init

agent/core/base_agent.py

@@ -0,0 +1,88 @@
+from typing import Any, Dict, Optional, cast
+from langchain_core.messages import HumanMessage
+from langgraph.graph import StateGraph, END
+
+from services.core.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) -> Any:
+        """构建代理状态图"""
+        workflow = StateGraph(cast(Any, AgentState))
+
+        self._add_shared_sql_nodes(workflow)
+        self._add_shared_sql_edges(workflow, start_node="process_input", end_node="generate_response")
+        workflow.add_edge("generate_response", END)
+
+        workflow.set_entry_point("process_input")
+
+        return workflow.compile()
+
+    def _add_shared_sql_nodes(self, workflow: Any) -> None:
+        """注册 SQL 规划相关共享节点。"""
+        workflow.add_node("process_input", cast(Any, nodes.process_input))
+        workflow.add_node("normalize_input", cast(Any, self._normalize_input))
+        workflow.add_node("classify_query_mode", cast(Any, nodes.classify_query_mode))
+        workflow.add_node("match_table", cast(Any, nodes.match_table))
+        workflow.add_node("load_sql_prompt", cast(Any, nodes.load_sql_prompt))
+        workflow.add_node("build_sql_plan", cast(Any, nodes.build_sql_plan))
+        workflow.add_node("generate_sql", cast(Any, self._generate_sql))
+        workflow.add_node("execute_sql", cast(Any, nodes.execute_sql))
+        workflow.add_node("check_empty_result", cast(Any, nodes.check_empty_result))
+        workflow.add_node("generate_response", cast(Any, self._generate_response))
+
+    @staticmethod
+    def _add_shared_sql_edges(workflow: Any, start_node: str, end_node: str) -> None:
+        """串联标准 SQL 工作流。"""
+        workflow.add_edge(start_node, "normalize_input")
+        workflow.add_edge("normalize_input", "classify_query_mode")
+        workflow.add_edge("classify_query_mode", "match_table")
+        workflow.add_edge("match_table", "load_sql_prompt")
+        workflow.add_edge("load_sql_prompt", "build_sql_plan")
+        workflow.add_edge("build_sql_plan", "generate_sql")
+        workflow.add_edge("generate_sql", "execute_sql")
+        workflow.add_edge("execute_sql", "check_empty_result")
+        workflow.add_edge("check_empty_result", end_node)
+
+    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 _generate_sql(self, state: AgentState) -> AgentState:
+        """生成 SQL"""
+        return nodes.generate_sql(state, self.model)
+
+    @staticmethod
+    def _coerce_state(initial_state: AgentState, result: Any) -> AgentState:
+        """兼容 LangGraph 返回 AgentState 或 dict。"""
+        if isinstance(result, AgentState):
+            return result
+        return initial_state.apply_graph_result(result)
+
+    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)
+        final_state = self._coerce_state(initial_state, result)
+
+        return {
+            "messages": final_state.messages,
+            "context": final_state.sync_context(),
+            "final_step": final_state.current_step,
+        }