landvex: Fixar och tester klara för alla komponenter

- Datafabrik: Dockerfile fix, agentorkestrering fungerar
- Vision: Identify-modell, FAISS, OCR alla testade
- API: Alla 7 integrationstester passerade
- Upplösare: Entitetsupplösning verifierad

life-agents/knowledge_graph.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+"""
+Knowledge Graph Agent
+Bygger och uppdaterar kunskapsgrafen kontinuerligt
+"""
+import sqlite3
+import json
+from datetime import datetime
+
+DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
+
+def build_knowledge_graph():
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    
+    # Hämta alla vägar och deras relationer
+    c.execute('''
+        SELECT r.id, r.name, r.county, r.type, r.length_km,
+               COUNT(o.id) as obs_count,
+               AVG(o.confidence) as avg_confidence
+        FROM roads r
+        LEFT JOIN observations o ON r.id = o.road_id
+        GROUP BY r.id
+    ''')
+    
+    nodes = []
+    for row in c.fetchall():
+        nodes.append({
+            "id": f"road-{row[0]}",
+            "type": "road",
+            "name": row[1],
+            "county": row[2],
+            "road_type": row[3],
+            "length_km": row[4],
+            "observation_count": row[5],
+            "avg_confidence": round(row[6] or 0, 2)
+        })
+    
+    # Skapa relationer mellan vägar i samma län
+    edges = []
+    counties = {}
+    for node in nodes:
+        county = node["county"]
+        if county not in counties:
+            counties[county] = []
+        counties[county].append(node["id"])
+    
+    for county, road_ids in counties.items():
+        for i in range(len(road_ids)):
+            for j in range(i+1, len(road_ids)):
+                edges.append({
+                    "source": road_ids[i],
+                    "target": road_ids[j],
+                    "type": "same_county",
+                    "weight": 0.5
+                })
+    
+    # Hämta observationer som noder
+    c.execute('''
+        SELECT o.id, o.observation_type, o.severity, o.confidence,
+               r.id as road_id, r.name as road_name
+        FROM observations o
+        JOIN roads r ON o.road_id = r.id
+        LIMIT 100
+    ''')
+    
+    for row in c.fetchall():
+        nodes.append({
+            "id": f"obs-{row[0]}",
+            "type": "observation",
+            "observation_type": row[1],
+            "severity": row[2],
+            "confidence": round(row[3], 2)
+        })
+        edges.append({
+            "source": f"obs-{row[0]}",
+            "target": f"road-{row[4]}",
+            "type": "observed_on",
+            "weight": row[3]
+        })
+    
+    conn.close()
+    
+    graph = {
+        "nodes": nodes,
+        "edges": edges,
+        "metadata": {
+            "generated": datetime.now().isoformat(),
+            "node_count": len(nodes),
+            "edge_count": len(edges)
+        }
+    }
+    
+    with open('/home/bernt/.openclaw/workspace/life-agents/knowledge_graph.json', 'w') as f:
+        json.dump(graph, f, indent=2)
+    
+    return graph
+
+if __name__ == "__main__":
+    print(f"[{datetime.now().isoformat()}] Knowledge Graph Agent")
+    graph = build_knowledge_graph()
+    print(f"Built graph with {graph['metadata']['node_count']} nodes and {graph['metadata']['edge_count']} edges")
+    print("Knowledge graph updated.")