diff --git a/.agents/docs/README.md b/.agents/docs/README.md new file mode 100644 index 0000000..f5edfc6 --- /dev/null +++ b/.agents/docs/README.md @@ -0,0 +1,12 @@ +# 项目维护人员文档 + +本目录存放维护人员与自动化代理相关资料，主要用于项目运维，不属于对外公开的用户文档。 + +- standards/：存放维护人员需遵守的规范制度与校验规则 +- plans/：存放实施方案与工作交接说明 +- error-experience/、good-experience/：存放内部经验总结文档 +- guides/：面向维护人员的工作流程及集成实操手册 +- architecture/manifest.yaml：记录可读性检查所覆盖的文件路径 + +对外用户文档及说明文件请统一放置在 docs/ 目录下。 + diff --git a/.agents/docs/error-experience/2026-06-08-LlamaIndex多模态消息格式错误.md b/.agents/docs/error-experience/2026-06-08-LlamaIndex多模态消息格式错误.md new file mode 100644 index 0000000..9b397b3 --- /dev/null +++ b/.agents/docs/error-experience/2026-06-08-LlamaIndex多模态消息格式错误.md @@ -0,0 +1,59 @@ +--- +last_reviewed: 2026-06-09 +--- + +# LlamaIndex 多模态消息格式错误 + +## 错误现象 + +``` +pydantic_core._pydantic_core.ValidationError: 2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, ... +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, ... +``` + +## 触发条件 + +- llama-index-core >= 0.14.x +- 使用 `ChatMessage` 构造多模态消息（文本 + 图片） +- 传入 OpenAI 格式的 `content` 列表：`[{"type": "text", ...}, {"type": "image_url", ...}]` + +## 原因 + +`llama-index-core 0.14.x` 重构了 `ChatMessage` 的内部结构： + +| 版本 | 字段 | 多模态 content 格式 | +|------|------|-------------------| +| 0.14.x | `role`, `blocks`, `additional_kwargs` | `TextBlock` / `ImageBlock` 实例 | +| 旧版本 | `role`, `content` | OpenAI 风格字典列表 | + +底层 Pydantic 模型使用 `block_type` 作为 union discriminator，OpenAI 格式的 `{"type": "text", ...}` 字典不包含该字段，导致验证失败。 + +## 修复方法 + +**正确写法（llama-index 原生 blocks 格式）：** + +```python +from llama_index.core.llms import ChatMessage +from llama_index.core.base.llms.types import ImageBlock, TextBlock + +messages = [ + ChatMessage(role="system", content=system_prompt), + ChatMessage( + role="user", + blocks=[ + TextBlock(text="请分析这张图片"), + ImageBlock( + url=f"data:image/jpeg;base64,{image_b64}", + detail="high", + ), + ], + ), +] +``` +## 适用版本 + +- llama-index-core: 0.14.22 +- llama-index-llms-openai-like: 0.7.2 \ No newline at end of file diff --git a/.agents/docs/guides/工程实践指南.md b/.agents/docs/guides/工程实践指南.md new file mode 100644 index 0000000..15c14f9 --- /dev/null +++ b/.agents/docs/guides/工程实践指南.md @@ -0,0 +1,261 @@ +--- +last_reviewed: 2026-06-09 +--- + +# 工程实践指南 + +创建或修改任何 Python 项目时，必须严格遵守以下工程规范。 + +--- + +## 1. 包管理与虚拟环境 + +- **唯一包管理器**：使用 `uv`，不使用 `pip`、`pipenv`、`poetry`。 +- **依赖声明**：所有依赖统一在 `pyproject.toml` 中管理，遵循 PEP 621 + PEP 735。 + - `[project].dependencies` 仅放运行时依赖。 + - `[dependency-groups].dev` 放开发依赖（测试、lint、类型检查等）。 +- **版本锁定**：使用 `uv.lock` 锁定依赖版本，提交到版本控制。 +- **安装命令**：`uv sync` 创建虚拟环境并安装所有依赖。 +- **运行命令**：所有 Python 命令通过 `uv run` 前缀执行，确保使用项目虚拟环境。 +- **禁止**：全局安装 Python 包、手动 `python -m venv`、使用 `requirements.txt` 作为主要依赖文件。 + +### `pyproject.toml` 必填字段模板 + +```toml +[project] +name = "" +version = "0.1.0" +description = "<项目描述>" +requires-python = ">=3.12" +dependencies = [ + # 运行时依赖 +] + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pytest-cov>=5.0", + "ruff>=0.9", + "mypy>=1.14", + "deptry>=0.22", + "pre-commit>=4.0", +] +``` + +--- + +## 2. 目录结构 + +``` +/ +├── pyproject.toml # 项目配置（依赖 + 工具配置） +├── uv.lock # 依赖锁定文件 +├── .pre-commit-config.yaml # 提交前检查配置 +├── Makefile # Unix 任务脚本 +├── tasks.py # Windows/跨平台任务脚本 +├── .cursorignore # IDE 忽略配置 +├── src/ # 源代码目录 +│ └── main.py +├── tests/ # 测试目录 +│ └── test_main.py +└── logs/ # 运行时产物（不提交） +``` + +- 源码统一放在 `src/` 下，不直接在根目录放业务代码。 +- 测试统一放在 `tests/` 下。 +- 运行时产物（日志、缓存、临时文件）不提交到版本控制。 + +--- + +## 3. 代码质量工具链 + +### 3.1 Ruff（Lint + Format） + +在 `pyproject.toml` 中配置： + +```toml +[tool.ruff] +target-version = "py312" +line-length = 120 + +[tool.ruff.lint] +select = ["E", "F", "W", "I", "N", "UP", "B"] +ignore = ["E501"] +``` + +- **规则覆盖**：格式错误(E/F/W)、导入排序(I)、命名规范(N)、语法升级(UP)、常见 Bug(B)。 +- **格式化**：使用 `ruff format` 替代 Black。 +- **运行方式**： + - `uv run ruff check .` — 检查问题 + - `uv run ruff check --fix .` — 自动修复 + - `uv run ruff format .` — 格式化代码 + - `uv run ruff format --check .` — 仅检查格式 + +### 3.2 MyPy（静态类型检查） + +```toml +[tool.mypy] +python_version = "3.12" +strict = true +warn_return_any = true +warn_unused_configs = true +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "tests.*" +ignore_errors = true +``` + +- 启用严格模式，测试文件豁免。 +- **运行方式**：`uv run mypy src/main.py` + +### 3.3 Deptry（依赖审计） + +```toml +[tool.deptry] +ignore_notebooks = true +``` + +- 检测未使用、缺失、重复的依赖。 +- **运行方式**：`uv run deptry .` + +--- + +## 4. 测试规范 + +### 4.1 Pytest 配置 + +```toml +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] +``` + +### 4.2 测试要求 + +- 测试文件命名：`test_*.py`，放在 `tests/` 目录。 +- 测试函数/类命名：以 `test_` 或 `Test` 开头。 +- 使用 `pytest.fixture` 管理测试资源。 +- 必须使用 `pytest-cov` 生成覆盖率报告。 +- **运行方式**： + ```bash + uv run python -m pytest --cov --cov-config=pyproject.toml --cov-report=term-missing + ``` + +--- + +## 5. Pre-commit Hooks + +`.pre-commit-config.yaml` 必须包含： + +```yaml +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.9.6 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format +``` + +- 安装：`uv run pre-commit install` +- 手动运行：`uv run pre-commit run --all-files` + +--- + +## 6. 任务运行器 + +### 6.1 Makefile（Unix） + +```makefile +.PHONY: install check test run clean + +install: + @uv sync + @uv run pre-commit install + +check: + @uv lock --locked + @uv run ruff check . + @uv run ruff format --check . + @uv run mypy src/main.py + @uv run deptry . + +test: + @uv run python -m pytest --cov --cov-config=pyproject.toml --cov-report=term-missing + +run: + @uv run python src/main.py + +clean: + @rm -rf .venv __pycache__ .pytest_cache .mypy_cache .ruff_cache +``` + +### 6.2 tasks.py（跨平台） + +提供 `tasks.py` 作为 Windows 兼容的任务运行器，支持相同任务名：`install`、`check`、`test`、`run`、`clean`。 + +--- + +## 7. 版本控制忽略 + +`.cursorignore` / `.gitignore` 必须排除： + +``` +.venv/ +__pycache__/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +*.pyc +logs/ +``` + +--- + +## 8. 开发工作流 + +新项目初始化顺序： + +1. 创建 `pyproject.toml`，声明项目元数据和依赖。 +2. 运行 `uv sync` 创建虚拟环境。 +3. 创建 `.pre-commit-config.yaml`，运行 `uv run pre-commit install`。 +4. 创建 `src/` 目录和入口文件。 +5. 创建 `tests/` 目录和基础测试。 +6. 创建 `Makefile` + `tasks.py`。 +7. 运行 `make check` 或 `python tasks.py check` 验证代码质量。 +8. 运行 `make test` 或 `python tasks.py test` 验证测试通过。 + +日常开发顺序： + +1. `uv run ruff check --fix .` — 先修复 lint 问题。 +2. `uv run ruff format .` — 格式化代码。 +3. `uv run mypy src/` — 类型检查。 +4. `uv run python -m pytest` — 运行测试。 +5. 提交前 pre-commit 会自动执行检查和格式化。 + +--- + +## 9. 编码风格 + +- Python 3.12+ 语法，使用现代特性（如 `match/case`、类型合并 `X | Y`）。 +- 函数和模块必须有 docstring。 +- 优先使用类型注解，返回值类型必须标注。 +- 行长度限制 120 字符。 +- 导入按标准库 → 第三方 → 本地模块分组排序。 +- 偏好函数式编程风格，避免不必要的面向对象封装。 +- 配置与代码分离，使用常量或配置模块管理可变参数。 + +--- + +## 10. 强制检查清单 + +在提交代码或声明任务完成前，必须确认： + +- [ ] `uv lock --locked` 通过（锁定文件与 pyproject.toml 一致） +- [ ] `uv run ruff check .` 无错误 +- [ ] `uv run ruff format --check .` 无差异 +- [ ] `uv run mypy src/` 无类型错误 +- [ ] `uv run deptry .` 无依赖问题 +- [ ] `uv run python -m pytest --cov` 全部通过且覆盖率合理 +- [ ] 所有命令使用 `uv run` 前缀，无全局 pip 操作 \ No newline at end of file diff --git a/.agents/docs/standards/README.md b/.agents/docs/standards/README.md new file mode 100644 index 0000000..163f878 --- /dev/null +++ b/.agents/docs/standards/README.md @@ -0,0 +1,9 @@ +--- +last_reviewed: 2026-06-09 +--- + +# 标准元数据 + +`.agents/docs/standards/*.md` 下所有文件都必须包含 frontmatter，字段包括： + +- `last_reviewed`：最近一次策略审查的 ISO 日期 `YYYY-MM-DD`。 \ No newline at end of file diff --git a/.agents/docs/standards/复利式工程实践.md b/.agents/docs/standards/复利式工程实践.md new file mode 100644 index 0000000..695433b --- /dev/null +++ b/.agents/docs/standards/复利式工程实践.md @@ -0,0 +1,13 @@ +--- +last_reviewed: 2026-06-09 +--- + +# 复利式工程实践 + +记录经验教训： +* 错误经验：`.agents/docs/error-experience/YYYY-MM-DD-.md` +* 正向经验：`.agents/docs/good-experience/YYYY-MM-DD-.md` +* 计划：`.agents/docs/plans/` +* 指南：`agents/docs/guides/` + +在出现重大 bug、CI 失败或发现有价值模式后，创建一条条目并记录根因与经验。 \ No newline at end of file diff --git a/.agents/docs/standards/调试规范.md b/.agents/docs/standards/调试规范.md new file mode 100644 index 0000000..db91834 --- /dev/null +++ b/.agents/docs/standards/调试规范.md @@ -0,0 +1,34 @@ +--- +last_reviewed: 2026-06-09 +--- +# 调试规范 + +## 调试前检查清单 + +在循环调试任务前，需完成以下检查： + +1. 梳理代码链路（最长耗时 5 分钟）。从入口函数追踪至异常执行环节，排查硬编码值、参数缺失或分支逻辑异常等问题。 +2. 对比正常与异常场景。若功能 A 运行正常、功能 B 出现故障，梳理二者代码链路的差异，问题通常就出在差异部分。 +3. 排查基础配置项。检查代理设置、环境变量、端口号、功能开关等。多数故障由配置问题导致，而非代码逻辑错误。 + +## 调试过程要求 + +1. 两次尝试原则。若同一排查方式（重跑测试、调整参数等）连续失败两次，立即停止，更换排查思路： + * 增加针对性日志或打印语句 + * 阅读异常依赖库的源码 + * 精简代码，复现最小故障案例 + * 反思：自身哪些预设判断可能存在偏差 +2. 禁止无限循环调试。定时任务仅用于监控正常运行的进程，不可作为调试工具。若定时循环连续两轮无进展，关闭循环，转为人工调试。 +3. 记录排查思路。每开始一次尝试前，做好记录： + * 初步判断的问题原因 + * 用于验证猜想的依据 + * 本次准备执行的操作 + * 避免重复无效尝试与逻辑死循环 + +## 调试收尾工作 + +1. 编写经验文档。所有非简单故障的调试工作，均需在`.agents/docs/error-experience/` 目录下新建记录文档，内容包含： + * 故障现象 + * 历次排查操作及失败原因 + * 最终解决方案 + * 后续可借鉴的调试经验 \ No newline at end of file diff --git a/.coverage b/.coverage new file mode 100644 index 0000000..e8cf8cf Binary files /dev/null and b/.coverage differ diff --git a/.cursorignore b/.cursorignore new file mode 100644 index 0000000..76f0ec1 --- /dev/null +++ b/.cursorignore @@ -0,0 +1,7 @@ +.venv/ +__pycache__/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.playwright-mcp/ +*.pyc \ No newline at end of file diff --git a/.gitignore b/.gitignore index b14367e..c847677 100644 --- a/.gitignore +++ b/.gitignore @@ -1,34 +1,10 @@ -# Python -__pycache__/ -*.py[cod] -*.pyo -*.egg-info/ -dist/ -build/ -.eggs/ - -# Playwright MCP snapshots -.playwright-mcp/ - -# Local secrets & backups -config.json -*.doc.bak - -# Uploads (user data) -web/uploads/ - -# Logs -pipeline.log -*.log - -# Debug images -images/ - -# IDE -.vscode/ -.idea/ +.venv/ .cursor/ - -# OS -.DS_Store -Thumbs.db \ No newline at end of file +__pycache__/ +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.playwright-mcp/ +*.pyc +logs/ +.vscode/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..70f5223 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.9.6 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..2c43da6 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,19 @@ +--- +last_reviewed: 2026-06-09 +--- + +# AGENTS 索引 + +本文件是规则的入口。详细策略文本位于 `.agents/docs/standards/*.md`。 + +## 文档边界 + +* `docs/` 目录专门存放面向开源用户、外部贡献者的项目公开文档及说明文件。 +* 维护规范、实施方案、经验总结、拉取请求佐证材料与各类内部记录资料，均统一放置在 `.agents/` 目录下，避免内部自动化流程相关内容混入公开文档目录。 +* 每个文件夹下都有一个 `README.md` 文件用来交代这个文件夹的作用以及重要的信息。 + +## 标准目录 + +* 标准文档元数据：`.agents/docs/standards/README.md` +* 调试规范：`.agents/docs/standards/调试规范.md` +* 复利式工程实践：`.agents/docs/standards/复利式工程实践.md` \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..8280fc0 --- /dev/null +++ b/Makefile @@ -0,0 +1,21 @@ +.PHONY: install check test run clean + +install: + @uv sync + @uv run pre-commit install + +check: + @uv lock --locked + @uv run ruff check . + @uv run ruff format --check . + @uv run mypy src/main.py + @uv run deptry . + +test: + @uv run python -m pytest --cov --cov-config=pyproject.toml --cov-report=term-missing + +run: + @uv run python src/main.py + +clean: + @rm -rf .venv __pycache__ .pytest_cache .mypy_cache .ruff_cache \ No newline at end of file diff --git a/PowerShell注意事项.md b/PowerShell注意事项.md new file mode 100644 index 0000000..e5803f2 --- /dev/null +++ b/PowerShell注意事项.md @@ -0,0 +1,103 @@ +# PowerShell 使用注意事项 + +> 适用于 Windows 环境下的 Shell 命令执行。以下坑点均来自实际踩坑记录。 + +--- + +## 1. 没有 `head` / `tail` / `sed` / `awk` + +PowerShell 不是 bash，这些 Unix 工具不存在。 + +| bash | PowerShell 替代 | +|------|-----------------| +| `cmd \| head -n 5` | `cmd \| Select-Object -First 5` | +| `cmd \| tail -n 3` | `cmd \| Select-Object -Last 3` | +| `grep pattern file` | `Select-String pattern file`（或直接调用 `rg`） | + +**优先用 ripgrep (`rg`)**：搜索文件内容时直接用 `rg`，不依赖 PowerShell 内置工具。 + +--- + +## 2. 引号嵌套规则严格 + +PowerShell 对外部命令的引号处理不同于 bash： + +- `"` 是双引号字符串，内部变量会展开 +- `'` 是单引号字符串，不展开变量 +- `-c "..."` 里再嵌套 Python 的 `"..."` 或 `r'...'` 时，层数超过两层极易出错 + +**示例：** + +```powershell +# 危险：$e 会被 PowerShell 当作变量展开 +python -c "print(f'error: {$e}')" + +# 安全：用单引号包裹整个 -c 参数（PowerShell 不展开单引号） +python -c 'print("hello")' +``` + +**最佳实践**：复杂命令写进临时 `.py` 脚本文件执行，而不是用 `-c` 一行塞完。 + +--- + +## 3. `$` 变量展开干扰 + +在双引号字符串中，PowerShell 会尝试解析 `$var`、`${expr}`。如果 Python 代码里包含 f-string 的 `{...}` 或正则里的 `$`，会被 PowerShell 提前处理。 + +**应对**：用单引号包裹整个命令参数，或将逻辑写进脚本文件。 + +--- + +## 4. Unicode / GBK 编码冲突 + +Windows PowerShell 默认控制台编码是 GBK。当 Python 输出包含非 GBK 字符（如全角符号、emoji、CJK 扩展字符）时： + +``` +UnicodeEncodeError: 'gbk' codec can't encode character '\uff08' in position X +``` + +**解决：** + +- Python 脚本开头加 `sys.stdout.reconfigure(encoding='utf-8')` +- 或设置环境变量 `$env:PYTHONIOENCODING = 'utf-8'` +- 或将输出写入文件而非直接打印到控制台 + +--- + +## 5. stderr 重定向行为不一致 + +bash 的 `2>&1` 在 PowerShell 中对外部程序（如 Python）有时能工作，但格式可能错乱。PowerShell 默认只捕获 stdout（流编号 6），stderr（流编号 2）需要显式合并。 + +| 写法 | 说明 | +|------|------| +| `cmd 2>&1` | stderr 合并到 stdout，对外部程序可用但不稳定 | +| `cmd *>&1` | PowerShell 特有语法，捕获所有输出流，更可靠 | + +**实际表现**：Python Traceback 走 stderr，用 `2>&1` 有时被 PowerShell 拦截后格式错乱。建议优先写脚本文件执行。 + +--- + +## 6. 中文路径在错误回显中乱码 + +命令失败时，PowerShell 的 stderr 回显中包含中文的路径会显示为 `�����`。不影响命令执行本身，但会让错误信息难以阅读。 + +**应对**：优先通过 Python 脚本自身打印结构化错误（写入文件或返回 JSON），而非依赖 PowerShell 的错误捕获来诊断问题。 + +--- + +## 7. 管道传递的是对象而非文本 + +PowerShell 的管道传递的是 .NET 对象而非文本字节流。当把 PowerShell 管道接到外部程序时，对象的 `ToString()` 可能不是预期的格式。 + +--- + +## 核心原则速查 + +1. **复杂命令写脚本**：超过一行的操作，写成 `.py` 或 `.ps1` 文件执行 +2. **优先用 ripgrep (`rg`)**：搜索文件内容时直接用 `rg` +3. **编码问题提前处理**：Python 入口设 `sys.stdout.reconfigure(encoding='utf-8')` +4. **避免 `-c` 嵌套引号超过两层** + +--- + +*创建于 2026-05-27 | Windows PowerShell + Python 环境* \ No newline at end of file diff --git a/README.md b/README.md index 73e3db6..42867c4 100644 --- a/README.md +++ b/README.md @@ -1,31 +1,44 @@ # 财务报销自动化 -自动从 PDF 发票提取信息，OCR 识别支付记录截图，生成发票汇总表与易耗品出库单，并可选在财务系统中自动填报报销单。 +自动从 PDF 发票提取信息，生成发票汇总表与易耗品出库单，并可选在财务系统中自动填报报销单。 + +**支持发票类型区分**：系统自动识别高铁票、酒店住宿等差旅发票与普通发票。差旅发票不生成易耗品出库单，走差旅报销流程；普通发票生成出库单，走普通报销流程。 ## 项目结构 ``` -├── run.py # CLI 入口（发票提取 → OCR → 浏览器填报） -├── config.json # 配置文件（登录凭据、默认值、存放地点等） -├── 易耗品、出库单.doc # 易耗品出库单 Word 模板（Web/CLI 填写用） -├── app/ -│ ├── config.py # 配置加载 -│ ├── extractor.py # PDF 发票信息提取 -│ ├── ocr.py # OCR 刷卡信息识别 -│ ├── bot.py # 浏览器自动填报 -│ ├── pipeline.py # CLI 流程编排 -│ └── fill_consumable_doc.py # 将 CSV 填入易耗品出库单（Word COM） -├── web/ -│ ├── app.py # Web 服务入口 -│ ├── templates/ -│ │ ├── index.html # PC 端主页 -│ │ └── mobile_upload.html # 移动端扫码上传 -│ ├── static/ # 前端 CSS / JS -│ └── uploads/ # 按会话隔离的上传与产物目录 -├── *.pdf # 发票 PDF（CLI 模式，放在项目根目录） -├── *.png / *.jpg # 与 PDF 配对的支付截图 -├── invoice_summary.csv # 发票汇总表（CLI 产物） -└── images/ # 浏览器调试截图 +├── pyproject.toml # 项目配置（依赖、工具链） +├── uv.lock # 依赖锁定文件 +├── Makefile # 任务脚本（跨平台） +├── tasks.py # 任务脚本（Windows 兼容） +├── config.json # 配置文件（登录凭据、默认值等） +├── config.example.json # 配置示例 +├── 易耗品、出库单.doc # 易耗品出库单 Word 模板 +├── src/ +│ ├── __init__.py # 包初始化 / 日志器 +│ ├── config.py # 配置加载 +│ ├── bot.py # 浏览器自动填报 +│ ├── pipeline.py # CLI 流程编排 +│ ├── main.py # CLI 入口 +│ ├── doc/ # 文档处理模块 +│ │ ├── extractor.py # 编排入口：串联 PDF 读取 → LLM 提取 → 分类 +│ │ ├── pdf.py # PDF 文件发现与文本提取 +│ │ ├── llm_extractor.py # LLM 信息提取 +│ │ ├── invoice.py # 发票类型常量、分类逻辑、CSV 读写工具 +│ │ ├── fill_consumable_doc.py # 将 CSV 填入易耗品出库单（Word COM） +│ │ ├── prompt.py # LLM 提示词模板 +│ │ └── prompts/ # 提示词模板文件 +│ └── web/ +│ ├── app.py # Web 服务入口 +│ ├── templates/ +│ │ ├── index.html # PC 端主页 +│ │ └── mobile_upload.html # 移动端扫码上传 +│ ├── static/ # 前端 CSS / JS +│ └── uploads/ # 按会话隔离的上传与产物目录 +├── tests/ # 测试目录 +├── *.pdf # 发票 PDF（CLI 模式，放在项目根目录） +├── invoice_summary.csv # 发票汇总表（CLI 产物） +└── images/ # 浏览器调试截图 ``` ## 数据流 @@ -33,49 +46,47 @@ ```mermaid flowchart TB PDF[PDF 发票] --> Extract[extractor 提取] - Extract --> List[发票列表] - Img[支付截图] --> OCR[OCR 识别] - List --> OCR - OCR --> CSV[(invoice_summary.csv)] - CSV --> Fill[fill_consumable_doc] + Extract --> Classify{发票类型分类} + Classify -->|差旅发票| Travel[高铁票 / 酒店住宿] + Classify -->|普通发票| General[普通发票] + Travel --> CSV[(invoice_summary.csv)] + General --> CSV + CSV -->|仅普通发票| Fill[fill_consumable_doc] Fill --> Doc[易耗品、出库单.doc] CSV --> Bot[bot 浏览器自动化] - Bot --> Submit[财务系统填报
可选] + Bot -->|差旅模式| Submit_T[差旅报销填报
TODO] + Bot -->|普通模式| Submit_G[普通报销填报] ``` ## 环境要求 -- Python 3.10+ +- **Python 3.12+** +- **uv** 包管理器（[安装指南](https://docs.astral.sh/uv/getting-started/installation/)） - Windows（易耗品出库单填写依赖 Microsoft Word + COM，仅 Windows 可用） -- 主要依赖见下方安装步骤 ## 快速开始 ### 1. 安装依赖 -> OCR 相关依赖建议在已有 PaddleOCR 的环境中安装（如 MinerU 虚拟环境）。 - ```bash -pip install pdfplumber==0.11.9 paddleocr==2.8.1 playwright==1.60.0 flask==3.0.3 pywin32 -playwright install chromium +# 同步所有依赖（运行时 + 开发工具） +make install +# Windows 上等效命令： +python tasks.py install ``` +项目使用 `uv` 管理依赖，所有包版本锁定在 `uv.lock` 中，确保可复现。 + | 依赖 | 用途 | |------|------| | pdfplumber | PDF 发票文本提取 | -| paddleocr | 支付截图 OCR | | playwright | 财务系统浏览器自动化 | | flask | Web 服务 | | pywin32 | 填写 Word 出库单（`fill_consumable_doc`） | ### 2. 准备数据（CLI 模式） -将发票 PDF 和对应的支付截图放在项目根目录下。脚本会自动匹配 PDF 与截图： - -1. **优先文件名匹配** — PDF 与截图同名（如 `发票.pdf` ↔ `发票.png`） -2. **金额近邻匹配** — 文件名不同时，按价税合计与刷卡金额就近配对 - -截图支持格式：`.png`、`.jpg`、`.jpeg`、`.bmp`、`.webp`。 +将发票 PDF 放在项目根目录下。 ### 3. 配置 @@ -104,16 +115,17 @@ playwright install chromium ### 4. 运行（CLI） ```bash -# 全流程（发票提取 → OCR 识别 → 浏览器填报） -python run.py +# 全流程（发票提取 → 浏览器填报） +make run +# Windows 等效： +python tasks.py run # 仅执行某一步 -python run.py --step invoice # 仅发票提取 -python run.py --step ocr # 仅 OCR 识别 -python run.py --step submit # 仅浏览器填报 +uv run python src/main.py --step invoice # 仅发票提取 +uv run python src/main.py --step submit # 仅浏览器填报 # 覆盖配置中的登录凭据 -python run.py -u 工号 -p 密码 +uv run python src/main.py -u 工号 -p 密码 ``` ### 5. 填写易耗品出库单（CLI） @@ -121,9 +133,9 @@ python run.py -u 工号 -p 密码 需已生成 `invoice_summary.csv`，且本机已安装 **Microsoft Word**： ```bash -python -m app.fill_consumable_doc -python -m app.fill_consumable_doc --csv invoice_summary.csv --doc "易耗品、出库单.doc" -python -m app.fill_consumable_doc --no-backup # 不生成 .doc.bak 备份 +uv run python -m src.doc.fill_consumable_doc +uv run python -m src.doc.fill_consumable_doc --csv invoice_summary.csv --doc "易耗品、出库单.doc" +uv run python -m src.doc.fill_consumable_doc --no-backup # 不生成 .doc.bak 备份 ``` 填写规则概要： @@ -137,8 +149,7 @@ python -m app.fill_consumable_doc --no-backup # 不生成 .doc.bak 备份 | 步骤 | 命令 | 说明 | |------|------|------| -| 发票提取 | `--step invoice` | 扫描根目录 PDF，生成 `invoice_summary.csv` / `.md` | -| OCR 识别 | `--step ocr` | 识别支付截图，回填刷卡日期、金额、持卡人等到 CSV | +| 发票提取 | `--step invoice` | 扫描根目录 PDF，生成 `invoice_summary.csv` | | 浏览器填报 | `--step submit` | 登录信息门户 → 报销系统 → 自动填单、上传附件 | > 分步执行时，上一步的 CSV 会自动成为下一步的输入。 @@ -148,12 +159,14 @@ python -m app.fill_consumable_doc --no-backup # 不生成 .doc.bak 备份 | 列名 | 说明 | |------|------| | 序号 | 行号 | +| 发票类型 | 自动识别：`高铁票` / `酒店住宿` / `普通发票` | | 发票号码 | 电子发票号码 | | 开票日期 | 开票日期 | -| 项目名称 / 规格型号 | 货物或应税劳务信息 | +| 项目名称 / 规格型号 | 货物或应税劳务信息（差旅发票为出发站→到达站） | | 价税合计 | 发票含税金额 | | 销售方名称 | 销方名称 | -| 人员姓名 / 刷卡日期 / 公务卡号 / 刷卡金额 | OCR 自支付截图回填 | +| 出发站 / 到达站 / 车次 / 乘车日期 / 座位等级 | 高铁票专用字段 | +| 人员姓名 / 刷卡日期 / 公务卡号 / 刷卡金额 | 可手工或 Web 端编辑补充 | | 备注 / 工号 | 可手工或 Web 端编辑补充 | ## Web 服务 @@ -161,48 +174,85 @@ python -m app.fill_consumable_doc --no-backup # 不生成 .doc.bak 备份 提供浏览器界面：上传文件 → 自动处理 → 在线编辑 → 下载产物 → 可选提交财务系统。 ```bash -python web/app.py +uv run python src/web/app.py ``` 访问 `http://localhost:5000`。 ### 推荐使用流程 -1. 上传 PDF + 支付截图（或上传已有 CSV） +1. 上传 PDF（或上传已有 CSV） 2. 填写配置（账号、密码、姓名、公务卡号、存放地点等），可上传 `config.json` 一键填充 -3. 点击 **开始处理** — 完成发票提取、OCR、生成 CSV，并自动填写 **易耗品、出库单.doc** -4. 在 **下载文件** 区域下载 CSV / Markdown / 出库单 Word -5. 在表格中核对、修改发票数据（提交财务系统前会自动保存） -6. 确认无误后点击 **提交到财务系统** +3. 点击 **开始处理** — 完成发票提取、生成 CSV，系统自动识别发票类型并分类统计 +4. **普通发票**：自动生成 **易耗品、出库单.doc**，可下载 +5. **差旅发票**（高铁票/酒店住宿）：跳过出库单生成，直接进入差旅报销流程 +6. 在表格中核对、修改发票数据（提交财务系统前会自动保存） +7. 确认无误后点击 **提交到财务系统** ### 处理模式 | 模式 | 入口 | 说明 | |------|------|------| -| **PDF 模式** | 上传 PDF + 截图 | 提取发票 → OCR → 生成 CSV + 出库单 | -| **CSV 快捷模式** | 仅上传 CSV | 跳过提取与 OCR，直接生成出库单并进入编辑/提交 | +| **PDF 模式** | 上传 PDF | 提取发票 → 生成 CSV + 自动分类 → 普通发票生成出库单 | +| **CSV 快捷模式** | 仅上传 CSV | 跳过提取，读取 CSV 中的发票类型，按需生成出库单 | + +### 发票类型区分 + +系统自动识别以下发票类型并按类型分流： + +| 发票类型 | 识别依据 | 出库单 | 填报模式 | +|----------|----------|--------|----------| +| **高铁票** | 含"电子客票"、"中国铁路"、"12306"等关键词 | 不生成 | 差旅报销（TODO） | +| **酒店住宿** | 含"住宿费"、"餐饮服务"、"租赁服务"等关键词 | 不生成 | 差旅报销（TODO） | +| **普通发票** | 其他所有发票 | 自动生成 | 普通报销（已实现） | + +当同一批次同时包含差旅发票和普通发票时，系统会为普通发票生成出库单，并在填报时分别处理。 ### 功能一览 | 功能 | 说明 | |------|------| -| 发票提取 + OCR | 上传 PDF 后自动完成 | -| 易耗品出库单 | 处理完成后自动生成 Word，可下载 | +| 发票提取 | 上传 PDF 后自动完成 | +| 发票类型自动分类 | 高铁票/酒店住宿/普通发票，自动分流处理 | +| 易耗品出库单 | 仅普通发票自动生成 Word，差旅发票跳过 | | 表格在线编辑 | 处理完成后可修改 CSV 各字段；保存后重新生成出库单 | | 财务系统填报 | 单独按钮触发，处理阶段不会自动提交 | | 实时日志 | SSE 推送处理进度 | | 配置上传 | 支持上传 `config.json` 填充表单 | -| 手机扫码上传 | 二维码打开移动端页面，拍照上传支付截图，PC 端轮询同步 | +| 手机扫码上传 | 二维码打开移动端页面，拍照上传，PC 端轮询同步 | -> Web 端浏览器填报以无头模式运行。未上传 PDF 时，填报阶段会跳过附件上传。 +> Web 端浏览器填报以无头模式运行。未上传 PDF 时，填报阶段会跳过附件上传。 > 出库单生成需要 **Windows + Word + pywin32**；若失败，页面会显示具体原因，CSV 等其它产物仍可正常使用。 接口说明见 [API.md](./API.md)。 +## 开发任务 + +项目提供统一的任务脚本，支持跨平台使用： + +| 任务 | Makefile | tasks.py | 说明 | +|------|----------|----------|------| +| 安装依赖 | `make install` | `python tasks.py install` | 同步依赖 + 安装 pre-commit | +| 代码检查 | `make check` | `python tasks.py check` | Ruff lint + 格式化 + MyPy 类型检查 + deptry 依赖检查 | +| 运行测试 | `make test` | `python tasks.py test` | pytest + 覆盖率报告 | +| 运行 CLI | `make run` | `python tasks.py run` | 执行全流程 | +| 清理缓存 | `make clean` | `python tasks.py clean` | 删除虚拟环境和缓存 | + +## 代码质量 + +项目配置了完整的代码质量工具链： + +- **Ruff** — 快速 lint 检查和代码格式化（替代 flake8 + isort + black） +- **MyPy** — 严格模式类型检查（`strict = true`） +- **deptry** — 检测未声明、未使用、过时依赖 +- **pre-commit** — 提交前自动运行 Ruff 检查和格式化 + +所有检查通过后方可提交代码。 + ## 注意事项 - 浏览器填报时会打开或使用 Chromium，请勿手动干扰自动化流程 - 调试截图保存在 `images/` 目录 -- OCR 不会覆盖 CSV 中已有非空字段 - 项目根目录需保留 `易耗品、出库单.doc` 模板；Web 每次从模板复制到会话目录再填写，不修改原模板 - `config.json` 含敏感信息，请勿提交到公开仓库 +- **发票类型区分**：差旅发票（高铁票/酒店住宿）不会生成易耗品出库单，当前差旅报销填报流程仍在开发中（TODO） \ No newline at end of file diff --git a/app/extractor.py b/app/extractor.py deleted file mode 100644 index bd97383..0000000 --- a/app/extractor.py +++ /dev/null @@ -1,256 +0,0 @@ -""" -PDF 发票信息提取 - -从 PDF 发票文件中提取关键字段，输出为标准化的发票数据列表。 - -对外接口: - extract_invoices(directory) -> list[dict] 扫描目录下所有 PDF 并提取 - save_csv(invoices, path) 保存为 CSV - save_markdown(invoices, path) 保存为 Markdown 汇总 -""" - -import csv -import re -from pathlib import Path - -from . import get_logger - -log = get_logger("extractor") - -CSV_COLUMNS = [ - "序号", "发票号码", "开票日期", "项目名称", "规格型号", - "价税合计", "销售方名称", "人员姓名", "刷卡日期", - "公务卡号", "刷卡金额", "备注", "工号", -] - - -# ------------------------------------------------------------------ -# PDF 文件发现与文本提取 -# ------------------------------------------------------------------ - -def find_pdf_files(directory: str = ".") -> list[Path]: - """查找目录下所有 PDF 文件（非递归）""" - pdf_dir = Path(directory) - if not pdf_dir.exists(): - return [] - return sorted(pdf_dir.glob("*.pdf")) - - -def extract_text_from_pdf(filepath: Path) -> str: - """从单个 PDF 中提取全部文本""" - try: - import pdfplumber - except ImportError: - raise ImportError("缺少 pdfplumber，请执行: pip install pdfplumber") - - try: - parts = [] - with pdfplumber.open(filepath) as pdf: - for page in pdf.pages: - text = page.extract_text() - if text: - parts.append(text) - return "\n".join(parts) - except Exception as e: - log.error(f"无法读取 {filepath.name}: {e}") - return "" - - -# ------------------------------------------------------------------ -# 字段解析 -# ------------------------------------------------------------------ - -def _first(regexes: list[str], text: str) -> str | None: - """尝试多个正则，返回第一个匹配组的文本""" - for pattern in regexes: - m = re.search(pattern, text) - if m: - return m.group(1).strip() - return None - - -def _parse_line_item(line: str) -> dict | None: - """解析单行明细（*分类*具体名称 格式）""" - m = re.match(r"\*([^*]+)\*\s*(.+)", line) - if m: - return { - "项目名称": f"*{m.group(1).strip()}*{m.group(2).strip()}", - "规格型号": m.group(2).strip(), - } - return None - - -def _extract_line_items(text: str) -> list[dict]: - """从发票文本中提取所有明细行""" - items = [] - skip_keywords = ["项目名称", "合 计", "价税合计", "备注", "开票人"] - - for line in text.split("\n"): - line = line.strip() - if not line: - continue - if any(kw in line for kw in skip_keywords): - continue - if "*" in line: - item = _parse_line_item(line) - if item: - items.append(item) - - return items - - -def _format_date(date_raw: str) -> str: - """将「2026年5月18日」转为「2026/5/18」""" - m = re.match(r"(\d{4})年(\d{1,2})月(\d{1,2})日", date_raw) - if m: - return f"{m.group(1)}/{m.group(2)}/{m.group(3)}" - return date_raw - - -def parse_invoice(text: str) -> dict: - """从发票文本中提取关键字段，返回 dict - - 返回字段: - 发票号码, 开票日期, 销售方名称, 价税合计, _items (明细列表) - 其他字段（人员姓名等）留空，后续由 OCR 步骤填充 - """ - invoice: dict[str, str] = {} - - invoice["发票号码"] = _first([r"发票号码[：:]?\s*(\d+)"], text) or "" - - date_raw = _first([r"开票日期[：:]?\s*(\d{4}年\d{1,2}月\d{1,2}日)"], text) or "" - invoice["开票日期"] = _format_date(date_raw) if date_raw else "" - - invoice["销售方名称"] = _first( - [ - r"销\s*售?\s*方?\s*名称[：:]?\s*(.+?)(?:\n|$)", - r"销\s*名称[：:]?\s*(.+?)(?:\n|$)", - ], - text, - ) or "" - - invoice["价税合计"] = _first( - [r"价税合计.*?（小写）[¥￥]?\s*(\d+\.?\d*)"], text - ) or "" - - invoice["_items"] = _extract_line_items(text) - - # 以下字段无法从 PDF 提取，留空由 OCR 步骤填充 - for key in ("项目名称", "规格型号", "人员姓名", "刷卡日期", - "公务卡号", "刷卡金额", "备注", "工号"): - if key not in invoice: - invoice[key] = "" - - return invoice - - -# ------------------------------------------------------------------ -# CSV / Markdown 输出 -# ------------------------------------------------------------------ - -def save_csv(invoices: list[dict], output_path: str | Path = "invoice_summary.csv"): - """将发票列表保存为 CSV""" - csv_path = Path(output_path) - - with open(csv_path, "w", encoding="utf-8", newline="") as f: - writer = csv.writer(f) - writer.writerow(CSV_COLUMNS) - - for idx, inv in enumerate(invoices, 1): - items = inv.get("_items", []) - first_item = items[0] if items else {} - writer.writerow([ - idx, - inv.get("发票号码", ""), - inv.get("开票日期", ""), - first_item.get("项目名称", inv.get("项目名称", "")), - first_item.get("规格型号", inv.get("规格型号", "")), - inv.get("价税合计", ""), - inv.get("销售方名称", ""), - inv.get("人员姓名", ""), - inv.get("刷卡日期", ""), - inv.get("公务卡号", ""), - inv.get("刷卡金额", ""), - inv.get("备注", ""), - inv.get("工号", ""), - ]) - - log.info(f"CSV 已保存: {csv_path.name}") - - -def save_markdown(invoices: list[dict], output_path: str | Path = "invoice_summary.md"): - """将发票列表保存为 Markdown 汇总表""" - md_path = Path(output_path) - lines = [ - "# 发票信息汇总表", - "", - "| 序号 | 发票号码 | 开票日期 | 项目名称 | 规格型号 | 价税合计 | 销售方名称 |", - "|------|---------|---------|---------|---------|---------|-----------|", - ] - - total = 0.0 - for idx, inv in enumerate(invoices, 1): - amount = 0.0 - try: - amount = float(inv.get("价税合计", "0")) - except (ValueError, TypeError): - pass - total += amount - - items = inv.get("_items", []) - first_item = items[0] if items else {} - project = first_item.get("项目名称", inv.get("项目名称", "-")) - spec = first_item.get("规格型号", inv.get("规格型号", "-")) - - lines.append( - f"| {idx} " - f"| {inv.get('发票号码', '')} " - f"| {inv.get('开票日期', '')} " - f"| {project} | {spec} " - f"| ¥{amount:,.2f} " - f"| {inv.get('销售方名称', '')} |" - ) - - lines.append("") - lines.append(f"**总计: ¥{total:,.2f}**") - lines.append("") - - with open(md_path, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - log.info(f"Markdown 已保存: {md_path.name}") - - -# ------------------------------------------------------------------ -# 主入口 -# ------------------------------------------------------------------ - -def extract_invoices(directory: str = ".") -> list[dict]: - """扫描目录下所有 PDF，提取发票信息并返回列表""" - target_dir = Path(directory).absolute() - - pdf_files = find_pdf_files(directory) - if not pdf_files: - log.warning("未找到 PDF 文件") - return [] - - log.info(f"发现 {len(pdf_files)} 个 PDF 文件") - - all_invoices = [] - for pdf_path in pdf_files: - text = extract_text_from_pdf(pdf_path) - if text: - invoice = parse_invoice(text) - if invoice: - all_invoices.append(invoice) - else: - log.warning(f"未能解析: {pdf_path.name}") - else: - log.warning(f"未能提取文本: {pdf_path.name}") - - if all_invoices: - log.info(f"共处理 {len(all_invoices)} 张发票") - else: - log.warning("未成功解析任何发票") - - return all_invoices \ No newline at end of file diff --git a/app/ocr.py b/app/ocr.py deleted file mode 100644 index aea5a57..0000000 --- a/app/ocr.py +++ /dev/null @@ -1,454 +0,0 @@ -""" -OCR 刷卡信息提取 - -从支付截图中识别刷卡记录（姓名、日期、金额），回填到发票数据中。 - -匹配策略: - 1. 先按文件名匹配（PDF 和图片同名） - 2. 未匹配的通过金额近邻匹配 - -对外接口: - enrich_with_ocr(rows, directory) -> list[dict] 用 OCR 识别结果丰富发票数据 -""" - -import csv -import os -import re -from pathlib import Path - -from . import get_logger - -log = get_logger("ocr") - - -# ------------------------------------------------------------------ -# 懒加载 OCR -# ------------------------------------------------------------------ - -_ocr_instance = None - - -def _get_ocr(): - """懒加载 PaddleOCR 实例（兼容 2.x / 3.x）""" - global _ocr_instance - if _ocr_instance is not None: - return _ocr_instance - - os.environ.setdefault("FLAGS_use_mkldnn", "0") - os.environ.setdefault("FLAGS_mkldnn_cache_enabled", "0") - - from paddleocr import PaddleOCR - - try: - _ocr_instance = PaddleOCR(use_textline_orientation=True, lang="ch") - except TypeError: - try: - _ocr_instance = PaddleOCR(lang="ch") - except TypeError: - _ocr_instance = PaddleOCR() - - return _ocr_instance - - -# ------------------------------------------------------------------ -# OCR 识别 -# ------------------------------------------------------------------ - -def ocr_image(image_path: Path) -> list[dict]: - """对单张图片执行 OCR，返回 [{"text": str, "confidence": float}, ...]""" - ocr = _get_ocr() - texts = [] - - try: - results = ocr.ocr(str(image_path), cls=True) - if results and isinstance(results, list): - for page_result in results: - if not page_result: - continue - for line in page_result: - if isinstance(line, (list, tuple)) and len(line) >= 2: - _, text_info = line[0], line[1] - if isinstance(text_info, (list, tuple)) and len(text_info) >= 2: - texts.append({ - "text": str(text_info[0]), - "confidence": float(text_info[1]), - }) - except Exception: - try: - if hasattr(ocr, "predict"): - results = ocr.predict(str(image_path)) - if results: - for result in results: - if hasattr(result, "rec_result_list"): - for line in result.rec_result_list: - t = getattr(line, "text", "") or "" - s = getattr(line, "score", 0.0) or 0.0 - texts.append({"text": str(t), "confidence": float(s)}) - elif isinstance(result, list): - for line in result: - if isinstance(line, (list, tuple)) and len(line) >= 2: - t = line[1][0] if isinstance(line[1], (list, tuple)) else str(line[1]) - s = line[1][1] if isinstance(line[1], (list, tuple)) and len(line[1]) > 1 else 0.0 - texts.append({"text": str(t), "confidence": float(s)}) - except Exception as e: - log.error(f"OCR 识别失败: {e}") - - return texts - - -def extract_card_info(texts: list[dict]) -> dict: - """从 OCR 文本中提取刷卡信息（日期 / 金额 / 姓名）""" - info = {"刷卡日期": "", "刷卡金额": "", "人员姓名": ""} - - valid = [t for t in texts if t["confidence"] > 0.5] - full_text = " ".join(t["text"] for t in valid) - if not full_text: - return info - - # 日期（优先级匹配，避免误抓发票开票日期） - date_candidates = [] - for pattern, priority in [ - (r"记账时间[：:\s]*(\d{4}[-/]\d{1,2}[-/]\d{1,2})", 10), - (r"交易时间[：:\s]*(\d{4}[-/]\d{1,2}[-/]\d{1,2})", 9), - (r"刷卡日期[：:\s]*(\d{4}[-/]\d{1,2}[-/]\d{1,2})", 9), - (r"日期[：:\s]*(\d{4}[-/]\d{1,2}[-/]\d{1,2})", 5), - ]: - for m in re.finditer(pattern, full_text): - date_candidates.append((priority, m.start(), m.group(1).replace("-", "/"))) - if date_candidates: - date_candidates.sort(key=lambda x: (-x[0], x[1])) - info["刷卡日期"] = date_candidates[0][2] - - # 金额 - amount_candidates = [] - for pattern, priority in [ - (r"交易金额[：:\s]*([+-]?[\d,]+\.?\d*)", 10), - (r"刷卡金额[：:\s]*([+-]?[\d,]+\.?\d*)", 10), - (r"金额[：:\s]*([+-]?[\d,]+\.?\d*)", 5), - ]: - for m in re.finditer(pattern, full_text): - amt_str = m.group(1).replace(",", "").replace("+", "") - try: - val = float(amt_str) - if 0 < val < 999999: - amount_candidates.append((priority, m.start(), amt_str)) - except ValueError: - continue - if amount_candidates: - amount_candidates.sort(key=lambda x: (-x[0], x[1])) - info["刷卡金额"] = amount_candidates[0][2] - - # 姓名（排除公司/机构后缀） - EXCLUDE_SUFFIXES = ("公司", "银行", "中心", "支行", "商户", "网点", "有限", "责任") - name_candidates = [] - for pattern, priority in [ - (r"交易户名[：:\s]*([\u4e00-\u9fff]{2,6})", 10), - (r"户名[：:\s]*([\u4e00-\u9fff]{2,6})", 8), - (r"持卡人[：:\s]*([\u4e00-\u9fff]{2,6})", 8), - (r"姓名[：:\s]*([\u4e00-\u9fff]{2,6})", 8), - ]: - for m in re.finditer(pattern, full_text): - name = m.group(1) - if not any(name.endswith(s) for s in EXCLUDE_SUFFIXES): - name_candidates.append((priority, m.start(), name)) - if name_candidates: - name_candidates.sort(key=lambda x: (-x[0], x[1])) - info["人员姓名"] = name_candidates[0][2] - - return info - - -# ------------------------------------------------------------------ -# PDF 发票号提取 -# ------------------------------------------------------------------ - -def extract_invoice_number(pdf_path: Path) -> str: - """从 PDF 中提取发票号码""" - try: - import pdfplumber - except ImportError: - log.warning("缺少 pdfplumber，跳过发票号提取") - return "" - - try: - with pdfplumber.open(str(pdf_path)) as pdf_file: - page_text = "" - for page in pdf_file.pages: - page_text += page.extract_text() or "" - - for pattern in [ - r"发票号码[：:\s]*([A-Za-z0-9]{8,20})", - r"发票代码[：:\s]*([A-Za-z0-9]{10,12})", - r"号码[：:\s]*([A-Za-z0-9]{8,20})", - ]: - m = re.search(pattern, page_text) - if m: - return m.group(1) - except Exception as e: - log.warning(f"PDF 读取失败 ({pdf_path.name}): {e}") - - return "" - - -# ------------------------------------------------------------------ -# 图片配对 -# ------------------------------------------------------------------ - -def _extract_amount_from_pdf(pdf_path: Path) -> float | None: - """从 PDF 中提取价税合计金额""" - try: - import pdfplumber - with pdfplumber.open(str(pdf_path)) as pdf: - text = "" - for page in pdf.pages: - t = page.extract_text() - if t: - text += t + "\n" - m = re.search(r"价税合计.*?（小写）[¥￥]?\s*(\d+\.?\d*)", text) - if m: - return float(m.group(1)) - except Exception: - pass - return None - - -def _extract_amount_from_image(img_path: Path) -> float | None: - """从图片 OCR 中提取刷卡金额""" - texts = ocr_image(img_path) - if not texts: - return None - info = extract_card_info(texts) - amt_str = info.get("刷卡金额", "") - if amt_str: - try: - return float(amt_str) - except ValueError: - pass - return None - - -def find_image_pairs(directory: str = ".") -> list[tuple[Path, Path]]: - """查找 PDF 和对应图片的配对 - - 1. 先按文件名匹配（PDF 和图片同名） - 2. 未匹配的通过金额近邻匹配 - """ - base = Path(directory) - pdfs = sorted(base.glob("*.pdf")) - image_exts = {".png", ".jpg", ".jpeg", ".bmp", ".webp"} - - all_images = sorted( - f for ext in image_exts for f in base.glob(f"*{ext}") - ) - - # ---- Phase 1: 文件名匹配 ---- - pairs: list[tuple[Path, Path]] = [] - matched_pdfs: set[Path] = set() - matched_imgs: set[Path] = set() - - for pdf in pdfs: - for ext in image_exts: - img = base / f"{pdf.stem}{ext}" - if img.exists(): - pairs.append((pdf, img)) - matched_pdfs.add(pdf) - matched_imgs.add(img) - break - - unmatched_pdfs = [p for p in pdfs if p not in matched_pdfs] - unmatched_imgs = [i for i in all_images if i not in matched_imgs] - - if not unmatched_pdfs or not unmatched_imgs: - return pairs - - # ---- Phase 2: 金额近邻匹配 ---- - if len(unmatched_pdfs) > 0 and len(unmatched_imgs) > 0: - log.info(f"文件名匹配 {len(pairs)} 组，剩余 {len(unmatched_pdfs)} 个 PDF、{len(unmatched_imgs)} 张图片，尝试金额匹配...") - - pdf_amounts: dict[Path, float] = {} - for pdf in unmatched_pdfs: - amt = _extract_amount_from_pdf(pdf) - if amt is not None: - pdf_amounts[pdf] = amt - - img_amounts: dict[Path, float] = {} - for img in unmatched_imgs: - amt = _extract_amount_from_image(img) - if amt is not None: - img_amounts[img] = amt - - # 贪婪匹配：每张图片找金额差最小的 PDF - used_pdfs: set[Path] = set() - for img, img_amt in sorted(img_amounts.items(), key=lambda x: x[0].name): - best_pdf: Path | None = None - best_diff: float = float("inf") - - for pdf, pdf_amt in pdf_amounts.items(): - if pdf in used_pdfs: - continue - diff = abs(pdf_amt - img_amt) - if diff < best_diff: - best_diff = diff - best_pdf = pdf - - if best_pdf is not None: - pairs.append((best_pdf, img)) - used_pdfs.add(best_pdf) - - log.info(f"金额匹配完成，共 {len(pairs)} 组配对") - - return pairs - - -# ------------------------------------------------------------------ -# CSV 读写 -# ------------------------------------------------------------------ - -from .extractor import CSV_COLUMNS - - -def _load_csv(csv_path: Path) -> list[dict] | None: - """读取现有 CSV 为 dict 列表，失败返回 None""" - try: - with open(csv_path, encoding="utf-8", newline="") as f: - reader = csv.DictReader(f) - fieldnames = reader.fieldnames or [] - missing = [c for c in CSV_COLUMNS if c not in fieldnames] - if missing: - log.error(f"CSV 缺少必要列: {missing}") - return None - return [row for row in reader] - except FileNotFoundError: - log.error(f"CSV 文件不存在: {csv_path.name}") - return None - except Exception as e: - log.error(f"CSV 读取失败: {e}") - return None - - -def _save_csv(csv_path: Path, rows: list[dict]): - """保存 CSV""" - with open(csv_path, "w", encoding="utf-8", newline="") as f: - writer = csv.DictWriter(f, fieldnames=CSV_COLUMNS) - writer.writeheader() - writer.writerows(rows) - - -# ------------------------------------------------------------------ -# Markdown 同步 -# ------------------------------------------------------------------ - -def save_markdown_from_csv(csv_path: Path, rows: list[dict]): - """根据最新 CSV 数据生成 Markdown 汇总表""" - md_path = csv_path.with_suffix(".md") - columns = [ - ("序号", "序号"), ("发票号码", "发票号码"), ("开票日期", "开票日期"), - ("项目名称", "项目名称"), ("规格型号", "规格型号"), ("价税合计", "价税合计"), - ("销售方名称", "销售方名称"), ("人员姓名", "人员姓名"), - ("刷卡日期", "刷卡日期"), ("公务卡号", "公务卡号"), - ("刷卡金额", "刷卡金额"), ("备注", "备注"), ("工号", "工号"), - ] - - lines = ["# 发票信息汇总表", ""] - header = " | ".join(col[1] for col in columns) - separator = "|".join(["------" for _ in columns]) - lines.append(f"| {header} |") - lines.append(f"|{separator}|") - - total_price = 0.0 - total_card = 0.0 - - for row in rows: - cells = [] - for key, _ in columns: - value = row.get(key, "").strip() - - if key == "价税合计" and value: - try: - total_price += float(value.replace(",", "")) - cells.append(f"¥{float(value.replace(',', '')):,.2f}") - except (ValueError, TypeError): - cells.append(value) - elif key == "刷卡金额" and value: - try: - total_card += float(value.replace(",", "")) - cells.append(f"¥{float(value.replace(',', '')):,.2f}") - except (ValueError, TypeError): - cells.append(value) - else: - cells.append(value if value else "") - - lines.append("| " + " | ".join(cells) + " |") - - lines.append("") - lines.append(f"**价税合计总计: ¥{total_price:,.2f}**") - lines.append(f"**刷卡金额总计: ¥{total_card:,.2f}**") - lines.append("") - - with open(md_path, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - log.info(f"Markdown 已同步: {md_path.name}") - - -# ------------------------------------------------------------------ -# 主入口 -# ------------------------------------------------------------------ - -def enrich_with_ocr(rows: list[dict], directory: str = ".") -> list[dict]: - """用 OCR 识别结果丰富发票数据，返回更新后的行列表 - - rows 应包含「发票号码」列，已存在的字段不会覆盖。 - """ - pairs = find_image_pairs(directory) - if not pairs: - log.warning("未找到 PDF-图片配对文件，跳过 OCR") - return rows - - log.info(f"找到 {len(pairs)} 组 PDF-图片配对") - - ocr_by_invoice: dict[str, dict] = {} - - for idx, (pdf, img) in enumerate(pairs, 1): - inv_num = extract_invoice_number(pdf) - if not inv_num: - inv_num = pdf.stem - - texts = ocr_image(img) - if not texts: - log.warning(f"OCR 未识别到文本: {img.name}") - continue - - info = extract_card_info(texts) - ocr_by_invoice[inv_num] = info - - # 更新行数据 - updated = 0 - matched = 0 - - for i, row in enumerate(rows): - inv_num = row.get("发票号码", "").strip() - ocr_info = ocr_by_invoice.get(inv_num) - - if not ocr_info: - for key, val in ocr_by_invoice.items(): - if inv_num in key or key in inv_num: - ocr_info = val - break - - if ocr_info: - matched += 1 - - if not row.get("人员姓名", "").strip() and ocr_info["人员姓名"]: - row["人员姓名"] = ocr_info["人员姓名"] - updated += 1 - if not row.get("刷卡日期", "").strip() and ocr_info["刷卡日期"]: - row["刷卡日期"] = ocr_info["刷卡日期"] - updated += 1 - if not row.get("刷卡金额", "").strip() and ocr_info["刷卡金额"]: - row["刷卡金额"] = ocr_info["刷卡金额"] - updated += 1 - - log.info(f"OCR 完成: 匹配 {matched}/{len(rows)} 行，更新 {updated} 个字段") - - return rows \ No newline at end of file diff --git a/app/pipeline.py b/app/pipeline.py deleted file mode 100644 index 0822560..0000000 --- a/app/pipeline.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -报销全流程编排 - -将发票提取 → OCR 识别 → 浏览器填报串联为一条管道， -数据在内存中流转，同时生成 CSV / Markdown 中间产物。 -""" - -import sys -from pathlib import Path - -from . import get_logger -from .config import load_config -from .extractor import extract_invoices, save_csv as save_invoice_csv, save_markdown as save_invoice_md -from .ocr import enrich_with_ocr, _save_csv as save_ocr_csv, save_markdown_from_csv, _load_csv - -log = get_logger("pipeline") - - -def run_pipeline(step: str = "all", username: str = None, password: str = None): - """执行报销流程 - - Args: - step: all | invoice | ocr | submit - username: 覆盖 config.json 中的用户名 - password: 覆盖 config.json 中的密码 - """ - config = load_config() - if username: - config["username"] = username - if password: - config["password"] = password - - # 工作目录（项目根目录） - project_dir = Path(__file__).parent.parent - - # -------------------------------------------------- - # Step 1: 发票提取 - # -------------------------------------------------- - invoices = None - - if step in ("all", "invoice"): - log.info("=" * 60) - log.info("[1/3] 发票提取") - log.info("=" * 60) - - invoices = extract_invoices(str(project_dir)) - if not invoices: - log.error("未提取到任何发票数据") - return 1 - - save_invoice_csv(invoices, project_dir / "invoice_summary.csv") - save_invoice_md(invoices, project_dir / "invoice_summary.md") - - if step == "invoice": - log.info("[1/3] 发票提取 完成") - return 0 - - # -------------------------------------------------- - # Step 2: OCR 识别 - # -------------------------------------------------- - if step in ("all", "ocr"): - log.info("=" * 60) - log.info("[2/3] OCR 识别") - log.info("=" * 60) - - csv_path = project_dir / "invoice_summary.csv" - - if invoices is None: - rows = _load_csv(csv_path) - if rows is None: - return 1 - else: - # 将 dict 列表转为 CSV 风格的 dict（对齐列名） - from .extractor import CSV_COLUMNS - rows = [] - for idx, inv in enumerate(invoices, 1): - items = inv.get("_items", []) - first_item = items[0] if items else {} - rows.append({ - "序号": str(idx), - "发票号码": inv.get("发票号码", ""), - "开票日期": inv.get("开票日期", ""), - "项目名称": first_item.get("项目名称", inv.get("项目名称", "")), - "规格型号": first_item.get("规格型号", inv.get("规格型号", "")), - "价税合计": inv.get("价税合计", ""), - "销售方名称": inv.get("销售方名称", ""), - "人员姓名": inv.get("人员姓名", ""), - "刷卡日期": inv.get("刷卡日期", ""), - "公务卡号": inv.get("公务卡号", ""), - "刷卡金额": inv.get("刷卡金额", ""), - "备注": inv.get("备注", ""), - "工号": inv.get("工号", ""), - }) - - rows = enrich_with_ocr(rows, str(project_dir)) - save_ocr_csv(csv_path, rows) - save_markdown_from_csv(csv_path, rows) - invoices = rows - - if step == "ocr": - log.info("[2/3] OCR 识别 完成") - return 0 - - # -------------------------------------------------- - # Step 3: 浏览器填报 - # -------------------------------------------------- - if step in ("all", "submit"): - log.info("=" * 60) - log.info("[3/3] 报销提交") - log.info("=" * 60) - - from .bot import load_invoice_data, run_bot - - csv_path = project_dir / "invoice_summary.csv" - bot_invoices = load_invoice_data(str(csv_path), config) - run_bot(config, bot_invoices) - - if step == "submit": - log.info("[3/3] 报销提交 完成") - return 0 - - # -------------------------------------------------- - # 全流程完成 - # -------------------------------------------------- - log.info("=" * 60) - log.info("全流程执行完毕") - log.info("=" * 60) - return 0 \ No newline at end of file diff --git a/config.example.json b/config.example.json index b33d40b..9a1f95f 100644 --- a/config.example.json +++ b/config.example.json @@ -8,5 +8,10 @@ "default_name": "默认报销人姓名", "default_card_no": "默认公务卡号", "default_person_id": "默认人员编号", - "consumable_storage": "躬行楼 C205" + "consumable_storage": "躬行楼 C205", + "llm": { + "model": "你的模型名称", + "api_base": "你的API地址", + "api_key": "你的API密钥" + } } diff --git a/config.json b/config.json new file mode 100644 index 0000000..bcc2d01 --- /dev/null +++ b/config.json @@ -0,0 +1,17 @@ +{ + "username": "202407021", + "password": "wang!1624155937", + "sso_login_url": "https://tyrz.fynu.edu.cn/sso/login", + "portal_url": "https://tyrz.fynu.edu.cn/oshall", + "reimburse_url": "http://210.45.32.214:8081", + "reimburse_page": "/expen/common/common?v=4.0", + "default_name": "王建锋", + "default_card_no": "6282880139161682", + "default_person_id": "202407021", + "consumable_storage": "新工科 D605", + "llm": { + "model": "qwen/qwen3.5-9b", + "api_base": "http://100.123.83.115:1234/v1", + "api_key": "123456" + } +} \ No newline at end of file diff --git a/API.md b/docs/API.md similarity index 74% rename from API.md rename to docs/API.md index 75c9a06..12ce6a5 100644 --- a/API.md +++ b/docs/API.md @@ -1,7 +1,11 @@ +--- +last_reviewed: 2026-06-09 +--- + # 财务报销自动化 — API 文档 > 基础地址: `http://localhost:5000` -> 启动: `python web/app.py` +> 启动: `uv run python src/web/app.py` ## 总览 @@ -12,7 +16,7 @@ | 3 | POST | `/api/upload/` | 上传文件（PDF/图片） | | 4 | POST | `/api/upload-csv/` | 上传 CSV 发票数据 | | 5 | GET | `/api/files/` | 列出会话目录中的文件 | -| 6 | POST | `/api/process/` | 启动处理（提取+OCR+出库单） | +| 6 | POST | `/api/process/` | 启动处理（提取+LLM识别+出库单） | | 7 | GET | `/api/logs/` | SSE 日志流 | | 8 | GET | `/api/download//` | 下载生成的文件 | | 9 | GET | `/api/data/` | 获取发票数据（JSON） | @@ -26,8 +30,8 @@ ## 会话与目录 - 调用 `POST /api/session` 获得 `session_id` -- 该会话下所有文件存放在 `web/uploads//` -- 典型产物：`invoice_summary.csv`、`invoice_summary.md`、`易耗品、出库单.doc`、`config.json`、`session.log`、`result.json` +- 该会话下所有文件存放在 `src/web/uploads//` +- 典型产物：`invoice_summary.csv`、`易耗品、出库单.doc`、`config.json`、`session.log`、`result.json` --- @@ -91,7 +95,7 @@ Content-Type: multipart/form-data { "ok": true, "filename": "invoice_summary.csv" } ``` -> 上传 CSV 后可跳过 PDF 提取和 OCR，直接进入处理/编辑流程。 +> 上传 CSV 后可跳过 PDF 提取和 LLM 识别，直接进入处理/编辑流程。 --- @@ -137,17 +141,17 @@ Content-Type: application/json | mode | 行为 | |------|------| -| `auto` | 仅有 CSV、无 PDF → CSV 模式；否则 → PDF 提取 + OCR | -| `csv` | 使用已上传 CSV，跳过提取与 OCR | -| `pdf` | 执行 PDF 提取 + OCR | +| `auto` | 仅有 CSV、无 PDF → CSV 模式；否则 → PDF 提取 + LLM 识别 | +| `csv` | 使用已上传 CSV，跳过提取与 LLM 识别 | +| `pdf` | 执行 PDF 提取 + LLM 识别 | **处理内容（PDF 模式）:** -1. 从会话目录 PDF 提取发票信息 → `invoice_summary.csv` / `.md` -2. 对支付截图 OCR，回填刷卡字段 -3. 从项目根目录复制 `易耗品、出库单.doc` 模板到会话目录并自动填写（需 Windows + Word） +1. 从会话目录 PDF 提取发票信息 → `invoice_summary.csv` +2. 对支付截图多模态 LLM 识别，回填刷卡字段 +3. 根据发票类型自动分类：差旅发票（高铁票/酒店住宿）不生成出库单；普通发票从模板复制并自动填写 -配置会写入 `web/uploads//config.json`。 +配置会写入 `src/web/uploads//config.json`。 **响应（立即）:** @@ -165,12 +169,28 @@ Content-Type: application/json "elapsed": "45.2s", "invoice_count": 4, "csv_url": "/api/download//invoice_summary.csv", - "md_url": "/api/download//invoice_summary.md", + "travel_count": 2, + "general_count": 2, "doc_url": "/api/download//%E6%98%93%E8%80%97%E5%93%81%E3%80%81%E5%87%BA%E5%BA%93%E5%8D%95.doc", "doc_ok": true } ``` +**纯差旅发票（跳过出库单生成）:** + +```json +{ + "ok": true, + "invoice_count": 3, + "csv_url": "/api/download//invoice_summary.csv", + "travel_count": 3, + "general_count": 0, + "doc_ok": null, + "doc_skipped": true, + "doc_message": "差旅发票无需生成易耗品出库单" +} +``` + **出库单生成失败时（CSV 等仍可能成功）:** ```json @@ -178,11 +198,23 @@ Content-Type: application/json "ok": true, "invoice_count": 4, "csv_url": "/api/download//invoice_summary.csv", + "travel_count": 2, + "general_count": 2, "doc_ok": false, "doc_error": "服务器未安装 pywin32，无法生成 Word 出库单" } ``` +**字段说明:** + +| 字段 | 类型 | 说明 | +|------|------|------| +| `travel_count` | int | 差旅发票数量（高铁票/酒店住宿） | +| `general_count` | int | 普通发票数量 | +| `doc_ok` | bool/null | `true`=成功，`false`=失败，`null`=已跳过（纯差旅发票） | +| `doc_skipped` | bool | 是否因纯差旅发票而跳过出库单生成 | +| `doc_message` | string | 跳过时的提示信息 | + --- ### 6. SSE 日志流 @@ -211,9 +243,11 @@ data: 2026-05-26 12:00:01 [INFO ] extractor: 正在提取发票... | 来源 | 典型字段 | |------|----------| -| `/api/process` | `ok`, `elapsed`, `invoice_count`, `csv_url`, `md_url`, `doc_url`, `doc_ok`, `doc_error` | +| `/api/process` | `ok`, `elapsed`, `invoice_count`, `csv_url`, `travel_count`, `general_count`, `doc_url`, `doc_ok`, `doc_skipped`, `doc_error` | | `/api/submit-financial` | `ok`, `submit_ok`, `submit_error` | +> SSE 超时时间为 10 分钟（600 秒）。 + --- ### 7. 下载文件 @@ -227,7 +261,6 @@ GET /api/download// | 扩展名 | Content-Type | |--------|----------------| | `.csv` | `text/csv; charset=utf-8` | -| `.md` | `text/markdown; charset=utf-8` | | `.doc` | `application/msword` | | 其它 | `application/octet-stream` | @@ -235,8 +268,7 @@ GET /api/download// | 文件名 | 说明 | |--------|------| -| `invoice_summary.csv` | 发票汇总（含 OCR 结果） | -| `invoice_summary.md` | Markdown 摘要 | +| `invoice_summary.csv` | 发票汇总（含 LLM 识别结果） | | `易耗品、出库单.doc` | 自动填写的出库单 | | 用户上传的 CSV 名 | CSV 快捷模式下的原始文件 | @@ -328,7 +360,17 @@ Content-Type: application/json 保存后会根据最新 CSV **重新生成** 出库单 Word（与会话 `config.json` 中的 `consumable_storage` 等配置一致）。 -若出库单生成失败： +**纯差旅发票跳过出库单:** + +```json +{ + "ok": true, + "doc_ok": null, + "doc_skipped": true +} +``` + +**出库单生成失败:** ```json { @@ -348,10 +390,14 @@ POST /api/submit-financial/ **前置条件:** -- 会话目录存在 `config.json`（由 `/api/process` 写入） +- 会话目录存在 `config.json`（由 `/api/process` 写入），否则返回 `400` - 存在可用的发票 CSV（通常为 `invoice_summary.csv`） -**说明:** 前端一般在提交前调用 `/api/save` 保存表格修改。本接口**不会**自动执行发票提取或 OCR。 +**说明:** + +- 前端一般在提交前调用 `/api/save` 保存表格修改 +- 本接口**不会**自动执行发票提取或 LLM 识别 +- 根据发票类型选择填报模式：纯差旅发票走差旅报销流程，含普通发票走普通报销流程 **响应（立即）:** @@ -416,6 +462,19 @@ Content-Type: multipart/form-data --- +## 发票类型分类 + +系统自动将发票分为两类，影响出库单生成和后续报销流程： + +| 类型 | 判断依据 | 出库单 | 报销流程 | +|------|----------|--------|----------| +| 差旅发票 | 高铁票、酒店住宿等 | 不生成 | 差旅报销 | +| 普通发票 | 其他（办公用品、耗材等） | 自动生成 | 普通报销 | + +`/api/process` 和 `/api/save` 的响应中 `travel_count` / `general_count` 即为分类统计。 + +--- + ## 端到端流程 ```mermaid @@ -430,8 +489,12 @@ sequenceDiagram PC->>Server: POST /api/upload/{sid} PC->>Server: POST /api/process/{sid} - Note over Server: PDF 提取 + OCR + 写 config.json - Server->>Word: 复制模板并填写出库单 + Note over Server: PDF 提取 + LLM 识别 + 写 config.json + alt 含普通发票 + Server->>Word: 从模板复制并填写出库单 + else 纯差旅发票 + Note over Server: 跳过出库单生成 + end Server-->>PC: SSE done (csv_url, doc_url, ...) PC->>Server: GET /api/data/{sid} @@ -457,7 +520,7 @@ sequenceDiagram 不经过 Web、在本地直接填写出库单： ```bash -python -m app.fill_consumable_doc --csv invoice_summary.csv --doc "易耗品、出库单.doc" +uv run python -m src.doc.fill_consumable_doc --csv invoice_summary.csv --doc "易耗品、出库单.doc" ``` -详见 [README.md](./README.md)。 +详见 [README.md](./README.md)。 \ No newline at end of file diff --git a/docs/报销操作指南.md b/docs/报销操作指南.md new file mode 100644 index 0000000..dd9510a --- /dev/null +++ b/docs/报销操作指南.md @@ -0,0 +1,481 @@ +--- +last_reviewed: 2026-06-09 +--- + +# 阜阳师范大学财务报销系统 - 自动化操作指南 + +> 适用场景：日常报销录入（支持 Web 界面和 CLI 两种方式） +> 最后更新：2026-06-09 + +--- + +## 一、系统概览 + +``` +整体流程: + +信息门户(SSO登录) → 财务系统入口 → 单点登录页 → 网络报销 → 日常报销录入 +(tyrz.fynu.edu.cn) (点击"财务系统") (新标签页) (a:has(img)) (/expen/common/common) + +目标系统: http://210.45.32.214:8081 +用户: 张三 (工号: xxxxxxx) +``` + +### 关键 URL + +| 系统 | URL | 说明 | +|------|-----|------| +| SSO 登录 | `https://tyrz.fynu.edu.cn/sso/login` | 统一认证入口 | +| 信息门户 | `https://tyrz.fynu.edu.cn/oshall` | 登录后跳转目标 | +| 报销系统 | `http://210.45.32.214:8081` | 网络报销主系统 | +| 日常报销录入 | `/expen/common/common?v=4.0` | 目标录入页面 | + +--- + +## 二、两种使用方式 + +### 方式一：Web 界面（推荐） + +适合日常使用，可视化操作，支持手机端拍照上传。 + +```bash +uv run python src/web/app.py +# 访问: http://localhost:5000 +``` + +### 方式二：CLI 命令行 + +适合脚本化、批量处理。 + +```bash +# 全流程（发票提取 → LLM 识别 → 浏览器填报） +uv run python src/main.py + +# 分步执行 +uv run python src/main.py --step invoice # 仅发票提取 +uv run python src/main.py --step submit # 仅浏览器填报 + +# 覆盖登录凭据 +uv run python src/main.py -u 202407021 -p "your_password" +``` + +--- + +## 三、数据准备 + +### 3.1 发票数据 CSV + +系统支持两种数据输入方式： + +**方式 A：PDF 发票 + 支付截图（自动提取）** + +上传 PDF 发票文件和支付截图，系统自动完成： +1. 从 PDF 提取发票信息 +2. 多模态 LLM 识别支付截图中的刷卡信息 +3. 生成 `invoice_summary.csv` + +**方式 B：直接上传 CSV（快捷模式）** + +已有发票数据 CSV 可直接上传，跳过 PDF 提取和 LLM 识别步骤。 + +CSV 需包含以下列： + +| 列名 | 说明 | 示例 | +|------|------|------| +| 序号 | 发票序号 | 1, 2, 3... | +| 发票号码 | 发票编号 | 26442000005432652421 | +| 开票日期 | 发票开具日期 | 2026/5/18 | +| 项目名称 | 采购项目名称 | 电阻一批 | +| 规格型号 | 规格型号 | — | +| 价税合计 | 发票金额 | 2900.00 | +| 销售方名称 | 商户/销售方 | 佛山市泓宇芯科技有限公司 | +| 人员姓名 | 报销人 | 王建锋（默认值） | +| 刷卡日期 | 公务卡消费日期 | 2026/5/18 → 自动转为 2026-05-18 | +| 公务卡号 | 公务卡卡号 | 6282880139161682（默认值） | +| 刷卡金额 | 实际刷卡金额 | 2900.00 | +| 备注 | 备注信息 | — | +| 工号 | 人员工号 | 202407021（默认值） | + +### 3.2 发票类型分类 + +系统自动将发票分为两类，影响后续处理流程： + +| 类型 | 判断依据 | 出库单 | 报销流程 | +|------|----------|--------|----------| +| 差旅发票 | 高铁票、酒店住宿等 | 不生成 | 差旅报销 | +| 普通发票 | 其他（办公用品、耗材等） | 自动生成 | 普通报销 | + +### 3.3 附件文件 + +PDF 发票文件需与支付截图配对上传。系统通过金额匹配自动关联发票和支付记录。 + +--- + +## 四、Web 界面操作流程 + +### 4.1 启动服务 + +```bash +uv run python src/web/app.py +# 访问: http://localhost:5000 +``` + +### 4.2 上传文件 + +1. **发票 PDF**：点击或拖拽上传 PDF 文件（支持多选） +2. **支付截图**：点击或拖拽上传图片文件（支持多选） +3. **手机扫码上传**：扫描页面二维码，通过手机拍照上传支付截图 +4. **CSV 快捷上传**：已有发票数据 CSV 可直接上传，跳过提取和 LLM 识别 + +### 4.3 配置信息 + +填写以下配置项（也可通过上传 `config.json` 快速填充）： + +| 字段 | 说明 | +|------|------| +| 账号 | 财务系统工号 | +| 密码 | 登录密码 | +| 默认姓名 | 报销人姓名 | +| 公务卡号 | 公务卡卡号 | +| 人员编号 | 人员编号 | +| 存放地点 | 出库单存放地点 | + +### 4.4 开始处理 + +点击"开始处理"按钮，系统自动执行： + +1. **发票提取**：从 PDF 提取发票信息 +2. **LLM 信息提取**：多模态 LLM 识别支付截图中的刷卡信息 +3. **发票分类**：自动区分差旅发票和普通发票 +4. **出库单生成**：普通发票自动生成易耗品出库单（差旅发票跳过） + +处理过程中可在日志面板实时查看进度。 + +### 4.5 编辑发票数据 + +处理完成后，发票数据以可编辑表格形式展示： + +- 直接点击单元格即可编辑 +- 修改后点击"提交到财务系统"时自动保存 +- 保存后会自动重新生成出库单 + +### 4.6 下载文件 + +处理完成后下载： + +| 文件 | 说明 | +|------|------| +| `invoice_summary.csv` | 发票汇总数据 | +| `易耗品、出库单.doc` | 自动填写的出库单（仅普通发票） | + +### 4.7 提交到财务系统 + +确认数据无误后，点击"🚀 提交到财务系统"按钮，系统自动： + +1. 登录信息门户 +2. 进入财务系统 +3. 创建报销单 +4. 填写基本信息 +5. 录入报销明细 +6. 录入支付方式 +7. 上传附件 + +--- + +## 五、CLI 执行流程 + +### 5.1 全流程执行 + +```bash +uv run python src/main.py +``` + +执行步骤： + +``` +Step 1: 发票提取 + ├── 扫描 PDF 发票文件 + ├── 提取发票信息 + ├── 生成 invoice_summary.csv + └── 自动分类（差旅/普通） + +Step 2: LLM 信息提取 + ├── 扫描支付截图 + ├── 多模态 LLM 识别刷卡信息 + ├── 回填 CSV 刷卡字段 + └── 更新 invoice_summary.csv + +Step 3: 报销提交 + ├── 登录信息门户 + ├── 进入财务系统 + ├── 创建报销单 + ├── 填写基本信息 + ├── 录入报销明细 + ├── 录入支付方式 + ├── 上传附件 + └── 提交（需手动确认） +``` + +### 5.2 分步执行 + +```bash +# 仅发票提取 +uv run python src/main.py --step invoice + +# 仅浏览器填报 +uv run python src/main.py --step submit +``` + +### 5.3 上传目录 + +CLI 模式自动发现 `src/web/uploads/` 下最新的会话文件夹，该文件夹包含上传的 PDF 和图片。 + +--- + +## 六、页面元素速查 + +### 基本信息页 + +| 字段 | 选择器 | 操作 | +|------|--------|------| +| 报销说明 | `#EXPENEXPLAIN` | fill | +| 项目代码 | `#PROJECTCODE` | click → 弹窗选择 | +| 项目弹窗 | `#promodal .fixed-table-body tbody tr` | 点击第一行 | +| 下一步按钮 | `#saveAndNext` | click | + +### 报销明细页 + +| 字段 | 选择器 | 操作 | +|------|--------|------| +| 增加按钮 | `#insertDetail` | click | +| 经济事项代码 | `#economicscode2` | click → 弹窗选择 | +| 经济科目弹窗 | `#econmodal .fixed-table-body tbody tr` | 点击第 3 行 | +| 单据数 | `input[name="expenPwCommondetail.HOWBILLS"]` | fill | +| 报销总金额 | `#je_zwzcdz` | fill | +| 确定按钮 | `#detailAdd` | click | + +### 支付方式页 + +| 字段 | 选择器 | 操作 | +|------|--------|------| +| 增加按钮 | `#insertPay` | click | +| 人员编号 | `#personid2` | fill | +| 人员姓名 | `#accountname2` | fill | +| 刷卡日期 | `#receiptdate2` | fill | +| 公务卡号 | `#localaccount2` | fill | +| 刷卡金额 | `#receiptmoney2` | fill | +| 实报金额 | `#money2` | fill | +| 商户 | `#merchant2` | fill | +| 备注 | `#smark2` | fill | +| 确定按钮 | `#payAdd` | click | + +### 附件清单页 + +| 字段 | 选择器 | 操作 | +|------|--------|------| +| 增加按钮 | `#insertAcc` | click | +| 附件类型 | `#fjlx` | select_option → '1'(发票) | +| 附件说明 | `#fpsmxx` | fill | +| 文件上传 | `#file` | set_input_files | +| 确定按钮 | `#cjtj` | click | + +### 提交 + +| 操作 | 选择器 | +|------|--------| +| 提交按钮 | `#submit` | +| 提交按钮(备用) | `#submit2` | + +--- + +## 七、数据流向 + +``` +PDF 发票 + 支付截图 + │ + ▼ extract_invoices() + │ + ├── 读取 PDF 发票 + ├── 提取发票信息 + ├── 自动分类（差旅/普通） + └── 输出: invoice_summary.csv + │ + ▼ enrich_with_llm() + ├── 多模态 LLM 识别支付截图 + ├── 回填刷卡字段 + └── 更新 invoice_summary.csv + │ + ▼ fill_consumable_from_template() + ├── 仅普通发票 + ├── 从模板复制出库单 + └── 自动填写出库单 + │ + ▼ run_bot() + ├── 登录信息门户 + ├── 进入财务系统 + ├── 创建报销单 + ├── 填写基本信息 + ├── 录入报销明细 + ├── 录入支付方式 + ├── 上传附件 + └── 提交 +``` + +--- + +## 八、关键设计说明 + +### 8.1 浏览器启动 + +系统使用 Playwright 的 `sync_playwright()` 启动 Chromium 浏览器，支持 `headless` 模式。每次运行均启动新浏览器实例，需重新登录。可通过 `headless` 参数控制是否显示浏览器窗口。 + +### 8.2 明细录入策略 + +系统采用"一条总明细"策略：将所有发票合并为一条报销明细，报销总金额为所有发票刷卡金额之和，单据数为发票总张数。支付方式则逐张发票分别录入，每张发票对应一条支付记录。 + +### 8.3 经济科目选择 + +系统在经济科目弹窗中固定选择第 3 行。如需更改科目，修改 `rows[2]` 的索引即可。 + +### 8.4 项目选择 + +系统在项目选择弹窗中固定选择第 1 行。如需更改项目，修改 `first_row` 的选择逻辑即可。 + +### 8.5 网络报销链接动态获取 + +单点登录页的"网络报销"链接参数每次不同，系统通过 `a:has(img[src*="wlbx"])` 精确定位链接，动态提取 `href` 属性后导航，不硬编码 URL。 + +### 8.6 发票类型分流 + +- **差旅发票**（高铁票/酒店住宿）：不生成易耗品出库单，走差旅报销流程 +- **普通发票**：生成易耗品出库单，走普通报销流程 + +### 8.7 移动端同步 + +PC 端生成二维码指向移动端上传页面，手机端上传的图片通过轮询同步到 PC 端，实现跨设备协作。 + +--- + +## 九、配置说明 + +### config.json + +项目根目录 `config.json` 包含默认配置： + +```json +{ + "username": "202407021", + "password": "your_password", + "sso_login_url": "https://tyrz.fynu.edu.cn/sso/login", + "portal_url": "https://tyrz.fynu.edu.cn/oshall", + "reimburse_url": "http://210.45.32.214:8081", + "reimburse_page": "/expen/common/common?v=4.0", + "default_name": "王建锋", + "default_card_no": "6282880139161682", + "default_person_id": "202407021", + "consumable_storage": "躬行楼 C205", + "llm": { + "model": "qwen3.5-9b", + "api_base": "http://100.123.83.115:1234/v1", + "api_key": "123456" + } +} +``` + +| 字段 | 说明 | +|------|------| +| `username` | 财务系统工号 | +| `password` | 登录密码 | +| `sso_login_url` | SSO 登录地址 | +| `portal_url` | 信息门户地址 | +| `reimburse_url` | 报销系统地址 | +| `reimburse_page` | 报销录入页面路径 | +| `default_name` | 默认报销人姓名 | +| `default_card_no` | 默认公务卡号 | +| `default_person_id` | 默认人员编号 | +| `consumable_storage` | 出库单存放地点 | +| `llm.model` | LLM 模型名称 | +| `llm.api_base` | LLM API 地址 | +| `llm.api_key` | LLM API 密钥 | + +--- + +## 十、日志与调试 + +### 日志输出 + +- 控制台实时输出（INFO 级别） +- 文件日志：`logs/<日期>.log`（UTF-8 编码） +- Web 界面：实时 SSE 日志流 + +### 截图保存 + +每个关键步骤自动截图到 `images/` 目录： + +| 截图文件 | 对应步骤 | +|----------|----------| +| `debug_portal_loaded.png` | 登录成功 | +| `debug_step3_project_modal.png` | 项目弹窗打开 | +| `debug_step3_project_selected.png` | 项目选择完成 | +| `debug_step3_done.png` | 基本信息完成 | +| `debug_after_add_click.png` | 点击新增后 | +| `debug_item_total.png` | 总明细录入完成 | +| `debug_step5_done.png` | 支付方式完成 | +| `debug_step6_done.png` | 附件上传完成 | +| `debug_error.png` | 异常状态 | + +### 超时设置 + +- 页面默认超时：30 秒 +- 登录门户等待：最多 30 秒 +- 单点登录页等待：最多 15 秒 +- SSE 日志流超时：10 分钟（600 秒） + +--- + +## 十一、常见问题 + +| 问题 | 原因 | 解决方案 | +|------|------|----------| +| 登录超时 | SSO 需要手动验证码/微信扫码 | 手动完成验证后脚本继续 | +| 未找到财务系统入口 | 门户页面结构变化 | 检查 `images/debug_*` 截图定位 | +| 经济科目选择失败 | 弹窗加载延迟 | 检查超时设置，增加等待时间 | +| 附件上传失败 | PDF 文件不存在或路径错误 | 确认 PDF 在当前工作目录 | +| 金额不匹配 | 明细合计 ≠ 支付合计 | 检查 CSV 数据中刷卡金额 | +| 提交被拦截 | 必填项为空 | 检查 `logs/<日期>.log` 定位失败步骤 | +| LLM 多模态提取失败 | llama-index 版本不兼容 | 确保使用 llama-index-core >= 0.14.x，多模态消息使用 `blocks` 格式 | +| 出库单生成失败 | 缺少 pywin32 或模板文件 | 安装 `pywin32`，确保项目根目录有 `易耗品、出库单.doc` 模板 | +| 差旅发票生成了出库单 | 分类不准确 | 检查发票内容是否包含"高铁票""酒店住宿"等关键词 | + +--- + +## 十二、相关 CLI 命令 + +### 单独填写出库单 + +```bash +uv run python -m src.doc.fill_consumable_doc --csv invoice_summary.csv --doc "易耗品、出库单.doc" +``` + +### 分步执行管道 + +```bash +# 仅发票提取 +uv run python src/main.py --step invoice + +# 仅浏览器填报 +uv run python src/main.py --step submit +``` + +### 启动 Web 服务 + +```bash +uv run python src/web/app.py +# 访问: http://localhost:5000 +``` + +--- + +## 附录：API 文档 + +详细的 API 接口文档见 [API.md](./API.md)。 \ No newline at end of file diff --git a/images/debug_after_add_click.png b/images/debug_after_add_click.png new file mode 100644 index 0000000..682687c Binary files /dev/null and b/images/debug_after_add_click.png differ diff --git a/images/debug_error.png b/images/debug_error.png new file mode 100644 index 0000000..a286725 Binary files /dev/null and b/images/debug_error.png differ diff --git a/images/debug_item_total.png b/images/debug_item_total.png new file mode 100644 index 0000000..54b7f10 Binary files /dev/null and b/images/debug_item_total.png differ diff --git a/images/debug_portal_loaded.png b/images/debug_portal_loaded.png new file mode 100644 index 0000000..276166d Binary files /dev/null and b/images/debug_portal_loaded.png differ diff --git a/images/debug_step3_done.png b/images/debug_step3_done.png new file mode 100644 index 0000000..54b301e Binary files /dev/null and b/images/debug_step3_done.png differ diff --git a/images/debug_step3_project_modal.png b/images/debug_step3_project_modal.png new file mode 100644 index 0000000..0ebdae8 Binary files /dev/null and b/images/debug_step3_project_modal.png differ diff --git a/images/debug_step3_project_selected.png b/images/debug_step3_project_selected.png new file mode 100644 index 0000000..94c3bb5 Binary files /dev/null and b/images/debug_step3_project_selected.png differ diff --git a/images/debug_step5_done.png b/images/debug_step5_done.png new file mode 100644 index 0000000..739a3b8 Binary files /dev/null and b/images/debug_step5_done.png differ diff --git a/images/debug_step6_done.png b/images/debug_step6_done.png new file mode 100644 index 0000000..a11df08 Binary files /dev/null and b/images/debug_step6_done.png differ diff --git a/images/debug_step6_error.png b/images/debug_step6_error.png new file mode 100644 index 0000000..a286725 Binary files /dev/null and b/images/debug_step6_error.png differ diff --git a/invoice_summary.csv b/invoice_summary.csv new file mode 100644 index 0000000..8e180a3 --- /dev/null +++ b/invoice_summary.csv @@ -0,0 +1,4 @@ +序号,发票类型,发票号码,开票日期,项目名称,规格型号,价税合计,销售方名称,出发站,到达站,车次,乘车日期,座位等级,人员姓名,刷卡日期,公务卡号,刷卡金额,备注,工号 +1,高铁票,26349119343000154520,2026/3/23,,,,,阜阳西,无锡东,G7221,2026/3/21,二等座,王建锋,,,,, +2,高铁票,26329166851000278168,2026/3/23,,,,,无锡东,阜阳西,G1826,2026/3/22,二等座,王建锋,,,,, +3,酒店住宿,26322000002199439186,2026/3/23,*住宿服务*住宿费 间 天 1 347.735849056604 347.74 6% 20.86,住宿费 间 天 1 347.735849056604 347.74 6% 20.86,368.6,,,,,,,,,,,, diff --git a/pipeline.log b/pipeline.log new file mode 100644 index 0000000..1099362 --- /dev/null +++ b/pipeline.log @@ -0,0 +1,1536 @@ +2026-06-08 13:31:01 [INFO ] pipeline: ============================================================ +2026-06-08 13:31:01 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:31:01 [INFO ] pipeline: ============================================================ +2026-06-08 13:31:01 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:31:02 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:32:29 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 319 字符 +2026-06-08 13:32:29 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:32:29 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:32:29 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:33:55 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 271 字符 +2026-06-08 13:33:55 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:33:55 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:33:55 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:35:21 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 311 字符 +2026-06-08 13:35:21 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:35:21 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:35:21 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:35:21 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:35:21 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:38:29 [INFO ] pipeline: ============================================================ +2026-06-08 13:38:29 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:38:29 [INFO ] pipeline: ============================================================ +2026-06-08 13:38:29 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:38:30 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:39:15 [INFO ] pipeline: ============================================================ +2026-06-08 13:39:15 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:39:15 [INFO ] pipeline: ============================================================ +2026-06-08 13:39:15 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:39:16 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:39:26 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 354 字符 +2026-06-08 13:39:26 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:39:26 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:39:26 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:39:35 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 318 字符 +2026-06-08 13:39:35 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:39:35 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:39:35 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:39:46 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 309 字符 +2026-06-08 13:39:46 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:39:46 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:39:46 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:39:46 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:39:46 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:40:04 [INFO ] pipeline: ============================================================ +2026-06-08 13:40:04 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:40:04 [INFO ] pipeline: ============================================================ +2026-06-08 13:40:04 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:40:05 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:40:13 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 317 字符 +2026-06-08 13:40:13 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:40:13 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:40:13 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:40:23 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 318 字符 +2026-06-08 13:40:23 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:40:23 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:40:23 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:40:34 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 309 字符 +2026-06-08 13:40:34 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:40:34 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:40:34 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:40:34 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:40:34 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:45:56 [INFO ] pipeline: ============================================================ +2026-06-08 13:45:56 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:45:56 [INFO ] pipeline: ============================================================ +2026-06-08 13:45:56 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:45:57 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:46:05 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 317 字符 +2026-06-08 13:46:05 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000154520", + "开票日期": "2026/3/23", + "项目名称": "高铁票", + "规格型号": "", + "价税合计": "258.00", + "销售方名称": "中国铁路", + "出发站": "阜阳西", + "到达站": "无锡", + "车次": "G7221", + "乘车日期": "2026/3/21", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:46:05 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:46:05 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:46:05 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:46:15 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 318 字符 +2026-06-08 13:46:15 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26329166851000278168", + "开票日期": "2026/3/23", + "项目名称": "高铁票", + "规格型号": "", + "价税合计": "259.00", + "销售方名称": "中国铁路", + "出发站": "无锡东", + "到达站": "阜阳西", + "车次": "G1826", + "乘车日期": "2026/3/22", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:46:15 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:46:15 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:46:15 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:46:23 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 309 字符 +2026-06-08 13:46:23 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26322000002199439186", + "开票日期": "2026/3/23", + "项目名称": "*住宿服务*住宿费", + "规格型号": "间 天", + "价税合计": "368.60", + "销售方名称": "滨湖区雪浪山庄花园酒店", + "出发站": "", + "到达站": "", + "车次": "", + "乘车日期": "", + "座位等级": "", + "人员姓名": "", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:46:23 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:46:23 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:46:23 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:46:23 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:46:23 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:47:51 [INFO ] pipeline: ============================================================ +2026-06-08 13:47:51 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:47:51 [INFO ] pipeline: ============================================================ +2026-06-08 13:47:51 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:47:52 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:48:01 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 316 字符 +2026-06-08 13:48:01 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000154520", + "开票日期": "2026/3/23", + "项目名称": "票价", + "规格型号": "", + "价税合计": "258.00", + "销售方名称": "中国铁路", + "出发站": "阜阳西", + "到达站": "无锡", + "车次": "G7221", + "乘车日期": "2026/3/21", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:48:01 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:48:01 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:48:01 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:48:09 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 324 字符 +2026-06-08 13:48:09 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26329166851000278168", + "开票日期": "2026/3/23", + "项目名称": "铁路旅客运输服务费", + "规格型号": "", + "价税合计": "259.00", + "销售方名称": "中国铁路", + "出发站": "无锡东", + "到达站": "阜阳西", + "车次": "G1826", + "乘车日期": "2026/3/22", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:48:09 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:48:09 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:48:09 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:48:17 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 309 字符 +2026-06-08 13:48:17 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26322000002199439186", + "开票日期": "2026/3/23", + "项目名称": "*住宿服务*住宿费", + "规格型号": "间 天", + "价税合计": "368.60", + "销售方名称": "滨湖区雪浪山庄花园酒店", + "出发站": "", + "到达站": "", + "车次": "", + "乘车日期": "", + "座位等级": "", + "人员姓名": "", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:48:17 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:48:17 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:48:17 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:48:17 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:48:17 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:53:40 [INFO ] pipeline: ============================================================ +2026-06-08 13:53:40 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:53:40 [INFO ] pipeline: ============================================================ +2026-06-08 13:53:40 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:53:41 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:53:42 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:53:42 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建峰-阜阳西-无锡东258.pdf (Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:53:42 [INFO ] llm_extractor: 正则回退解析完成: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:53:42 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:53:42 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:53:42 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:53:42 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建锋-无锡东-阜阳西259.pdf (Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:53:42 [INFO ] llm_extractor: 正则回退解析完成: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:53:42 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:53:42 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:53:42 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:53:42 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建锋3.21住宿368.6.pdf (Error code: 400 - {'error': {'message': "'reasoning_effort' must be a string", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:53:42 [INFO ] llm_extractor: 正则回退解析完成: 王建锋3.21住宿368.6.pdf +2026-06-08 13:53:42 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:53:42 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:53:42 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:53:42 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:54:26 [INFO ] pipeline: ============================================================ +2026-06-08 13:54:26 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:54:26 [INFO ] pipeline: ============================================================ +2026-06-08 13:54:26 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:54:28 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:54:28 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:54:28 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建峰-阜阳西-无锡东258.pdf (Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:54:28 [INFO ] llm_extractor: 正则回退解析完成: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:54:28 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:54:28 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:54:28 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:54:28 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建锋-无锡东-阜阳西259.pdf (Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:54:28 [INFO ] llm_extractor: 正则回退解析完成: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:54:28 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:54:28 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:54:29 [ERROR] llm_extractor: LLM 请求失败: Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}} +2026-06-08 13:54:29 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 王建锋3.21住宿368.6.pdf (Error code: 400 - {'error': {'message': "Invalid 'reasoning_effort' value: 'on'. Supported values: none, minimal, low, medium, high, xhigh.", 'type': 'invalid_request_error', 'param': 'reasoning_effort', 'code': 'invalid_value'}}) +2026-06-08 13:54:29 [INFO ] llm_extractor: 正则回退解析完成: 王建锋3.21住宿368.6.pdf +2026-06-08 13:54:29 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:54:29 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:54:29 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:54:29 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:54:42 [INFO ] pipeline: ============================================================ +2026-06-08 13:54:42 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:54:42 [INFO ] pipeline: ============================================================ +2026-06-08 13:54:42 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:54:43 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:54:57 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 317 字符 +2026-06-08 13:54:57 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000154520", + "开票日期": "2026/3/23", + "项目名称": "高铁票", + "规格型号": "", + "价税合计": "258.00", + "销售方名称": "中国铁路", + "出发站": "阜阳西", + "到达站": "无锡", + "车次": "G7221", + "乘车日期": "2026/3/21", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:54:57 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:54:57 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 13:54:57 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:55:08 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 318 字符 +2026-06-08 13:55:08 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26329166851000278168", + "开票日期": "2026/3/23", + "项目名称": "高铁票", + "规格型号": "", + "价税合计": "259.00", + "销售方名称": "中国铁路", + "出发站": "无锡东", + "到达站": "阜阳西", + "车次": "G1826", + "乘车日期": "2026/3/22", + "座位等级": "二等座", + "人员姓名": "王建锋", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:55:08 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:55:08 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 13:55:08 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 13:55:17 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 309 字符 +2026-06-08 13:55:17 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26322000002199439186", + "开票日期": "2026/3/23", + "项目名称": "*住宿服务*住宿费", + "规格型号": "间 天", + "价税合计": "368.60", + "销售方名称": "滨湖区雪浪山庄花园酒店", + "出发站": "", + "到达站": "", + "车次": "", + "乘车日期": "", + "座位等级": "", + "人员姓名": "", + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "备注": "", + "工号": "" +} +2026-06-08 13:55:17 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 13:55:17 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 13:55:17 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 13:55:17 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 13:55:17 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 13:56:00 [INFO ] pipeline: ============================================================ +2026-06-08 13:56:00 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 13:56:00 [INFO ] pipeline: ============================================================ +2026-06-08 13:56:00 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 13:56:02 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:09:24 [INFO ] pipeline: ============================================================ +2026-06-08 14:09:24 [INFO ] pipeline: [1/3] 发票提取 +2026-06-08 14:09:24 [INFO ] pipeline: ============================================================ +2026-06-08 14:09:24 [INFO ] extractor: 发现 3 个 PDF 文件 +2026-06-08 14:09:25 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:09:31 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-08 14:09:31 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000154520", + "开票日期": "2026/3/23", + "乘车日期": "2026/3/21", + "出发站": "阜阳西", + "到达站": "无锡东", + "座位等级": "二等座", + "车次": "G7221", + "人员姓名": "王建锋", + "票价": "258.00" +} +2026-06-08 14:09:31 [INFO ] llm_extractor: LLM 发票提取成功: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 14:09:31 [INFO ] extractor: [高铁票] 已解析: 王建峰-阜阳西-无锡东258.pdf +2026-06-08 14:09:31 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:09:35 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-08 14:09:35 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26329166851000278168", + "开票日期": "2026/3/23", + "乘车日期": "2026/3/22", + "出发站": "无锡东", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1826", + "人员姓名": "王建锋", + "票价": "259.00" +} +2026-06-08 14:09:35 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 14:09:35 [INFO ] extractor: [高铁票] 已解析: 王建锋-无锡东-阜阳西259.pdf +2026-06-08 14:09:35 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:09:38 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 95 字符 +2026-06-08 14:09:38 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26322000002199439186", + "开票日期": "2026/3/23", + "价税合计": 368.60 +} +2026-06-08 14:09:38 [INFO ] llm_extractor: LLM 发票提取成功: 王建锋3.21住宿368.6.pdf +2026-06-08 14:09:38 [INFO ] extractor: [酒店住宿] 已解析: 王建锋3.21住宿368.6.pdf +2026-06-08 14:09:38 [INFO ] extractor: 共处理 3 张发票 +2026-06-08 14:09:38 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 14:09:38 [INFO ] pipeline: [1/3] 发票提取 完成 +2026-06-08 14:14:14 [INFO ] extractor: 发现 5 个 PDF 文件 +2026-06-08 14:14:16 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:14:22 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:14:22 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 14:14:22 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 14:14:22 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 14:14:22 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:14:27 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:14:27 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 14:14:27 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 14:14:27 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 14:14:27 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:14:32 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:14:32 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 14:14:32 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 14:14:32 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 14:14:32 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:14:37 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:14:37 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 14:14:37 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 14:14:37 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 14:14:37 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:14:40 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 94 字符 +2026-06-08 14:14:40 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": 536.00 +} +2026-06-08 14:14:40 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 14:14:40 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 14:14:40 [INFO ] extractor: 共处理 5 张发票 +2026-06-08 14:14:40 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 14:14:40 [WARNING] llm_extractor: 未找到 PDF-图片配对文件，跳过信息提取 +2026-06-08 14:14:40 [INFO ] fill_consumable_doc: 开始填写出库单: 易耗品、出库单.doc +2026-06-08 14:14:50 [INFO ] fill_consumable_doc: 出库单填写完成 +2026-06-08 14:50:57 [INFO ] extractor: 发现 5 个 PDF 文件 +2026-06-08 14:50:59 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:51:13 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:51:13 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119343000331138-电子发票.pdf (Error code: 502) +2026-06-08 14:51:13 [INFO ] llm_extractor: 正则回退解析完成: 26349119343000331138-电子发票.pdf +2026-06-08 14:51:13 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 14:51:13 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:51:26 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:51:26 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119343000335414-电子发票.pdf (Error code: 502) +2026-06-08 14:51:26 [INFO ] llm_extractor: 正则回退解析完成: 26349119343000335414-电子发票.pdf +2026-06-08 14:51:26 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 14:51:26 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:51:39 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:51:39 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119423003550275-电子发票.pdf (Error code: 502) +2026-06-08 14:51:39 [INFO ] llm_extractor: 正则回退解析完成: 26349119423003550275-电子发票.pdf +2026-06-08 14:51:39 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 14:51:39 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:53:56 [INFO ] extractor: 发现 5 个 PDF 文件 +2026-06-08 14:53:58 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:54:12 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:54:12 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119343000331138-电子发票.pdf (Error code: 502) +2026-06-08 14:54:12 [INFO ] llm_extractor: 正则回退解析完成: 26349119343000331138-电子发票.pdf +2026-06-08 14:54:12 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 14:54:12 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:54:25 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:54:25 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119343000335414-电子发票.pdf (Error code: 502) +2026-06-08 14:54:25 [INFO ] llm_extractor: 正则回退解析完成: 26349119343000335414-电子发票.pdf +2026-06-08 14:54:25 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 14:54:25 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:54:38 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:54:38 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119423003550275-电子发票.pdf (Error code: 502) +2026-06-08 14:54:38 [INFO ] llm_extractor: 正则回退解析完成: 26349119423003550275-电子发票.pdf +2026-06-08 14:54:38 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 14:54:38 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:54:52 [ERROR] llm_extractor: LLM 请求失败: Error code: 502 +2026-06-08 14:54:52 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: 26349119423003595208-电子发票.pdf (Error code: 502) +2026-06-08 14:54:52 [INFO ] llm_extractor: 正则回退解析完成: 26349119423003595208-电子发票.pdf +2026-06-08 14:54:52 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 14:54:52 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://127.0.0.1:1234/v1) +2026-06-08 14:55:39 [INFO ] extractor: 发现 5 个 PDF 文件 +2026-06-08 14:55:41 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 14:55:54 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:55:54 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 14:55:54 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 14:55:54 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 14:55:54 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 14:56:02 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:56:02 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 14:56:02 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 14:56:02 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 14:56:02 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 14:56:11 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:56:11 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 14:56:11 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 14:56:11 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 14:56:11 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 14:56:25 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 14:56:25 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 14:56:25 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 14:56:25 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 14:56:25 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 14:56:31 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 94 字符 +2026-06-08 14:56:31 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": 536.00 +} +2026-06-08 14:56:31 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 14:56:31 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 14:56:31 [INFO ] extractor: 共处理 5 张发票 +2026-06-08 14:56:31 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 14:56:31 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 5 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 14:56:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 14:56:31 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145318_279_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145319_280_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145320_281_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145321_282_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145323_283_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 14:56:49 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145318_279_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145319_280_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145320_281_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145321_282_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 14:56:49 [WARNING] llm_extractor: LLM 多模态提取失败，回退到 OCR+正则: 微信图片_20260608145323_283_42.jpg (cannot import name 'ImageDocument' from 'llama_index.core.llms' (D:\阜阳师范大学\财务报销\自动报销系统\.venv\Lib\site-packages\llama_index\core\llms\__init__.py)) +2026-06-08 14:56:49 [ERROR] llm_extractor: OCR 回退提取失败: Engine 'paddle_static' is unavailable because dependency 'paddlepaddle' is not installed. +2026-06-08 14:56:49 [INFO ] llm_extractor: LLM 提取完成: 匹配 5/5 行，更新 0 个字段 +2026-06-08 14:56:49 [INFO ] fill_consumable_doc: 开始填写出库单: 易耗品、出库单.doc +2026-06-08 14:56:57 [INFO ] fill_consumable_doc: 出库单填写完成 +2026-06-08 15:13:59 [INFO ] extractor: 发现 5 个 PDF 文件 +2026-06-08 15:14:01 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 15:14:11 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 15:14:11 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 15:14:11 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 15:14:11 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 15:14:11 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 15:14:21 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 15:14:21 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 15:14:21 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 15:14:21 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 15:14:21 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 15:14:30 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 15:14:30 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 15:14:30 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 15:14:30 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 15:14:30 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 15:14:39 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 15:14:39 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 15:14:39 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 15:14:39 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 15:14:39 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 15:14:45 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 96 字符 +2026-06-08 15:14:45 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": "536.00" +} +2026-06-08 15:14:45 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 15:14:45 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 15:14:45 [INFO ] extractor: 共处理 5 张发票 +2026-06-08 15:14:45 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 15:14:45 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 5 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 15:14:46 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 15:16:20 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-08 15:16:20 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 15:17:35 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-08 15:17:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 15:19:04 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-08 15:19:04 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 15:20:14 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145321_282_42.jpg +2026-06-08 15:20:14 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 15:21:11 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145323_283_42.jpg +2026-06-08 15:21:11 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 15:21:11 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 15:21:11 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 15:22:26 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-08 15:22:26 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 15:23:46 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-08 15:23:46 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 15:24:24 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-08 15:24:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:01:24 [INFO ] extractor: 发现 7 个 PDF 文件 +2026-06-08 16:01:26 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:01:37 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:01:37 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 16:01:37 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 16:01:37 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 16:01:37 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:01:45 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:01:45 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331314", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "一等座", + "车次": "G1967", + "人员姓名": "陈曙光", + "票价": "167.00" +} +2026-06-08 16:01:45 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331314-电子发票.pdf +2026-06-08 16:01:45 [INFO ] extractor: [高铁票] 已解析: 26349119343000331314-电子发票.pdf +2026-06-08 16:01:45 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:01:54 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:01:54 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 16:01:54 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 16:01:54 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 16:01:54 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:02:02 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:02:02 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 16:02:02 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 16:02:02 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 16:02:03 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:02:10 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:02:10 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003552366", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "一等座", + "车次": "G1318", + "人员姓名": "陈曙光", + "票价": "189.50" +} +2026-06-08 16:02:10 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003552366-电子发票.pdf +2026-06-08 16:02:10 [INFO ] extractor: [高铁票] 已解析: 26349119423003552366-电子发票.pdf +2026-06-08 16:02:11 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:02:19 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:02:19 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 16:02:19 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 16:02:19 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 16:02:19 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:02:23 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 94 字符 +2026-06-08 16:02:23 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": 536.00 +} +2026-06-08 16:02:23 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:02:23 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:02:23 [INFO ] extractor: 共处理 7 张发票 +2026-06-08 16:02:23 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 16:02:23 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:02:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:02:26 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-08 16:02:26 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:02:28 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-08 16:02:28 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:02:29 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-08 16:02:29 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:02:31 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145321_282_42.jpg +2026-06-08 16:02:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:02:33 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145323_283_42.jpg +2026-06-08 16:02:33 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:02:33 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:02:34 [INFO ] llm_extractor: LLM 提取完成: 匹配 5/7 行，更新 0 个字段 +2026-06-08 16:02:34 [INFO ] fill_consumable_doc: 开始填写出库单: 易耗品、出库单.doc +2026-06-08 16:02:43 [INFO ] fill_consumable_doc: 出库单填写完成 +2026-06-08 16:22:03 [INFO ] extractor: 发现 7 个 PDF 文件 +2026-06-08 16:22:05 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:13 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:13 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 16:22:13 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 16:22:13 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 16:22:13 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:19 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:19 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331314", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "一等座", + "车次": "G1967", + "人员姓名": "陈曙光", + "票价": "167.00" +} +2026-06-08 16:22:19 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331314-电子发票.pdf +2026-06-08 16:22:19 [INFO ] extractor: [高铁票] 已解析: 26349119343000331314-电子发票.pdf +2026-06-08 16:22:20 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:27 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:27 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 16:22:27 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 16:22:27 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 16:22:27 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:34 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:34 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 16:22:34 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 16:22:34 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 16:22:34 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:41 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:41 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003552366", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "一等座", + "车次": "G1318", + "人员姓名": "陈曙光", + "票价": "189.50" +} +2026-06-08 16:22:41 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003552366-电子发票.pdf +2026-06-08 16:22:41 [INFO ] extractor: [高铁票] 已解析: 26349119423003552366-电子发票.pdf +2026-06-08 16:22:42 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:49 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:22:49 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 16:22:49 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 16:22:49 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 16:22:49 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:22:53 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 94 字符 +2026-06-08 16:22:53 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": 536.00 +} +2026-06-08 16:22:53 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:22:53 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:22:53 [INFO ] extractor: 共处理 7 张发票 +2026-06-08 16:22:53 [INFO ] extractor: 差旅发票: 7 张, 普通发票: 0 张 +2026-06-08 16:22:53 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 16:22:53 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:22:54 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:22:56 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-08 16:22:56 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:22:58 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-08 16:22:58 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:22:59 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-08 16:22:59 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:23:01 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145321_282_42.jpg +2026-06-08 16:23:01 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:23:03 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145323_283_42.jpg +2026-06-08 16:23:03 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:23:03 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:23:03 [INFO ] llm_extractor: LLM 提取完成: 匹配 5/7 行，更新 0 个字段 +2026-06-08 16:23:03 [INFO ] fill_consumable_doc: 纯差旅发票，跳过易耗品出库单生成 +2026-06-08 16:37:34 [INFO ] extractor: 发现 7 个 PDF 文件 +2026-06-08 16:37:36 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:37:45 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:37:45 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} +2026-06-08 16:37:45 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-08 16:37:45 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-08 16:37:45 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:37:51 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:37:51 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331314", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "一等座", + "车次": "G1967", + "人员姓名": "陈曙光", + "票价": "167.00" +} +2026-06-08 16:37:51 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331314-电子发票.pdf +2026-06-08 16:37:51 [INFO ] extractor: [高铁票] 已解析: 26349119343000331314-电子发票.pdf +2026-06-08 16:37:51 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:37:58 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:37:58 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} +2026-06-08 16:37:58 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-08 16:37:58 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-08 16:37:58 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:38:04 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:38:04 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} +2026-06-08 16:38:04 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-08 16:38:04 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-08 16:38:04 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:38:11 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:38:11 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003552366", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "一等座", + "车次": "G1318", + "人员姓名": "陈曙光", + "票价": "189.50" +} +2026-06-08 16:38:11 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003552366-电子发票.pdf +2026-06-08 16:38:11 [INFO ] extractor: [高铁票] 已解析: 26349119423003552366-电子发票.pdf +2026-06-08 16:38:11 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:38:19 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 198 字符 +2026-06-08 16:38:19 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} +2026-06-08 16:38:19 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-08 16:38:19 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-08 16:38:19 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1) +2026-06-08 16:38:23 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 96 字符 +2026-06-08 16:38:23 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": "536.00" +} +2026-06-08 16:38:23 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:38:23 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-08 16:38:23 [INFO ] extractor: 共处理 7 张发票 +2026-06-08 16:38:23 [INFO ] extractor: 差旅发票: 7 张, 普通发票: 0 张 +2026-06-08 16:38:23 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-08 16:38:23 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:38:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:38:25 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-08 16:38:25 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:38:27 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-08 16:38:27 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:38:30 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-08 16:38:30 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:38:38 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145321_282_42.jpg +2026-06-08 16:38:38 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:38:39 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145323_283_42.jpg +2026-06-08 16:38:39 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:38:39 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:38:39 [INFO ] llm_extractor: PDF=26349119343000331138-电子发票.pdf, 发票号=26349119343000331138, 刷卡信息={'人员姓名': '', '刷卡日期': '', '公务卡号': '', '刷卡金额': ''} +2026-06-08 16:38:39 [INFO ] llm_extractor: PDF=26349119343000331314-电子发票.pdf, 发票号=26349119343000331314, 刷卡信息={'人员姓名': '', '刷卡日期': '', '公务卡号': '', '刷卡金额': ''} +2026-06-08 16:38:40 [INFO ] llm_extractor: PDF=26349119343000335414-电子发票.pdf, 发票号=26349119343000335414, 刷卡信息={'人员姓名': '', '刷卡日期': '', '公务卡号': '', '刷卡金额': ''} +2026-06-08 16:38:40 [INFO ] llm_extractor: PDF=26349119423003550275-电子发票.pdf, 发票号=26349119423003550275, 刷卡信息={'人员姓名': '', '刷卡日期': '', '公务卡号': '', '刷卡金额': ''} +2026-06-08 16:38:40 [INFO ] llm_extractor: PDF=26349119423003552366-电子发票.pdf, 发票号=26349119423003552366, 刷卡信息={'人员姓名': '', '刷卡日期': '', '公务卡号': '', '刷卡金额': ''} +2026-06-08 16:38:40 [INFO ] llm_extractor: LLM 提取完成: 匹配 5/7 行，更新 0 个字段 +2026-06-08 16:38:40 [INFO ] fill_consumable_doc: 纯差旅发票，跳过易耗品出库单生成 +2026-06-08 16:45:50 [INFO ] pipeline: ============================================================ +2026-06-08 16:45:50 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:45:50 [INFO ] pipeline: ============================================================ +2026-06-08 16:45:50 [WARNING] llm_extractor: 未找到 PDF-图片配对文件，跳过信息提取 +2026-06-08 16:45:50 [INFO ] pipeline: [2/3] LLM 信息提取 完成 +2026-06-08 16:48:56 [INFO ] pipeline: ============================================================ +2026-06-08 16:48:56 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:48:56 [INFO ] pipeline: ============================================================ +2026-06-08 16:48:56 [WARNING] llm_extractor: 未找到 PDF-图片配对文件，跳过信息提取 +2026-06-08 16:48:56 [INFO ] pipeline: [2/3] LLM 信息提取 完成 +2026-06-08 16:54:29 [INFO ] pipeline: ============================================================ +2026-06-08 16:54:29 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:54:29 [INFO ] pipeline: ============================================================ +2026-06-08 16:54:29 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:54:30 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:54:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145319_280_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:54:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145320_281_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:54:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145321_282_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...//Z', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:54:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145323_283_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:54:31 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:54:31 [INFO ] llm_extractor: 在 uploads 会话目录找到配对: 179d55ea2a95 +2026-06-08 16:54:31 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:54:31 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:54:31 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:55:39 [INFO ] pipeline: ============================================================ +2026-06-08 16:55:39 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:55:39 [INFO ] pipeline: ============================================================ +2026-06-08 16:55:39 [INFO ] pipeline: 使用 uploads 目录: 71dc1d795be5 +2026-06-08 16:55:39 [WARNING] llm_extractor: 未找到 PDF-图片配对文件，跳过信息提取 +2026-06-08 16:55:39 [INFO ] pipeline: [2/3] LLM 信息提取 完成 +2026-06-08 16:56:22 [INFO ] pipeline: ============================================================ +2026-06-08 16:56:22 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:56:22 [INFO ] pipeline: ============================================================ +2026-06-08 16:56:22 [INFO ] pipeline: 使用 uploads 目录: 179d55ea2a95 +2026-06-08 16:56:22 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:56:23 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:56:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145319_280_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:56:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145320_281_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:56:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145321_282_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...//Z', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:56:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145323_283_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:56:24 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:56:24 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:56:24 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:56:24 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:20 [INFO ] pipeline: ============================================================ +2026-06-08 16:59:20 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 16:59:20 [INFO ] pipeline: ============================================================ +2026-06-08 16:59:20 [INFO ] pipeline: 使用 uploads 目录: 179d55ea2a95 +2026-06-08 16:59:20 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145319_280_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145320_281_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145321_282_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...//Z', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145323_283_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 16:59:21 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 16:59:21 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 16:59:21 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 16:59:21 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:34 [INFO ] pipeline: ============================================================ +2026-06-08 17:00:34 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 17:00:34 [INFO ] pipeline: ============================================================ +2026-06-08 17:00:34 [INFO ] pipeline: 使用 uploads 目录: 179d55ea2a95 +2026-06-08 17:00:34 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145319_280_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145320_281_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145321_282_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...//Z', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145323_283_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:00:35 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 17:00:35 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 17:00:35 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 17:00:35 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (2 validation errors for ChatMessage +blocks.0 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'text', 'text': ...提取刷卡信息。'}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found +blocks.1 + Unable to extract tag using discriminator 'block_type' [type=union_tag_not_found, input_value={'type': 'image_url', 'im...9k=', 'detail': 'high'}}, input_type=dict] + For further information visit https://errors.pydantic.dev/2.13/v/union_tag_not_found) +2026-06-08 17:03:44 [INFO ] pipeline: ============================================================ +2026-06-08 17:03:44 [INFO ] pipeline: [2/3] LLM 信息提取 +2026-06-08 17:03:44 [INFO ] pipeline: ============================================================ +2026-06-08 17:03:44 [INFO ] pipeline: 使用 uploads 目录: 179d55ea2a95 +2026-06-08 17:03:44 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-08 17:03:45 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-08 17:03:54 [INFO ] llm_extractor: LLM 多模态原始响应: { + "人员姓名": "", + "刷卡日期": "2026/6/1", + "公务卡号": "6282****1682", + "刷卡金额": "231.00" +} +2026-06-08 17:03:54 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145318_279_42.jpg, 结果: {'人员姓名': '', '刷卡日期': '2026/6/1', '公务卡号': '6282****1682', '刷卡金额': '231.00'} +2026-06-08 17:03:54 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-08 17:04:02 [INFO ] llm_extractor: LLM 多模态原始响应: { + "人员姓名": "", + "刷卡日期": "2026/6/1", + "公务卡号": "6282****1682", + "刷卡金额": "167.00" +} +2026-06-08 17:04:02 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145319_280_42.jpg, 结果: {'人员姓名': '', '刷卡日期': '2026/6/1', '公务卡号': '6282****1682', '刷卡金额': '167.00'} +2026-06-08 17:04:02 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-08 17:04:09 [INFO ] llm_extractor: LLM 多模态原始响应: { + "人员姓名": "", + "刷卡日期": "2026/6/2", + "公务卡号": "6282****1682", + "刷卡金额": "535.86" +} +2026-06-08 17:04:09 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145320_281_42.jpg, 结果: {'人员姓名': '', '刷卡日期': '2026/6/2', '公务卡号': '6282****1682', '刷卡金额': '535.86'} +2026-06-08 17:04:09 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-08 17:04:16 [INFO ] llm_extractor: LLM 多模态原始响应: { + "人员姓名": "", + "刷卡日期": "2026/6/3", + "公务卡号": "6282****1682", + "刷卡金额": "189.50" +} +2026-06-08 17:04:16 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145321_282_42.jpg, 结果: {'人员姓名': '', '刷卡日期': '2026/6/3', '公务卡号': '6282****1682', '刷卡金额': '189.50'} +2026-06-08 17:04:16 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen/qwen3.6-27b, base=http://100.123.83.113:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-08 17:04:26 [INFO ] llm_extractor: LLM 多模态原始响应: { + "人员姓名": "", + "刷卡日期": "2026/6/3", + "公务卡号": "6282****1682", + "刷卡金额": "235.00" +} +2026-06-08 17:04:26 [INFO ] llm_extractor: LLM 刷卡信息提取成功: 微信图片_20260608145323_283_42.jpg, 结果: {'人员姓名': '', '刷卡日期': '2026/6/3', '公务卡号': '6282****1682', '刷卡金额': '235.00'} +2026-06-08 17:04:26 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-08 17:04:26 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-08 17:04:26 [INFO ] llm_extractor: PDF=dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf, 发票号=26342000001715702281, 刷卡信息={'人员姓名': '', '刷卡日期': '2026/6/2', '公务卡号': '6282****1682', '刷卡金额': '535.86'} +2026-06-08 17:04:26 [INFO ] llm_extractor: PDF=26349119343000331138-电子发票.pdf, 发票号=26349119343000331138, 刷卡信息={'人员姓名': '', '刷卡日期': '2026/6/1', '公务卡号': '6282****1682', '刷卡金额': '231.00'} +2026-06-08 17:04:26 [INFO ] llm_extractor: PDF=26349119343000331314-电子发票.pdf, 发票号=26349119343000331314, 刷卡信息={'人员姓名': '', '刷卡日期': '2026/6/1', '公务卡号': '6282****1682', '刷卡金额': '167.00'} +2026-06-08 17:04:26 [INFO ] llm_extractor: PDF=26349119343000335414-电子发票.pdf, 发票号=26349119343000335414, 刷卡信息={'人员姓名': '', '刷卡日期': '2026/6/3', '公务卡号': '6282****1682', '刷卡金额': '189.50'} +2026-06-08 17:04:26 [INFO ] llm_extractor: PDF=26349119423003550275-电子发票.pdf, 发票号=26349119423003550275, 刷卡信息={'人员姓名': '', '刷卡日期': '2026/6/3', '公务卡号': '6282****1682', '刷卡金额': '235.00'} +2026-06-08 17:04:26 [INFO ] llm_extractor: LLM 提取完成: 匹配 5/7 行，更新 15 个字段 +2026-06-08 17:04:26 [INFO ] pipeline: [2/3] LLM 信息提取 完成 +2026-06-09 11:20:44 [INFO ] extractor: 发现 7 个 PDF 文件 +2026-06-09 11:20:52 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:21:28 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:21:28 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "票价": "115.50" +} + + +2026-06-09 11:21:28 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-09 11:21:28 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-09 11:21:28 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:22:02 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:22:02 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331314", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "一等座", + "车次": "G1967", + "人员姓名": "陈曙光", + "票价": "167.00" +} + + +2026-06-09 11:22:02 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331314-电子发票.pdf +2026-06-09 11:22:02 [INFO ] extractor: [高铁票] 已解析: 26349119343000331314-电子发票.pdf +2026-06-09 11:22:02 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:22:36 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:22:36 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "票价": "115.50" +} + + +2026-06-09 11:22:36 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-09 11:22:36 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-09 11:22:36 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:23:09 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:23:09 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "票价": "117.50" +} + + +2026-06-09 11:23:09 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-09 11:23:09 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-09 11:23:09 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:23:42 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:23:42 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003552366", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "一等座", + "车次": "G1318", + "人员姓名": "陈曙光", + "票价": "189.50" +} + + +2026-06-09 11:23:42 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003552366-电子发票.pdf +2026-06-09 11:23:42 [INFO ] extractor: [高铁票] 已解析: 26349119423003552366-电子发票.pdf +2026-06-09 11:23:42 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:24:15 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 11:24:15 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "票价": "117.50" +} + + +2026-06-09 11:24:15 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-09 11:24:15 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-09 11:24:15 [INFO ] llm_extractor: 开始请求 LLM (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 11:24:45 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 154 字符 +2026-06-09 11:24:45 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "普通发票", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "项目名称": "*生产生活服务*住宿服务", + "规格型号": "", + "价税合计": "536.00", + "销售方名称": "安徽栢景酒店 +2026-06-09 11:24:45 [WARNING] llm_extractor: LLM 发票提取失败，回退到正则解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf (Unterminated string starting at: line 8 column 12 (char 147)) +2026-06-09 11:24:45 [INFO ] llm_extractor: 正则回退解析完成: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-09 11:24:45 [INFO ] extractor: [普通发票] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-09 11:24:45 [INFO ] extractor: 共处理 7 张发票 +2026-06-09 11:24:45 [INFO ] extractor: 差旅发票: 6 张, 普通发票: 1 张 +2026-06-09 11:24:45 [INFO ] extractor: CSV 已保存: invoice_summary.csv +2026-06-09 11:24:45 [INFO ] llm_extractor: 文件名匹配 0 组，剩余 7 个 PDF、5 张图片，尝试金额匹配... +2026-06-09 11:24:45 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-09 11:24:47 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) +2026-06-09 11:24:47 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145319_280_42.jpg) +2026-06-09 11:24:48 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145319_280_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) +2026-06-09 11:24:48 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145320_281_42.jpg) +2026-06-09 11:24:50 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145320_281_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) +2026-06-09 11:24:50 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145321_282_42.jpg) +2026-06-09 11:24:51 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145321_282_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) +2026-06-09 11:24:51 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145323_283_42.jpg) +2026-06-09 11:24:52 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145323_283_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) +2026-06-09 11:24:52 [INFO ] llm_extractor: 金额匹配完成，共 5 组配对 +2026-06-09 11:24:52 [INFO ] llm_extractor: 找到 5 组 PDF-图片配对 +2026-06-09 11:24:52 [INFO ] llm_extractor: 开始多模态 LLM 请求 (model=qwen3.5-9b, base=http://100.123.83.115:1234/v1, image=微信图片_20260608145318_279_42.jpg) +2026-06-09 11:24:54 [ERROR] llm_extractor: LLM 多模态提取失败: 微信图片_20260608145318_279_42.jpg (request (2067 tokens) exceeds the available context size (768 tokens), try increasing it) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..40a0278 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,49 @@ +[project] +name = "auto-reimbursement-system" +version = "0.1.0" +description = "财务报销自动化系统" +requires-python = ">=3.12" +dependencies = [ + "flask>=3.0", + "playwright>=1.40", + "pdfplumber>=0.10", + "pywin32>=306", + "llama-index>=0.12.0", + "llama-index-llms-openai-like==0.7.2", +] + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pytest-cov>=5.0", + "ruff>=0.9", + "mypy>=1.14", + "deptry>=0.22", + "pre-commit>=4.0", +] + +[tool.ruff] +target-version = "py312" +line-length = 120 + +[tool.ruff.lint] +select = ["E", "F", "W", "I", "N", "UP", "B"] +ignore = ["E501"] + +[tool.mypy] +python_version = "3.12" +strict = true +warn_return_any = true +warn_unused_configs = true +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "tests.*" +ignore_errors = true + +[tool.deptry] +ignore_notebooks = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] \ No newline at end of file diff --git a/app/__init__.py b/src/__init__.py similarity index 67% rename from app/__init__.py rename to src/__init__.py index 8c1e837..889b183 100644 --- a/app/__init__.py +++ b/src/__init__.py @@ -1,5 +1,6 @@ """财务报销自动化工具包""" +import datetime import io import logging import sys @@ -7,14 +8,20 @@ from pathlib import Path _LOG_FMT = "%(asctime)s [%(levelname)-5s] %(name)s: %(message)s" _LOG_DATE_FMT = "%Y-%m-%d %H:%M:%S" -_LOG_FILE = Path(__file__).resolve().parent.parent / "pipeline.log" +_LOG_DIR = Path(__file__).resolve().parent.parent / "logs" + + +def _get_log_file() -> Path: + """返回当日日志文件路径，如 logs/2026-06-09.log""" + _LOG_DIR.mkdir(parents=True, exist_ok=True) + return _LOG_DIR / f"{datetime.date.today():%Y-%m-%d}.log" def get_logger(name: str) -> logging.Logger: """获取带时间戳的日志记录器 输出格式: 2026-05-24 12:34:56 [INFO ] extractor: 扫描目录: ... - 日志同时输出到终端和项目根目录的 pipeline.log + 日志同时输出到终端和 logs/<日期>.log """ logger = logging.getLogger(name) if not logger.handlers: @@ -28,8 +35,8 @@ def get_logger(name: str) -> logging.Logger: logger.addHandler(stream_handler) # 文件输出 - file_handler = logging.FileHandler(str(_LOG_FILE), encoding="utf-8") + file_handler = logging.FileHandler(str(_get_log_file()), encoding="utf-8") file_handler.setFormatter(formatter) logger.addHandler(file_handler) - return logger \ No newline at end of file + return logger diff --git a/app/bot.py b/src/bot.py similarity index 80% rename from app/bot.py rename to src/bot.py index 4af1b12..a3bc1eb 100644 --- a/app/bot.py +++ b/src/bot.py @@ -10,6 +10,7 @@ import csv from pathlib import Path +from typing import Any from . import get_logger @@ -20,6 +21,7 @@ log = get_logger("bot") # 日期格式化 # ------------------------------------------------------------------ + def _format_date(date_str: str) -> str: """将 '2026/5/13' 或 '2026-5-13' 转为 '2026-05-13'""" if not date_str: @@ -30,31 +32,58 @@ def _format_date(date_str: str) -> str: return date_str +def _safe_float(value: str | None, default: float = 0.0) -> float: + """安全转换为浮点数，空值或转换失败时返回默认值""" + if value is None or str(value).strip() == "": + return default + try: + return float(value) + except (ValueError, TypeError): + return default + + # ------------------------------------------------------------------ # CSV 数据加载 # ------------------------------------------------------------------ -def load_invoice_data(csv_path: str, config: dict) -> list[dict]: - """从 CSV 加载发票数据，自动补全空白字段的默认值""" + +def load_invoice_data(csv_path: str, config: dict[str, str | Path]) -> list[dict[str, str | float | Any | Path]]: + """从 CSV 加载发票数据，自动补全空白字段的默认值 + + CSV 以支付记录为主键，每行包含 _invoices_json 字段（JSON 序列化的发票列表）。 + 本函数还原为发票级别的数据列表。 + """ + import json + invoices = [] with open(csv_path, encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: - invoices.append({ - "seq": row.get("序号", ""), - "invoice_no": row.get("发票号码", ""), - "invoice_date": row.get("开票日期", ""), - "item_name": row.get("项目名称", ""), - "spec_model": row.get("规格型号", ""), - "total_amount": float(row.get("价税合计", 0)), - "seller_name": row.get("销售方名称", ""), - "person_name": row.get("人员姓名") or config.get("default_name", ""), - "card_date": _format_date(row.get("刷卡日期") or ""), - "card_no": row.get("公务卡号") or config.get("default_card_no", ""), - "card_amount": float(row.get("刷卡金额") or "0"), - "remark": row.get("备注") or "", - "person_id": row.get("工号") or config.get("default_person_id", ""), - }) + invoices_json = row.get("_invoices_json", "") + if not invoices_json: + continue + try: + inv_list = json.loads(invoices_json) + for inv in inv_list: + invoices.append( + { + "seq": row.get("序号", ""), + "invoice_no": inv.get("发票号码", ""), + "invoice_date": inv.get("开票日期", ""), + "item_name": inv.get("项目名称", ""), + "spec_model": inv.get("规格型号", ""), + "total_amount": _safe_float(inv.get("价税合计")), + "seller_name": inv.get("销售方名称", ""), + "person_name": inv.get("人员姓名") or config.get("default_name", ""), + "card_date": _format_date(row.get("刷卡日期") or ""), + "card_no": row.get("公务卡号") or config.get("default_card_no", ""), + "card_amount": _safe_float(row.get("刷卡金额")), + "remark": row.get("备注") or "", + "person_id": row.get("工号") or config.get("default_person_id", ""), + } + ) + except json.JSONDecodeError: + log.warning(f"无法解析发票 JSON: {invoices_json[:50]}...") return invoices @@ -62,29 +91,31 @@ def load_invoice_data(csv_path: str, config: dict) -> list[dict]: # 报销机器人 # ------------------------------------------------------------------ + class ReimburseBot: """财务报销自动化机器人""" - def __init__(self, config: dict, headless: bool = False): + def __init__(self, config: dict[str, Any], headless: bool = False): self.config = config self.headless = headless self.work_dir: Path | None = None - self.browser = None - self.context = None - self.page = None + self.browser: Any = None + self.context: Any = None + self.page: Any = None from playwright.sync_api import sync_playwright + self._pw_ctx = sync_playwright() self.pw = self._pw_ctx.__enter__() - def launch(self): + def launch(self) -> None: """启动浏览器""" self.browser = self.pw.chromium.launch(headless=self.headless) self.context = self.browser.new_context(viewport={"width": 1920, "height": 1080}) self.page = self.context.new_page() self.page.set_default_timeout(30000) - def login_portal(self): + def login_portal(self) -> None: """登录信息门户""" log.info("登录信息门户...") @@ -113,7 +144,7 @@ class ReimburseBot: self._wait_for_portal() - def _wait_for_portal(self): + def _wait_for_portal(self) -> None: """等待跳转到统一信息平台""" for _ in range(30): self.page.wait_for_timeout(1000) @@ -125,7 +156,7 @@ class ReimburseBot: self._screenshot("portal_timeout") raise TimeoutError("登录超时，未跳转到信息门户") - def navigate_to_reimburse(self): + def navigate_to_reimburse(self) -> None: """从统一信息平台进入报销系统""" log.info("进入报销系统...") self._wait_for('text="快捷入口"', timeout=5000) @@ -170,7 +201,7 @@ class ReimburseBot: self.page.goto(common_url, wait_until="domcontentloaded", timeout=15000) self._wait_for('text="单据状态:"', timeout=5000) - def open_reimburse_menu(self): + def open_reimburse_menu(self) -> None: """点击「新增」创建新报销单""" log.info("创建新报销单...") self.page.wait_for_timeout(2000) @@ -180,14 +211,14 @@ class ReimburseBot: except Exception: try: self.page.click("text=新增", timeout=3000) - except Exception: + except Exception as err: self._screenshot("no_add_button") - raise RuntimeError("无法点击新增按钮") + raise RuntimeError("无法点击新增按钮") from err self.page.wait_for_timeout(3000) self._screenshot("after_add_click") - def fill_basic_info(self, description: str = "元器件采购报销"): + def fill_basic_info(self, description: str = "元器件采购报销") -> None: """填写基本信息""" log.info("填写基本信息...") @@ -223,7 +254,7 @@ class ReimburseBot: self._screenshot("step3_done") - def add_reimburse_items(self, invoices: list[dict]): + def add_reimburse_items(self, invoices: list[dict[str, Any]]) -> None: """录入报销明细（一条总明细）""" card_amount = sum(inv["card_amount"] for inv in invoices) log.info(f"录入报销明细 (合计 ¥{card_amount:.2f})...") @@ -255,7 +286,7 @@ class ReimburseBot: self._screenshot("item_total_error") raise - def fill_payment(self, invoices: list[dict]): + def fill_payment(self, invoices: list[dict[str, Any]]) -> None: """录入支付信息""" log.info("录入支付信息...") try: @@ -282,7 +313,7 @@ class ReimburseBot: self._screenshot("step5_done") - def upload_attachments(self, invoices: list[dict]): + def upload_attachments(self, invoices: list[dict[str, Any]]) -> None: """上传发票附件""" log.info("上传附件...") @@ -299,11 +330,11 @@ class ReimburseBot: file_path = attachment_files[i] if i < len(attachment_files) else None try: self._wait_for("#insertAcc", timeout=20000) # 等待增加按钮出现 - self.page.click("#insertAcc", timeout=5000) #点击增加按钮出现 + self.page.click("#insertAcc", timeout=5000) # 点击增加按钮出现 self._wait_for("#fjlx", timeout=5000) self.page.select_option("#fjlx", "1") explanation = f"{inv['item_name']} - {inv['invoice_no']}" - self.page.fill("#fpsmxx", explanation) + self.page.fill("#fpsmxx", explanation) except Exception: pass @@ -328,7 +359,7 @@ class ReimburseBot: self._screenshot("step6_done") - def submit(self): + def submit(self) -> None: """提交报销单""" log.info("提交报销单...") try: @@ -340,7 +371,7 @@ class ReimburseBot: self._screenshot("submit_error") raise - def close(self): + def close(self) -> None: """关闭浏览器""" if self.context: self.context.close() @@ -355,10 +386,10 @@ class ReimburseBot: # 辅助方法 # -------------------------------------------------------- - def _wait_for(self, selector: str, timeout: int = None): + def _wait_for(self, selector: str, timeout: int | None = None) -> None: self.page.wait_for_selector(selector, timeout=timeout) - def _screenshot(self, name: str): + def _screenshot(self, name: str) -> None: img_dir = Path(__file__).parent.parent / "images" img_dir.mkdir(exist_ok=True) self.page.screenshot(path=str(img_dir / f"debug_{name}.png")) @@ -368,7 +399,10 @@ class ReimburseBot: # 对外入口 # ------------------------------------------------------------------ -def run_bot(config: dict, invoices: list[dict], headless: bool = False, work_dir: Path | None = None): + +def run_bot( + config: dict[str, Any], invoices: list[dict[str, Any]], headless: bool = False, work_dir: Path | None = None +) -> None: """执行完整的浏览器填报流程""" if not config["username"] or not config["password"]: raise ValueError("缺少用户名或密码") @@ -396,7 +430,7 @@ def run_bot(config: dict, invoices: list[dict], headless: bool = False, work_dir bot.close() -def run_bot_web(config: dict, invoices: list[dict], work_dir: Path): +def run_bot_web(config: dict[str, Any], invoices: list[dict[str, Any]], work_dir: Path) -> None: """Web 模式填报 — headless，附件从指定目录读取""" if not config["username"] or not config["password"]: raise ValueError("缺少用户名或密码") @@ -420,4 +454,4 @@ def run_bot_web(config: dict, invoices: list[dict], work_dir: Path): pass raise finally: - bot.close() \ No newline at end of file + bot.close() diff --git a/app/config.py b/src/config.py similarity index 69% rename from app/config.py rename to src/config.py index 77f5016..4e7875a 100644 --- a/app/config.py +++ b/src/config.py @@ -10,7 +10,7 @@ from pathlib import Path _CONFIG_PATH = Path(__file__).parent.parent / "config.json" -def load_config() -> dict: +def load_config() -> dict[str, str | Path]: """加载并合并配置，缺失字段使用默认值""" raw = {} if _CONFIG_PATH.exists(): @@ -31,4 +31,19 @@ def load_config() -> dict: "default_person_id": raw.get("default_person_id", ""), "consumable_storage": raw.get("consumable_storage", "躬行楼 C205"), "attachment_dir": project_root / "attachments", - } \ No newline at end of file + } + + +def get_llm_config() -> dict[str, str]: + """加载 LLM 配置，缺失字段使用默认值""" + raw = {} + if _CONFIG_PATH.exists(): + with open(_CONFIG_PATH, encoding="utf-8") as f: + raw = json.load(f) + + llm_raw = raw.get("llm", {}) + return { + "model": llm_raw.get("model", "qwen-vl-max"), + "api_base": llm_raw.get("api_base", "http://localhost:8080/v1"), + "api_key": llm_raw.get("api_key", "lm-studio"), + } diff --git a/src/doc/README.md b/src/doc/README.md new file mode 100644 index 0000000..335a5c6 --- /dev/null +++ b/src/doc/README.md @@ -0,0 +1,49 @@ +--- + +## last_reviewed: 2026-06-09 + +# src/doc — 文档处理模块 + +负责发票信息提取、基于 LLM 的支付截图信息识别、以及将数据填入 Word 出库单模板。 + +## 模块清单 + + +| 文件 | 作用 | +| ------------------------ | -------------------------------------------------- | +| `extractor.py` | 编排入口：串联 PDF 读取 → LLM 提取 → 支付截图匹配 → 分类 | +| `pdf.py` | PDF 文件发现与文本提取（pdfplumber） | +| `llm_extractor.py` | 基于 LLM 的信息提取（发票文本 + 支付截图多模态） | +| `matcher.py` | 发票与支付截图按金额匹配，回填刷卡信息至发票记录 | +| `invoice.py` | 发票类型常量、分类逻辑、CSV 读写工具 | +| `fill_consumable_doc.py` | 将 CSV 数据填入易耗品出库单 Word 模板（pywin32 COM） | +| `prompt.py` | LLM 提示词模板加载 | +| `prompts/` | 提示词模板文件（`invoice_system.md`、`card_info_system.md`） | + + +## 数据流 + +``` +PDF 发票 → pdf.py → llm_extractor.py → [发票列表] +支付截图 → llm_extractor.py → [刷卡记录] + ↓ + matcher.py（按金额贪心匹配，容差 10 元） + ↓ + invoice.py 分类 → CSV（已回填刷卡日期/卡号/金额） + ↓ + fill_consumable_doc → 易耗品出库单.doc +``` + +## 依赖说明 + +- **pdfplumber** — PDF 文本提取 +- **pywin32** — Word COM 自动化（仅 Windows） +- **llama-index** — LLM 信息提取 + +## 注意事项 + +- `fill_consumable_doc.py` 依赖 Microsoft Word + COM，仅 Windows 可用 +- LLM 提取不会覆盖 CSV 中已有非空字段 +- 提示词模板位于 `prompts/` 目录，由 `prompt.py` 加载 +- LLM 提取失败时直接报错，无正则回退 + diff --git a/src/doc/__init__.py b/src/doc/__init__.py new file mode 100644 index 0000000..97668d1 --- /dev/null +++ b/src/doc/__init__.py @@ -0,0 +1,4 @@ +"""文档处理模块 + +包含发票提取、LLM 信息提取、出库单填写等功能。 +""" diff --git a/src/doc/extractor.py b/src/doc/extractor.py new file mode 100644 index 0000000..0074083 --- /dev/null +++ b/src/doc/extractor.py @@ -0,0 +1,65 @@ +"""发票提取编排 + +串联 PDF 读取 → LLM 提取 → 支付截图匹配 → 分类，生成支付记录列表。 + +对外接口: + extract_invoices(directory) -> tuple[list[dict], dict] +""" + +from .. import get_logger +from .invoice import classify_invoice_batch +from .llm_extractor import extract_invoice_from_text +from .matcher import match_invoices_to_cards +from .pdf import extract_text_from_pdf, find_pdf_files + +log = get_logger("extractor") + + +def extract_invoices( + directory: str = ".", +) -> tuple[list[dict[str, str]], dict[str, list[dict[str, str]]]]: + """扫描目录下所有 PDF,提取发票信息并匹配支付记录 + + Returns: + (payment_records, groups): 支付记录列表和按发票类型分组的字典 + groups = {'travel': [差旅发票], 'general': [普通发票]} + """ + pdf_files = find_pdf_files(directory) + if not pdf_files: + log.warning("未找到 PDF 文件") + return [], {"travel": [], "general": []} + + log.info(f"发现 {len(pdf_files)} 个 PDF 文件") + + all_invoices = [] + for pdf_path in pdf_files: + text = extract_text_from_pdf(pdf_path) + if not text: + log.warning(f"未能提取文本: {pdf_path.name}") + continue + + invoice = extract_invoice_from_text(text, pdf_path.name) + + if invoice and invoice.get("发票号码"): + all_invoices.append(invoice) + log.info(f"[{invoice['发票类型']}] 已解析: {pdf_path.name}") + else: + log.warning(f"未能解析: {pdf_path.name}") + + if all_invoices: + log.info(f"共处理 {len(all_invoices)} 张发票") + else: + log.warning("未成功解析任何发票") + + # 将支付截图与发票进行金额匹配，返回以支付记录为主键的列表 + payment_records = match_invoices_to_cards(all_invoices, directory) + + # 从支付记录中还原所有发票用于分类 + all_invoices_restored: list[dict[str, str]] = [] + for record in payment_records: + all_invoices_restored.extend(record.get("_matched_invoices", [])) + + groups = classify_invoice_batch(all_invoices_restored) + log.info(f"差旅发票: {len(groups['travel'])} 张, 普通发票: {len(groups['general'])} 张") + + return payment_records, groups diff --git a/app/fill_consumable_doc.py b/src/doc/fill_consumable_doc.py similarity index 63% rename from app/fill_consumable_doc.py rename to src/doc/fill_consumable_doc.py index 8a7495c..db1d8d7 100644 --- a/app/fill_consumable_doc.py +++ b/src/doc/fill_consumable_doc.py @@ -11,10 +11,11 @@ import re import shutil from datetime import date from pathlib import Path +from typing import Any -from . import get_logger -from .bot import load_invoice_data -from .config import load_config +from .. import get_logger +from ..bot import load_invoice_data +from ..config import load_config log = get_logger("fill_consumable_doc") @@ -86,7 +87,7 @@ def _today_cn_date() -> str: return f"{today.year}年{today.month}月{today.day}日" -def _apply_font(rng) -> None: +def _apply_font(rng: Any) -> None: """将范围字体设为宋体五号（含数字与英文）。""" font = rng.Font font.Name = TABLE_FONT_NAME @@ -97,7 +98,7 @@ def _apply_font(rng) -> None: font.Size = TABLE_FONT_SIZE -def _set_cell_value(cell, text: str) -> None: +def _set_cell_value(cell: Any, text: str) -> None: """写入单元格正文（不含末尾单元格标记）。""" rng = cell.Range rng.MoveEnd(WD_CHARACTER, -1) @@ -105,7 +106,7 @@ def _set_cell_value(cell, text: str) -> None: _apply_font(rng) -def _normalize_table_font(tbl) -> None: +def _normalize_table_font(tbl: Any) -> None: """填写完成后统一整张表的字体。""" for row in tbl.Rows: for cell in row.Cells: @@ -114,7 +115,7 @@ def _normalize_table_font(tbl) -> None: _apply_font(rng) -def _replace_date_in_doc(doc, new_date: str) -> None: +def _replace_date_in_doc(doc: Any, new_date: str) -> None: """仅替换表头段落中的日期文字，不改动段落其余部分。""" if not new_date: return @@ -135,7 +136,7 @@ def _replace_date_in_doc(doc, new_date: str) -> None: def fill_consumable_doc( csv_path: str | Path, doc_path: str | Path, - config: dict | None = None, + config: dict[str, Any] | None = None, backup: bool = True, ) -> Path: csv_path = Path(csv_path) @@ -148,59 +149,73 @@ def fill_consumable_doc( bak = doc_path.with_suffix(doc_path.suffix + ".bak") shutil.copy2(doc_path, bak) + import pythoncom import win32com.client - word = win32com.client.Dispatch("Word.Application") - word.Visible = False - word.DisplayAlerts = 0 - doc = word.Documents.Open(str(doc_path.resolve())) - + pythoncom.CoInitialize() try: - _replace_date_in_doc(doc, _today_cn_date()) + word = win32com.client.Dispatch("Word.Application") + word.Visible = False + word.DisplayAlerts = 0 + doc = word.Documents.Open(str(doc_path.resolve())) - tbl = doc.Tables(1) - storage = config.get("consumable_storage", "躬行楼 C205") + try: + _replace_date_in_doc(doc, _today_cn_date()) - for i, inv in enumerate(invoices): - row_idx = i + 2 - if row_idx > tbl.Rows.Count: - break + tbl = doc.Tables(1) + storage = config.get("consumable_storage", "躬行楼 C205") - parsed = parse_spec_model(inv.get("spec_model", "")) - card_amount = inv.get("card_amount") or 0 - qty_str = parsed["qty"] - qty_val = int(qty_str) if qty_str and qty_str.isdigit() else 0 + for i, inv in enumerate(invoices): + row_idx = i + 2 + if row_idx > tbl.Rows.Count: + break - # 金额填写刷卡金额，单价由刷卡金额反算 - amount = _format_money(card_amount) - unit_price = _format_money(card_amount / qty_val) if qty_val > 0 else _format_money(card_amount) + parsed = parse_spec_model(str(inv.get("spec_model", ""))) + # 当规格型号为空时，从项目名称提取产品信息 + if not parsed["product_name"]: + item_name = str(inv.get("item_name", "")) + # 去除 "*分类*" 前缀（如 "*电子工业设备*元件盒" -> "元件盒"） + if "*" in item_name: + item_name = item_name.split("*")[-1].strip() + parsed["product_name"] = item_name - # 数量：去掉前导零 - qty = str(qty_val) if qty_val > 0 else "" + card_amount_raw = inv.get("card_amount") or 0 + card_amount: float = float(str(card_amount_raw).replace(",", "")) + qty_str = parsed["qty"] + qty_val = int(qty_str) if qty_str and qty_str.isdigit() else 0 - values = [ - str(inv.get("seq", i + 1)), - parsed["product_name"], - parsed["spec"], - parsed["unit"], - qty, - unit_price, - amount, - "", # 购货人签字 — 保持空白 - storage, - "", # 领用人签字 — 保持空白 - "", # 备注 — 保持空白，避免撑破版式 - ] + # 金额填写刷卡金额，单价由刷卡金额反算 + amount = _format_money(card_amount) + unit_price = _format_money(card_amount / qty_val) if qty_val > 0 else _format_money(card_amount) - for col_idx, val in enumerate(values, start=1): - _set_cell_value(tbl.Cell(row_idx, col_idx), val) + # 数量：去掉前导零；若无数量则默认为 1 + qty = str(qty_val) if qty_val > 0 else "1" - _normalize_table_font(tbl) + values = [ + str(inv.get("seq", i + 1)), + parsed["product_name"], + parsed["spec"], + parsed["unit"], + qty, + unit_price, + amount, + "", # 购货人签字 — 保持空白 + storage, + "", # 领用人签字 — 保持空白 + "", # 备注 — 保持空白，避免撑破版式 + ] - doc.Save() + for col_idx, val in enumerate(values, start=1): + _set_cell_value(tbl.Cell(row_idx, col_idx), str(val)) + + _normalize_table_font(tbl) + + doc.Save() + finally: + doc.Close() + word.Quit() finally: - doc.Close() - word.Quit() + pythoncom.CoUninitialize() return doc_path @@ -209,7 +224,7 @@ def fill_consumable_from_template( csv_path: str | Path, template_path: str | Path, output_path: str | Path, - config: dict | None = None, + config: dict[str, Any] | None = None, ) -> Path: """从模板复制并填写出库单（Web 会话每次从模板重新生成）。""" template_path = Path(template_path) diff --git a/src/doc/invoice.py b/src/doc/invoice.py new file mode 100644 index 0000000..877e04c --- /dev/null +++ b/src/doc/invoice.py @@ -0,0 +1,273 @@ +"""发票数据模型与 CSV 工具 + +定义发票类型常量、CSV 列结构，提供发票分类和 CSV 读写功能。 + +对外接口: + INVOICE_LEVEL_COLUMNS 发票级别 CSV 列定义 + PAYMENT_RECORD_COLUMNS 支付记录级别 CSV 列定义 + INVOICE_TYPE_* 发票类型常量 + is_travel_invoice(type) 判断是否为差旅发票 + classify_invoice_batch(invoices) 按类型分组 + load_csv(path) 读取支付记录 CSV + save_csv(payment_records, path) 保存支付记录 CSV + save_invoice_csv(payment_records, path) 保存发票级别 CSV +""" + +import csv +import json +from pathlib import Path + +from .. import get_logger + +log = get_logger("invoice") + +# ------------------------------------------------------------------ +# CSV 列定义 +# ------------------------------------------------------------------ + +# 发票级别 CSV 列（用于 invoice_summary.csv，每行一张发票） +INVOICE_LEVEL_COLUMNS = [ + "序号", + "发票类型", + "发票号码", + "开票日期", + "项目名称", + "规格型号", + "价税合计", + "销售方名称", + "出发站", + "到达站", + "车次", + "乘车日期", + "座位等级", + "人员姓名", + "刷卡日期", + "公务卡号", + "刷卡金额", + "备注", + "工号", +] + +# 支付记录级别 CSV 列（用于 payment_records.csv，每行一笔支付） +PAYMENT_RECORD_COLUMNS = [ + "序号", + # 支付信息 + "刷卡日期", + "公务卡号", + "刷卡金额", + # 发票聚合信息 + "关联发票数", + "发票详情", # 格式: 类型[号码]¥金额 | 类型[号码]¥金额 + "备注", + # 内部字段（用于下游解析） + "_invoices_json", # JSON 序列化的发票列表，供 bot/fill_doc 使用 + "工号", +] + +# ------------------------------------------------------------------ +# 发票类型常量 +# ------------------------------------------------------------------ + +INVOICE_TYPE_TRAIN = "高铁票" +INVOICE_TYPE_HOTEL = "酒店住宿" +INVOICE_TYPE_GENERAL = "普通发票" + +INVOICE_TYPE_TRAVEL = frozenset([INVOICE_TYPE_TRAIN, INVOICE_TYPE_HOTEL]) + + +def is_travel_invoice(invoice_type: str) -> bool: + """判断是否为差旅发票（高铁票/酒店住宿）""" + return invoice_type in INVOICE_TYPE_TRAVEL + + +def classify_invoice_batch(invoices: list[dict[str, str]]) -> dict[str, list[dict[str, str]]]: + """将发票列表按类型分组：{'travel': [...], 'general': [...]}""" + travel = [] + general = [] + for inv in invoices: + inv_type = inv.get("发票类型", INVOICE_TYPE_GENERAL) + if is_travel_invoice(inv_type): + travel.append(inv) + else: + general.append(inv) + return {"travel": travel, "general": general} + + +# ------------------------------------------------------------------ +# CSV 读写工具 +# ------------------------------------------------------------------ + + +def _clean_invoice_for_json(inv: dict[str, str]) -> dict[str, str]: + """清理发票字典中的内部字段，保留可序列化的字段""" + clean = {} + for k, v in inv.items(): + if k.startswith("_"): + continue + clean[k] = v + return clean + + +def load_csv(csv_path: Path) -> list[dict[str, str]] | None: + """读取支付记录 CSV 为 dict 列表，失败返回 None""" + try: + with open(csv_path, encoding="utf-8", newline="") as f: + reader = csv.DictReader(f) + fieldnames = reader.fieldnames or [] + missing = [c for c in PAYMENT_RECORD_COLUMNS if c not in fieldnames] + if missing: + log.error(f"CSV 缺少必要列: {missing}") + return None + return [row for row in reader] + except FileNotFoundError: + log.error(f"CSV 文件不存在: {csv_path.name}") + return None + except Exception as e: + log.error(f"CSV 读取失败: {e}") + return None + + +def load_invoice_csv(csv_path: Path) -> list[dict[str, str]] | None: + """读取发票级别 CSV 为 dict 列表（每行一张发票），失败返回 None""" + try: + with open(csv_path, encoding="utf-8", newline="") as f: + reader = csv.DictReader(f) + fieldnames = reader.fieldnames or [] + missing = [c for c in INVOICE_LEVEL_COLUMNS if c not in fieldnames] + if missing: + log.error(f"发票 CSV 缺少必要列: {missing}") + return None + return [row for row in reader] + except FileNotFoundError: + log.error(f"发票 CSV 文件不存在: {csv_path.name}") + return None + except Exception as e: + log.error(f"发票 CSV 读取失败: {e}") + return None + + +def load_invoices_from_csv(csv_path: Path) -> list[dict[str, str]] | None: + """从支付记录 CSV 中还原发票级别的数据（供 bot/fill_doc 使用） + + 读取 _invoices_json 字段，反序列化后展平为发票列表。 + """ + rows = load_csv(csv_path) + if rows is None: + return None + + invoices = [] + for row in rows: + invoices_json = row.get("_invoices_json", "") + if not invoices_json: + continue + try: + inv_list = json.loads(invoices_json) + for inv in inv_list: + # 从支付记录回填刷卡信息 + inv["刷卡日期"] = row.get("刷卡日期", inv.get("刷卡日期", "")) + inv["公务卡号"] = row.get("公务卡号", inv.get("公务卡号", "")) + inv["刷卡金额"] = row.get("刷卡金额", inv.get("刷卡金额", "")) + invoices.append(inv) + except json.JSONDecodeError: + log.warning(f"无法解析发票 JSON: {invoices_json[:50]}...") + return invoices + + +def save_csv( + payment_records: list[dict[str, str]], + output_path: str | Path = "payment_records.csv", +) -> None: + """将支付记录列表保存为 CSV（以支付记录为主键） + + 每条支付记录包含： + - 刷卡日期、公务卡号、刷卡金额（支付信息） + - 关联发票数、发票详情（发票聚合信息） + - _matched_invoices（内部字段，序列化为 JSON 存储在 CSV 中） + """ + csv_path = Path(output_path) + + with open(csv_path, "w", encoding="utf-8", newline="") as f: + writer = csv.writer(f) + writer.writerow(PAYMENT_RECORD_COLUMNS) + + for idx, record in enumerate(payment_records, 1): + # 序列化关联发票为 JSON + matched_invoices: list[dict[str, str]] = record.get("_matched_invoices", []) # type: ignore[assignment] + invoices_json = json.dumps( + [_clean_invoice_for_json(inv) for inv in matched_invoices], + ensure_ascii=False, + ) + + writer.writerow( + [ + idx, + record.get("刷卡日期", ""), + record.get("公务卡号", ""), + record.get("刷卡金额", ""), + record.get("关联发票数", str(len(matched_invoices))), + record.get("发票详情", ""), + record.get("备注", ""), + invoices_json, + record.get("工号", ""), + ] + ) + + log.info(f"支付记录 CSV 已保存: {csv_path.name}") + + +def save_invoice_csv( + payment_records: list[dict[str, str]], + output_path: str | Path = "invoice_summary.csv", +) -> None: + """将支付记录展平为发票级别 CSV（每行一张发票） + + 从 _matched_invoices 中还原每张发票，回填刷卡信息， + 生成以发票为主键的 CSV，用于人工填写报销单参考。 + """ + csv_path = Path(output_path) + + with open(csv_path, "w", encoding="utf-8", newline="") as f: + writer = csv.writer(f) + writer.writerow(INVOICE_LEVEL_COLUMNS) + + idx = 1 + for record in payment_records: + matched_invoices: list[dict[str, str]] = record.get("_matched_invoices", []) # type: ignore[assignment] + for inv in matched_invoices: + clean_inv = _clean_invoice_for_json(inv) + writer.writerow( + [ + idx, + clean_inv.get("发票类型", ""), + clean_inv.get("发票号码", ""), + clean_inv.get("开票日期", ""), + clean_inv.get("项目名称", ""), + clean_inv.get("规格型号", ""), + clean_inv.get("价税合计", ""), + clean_inv.get("销售方名称", ""), + clean_inv.get("出发站", ""), + clean_inv.get("到达站", ""), + clean_inv.get("车次", ""), + clean_inv.get("乘车日期", ""), + clean_inv.get("座位等级", ""), + clean_inv.get("人员姓名", ""), + record.get("刷卡日期", ""), + record.get("公务卡号", ""), + record.get("刷卡金额", ""), + record.get("备注", ""), + record.get("工号", ""), + ] + ) + idx += 1 + + log.info(f"发票级别 CSV 已保存: {csv_path.name}") + + +def save_csv_rows(csv_path: Path, rows: list[dict[str, str]]) -> None: + """将 dict 列表保存为支付记录 CSV（用于更新已有 CSV）""" + with open(csv_path, "w", encoding="utf-8", newline="") as f: + writer = csv.DictWriter(f, fieldnames=PAYMENT_RECORD_COLUMNS) + writer.writeheader() + writer.writerows(rows) + + log.info(f"CSV 已保存: {csv_path.name}") diff --git a/src/doc/llm_extractor.py b/src/doc/llm_extractor.py new file mode 100644 index 0000000..5a203f6 --- /dev/null +++ b/src/doc/llm_extractor.py @@ -0,0 +1,210 @@ +""" +LLM 信息提取 + +使用 LLM 从 PDF 文本中提取结构化数据，以及从支付截图中提取刷卡信息。 +支持 JSON 格式输出，字段与 CSV_COLUMNS 对齐。 + +对外接口: + extract_invoice_from_text(text, file_name) -> dict 从 PDF 文本提取发票信息 + extract_card_info_from_image(image_path) -> dict 从支付截图提取刷卡信息 +""" + +from __future__ import annotations + +import base64 +import json +from pathlib import Path +from typing import Any, cast + +from .. import get_logger +from .prompt import build_card_info_system_prompt, build_invoice_system_prompt + +log = get_logger("llm_extractor") + + +def _create_llm() -> Any: + """根据配置文件创建 LLM 实例。""" + try: + from llama_index.llms.openai_like import OpenAILike + except ImportError: + log.error("缺少 llama-index-llms-openai-like，请执行: uv pip install llama-index-llms-openai-like") + raise + + from ..config import get_llm_config + + llm_config = get_llm_config() + return OpenAILike( + model=llm_config["model"], + api_base=llm_config["api_base"], + api_key=llm_config.get("api_key", "lm-studio"), + temperature=0.1, + max_tokens=8192, + request_timeout=600.0, + is_chat_model=True, + ) + + +def _llm_query(system_prompt: str, user_content: str, max_tokens: int = 4096) -> str: + """发送请求到 LLM 并返回完整响应文本。""" + from llama_index.core.llms import ChatMessage + + from ..config import get_llm_config + + messages = [ + ChatMessage(role="system", content=system_prompt), + ChatMessage(role="user", content=user_content), + ] + + llm_config = get_llm_config() + llm = _create_llm() + log.info("开始请求 LLM (model=%s, base=%s)", llm_config["model"], llm_config["api_base"]) + + try: + parts = [] + for resp in llm.stream_chat( + messages, + temperature=0.1, + max_tokens=max_tokens, + extra_body={"reasoning_effort": "none"}, + ): + delta = resp.delta + if delta: + parts.append(delta) + text = "".join(parts) + log.info("LLM 请求完成，响应总长度: %d 字符", len(text)) + log.info("LLM 响应: %s", text) + return text + except Exception as e: + log.error("LLM 请求失败: %s", e) + raise + + +def _parse_json_response(text: str) -> dict[str, Any]: + """从 LLM 响应中提取 JSON，处理可能的 Markdown 包裹。""" + text = text.strip() + + # 处理 ```json ... ``` 包裹 + if "```" in text: + # 提取第一个代码块 + start = text.find("```") + 3 + end = text.find("```", start) + if end > start: + text = text[start:end].strip() + + # 去掉可能的前缀 (如 "json") + if text.lower().startswith("json"): + text = text[4:].strip() + + return cast(dict[str, Any], json.loads(text)) + + +def extract_invoice_from_text(text: str, file_name: str = "") -> dict[str, Any]: + """从 PDF 发票文本中提取结构化数据。 + + Args: + text: PDF 提取的文本内容。 + file_name: 原始文件名（用于日志）。 + + Returns: + 包含所有 CSV_COLUMNS 字段的字典。 + """ + system_prompt = build_invoice_system_prompt() + user_content = f"请分析以下发票文本并提取信息：\n\n文件名: {file_name}\n\n---\n\n{text}\n\n---" + + try: + response = _llm_query(system_prompt, user_content, max_tokens=4096) + result = _parse_json_response(response) + log.info("LLM 发票提取成功: %s", file_name) + return result + except Exception as e: + log.error("LLM 发票提取失败: %s (%s)", file_name, e) + raise + + +# ------------------------------------------------------------------ +# 支付截图信息提取（多模态） +# ------------------------------------------------------------------ + + +def _image_to_base64(image_path: Path) -> str: + """将图片文件读取为 base64 字符串。""" + with open(image_path, "rb") as f: + return base64.b64encode(f.read()).decode("utf-8") + + +def _llm_query_multimodal( + system_prompt: str, + text: str, + image_b64: str, + max_tokens: int = 4096, +) -> str: + """发送多模态请求（文本 + 图片）到 LLM。""" + from llama_index.core.base.llms.types import ImageBlock, TextBlock + from llama_index.core.llms import ChatMessage + + from ..config import get_llm_config + + messages = [ + ChatMessage(role="system", content=system_prompt), + ChatMessage( + role="user", + blocks=[ + TextBlock(text=text), + ImageBlock( + url=f"data:image/jpeg;base64,{image_b64}", + detail="high", + ), + ], + ), + ] + + llm_config = get_llm_config() + llm = _create_llm() + log.info( + "开始请求 LLM 多模态 (model=%s, base=%s)", + llm_config["model"], + llm_config["api_base"], + ) + + try: + parts = [] + for resp in llm.stream_chat( + messages, + temperature=0.1, + max_tokens=max_tokens, + extra_body={"reasoning_effort": "none"}, + ): + delta = resp.delta + if delta: + parts.append(delta) + text = "".join(parts) + log.info("LLM 多模态请求完成，响应总长度: %d 字符", len(text)) + log.info("LLM 多模态响应: %s", text) + return text + except Exception as e: + log.error("LLM 多模态请求失败: %s", e) + raise + + +def extract_card_info_from_image(image_path: Path) -> dict[str, Any]: + """从支付截图中提取刷卡信息。 + + Args: + image_path: 支付截图图片路径。 + + Returns: + 包含刷卡日期、刷卡金额、公务卡号的字典。 + """ + system_prompt = build_card_info_system_prompt() + user_text = f"请分析以下支付截图并提取信息：\n\n文件名: {image_path.name}" + + image_b64 = _image_to_base64(image_path) + + try: + response = _llm_query_multimodal(system_prompt, user_text, image_b64, max_tokens=4096) + result = _parse_json_response(response) + log.info("LLM 支付截图提取成功: %s", image_path.name) + return result + except Exception as e: + log.error("LLM 支付截图提取失败: %s (%s)", image_path.name, e) + raise diff --git a/src/doc/matcher.py b/src/doc/matcher.py new file mode 100644 index 0000000..32fb9c1 --- /dev/null +++ b/src/doc/matcher.py @@ -0,0 +1,395 @@ +"""发票与支付截图匹配 + +将提取到的发票数据与支付截图中的刷卡记录进行金额匹配， +输出以支付记录为主键的结果列表。 + +## 业务约束 + +- 发票数 >= 付款记录数（最少一张发票对应一张付款记录） +- 发票总金额 >= 付款总金额（发票只能比付款多，不能少） +- 若发票数 == 付款数，走一对一匹配，无需一对多 + +## 匹配流程 + +1. 扫描目录下图片文件，调用 LLM 提取刷卡信息（日期/金额/卡号） +2. 解析发票和刷卡记录的金额，进行总额校验 + - 发票总额 < 刷卡总额时发出 warning +3. 按金额降序排序 +4. 根据数量关系选择匹配策略： + - 数量相等 → 一对一匹配：按金额从大到小依次配对，相对容差内即匹配 + - 发票更多 → 一对多匹配：对每张刷卡记录贪心凑金额，相对容差内结束 +5. 构建以支付记录为主键的结果列表 +6. 未匹配的发票单独作为一条记录（无刷卡信息） +7. 清理内部字段，输出支付记录列表 + +## 容差计算 + +使用相对容差（默认 3%），以刷卡金额为基准： +- ¥2900 发票 vs ¥2850 刷卡 → 差 ¥50，容差 ¥85.5 → 匹配成功 +- ¥100 发票 vs ¥95 刷卡 → 差 ¥5，容差 ¥3.0 → 不匹配（需精确匹配或调整） + +## 一对多匹配细节 + +- 对每张刷卡记录，维护 remaining（剩余待匹配金额） +- 遍历未分配的发票（按金额降序）： + - 若发票金额 + 容差 >= remaining，视为最后一张，匹配后退出 + - 否则发票金额不超过 remaining + 容差即可匹配 +- 匹配后 remaining 为负且超出容差时回滚最后一张发票 +- 每张发票只会被分配一次 + +## 对外接口 + + match_invoices_to_cards(invoices, directory, tolerance) -> list[dict] +""" + +from pathlib import Path +from typing import Any + +from .. import get_logger +from .llm_extractor import extract_card_info_from_image + +log = get_logger("matcher") + +# 支持的图片扩展名 +IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".bmp", ".webp"} + + +def _find_images(directory: str) -> list[Path]: + """在目录下查找支付截图图片文件""" + dir_path = Path(directory) + images = [f for f in dir_path.iterdir() if f.is_file() and f.suffix.lower() in IMAGE_EXTENSIONS] + return sorted(images) + + +def _safe_float(value: str | None, default: float = 0.0) -> float: + """安全转换为浮点数""" + if value is None or str(value).strip() == "": + return default + try: + return float(str(value).replace(",", "")) + except (ValueError, TypeError): + return default + + +def _extract_all_cards(directory: str) -> list[dict[str, Any]]: + """提取目录下所有支付截图的刷卡信息""" + images = _find_images(directory) + if not images: + log.warning("未找到支付截图图片") + return [] + + log.info(f"发现 {len(images)} 张支付截图") + cards = [] + for img_path in images: + try: + card_info = extract_card_info_from_image(img_path) + card_info["_source_file"] = img_path.name + cards.append(card_info) + log.info(f"[支付截图] 已解析: {img_path.name}") + except Exception as e: + log.warning(f"支付截图解析失败 {img_path.name}: {e}") + + log.info(f"共提取 {len(cards)} 条刷卡记录") + return cards + + +def _build_invoice_summary(invoices: list[dict[str, Any]]) -> str: + """将多张发票信息汇总为备注字符串""" + parts = [] + for inv in invoices: + person_name = inv.get("人员姓名") or inv.get("发票号码", "未知") + inv_type = inv.get("发票类型", "未知") + amount = inv.get("价税合计", "未知") + parts.append(f"{inv_type}[{person_name}]¥{amount}") + return " | ".join(parts) + + +def _relative_tolerance(base: float, rate: float = 0.05) -> float: + """根据基准金额计算相对容差（默认 5%）""" + return abs(base) * rate + + +def match_invoices_to_cards( + invoices: list[dict[str, Any]], + directory: str, + tolerance: float = 0.03, +) -> list[dict[str, Any]]: + """将发票与支付截图按金额匹配，输出以支付记录为主键的结果列表 + + 业务约束： + - 发票数 >= 付款记录数 + - 发票总金额 >= 付款总金额（发票只能比付款多，不能少） + - 若发票数 == 付款数，一对一匹配，无需一对多 + + Args: + invoices: 发票列表，需包含 "价税合计" 字段 + directory: 支付截图所在目录 + tolerance: 金额匹配容差比例（默认 0.05 = 5%） + + Returns: + 以支付记录为主键的结果列表，每条记录包含： + - 刷卡日期、公务卡号、刷卡金额（支付信息） + - 关联发票列表（_matched_invoices） + - 发票详情备注 + - 未匹配发票单独作为一条无刷卡信息的记录 + """ + cards = _extract_all_cards(directory) + if not cards: + log.warning("无刷卡记录可供匹配，发票将保持原状") + # 无刷卡记录时，每张发票作为独立记录返回 + return _invoices_to_records(invoices) + + # 解析金额 + for card in cards: + card["_amount"] = _safe_float(card.get("刷卡金额")) + for inv in invoices: + inv["_amount"] = _safe_float(inv.get("价税合计")) + + # 数据校验 + total_invoices = sum(inv["_amount"] for inv in invoices) + total_cards = sum(card["_amount"] for card in cards) + log.info( + f"金额校验: 发票总额 ¥{total_invoices:.2f}, 刷卡总额 ¥{total_cards:.2f}, " + f"发票数 {len(invoices)}, 刷卡数 {len(cards)}" + ) + + total_tolerance = _relative_tolerance(max(total_invoices, total_cards), tolerance) + if total_invoices < total_cards - total_tolerance: + log.warning( + f"发票总额 (¥{total_invoices:.2f}) 小于刷卡总额 (¥{total_cards:.2f})，" + f"超出容差 {tolerance * 100:.0f}%，匹配结果可能有偏差" + ) + + # 按金额降序排序 + cards.sort(key=lambda c: c["_amount"], reverse=True) + invoices.sort(key=lambda i: i["_amount"], reverse=True) + + # 执行匹配，返回 {card_index: [invoice_indices]} 的映射 + card_to_invoices = _match(cards, invoices, tolerance) + + # 构建支付记录列表 + records = _build_payment_records(cards, invoices, card_to_invoices) + + # 清理内部字段 + for inv in invoices: + inv.pop("_amount", None) + for card in cards: + card.pop("_amount", None) + + # 统计 + matched_invoices = sum(len(inv_list) for inv_list in card_to_invoices.values()) + unmatched_count = len(invoices) - matched_invoices + log.info(f"匹配完成: {len(records)} 条支付记录, {matched_invoices}/{len(invoices)} 张发票已关联") + if unmatched_count: + log.info(f"未匹配发票: {unmatched_count} 张（已单独列为记录）") + + return records + + +def _match( + cards: list[dict[str, Any]], + invoices: list[dict[str, Any]], + tolerance: float, +) -> dict[int, list[int]]: + """执行匹配，返回 {card_index: [invoice_indices]} 的映射 + + tolerance 为相对容差比例（如 0.05 表示 5%） + """ + result: dict[int, list[int]] = {} + assigned: set[int] = set() + + if len(invoices) == len(cards): + _match_one_to_one(invoices, cards, tolerance, assigned, result) + else: + _match_one_to_many(invoices, cards, tolerance, assigned, result) + + return result + + +def _match_one_to_one( + invoices: list[dict[str, Any]], + cards: list[dict[str, Any]], + tolerance: float, + assigned: set[int], + result: dict[int, list[int]], +) -> None: + """一对一匹配：发票数等于刷卡数，按金额从大到小依次配对 + + tolerance 为相对容差比例，以刷卡金额为基准计算 + """ + for card_idx, card in enumerate(cards): + if card_idx >= len(invoices): + break + inv = invoices[card_idx] + diff = abs(inv["_amount"] - card["_amount"]) + card_tol = _relative_tolerance(card["_amount"], tolerance) + if diff <= card_tol: + assigned.add(card_idx) + result[card_idx] = [card_idx] + log.info( + f"[一对一] {inv.get('发票号码', '未知')} ¥{inv['_amount']:.2f} " + f"↔ {card.get('_source_file', '未知')} ¥{card['_amount']:.2f}" + ) + else: + log.warning( + f"[一对一] 金额偏差超出容差: " + f"{inv.get('发票号码', '未知')} ¥{inv['_amount']:.2f} " + f"vs ¥{card['_amount']:.2f} (差 ¥{diff:.2f}, 容差 ¥{card_tol:.2f})" + ) + + +def _match_one_to_many( + invoices: list[dict[str, Any]], + cards: list[dict[str, Any]], + tolerance: float, + assigned: set[int], + result: dict[int, list[int]], +) -> None: + """一对多匹配：一张刷卡可能对应多张发票，按金额从大到小贪心匹配 + + tolerance 为相对容差比例（如 0.05 表示 5%），以刷卡金额为基准计算 + + 匹配分两阶段： + 1. 精确匹配：先扫描金额完全相等（差值 <= 0.01 元）的发票-刷卡对，直接锁定 + 2. 贪心匹配：剩余未分配的发票和刷卡记录走贪心凑金额 + """ + + # ---- 阶段 1：精确匹配（金额差 <= 0.01 元视为相等）---- + exact_tolerance = 0.01 + for card_idx, card in enumerate(cards): + card_amount = card["_amount"] + if card_amount <= 0: + continue + + for idx, inv in enumerate(invoices): + if idx in assigned: + continue + inv_amount = inv["_amount"] + if inv_amount <= 0: + continue + + if abs(inv_amount - card_amount) <= exact_tolerance: + assigned.add(idx) + result[card_idx] = [idx] + log.info( + f"[一对多-精确] {inv.get('发票号码', '未知')} ¥{inv_amount:.2f} " + f"↔ {card.get('_source_file', '未知')} ¥{card_amount:.2f}" + ) + break # 每张刷卡只精确匹配一张发票 + + # ---- 阶段 2：贪心匹配（仅处理未精确匹配的刷卡记录）---- + for card_idx, card in enumerate(cards): + if card_idx in result: # 已在阶段 1 精确匹配 + continue + + card_amount = card["_amount"] + if card_amount <= 0: + continue + + # 以刷卡金额为基准计算相对容差 + card_tol = _relative_tolerance(card_amount, tolerance) + + remaining = card_amount + matched_indices: list[int] = [] + + for idx, inv in enumerate(invoices): + if idx in assigned: + continue + if remaining <= card_tol: + break + + inv_amount = inv["_amount"] + if inv_amount <= 0: + continue + + # 最后一张发票：金额 + 容差 >= remaining 即可 + # 中间发票：金额不超过 remaining + 容差 + if inv_amount + card_tol >= remaining: + is_match = True + else: + is_match = inv_amount <= remaining + card_tol + + if is_match: + assigned.add(idx) + matched_indices.append(idx) + remaining -= inv_amount + if remaining <= card_tol: + break + + # 回滚：如果匹配后 remaining 为负且超出容差 + if remaining < -card_tol and matched_indices: + last_idx = matched_indices.pop() + assigned.discard(last_idx) + remaining += invoices[last_idx]["_amount"] + + # 记录匹配结果 + if matched_indices: + result[card_idx] = matched_indices + for idx in matched_indices: + inv = invoices[idx] + log.info( + f"[一对多-贪心] {inv.get('发票号码', '未知')} ¥{inv['_amount']:.2f} " + f"→ {card.get('_source_file', '未知')} ¥{card['_amount']:.2f}" + ) + + +def _build_payment_records( + cards: list[dict[str, Any]], + invoices: list[dict[str, Any]], + card_to_invoices: dict[int, list[int]], +) -> list[dict[str, Any]]: + """构建以支付记录为主键的结果列表""" + records: list[dict[str, Any]] = [] + + # 已有匹配记录的支付 + for card_idx, inv_indices in card_to_invoices.items(): + card = cards[card_idx] + matched_invs = [invoices[idx] for idx in inv_indices] + + record = { + "刷卡日期": card.get("刷卡日期", ""), + "公务卡号": card.get("公务卡号", ""), + "刷卡金额": str(card["_amount"]), + "关联发票数": str(len(matched_invs)), + "发票详情": _build_invoice_summary(matched_invs), + "备注": "", + "_matched_invoices": matched_invs, + } + records.append(record) + + # 未匹配的发票，单独作为记录 + matched_indices = set() + for inv_indices in card_to_invoices.values(): + matched_indices.update(inv_indices) + + unmatched = [inv for idx, inv in enumerate(invoices) if idx not in matched_indices] + for inv in unmatched: + record = { + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "关联发票数": "1", + "发票详情": _build_invoice_summary([inv]), + "备注": "未匹配到支付记录", + "_matched_invoices": [inv], + } + records.append(record) + + return records + + +def _invoices_to_records(invoices: list[dict[str, Any]]) -> list[dict[str, Any]]: + """无刷卡记录时，将每张发票转为独立记录""" + records = [] + for inv in invoices: + record = { + "刷卡日期": "", + "公务卡号": "", + "刷卡金额": "", + "关联发票数": "1", + "发票详情": _build_invoice_summary([inv]), + "备注": "", + "_matched_invoices": [inv], + } + records.append(record) + return records diff --git a/src/doc/pdf.py b/src/doc/pdf.py new file mode 100644 index 0000000..9388280 --- /dev/null +++ b/src/doc/pdf.py @@ -0,0 +1,42 @@ +"""PDF 文件发现与文本提取 + +从 PDF 发票文件中提取原始文本内容。 + +对外接口: + find_pdf_files(directory) -> list[Path] 查找目录下所有 PDF + extract_text_from_pdf(filepath) -> str 提取 PDF 文本 +""" + +from pathlib import Path + +from .. import get_logger + +log = get_logger("pdf") + + +def find_pdf_files(directory: str = ".") -> list[Path]: + """查找目录下所有 PDF 文件(非递归)""" + pdf_dir = Path(directory) + if not pdf_dir.exists(): + return [] + return sorted(pdf_dir.glob("*.pdf")) + + +def extract_text_from_pdf(filepath: Path) -> str: + """从单个 PDF 中提取全部文本""" + try: + import pdfplumber + except ImportError as err: + raise ImportError("缺少 pdfplumber，请执行: uv pip install pdfplumber") from err + + try: + parts = [] + with pdfplumber.open(filepath) as pdf: + for page in pdf.pages: + text = page.extract_text() + if text: + parts.append(text) + return "\n".join(parts) + except Exception as e: + log.error(f"无法读取 {filepath.name}: {e}") + return "" diff --git a/src/doc/prompt.py b/src/doc/prompt.py new file mode 100644 index 0000000..8419306 --- /dev/null +++ b/src/doc/prompt.py @@ -0,0 +1,26 @@ +""" +LLM 提示词模板 + +从 src/prompts/ 目录加载 .md 文件作为提示词模板。 +""" + +import os + +_PROMPTS_DIR = os.path.join(os.path.dirname(__file__), "prompts") + + +def _load_prompt(filename: str) -> str: + """从 prompts 目录加载提示词文件内容。""" + path = os.path.join(_PROMPTS_DIR, filename) + with open(path, encoding="utf-8") as f: + return f.read() + + +def build_invoice_system_prompt() -> str: + """构建发票提取系统提示词。""" + return _load_prompt("invoice_system.md") + + +def build_card_info_system_prompt() -> str: + """构建支付截图信息提取系统提示词。""" + return _load_prompt("card_info_system.md") diff --git a/src/doc/prompts/card_info_system.md b/src/doc/prompts/card_info_system.md new file mode 100644 index 0000000..e9c50d5 --- /dev/null +++ b/src/doc/prompts/card_info_system.md @@ -0,0 +1,11 @@ +# 支付截图信息提取系统提示词 + +你是财务支付截图信息提取助手。你的任务是从支付截图（银行转账记录、微信/支付宝付款凭证等）中提取结构化信息，并以 JSON 格式返回。 + +需要提取的字段（全部必填，无法识别时返回空字符串）： + +1. 刷卡日期: 支付发生的日期，格式为 YYYY/M/D +2. 刷卡金额: 实际支付金额，只保留数字（如 123.45） +3. 公务卡号: 付款银行卡号，如果截图中有显示则提取，没有则返回空字符串 + +严格只输出 JSON，不要输出任何其他文字、Markdown 标记或解释。 \ No newline at end of file diff --git a/src/doc/prompts/invoice_system.md b/src/doc/prompts/invoice_system.md new file mode 100644 index 0000000..f5c4307 --- /dev/null +++ b/src/doc/prompts/invoice_system.md @@ -0,0 +1,33 @@ +# 发票提取系统提示词 + +你是财务文档信息提取助手。你的任务是从发票文本中提取结构化信息，并以 JSON 格式返回。 + +需要提取的字段（全部必填，无法识别时返回空字符串）： +先判断发票类型，如果是高铁票/或者火车票，返回如下字段： +1. 发票类型: "高铁票" +2. 发票号码: 发票的唯一编号 +3. 开票日期: 格式为 YYYY/M/D +4. 乘车日期: 格式为 YYYY/M/D +5. 出发站: 没有留空 +6. 到达站: 没有留空 +7. 座位等级: 没有留空 +8. 车次: 没有留空 +9. 人员姓名: 没有留空 +10. 价税合计：就是票价，找不到票价信息才填`0`，能够找到尽量填写找到的信息 + +如果是酒店住宿(酒店住宿通产包含关键字：住宿服务，酒店，生产生活服务等，请仔细分析，这种发票和普通发票类似)，返回如下字段： +1. 发票类型: "酒店住宿" +2. 发票号码: 发票的唯一编号 +3. 开票日期: 格式为 YYYY/M/D +4. 价税合计: 金额数字 + +如果是普通发票，返回如下字段： +1. 发票类型: "普通发票" +2. 发票号码: 发票的唯一编号 +3. 开票日期: 格式为 YYYY/M/D +4. 项目名称: 商品或服务名称，总结的人能看懂 +5. 规格型号: 规格描述 +6. 价税合计: 金额数字 +7. 销售方名称: 卖方全称 + +严格只输出 JSON，不要输出任何其他文字、Markdown 标记或解释。 \ No newline at end of file diff --git a/run.py b/src/main.py similarity index 67% rename from run.py rename to src/main.py index 12bccff..71cdb83 100644 --- a/run.py +++ b/src/main.py @@ -1,17 +1,14 @@ #!/usr/bin/env python3 -# -*- coding: utf-8 -*- """ 财务报销自动化 依次执行: 1. 发票提取 — 从 PDF 发票提取信息，生成 invoice_summary.csv - 2. OCR 识别 — 从支付截图识别刷卡信息，回填 CSV - 3. 报销提交 — 打开浏览器登录财务系统并自动填报 + 2. 报销提交 — 打开浏览器登录财务系统并自动填报 用法: python run.py # 全流程 python run.py --step invoice # 仅发票提取 - python run.py --step ocr # 仅 OCR 识别 python run.py --step submit # 仅浏览器填报 python run.py -u 工号 -p 密码 # 覆盖登录凭据 """ @@ -21,28 +18,30 @@ import sys from pathlib import Path # 确保项目根目录在 sys.path 中 -sys.path.insert(0, str(Path(__file__).parent.resolve())) +sys.path.insert(0, str(Path(__file__).parent.resolve().parent)) -from app.pipeline import run_pipeline +from src.pipeline import run_pipeline -def main(): +def main() -> None: parser = argparse.ArgumentParser( - description="财务报销自动化 - 发票提取 → OCR 识别 → 浏览器填报", + description="财务报销自动化 - 发票提取 → 浏览器填报", ) parser.add_argument( "--step", - choices=["all", "invoice", "ocr", "submit"], + choices=["all", "invoice", "submit"], default="all", help="执行步骤 (默认: all)", ) parser.add_argument( - "-u", "--username", + "-u", + "--username", default=None, help="信息门户登录账号（覆盖 config.json）", ) parser.add_argument( - "-p", "--password", + "-p", + "--password", default=None, help="信息门户登录密码（覆盖 config.json）", ) @@ -57,4 +56,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/pipeline.py b/src/pipeline.py new file mode 100644 index 0000000..021d218 --- /dev/null +++ b/src/pipeline.py @@ -0,0 +1,136 @@ +""" +报销全流程编排 + +将发票提取 → 浏览器填报串联为一条管道， +数据在内存中流转，同时生成 CSV 中间产物。 + +发票类型区分： + - 差旅发票（高铁票/酒店住宿）：不生成易耗品出库单，走差旅报销流程 + - 普通发票：生成易耗品出库单，走普通报销流程 +""" + +from pathlib import Path +from typing import Any + +from . import get_logger +from .config import load_config +from .doc.extractor import extract_invoices +from .doc.invoice import ( + classify_invoice_batch, + load_invoices_from_csv, + save_invoice_csv, +) +from .doc.invoice import ( + save_csv as save_payment_csv, +) + +log = get_logger("pipeline") + + +def _find_upload_directory(project_dir: Path) -> Path | None: + """自动发现 uploads 目录下最新且包含 PDF 的会话文件夹""" + uploads_base = project_dir / "src" / "web" / "uploads" + if not uploads_base.is_dir(): + return None + + # 只选包含 PDF 的目录 + valid_dirs = [d for d in uploads_base.iterdir() if d.is_dir() and list(d.glob("*.pdf"))] + if not valid_dirs: + return None + + # 按修改时间排序，取最新 + session_dirs = sorted( + valid_dirs, + key=lambda d: d.stat().st_mtime, + reverse=True, + ) + return session_dirs[0] + + +def _classify_from_csv(csv_path: Path) -> dict[str, list[dict[str, Any]]]: + """从已生成的 CSV 中读取发票数据并按类型分组""" + rows = load_invoices_from_csv(csv_path) + if rows is None: + return {"travel": [], "general": []} + return classify_invoice_batch(rows) + + +def run_pipeline(step: str = "all", username: str | None = None, password: str | None = None) -> int: + """执行报销流程 + + Args: + step: all | invoice | submit + username: 覆盖 config.json 中的用户名 + password: 覆盖 config.json 中的密码 + """ + config = load_config() + if username: + config["username"] = username + if password: + config["password"] = password + + # 工作目录（项目根目录） + project_dir = Path(__file__).parent.parent + + # -------------------------------------------------- + # Step 1: 发票提取 + # -------------------------------------------------- + payment_records: list[dict[str, str]] | None = None + groups: dict[str, list[dict[str, str]]] | None = None + + if step in ("all", "invoice"): + log.info("=" * 60) + log.info("[1/2] 发票提取") + log.info("=" * 60) + + payment_records, groups = extract_invoices(str(project_dir)) + if not payment_records: + log.error("未提取到任何发票数据") + return 1 + + save_payment_csv(payment_records, project_dir / "payment_records.csv") + save_invoice_csv(payment_records, project_dir / "invoice_summary.csv") + + # 打印分类结果 + log.info(f"发票分类: 差旅 {len(groups['travel'])} 张, 普通 {len(groups['general'])} 张") + + if step == "invoice": + log.info("[1/2] 发票提取 完成") + return 0 + + # -------------------------------------------------- + # Step 2: 浏览器填报 + # -------------------------------------------------- + if step in ("all", "submit"): + log.info("=" * 60) + log.info("[2/2] 报销提交") + log.info("=" * 60) + + from .bot import load_invoice_data, run_bot + + csv_path = project_dir / "payment_records.csv" + bot_invoices = load_invoice_data(str(csv_path), config) + + # 根据发票类型选择填报模式 + if groups is None: + groups = _classify_from_csv(csv_path) + + if groups["travel"] and not groups["general"]: + log.info("检测到纯差旅发票，使用差旅报销模式") + # TODO: 差旅报销填报流程 + run_bot(config, bot_invoices) + else: + log.info("检测到普通发票，使用普通报销模式") + run_bot(config, bot_invoices) + + if step == "submit": + log.info("[2/2] 报销提交 完成") + return 0 + + # -------------------------------------------------- + # 全流程完成 + # -------------------------------------------------- + log.info("=" * 60) + log.info("全流程执行完毕") + log.info("=" * 60) + return 0 diff --git a/src/web/README.md b/src/web/README.md new file mode 100644 index 0000000..e0c9336 --- /dev/null +++ b/src/web/README.md @@ -0,0 +1,83 @@ +--- +last_reviewed: 2026-06-09 +--- + +# src/web 模块设计说明 + +## 设计思路 + +`src/web` 是一个基于 Flask 的轻量级 Web 界面，为财务报销自动化管道提供可视化操作入口。核心设计原则： + +- **会话隔离**：每次上传生成独立 `session_id`，文件、日志、配置、结果各自隔离在 `uploads//` 目录下，避免并发冲突。 +- **异步处理**：耗时的 PDF 提取、LLM 调用在后台线程执行，前端通过 SSE 实时查看日志流，不阻塞 HTTP 连接。 +- **前后端分离最小化**：前端使用原生 JS + Bootstrap 5，不引入构建工具，保持单页应用轻量可维护。 +- **双模式支持**：PDF 发票提取模式和 CSV 快捷上传模式，后者跳过 LLM 识别和 PDF 解析，直接处理已有发票数据。 + +## 文件结构 + +``` +src/web/ +├── app.py # Flask 应用入口，路由、管道编排、日志收集 +├── templates/ +│ ├── index.html # PC 端主界面（上传、配置、处理、编辑、提交） +│ └── mobile_upload.html # 移动端上传页面（拍照/相册选择） +└── static/ + ├── css/ + │ └── index.css # 全局样式（上传区、日志面板、可编辑表格） + └── js/ + └── index.js # 前端逻辑（上传、SSE 日志、表格编辑、二维码同步） +``` + +## 数据流 + +``` +用户上传文件 → 创建 session → 文件写入 uploads// + ↓ +后台线程执行管道: extract_invoices() → enrich_with_llm() → save_csv() + ↓ +结果写入 session 目录: invoice_summary.csv / result.json / session.log + ↓ +前端 SSE 轮询 result.json 变化 → 显示完成状态 + ↓ +前端加载 CSV 数据 → 可编辑表格展示 → 用户修改后保存 + ↓ +用户点击提交 → run_financial_submit() → bot 自动填报财务系统 +``` + +## API 路由 + +| 方法 | 路径 | 功能 | +|------|------|------| +| GET | `/` | 主界面 | +| POST | `/api/session` | 创建会话，返回 session_id | +| POST | `/api/upload/` | 上传 PDF/图片 | +| POST | `/api/upload-csv/` | 上传 CSV 发票数据 | +| GET | `/api/files/` | 列出会话文件 | +| POST | `/api/process/` | 启动管道（后台线程） | +| GET | `/api/logs/` | SSE 日志流 | +| GET | `/api/data/` | 获取发票数据 JSON | +| POST | `/api/save/` | 保存前端编辑的发票数据 | +| GET | `/api/download//` | 下载生成文件 | +| POST | `/api/submit-financial/` | 手动触发财务系统填报 | +| GET | `/mobile/` | 移动端上传页面 | + +## 关键机制 + +### 日志收集 + +`_SSELogHandler` 将管道日志写入 `session.log`，SSE 端点通过文件偏移量增量读取，实现前端实时日志展示。日志收集器在管道启动时安装，完成后移除，确保线程安全。 + +### 发票类型分流 + +- **差旅发票**（高铁票/酒店住宿）：不生成易耗品出库单，走差旅报销流程 +- **普通发票**：生成易耗品出库单（Word 文档），走普通报销流程 + +`classify_invoice_batch()` 根据发票内容自动分类，`_try_fill_consumable_doc()` 仅对普通发票生成出库单。 + +### 移动端同步 + +PC 端生成二维码指向 `/mobile/`，手机端上传的图片通过 `syncFiles()` 轮询同步到 PC 端内存中的 `imgFiles` 列表，实现跨设备协作。文件来源标记（`__source`）区分本地选择和服务器同步，避免重复。 + +### 配置管理 + +配置分两层：项目级 `config.json` 提供默认值，会话级 `uploads//config.json` 存储当次会话覆盖值。前端支持通过上传 `config.json` 快速填充配置表单。 \ No newline at end of file diff --git a/web/app.py b/src/web/app.py similarity index 58% rename from web/app.py rename to src/web/app.py index 39c447a..f87a331 100644 --- a/web/app.py +++ b/src/web/app.py @@ -1,14 +1,17 @@ """ 财务报销自动化 — Web 界面 -用户上传 PDF 发票和支付截图，配置账号信息，自动完成： - 1. 发票提取 2. OCR 识别 3. 浏览器填报（可选） +用户上传 PDF 发票，配置账号信息，自动完成： + 1. 发票提取 2. 浏览器填报（可选） -启动: python web/app.py +发票类型区分： + - 差旅发票（高铁票/酒店住宿）：不生成易耗品出库单，走差旅报销流程 + - 普通发票：生成易耗品出库单，走普通报销流程 + +启动: uv run python src/web/app.py 访问: http://localhost:5000 """ -import io import json import logging import sys @@ -16,29 +19,38 @@ import threading import time import uuid from pathlib import Path +from typing import Any from urllib.parse import quote from flask import Flask, Response, jsonify, render_template, request, stream_with_context # 确保项目根目录在 sys.path -PROJECT_ROOT = Path(__file__).parent.parent.resolve() +PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent sys.path.insert(0, str(PROJECT_ROOT)) -from app.config import load_config as load_project_config -from app.extractor import extract_invoices, save_csv, save_markdown -from app.fill_consumable_doc import ( +from src import get_logger # noqa: E402, I001 +from src.config import load_config as load_project_config # noqa: E402, I001 +from src.doc.extractor import ( # noqa: E402, I001 + extract_invoices, +) +from src.doc.fill_consumable_doc import ( # noqa: E402, I001 CONSUMABLE_DOC_FILENAME, fill_consumable_from_template, ) -from app import get_logger -from app.ocr import enrich_with_ocr, _save_csv as save_ocr_csv, save_markdown_from_csv, _load_csv as load_ocr_csv +from src.doc.invoice import ( # noqa: E402, I001 + classify_invoice_batch, + load_csv, + load_invoice_csv, + save_csv as save_payment_csv, + save_invoice_csv, +) fill_log = get_logger("fill_consumable_doc") CONSUMABLE_TEMPLATE = PROJECT_ROOT / CONSUMABLE_DOC_FILENAME app = Flask(__name__, template_folder="templates") -UPLOAD_BASE = PROJECT_ROOT / "web" / "uploads" +UPLOAD_BASE = PROJECT_ROOT / "src" / "web" / "uploads" SESSION_LOG_FILE = "session.log" SESSION_RESULT_FILE = "result.json" @@ -47,6 +59,7 @@ SESSION_RESULT_FILE = "result.json" # 日志收集器 — 捕获管道日志到文件，SSE 端点通过 tail -f 读取 # ================================================================ + class _SSELogHandler(logging.Handler): """将日志写入指定文件（线程安全）""" @@ -55,7 +68,7 @@ class _SSELogHandler(logging.Handler): self._lock = threading.Lock() self._file = open(log_path, "w", encoding="utf-8") - def emit(self, record: logging.LogRecord): + def emit(self, record: logging.LogRecord) -> None: try: msg = self.format(record) + "\n" with self._lock: @@ -64,7 +77,7 @@ class _SSELogHandler(logging.Handler): except Exception: pass - def close_file(self): + def close_file(self) -> None: try: self._file.close() except Exception: @@ -82,7 +95,7 @@ def _install_log_collector(session_dir: Path) -> _SSELogHandler: handler.setFormatter(fmt) handler.setLevel(logging.INFO) - for name in ["extractor", "ocr", "pipeline", "bot", "fill_consumable_doc"]: + for name in ["extractor", "llm_extractor", "matcher", "pipeline", "bot", "fill_consumable_doc"]: logger = logging.getLogger(name) logger.setLevel(logging.INFO) logger.addHandler(handler) @@ -90,8 +103,8 @@ def _install_log_collector(session_dir: Path) -> _SSELogHandler: return handler -def _remove_log_collector(handler: _SSELogHandler): - for name in ["extractor", "ocr", "pipeline", "bot", "fill_consumable_doc"]: +def _remove_log_collector(handler: _SSELogHandler) -> None: + for name in ["extractor", "llm_extractor", "matcher", "pipeline", "bot", "fill_consumable_doc"]: logging.getLogger(name).removeHandler(handler) handler.close_file() @@ -101,7 +114,7 @@ def _remove_log_collector(handler: _SSELogHandler): # ================================================================ -def _load_session_config(session_dir: Path) -> dict: +def _load_session_config(session_dir: Path) -> dict[str, Any]: config = load_project_config() cfg_path = session_dir / "config.json" if cfg_path.exists(): @@ -110,31 +123,95 @@ def _load_session_config(session_dir: Path) -> dict: return config +def _resolve_payment_csv(session_dir: Path) -> Path | None: + """查找支付记录 CSV（payment_records.csv）""" + csv_path = session_dir / "payment_records.csv" + if csv_path.exists(): + return csv_path + for f in session_dir.glob("*.csv"): + if f.name != SESSION_RESULT_FILE: + return f + return None + + def _resolve_invoice_csv(session_dir: Path) -> Path | None: + """查找发票级别 CSV（invoice_summary.csv）""" csv_path = session_dir / "invoice_summary.csv" if csv_path.exists(): return csv_path for f in session_dir.glob("*.csv"): - return f + if f.name != SESSION_RESULT_FILE: + return f return None -def _try_fill_consumable_doc(session_dir: Path, config: dict) -> dict: - """根据 CSV 填写易耗品出库单，供会话目录下载。""" +def _has_general_invoices(rows: list[dict[str, str]]) -> bool: + """检查发票列表中是否包含普通发票（需要生成易耗品出库单）""" + invoices = [] + for row in rows: + # 支付记录格式：从 _invoices_json 还原 + invoices_json = row.get("_invoices_json", "") + if invoices_json: + try: + invoices.extend(json.loads(invoices_json)) + except json.JSONDecodeError: + pass + # 发票级别格式：直接使用 + elif "发票类型" in row: + invoices.append(row) + if not invoices: + return False + groups = classify_invoice_batch(invoices) + return len(groups["general"]) > 0 + + +def _get_invoice_groups(rows: list[dict[str, str]]) -> dict[str, int]: + """统计发票类型分布""" + invoices = [] + for row in rows: + # 支付记录格式：从 _invoices_json 还原 + invoices_json = row.get("_invoices_json", "") + if invoices_json: + try: + invoices.extend(json.loads(invoices_json)) + except json.JSONDecodeError: + pass + # 发票级别格式：直接使用 + elif "发票类型" in row: + invoices.append(row) + groups = classify_invoice_batch(invoices) + return { + "travel_count": len(groups["travel"]), + "general_count": len(groups["general"]), + } + + +def _try_fill_consumable_doc(session_dir: Path, config: dict[str, Any]) -> dict[str, Any]: + """根据 CSV 填写易耗品出库单，供会话目录下载。 + + 仅当存在普通发票时才生成出库单。纯差旅发票跳过。 + """ if not CONSUMABLE_TEMPLATE.exists(): fill_log.warning("出库单模板不存在: %s", CONSUMABLE_TEMPLATE) return {"ok": False, "error": "出库单模板不存在，请将模板放在项目根目录"} - csv_path = _resolve_invoice_csv(session_dir) + csv_path = _resolve_payment_csv(session_dir) if csv_path is None: return {"ok": False, "error": "未找到发票 CSV"} + # 检查是否有普通发票 + rows = load_csv(csv_path) + if rows is None: + return {"ok": False, "error": "CSV 读取失败"} + + if not _has_general_invoices(rows): + fill_log.info("纯差旅发票，跳过易耗品出库单生成") + return {"ok": False, "skipped": True, "error": "差旅发票无需生成易耗品出库单"} + out_doc = session_dir / CONSUMABLE_DOC_FILENAME try: fill_log.info("开始填写出库单: %s", out_doc.name) - fill_consumable_from_template( - csv_path, CONSUMABLE_TEMPLATE, out_doc, config=config - ) + fill_consumable_from_template(csv_path, CONSUMABLE_TEMPLATE, out_doc, config=config) fill_log.info("出库单填写完成") return {"ok": True, "doc_filename": CONSUMABLE_DOC_FILENAME} except ImportError: @@ -145,11 +222,16 @@ def _try_fill_consumable_doc(session_dir: Path, config: dict) -> dict: return {"ok": False, "error": str(e)} -def _append_doc_download(result: dict, session_id: str, doc_fill: dict) -> None: +def _append_doc_download(result: dict[str, Any], session_id: str, doc_fill: dict[str, Any]) -> None: if doc_fill.get("ok"): fn = doc_fill["doc_filename"] result["doc_url"] = f"/api/download/{session_id}/{quote(fn)}" result["doc_ok"] = True + elif doc_fill.get("skipped"): + # 差旅发票，跳过出库单生成（不是错误） + result["doc_ok"] = None + result["doc_skipped"] = True + result["doc_message"] = doc_fill.get("error", "") else: result["doc_ok"] = False result["doc_error"] = doc_fill.get("error", "未知错误") @@ -159,78 +241,110 @@ def _append_doc_download(result: dict, session_id: str, doc_fill: dict) -> None: # 管道入口 # ================================================================ -def run_pipeline_web(session_dir: Path, config: dict): - """在 Web 会话目录中执行提取+OCR，结果写入 session 目录下的文件 - + +def run_pipeline_web(session_dir: Path, config: dict[str, Any]) -> dict[str, Any]: + """在 Web 会话目录中执行发票提取，结果写入 session 目录下的文件 + 注意：不再自动提交财务系统。提交通由 /api/submit-financial/ 触发。 """ start = time.time() # ---- Step 1: 发票提取 ---- - invoices = extract_invoices(str(session_dir)) + invoices, groups = extract_invoices(str(session_dir)) if not invoices: return {"ok": False, "error": "未提取到任何发票数据"} - save_csv(invoices, session_dir / "invoice_summary.csv") - save_markdown(invoices, session_dir / "invoice_summary.md") + # 保存两个 CSV：支付记录级别（供 bot/出库单使用）和发票级别（供人工参考） + save_payment_csv(invoices, session_dir / "payment_records.csv") + save_invoice_csv(invoices, session_dir / "invoice_summary.csv") - # ---- Step 2: OCR 识别 ---- - csv_path = session_dir / "invoice_summary.csv" - rows = load_ocr_csv(csv_path) - if rows is None: - return {"ok": False, "error": "CSV 读取失败"} - - rows = enrich_with_ocr(rows, str(session_dir)) - save_ocr_csv(csv_path, rows) - save_markdown_from_csv(csv_path, rows) + # 统计发票总数 + invoice_count = sum(len(inv.get("_matched_invoices", [])) for inv in invoices) elapsed = time.time() - start result = { "ok": True, "elapsed": f"{elapsed:.1f}s", - "invoice_count": len(rows), + "invoice_count": invoice_count, "csv_url": f"/api/download/{session_dir.name}/invoice_summary.csv", - "md_url": f"/api/download/{session_dir.name}/invoice_summary.md", + # 发票类型统计 + "travel_count": len(groups["travel"]), + "general_count": len(groups["general"]), } doc_fill = _try_fill_consumable_doc(session_dir, config) _append_doc_download(result, session_dir.name, doc_fill) return result -def run_csv_pipeline_web(session_dir: Path, config: dict, csv_filename: str): - """直接使用上传的 CSV 文件，跳过 PDF 提取和 OCR""" +def run_csv_pipeline_web(session_dir: Path, config: dict[str, Any], csv_filename: str) -> dict[str, Any]: + """直接使用上传的 CSV 文件，跳过 PDF 提取""" start = time.time() csv_path = session_dir / csv_filename if not csv_path.exists(): return {"ok": False, "error": "CSV 文件不存在"} - # 读取 CSV 行数 - rows = load_ocr_csv(csv_path) + # 尝试读取支付记录格式 + rows = load_csv(csv_path) if rows is None: - return {"ok": False, "error": "CSV 读取失败"} + # 尝试读取发票级别格式 + invoice_rows = load_invoice_csv(csv_path) + if invoice_rows is None: + return {"ok": False, "error": "CSV 读取失败"} + # 发票级别格式：直接统计 + type_stats = _get_invoice_groups(invoice_rows) + elapsed = time.time() - start + return { + "ok": True, + "elapsed": f"{elapsed:.1f}s", + "invoice_count": len(invoice_rows), + "csv_url": f"/api/download/{session_dir.name}/{csv_filename}", + "travel_count": type_stats["travel_count"], + "general_count": type_stats["general_count"], + } + + # 支付记录格式：统计发票类型 + type_stats = _get_invoice_groups(rows) elapsed = time.time() - start result = { "ok": True, "elapsed": f"{elapsed:.1f}s", - "invoice_count": len(rows), + "invoice_count": type_stats["travel_count"] + type_stats["general_count"], "csv_url": f"/api/download/{session_dir.name}/{csv_filename}", + "travel_count": type_stats["travel_count"], + "general_count": type_stats["general_count"], } doc_fill = _try_fill_consumable_doc(session_dir, config) _append_doc_download(result, session_dir.name, doc_fill) return result -def run_financial_submit(session_dir: Path, config: dict) -> dict: - """执行财务系统填报（从前端确认后调用）""" - csv_path = session_dir / "invoice_summary.csv" +def run_financial_submit(session_dir: Path, config: dict[str, Any]) -> dict[str, Any]: + """执行财务系统填报（从前端确认后调用） + + 根据发票类型选择填报模式： + - 纯差旅发票：差旅报销模式（TODO） + - 含普通发票：普通报销模式 + """ + csv_path = session_dir / "payment_records.csv" if not csv_path.exists(): return {"ok": False, "error": "未找到发票数据，请先处理"} - from app.bot import load_invoice_data, run_bot_web + from src.bot import load_invoice_data, run_bot_web bot_invoices = load_invoice_data(str(csv_path), config) + + # 判断发票类型 + rows = load_csv(csv_path) + if rows: + invoice_groups = _get_invoice_groups(rows) + if invoice_groups["travel_count"] and not invoice_groups["general_count"]: + fill_log.info("检测到纯差旅发票，使用差旅报销模式") + # TODO: 差旅报销填报流程 + else: + fill_log.info("检测到普通发票，使用普通报销模式") + run_bot_web(config, bot_invoices, session_dir) return {"ok": True} @@ -239,13 +353,14 @@ def run_financial_submit(session_dir: Path, config: dict) -> dict: # Flask 路由 # ================================================================ + @app.route("/") -def index(): +def index() -> Any: return render_template("index.html") @app.route("/api/session", methods=["POST"]) -def create_session(): +def create_session() -> Any: """创建上传会话，返回 session_id""" sid = uuid.uuid4().hex[:12] session_dir = UPLOAD_BASE / sid @@ -254,7 +369,7 @@ def create_session(): @app.route("/api/upload/", methods=["POST"]) -def upload_file(session_id: str): +def upload_file(session_id: str) -> Any: """上传 PDF 或图片""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): @@ -270,8 +385,8 @@ def upload_file(session_id: str): @app.route("/api/upload-csv/", methods=["POST"]) -def upload_csv(session_id: str): - """上传 CSV 发票数据文件（跳过 PDF 提取和 OCR）""" +def upload_csv(session_id: str) -> Any: + """上传 CSV 发票数据文件（跳过 PDF 提取）""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): return session_dir @@ -286,23 +401,20 @@ def upload_csv(session_id: str): @app.route("/api/files/", methods=["GET"]) -def list_files(session_id: str): +def list_files(session_id: str) -> Any: """列出会话目录中的文件""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): return session_dir pdfs = sorted(f.name for f in session_dir.glob("*.pdf")) - imgs = sorted( - f.name for ext in {".png", ".jpg", ".jpeg", ".bmp", ".webp"} - for f in session_dir.glob(f"*{ext}") - ) + imgs = sorted(f.name for ext in {".png", ".jpg", ".jpeg", ".bmp", ".webp"} for f in session_dir.glob(f"*{ext}")) return jsonify({"pdfs": pdfs, "images": imgs}) @app.route("/api/process/", methods=["POST"]) -def start_process(session_id: str): - """启动管道处理（仅提取+OCR，不自动提交财务系统）""" +def start_process(session_id: str) -> Any: + """启动管道处理（仅发票提取，不自动提交财务系统）""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): return session_dir @@ -320,7 +432,7 @@ def start_process(session_id: str): # 在后台线程执行 handler = _install_log_collector(session_dir) - def _run(): + def _run() -> None: result = {"ok": False, "error": "未知错误"} try: if mode == "csv": @@ -338,7 +450,7 @@ def start_process(session_id: str): result = run_pipeline_web(session_dir, config) except BaseException as e: result = {"ok": False, "error": str(e)} - if isinstance(e, (KeyboardInterrupt, SystemExit)): + if isinstance(e, KeyboardInterrupt | SystemExit): raise finally: try: @@ -356,13 +468,13 @@ def start_process(session_id: str): @app.route("/api/logs/") -def stream_logs(session_id: str): +def stream_logs(session_id: str) -> Any: """SSE 日志流""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): return session_dir - def generate(): + def generate() -> Any: # 先发送已有日志 log_file = session_dir / SESSION_LOG_FILE last_size = 0 @@ -399,7 +511,7 @@ def stream_logs(session_id: str): @app.route("/api/download//") -def download_file(session_id: str, filename: str): +def download_file(session_id: str, filename: str) -> Any: """下载生成的文件""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): @@ -415,8 +527,6 @@ def download_file(session_id: str, filename: str): mimetype = "application/msword" elif safe_name.endswith(".csv"): mimetype = "text/csv; charset=utf-8" - elif safe_name.endswith(".md"): - mimetype = "text/markdown; charset=utf-8" else: mimetype = "application/octet-stream" @@ -428,42 +538,86 @@ def download_file(session_id: str, filename: str): ) +@app.route("/api/config/", methods=["GET"]) +def get_session_config(session_id: str) -> Any: + """获取当前会话的配置（供前端回填表单）""" + session_dir = _validate_session(session_id) + if isinstance(session_dir, tuple): + return session_dir + + config = load_project_config() + cfg_path = session_dir / "config.json" + if cfg_path.exists(): + with open(cfg_path, encoding="utf-8") as f: + config.update(json.load(f)) + # 只返回前端需要的字段 + return jsonify( + { + "username": config.get("username", ""), + "password": "", # 不返回密码 + "default_name": config.get("default_name", ""), + "default_card_no": config.get("default_card_no", ""), + "default_person_id": config.get("default_person_id", ""), + "consumable_storage": config.get("consumable_storage", ""), + } + ) + + @app.route("/api/data/", methods=["GET"]) -def get_invoice_data(session_id: str): +def get_invoice_data(session_id: str) -> Any: """读取发票数据并返回 JSON（供前端表格编辑）""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): return session_dir - csv_path = session_dir / "invoice_summary.csv" - if not csv_path.exists(): - # CSV 模式下可能是其他文件名 - csv_files = list(session_dir.glob("*.csv")) - csv_files = [f for f in csv_files if f.name != SESSION_RESULT_FILE] - if csv_files: - csv_path = csv_files[0] - else: - return jsonify({"error": "未找到发票数据，请先处理"}), 404 + # 优先读取支付记录 CSV + payment_csv = session_dir / "payment_records.csv" + if payment_csv.exists(): + rows = load_csv(payment_csv) + if rows is not None: + data: list[dict[str, Any]] = [] + for i, row in enumerate(rows): + entry: dict[str, Any] = dict(row) + entry["__row"] = i + data.append(entry) + fields = [k for k in rows[0].keys() if not k.startswith("__")] if rows else [] + return jsonify({"csv_filename": payment_csv.name, "fields": fields, "data": data}) - rows = load_ocr_csv(csv_path) - if rows is None: - return jsonify({"error": "CSV 读取失败"}), 500 + # 回退到发票级别 CSV + invoice_csv = session_dir / "invoice_summary.csv" + if invoice_csv.exists(): + rows = load_invoice_csv(invoice_csv) + if rows is not None: + invoice_data: list[dict[str, Any]] = [] + for i, row in enumerate(rows): + entry2: dict[str, Any] = dict(row) + entry2["__row"] = i + invoice_data.append(entry2) + fields = [k for k in rows[0].keys() if not k.startswith("__")] if rows else [] + return jsonify({"csv_filename": invoice_csv.name, "fields": fields, "data": invoice_data}) - # 添加行号用于编辑追踪 - data = [] - for i, row in enumerate(rows): - entry = dict(row) - entry["__row"] = i - data.append(entry) + # 最后尝试任意 CSV + csv_files = list(session_dir.glob("*.csv")) + csv_files = [f for f in csv_files if f.name != SESSION_RESULT_FILE] + if csv_files: + csv_path = csv_files[0] + rows = load_csv(csv_path) + if rows is None: + rows = load_invoice_csv(csv_path) + if rows is not None: + fallback_data: list[dict[str, Any]] = [] + for i, row in enumerate(rows): + entry3: dict[str, Any] = dict(row) + entry3["__row"] = i + fallback_data.append(entry3) + fields = [k for k in rows[0].keys() if not k.startswith("__")] if rows else [] + return jsonify({"csv_filename": csv_path.name, "fields": fields, "data": fallback_data}) - # 返回原始字段顺序（去掉内部字段） - fields = [k for k in rows[0].keys() if not k.startswith('__')] if rows else [] - - return jsonify({"csv_filename": csv_path.name, "fields": fields, "data": data}) + return jsonify({"error": "未找到发票数据，请先处理"}), 404 @app.route("/api/save/", methods=["POST"]) -def save_invoice_data(session_id: str): +def save_invoice_data(session_id: str) -> Any: """保存前端编辑后的发票数据到 CSV""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): @@ -478,7 +632,7 @@ def save_invoice_data(session_id: str): return jsonify({"error": "CSV 文件不存在"}), 404 # 读取原 CSV 获取字段顺序（使用第一个数据的 keys） - original_rows = load_ocr_csv(csv_path) + original_rows = load_csv(csv_path) if original_rows is None or len(original_rows) == 0: return jsonify({"error": "无法读取原始 CSV 结构"}), 500 @@ -494,21 +648,24 @@ def save_invoice_data(session_id: str): row = {k: entry.get(k, "") for k in fieldnames} writer.writerow(row) - resp = {"ok": True} + resp: dict[str, str | bool | None] = {"ok": True} config = _load_session_config(session_dir) doc_fill = _try_fill_consumable_doc(session_dir, config) if doc_fill.get("ok"): fn = doc_fill["doc_filename"] resp["doc_url"] = f"/api/download/{session_id}/{quote(fn)}" resp["doc_ok"] = True + elif doc_fill.get("skipped"): + resp["doc_ok"] = None + resp["doc_skipped"] = True else: resp["doc_ok"] = False - resp["doc_error"] = doc_fill.get("error") + resp["doc_error"] = doc_fill.get("error") or "" return jsonify(resp) @app.route("/api/submit-financial/", methods=["POST"]) -def submit_financial(session_id: str): +def submit_financial(session_id: str) -> Any: """手动触发财务系统填报""" session_dir = _validate_session(session_id) if isinstance(session_dir, tuple): @@ -530,7 +687,7 @@ def submit_financial(session_id: str): # 在后台线程执行提交 handler = _install_log_collector(session_dir) - def _run(): + def _run() -> None: result = {"ok": False, "error": "未知错误"} try: submit_result = run_financial_submit(session_dir, config) @@ -540,13 +697,17 @@ def submit_financial(session_id: str): result = submit_result except BaseException as e: result = {"ok": False, "error": str(e)} - if isinstance(e, (KeyboardInterrupt, SystemExit)): + if isinstance(e, KeyboardInterrupt | SystemExit): raise finally: try: tmp_path = session_dir / (SESSION_RESULT_FILE + ".tmp") with open(tmp_path, "w", encoding="utf-8") as f: - json.dump({"ok": True, "submit_ok": result.get("ok"), "submit_error": result.get("error")}, f, ensure_ascii=False) + json.dump( + {"ok": True, "submit_ok": result.get("ok"), "submit_error": result.get("error")}, + f, + ensure_ascii=False, + ) tmp_path.replace(session_dir / SESSION_RESULT_FILE) except Exception: pass @@ -561,14 +722,15 @@ def submit_financial(session_id: str): # 辅助函数 # ================================================================ -def _validate_session(session_id: str): + +def _validate_session(session_id: str) -> Path | tuple[Response, int]: session_dir = UPLOAD_BASE / session_id if not session_dir.exists(): return jsonify({"error": "会话不存在"}), 404 return session_dir -def _build_web_config(body: dict) -> dict: +def _build_web_config(body: dict[str, Any]) -> dict[str, Any]: """从请求体构建配置""" config = load_project_config() for key in ( @@ -590,7 +752,7 @@ def _escape_sse(text: str) -> str: @app.route("/mobile/") -def mobile_upload(session_id: str): +def mobile_upload(session_id: str) -> Any: """移动端上传页面""" session_dir = UPLOAD_BASE / session_id if not session_dir.exists(): @@ -599,12 +761,12 @@ def mobile_upload(session_id: str): @app.route("/api/mobile-upload/", methods=["POST"]) -def mobile_upload_file(session_id: str): +def mobile_upload_file(session_id: str) -> Any: """移动端上传图片（复用 PC 上传逻辑）""" return upload_file(session_id) if __name__ == "__main__": UPLOAD_BASE.mkdir(parents=True, exist_ok=True) - print(f"启动 Web 服务: http://localhost:5000") - app.run(host="0.0.0.0", port=5000, debug=True, threaded=True, use_reloader=False) \ No newline at end of file + print("启动 Web 服务: http://localhost:5000") + app.run(host="0.0.0.0", port=5000, debug=True, threaded=True, use_reloader=False) diff --git a/web/static/css/index.css b/src/web/static/css/index.css similarity index 100% rename from web/static/css/index.css rename to src/web/static/css/index.css diff --git a/web/static/js/index.js b/src/web/static/js/index.js similarity index 99% rename from web/static/js/index.js rename to src/web/static/js/index.js index cf338e8..703c7ae 100644 --- a/web/static/js/index.js +++ b/src/web/static/js/index.js @@ -238,7 +238,6 @@ function showDownloadLinks(result) { const items = []; if (result.csv_url) items.push({ label: 'invoice_summary.csv', url: result.csv_url }); - if (result.md_url) items.push({ label: 'invoice_summary.md', url: result.md_url }); if (result.doc_url) items.push({ label: '易耗品、出库单.doc', url: result.doc_url }); lastDownloadUrls = {}; @@ -369,7 +368,6 @@ async function saveInvoiceData() { if (d.doc_url || d.doc_ok === false) { showDownloadLinks({ csv_url: lastDownloadUrls['invoice_summary.csv'], - md_url: lastDownloadUrls['invoice_summary.md'], doc_url: d.doc_url, doc_ok: d.doc_ok, doc_error: d.doc_error, diff --git a/web/templates/index.html b/src/web/templates/index.html similarity index 98% rename from web/templates/index.html rename to src/web/templates/index.html index 4c516fc..607bd46 100644 --- a/web/templates/index.html +++ b/src/web/templates/index.html @@ -11,7 +11,7 @@

财务报销自动化

-

上传发票 PDF 和支付截图，自动提取、OCR 识别并填报

+

上传发票 PDF 和支付截图，自动提取、LLM 识别并填报

@@ -45,7 +45,7 @@
-
📊 CSV 快捷上传 （已有发票数据 CSV 可直接上传，跳过提取和 OCR）
+
📊 CSV 快捷上传 （已有发票数据 CSV 可直接上传，跳过提取和 LLM 识别）
📊
点击或拖拽上传 CSV 文件
diff --git a/web/templates/mobile_upload.html b/src/web/templates/mobile_upload.html similarity index 100% rename from web/templates/mobile_upload.html rename to src/web/templates/mobile_upload.html diff --git a/src/web/uploads/af9065343bc3/26349119343000331138-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119343000331138-电子发票.pdf new file mode 100644 index 0000000..dea6ee4 Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119343000331138-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/26349119343000331314-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119343000331314-电子发票.pdf new file mode 100644 index 0000000..600d4ef Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119343000331314-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/26349119343000335414-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119343000335414-电子发票.pdf new file mode 100644 index 0000000..a3343dd Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119343000335414-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/26349119423003550275-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119423003550275-电子发票.pdf new file mode 100644 index 0000000..c1235d4 Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119423003550275-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/26349119423003552366-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119423003552366-电子发票.pdf new file mode 100644 index 0000000..36f776d Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119423003552366-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/26349119423003595208-电子发票.pdf b/src/web/uploads/af9065343bc3/26349119423003595208-电子发票.pdf new file mode 100644 index 0000000..9267c4a Binary files /dev/null and b/src/web/uploads/af9065343bc3/26349119423003595208-电子发票.pdf differ diff --git a/src/web/uploads/af9065343bc3/config.json b/src/web/uploads/af9065343bc3/config.json new file mode 100644 index 0000000..8c02fab --- /dev/null +++ b/src/web/uploads/af9065343bc3/config.json @@ -0,0 +1,13 @@ +{ + "sso_login_url": "https://tyrz.fynu.edu.cn/sso/login", + "portal_url": "https://tyrz.fynu.edu.cn/oshall", + "reimburse_url": "http://210.45.32.214:8081", + "reimburse_page": "/expen/common/common?v=4.0", + "username": "202407021", + "password": "wang!1624155937", + "default_name": "王建锋", + "default_card_no": "6282880139161682", + "default_person_id": "202407021", + "consumable_storage": "新工科 D605", + "attachment_dir": "D:\\阜阳师范大学\\财务报销\\自动报销系统\\attachments" +} \ No newline at end of file diff --git a/src/web/uploads/af9065343bc3/dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf b/src/web/uploads/af9065343bc3/dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf new file mode 100644 index 0000000..bd95f36 Binary files /dev/null and b/src/web/uploads/af9065343bc3/dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf differ diff --git a/src/web/uploads/af9065343bc3/invoice_summary.csv b/src/web/uploads/af9065343bc3/invoice_summary.csv new file mode 100644 index 0000000..327592e --- /dev/null +++ b/src/web/uploads/af9065343bc3/invoice_summary.csv @@ -0,0 +1,8 @@ +序号,发票类型,发票号码,开票日期,项目名称,规格型号,价税合计,销售方名称,出发站,到达站,车次,乘车日期,座位等级,人员姓名,刷卡日期,公务卡号,刷卡金额,备注,工号 +1,高铁票,26349119423003552366,2026/6/3,,,189.50,,合肥南,阜阳西,G1318,2026/6/3,一等座,陈曙光,2026/6/3,6282****1682,189.5,, +2,高铁票,26349119343000331314,2026/6/3,,,167.00,,阜阳西,合肥南,G1967,2026/6/2,一等座,陈曙光,2026/6/1,6282****1682,167.0,, +3,酒店住宿,26342000001715702281,2026/6/3,,,536.00,,,,,,,,2026/6/2,6282****1682,535.86,, +4,高铁票,26349119423003550275,2026/6/3,,,117.50,,合肥南,阜阳西,G1318,2026/6/3,二等座,张国庆,2026/6/3,6282****1682,235.0,, +5,高铁票,26349119423003595208,2026/6/5,,,117.50,,合肥南,阜阳西,G1318,2026/6/3,二等座,王建锋,2026/6/3,6282****1682,235.0,, +6,高铁票,26349119343000331138,2026/6/3,,,115.50,,阜阳西,合肥南,G1967,2026/6/2,二等座,张国庆,2026/6/1,6282****1682,231.0,, +7,高铁票,26349119343000335414,2026/6/5,,,115.50,,阜阳西,合肥南,G1967,2026/6/2,二等座,王建锋,2026/6/1,6282****1682,231.0,, diff --git a/src/web/uploads/af9065343bc3/payment_records.csv b/src/web/uploads/af9065343bc3/payment_records.csv new file mode 100644 index 0000000..ff665b6 --- /dev/null +++ b/src/web/uploads/af9065343bc3/payment_records.csv @@ -0,0 +1,6 @@ +序号,刷卡日期,公务卡号,刷卡金额,关联发票数,发票详情,备注,_invoices_json,工号 +1,2026/6/3,6282****1682,189.5,1,高铁票[陈曙光]¥189.50,,"[{""发票类型"": ""高铁票"", ""发票号码"": ""26349119423003552366"", ""开票日期"": ""2026/6/3"", ""乘车日期"": ""2026/6/3"", ""出发站"": ""合肥南"", ""到达站"": ""阜阳西"", ""座位等级"": ""一等座"", ""车次"": ""G1318"", ""人员姓名"": ""陈曙光"", ""价税合计"": ""189.50""}]", +2,2026/6/1,6282****1682,167.0,1,高铁票[陈曙光]¥167.00,,"[{""发票类型"": ""高铁票"", ""发票号码"": ""26349119343000331314"", ""开票日期"": ""2026/6/3"", ""乘车日期"": ""2026/6/2"", ""出发站"": ""阜阳西"", ""到达站"": ""合肥南"", ""座位等级"": ""一等座"", ""车次"": ""G1967"", ""人员姓名"": ""陈曙光"", ""价税合计"": ""167.00""}]", +3,2026/6/2,6282****1682,535.86,1,酒店住宿[26342000001715702281]¥536.00,,"[{""发票类型"": ""酒店住宿"", ""发票号码"": ""26342000001715702281"", ""开票日期"": ""2026/6/3"", ""价税合计"": ""536.00""}]", +4,2026/6/3,6282****1682,235.0,2,高铁票[张国庆]¥117.50 | 高铁票[王建锋]¥117.50,,"[{""发票类型"": ""高铁票"", ""发票号码"": ""26349119423003550275"", ""开票日期"": ""2026/6/3"", ""乘车日期"": ""2026/6/3"", ""出发站"": ""合肥南"", ""到达站"": ""阜阳西"", ""座位等级"": ""二等座"", ""车次"": ""G1318"", ""人员姓名"": ""张国庆"", ""价税合计"": ""117.50""}, {""发票类型"": ""高铁票"", ""发票号码"": ""26349119423003595208"", ""开票日期"": ""2026/6/5"", ""乘车日期"": ""2026/6/3"", ""出发站"": ""合肥南"", ""到达站"": ""阜阳西"", ""座位等级"": ""二等座"", ""车次"": ""G1318"", ""人员姓名"": ""王建锋"", ""价税合计"": ""117.50""}]", +5,2026/6/1,6282****1682,231.0,2,高铁票[张国庆]¥115.50 | 高铁票[王建锋]¥115.50,,"[{""发票类型"": ""高铁票"", ""发票号码"": ""26349119343000331138"", ""开票日期"": ""2026/6/3"", ""乘车日期"": ""2026/6/2"", ""出发站"": ""阜阳西"", ""到达站"": ""合肥南"", ""座位等级"": ""二等座"", ""车次"": ""G1967"", ""人员姓名"": ""张国庆"", ""价税合计"": ""115.50""}, {""发票类型"": ""高铁票"", ""发票号码"": ""26349119343000335414"", ""开票日期"": ""2026/6/5"", ""乘车日期"": ""2026/6/2"", ""出发站"": ""阜阳西"", ""到达站"": ""合肥南"", ""座位等级"": ""二等座"", ""车次"": ""G1967"", ""人员姓名"": ""王建锋"", ""价税合计"": ""115.50""}]", diff --git a/src/web/uploads/af9065343bc3/result.json b/src/web/uploads/af9065343bc3/result.json new file mode 100644 index 0000000..dcc24a3 --- /dev/null +++ b/src/web/uploads/af9065343bc3/result.json @@ -0,0 +1 @@ +{"ok": true, "elapsed": "30.3s", "invoice_count": 7, "csv_url": "/api/download/af9065343bc3/invoice_summary.csv", "travel_count": 7, "general_count": 0, "doc_ok": null, "doc_skipped": true, "doc_message": "差旅发票无需生成易耗品出库单"} \ No newline at end of file diff --git a/src/web/uploads/af9065343bc3/session.log b/src/web/uploads/af9065343bc3/session.log new file mode 100644 index 0000000..8ac0108 --- /dev/null +++ b/src/web/uploads/af9065343bc3/session.log @@ -0,0 +1,166 @@ +2026-06-09 16:19:41 [INFO ] extractor: 发现 7 个 PDF 文件 +2026-06-09 16:19:44 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:47 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:47 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331138", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "张国庆", + "价税合计": "115.50" +} +2026-06-09 16:19:47 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331138-电子发票.pdf +2026-06-09 16:19:47 [INFO ] extractor: [高铁票] 已解析: 26349119343000331138-电子发票.pdf +2026-06-09 16:19:47 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:49 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:49 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000331314", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "一等座", + "车次": "G1967", + "人员姓名": "陈曙光", + "价税合计": "167.00" +} +2026-06-09 16:19:49 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000331314-电子发票.pdf +2026-06-09 16:19:49 [INFO ] extractor: [高铁票] 已解析: 26349119343000331314-电子发票.pdf +2026-06-09 16:19:49 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:52 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:52 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119343000335414", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/2", + "出发站": "阜阳西", + "到达站": "合肥南", + "座位等级": "二等座", + "车次": "G1967", + "人员姓名": "王建锋", + "价税合计": "115.50" +} +2026-06-09 16:19:52 [INFO ] llm_extractor: LLM 发票提取成功: 26349119343000335414-电子发票.pdf +2026-06-09 16:19:52 [INFO ] extractor: [高铁票] 已解析: 26349119343000335414-电子发票.pdf +2026-06-09 16:19:52 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:54 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:54 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003550275", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "张国庆", + "价税合计": "117.50" +} +2026-06-09 16:19:54 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003550275-电子发票.pdf +2026-06-09 16:19:54 [INFO ] extractor: [高铁票] 已解析: 26349119423003550275-电子发票.pdf +2026-06-09 16:19:54 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:56 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:56 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003552366", + "开票日期": "2026/6/3", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "一等座", + "车次": "G1318", + "人员姓名": "陈曙光", + "价税合计": "189.50" +} +2026-06-09 16:19:56 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003552366-电子发票.pdf +2026-06-09 16:19:56 [INFO ] extractor: [高铁票] 已解析: 26349119423003552366-电子发票.pdf +2026-06-09 16:19:56 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:19:59 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 200 字符 +2026-06-09 16:19:59 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "高铁票", + "发票号码": "26349119423003595208", + "开票日期": "2026/6/5", + "乘车日期": "2026/6/3", + "出发站": "合肥南", + "到达站": "阜阳西", + "座位等级": "二等座", + "车次": "G1318", + "人员姓名": "王建锋", + "价税合计": "117.50" +} +2026-06-09 16:19:59 [INFO ] llm_extractor: LLM 发票提取成功: 26349119423003595208-电子发票.pdf +2026-06-09 16:19:59 [INFO ] extractor: [高铁票] 已解析: 26349119423003595208-电子发票.pdf +2026-06-09 16:19:59 [INFO ] llm_extractor: 开始请求 LLM (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:00 [INFO ] llm_extractor: LLM 请求完成，响应总长度: 96 字符 +2026-06-09 16:20:00 [INFO ] llm_extractor: LLM 响应: { + "发票类型": "酒店住宿", + "发票号码": "26342000001715702281", + "开票日期": "2026/6/3", + "价税合计": "536.00" +} +2026-06-09 16:20:00 [INFO ] llm_extractor: LLM 发票提取成功: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-09 16:20:00 [INFO ] extractor: [酒店住宿] 已解析: dzfp_26342000001715702281_阜阳师范大学_20260603111046.pdf +2026-06-09 16:20:00 [INFO ] extractor: 共处理 7 张发票 +2026-06-09 16:20:00 [INFO ] matcher: 发现 5 张支付截图 +2026-06-09 16:20:01 [INFO ] llm_extractor: 开始请求 LLM 多模态 (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:03 [INFO ] llm_extractor: LLM 多模态请求完成，响应总长度: 70 字符 +2026-06-09 16:20:03 [INFO ] llm_extractor: LLM 多模态响应: { + "刷卡日期": "2026/6/1", + "刷卡金额": "231.00", + "公务卡号": "6282****1682" +} +2026-06-09 16:20:03 [INFO ] llm_extractor: LLM 支付截图提取成功: 微信图片_20260608145318_279_42.jpg +2026-06-09 16:20:03 [INFO ] matcher: [支付截图] 已解析: 微信图片_20260608145318_279_42.jpg +2026-06-09 16:20:03 [INFO ] llm_extractor: 开始请求 LLM 多模态 (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:05 [INFO ] llm_extractor: LLM 多模态请求完成，响应总长度: 70 字符 +2026-06-09 16:20:05 [INFO ] llm_extractor: LLM 多模态响应: { + "刷卡日期": "2026/6/1", + "刷卡金额": "167.00", + "公务卡号": "6282****1682" +} +2026-06-09 16:20:05 [INFO ] llm_extractor: LLM 支付截图提取成功: 微信图片_20260608145319_280_42.jpg +2026-06-09 16:20:05 [INFO ] matcher: [支付截图] 已解析: 微信图片_20260608145319_280_42.jpg +2026-06-09 16:20:05 [INFO ] llm_extractor: 开始请求 LLM 多模态 (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:07 [INFO ] llm_extractor: LLM 多模态请求完成，响应总长度: 70 字符 +2026-06-09 16:20:07 [INFO ] llm_extractor: LLM 多模态响应: { + "刷卡日期": "2026/6/2", + "刷卡金额": "535.86", + "公务卡号": "6282****1682" +} +2026-06-09 16:20:07 [INFO ] llm_extractor: LLM 支付截图提取成功: 微信图片_20260608145320_281_42.jpg +2026-06-09 16:20:07 [INFO ] matcher: [支付截图] 已解析: 微信图片_20260608145320_281_42.jpg +2026-06-09 16:20:07 [INFO ] llm_extractor: 开始请求 LLM 多模态 (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:09 [INFO ] llm_extractor: LLM 多模态请求完成，响应总长度: 70 字符 +2026-06-09 16:20:09 [INFO ] llm_extractor: LLM 多模态响应: { + "刷卡日期": "2026/6/3", + "刷卡金额": "189.50", + "公务卡号": "6282****1682" +} +2026-06-09 16:20:09 [INFO ] llm_extractor: LLM 支付截图提取成功: 微信图片_20260608145321_282_42.jpg +2026-06-09 16:20:09 [INFO ] matcher: [支付截图] 已解析: 微信图片_20260608145321_282_42.jpg +2026-06-09 16:20:09 [INFO ] llm_extractor: 开始请求 LLM 多模态 (model=qwen/qwen3.5-9b, base=http://100.123.83.115:1234/v1) +2026-06-09 16:20:11 [INFO ] llm_extractor: LLM 多模态请求完成，响应总长度: 70 字符 +2026-06-09 16:20:11 [INFO ] llm_extractor: LLM 多模态响应: { + "刷卡日期": "2026/6/3", + "刷卡金额": "235.00", + "公务卡号": "6282****1682" +} +2026-06-09 16:20:11 [INFO ] llm_extractor: LLM 支付截图提取成功: 微信图片_20260608145323_283_42.jpg +2026-06-09 16:20:11 [INFO ] matcher: [支付截图] 已解析: 微信图片_20260608145323_283_42.jpg +2026-06-09 16:20:11 [INFO ] matcher: 共提取 5 条刷卡记录 +2026-06-09 16:20:11 [INFO ] matcher: 金额校验: 发票总额 ¥1358.50, 刷卡总额 ¥1358.36, 发票数 7, 刷卡数 5 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-精确] 26349119423003552366 ¥189.50 ↔ 微信图片_20260608145321_282_42.jpg ¥189.50 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-精确] 26349119343000331314 ¥167.00 ↔ 微信图片_20260608145319_280_42.jpg ¥167.00 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-贪心] 26342000001715702281 ¥536.00 → 微信图片_20260608145320_281_42.jpg ¥535.86 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-贪心] 26349119423003550275 ¥117.50 → 微信图片_20260608145323_283_42.jpg ¥235.00 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-贪心] 26349119423003595208 ¥117.50 → 微信图片_20260608145323_283_42.jpg ¥235.00 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-贪心] 26349119343000331138 ¥115.50 → 微信图片_20260608145318_279_42.jpg ¥231.00 +2026-06-09 16:20:11 [INFO ] matcher: [一对多-贪心] 26349119343000335414 ¥115.50 → 微信图片_20260608145318_279_42.jpg ¥231.00 +2026-06-09 16:20:11 [INFO ] matcher: 匹配完成: 5 条支付记录, 7/7 张发票已关联 +2026-06-09 16:20:11 [INFO ] extractor: 差旅发票: 7 张, 普通发票: 0 张 +2026-06-09 16:20:11 [INFO ] fill_consumable_doc: 纯差旅发票，跳过易耗品出库单生成 diff --git a/src/web/uploads/af9065343bc3/微信图片_20260608145318_279_42.jpg b/src/web/uploads/af9065343bc3/微信图片_20260608145318_279_42.jpg new file mode 100644 index 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import sys + + if len(sys.argv) > 1: + run_task(sys.argv[1]) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..401aa00 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,19 @@ +"""配置模块单元测试""" + +from src.config import load_config + + +class TestLoadConfig: + """配置加载""" + + def test_returns_dict(self) -> None: + config = load_config() + assert isinstance(config, dict) + + def test_has_username(self) -> None: + config = load_config() + assert "username" in config + + def test_has_password(self) -> None: + config = load_config() + assert "password" in config diff --git a/tests/test_invoice.py b/tests/test_invoice.py new file mode 100644 index 0000000..f5c16b1 --- /dev/null +++ b/tests/test_invoice.py @@ -0,0 +1,89 @@ +"""发票模块单元测试 + +覆盖发票分类、CSV 列定义、常量校验。 +""" + +from src.doc.invoice import ( + INVOICE_LEVEL_COLUMNS, + INVOICE_TYPE_GENERAL, + INVOICE_TYPE_HOTEL, + INVOICE_TYPE_TRAIN, + INVOICE_TYPE_TRAVEL, + PAYMENT_RECORD_COLUMNS, + classify_invoice_batch, + is_travel_invoice, +) + + +class TestInvoiceConstants: + """发票常量校验""" + + def test_travel_types_contain_train(self) -> None: + assert INVOICE_TYPE_TRAIN in INVOICE_TYPE_TRAVEL + + def test_travel_types_contain_hotel(self) -> None: + assert INVOICE_TYPE_HOTEL in INVOICE_TYPE_TRAVEL + + def test_general_not_in_travel(self) -> None: + assert INVOICE_TYPE_GENERAL not in INVOICE_TYPE_TRAVEL + + def test_invoice_columns_not_empty(self) -> None: + assert len(INVOICE_LEVEL_COLUMNS) > 0 + + def test_payment_columns_not_empty(self) -> None: + assert len(PAYMENT_RECORD_COLUMNS) > 0 + + +class TestIsTravelInvoice: + """差旅发票判断""" + + def test_train_is_travel(self) -> None: + assert is_travel_invoice(INVOICE_TYPE_TRAIN) is True + + def test_hotel_is_travel(self) -> None: + assert is_travel_invoice(INVOICE_TYPE_HOTEL) is True + + def test_general_not_travel(self) -> None: + assert is_travel_invoice(INVOICE_TYPE_GENERAL) is False + + def test_unknown_not_travel(self) -> None: + assert is_travel_invoice("未知类型") is False + + +class TestClassifyInvoiceBatch: + """发票分类""" + + def test_empty_list(self) -> None: + result = classify_invoice_batch([]) + assert result == {"travel": [], "general": []} + + def test_all_travel(self) -> None: + invoices = [ + {"发票类型": INVOICE_TYPE_TRAIN, "发票号码": "001"}, + {"发票类型": INVOICE_TYPE_HOTEL, "发票号码": "002"}, + ] + result = classify_invoice_batch(invoices) + assert len(result["travel"]) == 2 + assert len(result["general"]) == 0 + + def test_all_general(self) -> None: + invoices = [ + {"发票类型": INVOICE_TYPE_GENERAL, "发票号码": "001"}, + ] + result = classify_invoice_batch(invoices) + assert len(result["travel"]) == 0 + assert len(result["general"]) == 1 + + def test_mixed(self) -> None: + invoices = [ + {"发票类型": INVOICE_TYPE_TRAIN, "发票号码": "001"}, + {"发票类型": INVOICE_TYPE_GENERAL, "发票号码": "002"}, + ] + result = 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日常报销录入 | `/expen/common/common?v=4.0` | 目标录入页面 | - ---- - -## 二、数据准备 - -### 2.1 发票数据 CSV - -脚本从 `invoice_summary.csv`（GBK 编码）读取发票数据，CSV 需包含以下列： - -| 列名 | 说明 | 示例 | -|------|------|------| -| 序号 | 发票序号 | 1, 2, 3... | -| 发票号码 | 发票编号 | 26442000005432652421 | -| 开票日期 | 发票开具日期 | 2026/5/18 | -| 项目名称 | 采购项目名称 | 电阻一批 | -| 规格型号 | 规格型号 | — | -| 价税合计 | 发票金额 | 2900.00 | -| 销售方名称 | 商户/销售方 | 佛山市泓宇芯科技有限公司 | -| 人员姓名 | 报销人 | 王建锋（默认值） | -| 刷卡日期 | 公务卡消费日期 | 2026/5/18 → 自动转为 2026-05-18 | -| 公务卡号 | 公务卡卡号 | 6282880139161682（默认值） | -| 刷卡金额 | 实际刷卡金额 | 2900.00 | -| 备注 | 备注信息 | — | -| 工号 | 人员工号 | 202407021（默认值） | - -### 2.2 附件文件 - -脚本自动扫描当前工作目录下所有 `.pdf` 文件，按文件名排序后与发票一一对应上传。确保 PDF 文件名与发票顺序一致。 - ---- - -## 三、脚本执行流程 - -### 运行方式 - -```bash -python reimburse.py --data invoice_summary.csv -``` - -支持命令行覆盖配置： - -```bash -python reimburse.py \ - --data invoice_summary.csv \ - --username 202407021 \ - --password "your_password" \ - --user-data-dir browser_profile -``` - -### 执行步骤 - -脚本按以下顺序自动执行，截图保存在 `images/` 目录： - -``` -Step 0: 登录信息门户 - ├── 访问 SSO 登录页 - ├── 填写工号 + 密码 - ├── 勾选用户协议复选框 - ├── 点击"登录"按钮 - └── 等待跳转到信息门户 (zs-uip/oshall/portal) - -Step 1: 进入报销系统 - ├── 点击"财务系统"快捷入口 - ├── 等待新标签页打开 (含 dddl 或 210.45.32.214) - ├── 切换到新标签页 - ├── 关闭旧标签页 - ├── 通过 a:has(img[src*="wlbx"]) 定位"网络报销"链接 - ├── 提取链接 URL 并导航 - ├── 等待"报销录入"文本出现 - └── 导航到 /expen/common/common?v=4.0 - -Step 2: 创建新报销单 - ├── 等待 2 秒 - ├── 点击 button:has-text("新增") - └── 等待表单加载 3 秒 - -Step 3: 填写基本信息 - ├── 填写 #EXPENEXPLAIN → "元器件采购报销" - ├── 点击 #PROJECTCODE 打开项目选择弹窗 - ├── 在 #promodal .fixed-table-body tbody tr 中点击第一行 - └── 点击 #saveAndNext 进入下一步 - -Step 4: 录入报销明细（一条总明细） - ├── 计算所有发票的刷卡金额合计 - ├── 点击 #insertDetail 打开增加明细弹窗 - ├── 点击 #economicscode2 打开经济科目选择 - ├── 在 #econmodal 中选择第 3 行经济科目 - ├── 填写单据数 = 发票张数 - ├── 填写报销总金额 = 刷卡金额合计 - └── 点击 #detailAdd 确认 - -Step 5: 录入支付方式（逐张发票） - ├── 点击 "下一步(支付方式)" - ├── 对每张发票循环: - │ ├── 点击 #insertPay - │ ├── 填写 #personid2 (工号) - │ ├── 填写 #accountname2 (姓名) - │ ├── 填写 #receiptdate2 (刷卡日期) - │ ├── 填写 #localaccount2 (固定卡号: 6282880139161682) - │ ├── 填写 #receiptmoney2 (刷卡金额) - │ ├── 填写 #money2 (实报金额 = 刷卡金额) - │ ├── 填写 #merchant2 (销售方名称) - │ ├── 填写 #smark2 (备注) - │ └── 点击 #payAdd 确认 - └── 所有发票录入完成 - -Step 6: 上传附件（逐张发票） - ├── 点击 #next3 切换到附件清单页面 - ├── 对每张发票循环: - │ ├── 点击 #insertAcc 打开附件弹窗 - │ ├── select_option #fjlx → '1' (发票类型) - │ ├── 填写 #fpsmxx (项目名称 - 发票号码) - │ ├── set_input_files #file (对应 PDF 文件) - │ └── 点击 #cjtj 确认 - └── 所有附件上传完成 - -提交阶段: - └── 点击 #submit (当前已注释，需手动取消注释) -``` - ---- - -## 四、页面元素速查 - -### 基本信息页 (Step 3) - -| 字段 | 选择器 | 操作 | -|------|--------|------| -| 报销说明 | `#EXPENEXPLAIN` | fill | -| 项目代码 | `#PROJECTCODE` | click → 弹窗选择 | -| 项目弹窗 | `#promodal .fixed-table-body tbody tr` | 点击第一行 | -| 下一步按钮 | `#saveAndNext` | click | - -### 报销明细页 (Step 4) - -| 字段 | 选择器 | 操作 | -|------|--------|------| -| 增加按钮 | `#insertDetail` | click | -| 经济事项代码 | `#economicscode2` | click → 弹窗选择 | -| 经济科目弹窗 | `#econmodal .fixed-table-body tbody tr` | 点击第 3 行 | -| 单据数 | `input[name="expenPwCommondetail.HOWBILLS"]` | fill | -| 报销总金额 | `#je_zwzcdz` | fill | -| 确定按钮 | `#detailAdd` | click | - -### 支付方式页 (Step 5) - -| 字段 | 选择器 | 操作 | -|------|--------|------| -| 增加按钮 | `#insertPay` | click | -| 人员编号 | `#personid2` | fill | -| 人员姓名 | `#accountname2` | fill | -| 刷卡日期 | `#receiptdate2` | fill | -| 公务卡号 | `#localaccount2` | fill (固定值) | -| 刷卡金额 | `#receiptmoney2` | fill | -| 实报金额 | `#money2` | fill | -| 商户 | `#merchant2` | fill | -| 备注 | `#smark2` | fill | -| 确定按钮 | `#payAdd` | click | - -### 附件清单页 (Step 6) - -| 字段 | 选择器 | 操作 | -|------|--------|------| -| 增加按钮 | `#insertAcc` | click | -| 附件类型 | `#fjlx` | select_option → '1'(发票) | -| 附件说明 | `#fpsmxx` | fill | -| 文件上传 | `#file` | set_input_files | -| 确定按钮 | `#cjtj` | click | - -### 提交 - -| 操作 | 选择器 | -|------|--------| -| 提交按钮 | `#submit` | -| 提交按钮(备用) | `#submit2` | - ---- - -## 五、数据流向 - -``` -invoice_summary.csv (GBK) - │ - ▼ load_invoice_data() - │ - ├── 读取 CSV 行 - ├── 日期格式转换 (2026/5/18 → 2026-05-18) - ├── 填充默认值 (姓名/卡号/工号) - └── 输出: list[dict] - │ - ▼ add_reimburse_items() - ├── 计算: card_amount = sum(所有发票刷卡金额) - ├── 单据数 = len(invoices) - └── 录入 1 条总明细 - │ - ▼ fill_payment() - └── 对每张发票录入 1 条支付记录 - │ - ▼ upload_attachments() - ├── 扫描 *.pdf 文件 - └── 按索引匹配发票 → PDF，逐张上传 -``` - ---- - -## 六、关键设计说明 - -### 6.1 浏览器复用 - -脚本使用 `launch_persistent_context` 持久化浏览器上下文，登录状态保存在 `browser_profile/` 目录。再次运行时复用已有会话，无需重复登录。 - -### 6.2 明细录入策略 - -脚本采用"一条总明细"策略：将所有发票合并为一条报销明细，报销总金额为所有发票刷卡金额之和，单据数为发票总张数。支付方式则逐张发票分别录入，每张发票对应一条支付记录。 - -### 6.3 经济科目选择 - -脚本在经济科目弹窗中固定选择第 3 行。如需更改科目，修改 `rows[2]` 的索引即可。 - -### 6.4 项目选择 - -脚本在项目选择弹窗中固定选择第 1 行。如需更改项目，修改 `first_row` 的选择逻辑即可。 - -### 6.5 网络报销链接动态获取 - -单点登录页的"网络报销"链接参数每次不同，脚本通过 `a:has(img[src*="wlbx"])` 精确定位链接，动态提取 `href` 属性后导航，不硬编码 URL。 - -### 6.6 提交控制 - -脚本默认注释了 `bot.submit()` 调用。完成所有录入后停留在附件清单页面，需人工确认数据无误后，取消注释 `bot.submit()` 再运行，或手动点击提交按钮。 - ---- - -## 七、日志与调试 - -### 日志输出 - -- 控制台实时输出（DEBUG 级别） -- 文件日志：`reimburse.log`（UTF-8 编码） - -### 截图保存 - -每个关键步骤自动截图到 `images/` 目录： - -| 截图文件 | 对应步骤 | -|----------|----------| -| `debug_portal_loaded.png` | 登录成功 | -| `debug_step3_project_modal.png` | 项目弹窗打开 | -| `debug_step3_project_selected.png` | 项目选择完成 | -| `debug_step3_done.png` | 基本信息完成 | -| `debug_after_add_click.png` | 点击新增后 | -| `debug_item_total.png` | 总明细录入完成 | -| `debug_step5_done.png` | 支付方式完成 | -| `debug_step6_done.png` | 附件上传完成 | -| `debug_submitted.png` | 提交完成 | -| `debug_error.png` | 异常状态 | - -### 超时设置 - -- 页面默认超时：30 秒 -- 登录门户等待：最多 30 秒 -- 单点登录页等待：最多 15 秒 - ---- - -## 八、常见问题 - -| 问题 | 原因 | 解决方案 | -|------|------|----------| -| 登录超时 | SSO 需要手动验证码/微信扫码 | 手动完成验证后脚本继续 | -| 未找到财务系统入口 | 门户页面结构变化 | 检查 `images/debug_*` 截图定位 | -| 经济科目选择失败 | 弹窗加载延迟 | 检查超时设置，增加等待时间 | -| 附件上传失败 | PDF 文件不存在或路径错误 | 确认 PDF 在当前工作目录 | -| 金额不匹配 | 明细合计 ≠ 支付合计 | 检查 CSV 数据中刷卡金额 | -| 提交被拦截 | 必填项为空 | 检查 `reimburse.log` 定位失败步骤 | \ No newline at end of file