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构建并部署 AI Agent 服务 / deploy (push) Failing after 6m15s
- 更新 intent.py 为 React 模式推理器 - 新增 react_nodes.py: React 模式节点 - 新增 retry_utils.py: 超时和重试工具 - 更新 state.py: 支持循环步数和错误记录 - 重写 subgraph_builder.py: 完整 React 循环流程 - 结构化错误输出，符合 Agent 执行循环最佳实践 - 限制最大推理步数 ≤40，防止无限循环 - RAG 检索带重试和超时保护 - 子图错误可传递给主图处理
389 lines
12 KiB
Python
389 lines
12 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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- rag_retrieve_node: RAG 检索节点（带重试）
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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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"""
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import sys
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import time
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from typing import Dict, Any, Optional
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from datetime import datetime
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from functools import wraps
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# 导入我们的 intent.py
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from ..agent_subgraphs.common.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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RetrievalConfig,
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ReasoningResult
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)
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from ..agent_subgraphs.common.state_base import StateUtils
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from .state import MainGraphState, ErrorRecord, ErrorSeverity
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from .retry_utils import (
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RetryConfig,
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RetryResult,
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with_retry,
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create_retry_wrapper_for_node,
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RAG_RETRY_CONFIG,
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SUBGRAPH_RETRY_CONFIG
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)
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def get_rag_tool():
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"""
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获取 RAG 工具（延迟导入，避免循环依赖）
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"""
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try:
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# 尝试导入现有的 RAG 工具
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from ..rag.tools import create_rag_tool_sync
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# 注意：这里简化处理，实际使用时应该从全局获取初始化好的工具
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return None # 先返回 None，后面通过注入方式
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except Exception:
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return None
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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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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（供条件路由使用）
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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. RAG 检索节点（带超时和重试） ==========
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def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
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"""
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RAG 检索核心逻辑（不带重试）
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"""
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# 获取推理结果中的检索配置
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reasoning_result: Optional[ReasoningResult] = state.debug_info.get("reasoning_result")
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retrieval_query = state.user_query
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if reasoning_result and reasoning_result.retrieval_config:
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cfg: RetrievalConfig = reasoning_result.retrieval_config
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if cfg.retrieval_query:
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retrieval_query = cfg.retrieval_query
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# 尝试获取 RAG 工具并调用
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# 这里演示如何调用，实际使用时需要确保 RAG 已初始化
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# 暂时用模拟数据
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state.rag_context = (
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f"[模拟RAG检索结果]\n"
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f"查询: {retrieval_query}\n"
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f"这是一个来自知识库的示例回答。"
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)
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state.rag_docs = [
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{"source": "doc1.txt", "content": "示例内容1"},
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{"source": "doc2.txt", "content": "示例内容2"}
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]
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state.rag_retrieved = True
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state.success = True
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return state
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def rag_retrieve_node(state: MainGraphState) -> MainGraphState:
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"""
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RAG 检索节点：带超时和重试
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Returns: 更新后的状态
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"""
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state.current_phase = "rag_retrieving"
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# 使用重试包装器
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start_time = time.time()
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last_error = None
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for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
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try:
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# 执行核心逻辑
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result = _rag_retrieve_core(state)
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# 成功
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state.debug_info["rag_retrieval"] = {
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"attempt": attempt + 1,
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"success": True,
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"time": time.time() - start_time
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}
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return result
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except Exception as e:
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last_error = e
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if attempt >= RAG_RETRY_CONFIG.max_retries:
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break
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# 等待后重试（指数退避）
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delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
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time.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
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# 所有重试都失败，记录结构化错误
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error_record = ErrorRecord(
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error_type="RAGRetrievalError",
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error_message=str(last_error) if last_error else "RAG 检索超时",
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severity=ErrorSeverity.WARNING,
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source="rag_retrieve_node",
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timestamp=datetime.now().isoformat(),
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retry_count=RAG_RETRY_CONFIG.max_retries,
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max_retries=RAG_RETRY_CONFIG.max_retries,
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context={
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"query": state.user_query,
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"total_time": time.time() - start_time,
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"timeout": RAG_RETRY_CONFIG.timeout
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}
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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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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_exhausted": 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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# 没有错误，直接返回
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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（用于 LLM 决策）
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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_exhausted": 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，我会用现有知识回答"
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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，请尝试简化查询或使用其他功能"
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elif "timeout" in error.error_message.lower():
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structured_error["suggestion"] = "请求超时，请稍后再试或简化查询"
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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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# 重试策略
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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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# 降级到直接回答模式
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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，无法继续
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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. 最终回答节点 ==========
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def final_response_node(state: MainGraphState) -> MainGraphState:
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"""
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最终回答节点：整理并生成最终回答
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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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# 构建最终回答
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parts = []
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# 添加 RAG 上下文（如果有）
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if state.rag_context:
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parts.append(state.rag_context)
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parts.append("---")
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# 添加子图结果（如果有）
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if state.contact_result and state.contact_result.get("final_result"):
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parts.append(state.contact_result["final_result"])
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if state.dictionary_result and state.dictionary_result.get("final_result"):
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parts.append(state.dictionary_result["final_result"])
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if state.news_result and state.news_result.get("final_result"):
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parts.append(state.news_result["final_result"])
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# 如果都没有，用默认回答
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if not parts:
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parts.append(f"我理解了您的问题：{state.user_query}")
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state.final_result = "\n".join(parts)
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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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return state
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# ========== 5. 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（如果 user_query 为空）
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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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# ========== 6. 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: 路由字符串
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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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# 重试路由
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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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# 没有推理结果，直接结束
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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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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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"clarify": "final_response", # 简化：澄清直接回答让用户补充
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"call_tool": "final_response", # 简化：工具调用暂未实现
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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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