0.1.1 暂存

2026-04-14 10:28:22 +08:00
parent 4a0638ba2e
commit cc83fe963a
83 changed files with 4005 additions and 179 deletions
@@ -0,0 +1,135 @@
+"""
+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。
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+import unicodedata
+from enum import Enum
+from typing import NamedTuple, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class DialogIntent(str, Enum):
+    TEXT2SQL = "text2sql"
+    CONVERSATION = "conversation"
+
+
+class DialogClassifyResult(NamedTuple):
+    intent: DialogIntent
+    """若为 CONVERSATION，可展示给用户的引导文案；TEXT2SQL 时为 None。"""
+    reply_suggestion: Optional[str] = None
+
+
+DEFAULT_CONVERSATION_REPLY = (
+    "您好，我是业务库 Text2SQL 助手。\n"
+    "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"
+    "输入 quit 或 exit 可退出。"
+)
+
+_EMPTY_INPUT_REPLY = "请输入具体的业务查询问题，或输入 quit 退出。"
+
+# 一旦出现，倾向于按「要查数据」处理（含常见业务词，避免误判）
+_SQL_OR_QUERY_HINT_RE = re.compile(
+    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|"
+    r"多少|几个|几张|哪些|占比|同比|环比|"
+    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|"
+    r"query|select|list|show|count|sum|avg|how\s+many|statistics|\bfrom\b|\bwhere\b|\btable\b)",
+    re.IGNORECASE,
+)
+
+_CHITCHAT_PHRASES = frozenset(
+    {
+        "你好",
+        "您好",
+        "嗨",
+        "哈喽",
+        "hello",
+        "hi",
+        "hey",
+        "早上好",
+        "下午好",
+        "晚上好",
+        "在吗",
+        "在不在",
+        "谢谢",
+        "多谢",
+        "感谢",
+        "thanks",
+        "thank you",
+        "thx",
+        "再见",
+        "拜拜",
+        "bye",
+        "goodbye",
+        "哈哈",
+        "哈哈哈",
+        "嗯",
+        "嗯嗯",
+        "好的",
+        "好",
+        "ok",
+        "okay",
+        "行",
+        "收到",
+        "👋",
+        "😀",
+        "哈哈谢谢",
+    }
+)
+
+_CHITCHAT_KEYS = frozenset(p.casefold() for p in _CHITCHAT_PHRASES)
+
+_META_QUESTION_RE = re.compile(
+    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|"
+    r"什么功能|干啥的)",
+    re.IGNORECASE,
+)
+
+
+def _normalize(text: str) -> str:
+    t = unicodedata.normalize("NFKC", text or "").strip()
+    t = re.sub(r"\s+", " ", t)
+    return t
+
+
+def _strip_trailing_punct(t: str) -> str:
+    return re.sub(r"[！!。.?？,，;；:~～…、]+$", "", t).strip()
+
+
+def classify_dialog(user_text: str) -> DialogClassifyResult:
+    """
+    对用户一轮输入做粗分类。
+
+    策略：优先用「查询/业务」关键词锁定 TEXT2SQL；否则对短寒暄、致谢、元问题判为 CONVERSATION；
+    其余默认 TEXT2SQL，避免漏判真实查询。
+    """
+    t = _normalize(user_text)
+    if not t:
+        return DialogClassifyResult(
+            DialogIntent.CONVERSATION, reply_suggestion=_EMPTY_INPUT_REPLY
+        )
+
+    if _SQL_OR_QUERY_HINT_RE.search(t):
+        logger.debug("[dialog] intent=text2sql (query/business hint)")
+        return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
+
+    core = _strip_trailing_punct(t)
+    if core.casefold() in _CHITCHAT_KEYS:
+        logger.debug("[dialog] intent=conversation (chitchat phrase)")
+        return DialogClassifyResult(
+            DialogIntent.CONVERSATION, reply_suggestion=DEFAULT_CONVERSATION_REPLY
+        )
+
+    if _META_QUESTION_RE.search(t):
+        logger.debug("[dialog] intent=conversation (meta question)")
+        return DialogClassifyResult(
+            DialogIntent.CONVERSATION,
+            reply_suggestion=DEFAULT_CONVERSATION_REPLY,
+        )
+
+    logger.debug("[dialog] intent=text2sql (default)")
+    return DialogClassifyResult(DialogIntent.TEXT2SQL, None)