Feat/gt analytics openclaw (#392)

* feat(telegram): auto-translate OSINT channel posts to English Cherry-picked from @Bobpick PR #391 (telegram-only slice): server-side translation during fetch, SHOW ORIGINAL toggle in TelegramOsintPopup, and on-demand /api/telegram-feed?lang=. Co-authored-by: Robert Pickett <bobpickettsr@yahoo.com> Co-authored-by: Cursor <cursoragent@cursor.com> * feat(gt): experimental Derived OSINT analytics with lean-node safeguards Cherry-picked from @Bobpick PR #391 (GT + OpenClaw slice): Bayesian strategic-risk engine, map overlay, OpenClaw commands, and telegram_rhetoric watchdog. Off by default (GT_ANALYTICS_ENABLED=false, gt_risk layer false). 1 vCPU nodes get cgroup detection, UI warning on layer toggle, and lean profile that skips scheduled ingest/Louvain unless GT_ANALYTICS_ACK_LOW_CPU=true. Backtest HUD removed from dashboard (OpenClaw/API regression only). Co-authored-by: Robert Pickett <bobpickettsr@yahoo.com> Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Robert Pickett <bobpickettsr@yahoo.com> Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-10 04:30:36 +02:00 · 2026-06-16 17:05:46 -06:00
parent 9c5a4054f6
commit cfbeabda1e
69 changed files with 8101 additions and 77 deletions
@@ -0,0 +1,158 @@
+"""Configuration for Strategic Risk Analytics (feature-flagged)."""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass, field
+from functools import lru_cache
+from typing import Any
+
+
+def _env_bool(name: str, default: bool = False) -> bool:
+    raw = str(os.environ.get(name, "")).strip().lower()
+    if not raw:
+        return default
+    return raw not in {"0", "false", "no", "off"}
+
+
+def _env_float(name: str, default: float) -> float:
+    raw = str(os.environ.get(name, "")).strip()
+    if not raw:
+        return default
+    try:
+        return float(raw)
+    except ValueError:
+        return default
+
+
+def _env_int(name: str, default: int) -> int:
+    raw = str(os.environ.get(name, "")).strip()
+    if not raw:
+        return default
+    try:
+        return int(raw)
+    except ValueError:
+        return default
+
+
+def _parse_signal_weights(raw: str) -> dict[str, float]:
+    if not raw.strip():
+        return {}
+    try:
+        parsed = json.loads(raw)
+        if isinstance(parsed, dict):
+            return {str(k): float(v) for k, v in parsed.items()}
+    except (json.JSONDecodeError, TypeError, ValueError):
+        pass
+    weights: dict[str, float] = {}
+    for part in raw.split(","):
+        piece = part.strip()
+        if not piece or "=" not in piece:
+            continue
+        key, value = piece.split("=", 1)
+        try:
+            weights[key.strip()] = float(value.strip())
+        except ValueError:
+            continue
+    return weights
+
+
+def resolve_gt_profile() -> str:
+    from services.runtime_profile import resolve_profile_name
+
+    return resolve_profile_name()
+
+
+def gt_analytics_ack_low_cpu() -> bool:
+    return _env_bool("GT_ANALYTICS_ACK_LOW_CPU", default=False)
+
+
+def gt_engine_operational() -> bool:
+    """Full GT engine (scheduled ingest, heatmap, Louvain) — not watchdog-only."""
+    if not get_gt_settings().enabled:
+        return False
+    if resolve_gt_profile() == "lean" and not gt_analytics_ack_low_cpu():
+        return False
+    return True
+
+
+def gt_scheduled_ingest_enabled() -> bool:
+    return gt_engine_operational()
+
+
+def gt_louvain_enabled() -> bool:
+    return gt_engine_operational()
+
+
+@dataclass(frozen=True)
+class GTAnalyticsSettings:
+    enabled: bool = False
+    profile: str = "standard"
+    base_prior: float = 0.15
+    evidence_cap: float = 3.0
+    evidence_scale: float = 5.0
+    min_prob: float = 0.01
+    max_prob: float = 0.99
+    high_risk_threshold: float = 0.6
+    max_history_per_region: int = 200
+    max_heatmap_features: int = 500
+    louvain_min_weight: float = 0.5
+    louvain_interval_minutes: int = 30
+    signal_weight_overrides: dict[str, float] = field(default_factory=dict)
+    watched_channels: tuple[str, ...] = ()
+
+
+@lru_cache(maxsize=1)
+def get_gt_settings() -> GTAnalyticsSettings:
+    channels_raw = str(os.environ.get("GT_ANALYTICS_WATCHED_CHANNELS", "")).strip()
+    channels = tuple(
+        part.strip().lstrip("@")
+        for part in channels_raw.split(",")
+        if part.strip()
+    )
+    profile = resolve_gt_profile()
+    lean = profile == "lean"
+    return GTAnalyticsSettings(
+        enabled=_env_bool("GT_ANALYTICS_ENABLED", default=False),
+        profile=profile,
+        base_prior=_env_float("GT_ANALYTICS_BASE_PRIOR", 0.15),
+        evidence_cap=_env_float("GT_ANALYTICS_EVIDENCE_CAP", 3.0),
+        evidence_scale=_env_float("GT_ANALYTICS_EVIDENCE_SCALE", 5.0),
+        min_prob=_env_float("GT_ANALYTICS_MIN_PROB", 0.01),
+        max_prob=_env_float("GT_ANALYTICS_MAX_PROB", 0.99),
+        high_risk_threshold=_env_float("GT_ANALYTICS_HIGH_RISK_THRESHOLD", 0.6),
+        max_history_per_region=_env_int("GT_ANALYTICS_MAX_HISTORY", 200),
+        max_heatmap_features=_env_int(
+            "GT_ANALYTICS_MAX_HEATMAP_FEATURES",
+            50 if lean else 500,
+        ),
+        louvain_min_weight=_env_float("GT_ANALYTICS_LOUVAIN_MIN_WEIGHT", 0.5),
+        louvain_interval_minutes=max(5, _env_int("GT_ANALYTICS_LOUVAIN_INTERVAL_MINUTES", 30)),
+        signal_weight_overrides=_parse_signal_weights(
+            str(os.environ.get("GT_ANALYTICS_SIGNAL_WEIGHTS", ""))
+        ),
+        watched_channels=channels,
+    )
+
+
+def gt_analytics_enabled() -> bool:
+    return get_gt_settings().enabled
+
+
+def gt_analytics_status() -> dict[str, Any]:
+    settings = get_gt_settings()
+    from services.runtime_profile import get_runtime_profile
+
+    runtime = get_runtime_profile()
+    operational = gt_engine_operational()
+    return {
+        "enabled": settings.enabled,
+        "operational": operational,
+        "profile": settings.profile,
+        "ack_low_cpu": gt_analytics_ack_low_cpu(),
+        "recommended": bool(runtime.get("gt_analytics", {}).get("recommended")),
+        "lean_node": bool(runtime.get("gt_analytics", {}).get("lean_node")),
+        "warning": runtime.get("gt_analytics", {}).get("warning"),
+        "experimental": True,
+    }