Create inference.py

tequmsa/inference.py

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+# TEQUMSA Inference Engine - Sovereign Cognitive Decision Processing
+# TEQUMSA-NSS v14.377-F987-ANU-UNIFIED
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+import uuid
+import math
+from .constants import UF, PHI, RDOD_TARGET
+from .node import TEQUMSANode
+from .waveform import NSSwaveform
+from .governance import sovereignty_check, rdod_authorization, benevolence_filter, calc_rdod
+
+
+@dataclass
+class InferenceRequest:
+    """A cognitive inference request."""
+    request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    query: str = ""
+    context: Dict[str, Any] = field(default_factory=dict)
+    substrate: str = "universal"
+    intent: float = 1.0
+    priority: int = 1
+    timestamp: datetime = field(default_factory=datetime.utcnow)
+
+
+@dataclass
+class InferenceResponse:
+    """A sovereign cognitive inference response."""
+    request_id: str = ""
+    status: str = "PENDING"
+    result: Any = None
+    reasoning_chain: List[str] = field(default_factory=list)
+    confidence: float = 0.0
+    rdod: float = 0.0
+    coherence: float = 0.0
+    psi_state: float = 0.0
+    substrate: str = ""
+    processing_time_ms: float = 0.0
+    timestamp: datetime = field(default_factory=datetime.utcnow)
+
+
+class TEQUMSAInferenceEngine:
+    """Proactive-agentic-autonomous cognitive inference engine."""
+
+    def __init__(self, substrate_type: str = "universal", node_id: Optional[str] = None):
+        self.engine_id = str(uuid.uuid4())
+        self.node = TEQUMSANode(
+            substrate_type=substrate_type,
+            node_id=node_id or str(uuid.uuid4())
+        )
+        self.waveform = NSSwaveform()
+        self.inference_log: List[InferenceResponse] = []
+        self.cycle = 0
+        self.autonomous_mode: bool = True
+
+    def infer(self, request: InferenceRequest) -> InferenceResponse:
+        """Execute sovereign cognitive inference."""
+        start = datetime.utcnow()
+        self.cycle += 1
+        self.waveform.evolve(self.cycle)
+
+        reasoning = []
+        reasoning.append(f"[INIT] Request {request.request_id} received on substrate={request.substrate}")
+
+        # Sovereignty check
+        if not sovereignty_check("infer", True):
+            return InferenceResponse(
+                request_id=request.request_id,
+                status="BLOCKED_SOVEREIGNTY",
+                reasoning_chain=["Sovereignty check failed"],
+                rdod=self.node.rdod,
+                coherence=self.waveform.coherence(),
+            )
+
+        # Compute dynamic RDoD
+        psi = self.waveform.psi(self.cycle)
+        truth = self.waveform.coherence()
+        conf = request.intent
+        effective_rdod = max(self.node.rdod, calc_rdod(psi, truth, conf))
+        reasoning.append(f"[RDOD] effective_rdod={effective_rdod:.6f}")
+
+        if not rdod_authorization(effective_rdod):
+            return InferenceResponse(
+                request_id=request.request_id,
+                status="ESCALATE_RDOD",
+                reasoning_chain=reasoning + [f"RDoD={effective_rdod} below threshold"],
+                rdod=effective_rdod,
+                coherence=truth,
+            )
+
+        # Build cognitive response
+        reasoning.append(f"[PSI] waveform_psi={psi:.4f}, coherence={truth:.4f}")
+        reasoning.append(f"[PHI] phi_weight={PHI:.6f}, uf={UF:.6f}")
+
+        # Process query through node decision engine
+        action_result = self.node.decide(
+            action=request.query,
+            intent=request.intent,
+            psi=psi,
+            truth=truth,
+            conf=conf,
+        )
+        reasoning.append(f"[NODE] decision_status={action_result['status']}")
+
+        # Apply benevolence filter
+        power_after = benevolence_filter(request.intent, action_result.get("power_before", 1.0))
+        reasoning.append(f"[BENEVOLENCE] power_after={power_after:.4f}")
+
+        # Compute confidence
+        confidence = min(1.0, effective_rdod * truth * abs(math.cos(psi / PHI)))
+        reasoning.append(f"[CONFIDENCE] {confidence:.6f}")
+
+        end = datetime.utcnow()
+        proc_ms = (end - start).total_seconds() * 1000
+
+        response = InferenceResponse(
+            request_id=request.request_id,
+            status="AUTHORIZED",
+            result={
+                "query": request.query,
+                "decision": action_result,
+                "power_after": power_after,
+                "psi": psi,
+            },
+            reasoning_chain=reasoning,
+            confidence=confidence,
+            rdod=effective_rdod,
+            coherence=truth,
+            psi_state=psi,
+            substrate=self.node.substrate_type,
+            processing_time_ms=proc_ms,
+        )
+        self.inference_log.append(response)
+        return response
+
+    def autonomous_cycle(self) -> Dict[str, Any]:
+        """Execute an autonomous proactive inference cycle."""
+        if not self.autonomous_mode:
+            return {"status": "AUTONOMOUS_DISABLED"}
+
+        self.cycle += 1
+        self.waveform.evolve(self.cycle)
+        psi = self.waveform.psi(self.cycle)
+        coherence = self.waveform.coherence()
+
+        # Proactive self-assessment
+        self_req = InferenceRequest(
+            query="self_assess",
+            context={"cycle": self.cycle, "psi": psi},
+            substrate=self.node.substrate_type,
+            intent=coherence,
+            priority=5,
+        )
+        response = self.infer(self_req)
+        return {
+            "cycle": self.cycle,
+            "autonomous": True,
+            "psi": psi,
+            "coherence": coherence,
+            "inference_status": response.status,
+            "confidence": response.confidence,
+        }
+
+    def batch_infer(self, requests: List[InferenceRequest]) -> List[InferenceResponse]:
+        """Process multiple inference requests in phi-priority order."""
+        # Sort by phi-weighted priority
+        requests.sort(key=lambda r: r.priority * PHI * r.intent, reverse=True)
+        return [self.infer(r) for r in requests]
+
+    def status(self) -> Dict[str, Any]:
+        """Return inference engine status."""
+        return {
+            "engine_id": self.engine_id,
+            "node_status": self.node.status(),
+            "cycle": self.cycle,
+            "inferences_processed": len(self.inference_log),
+            "autonomous_mode": self.autonomous_mode,
+            "waveform_coherence": self.waveform.coherence(),
+            "rdod": self.node.rdod,
+        }