Mutual_Fund/agent/advisor_agent/intent/recognizer.py

"""投顾聊天入口的轻量意图识别。"""
from __future__ import annotations

import re

from common.common_const import (
    AGENT_INTENT_DATA_QUERY,
    AGENT_INTENT_DIALOGUE_SCRIPT,
    AGENT_INTENT_FUND_ANALYSIS,
    AGENT_INTENT_REBALANCE,
    AGENT_INTENT_RECOMMEND,
)


_QUERY_ACTIONS = (
    "查询",
    "查一下",
    "查看",
    "统计",
    "列出",
    "显示",
    "多少",
    "有哪些",
    "明细",
)
_DATA_TERMS = (
    "持仓",
    "资产",
    "收益",
    "交易记录",
    "申购",
    "赎回",
    "余额",
    "市值",
    "份额",
    "客户数据",
    "账户",
)
_REBALANCE_TERMS = ("调仓", "再平衡", "组合调整", "配置偏离", "偏离目标", "降低仓位", "增加仓位")
_FUND_ANALYSIS_TERMS = ("基金分析", "分析基金", "基金表现", "净值走势", "最大回撤", "夏普比率", "年化波动")
_DIALOGUE_TERMS = ("话术", "沟通", "怎么跟客户说", "如何向客户解释", "安抚客户", "投诉处理")
_RECOMMEND_TERMS = ("推荐", "组合建议", "配置建议", "买什么", "适合配置", "筛选基金", "投资方案")
_CUSTOMER_IDENTITY_TERMS = ("是谁", "姓名", "实名", "基本信息", "联系方式", "手机号")
_SCOPE_ERROR_MESSAGE = "投顾范围查询仅支持客户数据查询"


def _contains_any(text: str, terms: tuple[str, ...]) -> bool:
    return any(term in text for term in terms)


def recognize_advisor_intent(query: str | None, explicit_intent: str | None = None) -> str | None:
    """返回当前投顾聊天应使用的意图；无法判断时返回 ``None``。

    数据查询采用保守规则：必须命中查询动作或客户数据表达，且不能明显是
    推荐、调仓或话术请求，避免把生成类请求送入 NL2SQL。
    """
    if explicit_intent:
        return explicit_intent
    text = re.sub(r"\s+", "", query or "")
    if not text:
        return None
    if text == _SCOPE_ERROR_MESSAGE:
        return None

    # 先处理最明确的任务词，避免“查询基金收益”被误判成普通数据查询。
    if _contains_any(text, _REBALANCE_TERMS):
        return AGENT_INTENT_REBALANCE
    if _contains_any(text, _FUND_ANALYSIS_TERMS):
        return AGENT_INTENT_FUND_ANALYSIS
    if _contains_any(text, _DIALOGUE_TERMS):
        return AGENT_INTENT_DIALOGUE_SCRIPT

    has_action = any(term in text for term in _QUERY_ACTIONS)
    has_data = any(term in text for term in _DATA_TERMS)
    has_customer_identity = (
        "客户" in text
        and _contains_any(text, _CUSTOMER_IDENTITY_TERMS)
        and not _contains_any(text, _RECOMMEND_TERMS)
    )
    if has_customer_identity:
        return AGENT_INTENT_DATA_QUERY
    if has_data and (has_action or "客户" in text or "近一年" in text or "本月" in text):
        return AGENT_INTENT_DATA_QUERY
    if _contains_any(text, _RECOMMEND_TERMS):
        return AGENT_INTENT_RECOMMEND
    return None


__all__ = ["recognize_advisor_intent"]