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构建并部署 AI Agent 服务 / deploy (push) Successful in 12m9s
## 核心改动 ### 1. 单图方案重构 - 删除了多图（self.graphs），改为单图（self.graph） - 新增 MainGraphState.current_model 字段用于运行时注入模型 - llm_call 节点改为动态选择模型（create_dynamic_llm_call_node） ### 2. chat_services 优化 - 添加 _cached_services 缓存，避免重复初始化 - 新增 get_cached_chat_services() 函数，用于单图注入 - 新增 _check_http_service_available() 统一HTTP探测逻辑 - 减少重复代码，LocalVLLMChatProvider和LocalSmallModelProvider共用探测方法 ### 3. AIAgentService 重构 - initialize() 只构建一次图，传入 chat_services 字典 - 新增 _resolve_model() 模型回退逻辑 - 新增 _build_invocation() 统一构建调用参数 - process_message() 和 process_message_stream() 改为注入 current_model - 流式处理代码拆分，增加可读性 ### 4. 新增和删除文件 - 新增：backend/app/main_graph/main_graph_builder.py（图构建） - 新增：backend/app/main_graph/subgraph_wrapper.py（子图封装） - 新增：tools/test/test_tavily_search.py（测试） - 删除：backend/app/main_graph/graph.py（旧图） - 删除：backend/app/main_graph/utils/main_graph_builder.py（旧构建器） - 删除：backend/app/main_graph/utils/__init__.py ### 5. 其他更新 - README.md：新增模型服务使用情况详解章节 - backend/app/model_services/__init__.py：新增 get_cached_chat_services 导出 ## 方案优势 - 内存优化：N张图 → 1张图 - 灵活性：运行时动态选择模型，支持同会话不同模型 - 性能：模型服务缓存，初始化仅一次 - 可维护性：减少重复代码，统一HTTP探测逻辑
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
"""
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主图状态定义 - React 模式增强版
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Main Graph State Definition - React Mode Enhanced
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"""
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from enum import Enum, auto
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from typing import Optional, Dict, Any, Annotated, Sequence, TypedDict, List
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from dataclasses import dataclass, field
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from langgraph.graph import add_messages
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from langchain_core.messages import BaseMessage
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# ========== 新的类型 ==========
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class CurrentAction(Enum):
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"""主图当前操作类型"""
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NONE = auto()
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GENERAL_CHAT = auto()
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NEWS_ANALYSIS = auto()
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DICTIONARY = auto()
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CONTACT = auto()
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class ErrorSeverity(Enum):
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"""错误严重程度"""
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INFO = auto() # 信息级别，继续执行
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WARNING = auto() # 警告级别，可以重试
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ERROR = auto() # 错误级别，需要处理
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FATAL = auto() # 致命错误，终止执行
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@dataclass
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class ErrorRecord:
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"""错误记录"""
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error_type: str
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error_message: str
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severity: ErrorSeverity = ErrorSeverity.ERROR
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source: str = "" # 来源：哪个节点/子图/工具
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timestamp: str = ""
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retry_count: int = 0 # 已重试次数
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max_retries: int = 3 # 最大重试次数
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context: Dict[str, Any] = field(default_factory=dict) # 错误上下文
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@dataclass
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class MainGraphState:
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"""
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- 旧代码的 MessagesState 兼容性字段
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- React 推理控制字段
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- 循环和错误处理
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- 子图结果占位
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- 用户信息
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"""
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# ========== 旧 MessagesState 兼容性字段 ==========
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messages: Annotated[Sequence[BaseMessage], add_messages] = field(default_factory=list)
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llm_calls: int = 0
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memory_context: str = ""
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system_prompt: str = ""
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turns_since_last_summary: int = 0 # 新增：来自旧状态
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last_token_usage: Dict[str, Any] = field(default_factory=dict) # 新增：来自旧状态
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last_elapsed_time: float = 0.0 # 新增：来自旧状态
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# ========== 主图控制字段 ==========
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user_query: str = ""
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current_action: CurrentAction = CurrentAction.NONE
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current_model: str = "" # 新增：本次请求使用的模型
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intent_confidence: float = 0.0
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# ========== React 推理专用字段 ==========
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reasoning_step: int = 0
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max_steps: int = 10 # 从 40 改到 10，避免过长循环
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last_action: str = ""
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reasoning_history: List[Dict[str, Any]] = field(default_factory=list)
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# ========== RAG 相关字段 ==========
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rag_context: str = ""
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rag_retrieved: bool = False
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rag_docs: List[Dict[str, Any]] = field(default_factory=list)
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# ========== 联网搜索相关字段 ==========
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web_search_results: List[str] = field(default_factory=list)
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# ========== 错误处理字段 ==========
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errors: List[ErrorRecord] = field(default_factory=list)
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current_error: Optional[ErrorRecord] = None
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retry_action: Optional[str] = None
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# ========== 子图结果占位 ==========
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news_result: Optional[Dict[str, Any]] = None
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dictionary_result: Optional[Dict[str, Any]] = None
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contact_result: Optional[Dict[str, Any]] = None
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# ========== 用户信息 ==========
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user_id: str = ""
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# ========== 执行状态 ==========
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current_phase: str = "init"
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error_message: str = ""
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final_result: str = ""
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success: bool = False
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# ========== 元数据 ==========
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start_time: Optional[str] = None
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end_time: Optional[str] = None
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debug_info: Dict[str, Any] = field(default_factory=dict)
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