dd logging

langraph_agent.py

@@ -81,11 +81,9 @@ class CyberLegalAgent:
             
             # Log LightRAG response
             lightrag_context = lightrag_response.get("response", "")
-            logger.info(f"🔍 LightRAG response received:")
-            logger.info(f"📄 Context length: {len(lightrag_context)} characters")
-            logger.info(f"📚 References found: {len(state.get('relevant_documents', []))}")
+            logger.info(f"🔍 LightRAG response received\n📄 Context length: {len(lightrag_context)} characters\n📚 References found: {len(state.get('relevant_documents', []))}")
             if len(lightrag_context) > 1500:
-                logger.info(f"📝 Context preview: {lightrag_context[:500]}...")
+                logger.info(f"📝 Context preview: {lightrag_context[:1500]}...")
             else:
                 logger.info(f"📝 Full context: {lightrag_context}")
         
@@ -126,9 +124,9 @@ class CyberLegalAgent:
             response = await self.llm.ainvoke(messages)
             answer = response.content
             state["final_response"] = answer
-            
+            logger.info(f"🤖 Answer generated with success")
         except Exception as e:
-            state["lightrag_error"] = f"Answer generation failed: {str(e)}"
+            raise e
         
         answer_generation_duration = self.performance_monitor.end_timer("answer_generation")
         logger.info(f"⏱️  Answer generation processing time: {answer_generation_duration:.3f}s")
@@ -190,9 +188,11 @@ class CyberLegalAgent:
             
             # Run the workflow
             final_state = await self.workflow.ainvoke(initial_state)
-            
+            logger.info("=" * 80)
+            logger.info(f"final state: {final_state}")
+            logger.info("=" * 80)
             result = {
-                "response": final_state.get("final_response", ""),
+                "response": final_state["final_response"],
                 "processing_time": final_state.get("processing_time", 0.0),
                 "references": final_state.get("relevant_documents", []),
                 "timestamp": final_state.get("query_timestamp")