refactor: 重构快速路径流程，统一通过 llm_call 输出
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
- 重构 fast_paths.py，让 fast_chitchat 和 fast_rag 都进入 llm_call 而不是直接设置 final_result - 修改 check_fast_path_success 函数返回 'llm_call' 而不是 'success' - 更新 main_graph_builder.py 的条件边配置，支持路由到 llm_call - 在快速路径节点中添加清除 state.final_result 的逻辑，避免复用旧结果 - 重构 RAG 工具初始化方式，使用模块级变量管理 - 修改 finalize.py 让它返回 final_result - 更新 agent_service.py 的 RAG 工具注入方式 - 简化 hybrid_router.py 的代码结构 - 清理 rag_nodes.py 的全局变量相关代码 - 更新相关测试文件
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@@ -1,11 +1,11 @@
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"""
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RAG 检索节点模块
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包含 RAG 检索节点（带超时重试）
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使用模块级变量管理 RAG 工具
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"""
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import time
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import asyncio
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from typing import Dict, Any, Optional
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from typing import Optional
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from datetime import datetime
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from app.main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
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@@ -13,43 +13,15 @@ from app.main_graph.utils.retry_utils import RAG_RETRY_CONFIG
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from app.logger import info
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from ._utils import dispatch_custom_event, make_react_event
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from app.rag.tools import create_rag_tool
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from app.rag.pipeline import RAGPipeline
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# ========== 全局 RAG 工具实例 ==========
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_GLOBAL_RAG_TOOL: Optional[Any] = None
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_GLOBAL_RAG_PIPELINE: Optional[RAGPipeline] = None
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def get_global_rag_tool() -> Optional[Any]:
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return _GLOBAL_RAG_TOOL
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def set_global_rag_tool(tool: Any) -> None:
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global _GLOBAL_RAG_TOOL
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_GLOBAL_RAG_TOOL = tool
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def set_global_rag_pipeline(pipeline: RAGPipeline) -> None:
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global _GLOBAL_RAG_PIPELINE
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_GLOBAL_RAG_PIPELINE = pipeline
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def get_rag_tool_from_state(state: MainGraphState) -> Optional[Any]:
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"""从状态或全局获取 RAG 工具"""
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return state.debug_info.get("rag_tool") or get_global_rag_tool()
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def inject_rag_tool_to_state(state: MainGraphState, rag_tool: Any) -> MainGraphState:
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"""将 RAG 工具注入到状态中"""
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state.debug_info["rag_tool"] = rag_tool
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state.debug_info["rag_tool_injected"] = datetime.now().isoformat()
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return state
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def _get_rag_tool() -> Optional[callable]:
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"""获取 RAG 工具"""
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from app.main_graph.utils.rag_initializer import get_rag_tool
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return get_rag_tool()
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# ========== RAG 检索核心逻辑 ==========
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async def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
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async def _rag_retrieve_core(state: MainGraphState, rag_tool: callable) -> MainGraphState:
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"""执行 RAG 检索的核心逻辑"""
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retrieval_query = state.user_query
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@@ -60,55 +32,54 @@ async def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
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if cfg and cfg.retrieval_query:
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retrieval_query = cfg.retrieval_query
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rag_tool = get_rag_tool_from_state(state)
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# 调用 RAG 工具
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rag_context = await rag_tool.ainvoke(retrieval_query)
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info(f"[RAG Core] 获取到 rag_context: {type(rag_context)}, 长度={len(rag_context) if rag_context else 0}")
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if rag_tool:
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rag_context = await rag_tool.ainvoke(retrieval_query)
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state.rag_context = rag_context
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state.rag_docs = [{"source": "rag_retrieval", "content": rag_context}]
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state.rag_retrieved = True
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state.success = True
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state.debug_info["rag_source"] = "rag_tool"
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return state
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state.rag_context = rag_context
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state.rag_docs = [{"source": "rag_retrieval", "content": rag_context}]
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state.rag_retrieved = True
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state.success = True
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state.debug_info["rag_source"] = "tool"
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if _GLOBAL_RAG_PIPELINE:
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documents = await _GLOBAL_RAG_PIPELINE.aretrieve(retrieval_query)
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if documents:
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rag_context = _GLOBAL_RAG_PIPELINE.format_context(documents)
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state.rag_context = rag_context
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state.rag_docs = [
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{"source": doc.metadata.get("source", "unknown"), "content": doc.page_content}
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for doc in documents
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]
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else:
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state.rag_context = f"未找到与 '{retrieval_query}' 相关的知识库信息。"
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state.rag_docs = []
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state.rag_retrieved = True
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state.success = True
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state.debug_info["rag_source"] = "rag_pipeline"
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return state
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raise RuntimeError("RAG 工具未初始化，请先调用 set_global_rag_tool() 或 set_global_rag_pipeline()")
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info(f"[RAG Core] state.rag_docs 长度: {len(state.rag_docs)}")
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return state
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# ========== RAG 检索节点 ==========
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async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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async def rag_retrieve_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
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"""RAG 检索节点：带超时和重试"""
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state.current_phase = "rag_retrieving"
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start_time = time.time()
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last_error = None
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# 步骤1: 发送开始事件
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# 获取 RAG 工具
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rag_tool = _get_rag_tool()
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if not rag_tool:
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error_record = ErrorRecord(
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error_type="RAGRetrievalError",
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error_message="RAG 工具未初始化",
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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=0,
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max_retries=RAG_RETRY_CONFIG.max_retries,
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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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await dispatch_custom_event(
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"react_reasoning",
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make_react_event(state.reasoning_step, "rag_retrieve_start", 1.0, "开始执行 RAG 检索..."),
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config
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)
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# 步骤2: 执行检索（带重试）
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for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
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try:
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result = await _rag_retrieve_core(state)
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result = await _rag_retrieve_core(state, rag_tool)
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info(f"[RAG] 检索成功，上下文长度: {len(result.rag_context)} 字符")
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@@ -118,7 +89,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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"time": time.time() - start_time
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}
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# 记录成功到历史
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state.reasoning_history.append({
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"step": state.reasoning_step,
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"action": "RETRIEVE_RAG",
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@@ -127,7 +97,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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"timestamp": datetime.now().isoformat()
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})
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# 发送完成事件
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doc_count = len(result.rag_docs) if result.rag_docs else 0
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await dispatch_custom_event(
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"react_reasoning",
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@@ -144,7 +113,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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if attempt >= RAG_RETRY_CONFIG.max_retries:
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break
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# 发送重试事件
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await dispatch_custom_event(
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"react_reasoning",
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make_react_event(state.reasoning_step, "rag_retrieve_retry", 1.0,
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@@ -152,11 +120,10 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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config
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)
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# 指数退避
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delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
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await asyncio.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
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# 步骤3: 所有重试失败，记录到历史（避免推理循环）
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# 失败记录
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state.reasoning_history.append({
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"step": state.reasoning_step,
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"action": "RETRIEVE_RAG",
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@@ -165,7 +132,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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"timestamp": datetime.now().isoformat()
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})
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# 步骤4: 记录错误
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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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@@ -174,19 +140,12 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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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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"has_rag_tool": get_global_rag_tool() is not None,
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"has_rag_pipeline": _GLOBAL_RAG_PIPELINE is not None
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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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# 发送错误事件
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await dispatch_custom_event(
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"react_reasoning",
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make_react_event(state.reasoning_step, "rag_retrieve_error", 1.0,
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@@ -197,8 +156,7 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
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return state
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# ========== 重新检索节点 ==========
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async def rag_re_retrieve_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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async def rag_re_retrieve_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
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"""重新检索节点"""
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state.current_phase = "rag_re_retrieving"
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@@ -214,9 +172,4 @@ async def rag_re_retrieve_node(state: MainGraphState, config: Optional[Dict[str,
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__all__ = [
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"rag_retrieve_node",
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"rag_re_retrieve_node",
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"inject_rag_tool_to_state",
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"get_rag_tool_from_state",
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"get_global_rag_tool",
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"set_global_rag_tool",
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"set_global_rag_pipeline",
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]
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