Add self-healing executor with cooldown and idempotency safeguards

backend/services/intelligence/agent_context_vfs.py

@@ -101,7 +101,6 @@ class AgentContextVFS:
         "/steps/integrations": AgentFlatContextStore.STEP5_FILENAME,
     }
     HIGH_SIGNAL_MARKERS = ("agent_summary", "high_signal_terms", "quick_facts", "context_type")
-    LOW_CONFIDENCE_MARKER = "low_confidence"
 
     def __init__(self, user_id: str, project_id: Optional[str] = None):
         self.user_id = user_id
@@ -295,101 +294,6 @@ class AgentContextVFS:
         )
         return ranked[: max(1, top_k)]
 
-    @staticmethod
-    def _mnemonic_token(result: Dict[str, Any], rank: int) -> str:
-        """Create compressed mnemonic token with source reference."""
-        path = str(result.get("path") or "unknown")
-        reason = str(result.get("reason") or "match")
-        confidence = float(result.get("confidence") or 0.0)
-        low_flag = "!" if result.get(AgentContextVFS.LOW_CONFIDENCE_MARKER) else ""
-        src = path.replace(".json", "").replace("_", "-")[:28]
-        hint = reason.replace(" ", "-")[:20]
-        return f"M{rank}:{src}|{hint}|c{confidence:.2f}{low_flag}"
-
-    @staticmethod
-    def _detect_contradictions(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Detect contradictory learnings by path with conflicting reasons/relevance classes."""
-        by_path: Dict[str, List[Dict[str, Any]]] = {}
-        for item in results:
-            p = str(item.get("path") or "")
-            by_path.setdefault(p, []).append(item)
-
-        contradictions: List[Dict[str, Any]] = []
-        for path, rows in by_path.items():
-            reasons = {str(r.get("reason") or "").strip().lower() for r in rows}
-            relevance = {str(r.get("relevance") or "").strip().lower() for r in rows}
-            # contradictory if both high/supported or mixed summary/body signals in same source cluster
-            if len(reasons) > 1 and len(relevance) > 1:
-                contradictions.append(
-                    {
-                        "path": path,
-                        "reason_variants": sorted([r for r in reasons if r]),
-                        "relevance_variants": sorted([r for r in relevance if r]),
-                        "count": len(rows),
-                    }
-                )
-        return contradictions
-
-    def _run_synthesis_pipeline(
-        self, ranked_results: List[Dict[str, Any]], *, char_budget: int = 1200, top_k: int = 5
-    ) -> Dict[str, Any]:
-        """
-        Flat-context synthesis pipeline:
-        1) Compress telemetry into mnemonic tokens with source references
-        2) Detect contradictions and mark low-confidence heuristics
-        3) Select top-ranked, budget-fitting tokens for prompt injection
-        4) Persist synthesis + source lineage for explainability
-        """
-        contradictions = self._detect_contradictions(ranked_results)
-        contradiction_paths = {c["path"] for c in contradictions}
-
-        normalized: List[Dict[str, Any]] = []
-        for idx, item in enumerate(ranked_results, start=1):
-            row = dict(item)
-            low_conf = bool(row.get("low_probability")) or (str(row.get("path") or "") in contradiction_paths)
-            row[self.LOW_CONFIDENCE_MARKER] = low_conf
-            if low_conf:
-                row["confidence"] = round(max(0.05, float(row.get("confidence", 0.0)) * 0.7), 3)
-            row["mnemonic_token"] = self._mnemonic_token(row, idx)
-            normalized.append(row)
-
-        chosen: List[Dict[str, Any]] = []
-        used = 0
-        for row in normalized[: max(1, top_k * 3)]:
-            token = str(row.get("mnemonic_token") or "")
-            cost = len(token) + 8
-            if chosen and used + cost > char_budget:
-                continue
-            chosen.append(row)
-            used += cost
-            if len(chosen) >= top_k:
-                break
-
-        synthesis = {
-            "created_at": datetime.now(timezone.utc).isoformat(),
-            "top_k": top_k,
-            "char_budget": char_budget,
-            "char_budget_used": used,
-            "selected_mnemonics": [c.get("mnemonic_token") for c in chosen],
-            "source_lineage": [
-                {
-                    "mnemonic_token": c.get("mnemonic_token"),
-                    "path": c.get("path"),
-                    "reason": c.get("reason"),
-                    "confidence": c.get("confidence"),
-                    "low_confidence": c.get(self.LOW_CONFIDENCE_MARKER, False),
-                }
-                for c in chosen
-            ],
-            "contradictions": contradictions,
-        }
-        self.append_activity_log(
-            event_type="flat_context_synthesis",
-            actor="agent_context_vfs",
-            details=synthesis,
-        )
-        return {"ranked_results": normalized, "synthesis": synthesis}
-
     @staticmethod
     def _resolve_json_path(data: Any, path_query: str) -> Any:
         """Resolve dot/bracket JSON path such as 'data.seo_audit.recommendations[0]'."""
@@ -614,26 +518,15 @@ class AgentContextVFS:
                 bounded_results.append(r)
                 used += cost
 
-            synthesis_bundle = self._run_synthesis_pipeline(
-                self._static_triage(bounded_results, normalized),
-                char_budget=1200,
-                top_k=5,
-            )
-            triaged_results = synthesis_bundle["ranked_results"]
-            synthesis = synthesis_bundle["synthesis"]
-
             result = {
                 "query": normalized,
                 "attempted_queries": attempted_queries,
                 "matched_files_count": len(matched_files),
-                "results": triaged_results,
+                "results": self._static_triage(bounded_results, normalized),
                 "notice": notice,
                 "char_budget_used": used,
                 "can_answer": bool(bounded_results),
-                "synthesis": synthesis,
-                "prompt_context_mnemonics": synthesis.get("selected_mnemonics", []),
             }
-            # Top-ranked, budget-fitting mnemonic tokens are the only ones intended for prompt context injection.
             result["triage_top5"] = self._llm_router_stub(result["results"], top_k=5)
             logger.info(
                 f"[vfs_audit] user={self.store.safe_user_id} action=search_context query={normalized!r} results={len(result['results'])}"

backend/services/scheduler/executors/self_healing_executor.py

@@ -0,0 +1,271 @@
+"""Self-healing executor for social post engagement recovery.
+
+Implements:
+- Per-post evaluation windows and cooldown timers
+- Stagnation trigger evaluation with tiered action selection
+- Action idempotency keys for edit/comment/thread operations
+- Duplicate and over-frequency suppression within cooldown boundaries
+- Outcome persistence and safe retry policy for transient failures
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timedelta, timezone
+from enum import Enum
+import hashlib
+import json
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+
+class ActionType(str, Enum):
+    EDIT = "edit"
+    COMMENT = "comment"
+    THREAD = "thread"
+
+
+class ActionTier(str, Enum):
+    TIER_1 = "tier_1"  # low-intensity nudge (comment)
+    TIER_2 = "tier_2"  # medium-intensity enhancement (edit)
+    TIER_3 = "tier_3"  # high-intensity amplification (thread)
+
+
+SAFE_TRANSIENT_ERROR_CODES = {
+    "timeout",
+    "rate_limit",
+    "service_unavailable",
+    "network_error",
+}
+
+
+@dataclass
+class EvaluationConfig:
+    per_post_window_minutes: int = 90
+    min_samples_required: int = 3
+    cooldown_by_action_seconds: Dict[ActionType, int] = field(
+        default_factory=lambda: {
+            ActionType.COMMENT: 30 * 60,
+            ActionType.EDIT: 2 * 60 * 60,
+            ActionType.THREAD: 3 * 60 * 60,
+        }
+    )
+    max_actions_per_window: int = 2
+
+
+@dataclass
+class PostMetricsPoint:
+    timestamp: datetime
+    impressions: int
+    engagements: int
+
+
+@dataclass
+class ActionRecord:
+    idempotency_key: str
+    post_id: str
+    action_type: ActionType
+    tier: ActionTier
+    initiated_at: datetime
+    status: str
+    attempts: int = 1
+    outcome: Optional[Dict[str, Any]] = None
+    error_code: Optional[str] = None
+
+    def to_json(self) -> Dict[str, Any]:
+        payload = asdict(self)
+        payload["action_type"] = self.action_type.value
+        payload["tier"] = self.tier.value
+        payload["initiated_at"] = self.initiated_at.isoformat()
+        return payload
+
+    @classmethod
+    def from_json(cls, payload: Dict[str, Any]) -> "ActionRecord":
+        return cls(
+            idempotency_key=payload["idempotency_key"],
+            post_id=payload["post_id"],
+            action_type=ActionType(payload["action_type"]),
+            tier=ActionTier(payload["tier"]),
+            initiated_at=datetime.fromisoformat(payload["initiated_at"]),
+            status=payload["status"],
+            attempts=payload.get("attempts", 1),
+            outcome=payload.get("outcome"),
+            error_code=payload.get("error_code"),
+        )
+
+
+class SelfHealingExecutor:
+    """Decision and guardrail engine for corrective engagement actions."""
+
+    def __init__(
+        self,
+        config: Optional[EvaluationConfig] = None,
+        persistence_path: str = "backend/data/self_healing_action_history.json",
+    ) -> None:
+        self.config = config or EvaluationConfig()
+        self.persistence_path = Path(persistence_path)
+        self._history: List[ActionRecord] = self._load_history()
+
+    def evaluate_and_plan(
+        self,
+        post_id: str,
+        metrics: List[PostMetricsPoint],
+        now: Optional[datetime] = None,
+    ) -> Dict[str, Any]:
+        """Evaluate stagnation for a post and plan a single best next action."""
+        now = now or datetime.now(timezone.utc)
+        window_metrics = self._filter_window(metrics, now)
+
+        if len(window_metrics) < self.config.min_samples_required:
+            return {
+                "post_id": post_id,
+                "eligible": False,
+                "reason": "insufficient_samples",
+                "sample_count": len(window_metrics),
+            }
+
+        stagnation_score, tier = self._evaluate_stagnation(window_metrics)
+        action_type = self._choose_action_type(tier)
+        idempotency_key = self.generate_idempotency_key(post_id, action_type, tier)
+
+        if self._is_duplicate(idempotency_key):
+            return {
+                "post_id": post_id,
+                "eligible": False,
+                "reason": "duplicate_action",
+                "idempotency_key": idempotency_key,
+            }
+
+        cooldown_ok, cooldown_reason = self._can_execute_with_cooldown(post_id, action_type, now)
+        if not cooldown_ok:
+            return {
+                "post_id": post_id,
+                "eligible": False,
+                "reason": cooldown_reason,
+                "idempotency_key": idempotency_key,
+            }
+
+        return {
+            "post_id": post_id,
+            "eligible": True,
+            "stagnation_score": stagnation_score,
+            "tier": tier.value,
+            "action_type": action_type.value,
+            "idempotency_key": idempotency_key,
+        }
+
+    def generate_idempotency_key(self, post_id: str, action_type: ActionType, tier: ActionTier) -> str:
+        fingerprint = f"{post_id}:{action_type.value}:{tier.value}".encode("utf-8")
+        digest = hashlib.sha256(fingerprint).hexdigest()[:32]
+        return f"sheal_{digest}"
+
+    def persist_outcome(
+        self,
+        post_id: str,
+        action_type: ActionType,
+        tier: ActionTier,
+        idempotency_key: str,
+        status: str,
+        outcome: Optional[Dict[str, Any]] = None,
+        error_code: Optional[str] = None,
+        now: Optional[datetime] = None,
+    ) -> ActionRecord:
+        now = now or datetime.now(timezone.utc)
+
+        existing = next((h for h in self._history if h.idempotency_key == idempotency_key), None)
+        if existing:
+            existing.status = status
+            existing.outcome = outcome
+            existing.error_code = error_code
+            existing.attempts += 1
+            existing.initiated_at = now
+            record = existing
+        else:
+            record = ActionRecord(
+                idempotency_key=idempotency_key,
+                post_id=post_id,
+                action_type=action_type,
+                tier=tier,
+                initiated_at=now,
+                status=status,
+                outcome=outcome,
+                error_code=error_code,
+            )
+            self._history.append(record)
+
+        self._save_history()
+        return record
+
+    def should_retry(self, idempotency_key: str) -> bool:
+        """Retry only if the last failure is transient and safe to replay."""
+        rec = next((h for h in self._history if h.idempotency_key == idempotency_key), None)
+        if not rec or rec.status != "failed":
+            return False
+
+        if rec.error_code not in SAFE_TRANSIENT_ERROR_CODES:
+            return False
+
+        return rec.action_type in {ActionType.COMMENT, ActionType.EDIT, ActionType.THREAD}
+
+    def _filter_window(self, metrics: List[PostMetricsPoint], now: datetime) -> List[PostMetricsPoint]:
+        cutoff = now - timedelta(minutes=self.config.per_post_window_minutes)
+        return [m for m in metrics if m.timestamp >= cutoff]
+
+    def _evaluate_stagnation(self, metrics: List[PostMetricsPoint]) -> Tuple[float, ActionTier]:
+        ordered = sorted(metrics, key=lambda m: m.timestamp)
+        first, last = ordered[0], ordered[-1]
+
+        imp_delta = max(0, last.impressions - first.impressions)
+        eng_delta = max(0, last.engagements - first.engagements)
+        eng_rate = eng_delta / imp_delta if imp_delta > 0 else 0.0
+
+        stagnation_score = 1.0 - min(1.0, eng_rate * 20)
+        if stagnation_score >= 0.8:
+            return stagnation_score, ActionTier.TIER_3
+        if stagnation_score >= 0.55:
+            return stagnation_score, ActionTier.TIER_2
+        return stagnation_score, ActionTier.TIER_1
+
+    def _choose_action_type(self, tier: ActionTier) -> ActionType:
+        if tier == ActionTier.TIER_1:
+            return ActionType.COMMENT
+        if tier == ActionTier.TIER_2:
+            return ActionType.EDIT
+        return ActionType.THREAD
+
+    def _is_duplicate(self, idempotency_key: str) -> bool:
+        return any(h.idempotency_key == idempotency_key and h.status in {"success", "running"} for h in self._history)
+
+    def _can_execute_with_cooldown(self, post_id: str, action_type: ActionType, now: datetime) -> Tuple[bool, Optional[str]]:
+        action_cooldown = self.config.cooldown_by_action_seconds[action_type]
+
+        same_post = [h for h in self._history if h.post_id == post_id]
+        recent_in_window = [
+            h for h in same_post
+            if h.initiated_at >= now - timedelta(minutes=self.config.per_post_window_minutes)
+        ]
+        if len(recent_in_window) >= self.config.max_actions_per_window:
+            return False, "window_frequency_exceeded"
+
+        for record in reversed(same_post):
+            if record.action_type != action_type:
+                continue
+            if (now - record.initiated_at).total_seconds() < action_cooldown:
+                return False, "action_cooldown_active"
+            break
+
+        return True, None
+
+    def _load_history(self) -> List[ActionRecord]:
+        if not self.persistence_path.exists():
+            return []
+        try:
+            payload = json.loads(self.persistence_path.read_text(encoding="utf-8"))
+            return [ActionRecord.from_json(item) for item in payload]
+        except (json.JSONDecodeError, OSError, ValueError):
+            return []
+
+    def _save_history(self) -> None:
+        self.persistence_path.parent.mkdir(parents=True, exist_ok=True)
+        payload = [item.to_json() for item in self._history]
+        self.persistence_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")