init

agent/conversation.py

@@ -19,11 +19,13 @@ class ConversationAgent(BaseAgent):
 
         workflow.add_node("analyze_intent", self._analyze_intent)
         workflow.add_node("normalize_input", self._normalize_input)
+        workflow.add_node("generate_sql", self._generate_sql)
         workflow.add_node("generate_response", self._generate_response)
         workflow.add_node("update_context", self._update_context)
 
         workflow.add_edge("analyze_intent", "normalize_input")
-        workflow.add_edge("normalize_input", "generate_response")
+        workflow.add_edge("normalize_input", "generate_sql")
+        workflow.add_edge("generate_sql", "generate_response")
         workflow.add_edge("generate_response", "update_context")
         workflow.add_edge("update_context", END)
 
@@ -61,6 +63,11 @@ class ConversationAgent(BaseAgent):
         state.current_step = "response_generated"
         return state
 
+    def _generate_sql(self, state: AgentState) -> AgentState:
+        """生成 SQL"""
+        from . import nodes
+        return nodes.generate_sql(state, self.model)
+
     def _update_context(self, state: AgentState) -> AgentState:
         """更新对话上下文与历史"""
         for message in state.messages:

agent/graph.py

@@ -20,10 +20,12 @@ class BaseAgent:
 
         workflow.add_node("process_input", nodes.process_input)
         workflow.add_node("normalize_input", self._normalize_input)
+        workflow.add_node("generate_sql", self._generate_sql)
         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("normalize_input", "generate_sql")
+        workflow.add_edge("generate_sql", "generate_response")
         workflow.add_edge("generate_response", END)
 
         workflow.set_entry_point("process_input")
@@ -38,6 +40,10 @@ class BaseAgent:
         """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)
+
     def run(self, user_input: str, **kwargs) -> Dict[str, Any]:
         """运行代理并处理用户输入"""
         initial_state = AgentState(

agent/nodes.py

@@ -1,7 +1,11 @@
-from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
+import json
+
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage, AIMessage
 from .state import AgentState
-from services.prompt_manager import PromptManager
-from services.template_matcher import TemplateMatcher
+from services.prompt_manager import get_prompt_manager
+from services.template_matcher import get_template_matcher
+from services.sql_prompt_manager import SqlPromptManager
+from tools.sr_api_tool import SrApiQueryTool
 
 
 def process_input(state: AgentState) -> AgentState:
@@ -12,6 +16,14 @@ def process_input(state: AgentState) -> AgentState:
 
 def generate_response(state: AgentState, model) -> AgentState:
     """使用 LLM 生成回复"""
+    sr_api_result = state.context.get("sr_api_result")
+    if sr_api_result:
+        state.messages.append(AIMessage(content=str(sr_api_result)))
+        return state
+    final_sql = state.context.get("final_sql")
+    if final_sql:
+        state.messages.append(AIMessage(content=final_sql))
+        return state
     if state.messages:
         response = model.invoke(state.messages)
         state.messages.append(response)
@@ -27,7 +39,7 @@ def normalize_input(state: AgentState, model) -> AgentState:
     if not isinstance(last_message, HumanMessage):
         return state
 
-    prompt_manager = PromptManager()
+    prompt_manager = get_prompt_manager()
     system_prompt = SystemMessage(
         content=prompt_manager.get("system", "english_normalizer")
     )
@@ -38,6 +50,33 @@ def normalize_input(state: AgentState, model) -> AgentState:
     state.context["original_input"] = last_message.content
     state.context["normalized_input"] = normalized
 
-    matcher = TemplateMatcher()
+    matcher = get_template_matcher()
     state.context["table_match"] = matcher.match(normalized)
     return state
+
+
+def generate_sql(state: AgentState, model) -> AgentState:
+    """根据表名与提示词生成 SQL"""
+    table_match = state.context.get("table_match") or {}
+    table_name = table_match.get("table_name")
+    normalized = state.context.get("normalized_input")
+
+    if not table_name or not normalized:
+        return state
+
+    prompt_manager = SqlPromptManager()
+    prompt_data = prompt_manager.get_prompt(table_name)
+    if not prompt_data:
+        return state
+
+    prompt_text = json.dumps(prompt_data, ensure_ascii=False, indent=2)
+    system_template = get_prompt_manager().get("system", "sql_mysql_select_only")
+    system_content = system_template.format(table_prompt_json=prompt_text)
+    user_content = f"User question (normalized English): {normalized}"
+    response = model.invoke([SystemMessage(content=system_content), HumanMessage(content=user_content)])
+    sql_text = response.content if hasattr(response, "content") else str(response)
+
+    state.context["final_sql"] = sql_text
+    tool = SrApiQueryTool()
+    state.context["sr_api_result"] = tool.run(json.dumps({"sql": sql_text}, ensure_ascii=False))
+    return state

agent/tool.py

@@ -28,10 +28,12 @@ class ToolAgent(BaseAgent):
         workflow = StateGraph(AgentState)
 
         workflow.add_node("normalize_input", self._normalize_input)
+        workflow.add_node("generate_sql", self._generate_sql)
         workflow.add_node("agent", self._agent_node)
         workflow.add_node("tools", self.tool_node)
 
-        workflow.add_edge("normalize_input", "agent")
+        workflow.add_edge("normalize_input", "generate_sql")
+        workflow.add_edge("generate_sql", "agent")
         workflow.add_edge("tools", "agent")
 
         workflow.add_conditional_edges(
@@ -65,6 +67,11 @@ class ToolAgent(BaseAgent):
 
         return state
 
+    def _generate_sql(self, state: AgentState) -> AgentState:
+        """生成 SQL"""
+        from . import nodes
+        return nodes.generate_sql(state, self.model)
+
     def _should_use_tools(self, state: AgentState) -> str:
         """判断是否需要使用工具"""
         last_message = state.messages[-1]