feat: 添加线索引擎、NLQ、场景检测、前端界面等核心功能模块

backend/app/nlq/__init__.py

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+"""自然语言查询（NLQ）：审计员零门槛用自然语言查数/获取线索（R4/R20）。"""

backend/app/nlq/service.py

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+"""自然语言查询服务。
+
+采用"结构化意图优先 + LLM 兜底"策略：
+- 若问题命中线索检索意图（置信度/场景/状态/列出线索等），直接查审计数据库返回真实结果，
+  实现"数据找人"，不依赖外部模型，数据不出域。
+- 其余开放性问题再交给 LLMProvider（本地优先）。
+对应 R4 / R20 / R7。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from sqlalchemy.orm import Session
+
+from app.clues import service as clue_svc
+from app.clues.models import ClueStatus, ConfidenceTier
+from app.llm import ChatMessage, get_llm_provider
+
+SYSTEM_PROMPT = (
+    "你是电信运营商内部审计助手。基于审计数据中台的数据回答问题，"
+    "给出可解释的依据；无证据支撑时明确说明，不臆造数据。"
+)
+
+# 关键词 → 过滤条件映射
+_CONFIDENCE_KW = {"高置信": ConfidenceTier.HIGH, "高风险": ConfidenceTier.HIGH,
+                  "中置信": ConfidenceTier.MEDIUM, "低置信": ConfidenceTier.LOW}
+_SCENARIO_KW = {"拆单": "R8", "政企": "R8", "养卡": "R9", "骗补": "R9", "彩铃": "R9"}
+_STATUS_KW = {"待处理": ClueStatus.NEW, "已分派": ClueStatus.ASSIGNED,
+              "研判": ClueStatus.REVIEWING, "属实": ClueStatus.CONFIRMED,
+              "误报": ClueStatus.DISMISSED, "已销项": ClueStatus.CLOSED}
+_LIST_KW = ("线索", "列出", "查", "有哪些", "多少", "列表", "看看", "显示")
+
+_SCENARIO_NAME = {"R8": "政企拆单", "R9": "养卡骗补"}
+_CONF_NAME = {ConfidenceTier.HIGH: "高置信", ConfidenceTier.MEDIUM: "中置信",
+              ConfidenceTier.LOW: "低置信"}
+
+
+@dataclass
+class NLQAnswer:
+    question: str
+    answer: str
+    provider: str
+    model: str
+    egress: bool
+
+
+def _match_first(question: str, mapping: dict):
+    for kw, val in mapping.items():
+        if kw in question:
+            return val
+    return None
+
+
+def _is_clue_query(question: str) -> bool:
+    return any(kw in question for kw in _LIST_KW) or any(
+        kw in question for kw in {**_CONFIDENCE_KW, **_SCENARIO_KW, **_STATUS_KW}
+    )
+
+
+def _format_clue_answer(question: str, clues: list) -> str:
+    if not clues:
+        return "未检索到符合条件的线索。可调整筛选条件，或先运行扫描生成线索。"
+    lines = [f"共检索到 {len(clues)} 条线索："]
+    for i, c in enumerate(clues, 1):
+        amount = f"，涉及金额约 {c.amount_involved/10000:.1f} 万元" if c.amount_involved else ""
+        lines.append(
+            f"{i}. [{_SCENARIO_NAME.get(c.scenario_code, c.scenario_code)}] {c.title}"
+            f"（{_CONF_NAME.get(c.confidence, c.confidence.value)}，评分 {c.score:.2f}{amount}）"
+            f"——{c.rationale}"
+        )
+    return "\n".join(lines)
+
+
+def ask(question: str, session: Session | None = None) -> NLQAnswer:
+    """处理一次自然语言查询：优先结构化检索，其余交给 LLM。"""
+    # 结构化意图：检索线索（数据找人，不出域）
+    if session is not None and _is_clue_query(question):
+        confidence = _match_first(question, _CONFIDENCE_KW)
+        scenario = _match_first(question, _SCENARIO_KW)
+        status = _match_first(question, _STATUS_KW)
+        clues = clue_svc.list_clues(
+            session, status=status, scenario_code=scenario, confidence=confidence
+        )
+        return NLQAnswer(
+            question=question,
+            answer=_format_clue_answer(question, clues),
+            provider="datahub",
+            model="结构化检索",
+            egress=False,
+        )
+
+    # 开放性问题：交给 LLM（本地优先）
+    provider = get_llm_provider()
+    messages = [
+        ChatMessage(role="system", content=SYSTEM_PROMPT),
+        ChatMessage(role="user", content=question),
+    ]
+    resp = provider.chat(messages)
+    return NLQAnswer(
+        question=question,
+        answer=resp.content,
+        provider=resp.provider,
+        model=resp.model,
+        egress=resp.egress,
+    )