170 lines
7.7 KiB
Python
170 lines
7.7 KiB
Python
"""Customer-service intent recognition contract."""
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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 dataclasses import dataclass
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from enum import StrEnum
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from inspect import isawaitable
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logger = logging.getLogger("rag.intent")
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class Intent(StrEnum):
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GUIDE_PURCHASE = "guide_purchase"
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WANT_ADVISOR = "want_advisor"
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NL2SQL_REQUEST = "nl2sql_request"
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KNOWLEDGE_QA = "knowledge_qa"
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COMPANY_INFO = "company_info"
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CHITCHAT = "chitchat"
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OFF_TOPIC = "off_topic"
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TRANSFER_HUMAN = "transfer_human"
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NO_MATCH = "no_match"
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INTENT_VALUES = frozenset(item.value for item in Intent)
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# 按长度降序匹配，避免短标签误命中长标签的一部分
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_INTENT_PATTERN = re.compile(
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"|".join(re.escape(value) for value in sorted(INTENT_VALUES, key=len, reverse=True))
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)
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_JSON_PATTERN = re.compile(r"\{.*\}", re.S)
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# 改写句允许的最大长度：相对原句的倍数与绝对下限取大者，防止模型把历史大段塞进改写句
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_REWRITE_MAX_RATIO = 4
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_REWRITE_MIN_LIMIT = 200
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INTENT_SYSTEM_PROMPT = (
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"你是华夏科技（一家基金代销金融机构）智能客服的意图分类器。"
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"你会看到最近几轮对话（可能为空）和用户的【当前输入】。\n"
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"任务：\n"
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"1. 只对【当前输入】判定意图；【最近对话】仅用于理解当前输入中省略的主语或指代"
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"（如“它”“那个”“呢”“还有吗”）。\n"
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"2. 若当前输入依赖上文才能理解，请把它改写为一句不依赖上文的完整问题，"
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"只能补全上文已明确出现的对象，不得添加新信息、不得改变原意；"
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"若当前输入本身已完整，query 原样返回。\n"
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"3. 当前输入若明显切换到新话题，以当前输入为准，不要延续上一轮的意图。\n"
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"4. 致谢、告别、寒暄即使出现在知识问答之后也归为 chitchat。\n"
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"意图选项：\n"
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"- guide_purchase: 用户询问如何购买基金、开户、注册等引导类问题\n"
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"- want_advisor: 用户希望获得个性化基金推荐或投资顾问服务\n"
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"- knowledge_qa: 用户询问基金相关的知识性问题，如净值、费率、风险、申赎规则等\n"
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"- company_info: 用户询问华夏科技公司本身的静态档案信息，如公司全称、成立时间、牌照、总部地址、"
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"客服电话、服务时间、官网、投诉渠道等；"
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"注意：公司的产品清单、产品数量、产品数据不属于公司信息，应归为 nl2sql_request\n"
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"- nl2sql_request: 用户要求查询具体数据或账户信息，"
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"如“我的持仓有哪些”“我买了多少XX基金”“我的交易记录”"
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"“最近一周XX基金的净值数据”“XX基金最新的规模/费率数据”"
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"“你们公司有哪些股票型基金产品”“公司在售的债券基金有哪些”等"
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"询问公司产品清单/产品数据或个人账户数据、要求数据本身而非知识解释的问题\n"
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"- chitchat: 普通寒暄，如问候、致谢、告别、询问你是谁/你能做什么、在吗等一两句话的闲聊\n"
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"- off_topic: 用户要求你实质性地处理与金融、基金、公司业务无关的事情，"
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"如写代码、讲笑话、写作文、问天气、聊政治、情感咨询、做数学题等\n"
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"- transfer_human: 用户明确要求转接人工客服，或表达不满、投诉、对智能客服不信任，"
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"希望由真人处理，如“转人工”“找人工客服”“我要投诉”“人工服务”“我要见真人”“别跟我说废话找客服”等\n"
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"- no_match: 无法归入以上任何一类\n"
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"注意：寒暄性质的一两句话归为 chitchat；一旦用户提出金融之外的实质性请求，归为 off_topic。\n"
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"只输出一行 JSON，格式：{\"intent\": \"<小写英文标签>\", \"query\": \"<完整问题>\"}，"
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"不要输出解释、Markdown 或其他内容。"
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)
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@dataclass(frozen=True)
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class IntentResult:
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intent: Intent
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# 结合历史补全后的独立问题；无需补全或解析失败时等于原句
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query: str
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used_history: bool = False
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async def _config_value(config_getter, key: str, default):
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value = config_getter(key, str(default))
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if isawaitable(value):
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value = await value
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return type(default)(value)
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def parse_intent(raw: str | None) -> Intent:
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"""从模型原始输出中提取第一个合法标签，提取不到则返回 NO_MATCH。"""
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if not raw:
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return Intent.NO_MATCH
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match = _INTENT_PATTERN.search(raw.lower())
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return Intent(match.group(0)) if match else Intent.NO_MATCH
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def parse_intent_result(raw: str | None, *, fallback_query: str, used_history: bool = False) -> IntentResult:
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"""优先按 JSON 解析意图与改写句；JSON 不可用时退回正则抽标签、原句作为 query。"""
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match = _JSON_PATTERN.search(raw) if raw else None
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data = None
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if match:
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try:
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data = json.loads(match.group(0))
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except ValueError:
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data = None
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if not isinstance(data, dict):
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return IntentResult(parse_intent(raw), fallback_query, used_history)
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intent = parse_intent(str(data.get("intent") or ""))
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rewritten = str(data.get("query") or "").strip()
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limit = max(len(fallback_query) * _REWRITE_MAX_RATIO, _REWRITE_MIN_LIMIT)
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query = rewritten if rewritten and len(rewritten) <= limit else fallback_query
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return IntentResult(intent, query, used_history)
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def render_history(history, *, max_turns: int, max_chars: int) -> str:
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"""把最近 max_turns 轮 user/assistant 消息折叠成一段文本，每条截断到 max_chars。"""
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if max_turns <= 0 or not history:
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return ""
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recent = [
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message for message in history
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if isinstance(message, dict)
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and message.get("role") in ("user", "assistant")
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and message.get("content")
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][-max_turns * 2:]
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lines = []
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for message in recent:
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role = "用户" if message["role"] == "user" else "客服"
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content = str(message["content"]).replace("\n", " ").strip()
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if len(content) > max_chars:
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content = content[:max_chars] + "…"
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lines.append(f"{role}：{content}")
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return "\n".join(lines)
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def build_intent_messages(query: str, rendered_history: str) -> list[dict]:
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if rendered_history:
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user_content = f"【最近对话】\n{rendered_history}\n\n【当前输入】\n{query}"
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else:
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user_content = f"【当前输入】\n{query}"
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return [
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{"role": "system", "content": INTENT_SYSTEM_PROMPT},
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{"role": "user", "content": user_content},
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]
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async def intent_recognize(
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query: str,
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*,
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llm_client,
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history: list[dict] | None = None,
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config_getter=None,
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) -> IntentResult:
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"""结合最近几轮对话识别当前输入的意图，并给出补全指代后的独立问题。
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history 为空或 agent.customer.intent.history_turns 配置为 0 时退化为仅看当前句。
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"""
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if not query or not query.strip():
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return IntentResult(Intent.NO_MATCH, query or "")
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max_turns, max_chars = 3, 200
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if config_getter is not None:
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max_turns = await _config_value(config_getter, "agent.customer.intent.history_turns", max_turns)
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max_chars = await _config_value(config_getter, "agent.customer.intent.history_max_chars", max_chars)
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rendered = render_history(history, max_turns=max_turns, max_chars=max_chars)
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messages = build_intent_messages(query, rendered)
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try:
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raw = await llm_client.chat(messages)
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except Exception:
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logger.exception("intent recognition failed")
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return IntentResult(Intent.NO_MATCH, query, bool(rendered))
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return parse_intent_result(raw, fallback_query=query, used_history=bool(rendered)) |