init

agent/graph.py

@@ -0,0 +1,54 @@
+from typing import Any, Dict, Optional
+from langchain_core.messages import HumanMessage
+from langgraph.graph import StateGraph, END
+
+from services.llm_factory import create_chat_model
+from .state import AgentState
+from . import nodes
+
+
+class BaseAgent:
+    """包含通用功能的基础代理类"""
+
+    def __init__(self, model_section: Optional[str] = None):
+        self.model = create_chat_model(model_section)
+        self.graph = self._build_graph()
+
+    def _build_graph(self) -> StateGraph:
+        """构建代理状态图"""
+        workflow = StateGraph(AgentState)
+
+        workflow.add_node("process_input", nodes.process_input)
+        workflow.add_node("normalize_input", self._normalize_input)
+        workflow.add_node("generate_response", self._generate_response)
+
+        workflow.add_edge("process_input", "normalize_input")
+        workflow.add_edge("normalize_input", "generate_response")
+        workflow.add_edge("generate_response", END)
+
+        workflow.set_entry_point("process_input")
+
+        return workflow.compile()
+
+    def _generate_response(self, state: AgentState) -> AgentState:
+        """使用 LLM 生成回复"""
+        return nodes.generate_response(state, self.model)
+
+    def _normalize_input(self, state: AgentState) -> AgentState:
+        """规范化用户输入"""
+        return nodes.normalize_input(state, self.model)
+
+    def run(self, user_input: str, **kwargs) -> Dict[str, Any]:
+        """运行代理并处理用户输入"""
+        initial_state = AgentState(
+            messages=[HumanMessage(content=user_input)],
+            context=kwargs
+        )
+
+        result = self.graph.invoke(initial_state)
+
+        return {
+            "messages": result.get("messages", []),
+            "context": result.get("context", {}),
+            "final_step": result.get("current_step", "unknown")
+        }