402 lines
13 KiB
Python
402 lines
13 KiB
Python
"""
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React 模式节点模块 - 带超时和重试功能
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包含：
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- react_reason_node: 使用 intent.py 进行推理
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- error_handling_node: 错误处理节点
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- final_response_node: 最终回答节点
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- init_state_node: 初始化状态节点
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注意：为了兼容 LangGraph 的同步接口，我们保留了同步的 react_reason 调用
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但内部会根据情况使用规则推理或尝试异步调用
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"""
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import sys
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from typing import Dict, Any, Optional
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from datetime import datetime
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# 导入我们的 intent.py
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from app.core.intent import (
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react_reason,
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get_route_by_reasoning,
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ReasoningAction,
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ReasoningResult
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)
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from app.core.state_base import StateUtils
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from app.main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
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from app.main_graph.utils.retry_utils import (
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RetryConfig,
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SUBGRAPH_RETRY_CONFIG
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)
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# ========== 1. React 推理节点 ==========
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def react_reason_node(state: MainGraphState) -> MainGraphState:
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"""
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React 模式推理节点：判断下一步做什么
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Returns: 更新后的状态
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"""
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state.current_phase = "react_reasoning"
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state.reasoning_step += 1
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# 检查是否超过最大步数
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if state.reasoning_step > state.max_steps:
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state.current_phase = "max_steps_exceeded"
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state.final_result = (
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f"❌ 推理步数超过限制（最大 {state.max_steps} 步），"
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f"已执行 {state.reasoning_step - 1} 步。"
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f"请简化您的问题或分批提问。"
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)
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state.success = False
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return state
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# 准备上下文
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context = {
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"retrieved_docs": state.rag_docs,
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"previous_actions": [h.get("action") for h in state.reasoning_history],
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"messages": state.messages,
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"errors": state.errors
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}
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# 使用 intent.py 进行推理
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# 注意：这里使用同步版本，内部会根据情况处理
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result: ReasoningResult = react_reason(state.user_query, context)
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# 记录推理历史
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state.reasoning_history.append({
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"step": state.reasoning_step,
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"action": result.action.name,
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"confidence": result.confidence,
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"reasoning": result.reasoning,
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"timestamp": datetime.now().isoformat()
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})
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# 更新状态
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state.debug_info["last_reasoning"] = {
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"action": result.action.name,
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"confidence": result.confidence,
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"reasoning": result.reasoning
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}
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# 保存推理结果到状态
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state.debug_info["reasoning_result"] = result
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# 确定下一步动作
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state.last_action = result.action.name
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return state
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# ========== 2. 联网搜索节点 ==========
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def web_search_node(state: MainGraphState) -> MainGraphState:
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"""
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联网搜索节点：执行搜索并将结果保存到状态
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"""
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state.current_phase = "web_searching"
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# 获取搜索查询
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reasoning_result = state.debug_info.get("reasoning_result")
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search_query = reasoning_result.metadata.get("search_query", state.user_query) if reasoning_result else state.user_query
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try:
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from app.core import web_search
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print(f"[WebSearch] 搜索: {search_query}")
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search_result = web_search(search_query, max_results=5)
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# 保存搜索结果到状态
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if not hasattr(state, "web_search_results"):
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state.web_search_results = []
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state.web_search_results.append(search_result)
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# 将搜索结果添加到 rag_context，供 LLM 使用
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if state.rag_context:
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state.rag_context = f"{state.rag_context}\n\n---\n\n## 🌐 联网搜索结果:\n{search_result}"
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else:
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state.rag_context = f"## 🌐 联网搜索结果:\n{search_result}"
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state.success = True
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print(f"[WebSearch] 搜索完成")
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except Exception as e:
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from app.main_graph.state import ErrorRecord, ErrorSeverity
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from datetime import datetime
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error_record = ErrorRecord(
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error_type="WebSearchError",
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error_message=str(e),
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severity=ErrorSeverity.WARNING,
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source="web_search_node",
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timestamp=datetime.now().isoformat(),
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retry_count=0,
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max_retries=2,
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context={"search_query": search_query}
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)
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state.errors.append(error_record)
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state.current_error = error_record
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state.current_phase = "error_handling"
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state.success = False
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return state
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# ========== 3. 错误处理节点 ==========
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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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# ========== 3. 最终回答节点 ==========
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from langchain_core.runnables.config import RunnableConfig
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from langchain_core.messages import AIMessage
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async def final_response_node(state: MainGraphState, config: RunnableConfig) -> MainGraphState:
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"""
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最终回答节点：调用 LLM 生成最终回答（支持流式输出）
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"""
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state.current_phase = "finalizing"
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# 如果已经有 final_result 了，直接返回
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if state.final_result:
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state.current_phase = "done"
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return state
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import time
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start_time = time.time()
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try:
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# 构建 LLM 调用链
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from app.agent.prompts import create_system_prompt
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from app.model_services.chat_services import get_chat_service
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from app.logger import debug, info
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llm = get_chat_service()
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prompt = create_system_prompt(tools=[])
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chain = prompt | llm
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# 构建上下文
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memory_context = getattr(state, "memory_context", "暂无用户信息")
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# 添加 RAG 上下文到消息
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messages_with_context = list(state.messages)
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if state.rag_context:
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# 把 RAG 上下文作为系统消息添加
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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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# 插入到第一个用户消息之前
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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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# 调用 LLM（流式输出）
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chunks = []
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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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# 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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elapsed_time = time.time() - start_time
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# 更新状态
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state.messages.append(response)
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state.final_result = response.content
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state.success = True
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state.current_phase = "done"
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state.end_time = datetime.now().isoformat()
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state.llm_calls = getattr(state, "llm_calls", 0) + 1
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info(f"⏱️ [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
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except Exception as e:
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from app.logger import error
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import traceback
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error(f"❌ [LLM错误] 调用失败: {e}")
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traceback.print_exc()
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state.final_result = "抱歉，模型暂时无法响应，请稍后再试。"
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state.success = False
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state.current_phase = "done"
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return state
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# ========== 4. 初始化状态节点 ==========
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def init_state_node(state: MainGraphState) -> MainGraphState:
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"""
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初始化状态节点：在流程开始时设置初始值
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"""
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state.current_phase = "initializing"
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state.reasoning_step = 0
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state.start_time = datetime.now().isoformat()
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# 从 messages 中提取用户查询
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if not state.user_query and state.messages:
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last_msg = state.messages[-1]
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state.user_query = getattr(last_msg, "content", str(last_msg))
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return state
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# ========== 5. 条件路由函数 ==========
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def route_by_reasoning(state: MainGraphState) -> str:
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"""
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根据推理结果决定下一步路由
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Returns: 路由标识，对应 graph_builder.py 中的边
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"""
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# 先检查特殊情况
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if state.current_phase == "max_steps_exceeded":
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return "final_response"
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if state.current_phase == "error_handling" or state.current_error:
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return "handle_error"
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if state.current_phase == "finalizing" or state.current_phase == "done":
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return "final_response"
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if state.current_phase == "retrying":
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if state.retry_action and "rag" in state.retry_action.lower():
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return "rag_retrieve"
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return "react_reason"
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# 获取推理结果
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reasoning_result: Optional[ReasoningResult] = state.debug_info.get("reasoning_result")
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if not reasoning_result:
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return "final_response"
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# 使用 intent.py 提供的路由函数
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route = get_route_by_reasoning(reasoning_result)
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# 映射到我们的节点名称
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# 注意：这些名称必须与 main_graph_builder.py 中定义的节点名称一致
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route_mapping = {
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"direct_response": "final_response",
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"retrieve_rag": "rag_retrieve",
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"re_retrieve_rag": "rag_retrieve",
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"web_search": "web_search", # ⭐ 新增：联网搜索
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"clarify": "final_response",
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"call_tool": "final_response", # 暂时映射到 final_response，后续可以扩展
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"contact": "contact_subgraph",
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"dictionary": "dictionary_subgraph",
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"news_analysis": "news_analysis_subgraph",
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}
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return route_mapping.get(route, "final_response")
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# ========== 导出 ==========
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__all__ = [
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"init_state_node",
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"react_reason_node",
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"web_search_node", # ⭐ 新增
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"error_handling_node",
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"final_response_node",
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"route_by_reasoning"
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]
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