feat: 完成极简 LangGraph 架构迁移，添加 Baosi API 支持

主要变更： - 迁移到极简 LangGraph 标准架构（START → init_state → 记忆 → Agent ⇄ Tools → finalize → END） - 添加 Baosi API 支持，配置 ops4.7 模型 - 保留本地模型作为默认首选，Baosi 作为备选 - 新架构使用 LangGraph 原生 ToolNode 和 bind_tools - 移除旧的混合路由、JSON 解析等复杂逻辑 - 把旧代码移到 deprecated/ 目录 - 添加新的 Agent 节点和 Tools 模块 - 添加测试脚本验证新架构 - 所有测试通过 ✓
2026-05-07 00:48:17 +08:00
parent 5e762da740
commit 22fdb625a4
23 changed files with 1232 additions and 494 deletions
--- a/backend/app/deprecated/fast_paths.py
+++ b/backend/app/deprecated/fast_paths.py
@@ -0,0 +1,226 @@
+"""
+快速路径节点模块
+包含闲聊、RAG、工具等快速处理节点
+"""
+
+from typing import Optional
+from langchain_core.runnables.config import RunnableConfig
+
+from ..state import MainGraphState
+from backend.app.logger import info, debug
+from ...model_services.chat_services import get_small_llm_service, get_chat_service
+from .rag_nodes import rag_retrieve_node
+from ._utils import dispatch_custom_event
+
+
+# ========== 闲聊回复模板 ==========
+CHITCHAT_TEMPLATES = {
+    "谢谢": "不客气！如果还有其他问题，请随时告诉我 😊",
+    "再见": "再见！期待下次为您服务 👋",
+    "你好": "你好！有什么我可以帮您的吗？",
+    "默认": None  # 使用 LLM 生成
+}
+
+CHITCHAT_KEYWORDS = {
+    "谢谢": ["谢谢", "感谢", "thanks", "thank you"],
+    "再见": ["再见", "拜拜", "bye", "goodbye"],
+    "你好": ["你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好"],
+}
+
+
+# ========== 闲聊节点 ==========
+async def fast_chitchat_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
+    """快速闲聊节点"""
+    state.current_phase = "fast_chitchat"
+    query = state.user_query or ""
+    info(f"[Fast Chitchat] 处理: {query[:50]}")
+
+    # 发送开始事件
+    await dispatch_custom_event("fast_path_start", {"path": "fast_chitchat"}, config)
+
+    # 清除之前的 final_result，让 llm_call 生成新回答
+    state.final_result = None
+    
+    # 标记快速路径成功，但不设置 final_result，让 llm_call 生成回答
+    state.success = True
+    state.current_phase = "llm_call"
+    state.fast_path.chitchat_success = True
+
+    # 发送完成事件
+    await dispatch_custom_event("fast_path_end", {"path": "fast_chitchat", "success": True}, config)
+
+    return state
+
+
+def _match_chitchat_template(query: str) -> str:
+    """匹配闲聊模板"""
+    query_clean = query.strip().lower()
+
+    for intent, keywords in CHITCHAT_KEYWORDS.items():
+        if any(kw in query_clean for kw in keywords):
+            return CHITCHAT_TEMPLATES[intent]
+
+    # 默认：使用 LLM 生成
+    try:
+        llm = get_small_llm_service()
+        response = llm.invoke(f"你是一个友好的助手。用户说：{query}。请简短友好地回复：")
+        return response.content
+    except Exception:
+        return "你好！有什么我可以帮您的吗？"
+
+
+# ========== 快速 RAG 节点 ==========
+async def fast_rag_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
+    """快速 RAG 节点：只负责 RAG 检索，然后交给 llm_call 生成回答"""
+    state.current_phase = "fast_rag"
+    query = state.user_query or ""
+    info(f"[Fast RAG] 开始处理: {query[:50]}")
+
+    # 获取 RAG 工具
+    from backend.app.main_graph.utils.rag_initializer import get_rag_tool
+    rag_tool = get_rag_tool()
+    info(f"[Fast RAG] 获取到 rag_tool: {rag_tool is not None}")
+
+    # 发送开始事件
+    await dispatch_custom_event("fast_path_start", {"path": "fast_rag"}, config)
+
+    # 清除之前的 final_result，让 llm_call 生成新回答
+    state.final_result = None
+
+    # 如果没有 rag_tool，升级到 React 循环
+    if not rag_tool:
+        info("[Fast RAG] 未找到 RAG 工具，升级到 React 循环")
+        return _mark_fast_path_failed(state, "未找到 RAG 工具")
+
+    try:
+        # 尝试 RAG 检索
+        state = await rag_retrieve_node(state, config)
+
+        # 检查检索结果
+        if _has_valid_rag_results(state):
+            info(f"[Fast RAG] 检索有效，进入 llm_call 生成回答")
+            await dispatch_custom_event("fast_path_end", {"path": "fast_rag", "success": True}, config)
+            # 注意：这里不设置 final_result，让 llm_call 节点处理
+            return state
+
+        # 检索结果无效：标记失败，升级到 React 循环
+        info("[Fast RAG] 无有效检索结果，升级到 React 循环")
+        await dispatch_custom_event("fast_path_end", {"path": "fast_rag", "success": False}, config)
+        return _mark_fast_path_failed(state, "无有效检索结果")
+
+    except Exception as e:
+        info(f"[Fast RAG] 执行失败: {e}")
+        return _mark_fast_path_failed(state, str(e))
+
+
+def _has_valid_rag_results(state: MainGraphState) -> bool:
+    """检查 RAG 结果是否有效（基于置信度）"""
+    from .rag_nodes import RAG_CONFIDENCE_THRESHOLD
+    rag_context = getattr(state, "rag_context", "")
+    rag_confidence = getattr(state, "rag_confidence", 0.0)
+
+    # 有结果且置信度足够
+    has_content = rag_context and len(rag_context) > 0
+    has_confidence = rag_confidence >= RAG_CONFIDENCE_THRESHOLD
+
+    info(f"[Fast RAG Check] has_content={has_content}, rag_confidence={rag_confidence:.2f}, threshold={RAG_CONFIDENCE_THRESHOLD}")
+
+    return has_content and has_confidence
+
+
+async def _generate_fast_answer(state: MainGraphState, query: str) -> MainGraphState:
+    """使用小模型快速生成回答"""
+    try:
+        chat_llm = get_chat_service()
+        rag_context = state.rag_context or str(state.rag_docs)[:2000]
+
+        prompt = f"""请根据以下信息回答用户问题：
+
+检索到的信息：
+{rag_context}
+
+用户问题：{query}
+
+请给出简洁、准确的回答："""
+
+        # 使用流式输出
+        from backend.app.main_graph.config import get_stream_writer
+        writer = get_stream_writer()
+
+        full_content = ""
+        async for chunk in chat_llm.astream(prompt):
+            content = getattr(chunk, 'content', '')
+            if content:
+                full_content += content
+                # 流式输出
+                if writer and hasattr(writer, '__call__'):
+                    try:
+                        writer({
+                            "type": "llm_token",
+                            "token": content
+                        })
+                    except Exception:
+                        pass
+
+        state.final_result = full_content
+        state.success = True
+        state.current_phase = "finalizing"
+        state.fast_path.rag_success = True
+        return state
+
+    except Exception as e:
+        info(f"[Fast RAG] 快速回答生成失败: {e}")
+        return _mark_fast_path_failed(state, "回答生成失败")
+
+
+# ========== 快速工具节点 ==========
+async def fast_tool_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
+    """快速工具节点"""
+    state.current_phase = "fast_tool"
+
+    decision = state.hybrid_router.decision
+    suggested_tools = decision.suggested_tools if (decision and hasattr(decision, 'suggested_tools')) else []
+    info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
+
+    await dispatch_custom_event("fast_path_start", {"path": "fast_tool", "suggested_tools": suggested_tools}, config)
+
+    # 无明确工具建议，升级到 React 循环
+    if not suggested_tools:
+        info("[Fast Tool] 无明确工具建议，升级到 React 循环")
+        return _mark_fast_path_failed(state, "无明确工具建议")
+
+    # 当前版本暂不支持快速工具调用，升级到 React 循环
+    info("[Fast Tool] 快速工具调用暂未完善，升级到 React 循环")
+    return _mark_fast_path_failed(state, "快速工具调用暂未完善")
+
+
+# ========== 条件路由函数 ==========
+def check_fast_path_success(state: MainGraphState) -> str:
+    """检查快速路径是否成功 - 使用新的结构化字段"""
+    if state.fast_path.failed:
+        info("[Fast Path Check] 快速路径失败，升级到 React 循环")
+        return "escalate"
+    
+    info("[Fast Path Check] 快速路径成功，进入 llm_call")
+    return "llm_call"
+
+
+# ========== 公共函数 ==========
+def _mark_fast_path_failed(state: MainGraphState, reason: str = "") -> MainGraphState:
+    """标记快速路径失败，准备升级到 React 循环 - 使用新的结构化字段"""
+    state.fast_path.failed = True
+    state.fast_path.fail_reason = reason
+    state.success = False
+    
+    info(f"[Fast Path] 标记失败，准备升级: {reason}")
+    return state
+
+
+# ========== 导出 ==========
+__all__ = [
+    "fast_chitchat_node",
+    "fast_rag_node",
+    "fast_tool_node",
+    "check_fast_path_success",
+    "_mark_fast_path_failed",
+]