288 lines
8.9 KiB
Python
288 lines
8.9 KiB
Python
"""
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用户输入意图分类：区分「自然语言查数 / Text2SQL」与「寒暄、致谢、元问题」等不适合直接生成 SQL 的对话。
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支持环境变量 ``DIALOG_INTENT_CLASSIFIER``：
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- ``rules``：仅用关键词与短语规则（无 LLM 调用）。
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- ``hybrid``（默认）：明显查数词/寒暄/元问题走规则；其余交 LLM 判断（需调用方传入 ``llm_client``）。
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"""
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from __future__ import annotations
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import logging
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import os
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import re
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import unicodedata
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from enum import Enum
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from typing import TYPE_CHECKING, NamedTuple, Optional
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if TYPE_CHECKING:
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from llm.deepseek_client import DeepSeekClient
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logger = logging.getLogger(__name__)
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_INTENT_CLASSIFIER_ENV = "DIALOG_INTENT_CLASSIFIER"
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class DialogIntent(str, Enum):
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TEXT2SQL = "text2sql"
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CONVERSATION = "conversation"
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class DialogClassifyResult(NamedTuple):
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intent: DialogIntent
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"""若为 CONVERSATION，可展示给用户的引导文案；TEXT2SQL 时为 None。"""
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reply_suggestion: Optional[str] = None
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DEFAULT_CONVERSATION_REPLY = (
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"您好，我是业务库 Text2SQL 助手。\n"
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"请用自然语言描述要查询或统计的内容（例如：查询某账户可用余额、按经纪商汇总未结算交易笔数）。\n"
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"输入 quit 或 exit 可退出。"
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)
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_EMPTY_INPUT_REPLY = "请输入具体的业务查询问题，或输入 quit 退出。"
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# 一旦出现，倾向于按「要查数据」处理（含常见业务词，避免误判）
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_SQL_OR_QUERY_HINT_RE = re.compile(
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r"(查|查询|查出|检索|统计|列出|汇总|求和|平均|分组|排序|排名|显示|导出|筛选|过滤|"
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r"多少|几个|几张|哪些|占比|同比|环比|"
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r"余额|交易|账户|持仓|报表|结算|合约|订单|流水|经纪商|对手方|证券|资金|"
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r"query|select|list|show|count|sum|avg|how\s+many|statistics|\bfrom\b|\bwhere\b|\btable\b)",
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re.IGNORECASE,
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)
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_CHITCHAT_PHRASES = frozenset(
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{
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"你好",
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"您好",
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"嗨",
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"哈喽",
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"hello",
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"hi",
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"hey",
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"早上好",
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"下午好",
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"晚上好",
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"在吗",
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"在不在",
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"谢谢",
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"多谢",
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"感谢",
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"thanks",
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"thank you",
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"thx",
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"再见",
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"拜拜",
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"bye",
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"goodbye",
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"哈哈",
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"哈哈哈",
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"嗯",
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"嗯嗯",
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"好的",
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"好",
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"ok",
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"okay",
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"行",
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"收到",
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"👋",
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"😀",
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"哈哈谢谢",
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}
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)
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_CHITCHAT_KEYS = frozenset(p.casefold() for p in _CHITCHAT_PHRASES)
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_META_QUESTION_RE = re.compile(
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r"(你是谁|你是什么|你能(做|干)什么|你会什么|怎么用|如何使用|使用说明|帮助|help\b|"
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r"什么功能|干啥的)",
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re.IGNORECASE,
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)
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def _normalize(text: str) -> str:
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t = unicodedata.normalize("NFKC", text or "").strip()
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t = re.sub(r"\s+", " ", t)
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return t
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def _strip_trailing_punct(t: str) -> str:
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return re.sub(r"[！!。.?？,，;；:~～…、]+$", "", t).strip()
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def _intent_classifier_mode() -> str:
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m = (os.getenv(_INTENT_CLASSIFIER_ENV) or "hybrid").strip().lower()
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if m not in ("rules", "hybrid"):
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logger.warning(
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"[dialog] unknown DIALOG_INTENT_CLASSIFIER=%r, use hybrid", m
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)
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return "hybrid"
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return m
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def _classify_dialog_rules(
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user_text: str,
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*,
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last_turn_was_data_query: bool = False,
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) -> DialogClassifyResult:
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"""
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规则分类（与历史行为一致）：优先查数关键词；寒暄/元问题为 conversation；
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若上一轮为数据查询且非寒暄/元问题，则倾向 text2sql。
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"""
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t = _normalize(user_text)
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if not t:
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return DialogClassifyResult(
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DialogIntent.CONVERSATION, reply_suggestion=_EMPTY_INPUT_REPLY
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)
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if _SQL_OR_QUERY_HINT_RE.search(t):
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logger.debug("[dialog] intent=text2sql (query/business hint)")
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return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
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core = _strip_trailing_punct(t)
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if core.casefold() in _CHITCHAT_KEYS:
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logger.debug("[dialog] intent=conversation (chitchat phrase)")
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return DialogClassifyResult(
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DialogIntent.CONVERSATION, reply_suggestion=DEFAULT_CONVERSATION_REPLY
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)
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if _META_QUESTION_RE.search(t):
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logger.debug("[dialog] intent=conversation (meta question)")
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return DialogClassifyResult(
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DialogIntent.CONVERSATION,
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reply_suggestion=DEFAULT_CONVERSATION_REPLY,
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)
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if last_turn_was_data_query:
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logger.debug("[dialog] intent=text2sql (follow-up after data query)")
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return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
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logger.debug("[dialog] intent=text2sql (default)")
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return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
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def _classify_dialog_llm(
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client: DeepSeekClient,
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user_text: str,
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*,
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last_turn_was_data_query: bool,
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dialog_context: Optional[str],
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) -> DialogClassifyResult:
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from config.prompts import (
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DIALOG_INTENT_CLASSIFIER_SYSTEM,
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DIALOG_INTENT_CLASSIFIER_USER,
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)
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last_label = "是" if last_turn_was_data_query else "否"
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ctx = (dialog_context or "").strip()
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if len(ctx) > 2400:
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ctx = ctx[:2400].rstrip() + "\n…(已截断)"
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if not ctx:
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ctx = "(无)"
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raw = client.chat_with_json(
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[
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{"role": "system", "content": DIALOG_INTENT_CLASSIFIER_SYSTEM},
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{
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"role": "user",
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"content": DIALOG_INTENT_CLASSIFIER_USER.format(
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last_turn_label=last_label,
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context_snip=ctx,
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user_message=_normalize(user_text),
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),
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},
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],
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temperature=0.0,
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max_tokens=256,
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top_p=1.0,
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)
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if raw.get("_json_decode_failed"):
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raise ValueError("intent JSON decode failed")
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intent_s = (raw.get("intent") or "").strip().lower()
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reply = (raw.get("reply_zh") or "").strip()
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if intent_s == "conversation":
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if not reply:
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reply = (
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"若上一版 SQL 或结果不符合预期，请具体说明：希望增加/修改哪些条件、"
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"时间范围或统计维度，以便重新生成。"
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)
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logger.info("[dialog] intent=conversation (LLM)")
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return DialogClassifyResult(DialogIntent.CONVERSATION, reply_suggestion=reply)
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if intent_s == "text2sql":
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logger.info("[dialog] intent=text2sql (LLM)")
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return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
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raise ValueError(f"unexpected intent field: {intent_s!r}")
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def classify_dialog(
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user_text: str,
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*,
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last_turn_was_data_query: bool = False,
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dialog_context: Optional[str] = None,
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llm_client: Optional[DeepSeekClient] = None,
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) -> DialogClassifyResult:
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"""
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对用户一轮输入做分类。
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``DIALOG_INTENT_CLASSIFIER``：
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- ``rules``：仅规则。
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- ``hybrid``：规则快速命中查数词/寒暄/元问题后返回；否则在有 ``llm_client`` 时用 LLM，
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失败或无客户端时回退规则。
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Args:
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user_text: 用户输入。
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last_turn_was_data_query: 上一轮助手是否为数据查询（供规则与 LLM 参考）。
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dialog_context: 会话摘要，供 LLM 参考（可选）。
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llm_client: DeepSeek 客户端；hybrid 下 LLM 分支需要。
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"""
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mode = _intent_classifier_mode()
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if mode == "rules":
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return _classify_dialog_rules(
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user_text, last_turn_was_data_query=last_turn_was_data_query
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)
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# hybrid
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t = _normalize(user_text)
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if not t:
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return DialogClassifyResult(
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DialogIntent.CONVERSATION, reply_suggestion=_EMPTY_INPUT_REPLY
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)
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if _SQL_OR_QUERY_HINT_RE.search(t):
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logger.debug("[dialog] intent=text2sql (query/business hint, hybrid fast)")
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return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
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core = _strip_trailing_punct(t)
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if core.casefold() in _CHITCHAT_KEYS:
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logger.debug("[dialog] intent=conversation (chitchat phrase, hybrid fast)")
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return DialogClassifyResult(
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DialogIntent.CONVERSATION, reply_suggestion=DEFAULT_CONVERSATION_REPLY
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)
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if _META_QUESTION_RE.search(t):
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logger.debug("[dialog] intent=conversation (meta question, hybrid fast)")
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return DialogClassifyResult(
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DialogIntent.CONVERSATION,
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reply_suggestion=DEFAULT_CONVERSATION_REPLY,
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)
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if llm_client is not None:
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try:
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return _classify_dialog_llm(
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llm_client,
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user_text,
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last_turn_was_data_query=last_turn_was_data_query,
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dialog_context=dialog_context,
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)
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except Exception as e:
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logger.warning("[dialog] LLM intent failed, fallback rules: %s", e)
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return _classify_dialog_rules(
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user_text, last_turn_was_data_query=last_turn_was_data_query
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)
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