Enhanced compact context: ALL 13 indices, clusters, prev_analysis, conv_history

prompts.py

@@ -731,7 +731,7 @@ def format_weather_context(weather_data: dict) -> str:
 def build_compact_context(context: Dict) -> str:
     """
     Build a compressed context string using abbreviations and key-value format.
-    Captures all essential data in ~50% fewer tokens.
+    Captures ALL essential data in ~50% fewer tokens.
     
     Format: KEY:value pairs, one per line, grouped by category.
     """
@@ -742,55 +742,120 @@ def build_compact_context(context: Dict) -> str:
     if field:
         lines.append(f"[FIELD] {field.get('name','?')} | {field.get('crop_type','?')} | {field.get('area_acres',0):.1f}ac")
     
-    # --- Vegetation Indices (compact key:value format) ---
+    # --- ALL Vegetation Indices (compact key:value format) ---
     veg = context.get("vegetation_indices", {})
     if veg:
-        veg_parts = []
+        # Primary indices (most important)
+        primary = []
         for k in ["ndvi", "evi", "ndre", "smi", "ndwi"]:
-            if k in veg and veg[k] is not None:
-                veg_parts.append(f"{k.upper()}:{veg[k]:.2f}")
-        if veg_parts:
-            lines.append(f"[VEG] " + " | ".join(veg_parts))
+            val = veg.get(k) or veg.get(k.upper())
+            if val is not None:
+                try:
+                    primary.append(f"{k.upper()}:{float(val):.2f}")
+                except (ValueError, TypeError):
+                    pass
+        if primary:
+            lines.append(f"[VEG1] " + " | ".join(primary))
+        
+        # Secondary indices (stress/health indicators)
+        secondary = []
+        for k in ["psri", "pri", "mcari", "osavi", "reci"]:
+            val = veg.get(k) or veg.get(k.upper())
+            if val is not None:
+                try:
+                    secondary.append(f"{k.upper()}:{float(val):.2f}")
+                except (ValueError, TypeError):
+                    pass
+        if secondary:
+            lines.append(f"[VEG2] " + " | ".join(secondary))
+        
+        # Soil indices
+        soil_idx = []
+        for k in ["sasi", "somi", "sfi"]:
+            val = veg.get(k) or veg.get(k.upper())
+            if val is not None:
+                try:
+                    soil_idx.append(f"{k.upper()}:{float(val):.2f}")
+                except (ValueError, TypeError):
+                    pass
+        if soil_idx:
+            lines.append(f"[SOIL_IDX] " + " | ".join(soil_idx))
     
     # --- Health Summary (single line) ---
     health = context.get("health_summary", {})
     if health:
         score = health.get("overall_stress", health.get("stress_score", health.get("average_stress_score", 0)))
         status = health.get("status", health.get("crop_health", "unknown"))
-        lines.append(f"[HEALTH] score:{score:.2f} status:{status}")
+        conf = health.get("confidence_score", health.get("confidence", 0))
+        try:
+            lines.append(f"[HEALTH] score:{float(score):.2f} status:{status} conf:{float(conf):.2f}")
+        except (ValueError, TypeError):
+            lines.append(f"[HEALTH] status:{status}")
     
-    # --- Stressed Patches (count + top 2) ---
+    # --- Stressed Patches (count + top 5) ---
     patches = context.get("stressed_patches", [])
     if patches:
         lines.append(f"[STRESS] {len(patches)} patches")
-        for p in patches[:2]:
-            lines.append(f"  - zone:{p.get('patch_id','?')} stress:{p.get('stress_score',0):.2f}")
+        for p in patches[:5]:  # Top 5 patches
+            pid = p.get('patch_id', p.get('id', '?'))
+            score = p.get('stress_score', p.get('score', 0))
+            try:
+                lines.append(f"  P{pid}:{float(score):.2f}")
+            except (ValueError, TypeError):
+                lines.append(f"  P{pid}")
+    
+    # --- Clustering Data (critical for zone analysis) ---
+    stress_analysis = context.get("stress_analysis", {})
+    clusters = stress_analysis.get("cluster_statistics", [])
+    if clusters:
+        lines.append(f"[CLUSTERS] {len(clusters)} zones")
+        for c in clusters[:3]:  # Top 3 clusters
+            cid = c.get('cluster_id', '?')
+            pct = c.get('percentage', 0)
+            stress = c.get('stress_score', {}).get('mean', 0) if isinstance(c.get('stress_score'), dict) else 0
+            lines.append(f"  C{cid}:{pct:.1f}% stress:{stress:.2f}")
     
     # --- SAR Bands (compact) ---
     sar = context.get("sar_bands", {})
     if sar:
         sar_parts = []
-        if "vv" in sar: sar_parts.append(f"VV:{sar['vv']:.2f}")
-        if "vh" in sar: sar_parts.append(f"VH:{sar['vh']:.2f}")
-        if "ratio" in sar: sar_parts.append(f"ratio:{sar['ratio']:.2f}")
+        for k in ["vv", "vh", "ratio", "VV", "VH"]:
+            if k.lower() in sar or k in sar:
+                val = sar.get(k.lower()) or sar.get(k)
+                if val is not None:
+                    try:
+                        sar_parts.append(f"{k.upper()}:{float(val):.2f}")
+                    except (ValueError, TypeError):
+                        pass
         if sar_parts:
-            lines.append(f"[SAR] " + " | ".join(sar_parts))
+            lines.append(f"[SAR] " + " | ".join(sar_parts[:3]))
     
-    # --- Weather (compressed) ---
+    # --- Weather (compressed with forecast) ---
     weather = context.get("weather", {})
     if weather:
         current = weather.get("current", {})
         if current:
             lines.append(f"[WX] T:{current.get('temp',0):.0f}°C H:{current.get('humidity',0):.0f}% Rain:{current.get('precip',0):.0f}mm")
+        
+        # Add 3-day forecast summary
+        forecast = weather.get("forecast_7d", weather.get("forecast", []))
+        if forecast and len(forecast) > 0:
+            rain_days = sum(1 for d in forecast[:3] if d.get('precipitation', 0) > 5)
+            max_temp = max((d.get('temp_max', 0) for d in forecast[:3]), default=0)
+            lines.append(f"[FORECAST] 3d_rain_days:{rain_days} max_T:{max_temp:.0f}°C")
+        
+        # Weather alerts
         stress = weather.get("stress_indicators", {})
         flags = []
         if stress.get("current_heat_stress"): flags.append("HEAT")
+        if stress.get("predicted_heat_stress"): flags.append("HEAT_RISK")
         if stress.get("drought_risk"): flags.append("DROUGHT")
         if stress.get("suitable_for_irrigation"): flags.append("OK_IRRIG")
+        if stress.get("suitable_for_spraying"): flags.append("OK_SPRAY")
         if flags:
             lines.append(f"[WX_ALERT] " + ",".join(flags))
     
-    # --- Soil (compact) ---
+    # --- Soil Indicators (compact) ---
     soil = context.get("soil_indicators", {})
     if soil:
         soil_parts = []
@@ -801,20 +866,62 @@ def build_compact_context(context: Dict) -> str:
         if soil_parts:
             lines.append(f"[SOIL] " + " | ".join(soil_parts))
     
-    # --- Trends (single line) ---
+    # --- Historical Trends (more detail) ---
     trends = context.get("historical_trends", {})
-    if trends and trends.get("summary"):
-        lines.append(f"[TREND] {trends['summary'][:80]}")
-    
-    # --- Zone Analysis (compact) ---
+    if trends:
+        summary = trends.get("summary", "")
+        if summary:
+            lines.append(f"[TREND] {summary[:120]}")
+        # Add specific trend data
+        ndvi_trend = trends.get("ndvi_change") or trends.get("NDVI_change")
+        smi_trend = trends.get("smi_change") or trends.get("SMI_change")
+        if ndvi_trend or smi_trend:
+            parts = []
+            if ndvi_trend: parts.append(f"NDVI:{ndvi_trend:+.2f}")
+            if smi_trend: parts.append(f"SMI:{smi_trend:+.2f}")
+            if parts:
+                lines.append(f"[TREND_DATA] " + " ".join(parts))
+    
+    # --- Zone Analysis (all critical zones) ---
     zones = context.get("zone_analysis", {})
-    if zones and zones.get("most_critical"):
-        mc = zones["most_critical"]
-        lines.append(f"[ZONE_ALERT] {mc.get('location','?')} stress:{mc.get('stress_score',0):.2f}")
+    if zones:
+        priority_zones = zones.get("priority_zones", [])
+        if priority_zones:
+            lines.append(f"[ZONES] {len(priority_zones)} priority areas")
+            for z in priority_zones[:3]:
+                loc = z.get('location', '?')
+                score = z.get('stress_score', 0)
+                lines.append(f"  {loc}: stress:{score:.2f}")
+        elif zones.get("most_critical"):
+            mc = zones["most_critical"]
+            lines.append(f"[ZONE_ALERT] {mc.get('location','?')} stress:{mc.get('stress_score',0):.2f}")
+    
+    # --- Previous Analysis (LLM insights from satellite) ---
+    prev = context.get("previous_analysis", {})
+    if prev:
+        rec = prev.get("recommendation", prev.get("recommendations", ""))
+        if rec:
+            rec_text = rec[0] if isinstance(rec, list) else str(rec)
+            lines.append(f"[PREV_REC] {rec_text[:80]}")
+        
+        concerns = prev.get("key_concerns", [])
+        if concerns and isinstance(concerns, list):
+            lines.append(f"[CONCERNS] " + ", ".join(str(c)[:30] for c in concerns[:3]))
+    
+    # --- Conversation History (for follow-ups) ---
+    conv = context.get("conversation_history", [])
+    if conv:
+        lines.append(f"[CONV] {len(conv)} prior turns")
+        if len(conv) > 0:
+            last = conv[-1]
+            role = last.get("role", "")
+            content = last.get("content", "")[:50]
+            lines.append(f"  Last: {role}: {content}...")
     
     return "\n".join(lines) if lines else "No data"
 
 
+
 # =============================================================================
 # COMPRESSED STAGE PROMPTS - Reduce Token Usage
 # =============================================================================