first commit

backend/app/services/assessment_service.py

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+"""Fraud amount assessment and inquiry suggestion generation."""
+import logging
+from uuid import UUID
+
+import httpx
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.config import settings
+from app.models.transaction import TransactionRecord
+from app.models.assessment import FraudAssessment, ConfidenceLevel, ReviewStatus
+from app.rules.assessment_rules import classify_transaction
+
+logger = logging.getLogger(__name__)
+
+
+async def assess_case(case_id: UUID, db: AsyncSession) -> list[FraudAssessment]:
+    """Run rule-based assessment on all non-duplicate transactions and generate reasons."""
+    result = await db.execute(
+        select(TransactionRecord)
+        .where(TransactionRecord.case_id == case_id)
+        .where(TransactionRecord.is_duplicate.is_(False))
+        .order_by(TransactionRecord.trade_time.asc())
+    )
+    transactions = list(result.scalars().all())
+
+    assessments: list[FraudAssessment] = []
+    for tx in transactions:
+        level, reason, exclude_reason = classify_transaction(tx)
+
+        fa = FraudAssessment(
+            case_id=case_id,
+            transaction_id=tx.id,
+            confidence_level=level,
+            assessed_amount=float(tx.amount) if level != ConfidenceLevel.low else 0,
+            reason=reason,
+            exclude_reason=exclude_reason,
+            review_status=ReviewStatus.pending,
+        )
+        db.add(fa)
+        assessments.append(fa)
+
+    await db.flush()
+
+    # try to enhance reasons via LLM
+    if settings.LLM_API_KEY and settings.LLM_API_URL:
+        for fa in assessments:
+            try:
+                enhanced = await _enhance_reason_via_llm(fa, transactions)
+                if enhanced:
+                    fa.reason = enhanced
+            except Exception as e:
+                logger.debug("LLM reason enhancement skipped: %s", e)
+        await db.flush()
+
+    return assessments
+
+
+async def generate_inquiry_suggestions(case_id: UUID, db: AsyncSession) -> list[str]:
+    """Generate interview / inquiry suggestions based on assessment results."""
+    result = await db.execute(
+        select(FraudAssessment)
+        .where(FraudAssessment.case_id == case_id)
+        .order_by(FraudAssessment.created_at.asc())
+    )
+    assessments = list(result.scalars().all())
+
+    if not assessments:
+        return ["暂无分析结果，请先执行案件分析。"]
+
+    # try LLM generation
+    if settings.LLM_API_KEY and settings.LLM_API_URL:
+        try:
+            return await _generate_suggestions_via_llm(assessments)
+        except Exception as e:
+            logger.debug("LLM suggestions skipped: %s", e)
+
+    return _generate_suggestions_rule_based(assessments)
+
+
+def _generate_suggestions_rule_based(assessments: list[FraudAssessment]) -> list[str]:
+    suggestions: list[str] = []
+    pending = [a for a in assessments if a.review_status == ReviewStatus.pending]
+    medium = [a for a in assessments if a.confidence_level == ConfidenceLevel.medium]
+
+    if pending:
+        suggestions.append(
+            f"有 {len(pending)} 笔交易尚未确认，建议逐笔向受害人核实是否受到诱导操作。"
+        )
+    if medium:
+        suggestions.append(
+            "部分交易置信度为中等，建议追问受害人交易的具体背景和对方的诱导话术。"
+        )
+    suggestions.append("是否还有其他未截图的转账记录或 APP 需要补充？")
+    suggestions.append("涉案金额中是否有已部分追回或返还的款项？")
+    suggestions.append(
+        "除了截图所示的 APP 外，是否还存在银行柜台、ATM、其他支付平台等转账渠道？"
+    )
+    return suggestions
+
+
+async def _enhance_reason_via_llm(fa: FraudAssessment, all_tx: list) -> str | None:
+    prompt = (
+        f"这笔交易金额{fa.assessed_amount}元，置信等级{fa.confidence_level.value}。"
+        f"原始认定理由：{fa.reason}。"
+        "请用简洁中文优化认定理由表述，使之适合出现在办案文书中。只返回优化后的理由文字。"
+    )
+    async with httpx.AsyncClient(timeout=15) as client:
+        resp = await client.post(
+            settings.LLM_API_URL,
+            headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+            json={
+                "model": settings.LLM_MODEL,
+                "messages": [{"role": "user", "content": prompt}],
+                "max_tokens": 300,
+            },
+        )
+        resp.raise_for_status()
+        return resp.json()["choices"][0]["message"]["content"].strip()
+
+
+async def _generate_suggestions_via_llm(assessments: list[FraudAssessment]) -> list[str]:
+    summary_lines = []
+    for a in assessments:
+        summary_lines.append(
+            f"- 金额{a.assessed_amount}元, 置信{a.confidence_level.value}, "
+            f"状态{a.review_status.value}, 理由: {a.reason[:60]}"
+        )
+    summary = "\n".join(summary_lines)
+
+    prompt = (
+        "你是一名反诈案件办案助手。以下是某诈骗案件的交易认定摘要：\n"
+        f"{summary}\n\n"
+        "请生成5条笔录辅助问询建议，帮助民警追问受害人以完善证据链。"
+        "只返回JSON数组格式的5个字符串。"
+    )
+    import json
+    async with httpx.AsyncClient(timeout=20) as client:
+        resp = await client.post(
+            settings.LLM_API_URL,
+            headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+            json={
+                "model": settings.LLM_MODEL,
+                "messages": [{"role": "user", "content": prompt}],
+                "max_tokens": 600,
+            },
+        )
+        resp.raise_for_status()
+        text = resp.json()["choices"][0]["message"]["content"].strip()
+        return json.loads(text.strip().strip("`").removeprefix("json").strip())