实现Agent对话，合格自动提交，不合格补充材料的能力
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wandering
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---
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last_reviewed: 2026-06-11
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---
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# scripts — 测试脚本目录
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存放用于测试各模块功能的独立脚本，可直接运行。
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## 脚本清单
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| 文件 | 说明 |
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|------|------|
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| `test_multimodal.py` | 测试 PDF 多模态提取完整链路（PDF 渲染 + LLM 提取） |
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| `test_travel_info.py` | 测试差旅信息提取函数（数据从 `data/.invoice_cache` 缓存加载） |
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## 运行方式
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```bash
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uv run python scripts/test_multimodal.py
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uv run python scripts/test_travel_info.py
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```
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69
scripts/debug_stream_fields.py
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69
scripts/debug_stream_fields.py
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"""诊断脚本：检查 stream_chat 返回对象的字段结构"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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# 加载 .env
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from dotenv import load_dotenv # noqa: E402
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from llama_index.core.llms import ChatMessage # noqa: E402
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from src.config import get_llm_config # noqa: E402
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from src.doc.llm_extractor import _create_llm # noqa: E402
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load_dotenv(Path(__file__).parent / ".env")
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llm_config = get_llm_config()
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print(f"LLM config: model={llm_config['model']}, api_base={llm_config['api_base']}")
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llm = _create_llm()
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messages = [
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ChatMessage(role="system", content="你是一个助手"),
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ChatMessage(role="user", content="1+1 等于几？"),
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]
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print("\n=== 检查 stream_chat 返回对象的字段 ===")
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count = 0
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try:
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for resp in llm.stream_chat(messages, temperature=0.1):
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count += 1
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if count <= 3:
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print(f"\n--- chunk #{count} ---")
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print(f" type: {type(resp).__name__}")
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print(f" delta: {repr(resp.delta)[:200]}")
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if hasattr(resp, "additional_kwargs") and resp.additional_kwargs:
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print(f" additional_kwargs keys: {list(resp.additional_kwargs.keys())}")
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for k, v in resp.additional_kwargs.items():
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val_preview = str(v)[:200]
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print(f" additional_kwargs['{k}']: {val_preview}")
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if hasattr(resp, "raw"):
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raw = resp.raw
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if isinstance(raw, dict):
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print(f" raw keys: {list(raw.keys())}")
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for k, v in raw.items():
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val_preview = str(v)[:200]
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print(f" raw['{k}']: {val_preview}")
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else:
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print(f" raw type: {type(raw)}")
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if hasattr(resp, "message") and resp.message:
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msg = resp.message
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print(f" message type: {type(msg).__name__}")
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if hasattr(msg, "additional_kwargs") and msg.additional_kwargs:
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print(f" message.additional_kwargs keys: {list(msg.additional_kwargs.keys())}")
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for k, v in msg.additional_kwargs.items():
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val_preview = str(v)[:200]
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print(f" message.additional_kwargs['{k}']: {val_preview}")
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if hasattr(msg, "reasoning_content"):
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rc = msg.reasoning_content
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print(f" message.reasoning_content: {repr(rc)[:200]}")
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elif count == 4:
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print("\n... (更多 chunk 省略)")
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if count >= 10:
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break
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print(f"\n=== 共收到 {count} 个 chunk ===")
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except Exception as e:
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print(f"\n错误: {e}")
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import traceback
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traceback.print_exc()
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208
scripts/test_application_extract.py
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scripts/test_application_extract.py
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#!/usr/bin/env python3
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"""单独测试事前申请单的信息提取功能
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用于调试 LLM 对事前申请单的提取准确率。
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用法:
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# 测试项目根目录的事前申请单.pdf
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python scripts/test_application_extract.py
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# 测试指定文件
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python scripts/test_application_extract.py --file path/to/file.pdf
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# 测试 scripts/data 目录下的事前申请单
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python scripts/test_application_extract.py --dir scripts/data
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"""
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import argparse
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import io
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import json
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import sys
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from pathlib import Path
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# 加载 .env 环境变量（在导入 src 模块之前）
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from dotenv import load_dotenv
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ROOT = Path(__file__).resolve().parent.parent
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load_dotenv(ROOT / ".env")
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# Windows 终端强制 UTF-8
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
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sys.path.insert(0, str(ROOT)) # noqa: E402
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from src.doc.llm_extractor import extract_document # noqa: E402
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def test_single_file(file_path: Path) -> None:
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"""测试单个文件的提取效果"""
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print("=" * 60)
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print(f"测试文件: {file_path.name}")
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print("=" * 60)
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if not file_path.exists():
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print(f"文件不存在: {file_path}")
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return
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try:
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# 调用统一提取接口
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result = extract_document(file_path)
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if not result:
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print("[ERROR] 提取返回空结果")
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return
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# 检查类型判断
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inv_type = result.get("invoice_type", "")
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print(f"\n[类型判断] invoice_type = {inv_type}")
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if inv_type != "application":
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print(f"[WARNING] 类型判断错误！期望 'application'，实际得到 '{inv_type}'")
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else:
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print("[OK] 类型判断正确")
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# 展示提取结果
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print("\n[提取结果]")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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# 字段完整性检查
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print("\n[字段检查]")
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expected_fields = {
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"invoice_type": "类型标识",
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"project_name": "项目名称",
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"purpose": "出差事由",
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"start_date": "开始日期",
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"end_date": "结束日期",
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"person_info": "人员信息",
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}
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for field, desc in expected_fields.items():
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value = result.get(field)
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if value is None:
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print(f" [MISSING] {desc} ({field}) - 字段缺失")
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elif value == "" or value == []:
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print(f" [EMPTY] {desc} ({field}) - 字段为空")
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else:
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print(f" [OK] {desc} ({field})")
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# 日期格式检查
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for date_field in ["start_date", "end_date"]:
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date_value = result.get(date_field, "")
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if date_value and len(date_value) == 10:
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try:
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parts = date_value.split("-")
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if len(parts) == 3:
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int(parts[0]) # year
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int(parts[1]) # month
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int(parts[2]) # day
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print(f" [OK] {date_field} 格式正确 (YYYY-MM-DD)")
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except (ValueError, IndexError):
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print(f" [ERROR] {date_field} 格式错误: {date_value}")
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elif date_value:
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print(f" [ERROR] {date_field} 格式错误: {date_value}")
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# 人员信息结构检查
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person_info = result.get("person_info")
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if person_info:
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if isinstance(person_info, list):
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print(f"\n[人员信息] 共 {len(person_info)} 人")
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for i, person in enumerate(person_info, 1):
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pid = person.get("person_id", "")
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pname = person.get("person_name", "")
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print(f" 人员 {i}: {pname} ({pid})")
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elif isinstance(person_info, dict):
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print("\n[人员信息] 单人格式")
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print(f" 姓名: {person_info.get('person_name', '')}")
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print(f" 编号: {person_info.get('person_id', '')}")
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except Exception as e:
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print(f"\n[EXCEPTION] 提取失败: {e}")
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import traceback
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traceback.print_exc()
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def test_cache_comparison(file_path: Path) -> None:
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"""对比原始提取和缓存结果"""
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cache_dir = file_path.parent / ".invoice_cache"
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cache_file = cache_dir / f"{file_path.stem}{file_path.suffix}.json"
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if not cache_file.exists():
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print(f"\n[INFO] 无缓存文件对比: {cache_file}")
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return
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print("\n" + "=" * 60)
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print("[缓存对比]")
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print("=" * 60)
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try:
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with open(cache_file, encoding="utf-8") as f:
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cache_data = json.load(f)
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cached_result = cache_data.get("extracted_data", {})
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print("\n[缓存数据]")
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print(json.dumps(cached_result, ensure_ascii=False, indent=2))
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# 对比关键字段
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print("\n[字段对比]")
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for key in ["invoice_type", "project_name", "purpose", "start_date", "end_date"]:
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cached_value = cached_result.get(key, "<缺失>")
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print(f" {key}: {cached_value}")
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except Exception as e:
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print(f"[ERROR] 读取缓存失败: {e}")
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def main() -> None:
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parser = argparse.ArgumentParser(description="测试事前申请单信息提取")
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parser.add_argument(
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"--file",
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type=str,
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default=None,
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help="要测试的文件路径 (默认: 扫描 scripts/data 目录)",
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)
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parser.add_argument(
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"--dir",
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type=str,
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default=str(ROOT / "scripts" / "data"),
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help="扫描目录下所有事前申请单文件 (默认: scripts/data)",
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)
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parser.add_argument(
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"--no-cache-compare",
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action="store_true",
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help="不执行缓存对比",
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)
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args = parser.parse_args()
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if args.dir:
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# 目录模式：扫描所有PDF文件
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dir_path = Path(args.dir)
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if not dir_path.exists():
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print(f"目录不存在: {dir_path}")
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sys.exit(1)
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pdf_files = sorted(dir_path.glob("*.pdf"))
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if not pdf_files:
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print(f"目录下没有找到PDF文件: {dir_path}")
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sys.exit(1)
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print(f"发现 {len(pdf_files)} 个PDF文件，开始逐个测试...\n")
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for pdf in pdf_files:
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if "事前申请" in pdf.name or "申请单" in pdf.name:
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test_single_file(pdf)
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if not args.no_cache_compare:
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test_cache_comparison(pdf)
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print()
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else:
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# 单文件模式
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file_path = Path(args.file)
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test_single_file(file_path)
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if not args.no_cache_compare:
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test_cache_comparison(file_path)
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print("\n测试完成!")
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if __name__ == "__main__":
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main()
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