205 lines
7.2 KiB
Python
205 lines
7.2 KiB
Python
"""
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快速路径节点模块
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包含闲聊、RAG、工具等快速处理节点
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"""
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from typing import Optional
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from langchain_core.runnables.config import RunnableConfig
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from ..state import MainGraphState
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from ...logger import info, debug
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from ...model_services.chat_services import get_small_llm_service, get_chat_service
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from .rag_nodes import rag_retrieve_node
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from ._utils import dispatch_custom_event
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# ========== 闲聊回复模板 ==========
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CHITCHAT_TEMPLATES = {
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"谢谢": "不客气！如果还有其他问题，请随时告诉我 😊",
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"再见": "再见！期待下次为您服务 👋",
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"你好": "你好！有什么我可以帮您的吗？",
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"默认": None # 使用 LLM 生成
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}
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CHITCHAT_KEYWORDS = {
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"谢谢": ["谢谢", "感谢", "thanks", "thank you"],
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"再见": ["再见", "拜拜", "bye", "goodbye"],
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"你好": ["你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好"],
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}
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# ========== 闲聊节点 ==========
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async def fast_chitchat_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
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"""快速闲聊节点"""
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state.current_phase = "fast_chitchat"
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query = state.user_query or ""
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info(f"[Fast Chitchat] 处理: {query[:50]}")
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# 发送开始事件
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await dispatch_custom_event("fast_path_start", {"path": "fast_chitchat"}, config)
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# 清除之前的 final_result，让 llm_call 生成新回答
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state.final_result = None
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# 标记快速路径成功，但不设置 final_result，让 llm_call 生成回答
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state.success = True
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state.current_phase = "llm_call"
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state.debug_info["fast_chitchat_success"] = True
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# 发送完成事件
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await dispatch_custom_event("fast_path_end", {"path": "fast_chitchat", "success": True}, config)
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return state
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def _match_chitchat_template(query: str) -> str:
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"""匹配闲聊模板"""
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query_clean = query.strip().lower()
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for intent, keywords in CHITCHAT_KEYWORDS.items():
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if any(kw in query_clean for kw in keywords):
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return CHITCHAT_TEMPLATES[intent]
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# 默认：使用 LLM 生成
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try:
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llm = get_small_llm_service()
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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：")
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return response.content
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except Exception:
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return "你好！有什么我可以帮您的吗？"
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# ========== 快速 RAG 节点 ==========
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async def fast_rag_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
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"""快速 RAG 节点：只负责 RAG 检索，然后交给 llm_call 生成回答"""
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state.current_phase = "fast_rag"
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query = state.user_query or ""
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info(f"[Fast RAG] 开始处理: {query[:50]}")
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# 获取 RAG 工具
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from backend.app.main_graph.utils.rag_initializer import get_rag_tool
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rag_tool = get_rag_tool()
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info(f"[Fast RAG] 获取到 rag_tool: {rag_tool is not None}")
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# 发送开始事件
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await dispatch_custom_event("fast_path_start", {"path": "fast_rag"}, config)
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# 清除之前的 final_result，让 llm_call 生成新回答
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state.final_result = None
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# 如果没有 rag_tool，升级到 React 循环
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if not rag_tool:
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info("[Fast RAG] 未找到 RAG 工具，升级到 React 循环")
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return _mark_fast_path_failed(state, "未找到 RAG 工具")
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try:
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# 尝试 RAG 检索
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state = await rag_retrieve_node(state, config)
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# 检查检索结果
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if _has_valid_rag_results(state):
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info(f"[Fast RAG] 检索有效，进入 llm_call 生成回答")
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await dispatch_custom_event("fast_path_end", {"path": "fast_rag", "success": True}, config)
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# 注意：这里不设置 final_result，让 llm_call 节点处理
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return state
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# 无效结果：升级到 React 循环
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info("[Fast RAG] 无有效检索结果，升级到 React 循环")
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return _mark_fast_path_failed(state, "无有效检索结果")
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except Exception as e:
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info(f"[Fast RAG] 执行失败: {e}")
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return _mark_fast_path_failed(state, str(e))
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def _has_valid_rag_results(state: MainGraphState) -> bool:
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"""检查 RAG 结果是否有效"""
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rag_docs = getattr(state, "rag_docs", [])
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rag_context = getattr(state, "rag_context", "")
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return (rag_docs and len(rag_docs) > 0) or (rag_context and len(rag_context) > 10)
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async def _generate_fast_answer(state: MainGraphState, query: str) -> MainGraphState:
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"""使用小模型快速生成回答"""
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try:
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chat_llm = get_chat_service()
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rag_context = state.rag_context or str(state.rag_docs)[:2000]
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prompt = f"""请根据以下信息回答用户问题：
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检索到的信息：
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{rag_context}
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用户问题：{query}
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请给出简洁、准确的回答："""
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# 使用流式输出
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from backend.app.main_graph.config import get_stream_writer
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writer = get_stream_writer()
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full_content = ""
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async for chunk in chat_llm.astream(prompt):
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content = getattr(chunk, 'content', '')
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if content:
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full_content += content
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# 流式输出
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if writer and hasattr(writer, '__call__'):
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try:
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writer({
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"type": "llm_token",
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"token": content
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})
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except Exception:
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pass
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state.final_result = full_content
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state.success = True
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state.current_phase = "finalizing"
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state.debug_info["fast_rag_success"] = True
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return state
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except Exception as e:
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info(f"[Fast RAG] 快速回答生成失败: {e}")
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return _mark_fast_path_failed(state, "回答生成失败")
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# ========== 快速工具节点 ==========
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async def fast_tool_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
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"""快速工具节点"""
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state.current_phase = "fast_tool"
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decision = state.debug_info.get("hybrid_decision", {})
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suggested_tools = decision.get("suggested_tools", [])
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info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
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await dispatch_custom_event("fast_path_start", {"path": "fast_tool", "suggested_tools": suggested_tools}, config)
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# 无明确工具建议，升级到 React 循环
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if not suggested_tools:
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info("[Fast Tool] 无明确工具建议，升级到 React 循环")
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return _mark_fast_path_failed(state, "无明确工具建议")
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# 当前版本暂不支持快速工具调用，升级到 React 循环
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info("[Fast Tool] 快速工具调用暂未完善，升级到 React 循环")
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return _mark_fast_path_failed(state, "快速工具调用暂未完善")
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# ========== 公共函数 ==========
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def _mark_fast_path_failed(state: MainGraphState, reason: str = "") -> MainGraphState:
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"""标记快速路径失败，准备升级到 React 循环"""
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state.debug_info["fast_path_failed"] = True
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state.debug_info["fast_path_fail_reason"] = reason
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state.success = False
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info(f"[Fast Path] 标记失败，准备升级: {reason}")
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return state
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# ========== 导出 ==========
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__all__ = [
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"fast_chitchat_node",
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"fast_rag_node",
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"fast_tool_node",
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"_mark_fast_path_failed",
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]
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