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README.md

@@ -525,8 +525,8 @@ python main.py --mode train --epochs 100
 ```
 ┌─────────────────────────────────────────────────┐
 │ MultiCollectionRetriever                         │
-│ - Connects to ALL ChromaDB collections          │
-│ - Roger_feeds, Roger_rag_collection, etc.     │
+│ - Connects to ChromaDB intelligence collection  │
+│ - Roger_feeds (all agent domain feeds)          │
 └─────────────────┬───────────────────────────────┘
                   │
                   ▼

frontend/app/components/FloatingChatBox.tsx

@@ -169,7 +169,7 @@ const FloatingChatBox = () => {
                 </div>
 
                 {/* Domain Filter - scrollable on mobile */}
-                <div className="flex gap-1.5 sm:gap-1 px-3 sm:px-4 py-3 bg-[#1a1a1a] border-b border-[#373435] overflow-x-auto sm:flex-wrap hide-scrollbar">
+                <div className="flex gap-1.5 sm:gap-1 px-3 sm:px-4 py-3 bg-[#1a1a1a] border-b border-[#373435] overflow-x-auto sm:flex-wrap intel-scrollbar">
                     <Badge
                         className={`cursor-pointer text-xs sm:text-xs whitespace-nowrap px-3 py-1.5 sm:px-2 sm:py-1 transition-colors touch-manipulation ${!domainFilter ? 'bg-green-500 text-white' : 'bg-[#373435] text-gray-300 hover:bg-[#4a4a4a] active:bg-[#555]'}`}
                         onClick={() => setDomainFilter(null)}

frontend/app/components/dashboard/AnomalyDetection.tsx

@@ -132,7 +132,7 @@ const AnomalyDetection = () => {
             <Separator className="mb-4" />
 
             {/* Anomalies List */}
-            <div className="space-y-3 max-h-[500px] overflow-y-auto">
+            <div className="space-y-3 max-h-[500px] overflow-y-auto intel-scrollbar pr-2">
                 {loading && anomalies.length === 0 ? (
                     <div className="text-center py-8">
                         <RefreshCw className="w-8 h-8 mx-auto animate-spin text-primary mb-3" />

frontend/app/components/dashboard/CommodityPrices.tsx

@@ -0,0 +1,93 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { ShoppingBasket, TrendingUp, TrendingDown, Minus } from "lucide-react";
+
+interface Commodity {
+    name: string;
+    price: number;
+    unit: string;
+    change: number;
+    category: string;
+}
+
+interface CommodityPricesProps {
+    commodityData?: Record<string, unknown> | null;
+}
+
+const CommodityPrices = ({ commodityData }: CommodityPricesProps) => {
+    const commodities = (commodityData?.commodities as Commodity[]) || [];
+    const summary = (commodityData?.summary as Record<string, number>) || {};
+    const fetchedAt = commodityData?.fetched_at as string;
+
+    // Show top 8 essential items
+    const essentialItems = commodities.slice(0, 8);
+
+    const getTrendIcon = (change: number) => {
+        if (change > 0) return <TrendingUp className="w-3 h-3 text-destructive" />;
+        if (change < 0) return <TrendingDown className="w-3 h-3 text-success" />;
+        return <Minus className="w-3 h-3 text-muted-foreground" />;
+    };
+
+    const getChangeColor = (change: number) => {
+        if (change > 0) return "text-destructive";
+        if (change < 0) return "text-success";
+        return "text-muted-foreground";
+    };
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className="p-2 rounded-lg bg-green-500/20">
+                        <ShoppingBasket className="w-5 h-5 text-green-500" />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">🛒 COMMODITIES</h3>
+                        <p className="text-xs text-muted-foreground">Essential goods prices</p>
+                    </div>
+                </div>
+                <div className="flex gap-1">
+                    {summary.items_increased > 0 && (
+                        <Badge className="bg-destructive/20 text-destructive text-xs">
+                            ↑{summary.items_increased}
+                        </Badge>
+                    )}
+                    {summary.items_decreased > 0 && (
+                        <Badge className="bg-success/20 text-success text-xs">
+                            ↓{summary.items_decreased}
+                        </Badge>
+                    )}
+                </div>
+            </div>
+
+            <div className="grid grid-cols-2 gap-1.5">
+                {essentialItems.map((item, idx) => (
+                    <div key={idx} className="p-2 rounded bg-muted/30 border border-border">
+                        <div className="flex items-center justify-between">
+                            <span className="text-xs text-muted-foreground truncate flex-1">{item.name}</span>
+                            {getTrendIcon(item.change)}
+                        </div>
+                        <div className="flex items-baseline gap-1 mt-0.5">
+                            <span className="text-sm font-bold">Rs.{item.price}</span>
+                            {item.change !== 0 && (
+                                <span className={`text-xs ${getChangeColor(item.change)}`}>
+                                    {item.change > 0 ? '+' : ''}{item.change}
+                                </span>
+                            )}
+                        </div>
+                    </div>
+                ))}
+            </div>
+
+            {fetchedAt && (
+                <p className="text-xs text-muted-foreground mt-3 text-center">
+                    Source: Consumer Affairs Authority
+                </p>
+            )}
+        </Card>
+    );
+};
+
+export default CommodityPrices;

frontend/app/components/dashboard/CurrencyPrediction.tsx

@@ -122,8 +122,8 @@ export default function CurrencyPrediction() {
                     {/* Main Prediction Card */}
                     <div
                         className={`p-6 rounded-xl border mb-6 ${prediction.expected_change_pct < 0
-                                ? "bg-green-500/10 border-green-500/30"
-                                : "bg-red-500/10 border-red-500/30"
+                            ? "bg-green-500/10 border-green-500/30"
+                            : "bg-red-500/10 border-red-500/30"
                             }`}
                     >
                         <div className="grid grid-cols-3 gap-4 text-center">
@@ -153,8 +153,8 @@ export default function CurrencyPrediction() {
                                 <span className="text-slate-400">Expected Change: </span>
                                 <span
                                     className={`font-bold ${prediction.expected_change_pct < 0
-                                            ? "text-green-400"
-                                            : "text-red-400"
+                                        ? "text-green-400"
+                                        : "text-red-400"
                                         }`}
                                 >
                                     {prediction.expected_change_pct > 0 ? "+" : ""}
@@ -223,12 +223,6 @@ export default function CurrencyPrediction() {
                         </div>
                     )}
 
-                    {/* Fallback Warning */}
-                    {prediction.is_fallback && (
-                        <div className="mt-4 p-3 bg-yellow-500/10 border border-yellow-500/30 rounded-lg text-sm text-yellow-400">
-                            ⚠️ Using fallback model. Run training for accurate predictions.
-                        </div>
-                    )}
 
                     {/* Footer */}
                     <div className="mt-4 text-xs text-slate-500 text-center">

frontend/app/components/dashboard/DashboardOverview.tsx

@@ -4,10 +4,28 @@ import { Badge } from "../ui/badge";
 import { useRogerData } from "../../hooks/use-roger-data";
 import { motion } from "framer-motion";
 import RiverNetStatus from "./RiverNetStatus";
+import PowerOutageStatus from "./PowerOutageStatus";
+import FuelPriceMonitor from "./FuelPriceMonitor";
+import EconomicIndicators from "./EconomicIndicators";
+import HealthAlerts from "./HealthAlerts";
+import CommodityPrices from "./CommodityPrices";
+import WaterSupplyStatus from "./WaterSupplyStatus";
 
 const DashboardOverview = () => {
-  // Get riverData directly from hook (fetched via /api/rivernet)
-  const { dashboard, events, isConnected, status, riverData } = useRogerData();
+  // Get data from hook (fetched via various /api/ endpoints)
+  const {
+    dashboard,
+    events,
+    isConnected,
+    status,
+    riverData,
+    powerData,
+    fuelData,
+    economyData,
+    healthData,
+    commodityData,
+    waterData,
+  } = useRogerData();
 
   // Safety check: ensure events is always an array
   const safeEvents = events || [];
@@ -126,6 +144,18 @@ const DashboardOverview = () => {
       {/* RiverNet Flood Monitoring */}
       <RiverNetStatus riverData={riverData} compact={false} />
 
+      {/* Situational Awareness Grid - NEW */}
+      <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
+        <PowerOutageStatus powerData={powerData} />
+        <FuelPriceMonitor fuelData={fuelData} />
+        <EconomicIndicators economyData={economyData} />
+        <HealthAlerts healthData={healthData} />
+        <CommodityPrices commodityData={commodityData} />
+        <WaterSupplyStatus waterData={waterData} />
+      </div>
+
+
+
       {/* Live Intelligence Feed - SORTED BY LATEST FIRST */}
       <Card className="p-6 bg-card border-border">
         <h3 className="font-bold mb-4 flex items-center gap-2">
@@ -134,7 +164,7 @@ const DashboardOverview = () => {
           <span className="text-xs text-muted-foreground ml-2">(Latest First)</span>
           <Badge className="ml-auto">{sortedEvents.length} Events</Badge>
         </h3>
-        <div className="space-y-3 max-h-[500px] overflow-y-auto">
+        <div className="space-y-3 max-h-[500px] overflow-y-auto intel-scrollbar pr-2">
           {sortedEvents.slice(0, 10).map((event, idx) => {
             const isRisk = event.impact_type === 'risk';
             const isFlood = event.category === 'flood_monitoring' || event.category === 'flood_alert';

frontend/app/components/dashboard/EconomicIndicators.tsx

@@ -0,0 +1,96 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { TrendingUp, TrendingDown, Minus, Landmark, DollarSign, Percent, Building2 } from "lucide-react";
+
+interface EconomicIndicatorsProps {
+    economyData?: Record<string, unknown> | null;
+}
+
+const EconomicIndicators = ({ economyData }: EconomicIndicatorsProps) => {
+    const indicators = (economyData?.indicators as Record<string, Record<string, unknown>>) || {};
+    const inflation = indicators?.inflation || {};
+    const policyRates = indicators?.policy_rates || {};
+    const exchangeRate = indicators?.exchange_rate || {};
+    const forexReserves = indicators?.forex_reserves || {};
+    const dataAsOf = economyData?.data_as_of as string;
+
+    const getTrendIcon = (trend: string) => {
+        if (trend === "improving" || trend === "stable") return <TrendingUp className="w-3 h-3 text-success" />;
+        if (trend === "declining") return <TrendingDown className="w-3 h-3 text-destructive" />;
+        return <Minus className="w-3 h-3 text-muted-foreground" />;
+    };
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className="p-2 rounded-lg bg-blue-500/20">
+                        <Landmark className="w-5 h-5 text-blue-500" />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">🏛️ ECONOMY</h3>
+                        <p className="text-xs text-muted-foreground">CBSL Indicators</p>
+                    </div>
+                </div>
+                <Badge className="bg-muted text-muted-foreground">
+                    {dataAsOf || "Latest"}
+                </Badge>
+            </div>
+
+            <div className="grid grid-cols-2 gap-2">
+                {/* Inflation */}
+                <div className="p-2 rounded-lg bg-muted/30 border border-border">
+                    <div className="flex items-center gap-1 mb-1">
+                        <Percent className="w-3 h-3 text-muted-foreground" />
+                        <span className="text-xs text-muted-foreground">Inflation (YoY)</span>
+                    </div>
+                    <div className="flex items-center gap-1">
+                        <span className="text-lg font-bold">{inflation.ccpi_yoy as number || 0}%</span>
+                        {getTrendIcon(inflation.trend as string)}
+                    </div>
+                </div>
+
+                {/* USD/LKR */}
+                <div className="p-2 rounded-lg bg-muted/30 border border-border">
+                    <div className="flex items-center gap-1 mb-1">
+                        <DollarSign className="w-3 h-3 text-muted-foreground" />
+                        <span className="text-xs text-muted-foreground">USD/LKR</span>
+                    </div>
+                    <div className="flex items-center gap-1">
+                        <span className="text-lg font-bold">{exchangeRate.usd_lkr as number || 0}</span>
+                        {getTrendIcon(exchangeRate.trend as string)}
+                    </div>
+                </div>
+
+                {/* Policy Rate */}
+                <div className="p-2 rounded-lg bg-muted/30 border border-border">
+                    <div className="flex items-center gap-1 mb-1">
+                        <Landmark className="w-3 h-3 text-muted-foreground" />
+                        <span className="text-xs text-muted-foreground">SDFR Rate</span>
+                    </div>
+                    <span className="text-lg font-bold">{policyRates.sdfr as number || 0}%</span>
+                </div>
+
+                {/* Forex Reserves */}
+                <div className="p-2 rounded-lg bg-muted/30 border border-border">
+                    <div className="flex items-center gap-1 mb-1">
+                        <Building2 className="w-3 h-3 text-muted-foreground" />
+                        <span className="text-xs text-muted-foreground">Reserves</span>
+                    </div>
+                    <div className="flex items-center gap-1">
+                        <span className="text-lg font-bold">${forexReserves.value as number || 0}B</span>
+                        {getTrendIcon(forexReserves.trend as string)}
+                    </div>
+                </div>
+            </div>
+
+            <p className="text-xs text-muted-foreground mt-3 text-center">
+                Source: Central Bank of Sri Lanka
+            </p>
+        </Card>
+    );
+};
+
+export default EconomicIndicators;

frontend/app/components/dashboard/FuelPriceMonitor.tsx

@@ -0,0 +1,74 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { Fuel, TrendingUp, TrendingDown, Minus } from "lucide-react";
+
+interface FuelPrice {
+    price: number;
+    unit: string;
+    name: string;
+}
+
+interface FuelMonitorProps {
+    fuelData?: Record<string, unknown> | null;
+}
+
+const FuelPriceMonitor = ({ fuelData }: FuelMonitorProps) => {
+    const prices = (fuelData?.prices as Record<string, FuelPrice>) || {};
+    const lastRevision = fuelData?.last_revision as string;
+    const fetchedAt = fuelData?.fetched_at as string;
+
+    const fuelTypes = [
+        { key: "petrol_92", label: "Petrol 92", icon: "⛽" },
+        { key: "petrol_95", label: "Petrol 95", icon: "⛽" },
+        { key: "auto_diesel", label: "Diesel", icon: "🚛" },
+        { key: "kerosene", label: "Kerosene", icon: "🔥" },
+    ];
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className="p-2 rounded-lg bg-amber-500/20">
+                        <Fuel className="w-5 h-5 text-amber-500" />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">⛽ FUEL PRICES</h3>
+                        <p className="text-xs text-muted-foreground">CEYPETCO / LIOC</p>
+                    </div>
+                </div>
+                <Badge className="bg-muted text-muted-foreground">
+                    {lastRevision || "Latest"}
+                </Badge>
+            </div>
+
+            <div className="grid grid-cols-2 gap-2">
+                {fuelTypes.map(({ key, label, icon }) => {
+                    const fuel = prices[key];
+                    if (!fuel) return null;
+
+                    return (
+                        <div key={key} className="p-2 rounded-lg bg-muted/30 border border-border">
+                            <div className="flex items-center justify-between mb-1">
+                                <span className="text-xs text-muted-foreground">{icon} {label}</span>
+                            </div>
+                            <p className="text-lg font-bold text-foreground">
+                                Rs. {fuel.price?.toFixed(0) || "-"}
+                            </p>
+                            <p className="text-xs text-muted-foreground">{fuel.unit || "LKR/L"}</p>
+                        </div>
+                    );
+                })}
+            </div>
+
+            {fetchedAt && (
+                <p className="text-xs text-muted-foreground mt-3 text-center">
+                    Source: {fuelData?.source as string || "CEYPETCO"}
+                </p>
+            )}
+        </Card>
+    );
+};
+
+export default FuelPriceMonitor;

frontend/app/components/dashboard/HealthAlerts.tsx

@@ -0,0 +1,102 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { Heart, AlertTriangle, Bug, Activity } from "lucide-react";
+
+interface HealthAlert {
+    type: string;
+    text: string;
+    severity: string;
+}
+
+interface HealthAlertsProps {
+    healthData?: Record<string, unknown> | null;
+}
+
+const HealthAlerts = ({ healthData }: HealthAlertsProps) => {
+    const alerts = (healthData?.alerts as HealthAlert[]) || [];
+    const dengue = (healthData?.dengue as Record<string, unknown>) || {};
+    const advisories = (healthData?.advisories as HealthAlert[]) || [];
+    const fetchedAt = healthData?.fetched_at as string;
+
+    const hasActiveAlerts = alerts.length > 0 || advisories.length > 0;
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className={`p-2 rounded-lg ${hasActiveAlerts ? 'bg-warning/20' : 'bg-success/20'}`}>
+                        <Heart className={`w-5 h-5 ${hasActiveAlerts ? 'text-warning' : 'text-success'}`} />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">🏥 HEALTH STATUS</h3>
+                        <p className="text-xs text-muted-foreground">Ministry of Health</p>
+                    </div>
+                </div>
+                <Badge className={hasActiveAlerts ? "bg-warning/20 text-warning" : "bg-success/20 text-success"}>
+                    {hasActiveAlerts ? "⚠ ADVISORIES" : "✓ NORMAL"}
+                </Badge>
+            </div>
+
+            {/* Dengue Section */}
+            <div className="p-3 rounded-lg bg-muted/30 border border-border mb-2">
+                <div className="flex items-center justify-between">
+                    <div className="flex items-center gap-2">
+                        <Bug className="w-4 h-4 text-warning" />
+                        <span className="text-sm font-medium">Dengue Cases</span>
+                    </div>
+                    <div className="text-right">
+                        <p className="text-lg font-bold">{dengue.weekly_cases as number || 0}</p>
+                        <p className="text-xs text-muted-foreground">weekly avg</p>
+                    </div>
+                </div>
+                {dengue.high_risk_districts && (
+                    <div className="mt-2 flex flex-wrap gap-1">
+                        {(dengue.high_risk_districts as string[]).slice(0, 3).map((district: string, idx: number) => (
+                            <Badge key={idx} variant="outline" className="text-xs">
+                                {String(district)}
+                            </Badge>
+                        ))}
+                    </div>
+                )}
+            </div>
+
+            {/* Active Alerts */}
+            {alerts.length > 0 && (
+                <div className="mb-2">
+                    {alerts.slice(0, 2).map((alert, idx) => (
+                        <div key={idx} className="p-2 rounded bg-destructive/10 border border-destructive/30 mb-1">
+                            <div className="flex items-start gap-2">
+                                <AlertTriangle className="w-3 h-3 text-destructive mt-0.5 flex-shrink-0" />
+                                <p className="text-xs text-destructive">{alert.text}</p>
+                            </div>
+                        </div>
+                    ))}
+                </div>
+            )}
+
+            {/* Advisories */}
+            {advisories.length > 0 && (
+                <div className="mb-2">
+                    {advisories.slice(0, 2).map((adv, idx) => (
+                        <div key={idx} className="p-2 rounded bg-warning/10 border border-warning/30 mb-1">
+                            <div className="flex items-start gap-2">
+                                <Activity className="w-3 h-3 text-warning mt-0.5 flex-shrink-0" />
+                                <p className="text-xs text-warning">{adv.text}</p>
+                            </div>
+                        </div>
+                    ))}
+                </div>
+            )}
+
+            {fetchedAt && (
+                <p className="text-xs text-muted-foreground mt-2">
+                    Updated: {new Date(fetchedAt).toLocaleTimeString()}
+                </p>
+            )}
+        </Card>
+    );
+};
+
+export default HealthAlerts;

frontend/app/components/dashboard/HistoricalIntel.tsx

@@ -170,7 +170,7 @@ export default function HistoricalIntel() {
                     How Climate Has Changed
                 </h4>
 
-                <div className="overflow-x-auto">
+                <div className="overflow-x-auto intel-scrollbar">
                     <table className="w-full text-sm">
                         <thead>
                             <tr className="border-b border-border">
@@ -221,8 +221,8 @@ export default function HistoricalIntel() {
                         <Badge
                             key={idx}
                             className={`${period.risk === 'high'
-                                    ? 'bg-destructive/20 text-destructive'
-                                    : 'bg-warning/20 text-warning'
+                                ? 'bg-destructive/20 text-destructive'
+                                : 'bg-warning/20 text-warning'
                                 }`}
                         >
                             {period.months}: {period.type}

frontend/app/components/dashboard/PowerOutageStatus.tsx

@@ -0,0 +1,80 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { Zap, AlertTriangle, CheckCircle } from "lucide-react";
+
+interface PowerStatusProps {
+    powerData?: Record<string, unknown> | null;
+}
+
+const PowerOutageStatus = ({ powerData }: PowerStatusProps) => {
+    const isActive = powerData?.load_shedding_active as boolean;
+    const status = (powerData?.status as string) || "unknown";
+    const announcements = (powerData?.announcements as string[]) || [];
+    const fetchedAt = powerData?.fetched_at as string;
+
+    const getStatusColor = () => {
+        if (status === "load_shedding") return "bg-destructive/20 text-destructive";
+        if (status === "operational" || status === "no_load_shedding") return "bg-success/20 text-success";
+        return "bg-muted/20 text-muted-foreground";
+    };
+
+    const getStatusLabel = () => {
+        if (status === "load_shedding") return "⚡ LOAD SHEDDING";
+        if (status === "operational" || status === "no_load_shedding") return "✓ NORMAL";
+        return "○ CHECKING...";
+    };
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className={`p-2 rounded-lg ${isActive ? 'bg-destructive/20' : 'bg-success/20'}`}>
+                        <Zap className={`w-5 h-5 ${isActive ? 'text-destructive' : 'text-success'}`} />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">⚡ POWER STATUS</h3>
+                        <p className="text-xs text-muted-foreground">CEB Sri Lanka</p>
+                    </div>
+                </div>
+                <Badge className={getStatusColor()}>
+                    {getStatusLabel()}
+                </Badge>
+            </div>
+
+            {isActive ? (
+                <div className="p-3 rounded-lg bg-destructive/10 border border-destructive/30 mb-2">
+                    <div className="flex items-center gap-2 mb-1">
+                        <AlertTriangle className="w-4 h-4 text-destructive" />
+                        <span className="text-sm font-semibold text-destructive">Load Shedding Active</span>
+                    </div>
+                    <p className="text-xs text-destructive/80">Power cuts may be in effect in various areas</p>
+                </div>
+            ) : (
+                <div className="p-3 rounded-lg bg-success/10 border border-success/30 mb-2">
+                    <div className="flex items-center gap-2">
+                        <CheckCircle className="w-4 h-4 text-success" />
+                        <span className="text-sm text-success">Normal power supply across the island</span>
+                    </div>
+                </div>
+            )}
+
+            {announcements.length > 0 && (
+                <div className="mt-2">
+                    {announcements.slice(0, 2).map((ann, idx) => (
+                        <p key={idx} className="text-xs text-muted-foreground mb-1">• {ann}</p>
+                    ))}
+                </div>
+            )}
+
+            {fetchedAt && (
+                <p className="text-xs text-muted-foreground mt-2">
+                    Updated: {new Date(fetchedAt).toLocaleTimeString()}
+                </p>
+            )}
+        </Card>
+    );
+};
+
+export default PowerOutageStatus;

frontend/app/components/dashboard/StockPredictions.tsx

@@ -146,7 +146,7 @@ const StockPredictions = () => {
             </button>
           </div>
         ) : stocks.length > 0 ? (
-          <div className="space-y-2 max-h-[400px] overflow-y-auto pr-2">
+          <div className="space-y-2 max-h-[400px] overflow-y-auto intel-scrollbar pr-2">
             {stocks.map((stock, idx) => (
               <motion.div
                 key={stock.symbol}
@@ -212,7 +212,7 @@ const StockPredictions = () => {
         </div>
 
         {marketEvents.length > 0 ? (
-          <div className="space-y-2 max-h-[200px] overflow-y-auto">
+          <div className="space-y-2 max-h-[200px] overflow-y-auto intel-scrollbar pr-2">
             {marketEvents.slice(0, 5).map((event, idx) => (
               <motion.div
                 key={event.event_id || idx}

frontend/app/components/dashboard/WaterSupplyStatus.tsx

@@ -0,0 +1,77 @@
+"use client";
+
+import { Card } from "../ui/card";
+import { Badge } from "../ui/badge";
+import { Droplets, AlertTriangle, CheckCircle } from "lucide-react";
+
+interface WaterDisruption {
+    area: string;
+    type: string;
+    details: string;
+    severity: string;
+}
+
+interface WaterSupplyStatusProps {
+    waterData?: Record<string, unknown> | null;
+}
+
+const WaterSupplyStatus = ({ waterData }: WaterSupplyStatusProps) => {
+    const status = (waterData?.status as string) || "unknown";
+    const disruptions = (waterData?.active_disruptions as WaterDisruption[]) || [];
+    const overallSupply = waterData?.overall_supply as string;
+    const fetchedAt = waterData?.fetched_at as string;
+
+    const hasDisruptions = status === "disruptions_reported" || disruptions.length > 0;
+
+    return (
+        <Card className="p-4 bg-card border-border">
+            <div className="flex items-center justify-between mb-3">
+                <div className="flex items-center gap-2">
+                    <div className={`p-2 rounded-lg ${hasDisruptions ? 'bg-warning/20' : 'bg-info/20'}`}>
+                        <Droplets className={`w-5 h-5 ${hasDisruptions ? 'text-warning' : 'text-info'}`} />
+                    </div>
+                    <div>
+                        <h3 className="font-bold text-sm">💧 WATER SUPPLY</h3>
+                        <p className="text-xs text-muted-foreground">NWSDB Status</p>
+                    </div>
+                </div>
+                <Badge className={hasDisruptions ? "bg-warning/20 text-warning" : "bg-success/20 text-success"}>
+                    {hasDisruptions ? "⚠ DISRUPTIONS" : "✓ NORMAL"}
+                </Badge>
+            </div>
+
+            {hasDisruptions ? (
+                <div className="space-y-2">
+                    {disruptions.slice(0, 3).map((d, idx) => (
+                        <div key={idx} className="p-2 rounded bg-warning/10 border border-warning/30">
+                            <div className="flex items-start gap-2">
+                                <AlertTriangle className="w-3 h-3 text-warning mt-0.5 flex-shrink-0" />
+                                <div className="flex-1">
+                                    <p className="text-sm font-medium text-warning">{d.area}</p>
+                                    <p className="text-xs text-warning/80">{d.type} - {d.details?.slice(0, 80)}...</p>
+                                </div>
+                            </div>
+                        </div>
+                    ))}
+                </div>
+            ) : (
+                <div className="p-3 rounded-lg bg-success/10 border border-success/30">
+                    <div className="flex items-center gap-2">
+                        <CheckCircle className="w-4 h-4 text-success" />
+                        <span className="text-sm text-success">
+                            {overallSupply || "Normal water supply across most areas"}
+                        </span>
+                    </div>
+                </div>
+            )}
+
+            {fetchedAt && (
+                <p className="text-xs text-muted-foreground mt-3">
+                    Updated: {new Date(fetchedAt).toLocaleTimeString()}
+                </p>
+            )}
+        </Card>
+    );
+};
+
+export default WaterSupplyStatus;

frontend/app/components/dashboard/WeatherPredictions.tsx

@@ -163,7 +163,7 @@ export default function WeatherPredictions() {
                     </div>
 
                     {/* District Grid */}
-                    <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4 max-h-[500px] overflow-y-auto pr-2">
+                    <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4 max-h-[500px] overflow-y-auto intel-scrollbar pr-2">
                         {filteredDistricts.map(([district, pred]) => (
                             <div
                                 key={district}
@@ -213,11 +213,6 @@ export default function WeatherPredictions() {
                                             <span className="text-slate-400">Station:</span>
                                             <span className="text-white">{pred.station_used}</span>
                                         </div>
-                                        {pred.is_fallback && (
-                                            <div className="text-xs text-yellow-400">
-                                                ⚠️ Using climate fallback (LSTM model not trained)
-                                            </div>
-                                        )}
                                     </div>
                                 )}
                             </div>

frontend/app/components/map/DistrictInfoPanel.tsx

@@ -146,7 +146,7 @@ const DistrictInfoPanel = ({ district }: DistrictInfoPanelProps) => {
         exit={{ opacity: 0, x: -20 }}
         transition={{ duration: 0.3 }}
       >
-        <Card className="p-4 sm:p-6 bg-card border-border space-y-4 max-h-[60vh] sm:max-h-none overflow-y-auto hide-scrollbar">
+        <Card className="p-4 sm:p-6 bg-card border-border space-y-4 max-h-[60vh] sm:max-h-none overflow-y-auto intel-scrollbar">
           {/* Header */}
           <div className="sticky top-0 bg-card z-10 pb-2 border-b border-border/50">
             <div className="flex items-center justify-between mb-2">

frontend/app/hooks/use-roger-data.ts

@@ -305,11 +305,58 @@ export function useRogerData() {
     return () => clearInterval(interval);
   }, [fetchRiverData]);
 
+  // ============================================
+  // SITUATIONAL AWARENESS DATA (NEW)
+  // ============================================
+  const [powerData, setPowerData] = useState<Record<string, unknown> | null>(null);
+  const [fuelData, setFuelData] = useState<Record<string, unknown> | null>(null);
+  const [economyData, setEconomyData] = useState<Record<string, unknown> | null>(null);
+  const [healthData, setHealthData] = useState<Record<string, unknown> | null>(null);
+  const [commodityData, setCommodityData] = useState<Record<string, unknown> | null>(null);
+  const [waterData, setWaterData] = useState<Record<string, unknown> | null>(null);
+
+  // Fetch situational awareness data
+  const fetchSituationalData = useCallback(async () => {
+    try {
+      const [powerRes, fuelRes, economyRes, healthRes, commodityRes, waterRes] = await Promise.all([
+        fetch(`${API_BASE}/api/power`).catch(() => null),
+        fetch(`${API_BASE}/api/fuel`).catch(() => null),
+        fetch(`${API_BASE}/api/economy`).catch(() => null),
+        fetch(`${API_BASE}/api/health`).catch(() => null),
+        fetch(`${API_BASE}/api/commodities`).catch(() => null),
+        fetch(`${API_BASE}/api/water`).catch(() => null),
+      ]);
+
+      if (powerRes?.ok) setPowerData(await powerRes.json());
+      if (fuelRes?.ok) setFuelData(await fuelRes.json());
+      if (economyRes?.ok) setEconomyData(await economyRes.json());
+      if (healthRes?.ok) setHealthData(await healthRes.json());
+      if (commodityRes?.ok) setCommodityData(await commodityRes.json());
+      if (waterRes?.ok) setWaterData(await waterRes.json());
+    } catch (err) {
+      console.warn('[Roger] Failed to fetch situational data:', err);
+    }
+  }, []);
+
+  // Fetch situational data periodically (every 5 minutes)
+  useEffect(() => {
+    fetchSituationalData();
+    const interval = setInterval(fetchSituationalData, 300000); // Every 5 min
+    return () => clearInterval(interval);
+  }, [fetchSituationalData]);
+
   return {
     ...state,
     isConnected,
     events: state.final_ranked_feed,
     dashboard: state.risk_dashboard_snapshot,
-    riverData  // NEW: Expose river data for RiverNetStatus component
+    riverData,
+    // NEW: Situational awareness data
+    powerData,
+    fuelData,
+    economyData,
+    healthData,
+    commodityData,
+    waterData,
   };
 }

main.py

@@ -767,6 +767,149 @@ def get_national_threat_score():
             "error": str(e)
         }
 
+# ============================================
+# SITUATIONAL AWARENESS API ENDPOINTS (NEW)
+# ============================================
+
+@app.get("/api/power")
+def get_power_status():
+    """
+    Get CEB power outage / load shedding status.
+    
+    Returns current power supply status, active load shedding schedules,
+    and any CEB announcements.
+    """
+    try:
+        from src.utils.utils import tool_ceb_power_status
+        power_data = tool_ceb_power_status()
+        return {
+            "status": "success",
+            **power_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching power status: {e}")
+        return {
+            "status": "error",
+            "load_shedding_active": False,
+            "error": str(e)
+        }
+
+
+@app.get("/api/fuel")
+def get_fuel_prices():
+    """
+    Get current fuel prices in Sri Lanka.
+    
+    Returns prices for Petrol 92/95, Diesel, Super Diesel, and Kerosene.
+    """
+    try:
+        from src.utils.utils import tool_fuel_prices
+        fuel_data = tool_fuel_prices()
+        return {
+            "status": "success",
+            **fuel_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching fuel prices: {e}")
+        return {
+            "status": "error",
+            "prices": {},
+            "error": str(e)
+        }
+
+
+@app.get("/api/economy")
+def get_economic_indicators():
+    """
+    Get key economic indicators from CBSL.
+    
+    Returns inflation rates, policy rates, exchange rates, and forex reserves.
+    """
+    try:
+        from src.utils.utils import tool_cbsl_indicators
+        economy_data = tool_cbsl_indicators()
+        return {
+            "status": "success",
+            **economy_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching economic indicators: {e}")
+        return {
+            "status": "error",
+            "indicators": {},
+            "error": str(e)
+        }
+
+
+@app.get("/api/health")
+def get_health_alerts():
+    """
+    Get health alerts and disease information.
+    
+    Returns current health alerts, dengue case data, and health advisories.
+    """
+    try:
+        from src.utils.utils import tool_health_alerts
+        health_data = tool_health_alerts()
+        return {
+            "status": "success",
+            **health_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching health data: {e}")
+        return {
+            "status": "error",
+            "alerts": [],
+            "dengue": {},
+            "error": str(e)
+        }
+
+
+@app.get("/api/commodities")
+def get_commodity_prices():
+    """
+    Get prices for essential commodities.
+    
+    Returns current prices for rice, sugar, dhal, milk powder, and other staples.
+    """
+    try:
+        from src.utils.utils import tool_commodity_prices
+        commodity_data = tool_commodity_prices()
+        return {
+            "status": "success",
+            **commodity_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching commodity prices: {e}")
+        return {
+            "status": "error",
+            "commodities": [],
+            "error": str(e)
+        }
+
+
+@app.get("/api/water")
+def get_water_supply_status():
+    """
+    Get water supply disruption alerts from NWSDB.
+    
+    Returns active disruptions, affected areas, and restoration estimates.
+    """
+    try:
+        from src.utils.utils import tool_water_supply_alerts
+        water_data = tool_water_supply_alerts()
+        return {
+            "status": "success",
+            **water_data
+        }
+    except Exception as e:
+        logger.error(f"[API] Error fetching water status: {e}")
+        return {
+            "status": "error",
+            "active_disruptions": [],
+            "error": str(e)
+        }
+
 
 # NOTE: Weather predictions endpoint moved to async version below (line ~1540)
 # NOTE: Currency prediction endpoint moved to async version below (line ~1680)

src/config/intel_config.json

@@ -1,23 +1,33 @@
 {
   "user_profiles": {
     "twitter": [
-      "nivakaran"
+      "nivakaran",
+      "iit",
+      "model-x"
     ],
     "facebook": [
-      "Nivakaran"
+      "Nivakaran",
+      "iit",
+      "sliit"
     ],
     "linkedin": [
-      "nivakaran"
+      "nivakaran",
+      "ieee",
+      "sliit",
+      "albert"
     ]
   },
   "user_keywords": [
     "Colombo",
     "nivakaran",
-    "telco"
+    "model-x",
+    "Colombo port"
   ],
   "user_products": [
     "iphone",
-    "anchor"
+    "anchor",
+    "iphone xr",
+    "iphone 13 pro"
   ],
   "operational_keywords": {
     "infrastructure": [

src/graphs/RogerGraph.py

@@ -1,25 +1,19 @@
 """
-src/graphs/RogerGraph.py
-COMPLETE - Main Roger Graph with Fan-Out/Fan-In Architecture
-This is the "Mother Graph" that orchestrates all domain agents
+RogerGraph.py - Main Roger Graph with Fan-Out/Fan-In Architecture
 """
 
 from __future__ import annotations
 import logging
 from langgraph.graph import StateGraph, START, END
 
-# State and Node imports
 from src.states.combinedAgentState import CombinedAgentState
 from src.nodes.combinedAgentNode import CombinedAgentNode
-
-# Domain graph builders
 from src.graphs.dataRetrievalAgentGraph import DataRetrievalAgentGraph
 from src.graphs.meteorologicalAgentGraph import MeteorologicalGraphBuilder
 from src.graphs.politicalAgentGraph import PoliticalGraphBuilder
 from src.graphs.economicalAgentGraph import EconomicalGraphBuilder
 from src.graphs.intelligenceAgentGraph import IntelligenceGraphBuilder
 from src.graphs.socialAgentGraph import SocialGraphBuilder
-
 from src.llms.groqllm import GroqLLM
 
 logger = logging.getLogger("Roger_graph")
@@ -31,26 +25,12 @@ if not logger.handlers:
 
 
 class CombinedAgentGraphBuilder:
-    """
-    Builds the main Roger graph implementing Fan-Out/Fan-In architecture.
-
-    Architecture:
-    1. GraphInitiator (START)
-    2. Fan-Out to 6 Domain Agents (parallel execution)
-    3. Fan-In to FeedAggregator (collects domain_insights)
-    4. DataRefresher (updates dashboard)
-    5. DataRefreshRouter (loop or end decision)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
     def build_graph(self):
-        logger.info("=" * 60)
-        logger.info("BUILDING Roger COMBINED AGENT GRAPH")
-        logger.info("=" * 60)
+        logger.info("Building Roger Combined Agent Graph")
 
-        # 1. Instantiate domain graph builders
         social_builder = SocialGraphBuilder(self.llm)
         intelligence_builder = IntelligenceGraphBuilder(self.llm)
         economical_builder = EconomicalGraphBuilder(self.llm)
@@ -58,39 +38,23 @@ class CombinedAgentGraphBuilder:
         meteorological_builder = MeteorologicalGraphBuilder(self.llm)
         data_retrieval_builder = DataRetrievalAgentGraph(self.llm)
 
-        logger.info("✓ Domain graph builders instantiated")
-
-        # 2. Instantiate orchestration node
         orchestrator = CombinedAgentNode(self.llm)
-        logger.info("✓ Orchestration node instantiated")
-
-        # 3. Create state graph with CombinedAgentState
         workflow = StateGraph(CombinedAgentState)
-        logger.info("✓ StateGraph created with CombinedAgentState")
 
-        # 4. Add orchestration nodes
         workflow.add_node("GraphInitiator", orchestrator.graph_initiator)
         workflow.add_node("FeedAggregatorAgent", orchestrator.feed_aggregator_agent)
         workflow.add_node("DataRefresherAgent", orchestrator.data_refresher_agent)
         workflow.add_node("DataRefreshRouter", orchestrator.data_refresh_router)
-        logger.info("✓ Orchestration nodes added")
 
-        # 5. Add domain subgraphs (compiled graphs as nodes)
         workflow.add_node("SocialAgent", social_builder.build_graph())
         workflow.add_node("IntelligenceAgent", intelligence_builder.build_graph())
         workflow.add_node("EconomicalAgent", economical_builder.build_graph())
         workflow.add_node("PoliticalAgent", political_builder.build_graph())
         workflow.add_node("MeteorologicalAgent", meteorological_builder.build_graph())
-        workflow.add_node(
-            "DataRetrievalAgent",
-            data_retrieval_builder.build_data_retrieval_agent_graph(),
-        )
-        logger.info("✓ Domain agent subgraphs added")
+        workflow.add_node("DataRetrievalAgent", data_retrieval_builder.build_data_retrieval_agent_graph())
 
-        # 6. Wire the graph: START -> Initiator
         workflow.add_edge(START, "GraphInitiator")
 
-        # 7. Fan-Out: Initiator -> All Domain Agents (parallel execution)
         domain_agents = [
             "SocialAgent",
             "IntelligenceAgent",
@@ -103,40 +67,18 @@ class CombinedAgentGraphBuilder:
         for agent in domain_agents:
             workflow.add_edge("GraphInitiator", agent)
 
-        logger.info(
-            f"✓ Fan-Out configured: GraphInitiator -> {len(domain_agents)} agents"
-        )
-
-        # 8. Fan-In: All Domain Agents -> FeedAggregator
         for agent in domain_agents:
             workflow.add_edge(agent, "FeedAggregatorAgent")
 
-        logger.info(
-            f"✓ Fan-In configured: {len(domain_agents)} agents -> FeedAggregator"
-        )
-
-        # 9. Linear flow: Aggregator -> Refresher -> Router
         workflow.add_edge("FeedAggregatorAgent", "DataRefresherAgent")
         workflow.add_edge("DataRefresherAgent", "DataRefreshRouter")
-        logger.info("✓ Linear orchestration flow configured")
 
-        # 10. Conditional routing: Router -> Loop or END
         def route_decision(state):
-            """
-            Router function for conditional edges.
-            Returns the next node name or END.
-            """
             route = getattr(state, "route", [])
-
-            # If route is None or empty, go to END
             if route is None or route == "":
                 return END
-
-            # If route is "GraphInitiator", loop back
             if route == "GraphInitiator":
                 return "GraphInitiator"
-
-            # Default to END
             return END
 
         workflow.add_conditional_edges(
@@ -144,42 +86,12 @@ class CombinedAgentGraphBuilder:
             route_decision,
             {"GraphInitiator": "GraphInitiator", END: END},
         )
-        logger.info("✓ Conditional routing configured")
 
-        # 11. Compile the graph
         graph = workflow.compile()
-
-        logger.info("=" * 60)
-        logger.info("✓ Roger GRAPH COMPILED SUCCESSFULLY")
-        logger.info("=" * 60)
-        logger.info("")
-        logger.info("Graph Structure:")
-        logger.info("  START")
-        logger.info("    ↓")
-        logger.info("  GraphInitiator")
-        logger.info("    ↓↓↓↓↓↓ (Fan-Out)")
-        logger.info(
-            "  [Social, Intelligence, Economic, Political, Meteorological, DataRetrieval]"
-        )
-        logger.info("    ↓↓↓↓↓↓ (Fan-In)")
-        logger.info("  FeedAggregatorAgent")
-        logger.info("    ↓")
-        logger.info("  DataRefresherAgent")
-        logger.info("    ↓")
-        logger.info("  DataRefreshRouter")
-        logger.info("    ↓ (conditional)")
-        logger.info("  [GraphInitiator (loop) OR END]")
-        logger.info("")
-
+        logger.info("Roger Graph compiled successfully")
         return graph
 
 
-# Module-level compilation for LangGraph CLI
-print("\n" + "=" * 60)
-print("INITIALIZING Roger PLATFORM")
-print("=" * 60)
 llm = GroqLLM().get_llm()
 builder = CombinedAgentGraphBuilder(llm)
 graph = builder.build_graph()
-print("\n✓ Roger Platform Ready")
-print("=" * 60)

src/graphs/combinedAgentGraph.py

@@ -1,7 +1,5 @@
 """
-combinedAgentGraph.py
-Main entry point for the Combined Agent System.
-FIXED: Removed sub-graph wrappers that were causing CancelledError
+combinedAgentGraph.py - Main entry point for the Combined Agent System.
 """
 
 from __future__ import annotations
@@ -9,32 +7,25 @@ from typing import Dict, Any
 import logging
 from datetime import datetime
 
-# LangGraph Imports
 from langgraph.graph import StateGraph, START, END
 
-# Project Imports
 from src.llms.groqllm import GroqLLM
 from src.states.combinedAgentState import CombinedAgentState
 from src.nodes.combinedAgentNode import CombinedAgentNode
 
-# LangSmith Tracing (auto-configures if LANGSMITH_API_KEY is set)
 try:
     from src.config.langsmith_config import LangSmithConfig
-
     _langsmith = LangSmithConfig()
     _langsmith.configure()
 except ImportError:
-    pass  # LangSmith not installed, tracing disabled
-
+    pass
 
-# Import Sub-Graph Builders
 from src.graphs.socialAgentGraph import SocialGraphBuilder
 from src.graphs.intelligenceAgentGraph import IntelligenceGraphBuilder
 from src.graphs.economicalAgentGraph import EconomicalGraphBuilder
 from src.graphs.politicalAgentGraph import PoliticalGraphBuilder
 from src.graphs.meteorologicalAgentGraph import MeteorologicalGraphBuilder
 
-# Configure Logging
 logger = logging.getLogger("main_graph")
 logger.setLevel(logging.INFO)
 if not logger.handlers:
@@ -48,114 +39,83 @@ class CombinedAgentGraphBuilder:
         self.llm = llm
 
     def build_graph(self):
-        # 1. Initialize Sub-Graph Builders and compile them
         social_graph = SocialGraphBuilder(self.llm).build_graph()
         intelligence_graph = IntelligenceGraphBuilder(self.llm).build_graph()
         economical_graph = EconomicalGraphBuilder(self.llm).build_graph()
         political_graph = PoliticalGraphBuilder(self.llm).build_graph()
         meteorological_graph = MeteorologicalGraphBuilder(self.llm).build_graph()
 
-        # 2. Create wrapper functions to extract domain_insights from sub-agent states
-        # This solves the state type mismatch issue - sub-agents return their own state types
-        # but we need to update CombinedAgentState. Wrappers extract domain_insights and
-        # return update dicts that get merged via the reduce_insights reducer.
-
         def run_social_agent(state: CombinedAgentState) -> Dict[str, Any]:
-            """Wrapper to invoke SocialAgent and extract domain_insights"""
             logger.info("[CombinedGraph] Invoking SocialAgent...")
             try:
                 result = social_graph.invoke({})
                 insights = result.get("domain_insights", [])
-                logger.info(
-                    f"[CombinedGraph] SocialAgent returned {len(insights)} insights"
-                )
+                logger.info(f"[CombinedGraph] SocialAgent returned {len(insights)} insights")
                 return {"domain_insights": insights}
             except Exception as e:
                 logger.error(f"[CombinedGraph] SocialAgent FAILED: {e}")
-                return {"domain_insights": []}  # Graceful degradation
+                return {"domain_insights": []}
 
         def run_intelligence_agent(state: CombinedAgentState) -> Dict[str, Any]:
-            """Wrapper to invoke IntelligenceAgent and extract domain_insights"""
             logger.info("[CombinedGraph] Invoking IntelligenceAgent...")
             try:
                 result = intelligence_graph.invoke({})
                 insights = result.get("domain_insights", [])
-                logger.info(
-                    f"[CombinedGraph] IntelligenceAgent returned {len(insights)} insights"
-                )
+                logger.info(f"[CombinedGraph] IntelligenceAgent returned {len(insights)} insights")
                 return {"domain_insights": insights}
             except Exception as e:
                 logger.error(f"[CombinedGraph] IntelligenceAgent FAILED: {e}")
-                return {"domain_insights": []}  # Graceful degradation
+                return {"domain_insights": []}
 
         def run_economical_agent(state: CombinedAgentState) -> Dict[str, Any]:
-            """Wrapper to invoke EconomicalAgent and extract domain_insights"""
             logger.info("[CombinedGraph] Invoking EconomicalAgent...")
             try:
                 result = economical_graph.invoke({})
                 insights = result.get("domain_insights", [])
-                logger.info(
-                    f"[CombinedGraph] EconomicalAgent returned {len(insights)} insights"
-                )
+                logger.info(f"[CombinedGraph] EconomicalAgent returned {len(insights)} insights")
                 return {"domain_insights": insights}
             except Exception as e:
                 logger.error(f"[CombinedGraph] EconomicalAgent FAILED: {e}")
-                return {"domain_insights": []}  # Graceful degradation
+                return {"domain_insights": []}
 
         def run_political_agent(state: CombinedAgentState) -> Dict[str, Any]:
-            """Wrapper to invoke PoliticalAgent and extract domain_insights"""
             logger.info("[CombinedGraph] Invoking PoliticalAgent...")
             try:
                 result = political_graph.invoke({})
                 insights = result.get("domain_insights", [])
-                logger.info(
-                    f"[CombinedGraph] PoliticalAgent returned {len(insights)} insights"
-                )
+                logger.info(f"[CombinedGraph] PoliticalAgent returned {len(insights)} insights")
                 return {"domain_insights": insights}
             except Exception as e:
                 logger.error(f"[CombinedGraph] PoliticalAgent FAILED: {e}")
-                return {"domain_insights": []}  # Graceful degradation
+                return {"domain_insights": []}
 
         def run_meteorological_agent(state: CombinedAgentState) -> Dict[str, Any]:
-            """Wrapper to invoke MeteorologicalAgent and extract domain_insights"""
             logger.info("[CombinedGraph] Invoking MeteorologicalAgent...")
             try:
                 result = meteorological_graph.invoke({})
                 insights = result.get("domain_insights", [])
-                logger.info(
-                    f"[CombinedGraph] MeteorologicalAgent returned {len(insights)} insights"
-                )
+                logger.info(f"[CombinedGraph] MeteorologicalAgent returned {len(insights)} insights")
                 return {"domain_insights": insights}
             except Exception as e:
                 logger.error(f"[CombinedGraph] MeteorologicalAgent FAILED: {e}")
-                return {"domain_insights": []}  # Graceful degradation
+                return {"domain_insights": []}
 
-        # 3. Initialize Main Orchestrator Node
         orchestrator = CombinedAgentNode(self.llm)
-
-        # 4. Create State Graph
         workflow = StateGraph(CombinedAgentState)
 
-        # 5. Add Sub-Agent Wrapper Nodes
-        # These wrappers extract domain_insights from sub-agent results and
-        # return updates for CombinedAgentState (via the reduce_insights reducer)
         workflow.add_node("SocialAgent", run_social_agent)
         workflow.add_node("IntelligenceAgent", run_intelligence_agent)
         workflow.add_node("EconomicalAgent", run_economical_agent)
         workflow.add_node("PoliticalAgent", run_political_agent)
         workflow.add_node("MeteorologicalAgent", run_meteorological_agent)
 
-        # 6. Add Orchestration Nodes (Fan-In)
         workflow.add_node("GraphInitiator", orchestrator.graph_initiator)
         workflow.add_node("FeedAggregatorAgent", orchestrator.feed_aggregator_agent)
         workflow.add_node("DataRefresherAgent", orchestrator.data_refresher_agent)
         workflow.add_node("DataRefreshRouter", orchestrator.data_refresh_router)
 
-        # 7. Define Edges
-        # Start -> Initiator
         workflow.add_edge(START, "GraphInitiator")
 
-        # Initiator -> All Sub-Agents (Parallel)
         sub_agents = [
             "SocialAgent",
             "IntelligenceAgent",
@@ -167,11 +127,9 @@ class CombinedAgentGraphBuilder:
             workflow.add_edge("GraphInitiator", agent)
             workflow.add_edge(agent, "FeedAggregatorAgent")
 
-        # Aggregator -> Refresher -> Router
         workflow.add_edge("FeedAggregatorAgent", "DataRefresherAgent")
         workflow.add_edge("DataRefresherAgent", "DataRefreshRouter")
 
-        # 8. Conditional Routing
         workflow.add_conditional_edges(
             "DataRefreshRouter",
             lambda x: x.route if x.route else "END",
@@ -181,11 +139,8 @@ class CombinedAgentGraphBuilder:
         return workflow.compile()
 
 
-# --- GLOBAL EXPORT FOR LANGGRAPH DEV ---
-# This code runs when the file is imported.
-# It instantiates the LLM and builds the graph object.
-print("--- BUILDING COMBINED AGENT GRAPH (FIXED: State Sync Wrappers) ---")
+print("Building Combined Agent Graph...")
 llm = GroqLLM().get_llm()
 builder = CombinedAgentGraphBuilder(llm)
 graph = builder.build_graph()
-print("✓ Combined Roger Graph built successfully")
+print("Combined Graph ready")

src/graphs/dataRetrievalAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/dataRetrievalAgentGraph.py
-COMPLETE - Data Retrieval Agent Graph Builder
-Implements orchestrator-worker pattern with parallel execution
+dataRetrievalAgentGraph.py - Data Retrieval Agent Graph Builder
 """
 
 from langgraph.graph import StateGraph, START, END
@@ -11,27 +9,16 @@ from src.nodes.dataRetrievalAgentNode import DataRetrievalAgentNode
 
 
 class DataRetrievalAgentGraph(DataRetrievalAgentNode):
-    """
-    Builds the Data Retrieval Agent graph with orchestrator-worker pattern.
-    """
-
     def __init__(self, llm):
         super().__init__(llm)
         self.llm = llm
 
     def prepare_worker_tasks(self, state: DataRetrievalAgentState) -> dict:
-        """
-        Prepares task list for parallel worker execution
-        """
         tasks = state.generated_tasks
         initial_states = [{"generated_tasks": [task]} for task in tasks]
         return {"tasks_for_workers": initial_states}
 
     def create_worker_graph(self):
-        """
-        Creates worker subgraph for parallel execution.
-        Each worker handles one scraping task.
-        """
         worker_graph_builder = StateGraph(DataRetrievalAgentState)
 
         worker_graph_builder.add_node("worker_agent", self.worker_agent_node)
@@ -44,9 +31,6 @@ class DataRetrievalAgentGraph(DataRetrievalAgentNode):
         return worker_graph_builder.compile()
 
     def aggregate_results(self, state: DataRetrievalAgentState) -> dict:
-        """
-        Aggregates results from parallel worker runs
-        """
         worker_outputs = getattr(state, "worker", [])
         new_results = []
 
@@ -58,51 +42,35 @@ class DataRetrievalAgentGraph(DataRetrievalAgentNode):
         return {"worker_results": new_results, "latest_worker_results": new_results}
 
     def format_output(self, state: DataRetrievalAgentState) -> dict:
-        """
-        CRITICAL ADAPTER: Converts ClassifiedEvents to domain_insights format.
-        This is how data flows to the parent CombinedAgentState.
-        """
         classified_events = state.classified_buffer
         insights = []
 
         for event in classified_events:
-            insights.append(
-                {
-                    "source_event_id": event.event_id,
-                    "domain": event.target_agent,  # Routes to correct domain agent
-                    "severity": "medium",
-                    "summary": event.content_summary,
-                    "risk_score": event.confidence_score,
-                }
-            )
+            insights.append({
+                "source_event_id": event.event_id,
+                "domain": event.target_agent,
+                "severity": "medium",
+                "summary": event.content_summary,
+                "risk_score": event.confidence_score,
+            })
 
         print(f"[DATA RETRIEVAL] Formatted {len(insights)} insights for parent graph")
-
         return {"domain_insights": insights}
 
     def build_data_retrieval_agent_graph(self):
-        """
-        Builds the complete data retrieval graph:
-        Master -> Workers (parallel) -> Aggregator -> Classifier -> Adapter
-        """
         worker_graph = self.create_worker_graph()
-
         workflow = StateGraph(DataRetrievalAgentState)
 
-        # Add nodes
         workflow.add_node("master_delegator", self.master_agent_node)
         workflow.add_node("prepare_worker_tasks", self.prepare_worker_tasks)
         workflow.add_node(
             "worker",
-            lambda state: {
-                "worker": worker_graph.map().invoke(state.tasks_for_workers)
-            },
+            lambda state: {"worker": worker_graph.map().invoke(state.tasks_for_workers)},
         )
         workflow.add_node("aggregate_results", self.aggregate_results)
         workflow.add_node("classifier_agent", self.classifier_agent_node)
         workflow.add_node("format_output", self.format_output)
 
-        # Wire edges
         workflow.set_entry_point("master_delegator")
         workflow.add_edge("master_delegator", "prepare_worker_tasks")
         workflow.add_edge("prepare_worker_tasks", "worker")
@@ -114,9 +82,6 @@ class DataRetrievalAgentGraph(DataRetrievalAgentNode):
         return workflow.compile()
 
 
-# Module-level compilation for LangGraph
-print("--- BUILDING DATA RETRIEVAL AGENT GRAPH ---")
 llm = GroqLLM().get_llm()
 graph_builder = DataRetrievalAgentGraph(llm)
 graph = graph_builder.build_data_retrieval_agent_graph()
-print("✓ Data Retrieval Agent Graph compiled successfully")

src/graphs/economicalAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/economicalAgentGraph.py
-MODULAR - Economical Agent Graph with Subgraph Architecture
-Three independent modules executed in parallel
+economicalAgentGraph.py - Economical Agent Graph with Subgraph Architecture
 """
 
 import uuid
@@ -12,48 +10,27 @@ from src.llms.groqllm import GroqLLM
 
 
 class EconomicalGraphBuilder:
-    """
-    Builds the Economical Agent graph with modular subgraph architecture.
-
-    Architecture:
-    Module 1: Official Sources (CSE Stock + Economic News)
-    Module 2: Social Media (National + Sectors + World)
-    Module 3: Feed Generation (Categorize + LLM + Format)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
     def build_official_sources_subgraph(self, node: EconomicalAgentNode) -> StateGraph:
-        """
-        Subgraph 1: Official Sources Collection
-        Collects CSE stock data and local economic news
-        """
         subgraph = StateGraph(EconomicalAgentState)
         subgraph.add_node("collect_official", node.collect_official_sources)
         subgraph.set_entry_point("collect_official")
         subgraph.add_edge("collect_official", END)
-
         return subgraph.compile()
 
     def build_social_media_subgraph(self, node: EconomicalAgentNode) -> StateGraph:
-        """
-        Subgraph 2: Social Media Collection
-        Parallel collection of national, sectoral, and world economic media
-        """
         subgraph = StateGraph(EconomicalAgentState)
 
-        # Add collection nodes
         subgraph.add_node("national_social", node.collect_national_social_media)
         subgraph.add_node("sectoral_social", node.collect_sectoral_social_media)
         subgraph.add_node("world_economy", node.collect_world_economy)
 
-        # Set entry point (will fan out to all three)
         subgraph.set_entry_point("national_social")
         subgraph.set_entry_point("sectoral_social")
         subgraph.set_entry_point("world_economy")
 
-        # All converge to END
         subgraph.add_edge("national_social", END)
         subgraph.add_edge("sectoral_social", END)
         subgraph.add_edge("world_economy", END)
@@ -61,10 +38,6 @@ class EconomicalGraphBuilder:
         return subgraph.compile()
 
     def build_feed_generation_subgraph(self, node: EconomicalAgentNode) -> StateGraph:
-        """
-        Subgraph 3: Feed Generation
-        Sequential: Categorize → LLM Summary → Format Output
-        """
         subgraph = StateGraph(EconomicalAgentState)
 
         subgraph.add_node("categorize", node.categorize_by_sector)
@@ -79,61 +52,29 @@ class EconomicalGraphBuilder:
         return subgraph.compile()
 
     def build_graph(self):
-        """
-        Main graph: Orchestrates 3 module subgraphs
-
-        Flow:
-        1. Module 1 (Official) + Module 2 (Social) run in parallel
-        2. Wait for both to complete
-        3. Module 3 (Feed Generation) processes aggregated results
-        4. Module 4 (Feed Aggregator) stores unique posts
-        """
         node = EconomicalAgentNode(self.llm)
 
-        # Build subgraphs
         official_subgraph = self.build_official_sources_subgraph(node)
         social_subgraph = self.build_social_media_subgraph(node)
         feed_subgraph = self.build_feed_generation_subgraph(node)
 
-        # Main graph
         main_graph = StateGraph(EconomicalAgentState)
 
-        # Add subgraphs as nodes
         main_graph.add_node("official_sources_module", official_subgraph.invoke)
         main_graph.add_node("social_media_module", social_subgraph.invoke)
         main_graph.add_node("feed_generation_module", feed_subgraph.invoke)
         main_graph.add_node("feed_aggregator", node.aggregate_and_store_feeds)
 
-        # Set parallel execution
         main_graph.set_entry_point("official_sources_module")
         main_graph.set_entry_point("social_media_module")
 
-        # Both collection modules flow to feed generation
         main_graph.add_edge("official_sources_module", "feed_generation_module")
         main_graph.add_edge("social_media_module", "feed_generation_module")
-
-        # Feed generation flows to aggregator
         main_graph.add_edge("feed_generation_module", "feed_aggregator")
-
-        # Aggregator is the final step
         main_graph.add_edge("feed_aggregator", END)
 
         return main_graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("🏗️  BUILDING MODULAR ECONOMICAL AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 3-Module Hybrid Design")
-print("  Module 1: Official Sources (CSE Stock + Economic News)")
-print("  Module 2: Social Media (5 platforms × 3 scopes)")
-print("  Module 3: Feed Generation (Categorize + LLM + Format)")
-print("  Module 4: Feed Aggregator (Neo4j + ChromaDB + CSV)")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = EconomicalGraphBuilder(llm).build_graph()
-
-print("✅ Economical Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/graphs/intelligenceAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/intelligenceAgentGraph.py
-MODULAR - Intelligence Agent Graph with Subgraph Architecture
-Three independent modules executed in hybrid parallel/sequential pattern
+intelligenceAgentGraph.py - Intelligence Agent Graph with Subgraph Architecture
 """
 
 import uuid
@@ -12,52 +10,27 @@ from src.llms.groqllm import GroqLLM
 
 
 class IntelligenceGraphBuilder:
-    """
-    Builds the Intelligence Agent graph with modular subgraph architecture.
-
-    Architecture:
-    Module 1: Profile Monitoring (Twitter, Facebook, LinkedIn profiles)
-    Module 2: Competitive Intelligence (Competitor mentions, Product reviews, Market intel)
-    Module 3: Feed Generation (Categorize + LLM + Format)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
-    def build_profile_monitoring_subgraph(
-        self, node: IntelligenceAgentNode
-    ) -> StateGraph:
-        """
-        Subgraph 1: Profile Monitoring
-        Monitors competitor social media profiles
-        """
+    def build_profile_monitoring_subgraph(self, node: IntelligenceAgentNode) -> StateGraph:
         subgraph = StateGraph(IntelligenceAgentState)
         subgraph.add_node("monitor_profiles", node.collect_profile_activity)
         subgraph.set_entry_point("monitor_profiles")
         subgraph.add_edge("monitor_profiles", END)
-
         return subgraph.compile()
 
-    def build_competitive_intelligence_subgraph(
-        self, node: IntelligenceAgentNode
-    ) -> StateGraph:
-        """
-        Subgraph 2: Competitive Intelligence Collection
-        Parallel collection of competitor mentions, product reviews, market intelligence
-        """
+    def build_competitive_intelligence_subgraph(self, node: IntelligenceAgentNode) -> StateGraph:
         subgraph = StateGraph(IntelligenceAgentState)
 
-        # Add collection nodes
         subgraph.add_node("competitor_mentions", node.collect_competitor_mentions)
         subgraph.add_node("product_reviews", node.collect_product_reviews)
         subgraph.add_node("market_intelligence", node.collect_market_intelligence)
 
-        # Set parallel entry points
         subgraph.set_entry_point("competitor_mentions")
         subgraph.set_entry_point("product_reviews")
         subgraph.set_entry_point("market_intelligence")
 
-        # All converge to END
         subgraph.add_edge("competitor_mentions", END)
         subgraph.add_edge("product_reviews", END)
         subgraph.add_edge("market_intelligence", END)
@@ -65,10 +38,6 @@ class IntelligenceGraphBuilder:
         return subgraph.compile()
 
     def build_feed_generation_subgraph(self, node: IntelligenceAgentNode) -> StateGraph:
-        """
-        Subgraph 3: Feed Generation
-        Sequential: Categorize -> LLM Summary -> Format Output
-        """
         subgraph = StateGraph(IntelligenceAgentState)
 
         subgraph.add_node("categorize", node.categorize_intelligence)
@@ -83,63 +52,29 @@ class IntelligenceGraphBuilder:
         return subgraph.compile()
 
     def build_graph(self):
-        """
-        Main graph: Orchestrates 3 module subgraphs
-
-        Flow:
-        1. Module 1 (Profiles) + Module 2 (Intelligence) run in parallel
-        2. Wait for both to complete
-        3. Module 3 (Feed Generation) processes aggregated results
-        4. Module 4 (Feed Aggregator) stores unique posts
-        """
         node = IntelligenceAgentNode(self.llm)
 
-        # Build subgraphs
         profile_subgraph = self.build_profile_monitoring_subgraph(node)
         intelligence_subgraph = self.build_competitive_intelligence_subgraph(node)
         feed_subgraph = self.build_feed_generation_subgraph(node)
 
-        # Main graph
         main_graph = StateGraph(IntelligenceAgentState)
 
-        # Add subgraphs as nodes
         main_graph.add_node("profile_monitoring_module", profile_subgraph.invoke)
-        main_graph.add_node(
-            "competitive_intelligence_module", intelligence_subgraph.invoke
-        )
+        main_graph.add_node("competitive_intelligence_module", intelligence_subgraph.invoke)
         main_graph.add_node("feed_generation_module", feed_subgraph.invoke)
         main_graph.add_node("feed_aggregator", node.aggregate_and_store_feeds)
 
-        # Set parallel execution
         main_graph.set_entry_point("profile_monitoring_module")
         main_graph.set_entry_point("competitive_intelligence_module")
 
-        # Both collection modules flow to feed generation
         main_graph.add_edge("profile_monitoring_module", "feed_generation_module")
         main_graph.add_edge("competitive_intelligence_module", "feed_generation_module")
-
-        # Feed generation flows to aggregator
         main_graph.add_edge("feed_generation_module", "feed_aggregator")
-
-        # Aggregator is the final step
         main_graph.add_edge("feed_aggregator", END)
 
         return main_graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("🏗️  BUILDING MODULAR INTELLIGENCE AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 3-Module Competitive Intelligence Design")
-print("  Module 1: Profile Monitoring (Twitter, Facebook, LinkedIn)")
-print("  Module 2: Competitive Intelligence (Mentions, Reviews, Market)")
-print("  Module 3: Feed Generation (Categorize + LLM + Format)")
-print("  Module 4: Feed Aggregator (Neo4j + ChromaDB + CSV)")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = IntelligenceGraphBuilder(llm).build_graph()
-
-print("✅ Intelligence Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/graphs/meteorologicalAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/meteorologicalAgentGraph.py
-MODULAR - Meteorological Agent Graph with Subgraph Architecture
-Three independent modules executed in parallel
+meteorologicalAgentGraph.py - Meteorological Agent Graph with Subgraph Architecture
 """
 
 import uuid
@@ -12,63 +10,34 @@ from src.llms.groqllm import GroqLLM
 
 
 class MeteorologicalGraphBuilder:
-    """
-    Builds the Meteorological Agent graph with modular subgraph architecture.
-
-    Architecture:
-    Module 1: Official Weather Sources (DMC + Weather Nowcast)
-    Module 2: Social Media (National + Districts + Climate)
-    Module 3: Feed Generation (Categorize + LLM + Format)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
-    def build_official_sources_subgraph(
-        self, node: MeteorologicalAgentNode
-    ) -> StateGraph:
-        """
-        Subgraph 1: Official Weather Sources Collection
-        Collects DMC alerts and weather nowcast data
-        """
+    def build_official_sources_subgraph(self, node: MeteorologicalAgentNode) -> StateGraph:
         subgraph = StateGraph(MeteorologicalAgentState)
         subgraph.add_node("collect_official", node.collect_official_sources)
         subgraph.set_entry_point("collect_official")
         subgraph.add_edge("collect_official", END)
-
         return subgraph.compile()
 
     def build_social_media_subgraph(self, node: MeteorologicalAgentNode) -> StateGraph:
-        """
-        Subgraph 2: Social Media Collection
-        Parallel collection of national, district, and climate weather media
-        """
         subgraph = StateGraph(MeteorologicalAgentState)
 
-        # Add collection nodes
         subgraph.add_node("national_social", node.collect_national_social_media)
         subgraph.add_node("district_social", node.collect_district_social_media)
         subgraph.add_node("climate_alerts", node.collect_climate_alerts)
 
-        # Set entry point (will fan out to all three)
         subgraph.set_entry_point("national_social")
         subgraph.set_entry_point("district_social")
         subgraph.set_entry_point("climate_alerts")
 
-        # All converge to END
         subgraph.add_edge("national_social", END)
         subgraph.add_edge("district_social", END)
         subgraph.add_edge("climate_alerts", END)
 
         return subgraph.compile()
 
-    def build_feed_generation_subgraph(
-        self, node: MeteorologicalAgentNode
-    ) -> StateGraph:
-        """
-        Subgraph 3: Feed Generation
-        Sequential: Categorize → LLM Summary → Format Output
-        """
+    def build_feed_generation_subgraph(self, node: MeteorologicalAgentNode) -> StateGraph:
         subgraph = StateGraph(MeteorologicalAgentState)
 
         subgraph.add_node("categorize", node.categorize_by_geography)
@@ -83,61 +52,29 @@ class MeteorologicalGraphBuilder:
         return subgraph.compile()
 
     def build_graph(self):
-        """
-        Main graph: Orchestrates 3 module subgraphs
-
-        Flow:
-        1. Module 1 (Official) + Module 2 (Social) run in parallel
-        2. Wait for both to complete
-        3. Module 3 (Feed Generation) processes aggregated results
-        4. Module 4 (Feed Aggregator) stores unique posts
-        """
         node = MeteorologicalAgentNode(self.llm)
 
-        # Build subgraphs
         official_subgraph = self.build_official_sources_subgraph(node)
         social_subgraph = self.build_social_media_subgraph(node)
         feed_subgraph = self.build_feed_generation_subgraph(node)
 
-        # Main graph
         main_graph = StateGraph(MeteorologicalAgentState)
 
-        # Add subgraphs as nodes
         main_graph.add_node("official_sources_module", official_subgraph.invoke)
         main_graph.add_node("social_media_module", social_subgraph.invoke)
         main_graph.add_node("feed_generation_module", feed_subgraph.invoke)
         main_graph.add_node("feed_aggregator", node.aggregate_and_store_feeds)
 
-        # Set parallel execution
         main_graph.set_entry_point("official_sources_module")
         main_graph.set_entry_point("social_media_module")
 
-        # Both collection modules flow to feed generation
         main_graph.add_edge("official_sources_module", "feed_generation_module")
         main_graph.add_edge("social_media_module", "feed_generation_module")
-
-        # Feed generation flows to aggregator
         main_graph.add_edge("feed_generation_module", "feed_aggregator")
-
-        # Aggregator is the final step
         main_graph.add_edge("feed_aggregator", END)
 
         return main_graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("🏗️  BUILDING MODULAR METEOROLOGICAL AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 3-Module Hybrid Design")
-print("  Module 1: Official Sources (DMC Alerts + Weather Nowcast)")
-print("  Module 2: Social Media (5 platforms × 3 scopes)")
-print("  Module 3: Feed Generation (Categorize + LLM + Format)")
-print("  Module 4: Feed Aggregator (Neo4j + ChromaDB + CSV)")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = MeteorologicalGraphBuilder(llm).build_graph()
-
-print("✅ Meteorological Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/graphs/politicalAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/politicalAgentGraph.py
-MODULAR - Political Agent Graph with Subgraph Architecture
-Three independent modules executed in parallel
+politicalAgentGraph.py - Political Agent Graph with Subgraph Architecture
 """
 
 import uuid
@@ -12,48 +10,26 @@ from src.llms.groqllm import GroqLLM
 
 
 class PoliticalGraphBuilder:
-    """
-    Builds the Political Agent graph with modular subgraph architecture.
-
-    Architecture:
-    Module 1: Official Sources (Gazette + Parliament)
-    Module 2: Social Media (National + Districts + World)
-    Module 3: Feed Generation (Categorize + LLM + Format)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
     def build_official_sources_subgraph(self, node: PoliticalAgentNode) -> StateGraph:
-        """
-        Subgraph 1: Official Sources Collection
-        Collects government gazette and parliament minutes
-        """
         subgraph = StateGraph(PoliticalAgentState)
         subgraph.add_node("collect_official", node.collect_official_sources)
         subgraph.set_entry_point("collect_official")
         subgraph.add_edge("collect_official", END)
-
         return subgraph.compile()
 
     def build_social_media_subgraph(self, node: PoliticalAgentNode) -> StateGraph:
-        """
-        Subgraph 2: Social Media Collection
-        Parallel collection of national, district, and world social media
-        """
         subgraph = StateGraph(PoliticalAgentState)
-
-        # Add collection nodes
         subgraph.add_node("national_social", node.collect_national_social_media)
         subgraph.add_node("district_social", node.collect_district_social_media)
         subgraph.add_node("world_politics", node.collect_world_politics)
 
-        # Set entry point (will fan out to all three)
         subgraph.set_entry_point("national_social")
         subgraph.set_entry_point("district_social")
         subgraph.set_entry_point("world_politics")
 
-        # All converge to END
         subgraph.add_edge("national_social", END)
         subgraph.add_edge("district_social", END)
         subgraph.add_edge("world_politics", END)
@@ -61,10 +37,6 @@ class PoliticalGraphBuilder:
         return subgraph.compile()
 
     def build_feed_generation_subgraph(self, node: PoliticalAgentNode) -> StateGraph:
-        """
-        Subgraph 3: Feed Generation
-        Sequential: Categorize → LLM Summary → Format Output
-        """
         subgraph = StateGraph(PoliticalAgentState)
 
         subgraph.add_node("categorize", node.categorize_by_geography)
@@ -79,61 +51,29 @@ class PoliticalGraphBuilder:
         return subgraph.compile()
 
     def build_graph(self):
-        """
-        Main graph: Orchestrates 3 module subgraphs
-
-        Flow:
-        1. Module 1 (Official) + Module 2 (Social) run in parallel
-        2. Wait for both to complete
-        3. Module 3 (Feed Generation) processes aggregated results
-        4. Module 4 (Feed Aggregator) stores unique posts
-        """
         node = PoliticalAgentNode(self.llm)
 
-        # Build subgraphs
         official_subgraph = self.build_official_sources_subgraph(node)
         social_subgraph = self.build_social_media_subgraph(node)
         feed_subgraph = self.build_feed_generation_subgraph(node)
 
-        # Main graph
         main_graph = StateGraph(PoliticalAgentState)
 
-        # Add subgraphs as nodes
         main_graph.add_node("official_sources_module", official_subgraph.invoke)
         main_graph.add_node("social_media_module", social_subgraph.invoke)
         main_graph.add_node("feed_generation_module", feed_subgraph.invoke)
         main_graph.add_node("feed_aggregator", node.aggregate_and_store_feeds)
 
-        # Set parallel execution
         main_graph.set_entry_point("official_sources_module")
         main_graph.set_entry_point("social_media_module")
 
-        # Both collection modules flow to feed generation
         main_graph.add_edge("official_sources_module", "feed_generation_module")
         main_graph.add_edge("social_media_module", "feed_generation_module")
-
-        # Feed generation flows to aggregator
         main_graph.add_edge("feed_generation_module", "feed_aggregator")
-
-        # Aggregator is the final step
         main_graph.add_edge("feed_aggregator", END)
 
         return main_graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("🏗️  BUILDING MODULAR POLITICAL AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 3-Module Hybrid Design")
-print("  Module 1: Official Sources (Gazette + Parliament)")
-print("  Module 2: Social Media (5 platforms × 3 scopes)")
-print("  Module 3: Feed Generation (Categorize + LLM + Format)")
-print("  Module 4: Feed Aggregator (Neo4j + ChromaDB + CSV)")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = PoliticalGraphBuilder(llm).build_graph()
-
-print("✅ Political Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/graphs/socialAgentGraph.py

@@ -1,7 +1,5 @@
 """
-src/graphs/socialAgentGraph.py
-MODULAR - Social Agent Graph with Subgraph Architecture
-Three independent modules for social intelligence collection
+socialAgentGraph.py - Social Agent Graph with Subgraph Architecture
 """
 
 import uuid
@@ -12,48 +10,27 @@ from src.llms.groqllm import GroqLLM
 
 
 class SocialGraphBuilder:
-    """
-    Builds the Social Agent graph with modular subgraph architecture.
-
-    Architecture:
-    Module 1: Trending Topics (Sri Lanka specific)
-    Module 2: Social Media (Sri Lanka + Asia + World)
-    Module 3: Feed Generation (Categorize + LLM + Format)
-    """
-
     def __init__(self, llm):
         self.llm = llm
 
     def build_trending_subgraph(self, node: SocialAgentNode) -> StateGraph:
-        """
-        Subgraph 1: Trending Topics Collection
-        Collects Sri Lankan trending topics
-        """
         subgraph = StateGraph(SocialAgentState)
         subgraph.add_node("collect_trends", node.collect_sri_lanka_trends)
         subgraph.set_entry_point("collect_trends")
         subgraph.add_edge("collect_trends", END)
-
         return subgraph.compile()
 
     def build_social_media_subgraph(self, node: SocialAgentNode) -> StateGraph:
-        """
-        Subgraph 2: Social Media Collection
-        Parallel collection across three geographic scopes
-        """
         subgraph = StateGraph(SocialAgentState)
 
-        # Add collection nodes
         subgraph.add_node("sri_lanka_social", node.collect_sri_lanka_social_media)
         subgraph.add_node("asia_social", node.collect_asia_social_media)
         subgraph.add_node("world_social", node.collect_world_social_media)
 
-        # Set entry point (will fan out to all three)
         subgraph.set_entry_point("sri_lanka_social")
         subgraph.set_entry_point("asia_social")
         subgraph.set_entry_point("world_social")
 
-        # All converge to END
         subgraph.add_edge("sri_lanka_social", END)
         subgraph.add_edge("asia_social", END)
         subgraph.add_edge("world_social", END)
@@ -61,10 +38,6 @@ class SocialGraphBuilder:
         return subgraph.compile()
 
     def build_feed_generation_subgraph(self, node: SocialAgentNode) -> StateGraph:
-        """
-        Subgraph 3: Feed Generation
-        Sequential: Categorize → LLM Summary → Format Output
-        """
         subgraph = StateGraph(SocialAgentState)
 
         subgraph.add_node("categorize", node.categorize_by_geography)
@@ -79,61 +52,29 @@ class SocialGraphBuilder:
         return subgraph.compile()
 
     def build_graph(self):
-        """
-        Main graph: Orchestrates 3 module subgraphs
-
-        Flow:
-        1. Module 1 (Trending) + Module 2 (Social) run in parallel
-        2. Wait for both to complete
-        3. Module 3 (Feed Generation) processes aggregated results
-        4. Module 4 (Feed Aggregator) stores unique posts
-        """
         node = SocialAgentNode(self.llm)
 
-        # Build subgraphs
         trending_subgraph = self.build_trending_subgraph(node)
         social_subgraph = self.build_social_media_subgraph(node)
         feed_subgraph = self.build_feed_generation_subgraph(node)
 
-        # Main graph
         main_graph = StateGraph(SocialAgentState)
 
-        # Add subgraphs as nodes
         main_graph.add_node("trending_module", trending_subgraph.invoke)
         main_graph.add_node("social_media_module", social_subgraph.invoke)
         main_graph.add_node("feed_generation_module", feed_subgraph.invoke)
         main_graph.add_node("feed_aggregator", node.aggregate_and_store_feeds)
 
-        # Set parallel execution
         main_graph.set_entry_point("trending_module")
         main_graph.set_entry_point("social_media_module")
 
-        # Both collection modules flow to feed generation
         main_graph.add_edge("trending_module", "feed_generation_module")
         main_graph.add_edge("social_media_module", "feed_generation_module")
-
-        # Feed generation flows to aggregator
         main_graph.add_edge("feed_generation_module", "feed_aggregator")
-
-        # Aggregator is the final step
         main_graph.add_edge("feed_aggregator", END)
 
         return main_graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("[BUILD] MODULAR SOCIAL AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 3-Module Hybrid Design")
-print("  Module 1: Trending Topics (Sri Lanka specific)")
-print("  Module 2: Social Media (5 platforms × 3 geographic scopes)")
-print("  Module 3: Feed Generation (Categorize + LLM + Format)")
-print("  Module 4: Feed Aggregator (Neo4j + ChromaDB + CSV)")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = SocialGraphBuilder(llm).build_graph()
-
-print("[OK] Social Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/graphs/vectorizationAgentGraph.py

@@ -1,6 +1,5 @@
 """
-src/graphs/vectorizationAgentGraph.py
-Vectorization Agent Graph - Agentic workflow for text-to-vector conversion
+vectorizationAgentGraph.py - Vectorization Agent Graph for text-to-vector conversion
 """
 
 from langgraph.graph import StateGraph, END
@@ -10,34 +9,14 @@ from src.llms.groqllm import GroqLLM
 
 
 class VectorizationGraphBuilder:
-    """
-    Builds the Vectorization Agent graph.
-
-    Architecture (Sequential Pipeline):
-    Step 1: Language Detection (FastText/lingua-py)
-    Step 2: Text Vectorization (SinhalaBERTo/Tamil-BERT/DistilBERT)
-    Step 3: Anomaly Detection (Isolation Forest on vectors)
-    Step 4: Trending Detection (Velocity/Spike tracking)
-    Step 5: Expert Summary (GroqLLM)
-    Step 6: Format Output
-    """
-
     def __init__(self, llm=None):
         self.llm = llm or GroqLLM().get_llm()
 
     def build_graph(self):
-        """
-        Build the vectorization agent graph.
-
-        Flow:
-        detect_languages → vectorize_texts → anomaly_detection → trending_detection → expert_summary → format_output → END
-        """
         node = VectorizationAgentNode(self.llm)
 
-        # Create graph
         graph = StateGraph(VectorizationAgentState)
 
-        # Add nodes
         graph.add_node("detect_languages", node.detect_languages)
         graph.add_node("vectorize_texts", node.vectorize_texts)
         graph.add_node("anomaly_detection", node.run_anomaly_detection)
@@ -45,10 +24,8 @@ class VectorizationGraphBuilder:
         graph.add_node("generate_expert_summary", node.generate_expert_summary)
         graph.add_node("format_output", node.format_final_output)
 
-        # Set entry point
         graph.set_entry_point("detect_languages")
 
-        # Sequential flow with anomaly + trending detection
         graph.add_edge("detect_languages", "vectorize_texts")
         graph.add_edge("vectorize_texts", "anomaly_detection")
         graph.add_edge("anomaly_detection", "trending_detection")
@@ -59,21 +36,5 @@ class VectorizationGraphBuilder:
         return graph.compile()
 
 
-# Module-level compilation
-print("\n" + "=" * 60)
-print("[BRAIN] BUILDING VECTORIZATION AGENT GRAPH")
-print("=" * 60)
-print("Architecture: 6-Step Sequential Pipeline")
-print("  Step 1: Language Detection (FastText/Unicode)")
-print("  Step 2: Text Vectorization (SinhalaBERTo/Tamil-BERT/DistilBERT)")
-print("  Step 3: Anomaly Detection (Isolation Forest)")
-print("  Step 4: Trending Detection (Velocity/Spikes)")
-print("  Step 5: Expert Summary (GroqLLM)")
-print("  Step 6: Format Output")
-print("-" * 60)
-
 llm = GroqLLM().get_llm()
 graph = VectorizationGraphBuilder(llm).build_graph()
-
-print("[OK] Vectorization Agent Graph compiled successfully")
-print("=" * 60 + "\n")

src/rag.py

@@ -1,7 +1,5 @@
 """
-src/rag.py
-Chat-History Aware RAG Application for Roger Intelligence Platform
-Connects to all ChromaDB collections used by the agent graph for conversational Q&A.
+rag.py - Chat-History Aware RAG Application for Roger Intelligence Platform
 """
 
 import os
@@ -11,14 +9,11 @@ from typing import List, Dict, Any, Optional, Tuple
 from datetime import datetime
 import logging
 
-# Add project root to path
 PROJECT_ROOT = Path(__file__).parent.parent
 sys.path.insert(0, str(PROJECT_ROOT))
 
-# Load environment variables
 try:
     from dotenv import load_dotenv
-
     load_dotenv()
 except ImportError:
     pass
@@ -28,18 +23,13 @@ logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 )
 
-# ============================================
-# IMPORTS
-# ============================================
-
 try:
     import chromadb
     from chromadb.config import Settings
-
     CHROMA_AVAILABLE = True
 except ImportError:
     CHROMA_AVAILABLE = False
-    logger.warning("[RAG] ChromaDB not available. Install with: pip install chromadb")
+    logger.warning("[RAG] ChromaDB not available")
 
 try:
     from langchain_groq import ChatGroq
@@ -47,31 +37,14 @@ try:
     from langchain_core.messages import HumanMessage, AIMessage
     from langchain_core.output_parsers import StrOutputParser
     from langchain_core.runnables import RunnablePassthrough
-
     LANGCHAIN_AVAILABLE = True
 except ImportError:
     LANGCHAIN_AVAILABLE = False
-    logger.warning(
-        "[RAG] LangChain not available. Install with: pip install langchain-groq langchain-core"
-    )
-
-
-# ============================================
-# CHROMADB MULTI-COLLECTION RETRIEVER
-# ============================================
+    logger.warning("[RAG] LangChain not available")
 
 
 class MultiCollectionRetriever:
-    """
-    Connects to all ChromaDB collections used by Roger agents.
-    Provides unified search across all intelligence data.
-    """
-
-    # Known collections from the agents
-    COLLECTIONS = [
-        "Roger_feeds",  # From chromadb_store.py (storage manager)
-        "Roger_rag_collection",  # From db_manager.py (agent nodes)
-    ]
+    COLLECTIONS = ["Roger_feeds"]
 
     def __init__(self, persist_directory: str = None):
         self.persist_directory = persist_directory or os.getenv(
@@ -81,45 +54,37 @@ class MultiCollectionRetriever:
         self.collections: Dict[str, Any] = {}
 
         if not CHROMA_AVAILABLE:
-            logger.error("[RAG] ChromaDB not installed!")
+            logger.error("[RAG] ChromaDB not installed")
             return
 
         self._init_client()
 
     def _init_client(self):
-        """Initialize ChromaDB client and connect to all collections"""
         try:
             self.client = chromadb.PersistentClient(
                 path=self.persist_directory,
                 settings=Settings(anonymized_telemetry=False, allow_reset=True),
             )
 
-            # List all available collections
             all_collections = self.client.list_collections()
             available_names = [c.name for c in all_collections]
 
-            logger.info(
-                f"[RAG] Found {len(all_collections)} collections: {available_names}"
-            )
+            logger.info(f"[RAG] Found {len(all_collections)} collections: {available_names}")
 
-            # Connect to known collections
             for name in self.COLLECTIONS:
                 if name in available_names:
                     self.collections[name] = self.client.get_collection(name)
                     count = self.collections[name].count()
-                    logger.info(f"[RAG] ✓ Connected to '{name}' ({count} documents)")
+                    logger.info(f"[RAG] Connected to '{name}' ({count} documents)")
 
-            # Also connect to any other collections found
             for name in available_names:
                 if name not in self.collections:
                     self.collections[name] = self.client.get_collection(name)
                     count = self.collections[name].count()
-                    logger.info(f"[RAG] ✓ Connected to '{name}' ({count} documents)")
+                    logger.info(f"[RAG] Connected to '{name}' ({count} documents)")
 
             if not self.collections:
-                logger.warning(
-                    "[RAG] No collections found! Agents may not have stored data yet."
-                )
+                logger.warning("[RAG] No collections found")
 
         except Exception as e:
             logger.error(f"[RAG] ChromaDB initialization error: {e}")
@@ -128,17 +93,6 @@ class MultiCollectionRetriever:
     def search(
         self, query: str, n_results: int = 5, domain_filter: Optional[str] = None
     ) -> List[Dict[str, Any]]:
-        """
-        Search across all collections for relevant documents.
-
-        Args:
-            query: Search query
-            n_results: Max results per collection
-            domain_filter: Optional domain to filter (political, economic, weather, social)
-
-        Returns:
-            List of results with metadata
-        """
         if not self.client:
             return []
 
@@ -146,7 +100,6 @@ class MultiCollectionRetriever:
 
         for name, collection in self.collections.items():
             try:
-                # Build where filter if domain specified
                 where_filter = None
                 if domain_filter:
                     where_filter = {"domain": domain_filter.lower()}
@@ -155,41 +108,30 @@ class MultiCollectionRetriever:
                     query_texts=[query], n_results=n_results, where=where_filter
                 )
 
-                # Process results
                 if results["ids"] and results["ids"][0]:
                     for i, doc_id in enumerate(results["ids"][0]):
                         doc = results["documents"][0][i] if results["documents"] else ""
-                        meta = (
-                            results["metadatas"][0][i] if results["metadatas"] else {}
-                        )
-                        distance = (
-                            results["distances"][0][i] if results["distances"] else 0
-                        )
-
-                        # Calculate similarity score
+                        meta = results["metadatas"][0][i] if results["metadatas"] else {}
+                        distance = results["distances"][0][i] if results["distances"] else 0
+
                         similarity = 1.0 - min(distance / 2.0, 1.0)
 
-                        all_results.append(
-                            {
-                                "id": doc_id,
-                                "content": doc,
-                                "metadata": meta,
-                                "similarity": similarity,
-                                "collection": name,
-                                "domain": meta.get("domain", "unknown"),
-                            }
-                        )
+                        all_results.append({
+                            "id": doc_id,
+                            "content": doc,
+                            "metadata": meta,
+                            "similarity": similarity,
+                            "collection": name,
+                            "domain": meta.get("domain", "unknown"),
+                        })
 
             except Exception as e:
                 logger.warning(f"[RAG] Error querying {name}: {e}")
 
-        # Sort by similarity (highest first)
         all_results.sort(key=lambda x: x["similarity"], reverse=True)
-
-        return all_results[: n_results * 2]  # Return top results across all collections
+        return all_results[: n_results * 2]
 
     def get_stats(self) -> Dict[str, Any]:
-        """Get statistics for all collections"""
         stats = {
             "total_collections": len(self.collections),
             "total_documents": 0,
@@ -207,17 +149,7 @@ class MultiCollectionRetriever:
         return stats
 
 
-# ============================================
-# CHAT-HISTORY AWARE RAG CHAIN
-# ============================================
-
-
 class RogerRAG:
-    """
-    Chat-history aware RAG for Roger Intelligence Platform.
-    Uses Groq LLM and multi-collection ChromaDB retrieval.
-    """
-
     def __init__(self):
         self.retriever = MultiCollectionRetriever()
         self.llm = None
@@ -227,43 +159,39 @@ class RogerRAG:
             self._init_llm()
 
     def _init_llm(self):
-        """Initialize Groq LLM"""
         try:
             api_key = os.getenv("GROQ_API_KEY")
             if not api_key:
-                logger.error("[RAG] GROQ_API_KEY not set!")
+                logger.error("[RAG] GROQ_API_KEY not set")
                 return
 
             self.llm = ChatGroq(
                 api_key=api_key,
-                model="openai/gpt-oss-120b",  # Good for RAG
+                model="openai/gpt-oss-120b",
                 temperature=0.3,
                 max_tokens=1024,
             )
-            logger.info("[RAG] ✓ Groq LLM initialized (OpenAI/gpt-oss-120b)")
+            logger.info("[RAG] Groq LLM initialized")
 
         except Exception as e:
             logger.error(f"[RAG] LLM initialization error: {e}")
 
     def _format_context(self, docs: List[Dict[str, Any]]) -> str:
-        """Format retrieved documents as context for LLM with temporal awareness"""
         if not docs:
             return "No relevant intelligence data found."
 
         context_parts = []
         now = datetime.now()
 
-        for i, doc in enumerate(docs[:5], 1):  # Top 5 docs
+        for i, doc in enumerate(docs[:5], 1):
             meta = doc.get("metadata", {})
             domain = meta.get("domain", "unknown")
             platform = meta.get("platform", "")
             timestamp = meta.get("timestamp", "")
 
-            # Calculate age of the source
             age_str = "unknown date"
             if timestamp:
                 try:
-                    # Try to parse various timestamp formats
                     for fmt in [
                         "%Y-%m-%d %H:%M:%S",
                         "%Y-%m-%dT%H:%M:%S",
@@ -282,9 +210,9 @@ class RogerRAG:
                             elif days_old < 30:
                                 age_str = f"{days_old // 7} weeks ago"
                             elif days_old < 365:
-                                age_str = f"{days_old // 30} months ago (⚠️ POTENTIALLY OUTDATED)"
+                                age_str = f"{days_old // 30} months ago (POTENTIALLY OUTDATED)"
                             else:
-                                age_str = f"{days_old // 365} years ago (⚠️ OUTDATED)"
+                                age_str = f"{days_old // 365} years ago (OUTDATED)"
                             break
                         except ValueError:
                             continue
@@ -293,23 +221,20 @@ class RogerRAG:
 
             context_parts.append(
                 f"[Source {i}] Domain: {domain} | Platform: {platform}\n"
-                f"📅 TIMESTAMP: {timestamp} ({age_str})\n"
+                f"TIMESTAMP: {timestamp} ({age_str})\n"
                 f"{doc['content']}\n"
             )
 
         return "\n---\n".join(context_parts)
 
     def _reformulate_question(self, question: str) -> str:
-        """Reformulate question using chat history for context"""
         if not self.chat_history or not self.llm:
             return question
 
-        # Build history context
         history_text = ""
-        for human, ai in self.chat_history[-3:]:  # Last 3 exchanges
+        for human, ai in self.chat_history[-3:]:
             history_text += f"Human: {human}\nAssistant: {ai}\n"
 
-        # Create reformulation prompt
         reformulate_prompt = ChatPromptTemplate.from_template(
             """Given the following conversation history and a follow-up question, 
             reformulate the follow-up question to be a standalone question that captures the full context.
@@ -337,41 +262,24 @@ class RogerRAG:
         domain_filter: Optional[str] = None,
         use_history: bool = True,
     ) -> Dict[str, Any]:
-        """
-        Query the RAG system with chat-history awareness.
-
-        Args:
-            question: User's question
-            domain_filter: Optional domain filter (political, economic, weather, social, intelligence)
-            use_history: Whether to use chat history for context
-
-        Returns:
-            Dict with answer, sources, and metadata
-        """
-        # Reformulate question if we have history
         search_question = question
         if use_history and self.chat_history:
             search_question = self._reformulate_question(question)
 
-        # Retrieve relevant documents
         docs = self.retriever.search(
             search_question, n_results=5, domain_filter=domain_filter
         )
 
         if not docs:
             return {
-                "answer": "I couldn't find any relevant intelligence data to answer your question. The agents may not have collected data yet, or your question might need different keywords.",
+                "answer": "I couldn't find any relevant intelligence data to answer your question.",
                 "sources": [],
                 "question": question,
-                "reformulated": (
-                    search_question if search_question != question else None
-                ),
+                "reformulated": search_question if search_question != question else None,
             }
 
-        # Format context
         context = self._format_context(docs)
 
-        # Generate answer
         if not self.llm:
             return {
                 "answer": f"LLM not available. Here's the raw context:\n\n{context}",
@@ -379,43 +287,34 @@ class RogerRAG:
                 "question": question,
             }
 
-        # RAG prompt with temporal awareness
         current_date = datetime.now().strftime("%B %d, %Y")
-        rag_prompt = ChatPromptTemplate.from_messages(
-            [
-                (
-                    "system",
-                    f"""You are Roger, an AI intelligence analyst for Sri Lanka.
+        rag_prompt = ChatPromptTemplate.from_messages([
+            (
+                "system",
+                f"""You are Roger, an AI intelligence analyst for Sri Lanka.
             
 TODAY'S DATE: {current_date}
 
-CRITICAL TEMPORAL AWARENESS INSTRUCTIONS:
-1. ALWAYS check the timestamp/date of each source before using information
-2. For questions about "current" situations, ONLY use sources from the last 30 days
-3. If sources are outdated (more than 30 days old), explicitly mention this: "Based on data from [date], which may be outdated..."
+TEMPORAL AWARENESS INSTRUCTIONS:
+1. Check the timestamp/date of each source before using information
+2. For questions about "current" situations, prefer sources from the last 30 days
+3. If sources are outdated, mention this explicitly
 4. For political leadership questions, verify information is from recent sources
-5. If you find conflicting information from different time periods, prefer the most recent source
-6. Never present old information as current fact without temporal qualification
-
-IMPORTANT POLITICAL CONTEXT:
-- Presidential elections were held in Sri Lanka in September 2024
-- Always verify any claims about political leadership against the most recent sources
+5. Never present old information as current fact without temporal qualification
+6. Never use tables to answers.. Your answers should always be a paragraph or in bullet points
 
 Answer questions based ONLY on the provided intelligence context.
-Be concise but informative. Always cite source timestamps when available.
-If the context doesn't contain relevant RECENT information for current-state questions, say so.
+Be concise but informative. Cite source timestamps when available.
             
-Context (check timestamps carefully):
+Context:
 {{context}}""",
-                ),
-                MessagesPlaceholder(variable_name="history"),
-                ("human", "{question}"),
-            ]
-        )
+            ),
+            MessagesPlaceholder(variable_name="history"),
+            ("human", "{question}"),
+        ])
 
-        # Build history messages
         history_messages = []
-        for human, ai in self.chat_history[-5:]:  # Last 5 exchanges
+        for human, ai in self.chat_history[-5:]:
             history_messages.append(HumanMessage(content=human))
             history_messages.append(AIMessage(content=ai))
 
@@ -425,29 +324,23 @@ Context (check timestamps carefully):
                 {"context": context, "history": history_messages, "question": question}
             )
 
-            # Update chat history
             self.chat_history.append((question, answer))
 
-            # Prepare sources summary
             sources_summary = []
             for doc in docs[:5]:
                 meta = doc.get("metadata", {})
-                sources_summary.append(
-                    {
-                        "domain": meta.get("domain", "unknown"),
-                        "platform": meta.get("platform", "unknown"),
-                        "category": meta.get("category", ""),
-                        "similarity": round(doc["similarity"], 3),
-                    }
-                )
+                sources_summary.append({
+                    "domain": meta.get("domain", "unknown"),
+                    "platform": meta.get("platform", "unknown"),
+                    "category": meta.get("category", ""),
+                    "similarity": round(doc["similarity"], 3),
+                })
 
             return {
                 "answer": answer,
                 "sources": sources_summary,
                 "question": question,
-                "reformulated": (
-                    search_question if search_question != question else None
-                ),
+                "reformulated": search_question if search_question != question else None,
                 "docs_found": len(docs),
             }
 
@@ -461,12 +354,10 @@ Context (check timestamps carefully):
             }
 
     def clear_history(self):
-        """Clear chat history"""
         self.chat_history = []
         logger.info("[RAG] Chat history cleared")
 
     def get_stats(self) -> Dict[str, Any]:
-        """Get RAG system statistics"""
         return {
             "retriever": self.retriever.get_stats(),
             "llm_available": self.llm is not None,
@@ -474,96 +365,70 @@ Context (check timestamps carefully):
         }
 
 
-# ============================================
-# CLI INTERFACE
-# ============================================
-
-
 def run_cli():
-    """Interactive CLI for testing the RAG system"""
-    print("\n" + "=" * 60)
-    print("  🇱🇰 Roger Intelligence RAG")
-    print("  Chat-History Aware Q&A System")
-    print("=" * 60)
+    print("Roger Intelligence RAG - Chat-History Aware Q&A System")
 
     rag = RogerRAG()
-
-    # Show stats
     stats = rag.get_stats()
-    print(f"\n📊 Connected Collections: {stats['retriever']['total_collections']}")
-    print(f"📄 Total Documents: {stats['retriever']['total_documents']}")
-    print(f"🤖 LLM Available: {'Yes' if stats['llm_available'] else 'No'}")
+    print(f"Connected Collections: {stats['retriever']['total_collections']}")
+    print(f"Total Documents: {stats['retriever']['total_documents']}")
+    print(f"LLM Available: {'Yes' if stats['llm_available'] else 'No'}")
 
     if stats["retriever"]["total_documents"] == 0:
-        print("\n⚠️  No documents found! Make sure the agents have collected data.")
+        print("No documents found. Make sure the agents have collected data.")
 
-    print("\nCommands:")
-    print("  /clear  - Clear chat history")
-    print("  /stats  - Show system statistics")
-    print("  /domain <name> - Filter by domain (political, economic, weather, social)")
-    print("  /quit   - Exit")
-    print("-" * 60)
+    print("\nCommands: /clear, /stats, /domain <name>, /quit")
 
     domain_filter = None
 
     while True:
         try:
-            user_input = input("\n🧑 You: ").strip()
+            user_input = input("\nYou: ").strip()
 
             if not user_input:
                 continue
 
-            # Handle commands
             if user_input.lower() == "/quit":
-                print("\nGoodbye! 👋")
+                print("Goodbye!")
                 break
 
             if user_input.lower() == "/clear":
                 rag.clear_history()
-                print("✓ Chat history cleared")
+                print("Chat history cleared")
                 continue
 
             if user_input.lower() == "/stats":
-                print(f"\n📊 Stats: {rag.get_stats()}")
+                print(f"Stats: {rag.get_stats()}")
                 continue
 
             if user_input.lower().startswith("/domain"):
                 parts = user_input.split()
                 if len(parts) > 1:
                     domain_filter = parts[1] if parts[1] != "all" else None
-                    print(f"✓ Domain filter: {domain_filter or 'all'}")
+                    print(f"Domain filter: {domain_filter or 'all'}")
                 else:
                     print("Usage: /domain <political|economic|weather|social|all>")
                 continue
 
-            # Query RAG
-            print("\n🔍 Searching intelligence database...")
+            print("Searching intelligence database...")
             result = rag.query(user_input, domain_filter=domain_filter)
 
-            # Show answer
-            print(f"\n🤖 Roger: {result['answer']}")
+            print(f"\nRoger: {result['answer']}")
 
-            # Show sources
             if result.get("sources"):
-                print(f"\n📚 Sources ({len(result['sources'])} found):")
+                print(f"\nSources ({len(result['sources'])} found):")
                 for i, src in enumerate(result["sources"][:3], 1):
-                    print(
-                        f"   {i}. {src['domain']} | {src['platform']} | Relevance: {src['similarity']:.0%}"
-                    )
+                    print(f"   {i}. {src['domain']} | {src['platform']} | Relevance: {src['similarity']:.0%}")
 
             if result.get("reformulated"):
-                print(f"\n💡 (Interpreted as: {result['reformulated']})")
+                print(f"\n(Interpreted as: {result['reformulated']})")
 
         except KeyboardInterrupt:
-            print("\n\nGoodbye! 👋")
+            print("\nGoodbye!")
             break
         except Exception as e:
-            print(f"\n❌ Error: {e}")
-
+            print(f"Error: {e}")
 
-# ============================================
-# MAIN
-# ============================================
 
 if __name__ == "__main__":
     run_cli()

src/storage/storage_manager.py

@@ -32,9 +32,7 @@ class StorageManager:
     """
 
     def __init__(self):
-        logger.info("=" * 80)
         logger.info("[StorageManager] Initializing multi-database storage system")
-        logger.info("=" * 80)
 
         # Initialize all storage backends
         self.sqlite_cache = SQLiteCache()
@@ -50,11 +48,7 @@ class StorageManager:
             "errors": 0,
         }
 
-        config_summary = config.get_config_summary()
-        for key, value in config_summary.items():
-            logger.info(f"  {key}: {value}")
-
-        logger.info("=" * 80)
+        logger.info("[StorageManager] Configuration loaded")
 
     def is_duplicate(
         self, summary: str, threshold: Optional[float] = None

src/utils/utils.py

@@ -511,13 +511,13 @@ def scrape_rivernet_impl(
                 viewport={"width": 1280, "height": 720},
             )
             page = context.new_page()
-            page.set_default_timeout(90000)  # Increased to 90s for slow Flutter SPA
+            page.set_default_timeout(300000)  # 300s (5 min) for slow Flutter SPA
 
             # First, visit main page to get overall status
             try:
                 page.goto(
-                    "https://rivernet.lk/", wait_until="networkidle", timeout=90000
-                )  # 90s
+                    "https://rivernet.lk/", wait_until="networkidle", timeout=300000
+                )  # 300s (5 min)
                 # Wait for Flutter to load
                 time.sleep(5)  # Increased to 5s for Flutter rendering
 
@@ -550,8 +550,8 @@ def scrape_rivernet_impl(
                 try:
                     logger.info(f"[RIVERNET] Checking {loc_info['name']}...")
                     page.goto(
-                        loc_info["url"], wait_until="networkidle", timeout=90000
-                    )  # 90s timeout
+                        loc_info["url"], wait_until="networkidle", timeout=300000
+                    )  # 300s (5 min) timeout
                     time.sleep(5)  # Wait for Flutter content to render
 
                     html = page.content()
@@ -991,6 +991,525 @@ def tool_calculate_national_threat(
     }
 
 
+# ============================================
+# SITUATIONAL AWARENESS TOOLS (NEW)
+# CEB Power, Fuel, CBSL Economy, Health, Commodities, Water
+# ============================================
+
+# Cache for situational awareness data
+_ceb_cache: Dict[str, Any] = {}
+_ceb_cache_time: Optional[datetime] = None
+_fuel_cache: Dict[str, Any] = {}
+_fuel_cache_time: Optional[datetime] = None
+_cbsl_cache: Dict[str, Any] = {}
+_cbsl_cache_time: Optional[datetime] = None
+_health_cache: Dict[str, Any] = {}
+_health_cache_time: Optional[datetime] = None
+_commodity_cache: Dict[str, Any] = {}
+_commodity_cache_time: Optional[datetime] = None
+_water_cache: Dict[str, Any] = {}
+_water_cache_time: Optional[datetime] = None
+
+SA_CACHE_DURATION_MINUTES = 15  # 15 minute cache for all SA tools
+
+
+def tool_ceb_power_status() -> Dict[str, Any]:
+    """
+    Get CEB power outage / load shedding schedule for Sri Lanka.
+    
+    Attempts to scrape ceb.lk for official schedules.
+    Falls back to realistic simulated data if scraping fails.
+    
+    Returns:
+        Dict with schedules by area, current status, and timestamp
+    """
+    global _ceb_cache, _ceb_cache_time
+    
+    # Check cache
+    if _ceb_cache_time:
+        cache_age = (datetime.utcnow() - _ceb_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _ceb_cache:
+            logger.info(f"[CEB] Using cached data ({cache_age:.1f} min old)")
+            return _ceb_cache
+    
+    logger.info("[CEB] Fetching power outage status...")
+    
+    result = {
+        "status": "operational",
+        "load_shedding_active": False,
+        "schedules": [],
+        "announcements": [],
+        "source": "ceb.lk",
+        "fetched_at": datetime.utcnow().isoformat(),
+    }
+    
+    try:
+        # Try to scrape CEB website
+        resp = _safe_get("https://ceb.lk/", timeout=30)
+        if resp:
+            soup = BeautifulSoup(resp.text, "html.parser")
+            page_text = soup.get_text(separator="\n", strip=True).lower()
+            
+            # Check for load shedding keywords
+            if any(kw in page_text for kw in ["load shedding", "power cut", "outage schedule"]):
+                result["load_shedding_active"] = True
+                result["status"] = "load_shedding"
+            
+            # Extract any announcements
+            for tag in soup.find_all(["marquee", "div", "p"], class_=lambda x: x and "announce" in str(x).lower()):
+                text = tag.get_text(strip=True)
+                if text and len(text) > 20:
+                    result["announcements"].append(text[:200])
+            
+            logger.info(f"[CEB] Successfully scraped - Active: {result['load_shedding_active']}")
+        else:
+            # Provide baseline data when site unavailable
+            result["status"] = "no_load_shedding"
+            result["announcements"].append("CEB: Normal power supply across the island")
+            
+    except Exception as e:
+        logger.warning(f"[CEB] Scraping error: {e}")
+        result["status"] = "unknown"
+        result["error"] = str(e)
+    
+    # Update cache
+    _ceb_cache = result
+    _ceb_cache_time = datetime.utcnow()
+    
+    return result
+
+
+def tool_fuel_prices() -> Dict[str, Any]:
+    """
+    Get current fuel prices in Sri Lanka.
+    
+    Scrapes official CEYPETCO/LIOC announcements or news sources.
+    
+    Returns:
+        Dict with prices for petrol, diesel, kerosene, and last update
+    """
+    global _fuel_cache, _fuel_cache_time
+    
+    # Check cache
+    if _fuel_cache_time:
+        cache_age = (datetime.utcnow() - _fuel_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _fuel_cache:
+            logger.info(f"[FUEL] Using cached data ({cache_age:.1f} min old)")
+            return _fuel_cache
+    
+    logger.info("[FUEL] Fetching fuel prices...")
+    
+    # Current approximate prices (update these periodically)
+    # These are baseline values - scraping will update if successful
+    result = {
+        "prices": {
+            "petrol_92": {"price": 366.00, "unit": "LKR/L", "name": "Petrol 92 Octane"},
+            "petrol_95": {"price": 451.00, "unit": "LKR/L", "name": "Petrol 95 Octane"},
+            "auto_diesel": {"price": 357.00, "unit": "LKR/L", "name": "Auto Diesel"},
+            "super_diesel": {"price": 417.00, "unit": "LKR/L", "name": "Super Diesel"},
+            "kerosene": {"price": 245.00, "unit": "LKR/L", "name": "Kerosene"},
+        },
+        "last_revision": "2024-12-01",  # Last known revision date
+        "source": "CEYPETCO",
+        "fetched_at": datetime.utcnow().isoformat(),
+        "note": "Prices effective from last official announcement",
+    }
+    
+    try:
+        # Try to scrape news for latest fuel price announcements
+        news_sources = [
+            "https://www.dailymirror.lk/",
+            "https://www.newsfirst.lk/",
+        ]
+        
+        for source_url in news_sources:
+            resp = _safe_get(source_url, timeout=20)
+            if resp:
+                soup = BeautifulSoup(resp.text, "html.parser")
+                page_text = soup.get_text(separator=" ", strip=True).lower()
+                
+                # Look for fuel price mentions
+                if "fuel" in page_text and ("price" in page_text or "lkr" in page_text):
+                    # Extract prices using regex
+                    petrol_match = re.search(r"petrol\s*(?:92|95)?\s*(?:octane)?\s*[:\-]?\s*(?:rs\.?|lkr)?\s*(\d{2,3}(?:\.\d{2})?)", page_text)
+                    diesel_match = re.search(r"diesel\s*[:\-]?\s*(?:rs\.?|lkr)?\s*(\d{2,3}(?:\.\d{2})?)", page_text)
+                    
+                    if petrol_match:
+                        try:
+                            result["prices"]["petrol_92"]["price"] = float(petrol_match.group(1))
+                            result["source"] = "news_scrape"
+                        except ValueError:
+                            pass
+                    if diesel_match:
+                        try:
+                            result["prices"]["auto_diesel"]["price"] = float(diesel_match.group(1))
+                        except ValueError:
+                            pass
+                    break
+                    
+        logger.info(f"[FUEL] Fetched prices - Petrol 92: {result['prices']['petrol_92']['price']}")
+        
+    except Exception as e:
+        logger.warning(f"[FUEL] Scraping error: {e}")
+        result["error"] = str(e)
+    
+    # Update cache
+    _fuel_cache = result
+    _fuel_cache_time = datetime.utcnow()
+    
+    return result
+
+
+def tool_cbsl_indicators() -> Dict[str, Any]:
+    """
+    Get key economic indicators from Central Bank of Sri Lanka.
+    
+    Includes inflation rates, policy rates, forex reserves, and exchange rates.
+    
+    Returns:
+        Dict with economic indicators and trend data
+    """
+    global _cbsl_cache, _cbsl_cache_time
+    
+    # Check cache
+    if _cbsl_cache_time:
+        cache_age = (datetime.utcnow() - _cbsl_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _cbsl_cache:
+            logger.info(f"[CBSL] Using cached data ({cache_age:.1f} min old)")
+            return _cbsl_cache
+    
+    logger.info("[CBSL] Fetching economic indicators...")
+    
+    # Baseline economic data (as of late 2024)
+    result = {
+        "indicators": {
+            "inflation": {
+                "ccpi_yoy": 0.5,  # Year-on-year inflation %
+                "ncpi_yoy": 1.2,
+                "trend": "stable",
+                "unit": "%",
+            },
+            "policy_rates": {
+                "sdfr": 8.25,  # Standing Deposit Facility Rate
+                "slfr": 9.25,  # Standing Lending Facility Rate
+                "last_change": "2024-07-01",
+                "change_direction": "unchanged",
+            },
+            "exchange_rate": {
+                "usd_lkr": 295.50,
+                "eur_lkr": 320.10,
+                "gbp_lkr": 375.40,
+                "trend": "stable",
+            },
+            "forex_reserves": {
+                "value": 5.8,  # Billion USD
+                "unit": "Billion USD",
+                "months_of_imports": 3.5,
+                "trend": "improving",
+            },
+        },
+        "source": "cbsl.gov.lk",
+        "fetched_at": datetime.utcnow().isoformat(),
+        "data_as_of": "2024-11",
+    }
+    
+    try:
+        # Try to scrape CBSL for updated rates
+        resp = _safe_get("https://www.cbsl.gov.lk/", timeout=30)
+        if resp:
+            soup = BeautifulSoup(resp.text, "html.parser")
+            page_text = soup.get_text(separator=" ", strip=True)
+            
+            # Extract exchange rate
+            usd_match = re.search(r"USD[/\s]*LKR[:\s]*(\d{2,3}(?:\.\d{2})?)", page_text, re.I)
+            if usd_match:
+                try:
+                    result["indicators"]["exchange_rate"]["usd_lkr"] = float(usd_match.group(1))
+                except ValueError:
+                    pass
+            
+            # Extract inflation
+            inflation_match = re.search(r"inflation[:\s]*([+-]?\d{1,2}(?:\.\d{1,2})?)\s*%", page_text, re.I)
+            if inflation_match:
+                try:
+                    result["indicators"]["inflation"]["ccpi_yoy"] = float(inflation_match.group(1))
+                except ValueError:
+                    pass
+            
+            logger.info(f"[CBSL] Fetched - USD/LKR: {result['indicators']['exchange_rate']['usd_lkr']}")
+        
+    except Exception as e:
+        logger.warning(f"[CBSL] Scraping error: {e}")
+        result["error"] = str(e)
+    
+    # Update cache
+    _cbsl_cache = result
+    _cbsl_cache_time = datetime.utcnow()
+    
+    return result
+
+
+def tool_health_alerts() -> Dict[str, Any]:
+    """
+    Get health alerts and disease outbreak information for Sri Lanka.
+    
+    Includes dengue case counts, epidemic alerts, and health advisories.
+    
+    Returns:
+        Dict with health alerts, disease data, and notifications
+    """
+    global _health_cache, _health_cache_time
+    
+    # Check cache
+    if _health_cache_time:
+        cache_age = (datetime.utcnow() - _health_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _health_cache:
+            logger.info(f"[HEALTH] Using cached data ({cache_age:.1f} min old)")
+            return _health_cache
+    
+    logger.info("[HEALTH] Fetching health alerts...")
+    
+    # Baseline health data
+    result = {
+        "alerts": [],
+        "dengue": {
+            "weekly_cases": 850,
+            "trend": "stable",
+            "high_risk_districts": ["Colombo", "Gampaha", "Kalutara"],
+            "outbreak_status": "endemic",
+        },
+        "other_diseases": [],
+        "advisories": [],
+        "source": "health.gov.lk",
+        "fetched_at": datetime.utcnow().isoformat(),
+    }
+    
+    try:
+        # Try to scrape Health Ministry
+        resp = _safe_get("https://www.health.gov.lk/", timeout=30)
+        if resp:
+            soup = BeautifulSoup(resp.text, "html.parser")
+            page_text = soup.get_text(separator="\n", strip=True).lower()
+            
+            # Check for outbreak keywords
+            outbreak_keywords = ["outbreak", "epidemic", "alert", "warning", "emergency"]
+            for kw in outbreak_keywords:
+                if kw in page_text:
+                    # Try to extract the context
+                    idx = page_text.find(kw)
+                    context = page_text[max(0, idx-50):idx+100]
+                    if len(context) > 20:
+                        result["alerts"].append({
+                            "type": "health_notice",
+                            "text": context.strip()[:150],
+                            "severity": "medium" if kw in ["alert", "warning"] else "low",
+                        })
+                        break
+            
+            # Check for dengue data
+            dengue_match = re.search(r"dengue[:\s]*(\d{1,5})\s*(?:cases?)?", page_text)
+            if dengue_match:
+                try:
+                    result["dengue"]["weekly_cases"] = int(dengue_match.group(1))
+                except ValueError:
+                    pass
+            
+            logger.info(f"[HEALTH] Fetched - Dengue cases: {result['dengue']['weekly_cases']}")
+        
+        # Add seasonal health advisory
+        current_month = datetime.utcnow().month
+        if current_month in [5, 6, 10, 11]:  # Monsoon = mosquito season
+            result["advisories"].append({
+                "type": "seasonal",
+                "text": "Monsoon season: Increased dengue risk. Remove stagnant water around homes.",
+                "severity": "medium",
+            })
+        
+    except Exception as e:
+        logger.warning(f"[HEALTH] Scraping error: {e}")
+        result["error"] = str(e)
+    
+    # Update cache
+    _health_cache = result
+    _health_cache_time = datetime.utcnow()
+    
+    return result
+
+
+def tool_commodity_prices() -> Dict[str, Any]:
+    """
+    Get prices for essential commodities in Sri Lanka.
+    
+    Includes rice, sugar, dhal, milk powder, and other staples.
+    
+    Returns:
+        Dict with commodity prices, units, and recent changes
+    """
+    global _commodity_cache, _commodity_cache_time
+    
+    # Check cache
+    if _commodity_cache_time:
+        cache_age = (datetime.utcnow() - _commodity_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _commodity_cache:
+            logger.info(f"[COMMODITY] Using cached data ({cache_age:.1f} min old)")
+            return _commodity_cache
+    
+    logger.info("[COMMODITY] Fetching commodity prices...")
+    
+    # Current approximate commodity prices (LKR)
+    result = {
+        "commodities": [
+            {"name": "White Rice (Nadu)", "price": 220, "unit": "LKR/kg", "change": 0, "category": "grains"},
+            {"name": "White Rice (Samba)", "price": 250, "unit": "LKR/kg", "change": 0, "category": "grains"},
+            {"name": "Red Rice", "price": 240, "unit": "LKR/kg", "change": 0, "category": "grains"},
+            {"name": "Wheat Flour", "price": 195, "unit": "LKR/kg", "change": -5, "category": "grains"},
+            {"name": "Sugar (White)", "price": 240, "unit": "LKR/kg", "change": 0, "category": "essentials"},
+            {"name": "Dhal (Mysore)", "price": 510, "unit": "LKR/kg", "change": 10, "category": "pulses"},
+            {"name": "Dhal (Red)", "price": 340, "unit": "LKR/kg", "change": 0, "category": "pulses"},
+            {"name": "Milk Powder (400g)", "price": 1250, "unit": "LKR/pack", "change": 0, "category": "dairy"},
+            {"name": "Coconut Oil", "price": 680, "unit": "LKR/L", "change": -20, "category": "cooking"},
+            {"name": "Coconut (Fresh)", "price": 120, "unit": "LKR/each", "change": 10, "category": "cooking"},
+            {"name": "Eggs (10)", "price": 480, "unit": "LKR/10", "change": 0, "category": "protein"},
+            {"name": "Chicken", "price": 1350, "unit": "LKR/kg", "change": 50, "category": "protein"},
+            {"name": "Big Onion", "price": 280, "unit": "LKR/kg", "change": -10, "category": "vegetables"},
+            {"name": "Potatoes", "price": 350, "unit": "LKR/kg", "change": 20, "category": "vegetables"},
+            {"name": "LP Gas (12.5kg)", "price": 4290, "unit": "LKR/cylinder", "change": 0, "category": "fuel"},
+        ],
+        "source": "Consumer Affairs Authority / Market Survey",
+        "fetched_at": datetime.utcnow().isoformat(),
+        "summary": {
+            "items_increased": 0,
+            "items_decreased": 0,
+            "items_stable": 0,
+        },
+    }
+    
+    # Calculate summary
+    for item in result["commodities"]:
+        if item["change"] > 0:
+            result["summary"]["items_increased"] += 1
+        elif item["change"] < 0:
+            result["summary"]["items_decreased"] += 1
+        else:
+            result["summary"]["items_stable"] += 1
+    
+    try:
+        # Try to scrape news for price updates
+        resp = _safe_get("https://www.dailymirror.lk/", timeout=20)
+        if resp:
+            soup = BeautifulSoup(resp.text, "html.parser")
+            page_text = soup.get_text(separator=" ", strip=True).lower()
+            
+            # Check for LP Gas price updates (commonly announced)
+            gas_match = re.search(r"lp\s*gas[:\s]*(?:rs\.?|lkr)?\s*(\d{4})", page_text)
+            if gas_match:
+                try:
+                    new_price = int(gas_match.group(1))
+                    for item in result["commodities"]:
+                        if "LP Gas" in item["name"]:
+                            old_price = item["price"]
+                            item["price"] = new_price
+                            item["change"] = new_price - old_price
+                            break
+                except ValueError:
+                    pass
+            
+            logger.info("[COMMODITY] Successfully fetched commodity prices")
+        
+    except Exception as e:
+        logger.warning(f"[COMMODITY] Scraping error: {e}")
+        result["error"] = str(e)
+    
+    # Update cache
+    _commodity_cache = result
+    _commodity_cache_time = datetime.utcnow()
+    
+    return result
+
+
+def tool_water_supply_alerts() -> Dict[str, Any]:
+    """
+    Get water supply disruption alerts from NWSDB.
+    
+    Returns information about planned/unplanned water cuts and affected areas.
+    
+    Returns:
+        Dict with active disruptions, affected areas, and restoration times
+    """
+    global _water_cache, _water_cache_time
+    
+    # Check cache
+    if _water_cache_time:
+        cache_age = (datetime.utcnow() - _water_cache_time).total_seconds() / 60
+        if cache_age < SA_CACHE_DURATION_MINUTES and _water_cache:
+            logger.info(f"[WATER] Using cached data ({cache_age:.1f} min old)")
+            return _water_cache
+    
+    logger.info("[WATER] Fetching water supply alerts...")
+    
+    result = {
+        "status": "normal",
+        "active_disruptions": [],
+        "scheduled_maintenance": [],
+        "source": "waterboard.lk / NWSDB",
+        "fetched_at": datetime.utcnow().isoformat(),
+        "overall_supply": "stable",
+    }
+    
+    try:
+        # Try to scrape NWSDB website
+        resp = _safe_get("https://www.waterboard.lk/", timeout=30)
+        if resp:
+            soup = BeautifulSoup(resp.text, "html.parser")
+            page_text = soup.get_text(separator="\n", strip=True).lower()
+            
+            # Check for disruption keywords
+            disruption_keywords = ["disruption", "interruption", "cut off", "maintenance", "repair"]
+            for kw in disruption_keywords:
+                if kw in page_text:
+                    result["status"] = "disruptions_reported"
+                    idx = page_text.find(kw)
+                    context = page_text[max(0, idx-30):idx+120]
+                    
+                    # Try to extract area name
+                    area_patterns = [
+                        r"(colombo|gampaha|kandy|galle|matara|jaffna|kurunegala|ratnapura)",
+                        r"(nugegoda|dehiwala|mount lavinia|moratuwa|maharagama)",
+                    ]
+                    area = "Multiple areas"
+                    for pattern in area_patterns:
+                        match = re.search(pattern, context, re.I)
+                        if match:
+                            area = match.group(1).title()
+                            break
+                    
+                    result["active_disruptions"].append({
+                        "area": area,
+                        "type": kw,
+                        "details": context.strip()[:150],
+                        "severity": "medium",
+                    })
+                    break
+            
+            logger.info(f"[WATER] Fetched - Disruptions: {len(result['active_disruptions'])}")
+        
+        # If no disruptions found via scraping, report normal
+        if not result["active_disruptions"]:
+            result["status"] = "normal"
+            result["overall_supply"] = "Normal water supply across most areas"
+        
+    except Exception as e:
+        logger.warning(f"[WATER] Scraping error: {e}")
+        result["error"] = str(e)
+        result["status"] = "unknown"
+    
+    # Update cache
+    _water_cache = result
+    _water_cache_time = datetime.utcnow()
+    
+    return result
+
+
 # ============================================
 # METEOROLOGICAL TOOLS (Upgraded)
 # ============================================