zhanghongyu_0626
9d4d4aaa6d
- Added `status` query parameter to `list_sessions_api` for filtering sessions by their status (active/closed). - Introduced `close_all_sessions_api` endpoint to allow users to close all active sessions for the current actor. - Updated `SessionRepository` to support status filtering in session listing and implemented logic for closing active sessions. - Improved Redis connection settings for better performance and reliability. This update enhances the chat functionality by providing more control over session management, improving user experience and system efficiency.
404 lines
13 KiB
Python
404 lines
13 KiB
Python
"""游客 Agent 编排：LangGraph 9 节点 + DeepSeek LLM。"""
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from __future__ import annotations
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from typing import TypedDict
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from langgraph.graph import END, StateGraph
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from langchain_openai import ChatOpenAI
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from app.config.settings import settings
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from app.model.schemas import AuthContext, IntentType
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from app.service.memory_service import VisitorMemoryService
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from app.service.rag_service import VisitorRagService
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from app.service.visitor_prompts import (
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CHITCHAT_SYSTEM,
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CHITCHAT_USER_TEMPLATE,
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FALLBACK_TEXT,
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GENERATE_SYSTEM,
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GENERATE_USER_TEMPLATE,
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INTENT_SYSTEM,
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INTENT_USER_TEMPLATE,
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TRANSFER_TEXT,
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)
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from app.utils.compliance_guard import (
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REJECT_ACCOUNT,
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REJECT_ADVICE,
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REJECT_COMPARE,
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REJECT_GENERAL,
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REJECT_PREDICT,
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REJECT_REALTIME,
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RISK_DISCLAIMER,
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should_add_disclaimer,
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)
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from app.utils.sanitize_postprocess import finalize_sanitized_reply
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# ---------------------------------------------------------------------------
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# DeepSeek LLM 客户端
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# ---------------------------------------------------------------------------
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def _build_llm() -> ChatOpenAI:
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"""构建 DeepSeek LLM 客户端（OpenAI 兼容）。"""
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return ChatOpenAI(
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model=settings.deepseek_model,
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api_key=settings.deepseek_api_key,
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base_url=settings.deepseek_base_url,
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temperature=settings.deepseek_temperature,
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max_tokens=settings.deepseek_max_tokens,
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)
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# ---------------------------------------------------------------------------
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# LangGraph State
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# ---------------------------------------------------------------------------
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class VisitorState(TypedDict):
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session_id: str
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trace_id: str
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message: str
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intent: str
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chitchat_memory: list[dict]
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consult_memory: list[dict]
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rag_context: str
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rag_sources: list[dict]
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reply: str
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has_disclaimer: bool
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transfer_to_human: bool
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# ---------------------------------------------------------------------------
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# 节点函数
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# ---------------------------------------------------------------------------
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# 意图关键词快速路由（不调 LLM）
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# 转人工关键词：用户主动要求 / 投诉 / 安全问题
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_TRANSFER_KEYWORDS = (
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"转人工", "人工客服", "人工服务", "联系客服", "找客服",
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"投诉", "不满", "纠纷", "账户异常", "被盗", "安全",
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)
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# 拒绝关键词：游客无法处理，直接拒绝，不转人工
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_REJECT_ADVICE_KW = (
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"推荐", "建议买", "建议卖", "包赚", "保本", "稳赚",
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"零风险", "无风险", "躺赚", "保本保息", "刚性兑付",
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)
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_REJECT_ACCOUNT_KW = (
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"我的持仓", "我的账户", "我的余额", "我的风评",
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"我的资产", "我的交易", "我的收益", "我的理财",
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)
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_REJECT_PREDICT_KW = (
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"会涨", "会跌", "收益预测", "走势预测",
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"能赚多少", "收益率多少", "涨跌", "明天行情",
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)
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_REJECT_COMPARE_KW = (
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"和其他", "和XX", "哪个平台好", "哪个好",
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"对比一下", "比一比", "哪家好",
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)
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_REJECT_REALTIME_KW = (
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"实时行情", "实时价格", "现在价格",
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"当前价格", "最新行情",
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)
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# FAQ 关键词：LLM 分类失败时仍可走 RAG（开户/账户操作类）
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_FAQ_KEYWORDS = (
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"开户", "开账户", "办理开户", "开户资料", "开户材料",
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"需要什么资料", "需要哪些资料", "需要什么材料", "需要哪些材料",
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"身份认证", "实名认证", "风险测评", "风评问卷", "忘记密码", "重置密码",
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)
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def _visitor_memory_prompt(session_id: str, kind: str) -> str:
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"""读取游客短期记忆；Redis 不可用时降级为空（与 recall_memory 口径一致）。"""
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try:
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return VisitorMemoryService().as_prompt_text(session_id, kind)
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except Exception:
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return ""
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def _degraded_reply_from_rag(rag_context: str) -> str | None:
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"""LLM 不可用时，从 RAG 首片段提取可读答案。"""
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text = (rag_context or "").strip()
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if not text:
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return None
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parts = text.split("\n", 1)
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if len(parts) < 2:
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return None
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body = parts[1].strip()
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if len(body) < 8:
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return None
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return body[:400]
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def recall_memory(state: VisitorState) -> VisitorState:
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"""节点 1：从 Redis 读取两类短期记忆（Redis 不可用则降级为空）。"""
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mem = VisitorMemoryService()
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sid = state["session_id"]
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try:
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chitchat_memory = mem.recall(sid, "chitchat")
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consult_memory = mem.recall(sid, "consult")
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except Exception:
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chitchat_memory = []
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consult_memory = []
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return {
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"chitchat_memory": chitchat_memory,
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"consult_memory": consult_memory,
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}
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def intent_classify(state: VisitorState) -> VisitorState:
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"""节点 2：意图分类（先关键词快速路由，再 DeepSeek）。"""
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msg = state["message"]
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# 转人工关键词（最高优先级）
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if any(k in msg for k in _TRANSFER_KEYWORDS):
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return {"intent": "transfer_human"}
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# 拒绝关键词（直接拒绝，不转人工）
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if any(k in msg for k in _REJECT_ADVICE_KW):
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return {"intent": "reject", "reply": REJECT_ADVICE}
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if any(k in msg for k in _REJECT_ACCOUNT_KW):
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return {"intent": "reject", "reply": REJECT_ACCOUNT}
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if any(k in msg for k in _REJECT_PREDICT_KW):
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return {"intent": "reject", "reply": REJECT_PREDICT}
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if any(k in msg for k in _REJECT_COMPARE_KW):
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return {"intent": "reject", "reply": REJECT_COMPARE}
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if any(k in msg for k in _REJECT_REALTIME_KW):
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return {"intent": "reject", "reply": REJECT_REALTIME}
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if any(k in msg for k in _FAQ_KEYWORDS):
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return {"intent": "faq"}
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# DeepSeek 分类
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try:
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llm = _build_llm()
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user_prompt = INTENT_USER_TEMPLATE.format(message=msg)
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resp = llm.invoke([
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{"role": "system", "content": INTENT_SYSTEM},
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{"role": "user", "content": user_prompt},
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])
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intent = resp.content.strip().lower()
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# 合法性校验
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valid = {"product_consult", "policy_interpret", "faq", "chit_chat", "reject", "transfer_human", "fallback"}
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if intent not in valid:
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intent = "fallback"
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except Exception:
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intent = "fallback"
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return {"intent": intent}
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def rag_search(state: VisitorState) -> VisitorState:
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"""节点 3：RAG 检索（product_consult/policy_interpret/faq 分支）。"""
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intent = state["intent"]
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rag = VisitorRagService()
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context, sources = rag.retrieve(intent, state["message"])
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if not context:
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# 空结果 → 走兜底
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return {
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"rag_context": "",
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"rag_sources": [],
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"reply": FALLBACK_TEXT,
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"intent": "fallback",
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}
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return {"rag_context": context, "rag_sources": sources}
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def generate(state: VisitorState) -> VisitorState:
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"""节点 4：基于 RAG 上下文 + 短期记忆生成回复。"""
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# 空上下文时跳过 LLM（兜底已在 rag_search 中处理）
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if not state.get("rag_context"):
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return {"reply": state.get("reply", FALLBACK_TEXT)}
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# 如果已经设置了兜底回复，直接返回
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if state.get("reply") and state.get("intent") == "fallback":
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has_disclaimer = should_add_disclaimer("faq")
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reply = state["reply"]
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if has_disclaimer:
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reply = f"{reply}\n\n{RISK_DISCLAIMER}"
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return {"reply": reply, "has_disclaimer": has_disclaimer}
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try:
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llm = _build_llm()
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mem_text = _visitor_memory_prompt(state["session_id"], "consult")
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user_prompt = GENERATE_USER_TEMPLATE.format(
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rag_context=state["rag_context"],
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memory=mem_text,
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message=state["message"],
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)
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resp = llm.invoke([
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{"role": "system", "content": GENERATE_SYSTEM},
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{"role": "user", "content": user_prompt},
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])
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reply = resp.content.strip()
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except Exception:
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degraded = _degraded_reply_from_rag(state.get("rag_context", ""))
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if not degraded:
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return {"reply": FALLBACK_TEXT, "intent": "fallback"}
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reply = degraded
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# 合规护栏（1B：命中不自动 transfer）
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reply, need_transfer = finalize_sanitized_reply(reply, intent=state.get("intent"))
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if need_transfer:
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return {"reply": reply, "transfer_to_human": True, "has_disclaimer": False}
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# 咨询类附带风险提示
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has_disclaimer = should_add_disclaimer(state["intent"])
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if has_disclaimer:
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reply = f"{reply}\n\n{RISK_DISCLAIMER}"
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return {"reply": reply, "has_disclaimer": has_disclaimer}
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def chitchat(state: VisitorState) -> VisitorState:
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"""节点 5：闲聊生成。"""
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try:
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llm = _build_llm()
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mem_text = _visitor_memory_prompt(state["session_id"], "chitchat")
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user_prompt = CHITCHAT_USER_TEMPLATE.format(
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memory=mem_text,
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message=state["message"],
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)
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resp = llm.invoke([
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{"role": "system", "content": CHITCHAT_SYSTEM},
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{"role": "user", "content": user_prompt},
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])
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reply = resp.content.strip()
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except Exception:
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return {"reply": FALLBACK_TEXT, "intent": "fallback"}
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# 合规护栏
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reply, need_transfer = finalize_sanitized_reply(reply, intent=state.get("intent"))
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if need_transfer:
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return {"reply": reply, "transfer_to_human": True, "has_disclaimer": False}
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return {"reply": reply, "has_disclaimer": False}
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def reject(state: VisitorState) -> VisitorState:
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"""节点：直接拒绝（不转人工）。"""
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# 关键词路由已设置 reply 时直接用
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if state.get("reply"):
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return {}
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# DeepSeek 返回 reject 但未设置 reply 时用通用拒绝
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return {"reply": REJECT_GENERAL}
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def transfer_human(state: VisitorState) -> VisitorState:
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"""节点 6：转人工。"""
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return {"reply": TRANSFER_TEXT, "transfer_to_human": True}
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def fallback(state: VisitorState) -> VisitorState:
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"""节点 7：兜底。"""
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return {"reply": FALLBACK_TEXT}
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def save_memory(state: VisitorState) -> VisitorState:
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"""节点 8：保存短期记忆（先写 user，再写 assistant；Redis 失败不阻断）。"""
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mem = VisitorMemoryService()
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sid = state["session_id"]
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intent = state.get("intent", "fallback")
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# 咨询类 → consult 记忆；闲聊/转人工/兜底 → chitchat 记忆
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if intent in ("product_consult", "policy_interpret", "faq"):
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kind = "consult"
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else:
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kind = "chitchat"
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try:
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mem.append(sid, kind, "user", state["message"])
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mem.append(sid, kind, "assistant", state["reply"])
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except Exception:
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pass
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return {}
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# ---------------------------------------------------------------------------
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# 图构建
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# ---------------------------------------------------------------------------
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def _build_graph():
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g = StateGraph(VisitorState)
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g.add_node("recall_memory", recall_memory)
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g.add_node("intent_classify", intent_classify)
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g.add_node("rag_search", rag_search)
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g.add_node("generate", generate)
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g.add_node("chitchat", chitchat)
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g.add_node("reject", reject)
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g.add_node("transfer_human", transfer_human)
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g.add_node("fallback", fallback)
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g.add_node("save_memory", save_memory)
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g.set_entry_point("recall_memory")
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g.add_edge("recall_memory", "intent_classify")
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def _route(state: VisitorState) -> str:
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intent = state["intent"]
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if intent in ("product_consult", "policy_interpret", "faq"):
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return "rag_search"
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if intent == "chit_chat":
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return "chitchat"
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if intent == "reject":
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return "reject"
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if intent == "transfer_human":
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return "transfer_human"
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return "fallback"
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g.add_conditional_edges("intent_classify", _route, {
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"rag_search": "rag_search",
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"chitchat": "chitchat",
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"reject": "reject",
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"transfer_human": "transfer_human",
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"fallback": "fallback",
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})
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g.add_edge("rag_search", "generate")
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g.add_edge("generate", "save_memory")
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g.add_edge("chitchat", "save_memory")
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g.add_edge("reject", "save_memory")
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g.add_edge("transfer_human", "save_memory")
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g.add_edge("fallback", "save_memory")
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g.add_edge("save_memory", END)
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return g.compile()
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_GRAPH = _build_graph()
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# ---------------------------------------------------------------------------
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# 编排入口（对齐 agent_service.run_chat 模式）
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# ---------------------------------------------------------------------------
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def run_visitor_chat(
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ctx: AuthContext,
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message: str,
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session_id: str,
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) -> tuple[str, bool, str, bool]:
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"""游客对话入口，返回 (reply, has_disclaimer, intent, transfer_to_human)。"""
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state: VisitorState = {
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"session_id": session_id,
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"trace_id": ctx.trace_id,
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"message": message,
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"intent": "",
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"chitchat_memory": [],
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"consult_memory": [],
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"rag_context": "",
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"rag_sources": [],
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"reply": "",
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"has_disclaimer": False,
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"transfer_to_human": False,
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}
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result = _GRAPH.invoke(state)
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return (
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result["reply"],
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result.get("has_disclaimer", False),
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result.get("intent", "fallback"),
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result.get("transfer_to_human", False),
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)
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