feat(server): 扩展文档智能识别服务，新增Azure Document Intelligence集成和测试用例

server/src/app/services/document_intelligence.py

@@ -59,6 +59,7 @@ class LlmDocumentClassification(BaseModel):
     expense_type: str = Field(default="other")
     confidence: float = Field(default=0.0, ge=0.0, le=1.0)
     evidence: list[str] = Field(default_factory=list)
+    fields: list[DocumentField] = Field(default_factory=list)
 
 
 DEFAULT_RULE = DocumentRule(
@@ -177,7 +178,11 @@ DOCUMENT_TYPE_RULE_MAP = {rule.document_type: rule for rule in DOCUMENT_RULES}
 SUPPORTED_DOCUMENT_TYPES = tuple(DOCUMENT_TYPE_RULE_MAP.keys()) + ("other",)
 
 AMOUNT_PATTERNS = (
-    re.compile(r"(?:价税合计|合计|金额|总额|票价|支付金额|实付金额|实收金额)[：:\s￥¥]*([0-9]+(?:[.,][0-9]{1,2})?)"),
+    re.compile(
+        r"(?:价税合计|合计金额|费用合计|订单(?:总)?金额|支付(?:金额)?|实付(?:金额)?|实收(?:金额)?|总(?:额|计|价)|票价|金额|车费|消费金额)"
+        r"[：:\s￥¥人民币]*([0-9]+(?:[.,][0-9]{1,2})?)"
+    ),
+    re.compile(r"[￥¥]\s*([0-9]+(?:[.,][0-9]{1,2})?)"),
     re.compile(r"([0-9]+(?:[.,][0-9]{1,2})?)\s*元"),
 )
 DATE_PATTERN = re.compile(r"((?:20\d{2}|19\d{2})[-/年.](?:1[0-2]|0?[1-9])[-/月.](?:3[01]|[12]\d|0?[1-9])日?)")
@@ -278,7 +283,7 @@ class DocumentIntelligenceService:
 
         system_prompt = (
             "你是企业报销票据识别复核器。"
-            "你的任务不是 OCR，而是在已有 OCR 文本和票据预览基础上判断票据类型。"
+            "你的任务不是 OCR，而是在已有 OCR 文本和票据预览基础上判断票据类型，并尽量复核关键字段。"
             "只输出 JSON 对象，不要输出 Markdown、解释或代码块。"
             "document_type 只能是："
             f"{', '.join(SUPPORTED_DOCUMENT_TYPES)}。"
@@ -286,7 +291,10 @@ class DocumentIntelligenceService:
             "严禁编造 OCR 中不存在的商户、酒店、航司、路线或金额。"
             "如果 OCR 出现冲突碎片，应优先依据票据主体信息，而不是单个噪声词。"
             "例如滴滴行程单/网约车发票，即使 OCR 混入酒店名称，也不能直接判成酒店票据。"
-            "输出字段：document_type, scene_code, scene_label, expense_type, confidence, evidence。"
+            "如果能从 OCR 或图片中明确确认字段，可在 fields 中返回。"
+            "fields 只允许包含 key, label, value，key 只能是 amount, date, merchant_name, invoice_number, "
+            "invoice_code, trip_no, route。无法确认就不要返回该字段。"
+            "输出字段：document_type, scene_code, scene_label, expense_type, confidence, evidence, fields。"
         )
         user_prompt = (
             "请根据以下票据事实给出最终分类 JSON：\n"
@@ -298,7 +306,8 @@ class DocumentIntelligenceService:
             '  "scene_label": "交通票据",\n'
             '  "expense_type": "transport",\n'
             '  "confidence": 0.86,\n'
-            '  "evidence": ["OCR 中出现 滴滴出行、订单号、上车/下车 等交通特征"]\n'
+            '  "evidence": ["OCR 中出现 滴滴出行、订单号、上车/下车 等交通特征"],\n'
+            '  "fields": [{"key": "amount", "label": "金额", "value": "32.5"}]\n'
             "}"
         )
 
@@ -357,6 +366,7 @@ class DocumentIntelligenceService:
             for item in parsed.evidence
             if str(item or "").strip()
         ][:4]
+        normalized_fields = _normalize_llm_document_fields(parsed.fields)
 
         return LlmDocumentClassification(
             document_type=normalized_type,
@@ -365,6 +375,7 @@ class DocumentIntelligenceService:
             expense_type=str(parsed.expense_type or base_rule.expense_type).strip() or base_rule.expense_type,
             confidence=float(parsed.confidence),
             evidence=evidence,
+            fields=normalized_fields,
         )
 
     @staticmethod
@@ -376,8 +387,28 @@ class DocumentIntelligenceService:
         has_preview: bool,
     ) -> DocumentInsight:
         source, parsed = llm_result
+        warnings = list(rule_insight.warnings)
+        merged_fields = rule_insight.fields
+        if parsed.fields and (has_preview or parsed.confidence >= 0.55):
+            merged_fields = _merge_document_fields(rule_insight.fields, tuple(parsed.fields))
+            if merged_fields != rule_insight.fields:
+                warnings.append("票据关键信息已结合大模型复核结果修正，建议人工再核对原图。")
+
         if parsed.confidence < 0.55:
-            return rule_insight
+            if merged_fields == rule_insight.fields:
+                return rule_insight
+            return DocumentInsight(
+                document_type=rule_insight.document_type,
+                document_type_label=rule_insight.document_type_label,
+                scene_code=rule_insight.scene_code,
+                scene_label=rule_insight.scene_label,
+                expense_type=rule_insight.expense_type,
+                fields=merged_fields,
+                classification_source=rule_insight.classification_source,
+                classification_confidence=rule_insight.classification_confidence,
+                evidence=rule_insight.evidence,
+                warnings=tuple(warnings),
+            )
 
         should_override = False
         if parsed.document_type == rule_insight.document_type:
@@ -389,10 +420,22 @@ class DocumentIntelligenceService:
             should_override = parsed.confidence >= threshold
 
         if not should_override:
-            return rule_insight
+            if merged_fields == rule_insight.fields:
+                return rule_insight
+            return DocumentInsight(
+                document_type=rule_insight.document_type,
+                document_type_label=rule_insight.document_type_label,
+                scene_code=rule_insight.scene_code,
+                scene_label=rule_insight.scene_label,
+                expense_type=rule_insight.expense_type,
+                fields=merged_fields,
+                classification_source=rule_insight.classification_source,
+                classification_confidence=rule_insight.classification_confidence,
+                evidence=rule_insight.evidence,
+                warnings=tuple(warnings),
+            )
 
         rule = DOCUMENT_TYPE_RULE_MAP.get(parsed.document_type, DEFAULT_RULE)
-        warnings = list(rule_insight.warnings)
         if parsed.document_type != rule_insight.document_type:
             warnings.append("票据类型已结合大模型复核结果修正，建议人工再核对原图。")
 
@@ -402,7 +445,7 @@ class DocumentIntelligenceService:
             scene_code=rule.scene_code if parsed.scene_code == "other" else parsed.scene_code,
             scene_label=rule.scene_label if parsed.scene_label == "其他票据" else parsed.scene_label,
             expense_type=rule.expense_type if parsed.expense_type == "other" else parsed.expense_type,
-            fields=fields,
+            fields=merged_fields,
             classification_source=source,
             classification_confidence=max(parsed.confidence, rule_insight.classification_confidence),
             evidence=tuple(parsed.evidence or rule_insight.evidence),
@@ -479,6 +522,115 @@ def _extract_json_payload(response_text: str | None) -> dict[str, Any] | None:
     return None
 
 
+def _normalize_llm_document_fields(raw_fields: list[DocumentField] | list[dict[str, Any]]) -> list[DocumentField]:
+    normalized: list[DocumentField] = []
+    seen_keys: set[str] = set()
+
+    for field in raw_fields:
+        raw_key = str(getattr(field, "key", "") if isinstance(field, DocumentField) else field.get("key") or "").strip()
+        raw_label = str(getattr(field, "label", "") if isinstance(field, DocumentField) else field.get("label") or "").strip()
+        raw_value = str(getattr(field, "value", "") if isinstance(field, DocumentField) else field.get("value") or "").strip()
+        key = _normalize_llm_document_field_key(raw_key, raw_label)
+        if not key or key in seen_keys:
+            continue
+        value = _normalize_llm_document_field_value(key, raw_value)
+        if not value:
+            continue
+        seen_keys.add(key)
+        normalized.append(
+            DocumentField(
+                key=key,
+                label=_llm_document_field_label(key),
+                value=value,
+            )
+        )
+
+    return normalized
+
+
+def _normalize_llm_document_field_key(key: str, label: str) -> str:
+    compact_key = str(key or "").strip().lower()
+    compact_label = str(label or "").replace(" ", "").lower()
+    if compact_key in {"amount", "total_amount", "payment_amount", "paid_amount"} or any(
+        token in compact_label for token in ("金额", "价税合计", "合计", "总额", "总计", "票价", "支付金额", "实付金额", "实收金额")
+    ):
+        return "amount"
+    if compact_key in {"date", "time", "issued_at", "invoice_date"} or any(
+        token in compact_label for token in ("日期", "时间", "开票日期", "发生时间")
+    ):
+        return "date"
+    if compact_key in {"merchant_name", "merchant", "seller_name", "vendor_name"} or any(
+        token in compact_label for token in ("商户", "酒店", "销售方", "开票方", "收款方")
+    ):
+        return "merchant_name"
+    if compact_key in {"invoice_number", "ticket_number", "order_no", "order_number"} or any(
+        token in compact_label for token in ("票据号码", "发票号码", "票号", "单号", "订单号")
+    ):
+        return "invoice_number"
+    if compact_key in {"invoice_code"} or "发票代码" in compact_label:
+        return "invoice_code"
+    if compact_key in {"trip_no", "flight_no", "train_no"} or any(
+        token in compact_label for token in ("车次", "航班")
+    ):
+        return "trip_no"
+    if compact_key in {"route", "trip_route"} or any(token in compact_label for token in ("行程", "路线")):
+        return "route"
+    return ""
+
+
+def _normalize_llm_document_field_value(key: str, value: str) -> str:
+    raw_value = str(value or "").strip()
+    if not raw_value:
+        return ""
+    if key == "amount":
+        amount = _extract_amount(raw_value)
+        if amount:
+            return amount
+        cleaned = raw_value.replace("￥", "").replace("¥", "").replace("元", "").replace(",", ".").strip()
+        try:
+            candidate = Decimal(cleaned)
+        except InvalidOperation:
+            return ""
+        if candidate <= Decimal("0.00"):
+            return ""
+        text_value = format(candidate.quantize(Decimal("0.01")), "f").rstrip("0").rstrip(".")
+        return f"{text_value}元"
+    if key == "date":
+        return _extract_date(raw_value) or _clean_field_value(raw_value)
+    if key == "route":
+        return _extract_route(raw_value) or _clean_field_value(
+            raw_value.replace("→", "-").replace("至", "-").replace("->", "-")
+        )
+    return _clean_field_value(raw_value)
+
+
+def _llm_document_field_label(key: str) -> str:
+    return {
+        "amount": "金额",
+        "date": "日期",
+        "merchant_name": "商户",
+        "invoice_number": "票据号码",
+        "invoice_code": "发票代码",
+        "trip_no": "车次/航班",
+        "route": "行程",
+    }.get(key, key)
+
+
+def _merge_document_fields(
+    base_fields: tuple[DocumentField, ...],
+    override_fields: tuple[DocumentField, ...],
+) -> tuple[DocumentField, ...]:
+    merged = {field.key: field for field in base_fields if field.key and field.value}
+    order = [field.key for field in base_fields if field.key and field.value]
+    for field in override_fields:
+        if not field.key or not field.value:
+            continue
+        merged[field.key] = field
+        if field.key not in order:
+            order.append(field.key)
+    return tuple(merged[key] for key in order if key in merged)
+
+
 def _extract_document_fields(text: str) -> list[DocumentField]:
     fields: list[DocumentField] = []
     amount = _extract_amount(text)
@@ -525,8 +677,6 @@ def _extract_amount(text: str) -> str:
                 continue
             if best_value is None or candidate > best_value:
                 best_value = candidate
-        if best_value is not None:
-            break
 
     if best_value is None:
         return ""

server/tests/test_document_intelligence.py

@@ -64,3 +64,55 @@ def test_document_intelligence_service_uses_vlm_result_when_preview_available(mo
     assert insight.document_type == "taxi_receipt"
     assert insight.classification_source == "llm_vision"
     assert calls[0] == ("vlm",)
+
+
+def test_document_intelligence_extracts_larger_decimal_amount_from_multiple_candidates() -> None:
+    insight = build_document_insight(
+        filename="taxi-amount.png",
+        summary="滴滴出行电子行程单",
+        text="滴滴出行 支付金额 1 元，实付 13.4 元，订单号 12345678",
+    )
+
+    assert any(field.label == "金额" and field.value == "13.4元" for field in insight.fields)
+
+
+def test_document_intelligence_service_uses_vlm_fields_to_correct_amount(monkeypatch) -> None:
+    def fake_complete(self, messages, *, slot_priority=("main", "backup"), max_tokens=500, temperature=0.2):
+        if slot_priority == ("vlm",):
+            return json.dumps(
+                {
+                    "document_type": "taxi_receipt",
+                    "scene_code": "transport",
+                    "scene_label": "交通票据",
+                    "expense_type": "transport",
+                    "confidence": 0.89,
+                    "evidence": ["图片主体为滴滴行程单，金额区域显示 13.4 元"],
+                    "fields": [
+                        {"key": "amount", "label": "金额", "value": "13.4"},
+                        {"key": "merchant_name", "label": "商户", "value": "滴滴出行"},
+                    ],
+                },
+                ensure_ascii=False,
+            )
+        return None
+
+    monkeypatch.setattr(RuntimeChatService, "complete", fake_complete)
+
+    engine = create_engine(
+        "sqlite+pysqlite:///:memory:",
+        connect_args={"check_same_thread": False},
+        poolclass=StaticPool,
+    )
+    session = sessionmaker(bind=engine, autoflush=False, autocommit=False)()
+    try:
+        insight = DocumentIntelligenceService(session).build_document_insight(
+            filename="didi-corrected.png",
+            summary="滴滴出行电子行程单",
+            text="滴滴出行 支付金额 1 元 订单号 12345678",
+            preview_data_url="data:image/png;base64,ZmFrZQ==",
+        )
+    finally:
+        session.close()
+
+    assert any(field.label == "金额" and field.value == "13.4元" for field in insight.fields)
+    assert any("大模型复核结果修正" in warning for warning in insight.warnings)