refactor: 整理文件夹结构，修复 create_serde 导入问题
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- 移动 main_graph/tools/ 到 deprecated/main_graph_tools/（旧架构工具） - 移动 rag_initializer.py 和 retry_utils.py 到 core/ - 清理 main_graph/nodes/ 里的旧节点到 deprecated/ - 修复 backend.py 中 create_serde 导入问题
This commit is contained in:
@@ -1,56 +0,0 @@
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"""
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主图节点通用工具模块
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包含事件发送、状态更新等通用功能
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"""
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from typing import Dict, Any, Optional
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from langchain_core.runnables.config import RunnableConfig
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async def dispatch_custom_event(
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event_name: str,
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data: Dict[str, Any],
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config: Optional[RunnableConfig] = None,
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) -> None:
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"""
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安全地发送自定义事件，忽略发送失败
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Args:
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event_name: 事件名称
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data: 事件数据
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config: LangChain 配置
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"""
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if not config:
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return
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try:
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from langchain_core.callbacks.manager import adispatch_custom_event
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await adispatch_custom_event(event_name, data, config=config)
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except Exception:
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# 事件发送失败不应中断主流程
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pass
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def make_react_event(
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step: int,
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action: str,
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confidence: float = 1.0,
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reasoning: str = ""
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) -> Dict[str, Any]:
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"""
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构造标准推理事件数据
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Args:
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step: 当前步数
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action: 动作名称
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confidence: 置信度
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reasoning: 推理过程
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Returns:
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事件数据字典
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"""
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return {
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"step": step,
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"action": action,
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"confidence": confidence,
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"reasoning": reasoning
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}
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@@ -1,95 +0,0 @@
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"""
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错误处理节点 - 处理子图/工具调用错误
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"""
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from ...main_graph.state import MainGraphState, ErrorSeverity
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from backend.app.logger import info
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def error_handling_node(state: MainGraphState) -> MainGraphState:
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"""
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错误处理节点：处理子图/工具调用错误
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返回结构化错误信息，格式如下：
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{
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"tool/node": "...",
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"status": "failed",
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"error": "...",
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"retries_exceeded": true/false,
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"suggestion": "..."
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}
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"""
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state.current_phase = "error_handling"
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if not state.current_error:
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state.current_phase = "react_reasoning"
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return state
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error = state.current_error
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# 更新错误状态
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state.error_message = f"{error.error_type}: {error.error_message}"
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# 记录结构化错误信息
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structured_error = {
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"tool": error.source,
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"status": "failed",
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"error": error.error_message,
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"retries_exceeded": error.retry_count >= error.max_retries,
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"retry_count": error.retry_count,
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"max_retries": error.max_retries
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}
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# 根据错误类型添加建议
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if "RAG" in error.error_type:
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structured_error["suggestion"] = "尝试重新表述问题或直接询问"
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elif "subgraph" in error.source or "contact" in error.source:
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structured_error["suggestion"] = "子图执行失败，请尝试简化查询"
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elif "timeout" in error.error_message.lower():
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structured_error["suggestion"] = "请求超时，请稍后再试"
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else:
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structured_error["suggestion"] = "请尝试其他方式提问"
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state.debug_info["structured_error"] = structured_error
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# 策略1: 检查是否可以重试
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can_retry = (
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error.severity in [ErrorSeverity.WARNING, ErrorSeverity.ERROR]
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and error.retry_count < error.max_retries
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)
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if can_retry:
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error.retry_count += 1
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state.retry_action = error.source
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state.debug_info["retry_count"] = error.retry_count
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if "RAG" in error.error_type:
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state.last_action = "RE_RETRIEVE_RAG"
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elif "subgraph" in error.source:
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state.last_action = "DIRECT_RESPONSE"
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else:
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state.last_action = "REASON"
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state.current_phase = "retrying"
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return state
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# 策略2: 无法重试，尝试降级方案
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if error.severity != ErrorSeverity.FATAL:
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state.final_result = (
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f"⚠️ 遇到一些问题：\n"
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f"```json\n{structured_error}\n```\n"
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f"但我会尽力用现有信息回答您。"
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)
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state.success = True
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state.current_phase = "finalizing"
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return state
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# 策略3: 致命错误
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state.final_result = (
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f"❌ 服务暂时不可用，请稍后再试。\n"
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f"```json\n{structured_error}\n```"
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)
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state.success = False
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state.current_phase = "finalizing"
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return state
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@@ -1,59 +0,0 @@
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"""
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完成事件节点模块（新架构版本）
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负责发送完成事件
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"""
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from typing import Any, Dict
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from datetime import datetime
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# 本地模块
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from .state import AgentState
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from backend.app.logger import info, warning
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from langchain_core.runnables.config import RunnableConfig
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async def finalize_node(state: AgentState, config: RunnableConfig) -> Dict[str, Any]:
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"""
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完成事件节点（新架构版本）
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Args:
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state: 当前对话状态
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config: 运行时配置
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Returns:
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空（不修改状态）
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"""
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info("[Finalize] 进入完成节点")
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try:
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# 获取流式写入器并发送完成事件
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from backend.app.main_graph.config import get_stream_writer
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writer = get_stream_writer()
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# 提取最后的回复
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final_reply = ""
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if state.messages:
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last_msg = state.messages[-1]
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final_reply = last_msg.content if hasattr(last_msg, 'content') else str(last_msg)
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# 只在 writer 存在且不是 noop 时才发送
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if writer and hasattr(writer, '__call__'):
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try:
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writer({
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"type": "custom",
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"data": {
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"type": "done",
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"token_usage": state.last_token_usage,
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"elapsed_time": state.last_elapsed_time,
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"final_result": final_reply
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}
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})
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info("🏁 [完成事件] 已发送完成事件")
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except Exception as e:
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warning(f"⚠️ [完成事件] 发送完成事件失败 (非致命): {e}")
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except Exception as e:
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warning(f"⚠️ [完成事件] 处理失败 (非致命): {e}")
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info("[Finalize] 离开完成节点")
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return {}
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@@ -1,214 +0,0 @@
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"""
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LLM 调用节点模块
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负责调用大语言模型并处理响应
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"""
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import time
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from typing import Any, Dict
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from langchain_core.language_models import BaseChatModel
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from langchain_core.messages import AIMessage
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# 本地模块
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from ...main_graph.state import MainGraphState
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from ...agent.prompts import create_system_prompt
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from ...utils.logging import log_state_change
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from backend.app.logger import debug, info, error
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def create_dynamic_llm_call_node(chat_services: Dict[str, BaseChatModel], tools: list):
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"""
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工厂函数：创建动态 LLM 调用节点（根据 state.current_model 选择模型）
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Args:
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chat_services: 模型名称 -> ChatModel 实例 的字典
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tools: 工具列表（llm_call 不使用工具，只负责回答）
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Returns:
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异步节点函数
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"""
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# llm_call 节点不使用工具，只负责生成回答
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# 直接使用原始模型，不绑定工具
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models = chat_services
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# 预构建 prompt（不带工具描述）
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prompt = create_system_prompt()
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from langchain_core.runnables.config import RunnableConfig
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async def call_llm(state: MainGraphState, config: RunnableConfig) -> Dict[str, Any]:
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"""
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LLM 调用节点（动态选择模型）
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Args:
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state: 当前对话状态
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config: LangChain/LangGraph 自动注入的配置，包含 callbacks 等信息
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Returns:
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更新后的状态字典
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"""
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log_state_change("llm_call", state, "进入")
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memory_context = getattr(state, "memory_context", "暂无用户信息")
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start_time = time.time()
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# 关键修复：如果 state.final_result 已经存在（比如子图执行完），直接返回
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if state.final_result:
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info(f"[llm_call] 检测到已有最终结果，直接返回: {state.final_result[:100]}...")
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elapsed_time = time.time() - start_time
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return {
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"final_result": state.final_result,
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"success": True,
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"current_phase": "done",
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"llm_calls": getattr(state, 'llm_calls', 0) + 1,
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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}
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# 动态选择模型
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model_name = getattr(state, "current_model", "")
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if not model_name or model_name not in models:
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# 回退到第一个可用模型
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fallback_name = next(iter(models.keys()))
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info(f"[llm_call] 模型 '{model_name}' 不可用，回退到 '{fallback_name}'")
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model_name = fallback_name
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llm = models[model_name]
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info(f"[llm_call] 使用模型（无工具）: {model_name}")
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try:
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# 添加上下文到消息
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messages_with_context = list(state.messages)
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info(f"[llm_call] 原始消息数量: {len(messages_with_context)}")
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for i, msg in enumerate(messages_with_context):
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msg_type = getattr(msg, 'type', 'unknown')
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msg_content = getattr(msg, 'content', '')[:100] if hasattr(msg, 'content') else str(msg)[:100]
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info(f"[llm_call] msg[{i}] type={msg_type}, content={repr(msg_content)}")
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if state.rag_context:
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from langchain_core.messages import SystemMessage
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rag_system_msg = SystemMessage(content=f"以下是检索到的相关信息：\n{state.rag_context}")
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inserted = False
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for i, msg in enumerate(messages_with_context):
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if msg.type == "human":
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messages_with_context.insert(i, rag_system_msg)
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inserted = True
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break
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if not inserted:
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messages_with_context.insert(0, rag_system_msg)
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info(f"[llm_call] RAG上下文已添加，长度: {len(state.rag_context)}")
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# 恢复为：手动进行 astream，并将所有的 chunk 拼接成最终的 response 返回。
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# LangGraph 会自动监听这期间产生的所有 token。
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chain = prompt | llm
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chunks = []
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info(f"[llm_call] 开始调用 LLM astream...")
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async for chunk in chain.astream(
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{
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"messages": messages_with_context,
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"memory_context": memory_context
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},
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config=config
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):
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chunks.append(chunk)
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info(f"[llm_call] LLM astream 完成，共收到 {len(chunks)} 个 chunks,info:{chunks[0].content[:50]}...{chunks[-1].content[:50]}")
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# 将所有 chunk 合并成最终的 AIMessage
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if chunks:
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response = chunks[0].content
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for chunk in chunks[1:]:
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response = response + chunk.content
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# 将所有 chunk 合并成最终的 AIMessage
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if chunks:
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response = chunks[0]
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for chunk in chunks[1:]:
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response = response + chunk
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else:
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response = AIMessage(content="")
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info(f"[llm_call] ⚠️ 警告: 没有收到任何 chunks！")
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elapsed_time = time.time() - start_time
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# 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
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token_usage = {}
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input_tokens = 0
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output_tokens = 0
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# 尝试从 response_metadata 中提取
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if hasattr(response, 'response_metadata') and response.response_metadata:
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meta = response.response_metadata
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if 'token_usage' in meta:
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token_usage = meta['token_usage']
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elif 'usage' in meta:
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token_usage = meta['usage']
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# 尝试从 additional_kwargs 中提取
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if not token_usage and hasattr(response, 'additional_kwargs'):
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add_kwargs = response.additional_kwargs
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if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
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token_usage = add_kwargs['llm_output']['token_usage']
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# 提取具体的 token 数值
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if token_usage:
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input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
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output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
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# 打印 LLM 的完整输出
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debug("\n" + "="*80)
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debug(f"📥 [LLM输出] 模型: {model_name} 返回的完整响应:")
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debug(f" 消息类型: {response.type.upper()}")
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debug(f" 内容长度: {len(str(response.content))} 字符")
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debug("-"*80)
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debug(f"{response.content}")
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# 打印响应统计信息
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info(f"⏱️ [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
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info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
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if token_usage:
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debug(f"📋 [LLM统计] 详细用量: {token_usage}")
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debug("="*80 + "\n")
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result = {
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"messages": [response],
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"llm_calls": getattr(state, 'llm_calls', 0) + 1,
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"last_token_usage": token_usage,
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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"final_result": response.content,
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"success": True,
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"current_phase": "done",
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"current_model": model_name # 记录实际使用的模型
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}
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log_state_change("llm_call", state, "离开")
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return result
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except Exception as e:
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elapsed_time = time.time() - start_time
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error(f"\n❌ [LLM错误] 模型 {model_name} 调用失败 (耗时: {elapsed_time:.2f}秒)")
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error(f" 错误类型: {type(e).__name__}")
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error(f" 错误信息: {str(e)}")
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import traceback
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error(f"📋 堆栈: {traceback.format_exc()}")
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debug("="*80 + "\n")
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# 返回一个友好的错误消息
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error_response = AIMessage(
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content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
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)
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error_result = {
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"messages": [error_response],
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"llm_calls": getattr(state, 'llm_calls', 0),
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"last_token_usage": {},
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"last_elapsed_time": elapsed_time,
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"turns_since_last_summary": getattr(state, 'turns_since_last_summary', 0) + 1,
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"final_result": "抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。",
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"success": False,
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"current_phase": "done",
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"current_model": model_name
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}
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log_state_change("llm_call", state, "离开（异常）")
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return error_result
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||||
return call_llm
|
||||
@@ -1,269 +0,0 @@
|
||||
"""
|
||||
RAG 检索节点模块
|
||||
包含：RAG 检索、置信度判断、重检索等节点
|
||||
"""
|
||||
|
||||
import time
|
||||
import asyncio
|
||||
from typing import Optional
|
||||
from datetime import datetime
|
||||
from langchain_core.runnables.config import RunnableConfig
|
||||
|
||||
from ...main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
|
||||
from ...main_graph.utils.retry_utils import RAG_RETRY_CONFIG
|
||||
from backend.app.logger import info, debug
|
||||
from ...model_services import get_small_llm_service
|
||||
from ._utils import dispatch_custom_event, make_react_event
|
||||
|
||||
|
||||
# 置信度阈值配置
|
||||
RAG_CONFIDENCE_THRESHOLD = 0.6 # 低于此值认为检索不相关
|
||||
|
||||
# 全局 pipeline 实例
|
||||
_rag_pipeline = None
|
||||
|
||||
|
||||
def _get_rag_pipeline():
|
||||
"""获取 RAG Pipeline 实例"""
|
||||
global _rag_pipeline
|
||||
if _rag_pipeline is None:
|
||||
from backend.app.rag.pipeline import RAGPipeline
|
||||
_rag_pipeline = RAGPipeline(
|
||||
num_queries=3,
|
||||
rerank_top_n=5,
|
||||
use_rerank=True,
|
||||
return_parent_docs=True,
|
||||
)
|
||||
return _rag_pipeline
|
||||
|
||||
|
||||
def _get_rag_tool() -> Optional[callable]:
|
||||
"""获取 RAG 工具"""
|
||||
from backend.app.main_graph.utils.rag_initializer import get_rag_tool
|
||||
return get_rag_tool()
|
||||
|
||||
|
||||
# ========== RAG 检索核心逻辑 ==========
|
||||
async def _rag_retrieve_core(state: MainGraphState, pipeline) -> MainGraphState:
|
||||
info(f"[RAG Core] _rag_retrieve_core 开始")
|
||||
retrieval_query = state.user_query
|
||||
|
||||
# 优先使用推理结果中的优化查询 - 从新的结构化字段获取
|
||||
reasoning_result = state.react_reasoning.reasoning_result
|
||||
if reasoning_result and hasattr(reasoning_result, "retrieval_config"):
|
||||
cfg = reasoning_result.retrieval_config
|
||||
if cfg and cfg.retrieval_query:
|
||||
retrieval_query = cfg.retrieval_query
|
||||
|
||||
info(f"[RAG Core] 使用检索查询: {retrieval_query[:50]}...")
|
||||
# 直接调用 pipeline 获取文档和上下文
|
||||
info(f"[RAG Core] 调用 pipeline.aretrieve")
|
||||
documents = await pipeline.aretrieve(retrieval_query)
|
||||
info(f"[RAG Core] pipeline.aretrieve 返回，得到 {len(documents)} 个文档")
|
||||
info(f"[RAG Core] 调用 pipeline.format_context")
|
||||
rag_context = pipeline.format_context(documents)
|
||||
info(f"[RAG Core] pipeline.format_context 返回")
|
||||
|
||||
info(f"[RAG Core] 获取到 rag_context: {type(rag_context)}, 长度={len(rag_context) if rag_context else 0}")
|
||||
info(f"[RAG Core] 获取到 rag_docs: {len(documents)} 个文档")
|
||||
|
||||
# 更新状态
|
||||
state.rag_context = rag_context
|
||||
state.rag_docs = documents # 保存文档用于置信度评估
|
||||
state.rag_retrieved = bool(documents) # 有文档才算检索成功
|
||||
state.rag_attempts = getattr(state, 'rag_attempts', 0) + 1
|
||||
# 移除对 debug_info 的依赖，不再保存 rag_scores
|
||||
|
||||
info(f"[RAG Core] _rag_retrieve_core 结束")
|
||||
return state
|
||||
|
||||
|
||||
# ========== RAG 检索节点 ==========
|
||||
async def rag_retrieve_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
|
||||
info(f"[RAG] rag_retrieve_node 开始")
|
||||
state.current_phase = "rag_retrieving"
|
||||
start_time = time.time()
|
||||
|
||||
info(f"[RAG] 调用 _get_rag_pipeline")
|
||||
pipeline = _get_rag_pipeline()
|
||||
|
||||
await dispatch_custom_event(
|
||||
"react_reasoning",
|
||||
make_react_event(state.reasoning_step, "rag_retrieve_start", 1.0, "开始执行 RAG 检索..."),
|
||||
config
|
||||
)
|
||||
|
||||
try:
|
||||
info(f"[RAG] 调用 _rag_retrieve_core")
|
||||
state = await _rag_retrieve_core(state, pipeline)
|
||||
info(f"[RAG] _rag_retrieve_core 返回")
|
||||
|
||||
# 评估置信度
|
||||
info(f"[RAG] 调用 _evaluate_rag_confidence")
|
||||
confidence = await _evaluate_rag_confidence(state)
|
||||
state.rag_confidence = confidence
|
||||
|
||||
info(f"[RAG] 检索完成，置信度={confidence:.2f}，RAG尝试次数={state.rag_attempts}")
|
||||
|
||||
state.reasoning_history.append({
|
||||
"step": state.reasoning_step,
|
||||
"action": "RETRIEVE_RAG",
|
||||
"confidence": confidence,
|
||||
"reasoning": f"RAG 检索完成，置信度={confidence:.2f}",
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
await dispatch_custom_event(
|
||||
"react_reasoning",
|
||||
make_react_event(state.reasoning_step, "rag_retrieve_complete", confidence,
|
||||
f"RAG 检索完成，置信度={confidence:.2f}"),
|
||||
config
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
info(f"[RAG] 检索失败: {e}", exc_info=True)
|
||||
state.rag_confidence = 0.0
|
||||
state.rag_retrieved = False
|
||||
|
||||
info(f"[RAG] rag_retrieve_node 结束")
|
||||
return state
|
||||
|
||||
|
||||
async def _evaluate_rag_confidence(state: MainGraphState) -> float:
|
||||
"""评估 RAG 检索结果置信度（综合向量相似度 + 重排分数 + 小模型判断）"""
|
||||
query = state.user_query or ""
|
||||
rag_context = state.rag_context or ""
|
||||
|
||||
if not rag_context:
|
||||
return 0.0
|
||||
|
||||
# 方式1: 向量相似度（从 rag_docs 中获取）
|
||||
embedding_score = _get_embedding_similarity(state)
|
||||
info(f"[RAG Confidence] 向量相似度={embedding_score:.3f}")
|
||||
|
||||
# 方式2: 重排序分数（从 rag_docs 中获取）
|
||||
rerank_score = _get_rerank_score(state)
|
||||
info(f"[RAG Confidence] 重排分数={rerank_score:.3f}")
|
||||
|
||||
# 方式3: 小模型判断
|
||||
llm_score = await _get_llm_score(state)
|
||||
info(f"[RAG Confidence] LLM评估={llm_score:.3f}")
|
||||
|
||||
# 综合得分（加权平均）
|
||||
# 向量相似度权重 0.3，重排权重 0.3，LLM 权重 0.4
|
||||
final_score = embedding_score * 0.3 + rerank_score * 0.3 + llm_score * 0.4
|
||||
info(f"[RAG Confidence] 综合置信度={final_score:.3f} (embedding={embedding_score:.3f}*0.3 + rerank={rerank_score:.3f}*0.3 + llm={llm_score:.3f}*0.4)")
|
||||
|
||||
return final_score
|
||||
|
||||
|
||||
def _get_embedding_similarity(state: MainGraphState) -> float:
|
||||
"""从 rag_docs 中获取向量相似度分数（不再从 debug_info 获取）"""
|
||||
# 降级：从 rag_docs 中获取
|
||||
rag_docs = getattr(state, "rag_docs", [])
|
||||
scores = []
|
||||
for doc in rag_docs:
|
||||
if isinstance(doc, dict):
|
||||
score = doc.get("score", 0.0)
|
||||
elif hasattr(doc, "metadata"):
|
||||
score = doc.metadata.get("embedding_score", doc.metadata.get("score", 0.0))
|
||||
else:
|
||||
continue
|
||||
if score > 1.0:
|
||||
score = min(score / 10.0, 1.0)
|
||||
scores.append(score)
|
||||
|
||||
return max(scores) if scores else 0.0
|
||||
|
||||
|
||||
def _get_rerank_score(state: MainGraphState) -> float:
|
||||
"""从 rag_docs 中获取重排序分数（不再从 debug_info 获取）"""
|
||||
# 降级：从 rag_docs 中获取
|
||||
rag_docs = getattr(state, "rag_docs", [])
|
||||
scores = []
|
||||
for doc in rag_docs:
|
||||
if isinstance(doc, dict):
|
||||
score = doc.get("rerank_score", 0.0)
|
||||
elif hasattr(doc, "metadata"):
|
||||
score = doc.metadata.get("rerank_score", 0.0)
|
||||
else:
|
||||
continue
|
||||
if score > 0:
|
||||
scores.append(score)
|
||||
|
||||
return max(scores) if scores else 0.0
|
||||
|
||||
|
||||
async def _get_llm_score(state: MainGraphState) -> float:
|
||||
"""使用小模型评估检索结果相关性"""
|
||||
query = state.user_query or ""
|
||||
rag_context = state.rag_context or ""
|
||||
|
||||
try:
|
||||
llm = get_small_llm_service()
|
||||
prompt = f"""评估以下检索结果与用户问题的相关性，返回 0.0-1.0 的分数：
|
||||
- 1.0 = 完全相关，能直接回答问题
|
||||
- 0.5 = 部分相关，有一定参考价值
|
||||
- 0.0 = 完全不相关，无法回答问题
|
||||
|
||||
用户问题：{query}
|
||||
|
||||
检索结果：{rag_context[:1500]}
|
||||
|
||||
只返回一个数字："""
|
||||
|
||||
response = await llm.ainvoke(prompt)
|
||||
content = response.content.strip()
|
||||
|
||||
import re
|
||||
match = re.search(r'(\d+\.?\d*)', content)
|
||||
if match:
|
||||
score = float(match.group(1))
|
||||
return max(0.0, min(1.0, score))
|
||||
|
||||
except Exception as e:
|
||||
info(f"[RAG Confidence] LLM评估失败: {e}")
|
||||
|
||||
return 0.5 # 默认中等置信度
|
||||
|
||||
|
||||
# ========== 置信度判断节点 ==========
|
||||
def check_rag_confidence(state: MainGraphState) -> str:
|
||||
"""
|
||||
根据 RAG 置信度判断下一步
|
||||
|
||||
Returns:
|
||||
"high_confidence" - 高置信度(>=0.6)，可直接生成回答
|
||||
"low_confidence" - 低置信度(<0.6)，需要联网搜索
|
||||
"no_rag" - 无检索结果，需要联网搜索
|
||||
"""
|
||||
rag_attempts = getattr(state, 'rag_attempts', 0)
|
||||
rag_confidence = getattr(state, 'rag_confidence', 0.0)
|
||||
|
||||
info(f"[Confidence Check] rag_attempts={rag_attempts}, rag_confidence={rag_confidence:.2f}")
|
||||
|
||||
# 情况1: 没有检索结果
|
||||
if not getattr(state, 'rag_retrieved', False) or not state.rag_context:
|
||||
info("[Confidence Check] 无检索结果，走联网")
|
||||
return "no_rag"
|
||||
|
||||
# 情况2: 置信度低于阈值
|
||||
if rag_confidence < RAG_CONFIDENCE_THRESHOLD:
|
||||
if rag_attempts >= 2:
|
||||
info(f"[Confidence Check] 置信度={rag_confidence:.2f}<{RAG_CONFIDENCE_THRESHOLD}，且RAG尝试{rag_attempts}次，走联网")
|
||||
return "low_confidence"
|
||||
else:
|
||||
info(f"[Confidence Check] 置信度={rag_confidence:.2f}<{RAG_CONFIDENCE_THRESHOLD}，可再尝试RAG一次")
|
||||
return "retry_rag"
|
||||
|
||||
# 情况3: 高置信度
|
||||
info(f"[Confidence Check] 高置信度={rag_confidence:.2f}>={RAG_CONFIDENCE_THRESHOLD}，直接生成回答")
|
||||
return "high_confidence"
|
||||
|
||||
|
||||
# ========== 导出 ==========
|
||||
__all__ = [
|
||||
"rag_retrieve_node",
|
||||
"check_rag_confidence",
|
||||
"RAG_CONFIDENCE_THRESHOLD",
|
||||
]
|
||||
@@ -1,220 +0,0 @@
|
||||
"""
|
||||
路由与初始化模块
|
||||
包含状态初始化节点和条件路由函数
|
||||
|
||||
三层统一循环防护：
|
||||
1. 全局步数硬上限（reasoning_step > max_steps）
|
||||
2. 路由模式检测（A→B→A→B 交替循环）
|
||||
3. 状态停滞检测（连续相同动作）
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
from backend.app.core.intent import get_route_by_reasoning, ReasoningAction
|
||||
from ...main_graph.state import (
|
||||
MainGraphState,
|
||||
CurrentAction,
|
||||
ReactReasoningState,
|
||||
HybridRouterState,
|
||||
FastPathState
|
||||
)
|
||||
from backend.app.logger import info
|
||||
|
||||
|
||||
# ========== 初始化状态节点 ==========
|
||||
def init_state_node(state: MainGraphState) -> MainGraphState:
|
||||
"""
|
||||
初始化状态节点：在流程开始时设置初始值
|
||||
|
||||
重置策略：
|
||||
- 持久化字段（如 messages、turns_since_last_summary）不重置
|
||||
- 临时字段（如 rag_context、final_result）重置为初始值
|
||||
"""
|
||||
# 持久化字段保留原样
|
||||
# - messages
|
||||
# - turns_since_last_summary
|
||||
# - user_id
|
||||
|
||||
# ========== 重置临时字段 ==========
|
||||
|
||||
# 主图控制字段
|
||||
state.user_query = ""
|
||||
state.current_action = CurrentAction.NONE
|
||||
state.current_model = ""
|
||||
state.intent_confidence = 0.0
|
||||
|
||||
# React 推理专用字段
|
||||
state.reasoning_step = 0
|
||||
state.last_action = ""
|
||||
state.reasoning_history = []
|
||||
|
||||
# RAG 相关字段
|
||||
state.rag_context = ""
|
||||
state.rag_retrieved = False
|
||||
state.rag_docs = []
|
||||
state.rag_confidence = 0.0
|
||||
state.rag_attempts = 0
|
||||
|
||||
# 联网搜索相关字段
|
||||
state.web_search_results = []
|
||||
|
||||
# 错误处理字段
|
||||
state.errors = []
|
||||
state.current_error = None
|
||||
state.retry_action = None
|
||||
state.error_message = ""
|
||||
|
||||
# 子图结果字段
|
||||
state.news_result = None
|
||||
state.dictionary_result = None
|
||||
state.contact_result = None
|
||||
|
||||
# 执行状态
|
||||
state.current_phase = "initializing"
|
||||
state.final_result = ""
|
||||
state.success = False
|
||||
|
||||
# 元数据
|
||||
state.start_time = None
|
||||
state.end_time = None
|
||||
|
||||
# 结构化状态
|
||||
state.react_reasoning = ReactReasoningState()
|
||||
state.hybrid_router = HybridRouterState()
|
||||
state.fast_path = FastPathState()
|
||||
|
||||
# 统计字段
|
||||
state.llm_calls = 0
|
||||
state.last_token_usage = {}
|
||||
state.last_elapsed_time = 0.0
|
||||
state.memory_context = ""
|
||||
|
||||
# 向后兼容字段
|
||||
state.debug_info = {}
|
||||
|
||||
# 设置初始值
|
||||
state.current_phase = "initializing"
|
||||
state.reasoning_step = 0
|
||||
state.start_time = datetime.now().isoformat()
|
||||
|
||||
# 从 messages 中提取 user_query（如果没有的话）
|
||||
if not state.user_query and state.messages:
|
||||
last_msg = state.messages[-1]
|
||||
state.user_query = getattr(last_msg, "content", str(last_msg))
|
||||
|
||||
return state
|
||||
|
||||
|
||||
# ========== 条件路由函数 ==========
|
||||
def route_by_reasoning(state: MainGraphState) -> str:
|
||||
"""
|
||||
根据推理结果决定下一步路由，带三层统一循环防护
|
||||
|
||||
核心逻辑：
|
||||
1. DIRECT_RESPONSE → 直接返回 llm_call
|
||||
2. 子图完成/已有结果 → 直接返回 llm_call
|
||||
3. 步数超限 → 直接返回 llm_call
|
||||
4. 其他 → 正常路由
|
||||
"""
|
||||
# 获取历史动作
|
||||
previous_actions = [h.get("action") for h in state.reasoning_history]
|
||||
|
||||
info(f"[条件路由] step={state.reasoning_step}, phase={state.current_phase}, history={previous_actions}")
|
||||
|
||||
# ========== 获取推理结果 - 从新的结构化字段获取 ==========
|
||||
reasoning_result = state.react_reasoning.reasoning_result
|
||||
latest_action = reasoning_result.action.name if reasoning_result else None
|
||||
|
||||
# ========== 核心检查：DIRECT_RESPONSE 优先 ==========
|
||||
# 从 reasoning_result 检查（最新）
|
||||
if latest_action == "DIRECT_RESPONSE":
|
||||
info(f"[条件路由] 推理结果为 DIRECT_RESPONSE，直接去 llm_call")
|
||||
return "llm_call"
|
||||
|
||||
# 备用：从历史记录检查
|
||||
if previous_actions and previous_actions[-1] == "DIRECT_RESPONSE":
|
||||
info(f"[条件路由] 历史记录最新动作为 DIRECT_RESPONSE，直接去 llm_call")
|
||||
return "llm_call"
|
||||
|
||||
# ========== 子图完成/已有结果 ==========
|
||||
if "subgraph_completed" in previous_actions or state.final_result:
|
||||
info("[条件路由] 子图已完成或已有结果，直接终止")
|
||||
return "llm_call"
|
||||
|
||||
# ========== 步数超限 ==========
|
||||
if state.reasoning_step > state.max_steps:
|
||||
info(f"[条件路由] 步数超限 ({state.reasoning_step}/{state.max_steps})，强制终止")
|
||||
return "llm_call"
|
||||
|
||||
# ========== 特殊阶段快速通道 ==========
|
||||
if state.current_phase in ("max_steps_exceeded", "finalizing", "done"):
|
||||
return "llm_call"
|
||||
if state.current_phase == "error_handling" or state.current_error:
|
||||
return "handle_error"
|
||||
|
||||
# ========== 无推理结果，默认终止 ==========
|
||||
if not reasoning_result:
|
||||
info("[条件路由] 无推理结果，默认去 llm_call")
|
||||
return "llm_call"
|
||||
|
||||
# ========== 计算目标路由 ==========
|
||||
route = get_route_by_reasoning(reasoning_result)
|
||||
|
||||
route_mapping = {
|
||||
"direct_response": "llm_call",
|
||||
"retrieve_rag": "rag_retrieve",
|
||||
"re_retrieve_rag": "rag_retrieve",
|
||||
"web_search": "web_search",
|
||||
"clarify": "llm_call",
|
||||
"call_tool": "llm_call",
|
||||
"contact": "contact_subgraph",
|
||||
"dictionary": "dictionary_subgraph",
|
||||
"news_analysis": "news_analysis_subgraph",
|
||||
}
|
||||
target = route_mapping.get(route, "llm_call")
|
||||
|
||||
# ========== RAG 次数硬限制 ==========
|
||||
rag_attempts = getattr(state, 'rag_attempts', 0)
|
||||
if target == "rag_retrieve" and rag_attempts >= 2:
|
||||
info(f"[条件路由] RAG已尝试{rag_attempts}次，强制走联网搜索")
|
||||
target = "web_search"
|
||||
|
||||
# ========== 循环防护检测 ==========
|
||||
# 1. 路由模式检测（A→B→A→B 交替）
|
||||
if len(previous_actions) >= 4:
|
||||
if (previous_actions[-4] == previous_actions[-2]
|
||||
and previous_actions[-3] == previous_actions[-1]
|
||||
and previous_actions[-2] != previous_actions[-1]):
|
||||
info(f"[条件路由] 检测到路由循环: {previous_actions[-4:]}，强制终止")
|
||||
return "llm_call"
|
||||
|
||||
# 2. 状态停滞检测（连续相同动作 TODO：本来应该是2）
|
||||
if len(previous_actions) >= 3 and previous_actions[-1] == previous_actions[-2] and previous_actions[-2] == previous_actions[-3]:
|
||||
info(f"[条件路由] 连续相同动作 '{previous_actions[-1]}'，强制终止")
|
||||
return "llm_call"
|
||||
|
||||
# ========== 智能优化 ==========
|
||||
if target == "rag_retrieve" and (state.rag_docs or state.rag_context):
|
||||
info("[条件路由] RAG 结果已存在，跳过检索")
|
||||
return "llm_call"
|
||||
|
||||
info(f"[条件路由] 动作={latest_action}, 目标={target}")
|
||||
return target
|
||||
|
||||
|
||||
# ========== 完成阶段条件路由函数 ==========
|
||||
|
||||
def should_summarize(state: MainGraphState) -> str:
|
||||
"""
|
||||
检查是否需要总结对话（对话足够长时）
|
||||
|
||||
Args:
|
||||
state: 当前图状态
|
||||
|
||||
Returns:
|
||||
"summarize" 或 "finalize"
|
||||
"""
|
||||
if state.turns_since_last_summary >= 5: # 每5轮对话总结一次
|
||||
return "summarize"
|
||||
else:
|
||||
return "finalize"
|
||||
@@ -1,100 +0,0 @@
|
||||
"""
|
||||
工具执行节点模块
|
||||
负责执行 AI 调用的工具函数
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict
|
||||
from langchain_core.messages import AIMessage, ToolMessage
|
||||
from ...main_graph.config import get_stream_writer
|
||||
|
||||
# 本地模块
|
||||
from ...main_graph.state import MainGraphState
|
||||
from ...utils.logging import log_state_change
|
||||
from backend.app.logger import debug, info
|
||||
|
||||
def create_tool_call_node(tools_by_name: Dict[str, Any]):
|
||||
"""
|
||||
工厂函数：创建工具执行节点
|
||||
|
||||
Args:
|
||||
tools_by_name: 名称到工具函数的映射字典
|
||||
|
||||
Returns:
|
||||
异步节点函数
|
||||
"""
|
||||
|
||||
from langchain_core.runnables.config import RunnableConfig
|
||||
|
||||
async def call_tools(state: MainGraphState, config: RunnableConfig) -> Dict[str, Any]:
|
||||
"""
|
||||
工具执行节点（异步方法）
|
||||
|
||||
Args:
|
||||
state: 当前对话状态
|
||||
config: 运行时配置
|
||||
|
||||
Returns:
|
||||
包含 ToolMessage 的状态更新
|
||||
"""
|
||||
log_state_change("tool_node", state, "进入")
|
||||
|
||||
last_message = state.messages[-1]
|
||||
if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
|
||||
log_state_change("tool_node", state, "离开（无工具调用）")
|
||||
return {"messages": []}
|
||||
|
||||
results = []
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
info(f"🛠️ [工具调用] 准备执行 {len(last_message.tool_calls)} 个工具")
|
||||
|
||||
for tool_call in last_message.tool_calls:
|
||||
tool_name = tool_call["name"]
|
||||
tool_args = tool_call["args"]
|
||||
tool_id = tool_call["id"]
|
||||
tool_func = tools_by_name.get(tool_name)
|
||||
|
||||
debug(f" ├─ 调用工具: {tool_name} 参数: {tool_args}")
|
||||
|
||||
if tool_func is None:
|
||||
err_msg = f"Tool {tool_name} not found"
|
||||
debug(f" └─ ❌ {err_msg}")
|
||||
results.append(ToolMessage(content=err_msg, tool_call_id=tool_id))
|
||||
continue
|
||||
|
||||
# 获取流式写入器并发送工具调用开始事件
|
||||
writer = get_stream_writer()
|
||||
writer({"type": "custom", "data": {"type": "tool_start", "tool": tool_name}})
|
||||
|
||||
try:
|
||||
# 修复闭包问题：将变量作为默认参数传入 lambda
|
||||
# 如果工具支持异步 (ainvoke)，优先使用异步调用
|
||||
if hasattr(tool_func, 'ainvoke'):
|
||||
observation = await tool_func.ainvoke(tool_args)
|
||||
else:
|
||||
observation = await loop.run_in_executor(
|
||||
None,
|
||||
lambda args=tool_args: tool_func.invoke(args)
|
||||
)
|
||||
|
||||
result_preview = str(observation).replace("\n", " ")
|
||||
debug(f" └─ ✅ 结果: {result_preview}")
|
||||
results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
|
||||
|
||||
# 发送工具调用完成事件
|
||||
writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": True}})
|
||||
except Exception as e:
|
||||
debug(f" └─ ❌ 异常: {e}")
|
||||
results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
|
||||
|
||||
# 发送工具调用失败事件
|
||||
writer({"type": "custom", "data": {"type": "tool_end", "tool": tool_name, "success": False, "error": str(e)}})
|
||||
|
||||
info(f"🛠️ [工具调用] 执行完成，返回 {len(results)} 条 ToolMessage")
|
||||
|
||||
result = {"messages": results}
|
||||
log_state_change("tool_node", state, "离开")
|
||||
return result
|
||||
|
||||
return call_tools
|
||||
@@ -1,116 +0,0 @@
|
||||
"""
|
||||
联网搜索节点 - 执行搜索并将结果保存到状态
|
||||
"""
|
||||
|
||||
from typing import Optional
|
||||
from datetime import datetime
|
||||
from langchain_core.runnables.config import RunnableConfig
|
||||
|
||||
from ...main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
|
||||
from backend.app.logger import info
|
||||
|
||||
|
||||
async def web_search_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
|
||||
"""
|
||||
联网搜索节点：执行搜索并将结果保存到状态
|
||||
"""
|
||||
state.current_phase = "web_searching"
|
||||
|
||||
# 发送开始事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"react_reasoning",
|
||||
{
|
||||
"step": state.reasoning_step,
|
||||
"action": "web_search_start",
|
||||
"confidence": 1.0,
|
||||
"reasoning": "开始执行联网搜索..."
|
||||
},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
info(f"[web_search_node] 无法发送开始事件: {e}")
|
||||
|
||||
# 获取搜索查询 - 从新的结构化字段获取
|
||||
reasoning_result = state.react_reasoning.reasoning_result
|
||||
search_query = reasoning_result.metadata.get("search_query", state.user_query) if reasoning_result else state.user_query
|
||||
|
||||
try:
|
||||
from backend.app.core import web_search
|
||||
|
||||
print(f"[WebSearch] 搜索: {search_query}")
|
||||
search_result = web_search(search_query, max_results=5)
|
||||
|
||||
# 保存搜索结果到状态
|
||||
if not hasattr(state, "web_search_results"):
|
||||
state.web_search_results = []
|
||||
state.web_search_results.append(search_result)
|
||||
|
||||
# 将搜索结果添加到 rag_context，供 LLM 使用
|
||||
if state.rag_context:
|
||||
state.rag_context = f"{state.rag_context}\n\n---\n\n## 🌐 联网搜索结果:\n{search_result}"
|
||||
else:
|
||||
state.rag_context = f"## 🌐 联网搜索结果:\n{search_result}"
|
||||
|
||||
state.success = True
|
||||
print(f"[WebSearch] 搜索完成")
|
||||
|
||||
# 发送完成事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"react_reasoning",
|
||||
{
|
||||
"step": state.reasoning_step,
|
||||
"action": "web_search_complete",
|
||||
"confidence": 1.0,
|
||||
"reasoning": f"联网搜索完成，找到 {len(search_result) if isinstance(search_result, list) else 1} 条结果"
|
||||
},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
info(f"[web_search_node] 无法发送完成事件: {e}")
|
||||
|
||||
except Exception as e:
|
||||
error_record = ErrorRecord(
|
||||
error_type="WebSearchError",
|
||||
error_message=str(e),
|
||||
severity=ErrorSeverity.WARNING,
|
||||
source="web_search_node",
|
||||
timestamp=datetime.now().isoformat(),
|
||||
retry_count=0,
|
||||
max_retries=2,
|
||||
context={"search_query": search_query}
|
||||
)
|
||||
state.errors.append(error_record)
|
||||
state.current_error = error_record
|
||||
state.current_phase = "error_handling"
|
||||
state.success = False
|
||||
|
||||
# 发送错误事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"react_reasoning",
|
||||
{
|
||||
"step": state.reasoning_step,
|
||||
"action": "web_search_error",
|
||||
"confidence": 1.0,
|
||||
"reasoning": f"联网搜索失败: {str(e)}"
|
||||
},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
info(f"[web_search_node] 无法发送错误事件: {e}")
|
||||
|
||||
return state
|
||||
Reference in New Issue
Block a user