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构建并部署 AI Agent 服务 / deploy (push) Failing after 6m5s
- 完善词典子图:添加生词本功能 - 创建API调用工具:dictionary_api - 添加前端格式化展示工具:result_formatter.py - 创建通讯录和资讯子图的基本结构 - 更新主图状态结构,添加MainGraphState - 添加subgraph_builder.py用于子图集成
158 lines
4.9 KiB
Python
158 lines
4.9 KiB
Python
"""
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子图整合主图构建器
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Subgraph Integration Main Graph Builder
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"""
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from langgraph.graph import StateGraph, START, END
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from typing import Dict, Any
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from .state import MainGraphState, CurrentAction
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from ..agent_subgraphs.contact import build_contact_subgraph
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from ..agent_subgraphs.dictionary import build_dictionary_subgraph
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from ..agent_subgraphs.news_analysis import build_news_analysis_subgraph
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def parse_user_intent(state: MainGraphState) -> MainGraphState:
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"""
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解析用户意图节点
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确定该路由到哪个子图
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"""
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state.current_phase = "intent_parsing"
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# 从messages中提取用户查询(如果user_query为空)
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if not state.user_query and state.messages:
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# 获取最后一条消息的内容
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last_msg = state.messages[-1]
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state.user_query = last_msg.content
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query_lower = state.user_query.lower()
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# 简单的关键词匹配
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if any(keyword in query_lower for keyword in ["通讯录", "联系人", "contact", "email"]):
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state.current_action = CurrentAction.CONTACT
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state.intent_confidence = 0.9
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elif any(keyword in query_lower for keyword in ["词典", "单词", "翻译", "dictionary", "translate"]):
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state.current_action = CurrentAction.DICTIONARY
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state.intent_confidence = 0.9
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elif any(keyword in query_lower for keyword in ["资讯", "新闻", "分析", "news", "report"]):
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state.current_action = CurrentAction.NEWS_ANALYSIS
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state.intent_confidence = 0.9
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else:
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# 默认是普通聊天
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state.current_action = CurrentAction.GENERAL_CHAT
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state.intent_confidence = 0.8
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return state
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def route_to_subgraph(state: MainGraphState) -> str:
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"""
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条件路由:决定路由到哪个子图
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"""
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if state.current_action == CurrentAction.NONE:
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return "general_chat"
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elif state.current_action == CurrentAction.GENERAL_CHAT:
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return "general_chat"
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elif state.current_action == CurrentAction.CONTACT:
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return "contact_subgraph"
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elif state.current_action == CurrentAction.DICTIONARY:
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return "dictionary_subgraph"
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elif state.current_action == CurrentAction.NEWS_ANALYSIS:
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return "news_analysis_subgraph"
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else:
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return "general_chat"
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def general_chat_node(state: MainGraphState) -> MainGraphState:
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"""
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普通聊天节点
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(目前是占位符,后续整合旧的LLM调用逻辑)
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"""
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state.current_phase = "general_chat"
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state.final_result = f"普通聊天模式:{state.user_query}"
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state.success = True
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return state
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def integrate_results(state: MainGraphState) -> MainGraphState:
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"""
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整合子图结果节点
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"""
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state.current_phase = "integrating"
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# 整合通讯录子图结果
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if state.contact_result:
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state.final_result = state.contact_result.get("final_result", "")
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# 整合词典子图结果
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elif state.dictionary_result:
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state.final_result = state.dictionary_result.get("final_result", "")
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# 整合资讯子图结果
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elif state.news_result:
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state.final_result = state.news_result.get("final_result", "")
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else:
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# 没有子图结果
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if not state.final_result:
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state.final_result = "处理完成"
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state.current_phase = "done"
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return state
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def build_main_graph() -> StateGraph:
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"""
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构建整合了子图的主图
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Returns:
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配置好的 StateGraph
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"""
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# 创建图
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graph = StateGraph(MainGraphState)
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# 添加节点
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graph.add_node("parse_intent", parse_user_intent)
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graph.add_node("general_chat", general_chat_node)
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graph.add_node("integrate_results", integrate_results)
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# 添加子图节点
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contact_graph = build_contact_subgraph()
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dictionary_graph = build_dictionary_subgraph()
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news_analysis_graph = build_news_analysis_subgraph()
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graph.add_node("contact_subgraph", contact_graph.compile())
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graph.add_node("dictionary_subgraph", dictionary_graph.compile())
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graph.add_node("news_analysis_subgraph", news_analysis_graph.compile())
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# 添加边
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# 从START开始
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graph.add_edge(START, "parse_intent")
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# 从parse_intent根据条件路由
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graph.add_conditional_edges(
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"parse_intent",
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route_to_subgraph,
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{
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"general_chat": "general_chat",
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"contact_subgraph": "contact_subgraph",
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"dictionary_subgraph": "dictionary_subgraph",
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"news_analysis_subgraph": "news_analysis_subgraph",
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}
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)
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# 从普通聊天和子图到结果整合
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graph.add_edge("general_chat", "integrate_results")
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graph.add_edge("contact_subgraph", "integrate_results")
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graph.add_edge("dictionary_subgraph", "integrate_results")
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graph.add_edge("news_analysis_subgraph", "integrate_results")
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# 最终到END
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graph.add_edge("integrate_results", END)
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return graph
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