252 lines
9.5 KiB
Python
252 lines
9.5 KiB
Python
"""Anonymous customer-service orchestration without private customer access."""
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from __future__ import annotations
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import json
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import logging
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import re
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from inspect import isawaitable
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from rag.intent import Intent, IntentResult
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logger = logging.getLogger(__name__)
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class QueryTooLongError(ValueError):
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pass
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class DataQueryRejected(ValueError):
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"""NL2SQL 数据查询被拒绝（未开放、无权限或配额不足），message 可直接回复用户。"""
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async def _config(config_getter, key: str, default: str):
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value = config_getter(key, default)
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if isawaitable(value):
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value = await value
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return value or default
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async def _maybe_await(value):
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return await value if isawaitable(value) else value
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class AnonymousCustomerAgent:
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def __init__(
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self,
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*,
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context,
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rag_retrieve,
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intent_recognize,
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generate_answer,
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audit_writer,
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config_getter,
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data_query=None,
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):
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self.context = context
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self.rag_retrieve = rag_retrieve
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self.intent_recognize = intent_recognize
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self.generate_answer = generate_answer
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self.audit_writer = audit_writer
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self.config_getter = config_getter
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# 可选 NL2SQL 数据查询依赖：签名 data_query(*, question, customer_id,
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# session_id, trace_id) -> dict；匿名 runtime 不装配（None），行为不变。
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self.data_query = data_query
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async def handle(
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self,
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session_id: str,
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query: str,
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*,
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trace_id: str,
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customer_id: int | None = None,
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) -> dict:
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if len(query) > 2000:
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raise QueryTooLongError("query长度不能超过2000字符")
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# 先取历史再写入当前问题，保证意图识别拿到的历史不含本轮输入；取不到历史不阻断请求
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try:
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history = await self.context.get(session_id)
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except Exception:
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logger.exception("load conversation history failed: session_id=%s", session_id)
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history = []
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await self.context.append(session_id, "user", query)
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if self._contains_sensitive_input(query):
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await _maybe_await(self.audit_writer(
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action="anon_sensitive_input",
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trace_id=trace_id,
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session_id=session_id,
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))
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recognized = await _maybe_await(self.intent_recognize(query, history))
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if isinstance(recognized, IntentResult):
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intent, search_query = recognized.intent, recognized.query
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else:
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intent, search_query = recognized, query
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sources = []
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data_query_meta = None
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if intent == Intent.GUIDE_PURCHASE:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.guide_purchase",
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"请前往开户页面办理。",
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)
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elif intent == Intent.WANT_ADVISOR:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.guide_advisor",
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"如需基金推荐，请联系投资顾问。",
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)
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elif intent == Intent.OFF_TOPIC:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.off_topic",
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"我是华夏科技的智能客服，只能解答基金与公司业务相关的问题，"
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"您可以问我基金知识、开户流程或公司信息～",
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)
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elif intent == Intent.CHITCHAT:
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answer = await self._chitchat(session_id)
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elif intent == Intent.NL2SQL_REQUEST:
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if self.data_query is None or customer_id is None:
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# 匿名会话或未装配数据查询能力：引导登录，不触发任何数据库查询
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answer = await _config(
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self.config_getter,
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"agent.customer.template.nl2sql_unavailable",
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"数据查询功能需要登录后使用，请先登录再来问我您的持仓和交易信息～",
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)
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else:
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answer, sources, data_query_meta = await self._run_data_query(
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question=search_query,
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customer_id=customer_id,
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session_id=session_id,
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trace_id=trace_id,
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)
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elif intent in (Intent.KNOWLEDGE_QA, Intent.COMPANY_INFO):
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try:
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# 用补全指代后的问题检索，省略主语的追问才能命中
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sources = await _maybe_await(self.rag_retrieve(search_query, None))
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except Exception:
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sources = []
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if not sources:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.fallback_human",
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"当前未找到匹配信息，请转人工客服。",
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)
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else:
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messages = await self.context.get(session_id)
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prompt = messages + [
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{
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"role": "system",
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"content": "仅根据提供的知识来源回答，不得编造基金推荐。",
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},
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{
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"role": "system",
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"content": f"知识来源：{json.dumps(sources, ensure_ascii=False)}",
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},
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]
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try:
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answer = await _maybe_await(self.generate_answer(prompt))
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except Exception:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.fallback_human",
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"当前服务繁忙，请转人工客服。",
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)
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else:
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answer = await _config(
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self.config_getter,
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"agent.customer.template.fallback_human",
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"当前未找到匹配信息，请转人工客服。",
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)
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await self.context.append(session_id, "assistant", answer)
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result = {
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"answer": answer,
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"sources": sources,
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"intent": intent.value,
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"rewritten_query": search_query,
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"trace_id": trace_id,
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}
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if data_query_meta is not None:
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result["data_query"] = data_query_meta
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return result
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async def _run_data_query(
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self,
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*,
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question: str,
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customer_id: int,
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session_id: str,
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trace_id: str,
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) -> tuple[str, list, dict]:
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"""调用注入的 NL2SQL 数据查询能力，失败时统一降级为客服话术。"""
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try:
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payload = await _maybe_await(
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self.data_query(
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question=question,
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customer_id=customer_id,
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session_id=session_id,
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trace_id=trace_id,
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)
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)
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except DataQueryRejected as exc:
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return str(exc), [], None
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except Exception:
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logger.exception(
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"client data query failed: trace_id=%s session_id=%s customer_id=%s",
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trace_id,
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session_id,
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customer_id,
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)
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answer = await _config(
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self.config_getter,
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"agent.customer.template.nl2sql_fallback",
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"暂时无法完成数据查询，请稍后再试或联系人工客服。",
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)
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return answer, [], None
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if not isinstance(payload, dict) or not str(payload.get("answer") or "").strip():
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return await _config(
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self.config_getter,
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"agent.customer.template.nl2sql_fallback",
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"暂时无法完成数据查询，请稍后再试或联系人工客服。",
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), [], None
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meta = {
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key: payload[key]
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for key in ("query_id", "row_count", "truncated", "chart")
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if payload.get(key) is not None
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}
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return str(payload["answer"]), list(payload.get("sources") or []), meta or None
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async def _chitchat(self, session_id: str) -> str:
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"""带对话历史调用 LLM 做受限闲聊，失败时退回固定话术。"""
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messages = await self.context.get(session_id)
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prompt = [
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{
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"role": "system",
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"content": (
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"你是华夏科技（基金代销金融机构）的智能客服助手。"
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"用户正在与你寒暄，请用一两句话简短、友好地回应，"
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"并自然地引导用户咨询基金知识、开户流程或公司信息。"
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"不得推荐任何基金产品，不得谈论具体收益，不得回答金融之外的实质性问题。"
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),
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},
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*messages,
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]
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try:
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return await _maybe_await(self.generate_answer(prompt))
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except Exception:
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return await _config(
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self.config_getter,
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"agent.customer.template.chitchat_fallback",
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"您好，我是华夏科技的智能客服，很高兴为您服务！"
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"您可以问我基金知识、开户流程或公司信息～",
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)
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@staticmethod
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def _contains_sensitive_input(query: str) -> bool:
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return bool(
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re.search(r"(?<!\d)1[3-9]\d{9}(?!\d)", query)
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or re.search(r"(?:客户|customer)[_ -]?(?:id|号)?\s*[:：]?\s*[A-Za-z0-9_-]{4,}", query, re.I)
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)
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