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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m38s
- 修改 react_nodes.py，在推理时保存推理过程到状态 - 修改 agent_service.py，检测并发送推理过程事件到前端 - 修改 chat_area.py，接收并显示推理过程 - 修改 useChat.ts，添加对推理过程事件的支持
339 lines
11 KiB
Python
339 lines
11 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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- 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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from app.logger import info
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# ========== 1. React 推理节点 ==========
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def react_reason_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> 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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# 发送推理开始事件
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if config:
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try:
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from langchain_core.runnables.config import RunnableConfig
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# 尝试获取回调管理器并发送自定义事件
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callbacks = config.get("callbacks")
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if callbacks:
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from langchain_core.callbacks.manager import adispatch_custom_event
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# 注意：这是异步操作，我们需要特殊处理
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# 这里我们使用自定义流式写入器（如果存在）
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pass
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except Exception as e:
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info(f"[react_reason] 无法发送回调事件: {e}")
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info(f"[react_reason] 第 {state.reasoning_step} 次推理开始")
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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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info(f"[react_reason] 推理结果: action={result.action.name}, confidence={result.confidence}")
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if result.reasoning:
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info(f"[react_reason] 推理过程: {result.reasoning}")
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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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# 发送推理完成事件（通过状态更新，agent_service 会处理）
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# 我们在状态中保存推理内容，以便 agent_service 可以通过 state_update 事件发送
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state.debug_info["latest_reasoning"] = {
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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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"sent": False # 标记是否已发送到前端
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}
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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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# ========== 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 "llm_call"
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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 "llm_call"
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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 "llm_call"
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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": "llm_call",
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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",
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"clarify": "llm_call",
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"call_tool": "llm_call",
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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, "llm_call")
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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",
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"error_handling_node",
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"route_by_reasoning"
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]
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