113 lines
4.6 KiB
Python
113 lines
4.6 KiB
Python
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from typing import Dict, Any, Optional, cast
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from langchain_core.messages import HumanMessage, AIMessage
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from langgraph.graph import StateGraph, END
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from agent.core.base_agent import BaseAgent
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from agent.core.state import AgentState
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from agent.core import nodes
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from config import CONVERSATION_MAX_HISTORY_MESSAGES
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class ConversationAgent(BaseAgent):
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"""处理多轮对话的代理"""
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def __init__(self, model_section: Optional[str] = None):
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super().__init__(model_section)
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def _build_graph(self) -> Any:
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"""构建对话专用图"""
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workflow = StateGraph(cast(Any, AgentState))
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workflow.add_node("analyze_intent", cast(Any, self._analyze_intent))
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self._add_shared_sql_nodes(workflow)
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workflow.add_node("update_context", cast(Any, self._update_context))
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workflow.add_edge("analyze_intent", "process_input")
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self._add_shared_sql_edges(workflow, start_node="process_input", end_node="generate_response")
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workflow.add_edge("generate_response", "update_context")
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workflow.add_edge("update_context", END)
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workflow.set_entry_point("analyze_intent")
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return workflow.compile()
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def _analyze_intent(self, state: AgentState) -> AgentState:
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"""分析用户意图与对话上下文"""
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user_message = state.messages[-1] if state.messages else None
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if user_message and isinstance(user_message, HumanMessage):
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content = user_message.content.lower()
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if any(word in content for word in ["hello", "hi", "hey", "greetings"]):
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state.intent = "greeting"
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elif any(word in content for word in ["help", "assist", "support"]):
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state.intent = "help"
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elif any(token in content for token in ["sql", "查询", "统计", "汇总", "top", "eta", "so ", "soid", "订单"]):
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state.intent = "sql_query"
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elif "?" in content:
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state.intent = "question"
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else:
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state.intent = "general"
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state.sync_context()
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state.set_current_step("intent_analyzed")
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return state
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def _generate_response(self, state: AgentState) -> AgentState:
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"""优先返回 SQL 执行结果，其次返回生成 SQL，再回退到模型回复"""
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return nodes.generate_response(state, self.model)
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def _update_context(self, state: AgentState) -> AgentState:
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"""更新当前会话的对话上下文与历史。"""
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conversation_history = list(state.context.get("conversation_history") or [])
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for message in state.messages:
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if isinstance(message, (HumanMessage, AIMessage)):
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conversation_history.append(message)
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# 使用配置文件中的最大消息数限制
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max_messages = CONVERSATION_MAX_HISTORY_MESSAGES
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if len(conversation_history) > max_messages:
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conversation_history = conversation_history[-max_messages:]
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state.context["conversation_history"] = conversation_history
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state.sync_context()
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last_context = dict(state.context)
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last_context.pop("conversation_history", None)
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last_context.pop("last_context", None)
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state.context["last_context"] = last_context
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state.set_current_step("context_updated")
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return state
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def run(self, user_input: str, **kwargs) -> Dict[str, Any]:
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"""运行对话，历史与上下文由调用方按会话维度传入。"""
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context = dict(kwargs)
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context["conversation_history"] = list(context.get("conversation_history") or [])
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context["last_context"] = dict(context.get("last_context") or {})
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initial_state = AgentState(
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messages=[HumanMessage(content=user_input)],
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context=context
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)
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result = self.graph.invoke(initial_state)
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final_state = self._coerce_state(initial_state, result)
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return {
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"messages": final_state.messages,
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"context": final_state.sync_context(),
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"conversation_history": list(final_state.context.get("conversation_history") or []),
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"final_step": final_state.current_step,
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}
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def stream_run(self, user_input: str, **kwargs):
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"""流式运行对话；会话历史需由调用方显式传入。"""
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conversation_history = list(kwargs.get("conversation_history") or [])
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all_messages = conversation_history + [HumanMessage(content=user_input)]
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full_text = ""
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for chunk in self.model.stream(all_messages):
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if hasattr(chunk, "content") and chunk.content:
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full_text += chunk.content
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yield chunk.content
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