162 lines
6.7 KiB
Python
162 lines
6.7 KiB
Python
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# -*- coding: utf-8 -*-
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"""Template-Ti 一期 · 自然语言查询接口（NL→SQL/API）—— issue #76。
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父 Issue #11「④ LLM 报警解释 / 交接班 / NL 查询」子任务：
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把驾驶舱/对话中的自然语言问题翻译为**结构化查询**：
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- 意图识别（intent）：trend（趋势）/ latest（最新值）/ kpi（统计指标）/ alarm（告警）；
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- 指标映射（metric）：自然语言指标名 → 点位（point_id），配置驱动
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（`config/nl_query.template.yaml` 指标字典）；
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- 时间范围（time_range）：从问句抽取（"最近 1 小时" → 1h）；
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- 产出：TDengine SQL（超级表查询）+ 驾驶舱 API 调用参数（to_api_params）。
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纯本地规则实现（无 LLM 依赖、可离线测试）；未识别意图/指标时给出
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结构化降级（intent=unsupported），由上层转 LLM 问答（query_cockpit）。
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"""
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from __future__ import annotations
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import os
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import re
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional
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#: 默认配置资产路径（相对本模块）
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DEFAULT_CONFIG_PATH = os.path.join(
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os.path.dirname(os.path.abspath(__file__)), "config",
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"nl_query.template.yaml")
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#: 默认 TDengine 超级表（对齐 data-bus tdengine_schema 命名）
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DEFAULT_TABLE = "tpl_ti_cl4.points"
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#: 时间范围抽取正则：最近 N 小时/分钟/天
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_TIME_RANGE_RE = re.compile(r"最近\s*(\d+)\s*(小时|分钟|天|h|min|d)")
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_TIME_UNIT = {"小时": "h", "分钟": "m", "天": "d", "h": "h", "min": "m", "d": "d"}
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@dataclass
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class NLQuery:
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"""一次 NL 查询的结构化结果。"""
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question: str
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intent: str # trend | latest | kpi | alarm | unsupported
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metric: str = ""
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point_id: str = ""
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device: str = ""
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time_range: str = "" # 如 "1h"；空 = 默认窗口
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sql: str = "" # TDengine SQL（intent=unsupported 时为空）
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meta: dict = field(default_factory=dict)
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def to_api_params(self) -> dict:
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"""驾驶舱 API 调用参数（供前端查询接口使用）。"""
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return {
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"intent": self.intent, "metric": self.metric,
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"point_id": self.point_id, "device": self.device,
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"time_range": self.time_range or "1h",
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}
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class NLQueryTranslator:
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"""自然语言 → 结构化查询（NL→SQL/API，规则 + 配置驱动）。"""
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#: 意图关键词（长词优先）
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_INTENT_KEYWORDS = [
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("trend", ["趋势", "走势", "曲线", "变化"]),
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("alarm", ["报警", "告警", "异常"]),
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("kpi", ["平均", "统计", "均值", "最大值", "最小值"]),
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("latest", ["最新", "现在", "当前", "多少", "数值"]),
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]
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def __init__(
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self,
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metrics: Optional[Dict[str, str]] = None,
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table: str = DEFAULT_TABLE,
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default_range: str = "1h",
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intent_keywords: Optional[Dict[str, List[str]]] = None,
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) -> None:
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"""Args:
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metrics: 自然语言指标名 → point_id（如 {"氯气流量": "CLF-01.FLOW"}）；
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table: TDengine 超级表名；
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default_range: 未识别时间范围时的默认窗口；
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intent_keywords: 意图关键词覆盖。
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"""
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self.metrics: Dict[str, str] = dict(metrics or {})
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self.table = table
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self.default_range = default_range
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self._intent = intent_keywords or dict(self._INTENT_KEYWORDS)
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# ------------------------------------------------------------------
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@classmethod
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def from_template_config(cls, path: str = DEFAULT_CONFIG_PATH) -> "NLQueryTranslator":
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"""从模板配置资产加载（config/nl_query.template.yaml）。"""
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import yaml
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with open(path, "r", encoding="utf-8") as fh:
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raw = yaml.safe_load(fh) or {}
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cfg = raw.get("nl_query", {}) or {}
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return cls(
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metrics=cfg.get("metrics", {}),
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table=cfg.get("table", DEFAULT_TABLE),
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default_range=cfg.get("default_time_range", "1h"),
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intent_keywords=cfg.get("intents"),
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)
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# ------------------------------------------------------------------
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def translate(self, question: str) -> NLQuery:
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"""把自然语言问题翻译为结构化查询。"""
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intent = self._detect_intent(question)
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if intent == "unsupported":
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return NLQuery(question=question, intent="unsupported",
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meta={"reason": "未识别查询意图"})
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metric = self._detect_metric(question)
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time_range = self._detect_time_range(question)
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point_id = self.metrics.get(metric, "") if metric else ""
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query = NLQuery(
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question=question, intent=intent, metric=metric,
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point_id=point_id, time_range=time_range,
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)
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query.sql = self._build_sql(query)
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query.meta = {"table": self.table}
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return query
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# ------------------------------------------------------------------
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def _detect_intent(self, question: str) -> str:
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for intent, keywords in self._intent.items():
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for kw in keywords:
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if kw in question:
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return intent
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return "unsupported"
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def _detect_metric(self, question: str) -> str:
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"""指标识别：配置字典中自然语言名作为子串匹配（长名优先）。"""
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candidates = sorted(self.metrics, key=len, reverse=True)
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for name in candidates:
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if name in question:
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return name
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return ""
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@staticmethod
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def _detect_time_range(question: str) -> str:
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m = _TIME_RANGE_RE.search(question)
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if not m:
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return ""
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return f"{int(m.group(1))}{_TIME_UNIT[m.group(2)]}"
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def _build_sql(self, query: NLQuery) -> str:
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"""生成 TDengine SQL（超级表，按 point_id 过滤）。"""
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point_filter = f"point_id = '{query.point_id}'" if query.point_id else "1=1"
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window = query.time_range or self.default_range
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if query.intent == "latest":
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return (f"SELECT last_row(value) AS value FROM {self.table} "
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f"WHERE {point_filter} AND ts >= now - {window}")
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if query.intent == "kpi":
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return (f"SELECT avg(value) AS value_avg FROM {self.table} "
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f"WHERE {point_filter} AND ts >= now - {window}")
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if query.intent == "alarm":
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return (f"SELECT count(*) AS alarms FROM {self.table} "
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f"WHERE {point_filter} AND value > threshold "
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f"AND ts >= now - {window}")
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# trend
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return (f"SELECT _wstart AS ts, avg(value) AS value_avg "
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f"FROM {self.table} WHERE {point_filter} "
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f"AND ts >= now - {window} INTERVAL(1m)")
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