init

agents/conversation_agent.py

@@ -0,0 +1,94 @@
+from typing import Dict, Any, List
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+from langgraph.graph import StateGraph, END
+from .base_agent import BaseAgent, AgentState
+
+
+class ConversationAgent(BaseAgent):
+    """Agent for handling multi-turn conversations"""
+    
+    def __init__(self, model_name: str = None):
+        super().__init__(model_name)
+        self.conversation_history: List[BaseMessage] = []
+    
+    def _build_graph(self) -> StateGraph:
+        """Build conversation-specific graph"""
+        workflow = StateGraph(AgentState)
+        
+        # Add nodes
+        workflow.add_node("analyze_intent", self._analyze_intent)
+        workflow.add_node("generate_response", self._generate_response)
+        workflow.add_node("update_context", self._update_context)
+        
+        # Define edges
+        workflow.add_edge("analyze_intent", "generate_response")
+        workflow.add_edge("generate_response", "update_context")
+        workflow.add_edge("update_context", END)
+        
+        # Set entry point
+        workflow.set_entry_point("analyze_intent")
+        
+        return workflow.compile()
+    
+    def _analyze_intent(self, state: AgentState) -> AgentState:
+        """Analyze user intent and conversation context"""
+        # Simple intent analysis - can be enhanced with more sophisticated logic
+        user_message = state.messages[-1] if state.messages else None
+        
+        if user_message and isinstance(user_message, HumanMessage):
+            content = user_message.content.lower()
+            
+            # Basic intent detection
+            if any(word in content for word in ["hello", "hi", "hey", "greetings"]):
+                state.context["intent"] = "greeting"
+            elif any(word in content for word in ["help", "assist", "support"]):
+                state.context["intent"] = "help"
+            elif "?" in content:
+                state.context["intent"] = "question"
+            else:
+                state.context["intent"] = "general"
+        
+        state.current_step = "intent_analyzed"
+        return state
+    
+    def _generate_response(self, state: AgentState) -> AgentState:
+        """Generate response considering conversation history"""
+        # Combine conversation history with current message
+        all_messages = self.conversation_history + state.messages
+        
+        if all_messages:
+            response = self.model.invoke(all_messages)
+            state.messages.append(response)
+        
+        state.current_step = "response_generated"
+        return state
+    
+    def _update_context(self, state: AgentState) -> AgentState:
+        """Update conversation context and history"""
+        # Add the conversation to history (excluding system messages)
+        for message in state.messages:
+            if isinstance(message, (HumanMessage, AIMessage)):
+                self.conversation_history.append(message)
+        
+        # Limit conversation history to avoid token limits
+        if len(self.conversation_history) > 10:
+            self.conversation_history = self.conversation_history[-10:]
+        
+        state.current_step = "context_updated"
+        return state
+    
+    def run(self, user_input: str, **kwargs) -> Dict[str, Any]:
+        """Run conversation with history management"""
+        initial_state = AgentState(
+            messages=[HumanMessage(content=user_input)],
+            context=kwargs
+        )
+        
+        result = self.graph.invoke(initial_state)
+        
+        return {
+            "messages": result.messages,
+            "context": result.context,
+            "conversation_history": self.conversation_history,
+            "final_step": result.current_step
+        }