Upload app.py

app.py

@@ -174,8 +174,20 @@ class ToolCallingAgentChat:
                 logger.info(f"Message: {message}")
                 logger.info(f"History length: {len(history)}")
             
-            # Convert history to messages for context
-            messages = []
+            # Convert history to messages for context with system message
+            from langchain_core.messages import SystemMessage
+            
+            messages = [SystemMessage(content="""You are a helpful AI assistant with web search capabilities. 
+
+IMPORTANT: You have access to a web_search tool. Use it when users ask about:
+- Current events, news, or recent information
+- Today's date or current time
+- Real-time data or recent updates
+- Specific facts that may have changed recently
+- When users explicitly ask you to search the web
+
+For general knowledge questions that don't require current information, answer directly without using tools.""")]
+            
             for user_msg, assistant_msg in history:
                 messages.append(HumanMessage(content=user_msg))
                 if assistant_msg:  # Only add if assistant responded
@@ -194,19 +206,43 @@ class ToolCallingAgentChat:
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"=== LLM RESPONSE ===")
                 logger.info(f"Response type: {type(response)}")
+                logger.info(f"Response content: {response.content}")
                 logger.info(f"Has tool calls: {bool(response.tool_calls if hasattr(response, 'tool_calls') else False)}")
+                if hasattr(response, 'tool_calls') and response.tool_calls:
+                    logger.info(f"Tool calls: {response.tool_calls}")
+                logger.info(f"Response additional_kwargs: {getattr(response, 'additional_kwargs', {})}")
             
             # Check if LLM wants to call tools
+            tool_calls_to_execute = []
+            
+            # Method 1: Proper tool calls
             if hasattr(response, 'tool_calls') and response.tool_calls:
+                tool_calls_to_execute = response.tool_calls
+            
+            # Method 2: Fallback - check if model mentioned search in content and user asked for current info
+            elif ("search" in message.lower() or "today" in message.lower() or "current" in message.lower() or "recent" in message.lower()) and self.tools:
+                if ENABLE_DETAILED_LOGGING:
+                    logger.info(f"=== FALLBACK TOOL CALLING ===")
+                    logger.info(f"Detected need for search based on keywords")
+                
+                # Manually trigger web search
+                import uuid
+                tool_calls_to_execute = [{
+                    'name': 'web_search',
+                    'args': {'query': message},
+                    'id': str(uuid.uuid4())
+                }]
+            
+            if tool_calls_to_execute:
                 if ENABLE_DETAILED_LOGGING:
                     logger.info(f"=== TOOL CALLS DETECTED ===")
-                    logger.info(f"Number of tool calls: {len(response.tool_calls)}")
+                    logger.info(f"Number of tool calls: {len(tool_calls_to_execute)}")
                 
                 # Add the LLM response to messages
                 messages.append(response)
                 
                 # Execute tool calls
-                for tool_call in response.tool_calls:
+                for tool_call in tool_calls_to_execute:
                     if ENABLE_DETAILED_LOGGING:
                         logger.info(f"Executing tool: {tool_call['name']} with args: {tool_call['args']}")