Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.

2026-04-14 18:02:12 +08:00
parent 026d3bf88c
commit ca8bc5e7de
87 changed files with 5808 additions and 6351 deletions
@@ -1,17 +1,27 @@
 """
 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。
+
+支持环境变量 ``DIALOG_INTENT_CLASSIFIER``：
+- ``rules``：仅用关键词与短语规则（无 LLM 调用）。
+- ``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``）。
 """

 from __future__ import annotations

 import logging
+import os
 import re
 import unicodedata
 from enum import Enum
-from typing import NamedTuple, Optional
+from typing import TYPE_CHECKING, NamedTuple, Optional
+
+if TYPE_CHECKING:
+    from llm.deepseek_client import DeepSeekClient

 logger = logging.getLogger(__name__)

+_INTENT_CLASSIFIER_ENV = "DIALOG_INTENT_CLASSIFIER"
+

 class DialogIntent(str, Enum):
    TEXT2SQL = "text2sql"
@@ -100,12 +110,24 @@ 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。
+def _intent_classifier_mode() -> str:
+    m = (os.getenv(_INTENT_CLASSIFIER_ENV) or "hybrid").strip().lower()
+    if m not in ("rules", "hybrid"):
+        logger.warning(
+            "[dialog] unknown DIALOG_INTENT_CLASSIFIER=%r, use hybrid", m
+        )
+        return "hybrid"
+    return m

-    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。
+
+def _classify_dialog_rules(
+    user_text: str,
+    *,
+    last_turn_was_data_query: bool = False,
+) -> DialogClassifyResult:
+    """
+    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；
+    若上一轮为数据查询且非寒暄/元问题，则倾向 text2sql。
    """
    t = _normalize(user_text)
    if not t:
@@ -131,5 +153,135 @@ def classify_dialog(user_text: str) -> DialogClassifyResult:
            reply_suggestion=DEFAULT_CONVERSATION_REPLY,
        )

+    if last_turn_was_data_query:
+        logger.debug("[dialog] intent=text2sql (follow-up after data query)")
+        return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
+
    logger.debug("[dialog] intent=text2sql (default)")
    return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
+
+
+def _classify_dialog_llm(
+    client: DeepSeekClient,
+    user_text: str,
+    *,
+    last_turn_was_data_query: bool,
+    dialog_context: Optional[str],
+) -> DialogClassifyResult:
+    from config.prompts import (
+        DIALOG_INTENT_CLASSIFIER_SYSTEM,
+        DIALOG_INTENT_CLASSIFIER_USER,
+    )
+
+    last_label = "是" if last_turn_was_data_query else "否"
+    ctx = (dialog_context or "").strip()
+    if len(ctx) > 2400:
+        ctx = ctx[:2400].rstrip() + "\n…(已截断)"
+    if not ctx:
+        ctx = "(无)"
+
+    raw = client.chat_with_json(
+        [
+            {"role": "system", "content": DIALOG_INTENT_CLASSIFIER_SYSTEM},
+            {
+                "role": "user",
+                "content": DIALOG_INTENT_CLASSIFIER_USER.format(
+                    last_turn_label=last_label,
+                    context_snip=ctx,
+                    user_message=_normalize(user_text),
+                ),
+            },
+        ],
+        temperature=0.0,
+        max_tokens=256,
+        top_p=1.0,
+    )
+    if raw.get("_json_decode_failed"):
+        raise ValueError("intent JSON decode failed")
+
+    intent_s = (raw.get("intent") or "").strip().lower()
+    reply = (raw.get("reply_zh") or "").strip()
+
+    if intent_s == "conversation":
+        if not reply:
+            reply = (
+                "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、"
+                "时间范围或统计维度，以便重新生成。"
+            )
+        logger.info("[dialog] intent=conversation (LLM)")
+        return DialogClassifyResult(DialogIntent.CONVERSATION, reply_suggestion=reply)
+
+    if intent_s == "text2sql":
+        logger.info("[dialog] intent=text2sql (LLM)")
+        return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
+
+    raise ValueError(f"unexpected intent field: {intent_s!r}")
+
+
+def classify_dialog(
+    user_text: str,
+    *,
+    last_turn_was_data_query: bool = False,
+    dialog_context: Optional[str] = None,
+    llm_client: Optional[DeepSeekClient] = None,
+) -> DialogClassifyResult:
+    """
+    对用户一轮输入做分类。
+
+    ``DIALOG_INTENT_CLASSIFIER``：
+    - ``rules``：仅规则。
+    - ``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，
+      失败或无客户端时回退规则。
+
+    Args:
+        user_text: 用户输入。
+        last_turn_was_data_query: 上一轮助手是否为数据查询（供规则与 LLM 参考）。
+        dialog_context: 会话摘要，供 LLM 参考（可选）。
+        llm_client: DeepSeek 客户端；hybrid 下 LLM 分支需要。
+    """
+    mode = _intent_classifier_mode()
+
+    if mode == "rules":
+        return _classify_dialog_rules(
+            user_text, last_turn_was_data_query=last_turn_was_data_query
+        )
+
+    # hybrid
+    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, hybrid fast)")
+        return DialogClassifyResult(DialogIntent.TEXT2SQL, None)
+
+    core = _strip_trailing_punct(t)
+    if core.casefold() in _CHITCHAT_KEYS:
+        logger.debug("[dialog] intent=conversation (chitchat phrase, hybrid fast)")
+        return DialogClassifyResult(
+            DialogIntent.CONVERSATION, reply_suggestion=DEFAULT_CONVERSATION_REPLY
+        )
+
+    if _META_QUESTION_RE.search(t):
+        logger.debug("[dialog] intent=conversation (meta question, hybrid fast)")
+        return DialogClassifyResult(
+            DialogIntent.CONVERSATION,
+            reply_suggestion=DEFAULT_CONVERSATION_REPLY,
+        )
+
+    if llm_client is not None:
+        try:
+            return _classify_dialog_llm(
+                llm_client,
+                user_text,
+                last_turn_was_data_query=last_turn_was_data_query,
+                dialog_context=dialog_context,
+            )
+        except Exception as e:
+            logger.warning("[dialog] LLM intent failed, fallback rules: %s", e)
+
+    return _classify_dialog_rules(
+        user_text, last_turn_was_data_query=last_turn_was_data_query
+    )