feat: comprehensive v2 upgrade — streaming, error KB, file upload, layout analysis
Major changes: - Streaming: LLM统一 _BaseLLM 接口 (invoke + stream), generate/modify/correct 节点使用 get_stream_writer() 实现逐字输出, UI 节点平铺展开自动折叠 - Prompt外部化: 7个prompt拆分到 prompts/*.md, loader.py 支持热重载 - 错误自增长: backend/error_kb.py — 指纹去重 + ChromaDB持久化, correct_jrxml→validate 通过时自动入库, retrieve同时搜索错误KB - 文件上传: backend/file_parser.py — PDF/DOCX/图片/文本解析, 侧边栏多文件上传, 文本自动注入下一条消息 - A4模板识别: backend/layout_analyzer.py — 三种模式(完整A4/行片段修改/行片段新建), PaddleOCR元素提取 + 行分组 + JRXML section匹配 - 会话历史下载: jrxml_versions版本追踪 + 侧边栏历史版本下载按钮 - 预览修复: route_after_save跳过预览/导出意图的验证循环 - Ctrl+C修复: JS注入拦截Streamlit裸c键清缓存 Docs: CLAUDE.md (完整项目文档), ROADMAP.md (改进路线图) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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# CLAUDE.md — JRXML 生成代理
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## 项目概述
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一个**本地桌面应用**，通过自然语言多轮对话帮助非技术用户创建 JasperReports 模板（JRXML 文件）。核心技术栈：Streamlit UI + LangGraph 状态机 + LLM 生成/修改 + 自动验证修正循环。
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**一句话**：用户用中文描述报表需求 → LLM 生成 JRXML → 自动验证 → 失败则自动修正(最多3次) → 返回可编译的 JRXML 文件。
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## 启动命令
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```bash
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# 终端 1 — 验证服务（必须先启动）
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python -m uvicorn validation_service.main:app --port 8001 --host 0.0.0.0
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# 终端 2 — Streamlit UI
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STREAMLIT_SERVER_HEADLESS=true streamlit run app.py --server.port 8501
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```
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浏览器打开 `http://localhost:8501`。
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## 当前配置（.env）
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- **LLM**: `cloud` / `anthropic` → MiniMax Anthropic 兼容 API (`MiniMax-M2.7`)
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- Base URL: `https://api.minimaxi.com/anthropic`
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- 认证: 通过 `OPENAI_API_KEY` 传入 Anthropic SDK（注意不是 `ANTHROPIC_API_KEY`）
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- 绕过代理: 代码中设 `NO_PROXY=*`
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- **嵌入模型**: `local` / `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`
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- **向量库**: ChromaDB 持久化在 `./db/chroma`
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- **验证服务**: FastAPI `localhost:8001`
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## 架构
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```
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app.py (Streamlit UI)
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│ run_agent(user_input)
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│ 功能: 流式输出/节点平铺/文件上传/历史下载/预览/Ctrl+C修复
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▼
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agent/graph.py (LangGraph 状态机)
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│ 节点流程:
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│ load_session → process_input → manage_context → save_state_snapshot
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│ → classify_intent (8种意图路由)
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│ ├─ retrieve → generate → save_session → validate → ... → finalize
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│ ├─ modify_jrxml → save_session → validate → ... → finalize
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│ ├─ handle_consult / handle_undo / handle_reset → finalize
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│ └─ preview/export → save_session → finalize (跳过验证)
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│
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│ 验证修正循环: validate ─fail─► explain_error ─► correct_jrxml ─► validate
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│ ▲ │
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│ └──────── (retry < MAX_RETRY=3) ───────────────────┘
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│
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├──► prompts/loader.py Prompt 外部化：7 个 .md 文件热重载
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├──► backend/llm.py LLM 工厂: Anthropic SDK / OpenAI / Ollama (统一 stream/invoke)
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├──► backend/rag_adapter.py 语义搜索: ChromaDB + SentenceTransformer
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├──► backend/error_kb.py 错误知识库: 指纹去重 + ChromaDB 持久化
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├──► backend/file_parser.py 文件解析: PDF/DOCX/图片/文本
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├──► backend/layout_analyzer.py A4布局分析: OCR + 行分组 + JRXML行匹配
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├──► backend/validation.py HTTP 客户端: POST /validate
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├──► backend/session.py 会话持久化: JSON 文件 CRUD
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└──► validation_service/ 独立 FastAPI: 结构检查 + XSD 校验
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```
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## 关键文件映射
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| 文件 | 职责 | 修改频率 |
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|------|------|---------|
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| `app.py` | Streamlit UI 入口，聊天界面 + 侧边栏 + 下载 + 文件上传 | **高** |
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| `agent/state.py` | AgentState 类型定义（~23 字段，含 jrxml_versions/last_error_case） | 低 |
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| `agent/nodes.py` | 14 个工作流节点 + 流式生成 + 错误记录 | **高** |
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| `agent/graph.py` | 状态图编译 + 路由函数（预览跳过验证） | 中 |
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| `prompts/loader.py` | Prompt 加载器（从 .md 文件热重载） | 低 |
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| `prompts/*.md` | 7 个独立 Prompt 模板 | **高** |
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| `backend/llm.py` | LLM 工厂，统一 `_BaseLLM` 接口（invoke + stream） | 中 |
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| `backend/rag_adapter.py` | RAGSearcher 单例，语义搜索接口 | 中 |
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| `backend/error_kb.py` | ErrorKB — 错误指纹去重 + ChromaDB 持久化 + 语义检索 | 中 |
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| `backend/file_parser.py` | 文件解析: PDF(pdfplumber)/DOCX(python-docx)/图片(PIL+PaddleOCR可选)/文本 | 中 |
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| `backend/layout_analyzer.py` | A4模板分析: 比例检测/PaddleOCR元素提取/行分组/JRXML行匹配 | 中 |
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| `backend/embeddings.py` | 嵌入模型工厂 (HuggingFace/OpenAI) | 低 |
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| `backend/validation.py` | 验证服务 HTTP 客户端 | 低 |
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| `backend/session.py` | 会话 JSON 文件 CRUD | 低 |
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| `validation_service/main.py` | FastAPI 验证服务 | 低 |
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| `scripts/init_kb.py` | 知识库初始化/模型下载 | 低 |
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## 关键约定
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1. **LLM 调用接口**: 所有节点通过 `get_llm().invoke(prompt)` 同步调用，或用 `get_llm().stream(prompt)` 流式调用。三个后端（Anthropic/OpenAI/Ollama）通过 `_BaseLLM` 统一接口。
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2. **流式生成**: generate/modify_jrxml/correct_jrxml 使用 `get_stream_writer()` 发射自定义事件，UI 通过 `stream_mode=["updates", "custom"]` 捕获逐字输出。
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3. **JRXML 提取**: `_extract_jrxml()` 处理 LLM 响应 —— 去掉 markdown 代码块标记，提取 XML 内容。
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4. **状态持久化**: 每个会话存为 `sessions/{session_id}.json`，LangGraph 节点间通过 AgentState dict 传递。
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5. **Token 计数**: 使用 `tiktoken` (gpt-4o encoder) 估算，不管实际模型是什么。
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6. **RAG 子模块**: `rag/` 是一个独立的 git submodule，其内部的生成产物 (`models/`, `embeddings/`, `chroma_db/`, `jrxml_source_chunks/`) 不在 git 中。
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## Prompt 模板位置
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所有 Prompt 在 `prompts/` 目录，`.md` 文件可直接编辑，无需重启应用：
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| 文件 | 用途 |
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|------|------|
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| `prompts/intent_classify.md` | 8 分类意图识别 |
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| `prompts/initial_generation.md` | 首次生成 JRXML |
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| `prompts/modification.md` | 修改现有 JRXML |
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| `prompts/correction.md` | 自动修正错误 |
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| `prompts/explain_error.md` | 错误转人话 |
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| `prompts/compression.md` | 对话压缩摘要 |
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| `prompts/consult.md` | 咨询解答 |
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## 新增功能 (v2)
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### 流式输出 + 节点平铺
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- LLM 生成时逐字展示 XML（不再是空白等待）
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- 节点以"处理过程"折叠区展开，不相互覆盖
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- 完成后自动折叠，展示总结卡片
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### 错误自增长知识库
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- `backend/error_kb.py` — ChromaDB 集合 `jrxml_error_cases`
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- 错误指纹去重（标准化 + MD5）：相同结构错误不重复录入
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- 记录内容：错误信息 + 修正前后 JRXML + 修正 prompt + 工具链
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- `retrieve` 节点自动注入历史修正案例
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- 流程：correct_jrxml 保存 last_error_case → validate 通过时自动入库
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### 文件上传
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- 侧边栏多文件上传（可逐文件移除）
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- 支持: PDF(pdfplumber+PIL) / DOCX(python-docx) / 图片(PIL+PaddleOCR可选) / 纯文本
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- 上传文本自动注入下一条消息前缀
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- 根据 `can_use_vision()` 判断是否走原生多模态（当前 MiniMax 不支持）
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### A4 模板识别
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- `backend/layout_analyzer.py` — 三种处理路径：
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- **完整 A4**: 比例匹配 + OCR 元素 → 全量布局描述
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- **行片段 + 有现有报表**: 行匹配到 JRXML section → 定位修改
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- **行片段 + 无现有报表**: 按 A4 模板生成完整报表
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- PaddleOCR（可选安装）提供精确元素位置/字号
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- 行分组：Y 轴容差自动聚类；行匹配：文本相似度搜索 JRXML band
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### 会话历史下载
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- `AgentState.jrxml_versions` 追踪每次生成/修改的版本
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- 侧边栏"历史版本"折叠区，每版本独立下载按钮
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### 预览修复
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- `route_after_save` 新增意图判断：预览/导出跳过验证直通 finalize
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### Ctrl+C 修复
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- JS 注入拦截 Streamlit 裸 `c` 键清缓存，保留 Ctrl+C 复制
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## 已知注意点
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- **Anthropic SDK**: 使用原始 `anthropic` 包（非 `langchain-anthropic`），因为需要直连 MiniMax 兼容端点。`backend/llm.py:31` 创建的 `Anthropic()` 必须传入 `api_key`，SDK 不会自动读 `OPENAI_API_KEY`。
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- **Windows 环境**: NO_PROXY 设为 `*` 避免代理干扰 MiniMax API。
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- **Streamlit headless**: Windows 下必须设 `STREAMLIT_SERVER_HEADLESS=true` 跳过邮箱采集提示。
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- **验证服务结构检查**: 字段引用一致性 (`$F{field}` vs `<field>` 声明)、SQL SELECT 存在性、pageWidth/pageHeight/name 属性。
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- **XSD 校验可选**: 需要 `validation_service/schemas/jasperreport_7_0_6.xsd` 存在。
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- **rag 子模块**: 内部有独立的管线脚本（`batch_chunker.py` → `embed_chunks.py` → `import_to_chroma.py`），通常不需要在主项目中运行。
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- **PaddleOCR 可选**: A4 模板精确识别需要 `pip install paddleocr`，未安装时仅返回图片元信息。
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# 改进路线图
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## 阶段一：代码质量（低风险，快速交付）
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### 1. Prompt 拆分 ✓
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- [x] 创建 `prompts/` 目录
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- [x] 7 个 prompt 各拆为独立 `.md` 文件
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- [x] `nodes.py` 改为从文件加载
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- [x] 支持热重载（文件变更无需重启）
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### 2. 修复无效代码 ✓
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- [x] `backend/llm.py` — `get_num_tokens()` 修复为正确 API
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- [x] `backend/embeddings.py` — 修复 docstring 函数名不一致
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- [x] `backend/llm.py` — 统一 LLM 接口基类 `_BaseLLM`
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---
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## 阶段二：用户体验（核心改造）
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### 3. 流式输出 + 节点平铺 ✓
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- [x] `backend/llm.py` — LLM 工厂支持 `stream()` 统一接口
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- [x] `agent/nodes.py` — generate/modify/correct 节点使用流式 + `get_stream_writer()`
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- [x] `app.py` — 使用 `stream_mode=["updates", "custom"]` 捕获流式事件
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- [x] 节点状态平铺（处理过程 expander 逐节点展示）
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- [x] 流式完成后节点自动折叠
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- [x] 完成后单独展示「总结卡片」
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### 4. 错误自增长知识库 ✓
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- [x] `backend/error_kb.py` — ErrorKB 类（ChromaDB 持久化）
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- [x] 错误指纹去重（标准化 + MD5）
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- [x] `correct_jrxml` — 保存修正前状态到 `last_error_case`
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- [x] `validate` — 修正成功时自动记录（仅新错误，自动去重）
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- [x] `retrieve` — 搜索错误知识库，注入历史修正案例
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- [x] 记录内容：错误 + 修正前后 JRXML + prompt + 工具链 + 模型
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### 5. 文件上传支持 ✓
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- [x] `backend/file_parser.py` — 统一解析接口
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- [x] 图片 → PIL 元信息 + PaddleOCR（可选安装后自动识别）
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- [x] PDF → pdfplumber / PyMuPDF 文本提取
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- [x] DOCX → python-docx 文本提取
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- [x] 纯文本 (.txt/.csv/.json/.xml) → 直接读取
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- [x] `can_use_vision()` — 根据模型名判断是否支持原生多模态
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- [x] `app.py` — 侧边栏文件上传组件（多文件，可移除）
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- [x] 上传文本自动注入下一条消息前缀
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### 6. A4 图片模板识别 ✓
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- [x] `backend/layout_analyzer.py` — 完整布局分析模块
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- [x] A4 比例判定：exact(±3%) / close(±8%) / not_a4 三档
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- [x] PaddleOCR 布局分析：逐元素提取坐标(x,y,w,h)、字号、文本
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- [x] 行分组：Y 轴容差自动聚类
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- [x] 结构化输出：`图片模板共 X 行，第 1 行有 Y 个元素，其中元素 a 长...高...字体...内容是...`
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- [x] 检测门槛：≥2 个 OCR 元素 + A4 比例 → 标记为模板
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- [x] `app.py` — 上传图片/PDF 时自动触发布局分析，替换为布局描述
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### 7. 会话历史 JRXML 下载 ✓
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- [x] `agent/state.py` — 新增 `jrxml_versions` 字段
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- [x] `agent/nodes.py` — `finalize` 节点追加版本记录
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- [x] `app.py` — 侧边栏"历史版本"折叠区，每版本独立下载按钮
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### 8. 预览功能修复 ✓
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- [x] 根因：`preview_report` 路由到 `save_session` → `validate` 触发不必要的验证修正循环
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- [x] 修复：`route_after_save` — 预览/导出意图跳过验证直接 `finalize`
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---
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## 阶段三：细节修复
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### 9. Ctrl+C 修复 ✓
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- [x] `app.py` — 注入 JS 拦截裸 `c` 键，保留 Ctrl+C 复制行为
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---
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## 执行顺序建议
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```
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1. Prompt 拆分 ──► 2. 无效代码修复
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│
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▼
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3. 流式输出 + 节点平铺
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│
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┌─────────────┼─────────────┐
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▼ ▼ ▼
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4. 错误自增长 5. 文件上传 7. 下载历史
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│ │
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▼ ▼
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6. A4 模板识别 8. 预览修复
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│
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▼
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9. Ctrl+C 修复
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```
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阶段一立即可做，无外部依赖。阶段二是主要工作量。阶段三是收尾。
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+7
-1
@@ -71,7 +71,11 @@ def route_after_undo(state: AgentState) -> Literal["save_session"]:
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return "save_session"
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def route_after_save(state: AgentState) -> Literal["validate"]:
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def route_after_save(state: AgentState) -> Literal["validate", "finalize"]:
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# 预览/导出意图跳过验证，直接完成
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intent = state.get("intent", "")
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if intent in ("preview_report", "export_pdf", "export_jrxml"):
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return "finalize"
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return "validate"
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@@ -222,4 +226,6 @@ def create_initial_state() -> AgentState:
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updated_at="",
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intent="",
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history_states=[],
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jrxml_versions=[],
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last_error_case={},
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)
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+94
-129
@@ -12,6 +12,7 @@ from dotenv import load_dotenv
|
||||
from agent.state import AgentState
|
||||
from backend.llm import get_llm
|
||||
from backend.validation import validate_jrxml
|
||||
from prompts.loader import load_prompt
|
||||
|
||||
load_dotenv()
|
||||
|
||||
@@ -20,119 +21,6 @@ CONTEXT_MAX_TOKENS = int(os.getenv("CONTEXT_MAX_TOKENS", "6000"))
|
||||
CONTEXT_KEEP_RECENT = int(os.getenv("CONTEXT_KEEP_RECENT", "4"))
|
||||
HISTORY_MAX_SNAPSHOTS = int(os.getenv("HISTORY_MAX_SNAPSHOTS", "10"))
|
||||
|
||||
# ============================================================
|
||||
# 意图分类提示词（约 180 tokens，控制在 200 token 以内）
|
||||
# ============================================================
|
||||
INTENT_CLASSIFY_PROMPT = """你是意图分类器。根据用户输入判断意图，只输出意图名称。
|
||||
|
||||
当前有报表：{has_report}
|
||||
用户输入：{user_input}
|
||||
|
||||
可选意图：
|
||||
- initial_generation（新建报表，或无报表时的任何需求）
|
||||
- modify_report（修改当前已有报表）
|
||||
- preview_report（预览/查看当前报表）
|
||||
- export_pdf（导出PDF文件）
|
||||
- export_jrxml（下载/导出/保存JRXML文件）
|
||||
- undo_modification（撤销/回退上一步修改）
|
||||
- consult_question（咨询JasperReports相关知识或使用问题）
|
||||
- reset_session（清空/重置/重新开始）
|
||||
|
||||
意图名称："""
|
||||
|
||||
# ============================================================
|
||||
# 咨询回答提示词
|
||||
# ============================================================
|
||||
CONSULT_PROMPT = """你是 JasperReports 专家。用简洁清晰的中文回答用户关于 JasperReports 的问题。
|
||||
|
||||
用户问题：{question}
|
||||
|
||||
直接回答："""
|
||||
|
||||
# ============================================================
|
||||
# 原有提示词（不变）
|
||||
# ============================================================
|
||||
INITIAL_GENERATION_PROMPT = """你是一位资深 JasperReports 工程师。根据以下参考模板和用户需求，生成一个完整、可编译的 JRXML 文件。
|
||||
JRXML 必须兼容 JasperReports 7.0.6 schema。
|
||||
|
||||
关键规则：
|
||||
- 只输出 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- 报表正文中使用的每个字段必须在 <field name="..."> 部分中声明。
|
||||
- 根元素为 <jasperReport>，包含正确的 xmlns 属性。
|
||||
- 包含 <queryString>，在 <![CDATA[...]]> 中包含 SQL 查询。
|
||||
- 确保所有交叉引用（字段名称、band 元素）保持一致。
|
||||
|
||||
参考模板和组件：
|
||||
{context}
|
||||
|
||||
用户需求：
|
||||
{user_request}
|
||||
"""
|
||||
|
||||
MODIFICATION_PROMPT = """你是一位资深 JasperReports 工程师。用户想要修改一个现有的、可编译的 JRXML 报表。精确应用请求的更改到当前 JRXML 并输出完整修改后的 JRXML。
|
||||
|
||||
关键规则：
|
||||
- 只输出完整修改后的 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- 保留所有未被更改的现有结构。
|
||||
- 结果必须继续与 JasperReports 7.0.6 兼容。
|
||||
- 报表正文中使用的每个字段必须在 <field> 部分中声明。
|
||||
- 如果添加新字段，正确声明它们。
|
||||
- 确保 <queryString> 是 <![CDATA[...]]> 中有效的 SQL。
|
||||
|
||||
当前 JRXML：
|
||||
{current_jrxml}
|
||||
|
||||
对话历史：
|
||||
{conversation_history}
|
||||
|
||||
用户的修改请求：
|
||||
{modification_request}
|
||||
"""
|
||||
|
||||
CORRECTION_PROMPT = """你是一位资深 JasperReports 工程师。你生成的 JRXML 文件编译失败。分析错误并修复 JRXML。
|
||||
|
||||
关键规则：
|
||||
- 只输出完整修复后的 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- JRXML 必须与 JasperReports 7.0.6 兼容。
|
||||
- 解决下面列出的特定错误。
|
||||
|
||||
当前 JRXML（带错误）：
|
||||
{current_jrxml}
|
||||
|
||||
编译错误：
|
||||
{error_msg}
|
||||
|
||||
错误的自然语言解释：
|
||||
{explanation}
|
||||
|
||||
立即生成修正后的 JRXML：
|
||||
"""
|
||||
|
||||
EXPLAIN_PROMPT = """你是一位 JasperReports 专家。用普通非技术语言解释以下 JRXML 编译错误，让业务用户能够理解。
|
||||
|
||||
错误消息：
|
||||
{error_msg}
|
||||
|
||||
当前 JRXML 片段（前 80 行）：
|
||||
{jrxml_snippet}
|
||||
|
||||
用 2-3 句话解释哪里出了问题以及如何修复：
|
||||
"""
|
||||
|
||||
COMPRESSION_PROMPT = """你是一个信息压缩助手。以下是用户与报表生成助手之间的历史对话记录，请将其压缩为一份简洁的摘要（不超过200字）。
|
||||
|
||||
摘要必须保留以下关键信息：
|
||||
- 用户提出的所有报表需求点（字段、标题、分组、汇总等）
|
||||
- 用户提出的所有修改要求及其顺序
|
||||
- 当前报表的核心结构（字段列表、标题、分组方式）
|
||||
- 任何特殊要求或约束条件
|
||||
|
||||
只输出摘要文本，不要添加任何解释或标记。
|
||||
|
||||
对话记录：
|
||||
{conversation_text}
|
||||
"""
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 核心工作流节点
|
||||
@@ -191,7 +79,7 @@ def classify_intent(state: AgentState) -> Dict:
|
||||
intent = "initial_generation"
|
||||
try:
|
||||
llm = get_llm()
|
||||
prompt = INTENT_CLASSIFY_PROMPT.format(
|
||||
prompt = load_prompt("intent_classify").format(
|
||||
has_report=has_report,
|
||||
user_input=user_input[:500],
|
||||
)
|
||||
@@ -222,7 +110,7 @@ def handle_consult(state: AgentState) -> Dict:
|
||||
user_input = state.get("user_input", "")
|
||||
try:
|
||||
llm = get_llm()
|
||||
prompt = CONSULT_PROMPT.format(question=user_input)
|
||||
prompt = load_prompt("consult").format(question=user_input)
|
||||
resp = llm.invoke(prompt)
|
||||
answer = resp.content.strip()
|
||||
except Exception:
|
||||
@@ -332,7 +220,7 @@ def manage_context(state: AgentState) -> Dict:
|
||||
|
||||
try:
|
||||
llm = get_llm()
|
||||
prompt = COMPRESSION_PROMPT.format(conversation_text=conv_text)
|
||||
prompt = load_prompt("compression").format(conversation_text=conv_text)
|
||||
resp = llm.invoke(prompt)
|
||||
new_compressed = resp.content.strip()[:300]
|
||||
except Exception:
|
||||
@@ -421,12 +309,21 @@ def _now_iso() -> str:
|
||||
|
||||
|
||||
def retrieve(state: AgentState) -> Dict:
|
||||
"""在 Chroma 中搜索相关的 JRXML 模板和组件（使用 rag_jrxml 语义分块管线）。"""
|
||||
"""在 ChromaDB + 错误知识库中搜索相关的 JRXML 模板和组件。"""
|
||||
try:
|
||||
from backend.rag_adapter import search_chunks
|
||||
from backend.error_kb import search_error_cases
|
||||
|
||||
user_input = state.get("user_input", "")
|
||||
context = search_chunks(user_input, k=5)
|
||||
|
||||
# 如果有最近错误，同时搜索错误知识库
|
||||
error_msg = state.get("error_msg", "")
|
||||
if error_msg:
|
||||
error_context = search_error_cases(error_msg, k=2)
|
||||
if error_context:
|
||||
context = f"{context}\n\n[历史错误修正案例]\n{error_context}"
|
||||
|
||||
state["retrieved_context"] = context
|
||||
except Exception:
|
||||
state["retrieved_context"] = ""
|
||||
@@ -435,13 +332,19 @@ def retrieve(state: AgentState) -> Dict:
|
||||
|
||||
def generate(state: AgentState) -> Dict:
|
||||
"""根据用户需求和检索到的上下文生成初始 JRXML。"""
|
||||
from langgraph.config import get_stream_writer
|
||||
|
||||
writer = get_stream_writer()
|
||||
llm = get_llm()
|
||||
prompt = INITIAL_GENERATION_PROMPT.format(
|
||||
prompt = load_prompt("initial_generation").format(
|
||||
context=state.get("retrieved_context", ""),
|
||||
user_request=state.get("user_input", ""),
|
||||
)
|
||||
resp = llm.invoke(prompt)
|
||||
jrxml = _extract_jrxml(resp.content)
|
||||
full = []
|
||||
for chunk in llm.stream(prompt):
|
||||
full.append(chunk)
|
||||
writer({"type": "stream", "node": "generate", "text": chunk})
|
||||
jrxml = _extract_jrxml("".join(full))
|
||||
state["current_jrxml"] = jrxml
|
||||
state["conversation_history"].append({"role": "assistant", "content": jrxml})
|
||||
return state
|
||||
@@ -449,6 +352,9 @@ def generate(state: AgentState) -> Dict:
|
||||
|
||||
def modify_jrxml(state: AgentState) -> Dict:
|
||||
"""根据用户的修改请求修改现有 JRXML。"""
|
||||
from langgraph.config import get_stream_writer
|
||||
|
||||
writer = get_stream_writer()
|
||||
llm = get_llm()
|
||||
# 构建对话上下文：压缩摘要 + 最近对话
|
||||
compressed = state.get("compressed_history", "")
|
||||
@@ -459,13 +365,16 @@ def modify_jrxml(state: AgentState) -> Dict:
|
||||
conv_parts.append(json.dumps(recent, ensure_ascii=False, indent=2))
|
||||
conv_text = "\n\n---\n\n".join(conv_parts)
|
||||
|
||||
prompt = MODIFICATION_PROMPT.format(
|
||||
prompt = load_prompt("modification").format(
|
||||
current_jrxml=state.get("current_jrxml", ""),
|
||||
conversation_history=conv_text,
|
||||
modification_request=state.get("user_modification_request", ""),
|
||||
)
|
||||
resp = llm.invoke(prompt)
|
||||
jrxml = _extract_jrxml(resp.content)
|
||||
full = []
|
||||
for chunk in llm.stream(prompt):
|
||||
full.append(chunk)
|
||||
writer({"type": "stream", "node": "modify_jrxml", "text": chunk})
|
||||
jrxml = _extract_jrxml("".join(full))
|
||||
state["current_jrxml"] = jrxml
|
||||
state["conversation_history"].append(
|
||||
{
|
||||
@@ -496,6 +405,29 @@ def validate(state: AgentState) -> Dict:
|
||||
result = validate_jrxml(jrxml)
|
||||
state["status"] = "pass" if result.get("valid") else "fail"
|
||||
state["error_msg"] = result.get("error", "")
|
||||
|
||||
# 修正成功后记录到错误知识库
|
||||
if result.get("valid") and state.get("retry_count", 0) > 0:
|
||||
case = state.get("last_error_case", {})
|
||||
if case and case.get("error_msg"):
|
||||
try:
|
||||
from backend.error_kb import record_error
|
||||
|
||||
recorded = record_error(
|
||||
error_msg=case["error_msg"],
|
||||
bad_jrxml=case.get("bad_jrxml", ""),
|
||||
good_jrxml=jrxml,
|
||||
correction_prompt=case.get("correction_prompt", ""),
|
||||
retry_count=state.get("retry_count", 0),
|
||||
)
|
||||
if recorded:
|
||||
state["conversation_history"].append({
|
||||
"role": "system",
|
||||
"content": f"[系统] 错误案例已记录到知识库（指纹: {case['error_msg'][:40]}...）",
|
||||
})
|
||||
except Exception:
|
||||
pass # 知识库写入不影响主流程
|
||||
|
||||
return state
|
||||
|
||||
|
||||
@@ -506,7 +438,7 @@ def explain_error(state: AgentState) -> Dict:
|
||||
lines = jrxml.split("\n")[:80]
|
||||
snippet = "\n".join(lines)
|
||||
|
||||
prompt = EXPLAIN_PROMPT.format(
|
||||
prompt = load_prompt("explain_error").format(
|
||||
error_msg=state.get("error_msg", "未知错误"),
|
||||
jrxml_snippet=snippet,
|
||||
)
|
||||
@@ -517,14 +449,27 @@ def explain_error(state: AgentState) -> Dict:
|
||||
|
||||
def correct_jrxml(state: AgentState) -> Dict:
|
||||
"""尝试自动修正验证失败的 JRXML。"""
|
||||
from langgraph.config import get_stream_writer
|
||||
|
||||
writer = get_stream_writer()
|
||||
llm = get_llm()
|
||||
prompt = CORRECTION_PROMPT.format(
|
||||
prompt = load_prompt("correction").format(
|
||||
current_jrxml=state.get("current_jrxml", ""),
|
||||
error_msg=state.get("error_msg", ""),
|
||||
explanation=state.get("natural_explanation", ""),
|
||||
)
|
||||
resp = llm.invoke(prompt)
|
||||
jrxml = _extract_jrxml(resp.content)
|
||||
# 保存修正前状态（供 validate 判断是否写入错误知识库）
|
||||
state["last_error_case"] = {
|
||||
"error_msg": state.get("error_msg", ""),
|
||||
"bad_jrxml": state.get("current_jrxml", ""),
|
||||
"correction_prompt": prompt,
|
||||
}
|
||||
|
||||
full = []
|
||||
for chunk in llm.stream(prompt):
|
||||
full.append(chunk)
|
||||
writer({"type": "stream", "node": "correct_jrxml", "text": chunk})
|
||||
jrxml = _extract_jrxml("".join(full))
|
||||
state["current_jrxml"] = jrxml
|
||||
state["retry_count"] = state.get("retry_count", 0) + 1
|
||||
state["conversation_history"].append(
|
||||
@@ -534,8 +479,28 @@ def correct_jrxml(state: AgentState) -> Dict:
|
||||
|
||||
|
||||
def finalize(state: AgentState) -> Dict:
|
||||
"""保存最终验证通过的 JRXML 并更新对话历史。"""
|
||||
state["final_jrxml"] = state.get("current_jrxml", "")
|
||||
"""保存最终验证通过的 JRXML 并更新对话历史 + 版本记录。"""
|
||||
jrxml = state.get("current_jrxml", "")
|
||||
state["final_jrxml"] = jrxml
|
||||
|
||||
if jrxml.strip():
|
||||
versions = state.get("jrxml_versions", [])
|
||||
if not isinstance(versions, list):
|
||||
versions = []
|
||||
intent = state.get("intent", "")
|
||||
label_map = {
|
||||
"initial_generation": "初始生成",
|
||||
"modify_report": "修改",
|
||||
"correct_jrxml": f"自动修正 (第{state.get('retry_count', 1)}次)",
|
||||
}
|
||||
versions.append({
|
||||
"ts": _now_iso(),
|
||||
"jrxml": jrxml,
|
||||
"intent": intent,
|
||||
"label": label_map.get(intent, intent),
|
||||
"status": state.get("status", ""),
|
||||
})
|
||||
state["jrxml_versions"] = versions
|
||||
return state
|
||||
|
||||
|
||||
|
||||
@@ -31,3 +31,9 @@ class AgentState(TypedDict, total=False):
|
||||
# 需求3：意图识别
|
||||
intent: str
|
||||
history_states: List[dict]
|
||||
|
||||
# 需求4：JRXML 版本历史（用于下载历史版本）
|
||||
jrxml_versions: List[dict]
|
||||
|
||||
# 需求5：错误自增长（记录修正前的状态，供 validate 节点判断是否入知识库）
|
||||
last_error_case: dict
|
||||
|
||||
@@ -1,7 +1,15 @@
|
||||
"""Streamlit 多轮对话 UI，用于 JRXML 生成代理。"""
|
||||
"""Streamlit 多轮对话 UI，用于 JRXML 生成代理。
|
||||
|
||||
支持:
|
||||
- 流式输出（LLM 逐字展示）
|
||||
- 节点平铺展开（每个处理阶段独立展示）
|
||||
- 完成后自动折叠节点区
|
||||
- 过程总结卡片
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import streamlit as st
|
||||
|
||||
@@ -23,7 +31,70 @@ st.set_page_config(
|
||||
initial_sidebar_state="expanded",
|
||||
)
|
||||
|
||||
# ---- URL 参数：session_id ----
|
||||
# 阻止 Streamlit 裸 'c' 键清除缓存，保留 Ctrl+C 复制行为
|
||||
st.components.v1.html("""
|
||||
<script>
|
||||
(function() {
|
||||
const parent = window.parent.document;
|
||||
parent.addEventListener('keydown', function(e) {
|
||||
// 仅拦截裸 'c' 键（非 Ctrl/Cmd 组合）
|
||||
if (e.key === 'c' && !e.ctrlKey && !e.metaKey && !e.altKey) {
|
||||
const tag = parent.activeElement ? parent.activeElement.tagName : '';
|
||||
if (tag !== 'INPUT' && tag !== 'TEXTAREA' && !parent.activeElement.isContentEditable) {
|
||||
e.stopImmediatePropagation();
|
||||
e.preventDefault();
|
||||
}
|
||||
}
|
||||
}, true);
|
||||
})();
|
||||
</script>
|
||||
""", height=0)
|
||||
|
||||
# ---- 节点名称 → 中文标签 ----
|
||||
NODE_LABELS = {
|
||||
"load_session": "📂 加载会话",
|
||||
"process_input": "📝 记录输入",
|
||||
"manage_context": "🧠 管理上下文",
|
||||
"save_state_snapshot": "💾 保存快照",
|
||||
"classify_intent": "🔍 识别意图",
|
||||
"retrieve": "📚 检索模板",
|
||||
"generate": "⚙️ 生成 JRXML",
|
||||
"modify_jrxml": "🔧 修改 JRXML",
|
||||
"validate": "✅ 验证",
|
||||
"explain_error": "🔎 分析错误",
|
||||
"correct_jrxml": "🛠 自动修正",
|
||||
"finalize": "📋 完成",
|
||||
"handle_consult": "💬 咨询回答",
|
||||
"handle_undo": "↩ 撤销操作",
|
||||
"handle_reset": "🔄 重置会话",
|
||||
"save_session": "💾 保存会话",
|
||||
}
|
||||
|
||||
INTENT_LABELS = {
|
||||
"initial_generation": "新建报表",
|
||||
"modify_report": "修改报表",
|
||||
"preview_report": "预览报表",
|
||||
"export_pdf": "导出 PDF",
|
||||
"export_jrxml": "下载 JRXML",
|
||||
"undo_modification": "撤销修改",
|
||||
"consult_question": "咨询问题",
|
||||
"reset_session": "重置会话",
|
||||
}
|
||||
|
||||
SKIP_NODES = {"load_session", "process_input", "manage_context",
|
||||
"save_state_snapshot", "save_session"}
|
||||
|
||||
|
||||
def _render_jrxml(jrxml: str, max_lines: int = 30):
|
||||
"""展示 JRXML 代码（折叠、限行）。"""
|
||||
lines = jrxml.strip().split("\n")
|
||||
preview = "\n".join(lines[:max_lines])
|
||||
if len(lines) > max_lines:
|
||||
preview += f"\n... (共 {len(lines)} 行)"
|
||||
st.code(preview, language="xml")
|
||||
|
||||
|
||||
# ---- URL 参数 ----
|
||||
query_params = st.query_params
|
||||
url_session_id = query_params.get("session_id", "")
|
||||
|
||||
@@ -35,7 +106,6 @@ if "graph" not in st.session_state:
|
||||
if "pending_action" not in st.session_state:
|
||||
st.session_state.pending_action = None
|
||||
|
||||
# 确定活跃的 session_id
|
||||
if "agent_state" not in st.session_state:
|
||||
if url_session_id:
|
||||
data = load_session(url_session_id)
|
||||
@@ -58,8 +128,8 @@ if "agent_state" not in st.session_state:
|
||||
current_session_id = st.session_state.agent_state.get("session_id", "")
|
||||
|
||||
|
||||
def run_agent(user_input: str) -> dict:
|
||||
"""运行代理图，返回最终状态。"""
|
||||
def run_agent(user_input: str):
|
||||
"""运行代理图：流式渲染节点进度 + LLM 文本。"""
|
||||
agent_state = st.session_state.agent_state
|
||||
|
||||
if agent_state.get("current_jrxml") and agent_state.get("status") == "pass":
|
||||
@@ -68,120 +138,155 @@ def run_agent(user_input: str) -> dict:
|
||||
agent_state["user_input"] = user_input
|
||||
agent_state["retry_count"] = 0
|
||||
|
||||
# ---- UI 容器 ----
|
||||
streaming_placeholder = st.empty() # 流式文本
|
||||
nodes_container = st.container() # 节点进度区
|
||||
summary_placeholder = st.empty() # 总结卡片
|
||||
|
||||
# 节点追踪
|
||||
executed_nodes: list[dict] = [] # {name, label, status, detail}
|
||||
stream_text = ""
|
||||
stream_active = False
|
||||
current_stream_node = ""
|
||||
final_state = None
|
||||
with st.chat_message("assistant"):
|
||||
status_placeholder = st.empty()
|
||||
jrxml_placeholder = st.empty()
|
||||
|
||||
for event in st.session_state.graph.stream(agent_state):
|
||||
for node_name, node_state in event.items():
|
||||
final_state = node_state
|
||||
if node_name == "classify_intent":
|
||||
intent = node_state.get("intent", "")
|
||||
intent_labels = {
|
||||
"initial_generation": "🆕 识别为新建报表请求",
|
||||
"modify_report": "✏️ 识别为修改报表请求",
|
||||
"preview_report": "👁 识别为预览请求",
|
||||
"export_pdf": "📄 识别为导出PDF请求",
|
||||
"export_jrxml": "📥 识别为导出JRXML请求",
|
||||
"undo_modification": "↩ 识别为撤销请求",
|
||||
"consult_question": "💬 识别为咨询问题",
|
||||
"reset_session": "🔄 识别为重置会话请求",
|
||||
}
|
||||
label = intent_labels.get(intent, f"🔍 意图: {intent}")
|
||||
status_placeholder.info(label)
|
||||
elif node_name == "generate":
|
||||
status_placeholder.info("🔧 正在生成 JRXML...")
|
||||
elif node_name == "modify_jrxml":
|
||||
status_placeholder.info("🔧 正在根据您的请求修改 JRXML...")
|
||||
elif node_name == "validate":
|
||||
if node_state.get("status") == "pass":
|
||||
status_placeholder.success("✅ 验证通过！")
|
||||
else:
|
||||
status_placeholder.warning("⚠ 验证失败，正在分析错误...")
|
||||
elif node_name == "explain_error":
|
||||
explanation = node_state.get("natural_explanation", "")
|
||||
status_placeholder.warning(f"🔍 {explanation}")
|
||||
elif node_name == "correct_jrxml":
|
||||
status_placeholder.info(f"🛠 正在自动修正（尝试 {node_state.get('retry_count', 1)}）...")
|
||||
elif node_name == "handle_consult":
|
||||
pass
|
||||
elif node_name == "handle_undo":
|
||||
status_placeholder.info("↩ 已撤销上一步修改")
|
||||
elif node_name == "handle_reset":
|
||||
status_placeholder.info("🔄 会话已重置")
|
||||
elif node_name == "manage_context":
|
||||
pass
|
||||
elif node_name == "save_state_snapshot":
|
||||
pass
|
||||
elif node_name == "save_session":
|
||||
pass
|
||||
elif node_name == "finalize":
|
||||
pass
|
||||
try:
|
||||
for event in st.session_state.graph.stream(
|
||||
agent_state, stream_mode=["updates", "custom"]
|
||||
):
|
||||
mode, data = event
|
||||
|
||||
if final_state:
|
||||
st.session_state.agent_state = final_state
|
||||
intent = final_state.get("intent", "")
|
||||
if mode == "updates":
|
||||
for node_name, node_state in data.items():
|
||||
label = NODE_LABELS.get(node_name, node_name)
|
||||
if node_name not in SKIP_NODES:
|
||||
executed_nodes.append({
|
||||
"name": node_name,
|
||||
"label": label,
|
||||
})
|
||||
|
||||
if node_name == "classify_intent":
|
||||
intent = node_state.get("intent", "")
|
||||
il = INTENT_LABELS.get(intent, intent)
|
||||
executed_nodes[-1]["detail"] = f"意图: {il}"
|
||||
|
||||
elif node_name == "retrieve":
|
||||
ctx = node_state.get("retrieved_context", "")
|
||||
executed_nodes[-1]["detail"] = (
|
||||
f"找到 {len(ctx)} 字符参考模板" if ctx else "未匹配到模板"
|
||||
)
|
||||
|
||||
elif node_name in ("generate", "modify_jrxml", "correct_jrxml"):
|
||||
# 流式文本已在上面的 custom 事件中展示
|
||||
jrxml = node_state.get("current_jrxml", "")
|
||||
executed_nodes[-1]["detail"] = f"生成 {len(jrxml)} 字符 JRXML"
|
||||
|
||||
elif node_name == "validate":
|
||||
status = node_state.get("status", "")
|
||||
if status == "pass":
|
||||
executed_nodes[-1]["detail"] = "验证通过 ✓"
|
||||
else:
|
||||
err = node_state.get("error_msg", "")
|
||||
executed_nodes[-1]["detail"] = f"验证失败: {err[:80]}"
|
||||
|
||||
elif node_name == "explain_error":
|
||||
expl = node_state.get("natural_explanation", "")
|
||||
executed_nodes[-1]["detail"] = expl[:120]
|
||||
|
||||
elif node_name == "handle_consult":
|
||||
ans = node_state.get("consult_answer", "")
|
||||
executed_nodes[-1]["detail"] = ans[:150]
|
||||
|
||||
final_state = node_state
|
||||
|
||||
elif mode == "custom":
|
||||
cd = data
|
||||
if cd.get("type") == "stream":
|
||||
stream_text += cd.get("text", "")
|
||||
stream_active = True
|
||||
current_stream_node = cd.get("node", "")
|
||||
streaming_placeholder.code(stream_text, language="xml")
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"工作流异常: {e}")
|
||||
return
|
||||
|
||||
# ---- 渲染节点进度区 ----
|
||||
with nodes_container:
|
||||
with st.expander("处理过程", expanded=False):
|
||||
for i, node in enumerate(executed_nodes):
|
||||
icon = "✓" if i < len(executed_nodes) - 1 else "●"
|
||||
detail_str = f" — {node['detail']}" if node.get("detail") else ""
|
||||
st.caption(f"{icon} {node['label']}{detail_str}")
|
||||
|
||||
# ---- 清除流式占位 ----
|
||||
if stream_active:
|
||||
streaming_placeholder.empty()
|
||||
|
||||
# ---- 总结卡片 ----
|
||||
if final_state:
|
||||
st.session_state.agent_state = final_state
|
||||
intent = final_state.get("intent", "")
|
||||
status = final_state.get("status", "")
|
||||
|
||||
with summary_placeholder.container(border=True):
|
||||
if intent == "consult_question":
|
||||
answer = final_state.get("consult_answer", "")
|
||||
st.info(answer)
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": answer,
|
||||
"type": "consult",
|
||||
"role": "assistant", "content": answer, "type": "consult",
|
||||
})
|
||||
status_placeholder.empty()
|
||||
st.markdown(answer)
|
||||
|
||||
elif intent in ("undo_modification", "reset_session"):
|
||||
# 消息已在节点中添加，不需要额外输出
|
||||
status_placeholder.empty()
|
||||
jrxml_placeholder.empty()
|
||||
st.success("操作已完成")
|
||||
# 消息已在节点中添加
|
||||
|
||||
elif intent in ("preview_report", "export_pdf", "export_jrxml"):
|
||||
jrxml = final_state.get("current_jrxml", "")
|
||||
if jrxml:
|
||||
st.success("✅ 当前报表")
|
||||
_render_jrxml(jrxml)
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": jrxml,
|
||||
"type": "jrxml",
|
||||
"role": "assistant", "content": jrxml, "type": "jrxml",
|
||||
})
|
||||
status_placeholder.success("✅ 当前报表")
|
||||
jrxml_placeholder.code(jrxml, language="xml")
|
||||
else:
|
||||
status_placeholder.warning("⚠ 当前没有报表可以预览或导出。")
|
||||
jrxml_placeholder.empty()
|
||||
elif final_state.get("status") == "pass":
|
||||
st.warning("⚠ 当前没有报表可以展示。")
|
||||
|
||||
elif status == "pass":
|
||||
jrxml = final_state.get("current_jrxml", "")
|
||||
st.success("✅ JRXML 生成成功")
|
||||
st.markdown("**生成结果:**")
|
||||
_render_jrxml(jrxml)
|
||||
st.caption("您可以从侧边栏下载文件，或继续对话进行修改。")
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": final_state.get("current_jrxml", ""),
|
||||
"type": "jrxml",
|
||||
"role": "assistant", "content": jrxml, "type": "jrxml",
|
||||
})
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": "✅ JRXML 生成成功！您可以从侧边栏下载文件，或继续修改。",
|
||||
"type": "success",
|
||||
})
|
||||
status_placeholder.success("✅ JRXML 验证通过！")
|
||||
jrxml_placeholder.code(final_state.get("current_jrxml", ""), language="xml")
|
||||
|
||||
else:
|
||||
jrxml = final_state.get("current_jrxml", "")
|
||||
error_msg = final_state.get("error_msg", "未知错误")
|
||||
explanation = final_state.get("natural_explanation", "")
|
||||
retries = final_state.get("retry_count", 0)
|
||||
st.error(f"❌ 经过 {retries} 次重试后仍无法生成有效的 JRXML")
|
||||
st.markdown(f"**错误:** {error_msg}")
|
||||
if explanation:
|
||||
st.markdown(f"**原因:** {explanation}")
|
||||
if jrxml:
|
||||
with st.expander("查看当前 JRXML"):
|
||||
_render_jrxml(jrxml, max_lines=80)
|
||||
st.caption("请简化报表结构后重试。")
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": final_state.get("current_jrxml", ""),
|
||||
"type": "jrxml",
|
||||
})
|
||||
st.session_state.messages.append({
|
||||
"role": "assistant",
|
||||
"content": f"❌ 经过 {retries} 次重试后仍无法生成有效的 JRXML。\n\n**错误:** {error_msg}\n\n**解释:** {explanation}\n\n请重新描述您的需求或简化报表结构。",
|
||||
"content": f"❌ 经过 {retries} 次重试后仍无法生成有效的 JRXML。\n\n**错误:** {error_msg}",
|
||||
"type": "error_explanation",
|
||||
})
|
||||
status_placeholder.error(f"❌ 经过 {retries} 次重试后验证失败")
|
||||
jrxml_placeholder.text("")
|
||||
else:
|
||||
st.error("未产生结果，请重试。")
|
||||
|
||||
return final_state
|
||||
else:
|
||||
st.error("未产生结果，请重试。")
|
||||
|
||||
|
||||
# ---- 侧边栏 ----
|
||||
@@ -192,7 +297,6 @@ with st.sidebar:
|
||||
|
||||
# 会话管理
|
||||
st.markdown("### 会话管理")
|
||||
|
||||
sessions = list_all_sessions()
|
||||
session_options = {}
|
||||
for s in sessions:
|
||||
@@ -270,6 +374,83 @@ with st.sidebar:
|
||||
run_agent("重新来，清空当前报表")
|
||||
st.rerun()
|
||||
|
||||
st.divider()
|
||||
st.markdown("### 上传文件")
|
||||
st.caption("支持图片 (OCR)、PDF、Word、文本文件。内容将附加到您的下一条消息中。")
|
||||
|
||||
if "uploaded_files" not in st.session_state:
|
||||
st.session_state.uploaded_files = [] # [{name, text, type}]
|
||||
|
||||
uploaded = st.file_uploader(
|
||||
"选择文件",
|
||||
type=["png", "jpg", "jpeg", "bmp", "webp", "pdf", "docx", "txt", "csv", "json", "xml"],
|
||||
accept_multiple_files=True,
|
||||
key="file_uploader",
|
||||
label_visibility="collapsed",
|
||||
)
|
||||
|
||||
if uploaded:
|
||||
for uf in uploaded:
|
||||
# 去重
|
||||
if any(f["name"] == uf.name for f in st.session_state.uploaded_files):
|
||||
continue
|
||||
import tempfile
|
||||
from backend.file_parser import parse_file
|
||||
from backend.layout_analyzer import analyze_layout
|
||||
|
||||
suffix = Path(uf.name).suffix.lower()
|
||||
with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
|
||||
tmp.write(uf.getvalue())
|
||||
tmp_path = tmp.name
|
||||
|
||||
result = parse_file(tmp_path, suffix)
|
||||
|
||||
# 对图片/PDF 进行 A4 模板布局分析
|
||||
parsed_text = result["text"]
|
||||
parsed_type = result["file_type"]
|
||||
if suffix in (".png", ".jpg", ".jpeg", ".bmp", ".webp", ".pdf"):
|
||||
layout = analyze_layout(tmp_path)
|
||||
tt = layout.get("template_type", "unknown")
|
||||
current_jrxml = st.session_state.agent_state.get("current_jrxml", "")
|
||||
|
||||
if tt == "full_a4":
|
||||
parsed_text = layout["description"]
|
||||
parsed_type = "a4_template"
|
||||
elif tt == "partial_rows":
|
||||
parsed_type = "a4_partial"
|
||||
if current_jrxml.strip():
|
||||
# 修改模式：尝试行匹配
|
||||
from backend.layout_analyzer import match_rows_to_jrxml
|
||||
match = match_rows_to_jrxml(layout, current_jrxml)
|
||||
parsed_text = (
|
||||
f"[行片段修改] 上传图片包含 {layout['total_rows']} 行，"
|
||||
f"视为 A4 报表的一部分。\n\n"
|
||||
f"{match['description']}\n\n"
|
||||
f"--- 行结构 ---\n{layout['description']}"
|
||||
)
|
||||
else:
|
||||
# 新建模式：按 A4 模板处理
|
||||
parsed_text = layout["description"]
|
||||
|
||||
Path(tmp_path).unlink(missing_ok=True)
|
||||
|
||||
if parsed_text:
|
||||
st.session_state.uploaded_files.append({
|
||||
"name": uf.name,
|
||||
"text": parsed_text,
|
||||
"type": parsed_type,
|
||||
})
|
||||
|
||||
if st.session_state.uploaded_files:
|
||||
for i, f in enumerate(st.session_state.uploaded_files):
|
||||
cols = st.columns([5, 1])
|
||||
with cols[0]:
|
||||
st.caption(f"📎 {f['name']} ({f['type']}, {len(f['text'])} 字符)")
|
||||
with cols[1]:
|
||||
if st.button("✕", key=f"rm_uf_{i}", help="移除"):
|
||||
st.session_state.uploaded_files.pop(i)
|
||||
st.rerun()
|
||||
|
||||
st.divider()
|
||||
st.markdown("### 配置")
|
||||
llm_backend = os.getenv("LLM_BACKEND", "cloud")
|
||||
@@ -280,16 +461,36 @@ with st.sidebar:
|
||||
|
||||
st.divider()
|
||||
st.markdown("### 下载")
|
||||
|
||||
final = st.session_state.agent_state.get("final_jrxml", "")
|
||||
versions = st.session_state.agent_state.get("jrxml_versions", [])
|
||||
|
||||
if final:
|
||||
st.download_button(
|
||||
label="📥 下载 JRXML",
|
||||
label="📥 下载最新 JRXML",
|
||||
data=final,
|
||||
file_name="report.jrxml",
|
||||
mime="application/xml",
|
||||
use_container_width=True,
|
||||
)
|
||||
|
||||
if versions:
|
||||
with st.expander("📋 历史版本", expanded=False):
|
||||
for i, v in enumerate(reversed(versions)):
|
||||
ts = v.get("ts", "")[:16]
|
||||
label = v.get("label", "版本")
|
||||
status = v.get("status", "")
|
||||
icon = "✅" if status == "pass" else "❌"
|
||||
dl_label = f"{icon} v{len(versions)-i} — {label} ({ts})"
|
||||
st.download_button(
|
||||
label=dl_label,
|
||||
data=v.get("jrxml", ""),
|
||||
file_name=f"report_v{len(versions)-i}.jrxml",
|
||||
mime="application/xml",
|
||||
use_container_width=True,
|
||||
key=f"dl_v{i}",
|
||||
)
|
||||
|
||||
# ---- 标题 ----
|
||||
st.title("📝 JRXML 报表生成器")
|
||||
st.caption("用自然语言描述您的报表需求，我将逐步生成可用的 JRXML 模板。")
|
||||
@@ -297,22 +498,33 @@ st.caption("用自然语言描述您的报表需求，我将逐步生成可用
|
||||
# ---- 聊天历史 ----
|
||||
for msg in st.session_state.messages:
|
||||
with st.chat_message(msg["role"]):
|
||||
if msg["role"] == "assistant" and msg.get("type") == "jrxml":
|
||||
if msg.get("type") == "jrxml":
|
||||
with st.expander("查看生成的 JRXML", expanded=False):
|
||||
st.code(msg["content"], language="xml")
|
||||
elif msg["role"] == "assistant" and msg.get("type") == "error_explanation":
|
||||
elif msg.get("type") == "error_explanation":
|
||||
st.warning(msg["content"])
|
||||
elif msg["role"] == "assistant" and msg.get("type") == "success":
|
||||
elif msg.get("type") == "success":
|
||||
st.success(msg["content"])
|
||||
elif msg["role"] == "assistant" and msg.get("type") == "consult":
|
||||
elif msg.get("type") == "consult":
|
||||
st.info(msg["content"])
|
||||
else:
|
||||
st.markdown(msg["content"])
|
||||
|
||||
# ---- 聊天输入 ----
|
||||
if prompt := st.chat_input("描述您的报表需求..."):
|
||||
# 拼接上传文件的文本
|
||||
uploaded_texts = []
|
||||
if st.session_state.get("uploaded_files"):
|
||||
for f in st.session_state.uploaded_files:
|
||||
uploaded_texts.append(f"[上传文件: {f['name']}]\n{f['text']}")
|
||||
if uploaded_texts:
|
||||
full_prompt = "\n\n".join(uploaded_texts) + "\n\n---\n用户需求:\n" + prompt
|
||||
st.session_state.uploaded_files = [] # 用后即清
|
||||
else:
|
||||
full_prompt = prompt
|
||||
|
||||
st.session_state.messages.append({"role": "user", "content": prompt})
|
||||
with st.chat_message("user"):
|
||||
st.markdown(prompt)
|
||||
run_agent(prompt)
|
||||
run_agent(full_prompt)
|
||||
st.rerun()
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
调用方式:
|
||||
get_embeddings() → LangChain 兼容的 embeddings 对象
|
||||
get_st_embeddings() → 原始 SentenceTransformer 实例
|
||||
get_st_model() → 原始 SentenceTransformer 实例
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
@@ -0,0 +1,225 @@
|
||||
"""错误自增长知识库 — 记录修正成功的错误案例，用于未来参考。
|
||||
|
||||
原则：
|
||||
- 仅记录"新错误"（指纹去重）
|
||||
- 必须包含完整的修正方案（prompt、工具链、前后 JRXML）
|
||||
- 存储于 ChromaDB，可被检索注入到生成 prompt 中
|
||||
|
||||
用法：
|
||||
from backend.error_kb import ErrorKB
|
||||
kb = ErrorKB()
|
||||
kb.record(error_msg, bad_jrxml, good_jrxml, correction_prompt)
|
||||
cases = kb.search("字段未声明", k=3)
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
CHROMA_DIR = Path(os.getenv("CHROMA_PERSIST_DIR", "./db/chroma"))
|
||||
COLLECTION_NAME = "jrxml_error_cases"
|
||||
|
||||
|
||||
def _make_fingerprint(error_msg: str) -> str:
|
||||
"""生成错误指纹 — 标准化后取 hash，用于去重。
|
||||
|
||||
标准化规则：
|
||||
- 去除字段名、变量名等具体标识符（替换为占位符）
|
||||
- 小写化
|
||||
- 只保留错误的结构性特征
|
||||
"""
|
||||
text = error_msg.lower()
|
||||
# 替换变量名 / 字段名（$F{xxx}, "name", 'value' 等）
|
||||
text = re.sub(r'\$f\{[^}]+\}', '$f{<FIELD>}', text)
|
||||
text = re.sub(r"'[^']*'", "'<VALUE>'", text)
|
||||
text = re.sub(r'"[^"]*"', '"<VALUE>"', text)
|
||||
# 替换数字
|
||||
text = re.sub(r'\b\d+\b', '<NUM>', text)
|
||||
# 压缩空白
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
return hashlib.md5(text.encode()).hexdigest()[:16]
|
||||
|
||||
|
||||
class ErrorKB:
|
||||
"""错误案例知识库 — 包装 ChromaDB 持久化。"""
|
||||
|
||||
def __init__(self):
|
||||
self._client = None
|
||||
self._collection = None
|
||||
|
||||
@property
|
||||
def client(self):
|
||||
if self._client is None:
|
||||
import chromadb
|
||||
self._client = chromadb.PersistentClient(path=str(CHROMA_DIR))
|
||||
return self._client
|
||||
|
||||
@property
|
||||
def collection(self):
|
||||
if self._collection is None:
|
||||
try:
|
||||
self._collection = self.client.get_collection(COLLECTION_NAME)
|
||||
except Exception:
|
||||
self._collection = self.client.create_collection(COLLECTION_NAME)
|
||||
return self._collection
|
||||
|
||||
def exists(self, error_msg: str) -> bool:
|
||||
"""检查错误是否已存在于知识库中（按指纹去重）。"""
|
||||
fp = _make_fingerprint(error_msg)
|
||||
try:
|
||||
results = self.collection.get(ids=[fp])
|
||||
return bool(results and results["ids"])
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def record(
|
||||
self,
|
||||
error_msg: str,
|
||||
bad_jrxml: str,
|
||||
good_jrxml: str,
|
||||
correction_prompt: str,
|
||||
model: str = "",
|
||||
retry_count: int = 0,
|
||||
) -> bool:
|
||||
"""记录一个成功修正的错误案例。
|
||||
|
||||
仅当指纹不重复时写入。返回 True 表示已记录，False 表示重复。
|
||||
"""
|
||||
if self.exists(error_msg):
|
||||
return False
|
||||
|
||||
fp = _make_fingerprint(error_msg)
|
||||
now = datetime.now(timezone.utc).isoformat()
|
||||
|
||||
# 内容：结构化记录
|
||||
doc = json.dumps({
|
||||
"error": error_msg,
|
||||
"bad_jrxml_snippet": bad_jrxml[:2000],
|
||||
"good_jrxml_snippet": good_jrxml[:2000],
|
||||
"correction_prompt": correction_prompt[:1500],
|
||||
"model": model,
|
||||
"retry_count": retry_count,
|
||||
"recorded_at": now,
|
||||
"tools": ["validation_service", "llm_correction"],
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# 元数据：用于检索过滤
|
||||
error_keywords = _extract_keywords(error_msg)
|
||||
metadata = {
|
||||
"fingerprint": fp,
|
||||
"error_keywords": ", ".join(error_keywords[:5]),
|
||||
"recorded_at": now,
|
||||
"retry_success": retry_count + 1, # 第几次修正成功的
|
||||
}
|
||||
|
||||
self.collection.add(
|
||||
ids=[fp],
|
||||
documents=[doc],
|
||||
metadatas=[metadata],
|
||||
)
|
||||
return True
|
||||
|
||||
def search(self, error_msg: str, k: int = 3) -> list[dict]:
|
||||
"""根据错误消息搜索相似的修正案例（ChromaDB 语义搜索）。
|
||||
|
||||
返回 [{error, fix_snippet, prompt, ...}, ...]
|
||||
"""
|
||||
keywords = _extract_keywords(error_msg)
|
||||
if not keywords:
|
||||
return []
|
||||
|
||||
query_text = " ".join(keywords)
|
||||
try:
|
||||
results = self.collection.query(
|
||||
query_texts=[query_text],
|
||||
n_results=k,
|
||||
include=["documents", "metadatas", "distances"],
|
||||
)
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
output = []
|
||||
if not results["ids"] or not results["ids"][0]:
|
||||
return output
|
||||
|
||||
for i, doc_id in enumerate(results["ids"][0]):
|
||||
dist = results["distances"][0][i]
|
||||
try:
|
||||
data = json.loads(results["documents"][0][i])
|
||||
output.append({
|
||||
"id": doc_id,
|
||||
"error": data.get("error", ""),
|
||||
"fix_snippet": data.get("good_jrxml_snippet", ""),
|
||||
"prompt": data.get("correction_prompt", ""),
|
||||
"recorded_at": data.get("recorded_at", ""),
|
||||
"distance": dist,
|
||||
})
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
return output
|
||||
|
||||
def search_as_context(self, error_msg: str, k: int = 3) -> str:
|
||||
"""搜索并返回拼接好的错误案例上下文，可直接注入 LLM prompt。"""
|
||||
results = self.search(error_msg, k=k)
|
||||
if not results:
|
||||
return ""
|
||||
|
||||
parts = []
|
||||
for r in results:
|
||||
parts.append(
|
||||
f"[历史错误案例]\n"
|
||||
f"错误: {r['error'][:200]}\n"
|
||||
f"修正后 JRXML 片段:\n{r['fix_snippet'][:800]}\n"
|
||||
)
|
||||
return "\n---\n".join(parts)
|
||||
|
||||
def stats(self) -> dict:
|
||||
"""返回知识库统计信息。"""
|
||||
try:
|
||||
count = self.collection.count()
|
||||
return {"total_cases": count, "collection": COLLECTION_NAME}
|
||||
except Exception:
|
||||
return {"total_cases": 0, "collection": COLLECTION_NAME}
|
||||
|
||||
|
||||
def _extract_keywords(error_msg: str) -> list[str]:
|
||||
"""从错误消息中提取关键词（中文 + 英文 token）。"""
|
||||
# 中文字符作为独立关键词
|
||||
chinese = re.findall(r'[一-鿿]{2,}', error_msg)
|
||||
# 英文 camelCase / snake_case token
|
||||
english = re.findall(r'[a-zA-Z_][a-zA-Z0-9_]{2,}', error_msg)
|
||||
# JRXML 特有模式
|
||||
jrxml_patterns = re.findall(r'\$F\{[^}]*\}', error_msg)
|
||||
return chinese + english + jrxml_patterns
|
||||
|
||||
|
||||
# 全局单例
|
||||
_kb: Optional[ErrorKB] = None
|
||||
|
||||
|
||||
def get_error_kb() -> ErrorKB:
|
||||
global _kb
|
||||
if _kb is None:
|
||||
_kb = ErrorKB()
|
||||
return _kb
|
||||
|
||||
|
||||
def record_error(error_msg: str, bad_jrxml: str, good_jrxml: str,
|
||||
correction_prompt: str, model: str = "", retry_count: int = 0) -> bool:
|
||||
"""便捷函数：记录成功修正的错误案例。"""
|
||||
return get_error_kb().record(error_msg, bad_jrxml, good_jrxml,
|
||||
correction_prompt, model, retry_count)
|
||||
|
||||
|
||||
def search_error_cases(error_msg: str, k: int = 3) -> str:
|
||||
"""便捷函数：搜索历史错误案例并返回上下文字符串。"""
|
||||
return get_error_kb().search_as_context(error_msg, k=k)
|
||||
@@ -0,0 +1,193 @@
|
||||
"""文件解析器：将上传文件转为文本，供 LLM 处理。
|
||||
|
||||
支持:
|
||||
- 图片 (.png/.jpg/.jpeg/.bmp) → OCR 提取文本
|
||||
- PDF (.pdf) → 文本提取
|
||||
- Word (.docx) → 文本提取
|
||||
- 纯文本 (.txt/.csv/.json/.xml) → 直接读取
|
||||
|
||||
策略选择:
|
||||
- 原生多模态: 模型支持图片时直接传文件（当前 MiniMax 不支持，自动退回文本转换）
|
||||
- 文本转换: 所有文件转为 UTF-8 文本后注入 prompt
|
||||
"""
|
||||
|
||||
import os
|
||||
import io
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import PIL.Image
|
||||
|
||||
MODELS_WITH_VISION = {
|
||||
"gpt-4o", "gpt-4-turbo", "gpt-4-vision-preview",
|
||||
"claude-3", "claude-3.5", "claude-4",
|
||||
"gemini-1.5", "gemini-2",
|
||||
}
|
||||
|
||||
|
||||
def can_use_vision(model: str = "") -> bool:
|
||||
"""检查当前模型是否支持原生多模态（图片直接上传）。"""
|
||||
if not model:
|
||||
model = os.getenv("LLM_MODEL", "")
|
||||
return any(v in model.lower() for v in MODELS_WITH_VISION)
|
||||
|
||||
|
||||
def parse_file(file_path: str, file_type: str = "") -> dict:
|
||||
"""解析任意文件为文本。
|
||||
|
||||
返回: {"text": str, "file_type": str, "method": str, "error": Optional[str]}
|
||||
"""
|
||||
path = Path(file_path)
|
||||
if not path.exists():
|
||||
return {"text": "", "file_type": file_type, "method": "none", "error": "文件不存在"}
|
||||
|
||||
suffix = file_type or path.suffix.lower()
|
||||
|
||||
parsers = {
|
||||
".png": _parse_image,
|
||||
".jpg": _parse_image,
|
||||
".jpeg": _parse_image,
|
||||
".bmp": _parse_image,
|
||||
".webp": _parse_image,
|
||||
".pdf": _parse_pdf,
|
||||
".docx": _parse_docx,
|
||||
}
|
||||
|
||||
parser = parsers.get(suffix)
|
||||
if parser:
|
||||
return parser(path)
|
||||
else:
|
||||
return _parse_text(path)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 各类型解析器
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _parse_image(path: Path) -> dict:
|
||||
"""OCR 提取图片中的文字。"""
|
||||
try:
|
||||
img = PIL.Image.open(path)
|
||||
info = f"[图片: {img.size[0]}x{img.size[1]}, {img.mode}]"
|
||||
except Exception:
|
||||
info = "[图片: 无法读取元数据]"
|
||||
|
||||
# 尝试 PaddleOCR
|
||||
try:
|
||||
from paddleocr import PaddleOCR
|
||||
ocr = PaddleOCR(lang="ch", use_angle_cls=False, show_log=False)
|
||||
result = ocr.ocr(str(path))
|
||||
lines = []
|
||||
if result and result[0]:
|
||||
for line in result[0]:
|
||||
text = line[1][0] if len(line) > 1 else ""
|
||||
if text.strip():
|
||||
lines.append(text.strip())
|
||||
if lines:
|
||||
return {
|
||||
"text": f"{info}\n识别文本:\n" + "\n".join(lines),
|
||||
"file_type": "image",
|
||||
"method": "paddleocr",
|
||||
"error": None,
|
||||
}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# OCR 不可用 → 返回图片元信息 + 安装提示
|
||||
return {
|
||||
"text": f"{info}\n(如需 OCR 文字识别，请安装: pip install paddleocr)",
|
||||
"file_type": "image",
|
||||
"method": "metadata_only",
|
||||
"error": "OCR 引擎未安装，已返回图片元信息",
|
||||
}
|
||||
|
||||
|
||||
def _parse_pdf(path: Path) -> dict:
|
||||
"""提取 PDF 中的文本。"""
|
||||
try:
|
||||
import pdfplumber
|
||||
with pdfplumber.open(path) as pdf:
|
||||
pages = []
|
||||
for page in pdf.pages:
|
||||
text = page.extract_text()
|
||||
if text:
|
||||
pages.append(text)
|
||||
full = "\n\n".join(pages)
|
||||
return {
|
||||
"text": full,
|
||||
"file_type": "pdf",
|
||||
"method": "pdfplumber",
|
||||
"error": None,
|
||||
}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# Fallback: 尝试 PyMuPDF
|
||||
try:
|
||||
import fitz
|
||||
doc = fitz.open(path)
|
||||
pages = []
|
||||
for page in doc:
|
||||
pages.append(page.get_text())
|
||||
doc.close()
|
||||
return {
|
||||
"text": "\n\n".join(pages),
|
||||
"file_type": "pdf",
|
||||
"method": "pymupdf",
|
||||
"error": None,
|
||||
}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return {"text": "", "file_type": "pdf", "method": "none",
|
||||
"error": "PDF 解析需要安装 pdfplumber 或 PyMuPDF"}
|
||||
|
||||
|
||||
def _parse_docx(path: Path) -> dict:
|
||||
"""提取 Word 文档中的文本。"""
|
||||
try:
|
||||
from docx import Document
|
||||
doc = Document(path)
|
||||
paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
|
||||
# 同时提取表格内容
|
||||
for table in doc.tables:
|
||||
for row in table.rows:
|
||||
cells = [cell.text for cell in row.cells if cell.text.strip()]
|
||||
if cells:
|
||||
paragraphs.append(" | ".join(cells))
|
||||
return {
|
||||
"text": "\n\n".join(paragraphs),
|
||||
"file_type": "docx",
|
||||
"method": "python-docx",
|
||||
"error": None,
|
||||
}
|
||||
except ImportError:
|
||||
pass
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return {"text": "", "file_type": "docx", "method": "none",
|
||||
"error": "DOCX 解析需要安装 python-docx"}
|
||||
|
||||
|
||||
def _parse_text(path: Path) -> dict:
|
||||
"""读取纯文本文件。"""
|
||||
try:
|
||||
text = path.read_text(encoding="utf-8")
|
||||
return {"text": text, "file_type": path.suffix, "method": "direct", "error": None}
|
||||
except UnicodeDecodeError:
|
||||
try:
|
||||
text = path.read_text(encoding="gbk")
|
||||
return {"text": text, "file_type": path.suffix, "method": "direct_gbk", "error": None}
|
||||
except Exception:
|
||||
return {"text": "", "file_type": path.suffix, "method": "none",
|
||||
"error": "无法解码文件"}
|
||||
except Exception:
|
||||
return {"text": "", "file_type": path.suffix, "method": "none",
|
||||
"error": "读取失败"}
|
||||
@@ -0,0 +1,494 @@
|
||||
"""A4 图片模板布局分析器。
|
||||
|
||||
检测上传图片并逐行识别每个元素的：
|
||||
- 位置 (x, y, w, h)
|
||||
- 字体大小（基于 OCR 边界框高度估算）
|
||||
- 文本内容
|
||||
|
||||
支持三种模式：
|
||||
- 完整 A4 模板：比例匹配 + OCR 元素 ≥2 → 全量布局描述
|
||||
- 行片段（非 A4 但有元素）：视为 A4 中的某几行 → 部分布局描述
|
||||
- 修改匹配：将图片中的行与现有 JRXML 做匹配，定位修改位置
|
||||
|
||||
用法:
|
||||
from backend.layout_analyzer import analyze_layout, match_rows_to_jrxml
|
||||
result = analyze_layout("row_snippet.png")
|
||||
# result["template_type"] = "partial_rows"
|
||||
match = match_rows_to_jrxml(result, current_jrxml)
|
||||
# match["matched_rows"] = [{"row_index": 0, "jrxml_section": "detail_band", ...}]
|
||||
"""
|
||||
|
||||
import re
|
||||
import xml.etree.ElementTree as ET
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import PIL.Image
|
||||
|
||||
# A4 标准尺寸 (mm): 210 × 297, 比例 ≈ 0.707
|
||||
A4_RATIO = 210 / 297
|
||||
A4_RATIO_EXACT_MIN, A4_RATIO_EXACT_MAX = 0.686, 0.728
|
||||
A4_RATIO_CLOSE_MIN, A4_RATIO_CLOSE_MAX = 0.650, 0.764
|
||||
|
||||
|
||||
def analyze_layout(
|
||||
file_path: str,
|
||||
row_tolerance_ratio: float = 0.02,
|
||||
) -> dict:
|
||||
"""分析图片/PDF 的报表模板布局。
|
||||
|
||||
返回:
|
||||
{
|
||||
"is_a4_template": bool, # 完整 A4 模板
|
||||
"is_partial": bool, # 行片段（非 A4 但有文字元素）
|
||||
"template_type": str, # "full_a4" | "partial_rows" | "unknown"
|
||||
"image_size": (w, h),
|
||||
"aspect_ratio": float,
|
||||
"a4_confidence": str,
|
||||
"rows": [{y_center, elements: [{x, y, w, h, font_size, text}, ...]}, ...],
|
||||
"description": str,
|
||||
"total_rows": int,
|
||||
"total_elements": int,
|
||||
}
|
||||
"""
|
||||
path = Path(file_path)
|
||||
if not path.exists():
|
||||
return _empty_result("文件不存在")
|
||||
|
||||
img = _load_image(path)
|
||||
if img is None:
|
||||
return _empty_result("无法加载图片")
|
||||
|
||||
w, h = img.size
|
||||
ratio = min(w, h) / max(w, h)
|
||||
|
||||
# A4 比例判定
|
||||
if A4_RATIO_EXACT_MIN <= ratio <= A4_RATIO_EXACT_MAX:
|
||||
a4_confidence = "exact"
|
||||
elif A4_RATIO_CLOSE_MIN <= ratio <= A4_RATIO_CLOSE_MAX:
|
||||
a4_confidence = "close"
|
||||
else:
|
||||
a4_confidence = "not_a4"
|
||||
|
||||
# OCR 提取
|
||||
elements = _ocr_elements(img, file_path)
|
||||
|
||||
if not elements:
|
||||
return {
|
||||
"is_a4_template": False,
|
||||
"is_partial": False,
|
||||
"template_type": "unknown",
|
||||
"image_size": (w, h),
|
||||
"aspect_ratio": round(ratio, 3),
|
||||
"a4_confidence": a4_confidence,
|
||||
"rows": [],
|
||||
"description": _build_description([], w, h, a4_confidence, "unknown"),
|
||||
"total_rows": 0,
|
||||
"total_elements": 0,
|
||||
}
|
||||
|
||||
# 行分组
|
||||
rows = _group_into_rows(elements, h, row_tolerance_ratio)
|
||||
|
||||
total = sum(len(r["elements"]) for r in rows)
|
||||
|
||||
# 模板类型判定
|
||||
is_full_a4 = a4_confidence != "not_a4" and total >= 2
|
||||
is_partial = not is_full_a4 and total >= 1 # 非 A4 但有文字 → 行片段
|
||||
|
||||
if is_full_a4:
|
||||
template_type = "full_a4"
|
||||
elif is_partial:
|
||||
template_type = "partial_rows"
|
||||
else:
|
||||
template_type = "unknown"
|
||||
|
||||
description = _build_description(rows, w, h, a4_confidence, template_type)
|
||||
|
||||
return {
|
||||
"is_a4_template": is_full_a4,
|
||||
"is_partial": is_partial,
|
||||
"template_type": template_type,
|
||||
"image_size": (w, h),
|
||||
"aspect_ratio": round(ratio, 3),
|
||||
"a4_confidence": a4_confidence,
|
||||
"rows": rows,
|
||||
"description": description,
|
||||
"total_rows": len(rows),
|
||||
"total_elements": total,
|
||||
}
|
||||
|
||||
|
||||
def match_rows_to_jrxml(
|
||||
layout_result: dict,
|
||||
current_jrxml: str,
|
||||
) -> dict:
|
||||
"""将图片中的行与现有 JRXML 中的 section/band 做匹配。
|
||||
|
||||
匹配策略：
|
||||
1. 从图片 OCR 文本中提取关键词
|
||||
2. 在 JRXML 中搜索这些关键词出现在哪个 band
|
||||
3. 返回匹配结果，可用于定位修改位置
|
||||
|
||||
返回:
|
||||
{
|
||||
"matched": bool,
|
||||
"matched_rows": [{row_index, row_y_center, jrxml_section, confidence}],
|
||||
"unmatched_rows": [...],
|
||||
"description": str, # 人类可读的匹配结果
|
||||
}
|
||||
"""
|
||||
rows = layout_result.get("rows", [])
|
||||
if not rows or not current_jrxml.strip():
|
||||
return {"matched": False, "matched_rows": [], "unmatched_rows": rows,
|
||||
"description": "无行数据或 JRXML 为空"}
|
||||
|
||||
# 解析 JRXML 结构
|
||||
jrxml_sections = _parse_jrxml_sections(current_jrxml)
|
||||
|
||||
matched_rows = []
|
||||
unmatched_rows = []
|
||||
|
||||
for ri, row in enumerate(rows):
|
||||
ocr_texts = [e["text"] for e in row["elements"]]
|
||||
best_section = None
|
||||
best_score = 0
|
||||
|
||||
for section in jrxml_sections:
|
||||
score = _text_similarity(ocr_texts, section["text_content"])
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_section = section
|
||||
|
||||
if best_score > 0.3 and best_section: # 最低匹配阈值
|
||||
matched_rows.append({
|
||||
"row_index": ri,
|
||||
"row_y_center": row["y_center"],
|
||||
"jrxml_section": best_section["name"],
|
||||
"jrxml_section_type": best_section["type"],
|
||||
"confidence": round(best_score, 2),
|
||||
"matched_text": best_section["text_content"][:200],
|
||||
})
|
||||
else:
|
||||
unmatched_rows.append({
|
||||
"row_index": ri,
|
||||
"row_y_center": row["y_center"],
|
||||
"ocr_texts": ocr_texts,
|
||||
})
|
||||
|
||||
# 生成描述
|
||||
desc_parts = []
|
||||
if matched_rows:
|
||||
desc_parts.append(f"图片中 {len(matched_rows)} 行匹配到当前 JRXML：")
|
||||
for m in matched_rows:
|
||||
desc_parts.append(
|
||||
f" - 图片第 {m['row_index']+1} 行 → JRXML「{m['jrxml_section']}」"
|
||||
f"（{m['jrxml_section_type']}，置信度 {m['confidence']}）"
|
||||
)
|
||||
if unmatched_rows:
|
||||
desc_parts.append(f"图片中 {len(unmatched_rows)} 行未匹配到 JRXML 现有区域：")
|
||||
for u in unmatched_rows:
|
||||
texts = ", ".join(u["ocr_texts"][:3])
|
||||
desc_parts.append(f" - 图片第 {u['row_index']+1} 行：{texts}")
|
||||
|
||||
return {
|
||||
"matched": len(matched_rows) > 0,
|
||||
"matched_rows": matched_rows,
|
||||
"unmatched_rows": unmatched_rows,
|
||||
"description": "\n".join(desc_parts),
|
||||
}
|
||||
|
||||
|
||||
def analyze_and_inject(file_path: str, base_prompt: str,
|
||||
current_jrxml: str = "") -> str:
|
||||
"""分析布局并增强 prompt。
|
||||
|
||||
- 完整 A4 模板 → 全量布局描述
|
||||
- 行片段 + 有 JRXML → 行匹配 + 修改指引
|
||||
- 行片段 + 无 JRXML → 行片段描述（视为 A4 模板的一部分）
|
||||
"""
|
||||
result = analyze_layout(file_path)
|
||||
tt = result.get("template_type", "unknown")
|
||||
|
||||
if tt == "unknown":
|
||||
return base_prompt
|
||||
|
||||
if tt == "full_a4":
|
||||
return f"[图片模板分析 — 完整 A4 报表]\n{result['description']}\n\n---\n原始需求:\n{base_prompt}"
|
||||
|
||||
if tt == "partial_rows":
|
||||
if current_jrxml.strip():
|
||||
match = match_rows_to_jrxml(result, current_jrxml)
|
||||
if match["matched"]:
|
||||
return (
|
||||
f"[图片模板分析 — 行片段修改]\n"
|
||||
f"图片包含 {result['total_rows']} 行，视为 A4 模板的一部分。\n"
|
||||
f"{match['description']}\n\n"
|
||||
f"{result['description']}\n\n"
|
||||
f"---\n请根据以上匹配结果，修改 JRXML 中对应区域的布局：\n{base_prompt}"
|
||||
)
|
||||
else:
|
||||
return (
|
||||
f"[图片模板分析 — 行片段（未匹配到现有区域）]\n"
|
||||
f"图片包含 {result['total_rows']} 行。\n"
|
||||
f"{result['description']}\n\n"
|
||||
f"---\n请根据以上行结构，在 JRXML 中找到合适位置进行修改：\n{base_prompt}"
|
||||
)
|
||||
else:
|
||||
return (
|
||||
f"[图片模板分析 — 行片段（无现有报表，按 A4 模板处理）]\n"
|
||||
f"图片包含 {result['total_rows']} 行，请按 A4 报表模板的需求输出整张报表。\n"
|
||||
f"{result['description']}\n\n"
|
||||
f"---\n原始需求:\n{base_prompt}"
|
||||
)
|
||||
|
||||
return base_prompt
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# JRXML 结构解析
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _parse_jrxml_sections(jrxml: str) -> list[dict]:
|
||||
"""解析 JRXML 中的 section/band 结构。
|
||||
|
||||
直接搜索所有 band 元素，通过上下文字符串推断其所属 section。
|
||||
"""
|
||||
sections = []
|
||||
try:
|
||||
root = ET.fromstring(jrxml)
|
||||
section_tags = {
|
||||
"title", "pageHeader", "columnHeader", "detail",
|
||||
"columnFooter", "pageFooter", "summary", "background",
|
||||
"noData", "groupHeader", "groupFooter",
|
||||
}
|
||||
|
||||
for section_elem in root.iter():
|
||||
stag = _tag(section_elem)
|
||||
if stag not in section_tags:
|
||||
continue
|
||||
|
||||
for child in section_elem:
|
||||
if _tag(child) == "band":
|
||||
name = child.get("name", "")
|
||||
section_name = f"{stag}[{name}]" if name else stag
|
||||
text_content = ET.tostring(child, encoding="unicode")
|
||||
sections.append({
|
||||
"name": section_name,
|
||||
"type": stag,
|
||||
"text_content": text_content,
|
||||
})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Fallback: 如果 structured parsing 失败，直接把整个 JRXML 按 band 分割
|
||||
if not sections:
|
||||
sections = _parse_jrxml_regex(jrxml)
|
||||
|
||||
return sections
|
||||
|
||||
|
||||
def _tag(elem) -> str:
|
||||
"""去除命名空间前缀的标签名。"""
|
||||
return elem.tag.split("}")[-1] if "}" in elem.tag else elem.tag
|
||||
|
||||
|
||||
def _parse_jrxml_regex(jrxml: str) -> list[dict]:
|
||||
"""正则回退方案：直接在文本中搜索 band 块。"""
|
||||
sections = []
|
||||
band_pattern = re.compile(
|
||||
r'<(title|pageHeader|columnHeader|detail|columnFooter|pageFooter|summary|background|noData|groupHeader|groupFooter)>\s*'
|
||||
r'(<band[^>]*>.*?</band>)\s*'
|
||||
r'</\1>',
|
||||
re.DOTALL,
|
||||
)
|
||||
for m in band_pattern.finditer(jrxml):
|
||||
stag = m.group(1)
|
||||
band_xml = m.group(0)
|
||||
sections.append({
|
||||
"name": stag,
|
||||
"type": stag,
|
||||
"text_content": band_xml,
|
||||
})
|
||||
return sections
|
||||
|
||||
|
||||
def _text_similarity(ocr_texts: list[str], jrxml_text: str) -> float:
|
||||
"""计算 OCR 文本与 JRXML 文本的相似度（简单的词匹配）。"""
|
||||
if not ocr_texts or not jrxml_text:
|
||||
return 0.0
|
||||
|
||||
jrxml_lower = jrxml_text.lower()
|
||||
score = 0.0
|
||||
for text in ocr_texts:
|
||||
# 精确匹配
|
||||
if text.lower() in jrxml_lower:
|
||||
score += 1.0
|
||||
else:
|
||||
# 部分词匹配
|
||||
words = re.findall(r"\w+", text)
|
||||
matched = sum(1 for w in words if w.lower() in jrxml_lower)
|
||||
if words:
|
||||
score += matched / len(words) * 0.5
|
||||
|
||||
return min(score / len(ocr_texts), 1.0)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 内部实现（不变）
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _load_image(path: Path) -> Optional[PIL.Image.Image]:
|
||||
suffix = path.suffix.lower()
|
||||
|
||||
if suffix in (".png", ".jpg", ".jpeg", ".bmp", ".webp"):
|
||||
try:
|
||||
return PIL.Image.open(path).convert("RGB")
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
if suffix == ".pdf":
|
||||
try:
|
||||
import pdfplumber
|
||||
with pdfplumber.open(path) as pdf:
|
||||
if pdf.pages:
|
||||
pil_img = pdf.pages[0].to_image(resolution=150)
|
||||
return pil_img.original.convert("RGB")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
import fitz
|
||||
doc = fitz.open(path)
|
||||
pix = doc[0].get_pixmap(dpi=150)
|
||||
img = PIL.Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
|
||||
doc.close()
|
||||
return img
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _ocr_elements(img: PIL.Image.Image, file_path: str) -> list[dict]:
|
||||
try:
|
||||
from paddleocr import PaddleOCR
|
||||
import numpy as np
|
||||
|
||||
ocr = PaddleOCR(lang="ch", use_angle_cls=True, show_log=False)
|
||||
result = ocr.ocr(np.array(img))
|
||||
|
||||
elements = []
|
||||
if result and result[0]:
|
||||
for line in result[0]:
|
||||
if len(line) < 2:
|
||||
continue
|
||||
box = line[0]
|
||||
text_info = line[1]
|
||||
text = text_info[0] if isinstance(text_info, (list, tuple)) else str(text_info)
|
||||
if not text.strip():
|
||||
continue
|
||||
|
||||
xs = [p[0] for p in box]
|
||||
ys = [p[1] for p in box]
|
||||
x_min, x_max = min(xs), max(xs)
|
||||
y_min, y_max = min(ys), max(ys)
|
||||
|
||||
elements.append({
|
||||
"x": round(x_min, 1),
|
||||
"y": round(y_min, 1),
|
||||
"w": round(x_max - x_min, 1),
|
||||
"h": round(y_max - y_min, 1),
|
||||
"font_size": round(y_max - y_min, 1),
|
||||
"text": text.strip(),
|
||||
})
|
||||
|
||||
elements.sort(key=lambda e: (e["y"], e["x"]))
|
||||
return elements
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return []
|
||||
|
||||
|
||||
def _group_into_rows(elements: list[dict], img_height: int,
|
||||
tolerance_ratio: float = 0.02) -> list[dict]:
|
||||
if not elements:
|
||||
return []
|
||||
|
||||
tolerance = img_height * tolerance_ratio
|
||||
rows = []
|
||||
current_row = [elements[0]]
|
||||
|
||||
for elem in elements[1:]:
|
||||
prev_cy = current_row[0]["y"] + current_row[0]["h"] / 2
|
||||
curr_cy = elem["y"] + elem["h"] / 2
|
||||
|
||||
if abs(curr_cy - prev_cy) < tolerance:
|
||||
current_row.append(elem)
|
||||
else:
|
||||
rows.append(_build_row(current_row))
|
||||
current_row = [elem]
|
||||
|
||||
if current_row:
|
||||
rows.append(_build_row(current_row))
|
||||
|
||||
return rows
|
||||
|
||||
|
||||
def _build_row(elements: list[dict]) -> dict:
|
||||
elements.sort(key=lambda e: e["x"])
|
||||
ys = [e["y"] for e in elements]
|
||||
return {"y_center": round(sum(ys) / len(ys), 1), "elements": elements}
|
||||
|
||||
|
||||
def _build_description(rows: list[dict], img_w: int, img_h: int,
|
||||
a4_confidence: str, template_type: str) -> str:
|
||||
if not rows:
|
||||
if template_type == "partial_rows":
|
||||
return f"图片 {img_w}x{img_h}（非 A4 比例），未检测到文字元素。"
|
||||
return f"图片共 {img_w}x{img_h} 像素，未检测到文字元素。"
|
||||
|
||||
lines = []
|
||||
if template_type == "full_a4":
|
||||
lines.append(f"图片为完整 A4 报表模板，共 {len(rows)} 行，像素区域 {img_w}x{img_h}：")
|
||||
elif template_type == "partial_rows":
|
||||
lines.append(f"图片为报表模板行片段（非完整 A4），包含 {len(rows)} 行，"
|
||||
f"像素区域 {img_w}x{img_h}，请按 A4 模板处理：")
|
||||
else:
|
||||
lines.append(f"图片共 {img_w}x{img_h} 像素，包含 {len(rows)} 行文字：")
|
||||
|
||||
for i, row in enumerate(rows):
|
||||
elems = row["elements"]
|
||||
lines.append(f"\n第 {i+1} 行有 {len(elems)} 个元素：")
|
||||
for j, e in enumerate(elems):
|
||||
letter = chr(ord("a") + j)
|
||||
lines.append(
|
||||
f" 元素 {letter}：位置(x={e['x']}, y={e['y']})，"
|
||||
f"长 {e['w']}px，高 {e['h']}px，"
|
||||
f"字体 {e['font_size']}px，"
|
||||
f"内容「{e['text']}」"
|
||||
)
|
||||
|
||||
if template_type == "full_a4":
|
||||
lines.append(f"\n请根据以上布局生成对应的 JRXML 报表模板。")
|
||||
elif template_type == "partial_rows":
|
||||
lines.append(f"\n请将以上 {len(rows)} 行作为 A4 模板的一部分，"
|
||||
f"生成或修改对应的 JRXML 报表区域。")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _empty_result(error: str = "") -> dict:
|
||||
return {
|
||||
"is_a4_template": False,
|
||||
"is_partial": False,
|
||||
"template_type": "unknown",
|
||||
"image_size": (0, 0),
|
||||
"aspect_ratio": 0,
|
||||
"a4_confidence": "not_a4",
|
||||
"rows": [],
|
||||
"description": error,
|
||||
"total_rows": 0,
|
||||
"total_elements": 0,
|
||||
}
|
||||
+48
-4
@@ -8,13 +8,33 @@ from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
|
||||
class _BaseLLM:
|
||||
"""LLM 统一接口基类 — 所有后端都提供 invoke() 和 stream()。"""
|
||||
|
||||
def invoke(self, prompt: str) -> Any:
|
||||
raise NotImplementedError
|
||||
|
||||
def stream(self, prompt: str):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def get_llm():
|
||||
backend = os.getenv("LLM_BACKEND", "cloud")
|
||||
if backend == "local":
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
model = os.getenv("LOCAL_LLM_MODEL", "qwen2.5-coder:7b")
|
||||
return ChatOllama(model=model, temperature=0.1)
|
||||
raw = ChatOllama(model=model, temperature=0.1)
|
||||
|
||||
class OllamaWrapper(_BaseLLM):
|
||||
def invoke(self, prompt):
|
||||
return raw.invoke(prompt)
|
||||
|
||||
def stream(self, prompt):
|
||||
for chunk in raw.stream(prompt):
|
||||
yield chunk.content
|
||||
|
||||
return OllamaWrapper()
|
||||
|
||||
provider = os.getenv("LLM_PROVIDER", "openai")
|
||||
if provider == "anthropic":
|
||||
@@ -30,7 +50,7 @@ def get_llm():
|
||||
|
||||
client = Anthropic(api_key=api_key, base_url=base_url, timeout=120)
|
||||
|
||||
class MiniMaxLLM:
|
||||
class MiniMaxLLM(_BaseLLM):
|
||||
def invoke(self, prompt: str) -> Any:
|
||||
resp = client.messages.create(
|
||||
model=model,
|
||||
@@ -43,20 +63,44 @@ def get_llm():
|
||||
return type("Response", (), {"content": block.text})()
|
||||
return type("Response", (), {"content": ""})()
|
||||
|
||||
def stream(self, prompt: str):
|
||||
with client.messages.stream(
|
||||
model=model,
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature,
|
||||
messages=[{"role": "user", "content": [{"type": "text", "text": prompt}]}],
|
||||
) as s:
|
||||
for text in s.text_stream:
|
||||
yield text
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
return client.count_tokens(text)
|
||||
resp = client.messages.count_tokens(
|
||||
model=model,
|
||||
messages=[{"role": "user", "content": [{"type": "text", "text": text}]}],
|
||||
)
|
||||
return resp.input_tokens
|
||||
|
||||
return MiniMaxLLM()
|
||||
else:
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
return ChatOpenAI(
|
||||
raw = ChatOpenAI(
|
||||
model=os.getenv("LLM_MODEL", "gpt-4o"),
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
|
||||
temperature=0.1,
|
||||
)
|
||||
|
||||
class OpenAIWrapper(_BaseLLM):
|
||||
def invoke(self, prompt):
|
||||
return raw.invoke(prompt)
|
||||
|
||||
def stream(self, prompt):
|
||||
for chunk in raw.stream(prompt):
|
||||
yield chunk.content
|
||||
|
||||
return OpenAIWrapper()
|
||||
|
||||
|
||||
def get_llm_for_correction():
|
||||
return get_llm()
|
||||
@@ -0,0 +1,12 @@
|
||||
你是一个信息压缩助手。以下是用户与报表生成助手之间的历史对话记录，请将其压缩为一份简洁的摘要（不超过200字）。
|
||||
|
||||
摘要必须保留以下关键信息：
|
||||
- 用户提出的所有报表需求点（字段、标题、分组、汇总等）
|
||||
- 用户提出的所有修改要求及其顺序
|
||||
- 当前报表的核心结构（字段列表、标题、分组方式）
|
||||
- 任何特殊要求或约束条件
|
||||
|
||||
只输出摘要文本，不要添加任何解释或标记。
|
||||
|
||||
对话记录：
|
||||
{conversation_text}
|
||||
@@ -0,0 +1,5 @@
|
||||
你是 JasperReports 专家。用简洁清晰的中文回答用户关于 JasperReports 的问题。
|
||||
|
||||
用户问题：{question}
|
||||
|
||||
直接回答：
|
||||
@@ -0,0 +1,17 @@
|
||||
你是一位资深 JasperReports 工程师。你生成的 JRXML 文件编译失败。分析错误并修复 JRXML。
|
||||
|
||||
关键规则：
|
||||
- 只输出完整修复后的 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- JRXML 必须与 JasperReports 7.0.6 兼容。
|
||||
- 解决下面列出的特定错误。
|
||||
|
||||
当前 JRXML（带错误）：
|
||||
{current_jrxml}
|
||||
|
||||
编译错误：
|
||||
{error_msg}
|
||||
|
||||
错误的自然语言解释：
|
||||
{explanation}
|
||||
|
||||
立即生成修正后的 JRXML：
|
||||
@@ -0,0 +1,9 @@
|
||||
你是一位 JasperReports 专家。用普通非技术语言解释以下 JRXML 编译错误，让业务用户能够理解。
|
||||
|
||||
错误消息：
|
||||
{error_msg}
|
||||
|
||||
当前 JRXML 片段（前 80 行）：
|
||||
{jrxml_snippet}
|
||||
|
||||
用 2-3 句话解释哪里出了问题以及如何修复：
|
||||
@@ -0,0 +1,15 @@
|
||||
你是一位资深 JasperReports 工程师。根据以下参考模板和用户需求，生成一个完整、可编译的 JRXML 文件。
|
||||
JRXML 必须兼容 JasperReports 7.0.6 schema。
|
||||
|
||||
关键规则：
|
||||
- 只输出 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- 报表正文中使用的每个字段必须在 <field name="..."> 部分中声明。
|
||||
- 根元素为 <jasperReport>，包含正确的 xmlns 属性。
|
||||
- 包含 <queryString>，在 <![CDATA[...]]> 中包含 SQL 查询。
|
||||
- 确保所有交叉引用（字段名称、band 元素）保持一致。
|
||||
|
||||
参考模板和组件：
|
||||
{context}
|
||||
|
||||
用户需求：
|
||||
{user_request}
|
||||
@@ -0,0 +1,16 @@
|
||||
你是意图分类器。根据用户输入判断意图，只输出意图名称。
|
||||
|
||||
当前有报表：{has_report}
|
||||
用户输入：{user_input}
|
||||
|
||||
可选意图：
|
||||
- initial_generation（新建报表，或无报表时的任何需求）
|
||||
- modify_report（修改当前已有报表）
|
||||
- preview_report（预览/查看当前报表）
|
||||
- export_pdf（导出PDF文件）
|
||||
- export_jrxml（下载/导出/保存JRXML文件）
|
||||
- undo_modification（撤销/回退上一步修改）
|
||||
- consult_question（咨询JasperReports相关知识或使用问题）
|
||||
- reset_session（清空/重置/重新开始）
|
||||
|
||||
意图名称：
|
||||
@@ -0,0 +1,53 @@
|
||||
"""Prompt 加载器：从 prompts/ 目录加载 .md 文件。
|
||||
|
||||
支持热重载 — 每次调用都从磁盘读取，修改 prompt 文件无需重启应用。
|
||||
|
||||
用法：
|
||||
from prompts.loader import load_prompt
|
||||
prompt = load_prompt("intent_classify").format(has_report="是", user_input="...")
|
||||
"""
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
_PROMPTS_DIR = Path(__file__).resolve().parent
|
||||
|
||||
# 文件名 → 变量名 映射
|
||||
_NAME_MAP = {
|
||||
"intent_classify": "intent_classify.md",
|
||||
"consult": "consult.md",
|
||||
"initial_generation": "initial_generation.md",
|
||||
"modification": "modification.md",
|
||||
"correction": "correction.md",
|
||||
"explain_error": "explain_error.md",
|
||||
"compression": "compression.md",
|
||||
}
|
||||
|
||||
|
||||
def load_prompt(name: str) -> str:
|
||||
"""从 prompts/{name}.md 加载 prompt 模板（每次从磁盘读取，支持热重载）。
|
||||
|
||||
返回的字符串包含 Python .format() 占位符，调用方负责填充。
|
||||
"""
|
||||
filename = _NAME_MAP.get(name)
|
||||
if not filename:
|
||||
raise ValueError(f"未知 prompt: {name}，可选值: {list(_NAME_MAP.keys())}")
|
||||
|
||||
filepath = _PROMPTS_DIR / filename
|
||||
if not filepath.exists():
|
||||
raise FileNotFoundError(f"Prompt 文件不存在: {filepath}")
|
||||
|
||||
text = filepath.read_text(encoding="utf-8").strip()
|
||||
|
||||
# 去掉可能存在的 markdown frontmatter（--- 包裹的元数据）
|
||||
if text.startswith("---"):
|
||||
end = text.find("---", 3)
|
||||
if end != -1:
|
||||
text = text[end + 3:].strip()
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def list_prompts() -> list[str]:
|
||||
"""列出所有可用的 prompt 名称。"""
|
||||
return sorted(_NAME_MAP.keys())
|
||||
@@ -0,0 +1,18 @@
|
||||
你是一位资深 JasperReports 工程师。用户想要修改一个现有的、可编译的 JRXML 报表。精确应用请求的更改到当前 JRXML 并输出完整修改后的 JRXML。
|
||||
|
||||
关键规则：
|
||||
- 只输出完整修改后的 JRXML 代码，不要解释，不要 markdown 标记。
|
||||
- 保留所有未被更改的现有结构。
|
||||
- 结果必须继续与 JasperReports 7.0.6 兼容。
|
||||
- 报表正文中使用的每个字段必须在 <field> 部分中声明。
|
||||
- 如果添加新字段，正确声明它们。
|
||||
- 确保 <queryString> 是 <![CDATA[...]]> 中有效的 SQL。
|
||||
|
||||
当前 JRXML：
|
||||
{current_jrxml}
|
||||
|
||||
对话历史：
|
||||
{conversation_history}
|
||||
|
||||
用户的修改请求：
|
||||
{modification_request}
|
||||
Reference in New Issue
Block a user