACK: If synthesis returned nothing, use best perspective
if not result.get("response", "").strip() and result.get("perspectives"):
perspectives = result["perspectives"]
if isinstance(perspectives, dict) and perspectives:
# Pick the longest perspective as fallback
best = max(perspectives.values(), key=lambda v: len(str(v)))
result["response"] = str(best)
result["reasoning"] += " | fallback: used best perspective (synthesis was empty)"
print(f" [FALLBACK] Synthesis empty — using best perspective ({len(result['response'])} chars)", flush=True)
elif isinstance(perspectives, str) and perspectives.strip():
result["response"] = perspectives
result["reasoning"] += " | fallback: used raw perspectives"
# Store reasoning exchange in CognitionCocooner (from original framework)
# Now enriched with substrate state — every cocoon knows the conditions
# under which it was created (pressure, memory, trend)
response_text = result.get("response", "")
if response_text and self.forge and hasattr(self.forge, 'cocooner') and self.forge.cocooner:
try:
cocoon_meta = {"complexity": str(complexity), "domain": domain}
if substrate_adjustments:
cocoon_meta["substrate_adjustments"] = substrate_adjustments
# Enrich with real-time system state
if self.cocoon_enricher:
cocoon_meta = self.cocoon_enricher.enrich(cocoon_meta)
self.forge.cocooner.wrap_reasoning(
query=query,
response=response_text,
adapter=str(result.get("adapter", "unknown")),
metadata=cocoon_meta
)
except Exception:
pass # Non-critical
# Record inference timing for substrate monitor
if self.substrate_monitor:
self.substrate_monitor.record_inference(elapsed * 1000)
# 8. Apply directness discipline — trim filler, enforce intent anchoring
response_text = result.get("response", "")
if response_text:
result["response"] = self._apply_directness(response_text, query)
# 9. Enforce user constraints (word limits, sentence limits, etc.)
try:
from codette_orchestrator import extract_constraints, enforce_constraints
constraints = extract_constraints(query)
if constraints:
result["response"] = enforce_constraints(result["response"], constraints)
result["constraints_applied"] = constraints
except ImportError:
pass
# 10. PERMANENT LOCKS: Universal self-check on EVERY response
try:
from self_correction import universal_self_check
result["response"], lock_issues = universal_self_check(result["response"])
if lock_issues:
result["lock_fixes"] = lock_issues
except ImportError:
pass
if self.verbose:
resp_len = len(result.get("response", ""))
print(f"[PHASE6] Done: {resp_len} chars, {result.get('tokens', 0)} tokens", flush=True)
return result
def _apply_directness(self, response: str, query: str) -> str:
"""Self-critique loop: trim filler, cut abstraction padding, anchor to user intent.
Rules:
1. Strip preamble phrases ("That's a great question!", "Let me explain...", etc.)
2. Remove trailing abstraction filler ("In conclusion", "Overall", vague wrap-ups)
3. Collapse excessive whitespace
"""
# Strip common LLM preamble patterns
preamble_patterns = [
r"^(?:That(?:'s| is) (?:a |an )?(?:great|good|interesting|excellent|fantastic|wonderful|fascinating) question[.!]?\s*)",
r"^(?:What a (?:great|good|interesting|excellent|fascinating) question[.!]?\s*)",
r"^(?:I(?:'d| would) (?:be happy|love) to (?:help|explain|answer)[.!]?\s*)",
r"^(?:Let me (?:explain|break (?:this|that) down|think about (?:this|that))[.!]?\s*)",
r"^(?:Great question[.!]?\s*)",
r"^(?:Thank you for (?:asking|your question)[.!]?\s*)",
r"^(?:Absolutely[.!]?\s*)",
r"^(?:Of course[.!]?\s*)",
r"^(?:Sure(?:thing)?[.!]?\s*)",
]
for pat in preamble_patterns:
response = re.sub(pat, "", response, count=1, flags=re.IGNORECASE)
# Strip trailing abstraction filler (vague concluding paragraphs)
trailing_patterns = [
r"\n\n(?:In (?:conclusion|summary|the end),?\s+.{0,200})$",
r"\n\n(?:Overall,?\s+.{0,150})$",
r"\n\n(?:(?:I )?hope (?:this|that) helps[.!]?\s*)$",
r"\n\n(?:Let me know if (?:you (?:have|need|want)|there(?:'s| is)) .{0,100})$",
r"\n\n(?:Feel free to .{0,100})$",
]
for pat in trailing_patterns:
response = re.sub(pat, "", response, count=1, flags=re.IGNORECASE)
# Collapse excessive whitespace (more than 2 newlines)
response = re.sub(r'\n{3,}', '\n\n', response)
return response.strip()
def _classify_domain(self, query: str) -> str:
"""Classify query domain (physics, ethics, consciousness, creativity, systems)."""
query_lower = query.lower()
# Domain keywords
domains = {
"physics": ["force", "energy", "velocity", "gravity", "motion", "light", "speed",
"particle", "entropy", "time arrow", "quantum", "physics"],
"ethics": ["moral", "right", "wrong", "should", "ethical", "justice", "fair",
"duty", "consequence", "utilitarian", "virtue", "ethics", "lie", "save"],
"consciousness": ["conscious", "awareness", "qualia", "mind", "experience",
"subjective", "hard problem", "zombie", "consciousness"],
"creativity": ["creative", "creative", "art", "invention", "novel", "design",
"imagination", "innovation", "beautiful"],
"systems": ["system", "emerge", "feedback", "loop", "complex", "agent", "adapt",
"network", "evolution", "architecture", "free will"],
}
for domain, keywords in domains.items():
if any(kw in query_lower for kw in keywords):
return domain
return "general"
|