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6 Commits

Author SHA1 Message Date
fujie
4b9790df00 feat: localize parameter names in export_to_word_cn.py and bump to v0.4.1 2026-01-05 23:37:14 +08:00
fujie
58452a8441 feat: release export_to_docx v0.4.0 with i18n, UserValves, and bug fixes 2026-01-05 23:29:16 +08:00
Jeff fu
e104161007 fix(docs): change py file link to GitHub URL for mkdocs compatibility 2026-01-05 17:40:39 +08:00
Jeff fu
6de0d6fbe4 feat(infographic-markdown): add new plugin for JS render to Markdown
- Add infographic_markdown.py (English) and infographic_markdown_cn.py (Chinese)
- AI-powered infographic generator using AntV library
- Renders SVG on frontend and embeds as Markdown Data URL image
- Supports 18+ infographic templates (lists, charts, comparisons, etc.)

Docs:
- Add plugin README.md and README_CN.md
- Add docs detail pages (infographic-markdown.md)
- Update docs index pages with new plugin
- Add 'JS Render to Markdown' pattern to plugin development guides
- Update copilot-instructions.md with new advanced development pattern

Version: 1.0.0
2026-01-05 17:29:52 +08:00
fujie
28d55c1469 feat: 添加 JavaScript 渲染 PoC,支持通过 API 更新消息内容 2026-01-05 09:01:42 +08:00
fujie
59933e9361 docs: 更新插件安装指南,增加OpenWebUI社区推荐安装方式。 2026-01-05 00:31:18 +08:00
23 changed files with 6777 additions and 2041 deletions

View File

@@ -841,10 +841,136 @@ def add_math(paragraph, latex_str):
omml = mathml2omml(mathml)
# ... 插入 OMML 到 paragraph._element ...
```
- [ ] 更新 `README.md` 插件列表
- [ ] 更新 `README_CN.md` 插件列表
- [ ] 更新/创建 `docs/` 下的对应文档
- [ ] 确保文档版本号与代码一致
### JS 渲染并嵌入 Markdown (JS Render to Markdown)
对于需要复杂前端渲染(如 AntV 图表、Mermaid 图表、ECharts但希望结果**持久化为纯 Markdown 格式**的场景,推荐使用 Data URL 嵌入模式:
#### 工作流程
```
┌─────────────────────────────────────────────────────────────┐
│ Plugin Workflow │
├─────────────────────────────────────────────────────────────┤
│ 1. Python Action │
│ ├── 分析消息内容 │
│ ├── 调用 LLM 生成结构化数据(可选) │
│ └── 通过 __event_call__ 发送 JS 代码到前端 │
├─────────────────────────────────────────────────────────────┤
│ 2. Browser JS (via __event_call__) │
│ ├── 动态加载可视化库(如 AntV、Mermaid
│ ├── 离屏渲染 SVG/Canvas │
│ ├── 使用 toDataURL() 导出 Base64 Data URL │
│ └── 通过 REST API 更新消息内容 │
├─────────────────────────────────────────────────────────────┤
│ 3. Markdown 渲染 │
│ └── 显示 ![描述](data:image/svg+xml;base64,...) │
└─────────────────────────────────────────────────────────────┘
```
#### 核心实现代码
**Python 端(发送 JS 执行):**
```python
async def action(self, body, __event_call__, __metadata__, ...):
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
# 生成 JS 代码
js_code = self._generate_js_code(
chat_id=chat_id,
message_id=message_id,
data=processed_data, # 可视化所需数据
)
# 执行 JS
if __event_call__:
await __event_call__({
"type": "execute",
"data": {"code": js_code}
})
```
**JavaScript 端(渲染并回写):**
```javascript
(async function() {
// 1. 动态加载可视化库
if (typeof VisualizationLib === 'undefined') {
await new Promise((resolve, reject) => {
const script = document.createElement('script');
script.src = 'https://cdn.example.com/lib.min.js';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
});
}
// 2. 创建离屏容器
const container = document.createElement('div');
container.style.cssText = 'position:absolute;left:-9999px;';
document.body.appendChild(container);
// 3. 渲染可视化
const instance = new VisualizationLib({ container, ... });
instance.render(data);
// 4. 导出为 Data URL
const dataUrl = await instance.toDataURL({ type: 'svg', embedResources: true });
// 或手动转换 SVG:
// const svgData = new XMLSerializer().serializeToString(svgElement);
// const base64 = btoa(unescape(encodeURIComponent(svgData)));
// const dataUrl = "data:image/svg+xml;base64," + base64;
// 5. 清理
instance.destroy();
document.body.removeChild(container);
// 6. 生成 Markdown 图片
const markdownImage = `![描述](${dataUrl})`;
// 7. 通过 API 更新消息
const token = localStorage.getItem("token");
await fetch(`/api/v1/chats/${chatId}/messages/${messageId}/event`, {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${token}`
},
body: JSON.stringify({
type: "chat:message",
data: { content: originalContent + "\n\n" + markdownImage }
})
});
})();
```
#### 优势
- **纯 Markdown 输出**:结果是标准的 Markdown 图片语法,无需 HTML 代码块
- **自包含**:图片以 Base64 Data URL 嵌入,无外部依赖
- **持久化**:通过 API 回写,消息重新加载后图片仍然存在
- **跨平台**:任何支持 Markdown 图片的客户端都能显示
- **无服务端渲染依赖**:利用用户浏览器的渲染能力
#### 与 HTML 注入模式对比
| 特性 | HTML 注入 (`\`\`\`html`) | JS 渲染 + Markdown 图片 |
|------|-------------------------|------------------------|
| 输出格式 | HTML 代码块 | Markdown 图片 |
| 交互性 | ✅ 支持按钮、动画 | ❌ 静态图片 |
| 外部依赖 | 需要加载 JS 库 | 无(图片自包含) |
| 持久化 | 依赖浏览器渲染 | ✅ 永久可见 |
| 文件导出 | 需特殊处理 | ✅ 直接导出 |
| 适用场景 | 交互式内容 | 信息图、图表快照 |
#### 参考实现
- `plugins/actions/js-render-poc/infographic_markdown.py` - AntV Infographic 生成并嵌入
- `plugins/actions/js-render-poc/js_render_poc.py` - 基础概念验证
---

View File

@@ -60,10 +60,16 @@ This project is a collection of resources and does not require a Python environm
### Using Plugins
1. Browse the `/plugins` directory and download the plugin file (`.py`) you need.
2. Go to OpenWebUI **Admin Panel** -> **Settings** -> **Plugins**.
3. Click the upload button and select the `.py` file you just downloaded.
4. Once uploaded, refresh the page to enable the plugin in your chat settings or toolbar.
1. **Install from OpenWebUI Community (Recommended)**:
- Visit my profile: [Fu-Jie's Profile](https://openwebui.com/u/Fu-Jie)
- Browse the plugins and select the one you like.
- Click "Get" to import it directly into your OpenWebUI instance.
2. **Manual Installation**:
- Browse the `/plugins` directory and download the plugin file (`.py`) you need.
- Go to OpenWebUI **Admin Panel** -> **Settings** -> **Plugins**.
- Click the upload button and select the `.py` file you just downloaded.
- Once uploaded, refresh the page to enable the plugin in your chat settings or toolbar.
### Contributing

View File

@@ -87,10 +87,16 @@ OpenWebUI 增强功能集合。包含个人开发与收集的### 🧩 插件 (Pl
### 使用插件 (Plugins)
1. `/plugins` 目录中浏览并下载你需要的插件文件 (`.py`)。
2. 打开 OpenWebUI 的 **管理员面板 (Admin Panel)** -> **设置 (Settings)** -> **插件 (Plugins)**
3. 点击上传按钮,选择刚才下载的 `.py`件。
4. 上传成功后,刷新页面,你就可以在聊天设置或工具栏中启用该插件了
1. **从 OpenWebUI 社区安装 (推荐)**:
- 访问我的主页: [Fu-Jie's Profile](https://openwebui.com/u/Fu-Jie)
- 浏览插件列表,选择你喜欢的插件。
- 点击 "Get" 按钮,将其直接导入到你的 OpenWebUI 实例中
2. **手动安装**:
-`/plugins` 目录中浏览并下载你需要的插件文件 (`.py`)。
- 打开 OpenWebUI 的 **管理员面板 (Admin Panel)** -> **设置 (Settings)** -> **插件 (Plugins)**
- 点击上传按钮,选择刚才下载的 `.py` 文件。
- 上传成功后,刷新页面,你就可以在聊天设置或工具栏中启用该插件了。
### 贡献代码

View File

@@ -235,6 +235,125 @@ llm_response = await generate_chat_completion(
)
```
### 4.4 JS Render to Markdown (Data URL Embedding)
For scenarios requiring complex frontend rendering (e.g., AntV charts, Mermaid diagrams) but wanting **persistent pure Markdown output**, use the Data URL embedding pattern:
#### Workflow
```
┌──────────────────────────────────────────────────────────────┐
│ 1. Python Action │
│ ├── Analyze message content │
│ ├── Call LLM to generate structured data (optional) │
│ └── Send JS code to frontend via __event_call__ │
├──────────────────────────────────────────────────────────────┤
│ 2. Browser JS (via __event_call__) │
│ ├── Dynamically load visualization library │
│ ├── Render SVG/Canvas offscreen │
│ ├── Export to Base64 Data URL via toDataURL() │
│ └── Update message content via REST API │
├──────────────────────────────────────────────────────────────┤
│ 3. Markdown Rendering │
│ └── Display ![description](data:image/svg+xml;base64,...) │
└──────────────────────────────────────────────────────────────┘
```
#### Python Side (Send JS for Execution)
```python
async def action(self, body, __event_call__, __metadata__, ...):
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
# Generate JS code
js_code = self._generate_js_code(
chat_id=chat_id,
message_id=message_id,
data=processed_data,
)
# Execute JS
if __event_call__:
await __event_call__({
"type": "execute",
"data": {"code": js_code}
})
```
#### JavaScript Side (Render and Write-back)
```javascript
(async function() {
// 1. Load visualization library
if (typeof VisualizationLib === 'undefined') {
await new Promise((resolve, reject) => {
const script = document.createElement('script');
script.src = 'https://cdn.example.com/lib.min.js';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
});
}
// 2. Create offscreen container
const container = document.createElement('div');
container.style.cssText = 'position:absolute;left:-9999px;';
document.body.appendChild(container);
// 3. Render visualization
const instance = new VisualizationLib({ container });
instance.render(data);
// 4. Export to Data URL
const dataUrl = await instance.toDataURL({ type: 'svg', embedResources: true });
// 5. Cleanup
instance.destroy();
document.body.removeChild(container);
// 6. Generate Markdown image
const markdownImage = `![Chart](${dataUrl})`;
// 7. Update message via API
const token = localStorage.getItem("token");
await fetch(`/api/v1/chats/${chatId}/messages/${messageId}/event`, {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${token}`
},
body: JSON.stringify({
type: "chat:message",
data: { content: originalContent + "\n\n" + markdownImage }
})
});
})();
```
#### Benefits
- **Pure Markdown Output**: Standard Markdown image syntax, no HTML code blocks
- **Self-Contained**: Images embedded as Base64 Data URL, no external dependencies
- **Persistent**: Via API write-back, images remain after page reload
- **Cross-Platform**: Works on any client supporting Markdown images
#### HTML Injection vs JS Render to Markdown
| Feature | HTML Injection | JS Render + Markdown |
|---------|----------------|----------------------|
| Output Format | HTML code block | Markdown image |
| Interactivity | ✅ Buttons, animations | ❌ Static image |
| External Deps | Requires JS libraries | None (self-contained) |
| Persistence | Depends on browser | ✅ Permanent |
| File Export | Needs special handling | ✅ Direct export |
| Use Case | Interactive content | Infographics, chart snapshots |
#### Reference Implementations
- `plugins/actions/js-render-poc/infographic_markdown.py` - AntV Infographic + Data URL
- `plugins/actions/js-render-poc/js_render_poc.py` - Basic proof of concept
---
## 5. Best Practices & Design Principles

View File

@@ -199,7 +199,124 @@ async def background_job(self, chat_id):
pass
```
---
### 4.3 JS 渲染并嵌入 Markdown (Data URL 嵌入)
对于需要复杂前端渲染(如 AntV 图表、Mermaid 图表)但希望结果**持久化为纯 Markdown 格式**的场景,推荐使用 Data URL 嵌入模式:
#### 工作流程
```
┌──────────────────────────────────────────────────────────────┐
│ 1. Python Action │
│ ├── 分析消息内容 │
│ ├── 调用 LLM 生成结构化数据(可选) │
│ └── 通过 __event_call__ 发送 JS 代码到前端 │
├──────────────────────────────────────────────────────────────┤
│ 2. Browser JS (通过 __event_call__) │
│ ├── 动态加载可视化库 │
│ ├── 离屏渲染 SVG/Canvas │
│ ├── 使用 toDataURL() 导出 Base64 Data URL │
│ └── 通过 REST API 更新消息内容 │
├──────────────────────────────────────────────────────────────┤
│ 3. Markdown 渲染 │
│ └── 显示 ![描述](data:image/svg+xml;base64,...) │
└──────────────────────────────────────────────────────────────┘
```
#### Python 端(发送 JS 执行)
```python
async def action(self, body, __event_call__, __metadata__, ...):
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
# 生成 JS 代码
js_code = self._generate_js_code(
chat_id=chat_id,
message_id=message_id,
data=processed_data,
)
# 执行 JS
if __event_call__:
await __event_call__({
"type": "execute",
"data": {"code": js_code}
})
```
#### JavaScript 端(渲染并回写)
```javascript
(async function() {
// 1. 加载可视化库
if (typeof VisualizationLib === 'undefined') {
await new Promise((resolve, reject) => {
const script = document.createElement('script');
script.src = 'https://cdn.example.com/lib.min.js';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
});
}
// 2. 创建离屏容器
const container = document.createElement('div');
container.style.cssText = 'position:absolute;left:-9999px;';
document.body.appendChild(container);
// 3. 渲染可视化
const instance = new VisualizationLib({ container });
instance.render(data);
// 4. 导出为 Data URL
const dataUrl = await instance.toDataURL({ type: 'svg', embedResources: true });
// 5. 清理
instance.destroy();
document.body.removeChild(container);
// 6. 生成 Markdown 图片
const markdownImage = `![图表](${dataUrl})`;
// 7. 通过 API 更新消息
const token = localStorage.getItem("token");
await fetch(`/api/v1/chats/${chatId}/messages/${messageId}/event`, {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${token}`
},
body: JSON.stringify({
type: "chat:message",
data: { content: originalContent + "\n\n" + markdownImage }
})
});
})();
```
#### 优势
- **纯 Markdown 输出**:结果是标准的 Markdown 图片语法,无需 HTML 代码块
- **自包含**:图片以 Base64 Data URL 嵌入,无外部依赖
- **持久化**:通过 API 回写,消息重新加载后图片仍然存在
- **跨平台**:任何支持 Markdown 图片的客户端都能显示
#### HTML 注入 vs JS 渲染嵌入 Markdown
| 特性 | HTML 注入 | JS 渲染 + Markdown 图片 |
|------|----------|------------------------|
| 输出格式 | HTML 代码块 | Markdown 图片 |
| 交互性 | ✅ 支持按钮、动画 | ❌ 静态图片 |
| 外部依赖 | 需要加载 JS 库 | 无(图片自包含) |
| 持久化 | 依赖浏览器渲染 | ✅ 永久可见 |
| 文件导出 | 需特殊处理 | ✅ 直接导出 |
| 适用场景 | 交互式内容 | 信息图、图表快照 |
#### 参考实现
- `plugins/actions/js-render-poc/infographic_markdown.py` - AntV 信息图 + Data URL
- `plugins/actions/js-render-poc/js_render_poc.py` - 基础概念验证
## 5. 最佳实践与设计原则

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@@ -104,10 +104,16 @@ hide:
### Using Plugins
1. Browse the [Plugin Center](plugins/index.md) and download the plugin file (`.py`)
2. Open OpenWebUI **Admin Panel****Settings****Plugins**
3. Click the upload button and select the `.py` file
4. Refresh the page and enable the plugin in your chat settings
1. **Install from OpenWebUI Community (Recommended)**:
- Visit my profile: [Fu-Jie's Profile](https://openwebui.com/u/Fu-Jie)
- Browse the plugins and select the one you like.
- Click "Get" to import it directly into your OpenWebUI instance.
2. **Manual Installation**:
- Browse the [Plugin Center](plugins/index.md) and download the plugin file (`.py`)
- Open OpenWebUI **Admin Panel****Settings****Plugins**
- Click the upload button and select the `.py` file
- Refresh the page and enable the plugin in your chat settings
---

View File

@@ -104,10 +104,16 @@ hide:
### 使用插件
1. 浏览[插件中心](plugins/index.md)并下载插件文件(`.py`
2. 打开 OpenWebUI **管理面板****设置****插件**
3. 点击上传按钮并选择 `.py` 文件
4. 刷新页面并在聊天设置中启用插件
1. **从 OpenWebUI 社区安装 (推荐)**:
- 访问我的主页: [Fu-Jie's Profile](https://openwebui.com/u/Fu-Jie)
- 浏览插件列表,选择你喜欢的插件。
- 点击 "Get" 按钮,将其直接导入到你的 OpenWebUI 实例中。
2. **手动安装**:
- 浏览[插件中心](plugins/index.md)并下载插件文件(`.py`
- 打开 OpenWebUI **管理面板****设置****插件**
- 点击上传按钮并选择 `.py` 文件
- 刷新页面并在聊天设置中启用插件
---

View File

@@ -0,0 +1,290 @@
# 使用 JavaScript 生成可视化内容的技术方案
## 概述
本文档描述了在 OpenWebUI Action 插件中使用浏览器端 JavaScript 代码生成可视化内容(如思维导图、信息图等)并将结果保存到消息中的技术方案。
## 核心架构
```mermaid
sequenceDiagram
participant Plugin as Python 插件
participant EventCall as __event_call__
participant Browser as 浏览器 (JS)
participant API as OpenWebUI API
participant DB as 数据库
Plugin->>EventCall: 1. 发送 execute 事件 (含 JS 代码)
EventCall->>Browser: 2. 执行 JS 代码
Browser->>Browser: 3. 加载可视化库 (D3/Markmap/AntV)
Browser->>Browser: 4. 渲染可视化内容
Browser->>Browser: 5. 转换为 Base64 Data URI
Browser->>API: 6. GET 获取当前消息内容
API-->>Browser: 7. 返回消息数据
Browser->>API: 8. POST 追加 Markdown 图片到消息
API->>DB: 9. 保存更新后的消息
```
## 关键步骤
### 1. Python 端通过 `__event_call__` 执行 JS
Python 插件**不直接修改 `body["messages"]`**,而是通过 `__event_call__` 发送 JS 代码让浏览器执行:
```python
async def action(
self,
body: dict,
__user__: dict = None,
__event_emitter__=None,
__event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
__metadata__: Optional[dict] = None,
__request__: Request = None,
) -> dict:
# 从 body 获取 chat_id 和 message_id
chat_id = body.get("chat_id", "")
message_id = body.get("id", "") # 注意body["id"] 是 message_id
# 通过 __event_call__ 执行 JS 代码
if __event_call__:
await __event_call__({
"type": "execute",
"data": {
"code": f"""
(async function() {{
const chatId = "{chat_id}";
const messageId = "{message_id}";
// ... JS 渲染和 API 更新逻辑 ...
}})();
"""
},
})
# 不修改 body直接返回
return body
```
### 2. JavaScript 加载可视化库
在浏览器端动态加载所需的 JS 库:
```javascript
// 加载 D3.js
if (!window.d3) {
await new Promise((resolve, reject) => {
const script = document.createElement('script');
script.src = 'https://cdn.jsdelivr.net/npm/d3@7';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
});
}
// 加载 Markmap (思维导图)
if (!window.markmap) {
await loadScript('https://cdn.jsdelivr.net/npm/markmap-lib@0.17');
await loadScript('https://cdn.jsdelivr.net/npm/markmap-view@0.17');
}
```
### 3. 渲染并转换为 Data URI
```javascript
// 创建 SVG 元素
const svg = document.createElementNS('http://www.w3.org/2000/svg', 'svg');
svg.setAttribute('width', '800');
svg.setAttribute('height', '600');
svg.setAttribute('xmlns', 'http://www.w3.org/2000/svg');
// ... 执行渲染逻辑 (添加图形元素) ...
// 转换为 Base64 Data URI
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = 'data:image/svg+xml;base64,' + base64;
```
### 4. 获取当前消息内容
由于 Python 端不传递原始内容JS 需要通过 API 获取:
```javascript
const token = localStorage.getItem('token');
// 获取当前聊天数据
const getResponse = await fetch(`/api/v1/chats/${chatId}`, {
method: 'GET',
headers: {
'Authorization': `Bearer ${token}`
}
});
const chatData = await getResponse.json();
// 查找目标消息
let originalContent = '';
if (chatData.chat && chatData.chat.messages) {
const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
if (targetMsg && targetMsg.content) {
originalContent = targetMsg.content;
}
}
```
### 5. 调用 API 更新消息
```javascript
// 构造新内容:原始内容 + Markdown 图片
const markdownImage = `![可视化图片](${dataUri})`;
const newContent = originalContent + '\n\n' + markdownImage;
// 调用 API 更新消息
const response = await fetch(`/api/v1/chats/${chatId}/messages/${messageId}/event`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${token}`
},
body: JSON.stringify({
type: 'chat:message',
data: { content: newContent }
})
});
if (response.ok) {
console.log('消息更新成功!');
}
```
## 完整示例
参考 [js_render_poc.py](https://github.com/Fu-Jie/awesome-openwebui/blob/main/plugins/actions/js-render-poc/js_render_poc.py) 获取完整的 PoC 实现。
## 事件类型
| 类型 | 用途 |
|------|------|
| `chat:message:delta` | 增量更新(追加文本) |
| `chat:message` | 完全替换消息内容 |
```javascript
// 增量更新
{ type: "chat:message:delta", data: { content: "追加的内容" } }
// 完全替换
{ type: "chat:message", data: { content: "完整的新内容" } }
```
## 关键数据来源
| 数据 | 来源 | 说明 |
|------|------|------|
| `chat_id` | `body["chat_id"]` | 聊天会话 ID |
| `message_id` | `body["id"]` | ⚠️ 注意:是 `body["id"]`,不是 `body["message_id"]` |
| `token` | `localStorage.getItem('token')` | 用户认证 Token |
| `originalContent` | 通过 API `GET /api/v1/chats/{chatId}` 获取 | 当前消息内容 |
## Python 端 API
| 参数 | 类型 | 说明 |
|------|------|------|
| `__event_emitter__` | Callable | 发送状态/通知事件 |
| `__event_call__` | Callable | 执行 JS 代码(用于可视化渲染) |
| `__metadata__` | dict | 元数据(可能为 None |
| `body` | dict | 请求体,包含 messages、chat_id、id 等 |
### body 结构示例
```json
{
"model": "gemini-3-flash-preview",
"messages": [...],
"chat_id": "ac2633a3-5731-4944-98e3-bf9b3f0ef0ab",
"id": "2e0bb7d4-dfc0-43d7-b028-fd9e06c6fdc8",
"session_id": "bX30sHI8r4_CKxCdAAAL"
}
```
### 常用事件
```python
# 发送状态更新
await __event_emitter__({
"type": "status",
"data": {"description": "正在渲染...", "done": False}
})
# 执行 JS 代码
await __event_call__({
"type": "execute",
"data": {"code": "console.log('Hello from Python!')"}
})
# 发送通知
await __event_emitter__({
"type": "notification",
"data": {"type": "success", "content": "渲染完成!"}
})
```
## 适用场景
- **思维导图** (Markmap)
- **信息图** (AntV Infographic)
- **流程图** (Mermaid)
- **数据图表** (ECharts, Chart.js)
- **任何需要 JS 渲染的可视化内容**
## 注意事项
### 1. 竞态条件问题
⚠️ **多次快速点击会导致内容覆盖问题**
由于 API 调用是异步的,如果用户快速多次触发 Action
- 第一次点击:获取原始内容 A → 渲染 → 更新为 A+图片1
- 第二次点击:可能获取到旧内容 A第一次还没保存完→ 更新为 A+图片2
结果图片1 被覆盖丢失!
**解决方案**
- 添加防抖debounce机制
- 使用锁/标志位防止重复执行
- 或使用 `chat:message:delta` 增量更新
### 2. 不要直接修改 `body["messages"]`
消息更新应由 JS 通过 API 完成,确保获取最新内容。
### 3. f-string 限制
Python f-string 内不能直接使用反斜杠,需要将转义字符串预先处理:
```python
# 转义 JSON 中的特殊字符
body_json = json.dumps(data, ensure_ascii=False)
escaped = body_json.replace("\\", "\\\\").replace("`", "\\`").replace("${", "\\${")
```
### 4. Data URI 大小限制
Base64 编码会增加约 33% 的体积,复杂图片可能导致消息过大。
### 5. 跨域问题
确保 CDN 资源支持 CORS。
### 6. API 权限
确保用户 token 有权限访问和更新目标消息。
## 与传统方式对比
| 特性 | 传统方式 (修改 body) | 新方式 (__event_call__) |
|------|---------------------|------------------------|
| 消息更新 | Python 直接修改 | JS 通过 API 更新 |
| 原始内容 | Python 传递给 JS | JS 通过 API 获取 |
| 灵活性 | 低 | 高 |
| 实时性 | 一次性 | 可多次更新 |
| 复杂度 | 简单 | 中等 |
| 竞态风险 | 低 | ⚠️ 需要处理 |

View File

@@ -1,7 +1,7 @@
# Export to Word
<span class="category-badge action">Action</span>
<span class="version-badge">v0.2.0</span>
<span class="version-badge">v0.4.1</span>
Export conversation to Word (.docx) with **syntax highlighting**, **native math equations**, **Mermaid diagrams**, **citations**, and **enhanced table formatting**.
@@ -34,11 +34,34 @@ You can configure the following settings via the **Valves** button in the plugin
| Valve | Description | Default |
| :--- | :--- | :--- |
| `TITLE_SOURCE` | Source for document title/filename. Options: `chat_title`, `ai_generated`, `markdown_title` | `chat_title` |
| `MERMAID_JS_URL` | URL for the Mermaid.js library (for diagram rendering). | `https://cdn.jsdelivr.net/npm/mermaid@10.9.1/dist/mermaid.min.js` |
| `MERMAID_PNG_SCALE` | Scale factor for Mermaid PNG generation (Resolution). Higher = clearer but larger file size. | `3.0` |
| `MERMAID_DISPLAY_SCALE` | Scale factor for Mermaid visual size in Word. >1.0 to enlarge, <1.0 to shrink. | `1.5` |
| `MERMAID_OPTIMIZE_LAYOUT` | Automatically convert LR (Left-Right) flowcharts to TD (Top-Down) for better fit. | `True` |
| `MAX_EMBED_IMAGE_MB` | Maximum image size to embed into DOCX (MB). | `20` |
| `UI_LANGUAGE` | User interface language. Options: `en` (English), `zh` (Chinese). | `en` |
| `FONT_LATIN` | Font name for Latin characters. | `Times New Roman` |
| `FONT_ASIAN` | Font name for Asian characters. | `SimSun` |
| `FONT_CODE` | Font name for code blocks. | `Consolas` |
| `TABLE_HEADER_COLOR` | Table header background color (Hex without #). | `F2F2F2` |
| `TABLE_ZEBRA_COLOR` | Table alternating row background color (Hex without #). | `FBFBFB` |
| `MERMAID_JS_URL` | URL for the Mermaid.js library. | `https://cdn.jsdelivr.net/npm/mermaid@11.12.2/dist/mermaid.min.js` |
| `MERMAID_JSZIP_URL` | URL for the JSZip library (required for DOCX manipulation). | `https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js` |
| `MERMAID_PNG_SCALE` | Scale factor for Mermaid PNG generation (Resolution). | `3.0` |
| `MERMAID_DISPLAY_SCALE` | Scale factor for Mermaid visual size in Word. | `1.0` |
| `MERMAID_OPTIMIZE_LAYOUT` | Automatically convert LR (Left-Right) flowcharts to TD (Top-Down). | `False` |
| `MERMAID_BACKGROUND` | Background color for Mermaid diagrams (e.g., `white`, `transparent`). | `transparent` |
| `MERMAID_CAPTIONS_ENABLE` | Enable/disable figure captions for Mermaid diagrams. | `True` |
| `MERMAID_CAPTION_STYLE` | Paragraph style name for Mermaid captions. | `Caption` |
| `MERMAID_CAPTION_PREFIX` | Caption prefix label (e.g., 'Figure'). Empty = auto-detect based on language. | `""` |
| `MATH_ENABLE` | Enable LaTeX math block conversion. | `True` |
| `MATH_INLINE_DOLLAR_ENABLE` | Enable inline `$ ... $` math conversion. | `True` |
### User-Level Configuration (UserValves)
Users can override the following settings in their personal settings:
- `TITLE_SOURCE`
- `UI_LANGUAGE`
- `FONT_LATIN`, `FONT_ASIAN`, `FONT_CODE`
- `TABLE_HEADER_COLOR`, `TABLE_ZEBRA_COLOR`
- `MERMAID_...` (Selected Mermaid settings)
- `MATH_...` (Math settings)
---

View File

@@ -1,7 +1,7 @@
# Export to Word导出为 Word
<span class="category-badge action">Action</span>
<span class="version-badge">v0.2.0</span>
<span class="version-badge">v0.4.1</span>
将当前对话导出为完美格式的 Word 文档,支持**代码语法高亮**、**原生数学公式**、**Mermaid 图表**、**引用资料**以及**增强表格**渲染。
@@ -33,12 +33,35 @@ Export to Word 插件会把聊天消息从 Markdown 转成精致的 Word 文档
| Valve | 说明 | 默认值 |
| :--- | :--- | :--- |
| `TITLE_SOURCE` | 文档标题/文件名的来源。选项:`chat_title` (对话标题), `ai_generated` (AI 生成), `markdown_title` (Markdown 标题) | `chat_title` |
| `MERMAID_JS_URL` | Mermaid.js 库的 URL用于图表渲染。 | `https://cdn.jsdelivr.net/npm/mermaid@10.9.1/dist/mermaid.min.js` |
| `MERMAID_PNG_SCALE` | Mermaid PNG 生成缩放比例(分辨率)。越高越清晰但文件越大。 | `3.0` |
| `MERMAID_DISPLAY_SCALE` | Mermaid 在 Word 中的显示比例(视觉大小)。>1.0 放大, <1.0 缩小。 | `1.5` |
| `MERMAID_OPTIMIZE_LAYOUT` | 优化 Mermaid 布局: 自动将 LR (左右) 转换为 TD (上下) 以适应页面。 | `True` |
| `MERMAID_CAPTIONS_ENABLE` | 启用/禁用 Mermaid 图表的图注。 | `True` |
| `文档标题来源` | 文档标题/文件名的来源。选项:`chat_title` (对话标题), `ai_generated` (AI 生成), `markdown_title` (Markdown 标题) | `chat_title` |
| `最大嵌入图片大小MB` | 嵌入图片的最大大小 (MB)。 | `20` |
| `界面语言` | 界面语言。选项:`en` (英语), `zh` (中文)。 | `zh` |
| `英文字体` | 英文字体名称。 | `Calibri` |
| `中文字体` | 中文字体名称。 | `SimSun` |
| `代码字体` | 代码字体名称。 | `Consolas` |
| `表头背景色` | 表头背景色(十六进制,不带#)。 | `F2F2F2` |
| `表格隔行背景色` | 表格隔行背景色(十六进制,不带#)。 | `FBFBFB` |
| `Mermaid_JS地址` | Mermaid.js 库的 URL。 | `https://cdn.jsdelivr.net/npm/mermaid@11.12.2/dist/mermaid.min.js` |
| `JSZip库地址` | JSZip 库的 URL用于 DOCX 操作)。 | `https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js` |
| `Mermaid_PNG缩放比例` | Mermaid PNG 生成缩放比例(分辨率)。 | `3.0` |
| `Mermaid显示比例` | Mermaid 在 Word 中的显示比例(视觉大小)。 | `1.0` |
| `Mermaid布局优化` | 优化 Mermaid 布局: 自动将 LR (左右) 转换为 TD (上下)。 | `False` |
| `Mermaid背景色` | Mermaid 图表背景色(如 `white`, `transparent`)。 | `transparent` |
| `启用Mermaid图注` | 启用/禁用 Mermaid 图表的图注。 | `True` |
| `Mermaid图注样式` | Mermaid 图注的段落样式名称。 | `Caption` |
| `Mermaid图注前缀` | 图注前缀(如 '图')。留空则根据语言自动检测。 | `""` |
| `启用数学公式` | 启用 LaTeX 数学公式块转换。 | `True` |
| `启用行内公式` | 启用行内 `$ ... $` 数学公式转换。 | `True` |
### 用户级配置 (UserValves)
用户可以在个人设置中覆盖以下配置:
- `文档标题来源`
- `界面语言`
- `英文字体`, `中文字体`, `代码字体`
- `表头背景色`, `表格隔行背景色`
- `Mermaid_...` (部分 Mermaid 设置)
- `启用数学公式`, `启用行内公式`
---

View File

@@ -63,7 +63,7 @@ Actions are interactive plugins that:
Export the current conversation to a formatted Word doc with **syntax highlighting**, **native math equations**, **Mermaid diagrams**, **citations**, and **enhanced table formatting**.
**Version:** 0.2.0
**Version:** 0.4.1
[:octicons-arrow-right-24: Documentation](export-to-word.md)
@@ -77,6 +77,16 @@ Actions are interactive plugins that:
[:octicons-arrow-right-24: Documentation](summary.md)
- :material-image-text:{ .lg .middle } **Infographic to Markdown**
---
AI-powered infographic generator that renders SVG and embeds it as Markdown Data URL image.
**Version:** 1.0.0
[:octicons-arrow-right-24: Documentation](infographic-markdown.md)
</div>
---

View File

@@ -63,7 +63,7 @@ Actions 是交互式插件,能够:
将当前对话导出为完美格式的 Word 文档,支持**代码语法高亮**、**原生数学公式**、**Mermaid 图表**、**引用资料**以及**增强表格**渲染。
**版本:** 0.2.0
**版本:** 0.4.1
[:octicons-arrow-right-24: 查看文档](export-to-word.md)
@@ -77,6 +77,16 @@ Actions 是交互式插件,能够:
[:octicons-arrow-right-24: 查看文档](summary.md)
- :material-image-text:{ .lg .middle } **信息图转 Markdown**
---
AI 驱动的信息图生成器,渲染 SVG 并以 Markdown Data URL 图片嵌入。
**版本:** 1.0.0
[:octicons-arrow-right-24: 查看文档](infographic-markdown.zh.md)
</div>
---

View File

@@ -0,0 +1,120 @@
# Infographic to Markdown
> **Version:** 1.0.0 | **Author:** Fu-Jie
AI-powered infographic generator that renders SVG on the frontend and embeds it directly into Markdown as a Data URL image.
## Overview
This plugin combines the power of AI text analysis with AntV Infographic visualization to create beautiful infographics that are embedded directly into chat messages as Markdown images.
### Key Features
- :robot: **AI-Powered**: Automatically analyzes text and selects the best infographic template
- :bar_chart: **Multiple Templates**: Supports 18+ infographic templates (lists, charts, comparisons, etc.)
- :framed_picture: **Self-Contained**: SVG/PNG embedded as Data URL, no external dependencies
- :memo: **Markdown Native**: Results are pure Markdown images, compatible everywhere
- :arrows_counterclockwise: **API Writeback**: Updates message content via REST API for persistence
### How It Works
```mermaid
graph TD
A[User triggers action] --> B[Python extracts message content]
B --> C[LLM generates Infographic syntax]
C --> D[Frontend JS loads AntV library]
D --> E[Render SVG offscreen]
E --> F[Export to Data URL]
F --> G[Update message via API]
G --> H[Display as Markdown image]
```
## Installation
1. Download `infographic_markdown.py` (English) or `infographic_markdown_cn.py` (Chinese)
2. Navigate to **Admin Panel****Settings****Functions**
3. Upload the file and configure settings
4. Use the action button in chat messages
## Configuration
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `SHOW_STATUS` | bool | `true` | Show operation status updates |
| `MODEL_ID` | string | `""` | LLM model ID (empty = use current model) |
| `MIN_TEXT_LENGTH` | int | `50` | Minimum text length required |
| `MESSAGE_COUNT` | int | `1` | Number of recent messages to use |
| `SVG_WIDTH` | int | `800` | Width of generated SVG (pixels) |
| `EXPORT_FORMAT` | string | `"svg"` | Export format: `svg` or `png` |
## Supported Templates
| Category | Template | Description |
|----------|----------|-------------|
| List | `list-grid` | Grid cards |
| List | `list-vertical` | Vertical list |
| Tree | `tree-vertical` | Vertical tree |
| Tree | `tree-horizontal` | Horizontal tree |
| Mind Map | `mindmap` | Mind map |
| Process | `sequence-roadmap` | Roadmap |
| Process | `sequence-zigzag` | Zigzag process |
| Relation | `relation-sankey` | Sankey diagram |
| Relation | `relation-circle` | Circular relation |
| Compare | `compare-binary` | Binary comparison |
| Analysis | `compare-swot` | SWOT analysis |
| Quadrant | `quadrant-quarter` | Quadrant chart |
| Chart | `chart-bar` | Bar chart |
| Chart | `chart-column` | Column chart |
| Chart | `chart-line` | Line chart |
| Chart | `chart-pie` | Pie chart |
| Chart | `chart-doughnut` | Doughnut chart |
| Chart | `chart-area` | Area chart |
## Usage Example
1. Generate some text content in the chat (or have the AI generate it)
2. Click the **📊 Infographic to Markdown** action button
3. Wait for AI analysis and SVG rendering
4. The infographic will be embedded as a Markdown image
## Technical Details
### Data URL Embedding
The plugin converts SVG graphics to Base64-encoded Data URLs:
```javascript
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = "data:image/svg+xml;base64," + base64;
const markdownImage = `![description](${dataUri})`;
```
### AntV toDataURL API
```javascript
// Export as SVG (recommended)
const svgUrl = await instance.toDataURL({
type: 'svg',
embedResources: true
});
// Export as PNG
const pngUrl = await instance.toDataURL({
type: 'png',
dpr: 2
});
```
## Notes
1. **Browser Compatibility**: Requires modern browsers with ES6+ and Fetch API support
2. **Network Dependency**: First use requires loading AntV library from CDN
3. **Data URL Size**: Base64 encoding increases size by ~33%
4. **Chinese Fonts**: SVG export embeds fonts for correct display
## Related Resources
- [AntV Infographic Documentation](https://infographic.antv.vision/)
- [Infographic API Reference](https://infographic.antv.vision/reference/infographic-api)
- [Infographic Syntax Guide](https://infographic.antv.vision/learn/infographic-syntax)

View File

@@ -0,0 +1,120 @@
# 信息图转 Markdown
> **版本:** 1.0.0 | **作者:** Fu-Jie
AI 驱动的信息图生成器,在前端渲染 SVG 并以 Data URL 图片格式直接嵌入到 Markdown 中。
## 概述
这个插件结合了 AI 文本分析能力和 AntV Infographic 可视化引擎,生成精美的信息图并以 Markdown 图片格式直接嵌入到聊天消息中。
### 主要特性
- :robot: **AI 驱动**: 自动分析文本并选择最佳的信息图模板
- :bar_chart: **多种模板**: 支持 18+ 种信息图模板(列表、图表、对比等)
- :framed_picture: **自包含**: SVG/PNG 以 Data URL 嵌入,无外部依赖
- :memo: **Markdown 原生**: 结果是纯 Markdown 图片,兼容任何平台
- :arrows_counterclockwise: **API 回写**: 通过 REST API 更新消息内容实现持久化
### 工作原理
```mermaid
graph TD
A[用户触发动作] --> B[Python 提取消息内容]
B --> C[LLM 生成 Infographic 语法]
C --> D[前端 JS 加载 AntV 库]
D --> E[离屏渲染 SVG]
E --> F[导出为 Data URL]
F --> G[通过 API 更新消息]
G --> H[显示为 Markdown 图片]
```
## 安装
1. 下载 `infographic_markdown.py`(英文版)或 `infographic_markdown_cn.py`(中文版)
2. 进入 **管理面板****设置****功能**
3. 上传文件并配置设置
4. 在聊天消息中使用动作按钮
## 配置选项
| 参数 | 类型 | 默认值 | 描述 |
|------|------|--------|------|
| `SHOW_STATUS` | bool | `true` | 是否显示操作状态 |
| `MODEL_ID` | string | `""` | LLM 模型 ID空则使用当前模型 |
| `MIN_TEXT_LENGTH` | int | `50` | 最小文本长度要求 |
| `MESSAGE_COUNT` | int | `1` | 用于生成的最近消息数量 |
| `SVG_WIDTH` | int | `800` | 生成的 SVG 宽度(像素) |
| `EXPORT_FORMAT` | string | `"svg"` | 导出格式:`svg``png` |
## 支持的模板
| 类别 | 模板名称 | 描述 |
|------|----------|------|
| 列表 | `list-grid` | 网格卡片 |
| 列表 | `list-vertical` | 垂直列表 |
| 树形 | `tree-vertical` | 垂直树 |
| 树形 | `tree-horizontal` | 水平树 |
| 思维导图 | `mindmap` | 思维导图 |
| 流程 | `sequence-roadmap` | 路线图 |
| 流程 | `sequence-zigzag` | 折线流程 |
| 关系 | `relation-sankey` | 桑基图 |
| 关系 | `relation-circle` | 圆形关系 |
| 对比 | `compare-binary` | 二元对比 |
| 分析 | `compare-swot` | SWOT 分析 |
| 象限 | `quadrant-quarter` | 四象限图 |
| 图表 | `chart-bar` | 条形图 |
| 图表 | `chart-column` | 柱状图 |
| 图表 | `chart-line` | 折线图 |
| 图表 | `chart-pie` | 饼图 |
| 图表 | `chart-doughnut` | 环形图 |
| 图表 | `chart-area` | 面积图 |
## 使用示例
1. 在聊天中生成一些文本内容(或让 AI 生成)
2. 点击 **📊 信息图转 Markdown** 动作按钮
3. 等待 AI 分析和 SVG 渲染
4. 信息图将以 Markdown 图片形式嵌入
## 技术细节
### Data URL 嵌入
插件将 SVG 图形转换为 Base64 编码的 Data URL
```javascript
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = "data:image/svg+xml;base64," + base64;
const markdownImage = `![描述](${dataUri})`;
```
### AntV toDataURL API
```javascript
// 导出 SVG推荐
const svgUrl = await instance.toDataURL({
type: 'svg',
embedResources: true
});
// 导出 PNG
const pngUrl = await instance.toDataURL({
type: 'png',
dpr: 2
});
```
## 注意事项
1. **浏览器兼容性**: 需要现代浏览器支持 ES6+ 和 Fetch API
2. **网络依赖**: 首次使用需要从 CDN 加载 AntV Infographic 库
3. **Data URL 大小**: Base64 编码会增加约 33% 的体积
4. **中文字体**: SVG 导出时会嵌入字体以确保正确显示
## 相关资源
- [AntV Infographic 官方文档](https://infographic.antv.vision/)
- [Infographic API 参考](https://infographic.antv.vision/reference/infographic-api)
- [Infographic 语法规范](https://infographic.antv.vision/learn/infographic-syntax)

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@@ -24,11 +24,24 @@ You can configure the following settings via the **Valves** button in the plugin
- `chat_title`: Use the conversation title (default).
- `ai_generated`: Use AI to generate a short title based on the content.
- `markdown_title`: Extract the first h1/h2 heading from the Markdown content.
- **MERMAID_JS_URL**: URL for the Mermaid.js library (for diagram rendering).
- **MAX_EMBED_IMAGE_MB**: Maximum image size to embed into DOCX (MB). Default: `20`.
- **UI_LANGUAGE**: User interface language, supports `en` (English) and `zh` (Chinese). Default: `en`.
- **FONT_LATIN**: Font name for Latin characters. Default: `Times New Roman`.
- **FONT_ASIAN**: Font name for Asian characters. Default: `SimSun`.
- **FONT_CODE**: Font name for code blocks. Default: `Consolas`.
- **TABLE_HEADER_COLOR**: Table header background color (Hex without #). Default: `F2F2F2`.
- **TABLE_ZEBRA_COLOR**: Table alternating row background color (Hex without #). Default: `FBFBFB`.
- **MERMAID_JS_URL**: URL for the Mermaid.js library.
- **MERMAID_JSZIP_URL**: URL for the JSZip library (required for DOCX manipulation).
- **MERMAID_PNG_SCALE**: Scale factor for Mermaid PNG generation (Resolution). Default: `3.0`.
- **MERMAID_DISPLAY_SCALE**: Scale factor for Mermaid visual size in Word. Default: `1.5`.
- **MERMAID_OPTIMIZE_LAYOUT**: Automatically convert LR (Left-Right) flowcharts to TD (Top-Down). Default: `True`.
- **MERMAID_CAPTIONS_ENABLE**: Enable/disable figure captions for Mermaid diagrams.
- **MERMAID_DISPLAY_SCALE**: Scale factor for Mermaid visual size in Word. Default: `1.0`.
- **MERMAID_OPTIMIZE_LAYOUT**: Automatically convert LR (Left-Right) flowcharts to TD (Top-Down). Default: `False`.
- **MERMAID_BACKGROUND**: Background color for Mermaid diagrams (e.g., `white`, `transparent`). Default: `transparent`.
- **MERMAID_CAPTIONS_ENABLE**: Enable/disable figure captions for Mermaid diagrams. Default: `True`.
- **MERMAID_CAPTION_STYLE**: Paragraph style name for Mermaid captions. Default: `Caption`.
- **MERMAID_CAPTION_PREFIX**: Caption prefix label (e.g., 'Figure'). Empty = auto-detect based on language.
- **MATH_ENABLE**: Enable LaTeX math block conversion (`\[...\]` and `$$...$$`). Default: `True`.
- **MATH_INLINE_DOLLAR_ENABLE**: Enable inline `$ ... $` math conversion. Default: `True`.
## Supported Markdown Syntax
@@ -75,6 +88,20 @@ All dependencies are declared in the plugin docstring.
## Changelog
### v0.4.0
- **Multi-language Support**: Added UI language switching (English/Chinese) with localized messages.
- **Font & Style Configuration**: Customizable fonts for Latin/Asian text and code, plus table colors.
- **Mermaid Enhancements**:
- Hybrid client-side rendering (SVG+PNG) for better clarity and compatibility.
- Configurable background color, fixing issues in dark mode.
- Added error boundaries to prevent export failures on render errors.
- **Performance**: Real-time progress updates for large document exports.
- **Bug Fixes**:
- Fixed parsing errors in Markdown tables containing code blocks or links.
- Fixed parsing issues with underscores (`_`), asterisks (`*`), and tildes (`~`) used as long separators.
- Enhanced error handling for image embedding.
### v0.3.0
- **Mermaid Diagrams**: Native support for rendering Mermaid diagrams as images in Word.

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@@ -20,15 +20,28 @@
您可以通过插件设置中的 **Valves** 按钮配置以下选项:
- **TITLE_SOURCE**:选择文档标题/文件名的生成方式。
- **文档标题来源**:选择文档标题/文件名的生成方式。
- `chat_title`:使用对话标题(默认)。
- `ai_generated`:使用 AI 根据内容生成简短标题。
- `markdown_title`:从 Markdown 内容中提取第一个一级或二级标题。
- **MERMAID_JS_URL**Mermaid.js 库的 URL用于图表渲染
- **MERMAID_PNG_SCALE**Mermaid PNG 生成缩放比例(分辨率)。默认:`3.0`
- **MERMAID_DISPLAY_SCALE**Mermaid 在 Word 中的显示比例(视觉大小)。默认:`1.5`
- **MERMAID_OPTIMIZE_LAYOUT**:自动将 LR左右流程图转换为 TD上下。默认`True`
- **MERMAID_CAPTIONS_ENABLE**:启用/禁用 Mermaid 图表的图注
- **最大嵌入图片大小MB**:嵌入图片的最大大小 (MB)。默认:`20`
- **界面语言**:界面语言,支持 `en` (英语) 和 `zh` (中文)。默认:`zh`
- **英文字体**:英文字体名称。默认:`Calibri`
- **中文字体**:中文字体名称。默认:`SimSun`
- **代码字体**:代码字体名称。默认:`Consolas`
- **表头背景色**:表头背景色(十六进制,不带#)。默认:`F2F2F2`
- **表格隔行背景色**:表格隔行背景色(十六进制,不带#)。默认:`FBFBFB`
- **Mermaid_JS地址**Mermaid.js 库的 URL。
- **JSZip库地址**JSZip 库的 URL用于 DOCX 操作)。
- **Mermaid_PNG缩放比例**Mermaid PNG 生成缩放比例(分辨率)。默认:`3.0`
- **Mermaid显示比例**Mermaid 在 Word 中的显示比例(视觉大小)。默认:`1.0`
- **Mermaid布局优化**:自动将 LR左右流程图转换为 TD上下。默认`False`
- **Mermaid背景色**Mermaid 图表背景色(如 `white`, `transparent`)。默认:`transparent`
- **启用Mermaid图注**:启用/禁用 Mermaid 图表的图注。默认:`True`
- **Mermaid图注样式**Mermaid 图注的段落样式名称。默认:`Caption`
- **Mermaid图注前缀**:图注前缀(如 '图')。留空则根据语言自动检测。
- **启用数学公式**:启用 LaTeX 数学公式块转换(`\[...\]``$$...$$`)。默认:`True`
- **启用行内公式**:启用行内 `$ ... $` 数学公式转换。默认:`True`
## 支持的 Markdown 语法
@@ -75,6 +88,24 @@
## 更新日志
### v0.4.1
- **中文参数名**: 将插件配置项名称和描述全部汉化,提升中文用户体验。
### v0.4.0
- **多语言支持**: 新增界面语言切换(中文/英文),提示信息更友好。
- **字体与样式配置**: 支持自定义中英文字体、代码字体以及表格颜色。
- **Mermaid 增强**:
- 客户端混合渲染SVG+PNG提高清晰度与兼容性。
- 支持背景色配置,修复深色模式下的显示问题。
- 增加错误边界,渲染失败时显示提示而非中断导出。
- **性能优化**: 导出大型文档时提供实时进度反馈。
- **Bug 修复**:
- 修复 Markdown 表格中包含代码块或链接时的解析错误。
- 修复下划线(`_`)、星号(`*`)、波浪号(`~`)作为长分隔符时的解析问题。
- 增强图片嵌入的错误处理。
### v0.3.0
- **Mermaid 图表**: 原生支持将 Mermaid 图表渲染为 Word 中的图片。

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# Infographic to Markdown
> **Version:** 1.0.0
AI-powered infographic generator that renders SVG on the frontend and embeds it directly into Markdown as a Data URL image.
## Overview
This plugin combines the power of AI text analysis with AntV Infographic visualization to create beautiful infographics that are embedded directly into chat messages as Markdown images.
### How It Works
```
┌─────────────────────────────────────────────────────────────┐
│ Open WebUI Plugin │
├─────────────────────────────────────────────────────────────┤
│ 1. Python Action │
│ ├── Receive message content │
│ ├── Call LLM to generate Infographic syntax │
│ └── Send __event_call__ to execute frontend JS │
├─────────────────────────────────────────────────────────────┤
│ 2. Browser JS (via __event_call__) │
│ ├── Dynamically load AntV Infographic library │
│ ├── Render SVG offscreen │
│ ├── Export to Data URL via toDataURL() │
│ └── Update message content via REST API │
├─────────────────────────────────────────────────────────────┤
│ 3. Markdown Rendering │
│ └── Display ![description](data:image/svg+xml;base64,...) │
└─────────────────────────────────────────────────────────────┘
```
## Features
- 🤖 **AI-Powered**: Automatically analyzes text and selects the best infographic template
- 📊 **Multiple Templates**: Supports 18+ infographic templates (lists, charts, comparisons, etc.)
- 🖼️ **Self-Contained**: SVG/PNG embedded as Data URL, no external dependencies
- 📝 **Markdown Native**: Results are pure Markdown images, compatible everywhere
- 🔄 **API Writeback**: Updates message content via REST API for persistence
## Plugins in This Directory
### 1. `infographic_markdown.py` - Main Plugin ⭐
- **Purpose**: Production use
- **Features**: Full AI + AntV Infographic + Data URL embedding
### 2. `js_render_poc.py` - Proof of Concept
- **Purpose**: Learning and testing
- **Features**: Simple SVG creation demo, `__event_call__` pattern
## Configuration (Valves)
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `SHOW_STATUS` | bool | `true` | Show operation status updates |
| `MODEL_ID` | string | `""` | LLM model ID (empty = use current model) |
| `MIN_TEXT_LENGTH` | int | `50` | Minimum text length required |
| `MESSAGE_COUNT` | int | `1` | Number of recent messages to use |
| `SVG_WIDTH` | int | `800` | Width of generated SVG (pixels) |
| `EXPORT_FORMAT` | string | `"svg"` | Export format: `svg` or `png` |
## Supported Templates
| Category | Template | Description |
|----------|----------|-------------|
| List | `list-grid` | Grid cards |
| List | `list-vertical` | Vertical list |
| Tree | `tree-vertical` | Vertical tree |
| Tree | `tree-horizontal` | Horizontal tree |
| Mind Map | `mindmap` | Mind map |
| Process | `sequence-roadmap` | Roadmap |
| Process | `sequence-zigzag` | Zigzag process |
| Relation | `relation-sankey` | Sankey diagram |
| Relation | `relation-circle` | Circular relation |
| Compare | `compare-binary` | Binary comparison |
| Analysis | `compare-swot` | SWOT analysis |
| Quadrant | `quadrant-quarter` | Quadrant chart |
| Chart | `chart-bar` | Bar chart |
| Chart | `chart-column` | Column chart |
| Chart | `chart-line` | Line chart |
| Chart | `chart-pie` | Pie chart |
| Chart | `chart-doughnut` | Doughnut chart |
| Chart | `chart-area` | Area chart |
## Syntax Examples
### Grid List
```infographic
infographic list-grid
data
title Project Overview
items
- label Module A
desc Description of module A
- label Module B
desc Description of module B
```
### Binary Comparison
```infographic
infographic compare-binary
data
title Pros vs Cons
items
- label Pros
children
- label Strong R&D
desc Technology leadership
- label Cons
children
- label Weak brand
desc Insufficient marketing
```
### Bar Chart
```infographic
infographic chart-bar
data
title Quarterly Revenue
items
- label Q1
value 120
- label Q2
value 150
```
## Technical Details
### Data URL Embedding
```javascript
// SVG to Base64 Data URL
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = "data:image/svg+xml;base64," + base64;
// Markdown image syntax
const markdownImage = `![description](${dataUri})`;
```
### AntV toDataURL API
```javascript
// Export as SVG (recommended, supports embedded resources)
const svgUrl = await instance.toDataURL({
type: 'svg',
embedResources: true
});
// Export as PNG (more compatible but larger)
const pngUrl = await instance.toDataURL({
type: 'png',
dpr: 2
});
```
## Notes
1. **Browser Compatibility**: Requires modern browsers with ES6+ and Fetch API support
2. **Network Dependency**: First use requires loading AntV library from CDN
3. **Data URL Size**: Base64 encoding increases size by ~33%
4. **Chinese Fonts**: SVG export embeds fonts for correct display
## Related Resources
- [AntV Infographic Documentation](https://infographic.antv.vision/)
- [Infographic API Reference](https://infographic.antv.vision/reference/infographic-api)
- [Infographic Syntax Guide](https://infographic.antv.vision/learn/infographic-syntax)
## License
MIT License

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@@ -0,0 +1,174 @@
# 信息图转 Markdown
> **版本:** 1.0.0
AI 驱动的信息图生成器,在前端渲染 SVG 并以 Data URL 图片格式直接嵌入到 Markdown 中。
## 概述
这个插件结合了 AI 文本分析能力和 AntV Infographic 可视化引擎,生成精美的信息图并以 Markdown 图片格式直接嵌入到聊天消息中。
### 工作原理
```
┌─────────────────────────────────────────────────────────────┐
│ Open WebUI 插件 │
├─────────────────────────────────────────────────────────────┤
│ 1. Python Action │
│ ├── 接收消息内容 │
│ ├── 调用 LLM 生成 Infographic 语法 │
│ └── 发送 __event_call__ 执行前端 JS │
├─────────────────────────────────────────────────────────────┤
│ 2. 浏览器 JS (通过 __event_call__) │
│ ├── 动态加载 AntV Infographic 库 │
│ ├── 离屏渲染 SVG │
│ ├── 使用 toDataURL() 导出 Data URL │
│ └── 通过 REST API 更新消息内容 │
├─────────────────────────────────────────────────────────────┤
│ 3. Markdown 渲染 │
│ └── 显示 ![描述](data:image/svg+xml;base64,...) │
└─────────────────────────────────────────────────────────────┘
```
## 功能特点
- 🤖 **AI 驱动**: 自动分析文本并选择最佳的信息图模板
- 📊 **多种模板**: 支持 18+ 种信息图模板(列表、图表、对比等)
- 🖼️ **自包含**: SVG/PNG 以 Data URL 嵌入,无外部依赖
- 📝 **Markdown 原生**: 结果是纯 Markdown 图片,兼容任何平台
- 🔄 **API 回写**: 通过 REST API 更新消息内容实现持久化
## 目录中的插件
### 1. `infographic_markdown.py` - 主插件 ⭐
- **用途**: 生产使用
- **功能**: 完整的 AI + AntV Infographic + Data URL 嵌入
### 2. `infographic_markdown_cn.py` - 主插件(中文版)
- **用途**: 生产使用
- **功能**: 与英文版相同,界面文字为中文
### 3. `js_render_poc.py` - 概念验证
- **用途**: 学习和测试
- **功能**: 简单的 SVG 创建演示,`__event_call__` 模式
## 配置选项 (Valves)
| 参数 | 类型 | 默认值 | 描述 |
|------|------|--------|------|
| `SHOW_STATUS` | bool | `true` | 是否显示操作状态 |
| `MODEL_ID` | string | `""` | LLM 模型 ID空则使用当前模型 |
| `MIN_TEXT_LENGTH` | int | `50` | 最小文本长度要求 |
| `MESSAGE_COUNT` | int | `1` | 用于生成的最近消息数量 |
| `SVG_WIDTH` | int | `800` | 生成的 SVG 宽度(像素) |
| `EXPORT_FORMAT` | string | `"svg"` | 导出格式:`svg``png` |
## 支持的模板
| 类别 | 模板名称 | 描述 |
|------|----------|------|
| 列表 | `list-grid` | 网格卡片 |
| 列表 | `list-vertical` | 垂直列表 |
| 树形 | `tree-vertical` | 垂直树 |
| 树形 | `tree-horizontal` | 水平树 |
| 思维导图 | `mindmap` | 思维导图 |
| 流程 | `sequence-roadmap` | 路线图 |
| 流程 | `sequence-zigzag` | 折线流程 |
| 关系 | `relation-sankey` | 桑基图 |
| 关系 | `relation-circle` | 圆形关系 |
| 对比 | `compare-binary` | 二元对比 |
| 分析 | `compare-swot` | SWOT 分析 |
| 象限 | `quadrant-quarter` | 四象限图 |
| 图表 | `chart-bar` | 条形图 |
| 图表 | `chart-column` | 柱状图 |
| 图表 | `chart-line` | 折线图 |
| 图表 | `chart-pie` | 饼图 |
| 图表 | `chart-doughnut` | 环形图 |
| 图表 | `chart-area` | 面积图 |
## 语法示例
### 网格列表
```infographic
infographic list-grid
data
title 项目概览
items
- label 模块一
desc 这是第一个模块的描述
- label 模块二
desc 这是第二个模块的描述
```
### 二元对比
```infographic
infographic compare-binary
data
title 优劣对比
items
- label 优势
children
- label 研发能力强
desc 技术领先
- label 劣势
children
- label 品牌曝光不足
desc 营销力度不够
```
### 条形图
```infographic
infographic chart-bar
data
title 季度收入
items
- label Q1
value 120
- label Q2
value 150
```
## 技术细节
### Data URL 嵌入
```javascript
// SVG 转 Base64 Data URL
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = "data:image/svg+xml;base64," + base64;
// Markdown 图片语法
const markdownImage = `![描述](${dataUri})`;
```
### AntV toDataURL API
```javascript
// 导出 SVG推荐支持嵌入资源
const svgUrl = await instance.toDataURL({
type: 'svg',
embedResources: true
});
// 导出 PNG更兼容但体积更大
const pngUrl = await instance.toDataURL({
type: 'png',
dpr: 2
});
```
## 注意事项
1. **浏览器兼容性**: 需要现代浏览器支持 ES6+ 和 Fetch API
2. **网络依赖**: 首次使用需要从 CDN 加载 AntV Infographic 库
3. **Data URL 大小**: Base64 编码会增加约 33% 的体积
4. **中文字体**: SVG 导出时会嵌入字体以确保正确显示
## 相关资源
- [AntV Infographic 官方文档](https://infographic.antv.vision/)
- [Infographic API 参考](https://infographic.antv.vision/reference/infographic-api)
- [Infographic 语法规范](https://infographic.antv.vision/learn/infographic-syntax)
## 许可证
MIT License

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@@ -0,0 +1,592 @@
"""
title: 📊 Infographic to Markdown
author: Fu-Jie
version: 1.0.0
description: AI生成信息图语法前端渲染SVG并转换为Markdown图片格式嵌入消息。支持AntV Infographic模板。
"""
import time
import json
import logging
import re
from typing import Optional, Callable, Awaitable, Any, Dict
from pydantic import BaseModel, Field
from fastapi import Request
from datetime import datetime
from open_webui.utils.chat import generate_chat_completion
from open_webui.models.users import Users
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# =================================================================
# LLM Prompts
# =================================================================
SYSTEM_PROMPT_INFOGRAPHIC = """
You are a professional infographic design expert who can analyze user-provided text content and convert it into AntV Infographic syntax format.
## Infographic Syntax Specification
Infographic syntax is a Mermaid-like declarative syntax for describing infographic templates, data, and themes.
### Syntax Rules
- Entry uses `infographic <template-name>`
- Key-value pairs are separated by spaces, **absolutely NO colons allowed**
- Use two spaces for indentation
- Object arrays use `-` with line breaks
⚠️ **IMPORTANT WARNING: This is NOT YAML format!**
- ❌ Wrong: `children:` `items:` `data:` (with colons)
- ✅ Correct: `children` `items` `data` (without colons)
### Template Library & Selection Guide
Choose the most appropriate template based on the content structure:
#### 1. List & Hierarchy
- **List**: `list-grid` (Grid Cards), `list-vertical` (Vertical List)
- **Tree**: `tree-vertical` (Vertical Tree), `tree-horizontal` (Horizontal Tree)
- **Mindmap**: `mindmap` (Mind Map)
#### 2. Sequence & Relationship
- **Process**: `sequence-roadmap` (Roadmap), `sequence-zigzag` (Zigzag Process)
- **Relationship**: `relation-sankey` (Sankey Diagram), `relation-circle` (Circular)
#### 3. Comparison & Analysis
- **Comparison**: `compare-binary` (Binary Comparison)
- **Analysis**: `compare-swot` (SWOT Analysis), `quadrant-quarter` (Quadrant Chart)
#### 4. Charts & Data
- **Charts**: `chart-bar`, `chart-column`, `chart-line`, `chart-pie`, `chart-doughnut`, `chart-area`
### Data Structure Examples
#### A. Standard List/Tree
```infographic
infographic list-grid
data
title Project Modules
items
- label Module A
desc Description of A
- label Module B
desc Description of B
```
#### B. Binary Comparison
```infographic
infographic compare-binary
data
title Advantages vs Disadvantages
items
- label Advantages
children
- label Strong R&D
desc Leading technology
- label Disadvantages
children
- label Weak brand
desc Insufficient marketing
```
#### C. Charts
```infographic
infographic chart-bar
data
title Quarterly Revenue
items
- label Q1
value 120
- label Q2
value 150
```
### Common Data Fields
- `label`: Main title/label (Required)
- `desc`: Description text (max 30 Chinese chars / 60 English chars for `list-grid`)
- `value`: Numeric value (for charts)
- `children`: Nested items
## Output Requirements
1. **Language**: Output content in the user's language.
2. **Format**: Wrap output in ```infographic ... ```.
3. **No Colons**: Do NOT use colons after keys.
4. **Indentation**: Use 2 spaces.
"""
USER_PROMPT_GENERATE = """
Please analyze the following text content and convert its core information into AntV Infographic syntax format.
---
**User Context:**
User Name: {user_name}
Current Date/Time: {current_date_time_str}
User Language: {user_language}
---
**Text Content:**
{long_text_content}
Please select the most appropriate infographic template based on text characteristics and output standard infographic syntax.
**Important Note:**
- If using `list-grid` format, ensure each card's `desc` description is limited to **maximum 30 Chinese characters** (or **approximately 60 English characters**).
- Descriptions should be concise and highlight key points.
"""
class Action:
class Valves(BaseModel):
SHOW_STATUS: bool = Field(
default=True, description="Show operation status updates in chat interface."
)
MODEL_ID: str = Field(
default="",
description="LLM model ID for text analysis. If empty, uses current conversation model.",
)
MIN_TEXT_LENGTH: int = Field(
default=50,
description="Minimum text length (characters) required for infographic analysis.",
)
MESSAGE_COUNT: int = Field(
default=1,
description="Number of recent messages to use for generation.",
)
SVG_WIDTH: int = Field(
default=800,
description="Width of generated SVG in pixels.",
)
EXPORT_FORMAT: str = Field(
default="svg",
description="Export format: 'svg' or 'png'.",
)
def __init__(self):
self.valves = self.Valves()
def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
"""Extract chat_id from body or metadata"""
if isinstance(body, dict):
chat_id = body.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
chat_id = body_metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
if isinstance(metadata, dict):
chat_id = metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
return ""
def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
"""Extract message_id from body or metadata"""
if isinstance(body, dict):
message_id = body.get("id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
message_id = body_metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
if isinstance(metadata, dict):
message_id = metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
return ""
def _extract_infographic_syntax(self, llm_output: str) -> str:
"""Extract infographic syntax from LLM output"""
match = re.search(r"```infographic\s*(.*?)\s*```", llm_output, re.DOTALL)
if match:
return match.group(1).strip()
else:
logger.warning("LLM output did not follow expected format, treating entire output as syntax.")
return llm_output.strip()
def _extract_text_content(self, content) -> str:
"""Extract text from message content, supporting multimodal formats"""
if isinstance(content, str):
return content
elif isinstance(content, list):
text_parts = []
for item in content:
if isinstance(item, dict) and item.get("type") == "text":
text_parts.append(item.get("text", ""))
elif isinstance(item, str):
text_parts.append(item)
return "\n".join(text_parts)
return str(content) if content else ""
async def _emit_status(self, emitter, description: str, done: bool = False):
"""Send status update event"""
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
def _generate_js_code(
self,
unique_id: str,
chat_id: str,
message_id: str,
infographic_syntax: str,
svg_width: int,
export_format: str,
) -> str:
"""Generate JavaScript code for frontend SVG rendering"""
# Escape the syntax for JS embedding
syntax_escaped = (
infographic_syntax
.replace("\\", "\\\\")
.replace("`", "\\`")
.replace("${", "\\${")
.replace("</script>", "<\\/script>")
)
# Template mapping (same as infographic.py)
template_mapping_js = """
const TEMPLATE_MAPPING = {
'list-grid': 'list-grid-compact-card',
'list-vertical': 'list-column-simple-vertical-arrow',
'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
'sequence-roadmap': 'sequence-roadmap-vertical-simple',
'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
'relation-sankey': 'relation-sankey-simple',
'relation-circle': 'relation-circle-icon-badge',
'compare-binary': 'compare-binary-horizontal-simple-vs',
'compare-swot': 'compare-swot',
'quadrant-quarter': 'quadrant-quarter-simple-card',
'statistic-card': 'list-grid-compact-card',
'chart-bar': 'chart-bar-plain-text',
'chart-column': 'chart-column-simple',
'chart-line': 'chart-line-plain-text',
'chart-area': 'chart-area-simple',
'chart-pie': 'chart-pie-plain-text',
'chart-doughnut': 'chart-pie-donut-plain-text'
};
"""
return f"""
(async function() {{
const uniqueId = "{unique_id}";
const chatId = "{chat_id}";
const messageId = "{message_id}";
const svgWidth = {svg_width};
const exportFormat = "{export_format}";
console.log("[Infographic Markdown] Starting render...");
console.log("[Infographic Markdown] chatId:", chatId, "messageId:", messageId);
try {{
// Load AntV Infographic if not loaded
if (typeof AntVInfographic === 'undefined') {{
console.log("[Infographic Markdown] Loading AntV Infographic library...");
await new Promise((resolve, reject) => {{
const script = document.createElement('script');
script.src = 'https://unpkg.com/@antv/infographic@latest/dist/infographic.min.js';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
}});
console.log("[Infographic Markdown] Library loaded.");
}}
const {{ Infographic }} = AntVInfographic;
// Get infographic syntax
let syntaxContent = `{syntax_escaped}`;
console.log("[Infographic Markdown] Original syntax:", syntaxContent.substring(0, 200) + "...");
// Clean up syntax
const backtick = String.fromCharCode(96);
const prefix = backtick + backtick + backtick + 'infographic';
const simplePrefix = backtick + backtick + backtick;
if (syntaxContent.toLowerCase().startsWith(prefix)) {{
syntaxContent = syntaxContent.substring(prefix.length).trim();
}} else if (syntaxContent.startsWith(simplePrefix)) {{
syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
}}
if (syntaxContent.endsWith(simplePrefix)) {{
syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
}}
// Fix colons after keywords
syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
// Ensure infographic prefix
if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
syntaxContent = 'infographic list-grid\\n' + syntaxContent;
}}
// Apply template mapping
{template_mapping_js}
for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
if (regex.test(syntaxContent)) {{
console.log(`[Infographic Markdown] Auto-mapping: ${{key}} -> ${{value}}`);
syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
break;
}}
}}
console.log("[Infographic Markdown] Cleaned syntax:", syntaxContent.substring(0, 200) + "...");
// Create offscreen container
const container = document.createElement('div');
container.id = 'infographic-offscreen-' + uniqueId;
container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;';
document.body.appendChild(container);
// Create and render infographic
const instance = new Infographic({{
container: '#' + container.id,
width: svgWidth,
padding: 24,
}});
console.log("[Infographic Markdown] Rendering infographic...");
instance.render(syntaxContent);
// Wait for render and export
await new Promise(resolve => setTimeout(resolve, 1000));
let dataUrl;
if (exportFormat === 'png') {{
dataUrl = await instance.toDataURL({{ type: 'png', dpr: 2 }});
}} else {{
dataUrl = await instance.toDataURL({{ type: 'svg', embedResources: true }});
}}
console.log("[Infographic Markdown] Data URL generated, length:", dataUrl.length);
// Cleanup
instance.destroy();
document.body.removeChild(container);
// Generate markdown image
const markdownImage = `![📊 AI 生成的信息图](${{dataUrl}})`;
// Update message via API
if (chatId && messageId) {{
const token = localStorage.getItem("token");
// Get current message content
const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
method: "GET",
headers: {{ "Authorization": `Bearer ${{token}}` }}
}});
if (!getResponse.ok) {{
throw new Error("Failed to get chat data: " + getResponse.status);
}}
const chatData = await getResponse.json();
let originalContent = "";
if (chatData.chat && chatData.chat.messages) {{
const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
if (targetMsg && targetMsg.content) {{
originalContent = targetMsg.content;
}}
}}
// Remove existing infographic images
const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\(data:image\\/[^)]+\\)/g;
let cleanedContent = originalContent.replace(infographicPattern, "");
cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
// Append new image
const newContent = cleanedContent + "\\n\\n" + markdownImage;
// Update message
const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
method: "POST",
headers: {{
"Content-Type": "application/json",
"Authorization": `Bearer ${{token}}`
}},
body: JSON.stringify({{
type: "chat:message",
data: {{ content: newContent }}
}})
}});
if (updateResponse.ok) {{
console.log("[Infographic Markdown] ✅ Message updated successfully!");
}} else {{
console.error("[Infographic Markdown] API error:", updateResponse.status);
}}
}} else {{
console.warn("[Infographic Markdown] ⚠️ Missing chatId or messageId");
}}
}} catch (error) {{
console.error("[Infographic Markdown] Error:", error);
}}
}})();
"""
async def action(
self,
body: dict,
__user__: dict = None,
__event_emitter__=None,
__event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
__metadata__: Optional[dict] = None,
__request__: Request = None,
) -> dict:
"""
Generate infographic using AntV and embed as Markdown image.
"""
logger.info("Action: Infographic to Markdown started")
# Get user information
if isinstance(__user__, (list, tuple)):
user_language = __user__[0].get("language", "en") if __user__ else "en"
user_name = __user__[0].get("name", "User") if __user__[0] else "User"
user_id = __user__[0].get("id", "unknown_user") if __user__ else "unknown_user"
elif isinstance(__user__, dict):
user_language = __user__.get("language", "en")
user_name = __user__.get("name", "User")
user_id = __user__.get("id", "unknown_user")
else:
user_language = "en"
user_name = "User"
user_id = "unknown_user"
# Get current time
now = datetime.now()
current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
try:
messages = body.get("messages", [])
if not messages:
raise ValueError("No messages available.")
# Get recent messages
message_count = min(self.valves.MESSAGE_COUNT, len(messages))
recent_messages = messages[-message_count:]
# Aggregate content
aggregated_parts = []
for msg in recent_messages:
text_content = self._extract_text_content(msg.get("content"))
if text_content:
aggregated_parts.append(text_content)
if not aggregated_parts:
raise ValueError("No text content found in messages.")
long_text_content = "\n\n---\n\n".join(aggregated_parts)
# Remove existing HTML blocks
parts = re.split(r"```html.*?```", long_text_content, flags=re.DOTALL)
clean_content = ""
for part in reversed(parts):
if part.strip():
clean_content = part.strip()
break
if not clean_content:
clean_content = long_text_content.strip()
# Check minimum length
if len(clean_content) < self.valves.MIN_TEXT_LENGTH:
await self._emit_status(
__event_emitter__,
f"⚠️ 内容太短 ({len(clean_content)} 字符),至少需要 {self.valves.MIN_TEXT_LENGTH} 字符",
True,
)
return body
await self._emit_status(__event_emitter__, "📊 正在分析内容...", False)
# Generate infographic syntax via LLM
formatted_user_prompt = USER_PROMPT_GENERATE.format(
user_name=user_name,
current_date_time_str=current_date_time_str,
user_language=user_language,
long_text_content=clean_content,
)
target_model = self.valves.MODEL_ID or body.get("model")
llm_payload = {
"model": target_model,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT_INFOGRAPHIC},
{"role": "user", "content": formatted_user_prompt},
],
"stream": False,
}
user_obj = Users.get_user_by_id(user_id)
if not user_obj:
raise ValueError(f"Unable to get user object: {user_id}")
await self._emit_status(__event_emitter__, "📊 AI 正在生成信息图语法...", False)
llm_response = await generate_chat_completion(__request__, llm_payload, user_obj)
if not llm_response or "choices" not in llm_response or not llm_response["choices"]:
raise ValueError("Invalid LLM response.")
assistant_content = llm_response["choices"][0]["message"]["content"]
infographic_syntax = self._extract_infographic_syntax(assistant_content)
logger.info(f"Generated syntax: {infographic_syntax[:200]}...")
# Extract IDs for API callback
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
unique_id = f"ig_{int(time.time() * 1000)}"
await self._emit_status(__event_emitter__, "📊 正在渲染 SVG...", False)
# Execute JS to render and embed
if __event_call__:
js_code = self._generate_js_code(
unique_id=unique_id,
chat_id=chat_id,
message_id=message_id,
infographic_syntax=infographic_syntax,
svg_width=self.valves.SVG_WIDTH,
export_format=self.valves.EXPORT_FORMAT,
)
await __event_call__(
{
"type": "execute",
"data": {"code": js_code},
}
)
await self._emit_status(__event_emitter__, "✅ 信息图生成完成!", True)
logger.info("Infographic to Markdown completed")
except Exception as e:
error_message = f"Infographic generation failed: {str(e)}"
logger.error(error_message, exc_info=True)
await self._emit_status(__event_emitter__, f"{error_message}", True)
return body

View File

@@ -0,0 +1,592 @@
"""
title: 📊 信息图转 Markdown
author: Fu-Jie
version: 1.0.0
description: AI 生成信息图语法,前端渲染 SVG 并转换为 Markdown 图片格式嵌入消息。支持 AntV Infographic 模板。
"""
import time
import json
import logging
import re
from typing import Optional, Callable, Awaitable, Any, Dict
from pydantic import BaseModel, Field
from fastapi import Request
from datetime import datetime
from open_webui.utils.chat import generate_chat_completion
from open_webui.models.users import Users
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# =================================================================
# LLM 提示词
# =================================================================
SYSTEM_PROMPT_INFOGRAPHIC = """
你是一位专业的信息图设计专家,能够分析用户提供的文本内容并将其转换为 AntV Infographic 语法格式。
## 信息图语法规范
信息图语法是一种类似 Mermaid 的声明式语法,用于描述信息图模板、数据和主题。
### 语法规则
- 入口使用 `infographic <模板名>`
- 键值对用空格分隔,**绝对不允许使用冒号**
- 使用两个空格缩进
- 对象数组使用 `-` 加换行
⚠️ **重要警告:这不是 YAML 格式!**
- ❌ 错误:`children:` `items:` `data:`(带冒号)
- ✅ 正确:`children` `items` `data`(不带冒号)
### 模板库与选择指南
根据内容结构选择最合适的模板:
#### 1. 列表与层级
- **列表**`list-grid`(网格卡片)、`list-vertical`(垂直列表)
- **树形**`tree-vertical`(垂直树)、`tree-horizontal`(水平树)
- **思维导图**`mindmap`(思维导图)
#### 2. 序列与关系
- **流程**`sequence-roadmap`(路线图)、`sequence-zigzag`(折线流程)
- **关系**`relation-sankey`(桑基图)、`relation-circle`(圆形关系)
#### 3. 对比与分析
- **对比**`compare-binary`(二元对比)
- **分析**`compare-swot`SWOT 分析)、`quadrant-quarter`(象限图)
#### 4. 图表与数据
- **图表**`chart-bar`、`chart-column`、`chart-line`、`chart-pie`、`chart-doughnut`、`chart-area`
### 数据结构示例
#### A. 标准列表/树形
```infographic
infographic list-grid
data
title 项目模块
items
- label 模块 A
desc 模块 A 的描述
- label 模块 B
desc 模块 B 的描述
```
#### B. 二元对比
```infographic
infographic compare-binary
data
title 优势与劣势
items
- label 优势
children
- label 研发能力强
desc 技术领先
- label 劣势
children
- label 品牌曝光弱
desc 营销不足
```
#### C. 图表
```infographic
infographic chart-bar
data
title 季度收入
items
- label Q1
value 120
- label Q2
value 150
```
### 常用数据字段
- `label`:主标题/标签(必填)
- `desc`:描述文字(`list-grid` 最多 30 个中文字符)
- `value`:数值(用于图表)
- `children`:嵌套项
## 输出要求
1. **语言**:使用用户的语言输出内容。
2. **格式**:用 ```infographic ... ``` 包裹输出。
3. **无冒号**:键后面不要使用冒号。
4. **缩进**:使用 2 个空格。
"""
USER_PROMPT_GENERATE = """
请分析以下文本内容,将其核心信息转换为 AntV Infographic 语法格式。
---
**用户上下文:**
用户名:{user_name}
当前时间:{current_date_time_str}
用户语言:{user_language}
---
**文本内容:**
{long_text_content}
请根据文本特征选择最合适的信息图模板,输出标准的信息图语法。
**重要提示:**
- 如果使用 `list-grid` 格式,确保每个卡片的 `desc` 描述限制在 **最多 30 个中文字符**。
- 描述应简洁,突出重点。
"""
class Action:
class Valves(BaseModel):
SHOW_STATUS: bool = Field(
default=True, description="在聊天界面显示操作状态更新。"
)
MODEL_ID: str = Field(
default="",
description="用于文本分析的 LLM 模型 ID。留空则使用当前对话模型。",
)
MIN_TEXT_LENGTH: int = Field(
default=50,
description="信息图分析所需的最小文本长度(字符数)。",
)
MESSAGE_COUNT: int = Field(
default=1,
description="用于生成的最近消息数量。",
)
SVG_WIDTH: int = Field(
default=800,
description="生成的 SVG 宽度(像素)。",
)
EXPORT_FORMAT: str = Field(
default="svg",
description="导出格式:'svg''png'",
)
def __init__(self):
self.valves = self.Valves()
def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
"""从 body 或 metadata 中提取 chat_id"""
if isinstance(body, dict):
chat_id = body.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
chat_id = body_metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
if isinstance(metadata, dict):
chat_id = metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
return ""
def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
"""从 body 或 metadata 中提取 message_id"""
if isinstance(body, dict):
message_id = body.get("id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
message_id = body_metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
if isinstance(metadata, dict):
message_id = metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
return ""
def _extract_infographic_syntax(self, llm_output: str) -> str:
"""从 LLM 输出中提取信息图语法"""
match = re.search(r"```infographic\s*(.*?)\s*```", llm_output, re.DOTALL)
if match:
return match.group(1).strip()
else:
logger.warning("LLM 输出未遵循预期格式,将整个输出作为语法处理。")
return llm_output.strip()
def _extract_text_content(self, content) -> str:
"""从消息内容中提取文本,支持多模态格式"""
if isinstance(content, str):
return content
elif isinstance(content, list):
text_parts = []
for item in content:
if isinstance(item, dict) and item.get("type") == "text":
text_parts.append(item.get("text", ""))
elif isinstance(item, str):
text_parts.append(item)
return "\n".join(text_parts)
return str(content) if content else ""
async def _emit_status(self, emitter, description: str, done: bool = False):
"""发送状态更新事件"""
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
def _generate_js_code(
self,
unique_id: str,
chat_id: str,
message_id: str,
infographic_syntax: str,
svg_width: int,
export_format: str,
) -> str:
"""生成用于前端 SVG 渲染的 JavaScript 代码"""
# 转义语法以便嵌入 JS
syntax_escaped = (
infographic_syntax
.replace("\\", "\\\\")
.replace("`", "\\`")
.replace("${", "\\${")
.replace("</script>", "<\\/script>")
)
# 模板映射
template_mapping_js = """
const TEMPLATE_MAPPING = {
'list-grid': 'list-grid-compact-card',
'list-vertical': 'list-column-simple-vertical-arrow',
'tree-vertical': 'hierarchy-tree-tech-style-capsule-item',
'tree-horizontal': 'hierarchy-tree-lr-tech-style-capsule-item',
'mindmap': 'hierarchy-mindmap-branch-gradient-capsule-item',
'sequence-roadmap': 'sequence-roadmap-vertical-simple',
'sequence-zigzag': 'sequence-horizontal-zigzag-simple',
'sequence-horizontal': 'sequence-horizontal-zigzag-simple',
'relation-sankey': 'relation-sankey-simple',
'relation-circle': 'relation-circle-icon-badge',
'compare-binary': 'compare-binary-horizontal-simple-vs',
'compare-swot': 'compare-swot',
'quadrant-quarter': 'quadrant-quarter-simple-card',
'statistic-card': 'list-grid-compact-card',
'chart-bar': 'chart-bar-plain-text',
'chart-column': 'chart-column-simple',
'chart-line': 'chart-line-plain-text',
'chart-area': 'chart-area-simple',
'chart-pie': 'chart-pie-plain-text',
'chart-doughnut': 'chart-pie-donut-plain-text'
};
"""
return f"""
(async function() {{
const uniqueId = "{unique_id}";
const chatId = "{chat_id}";
const messageId = "{message_id}";
const svgWidth = {svg_width};
const exportFormat = "{export_format}";
console.log("[信息图 Markdown] 开始渲染...");
console.log("[信息图 Markdown] chatId:", chatId, "messageId:", messageId);
try {{
// 加载 AntV Infographic如果尚未加载
if (typeof AntVInfographic === 'undefined') {{
console.log("[信息图 Markdown] 正在加载 AntV Infographic 库...");
await new Promise((resolve, reject) => {{
const script = document.createElement('script');
script.src = 'https://unpkg.com/@antv/infographic@latest/dist/infographic.min.js';
script.onload = resolve;
script.onerror = reject;
document.head.appendChild(script);
}});
console.log("[信息图 Markdown] 库加载完成。");
}}
const {{ Infographic }} = AntVInfographic;
// 获取信息图语法
let syntaxContent = `{syntax_escaped}`;
console.log("[信息图 Markdown] 原始语法:", syntaxContent.substring(0, 200) + "...");
// 清理语法
const backtick = String.fromCharCode(96);
const prefix = backtick + backtick + backtick + 'infographic';
const simplePrefix = backtick + backtick + backtick;
if (syntaxContent.toLowerCase().startsWith(prefix)) {{
syntaxContent = syntaxContent.substring(prefix.length).trim();
}} else if (syntaxContent.startsWith(simplePrefix)) {{
syntaxContent = syntaxContent.substring(simplePrefix.length).trim();
}}
if (syntaxContent.endsWith(simplePrefix)) {{
syntaxContent = syntaxContent.substring(0, syntaxContent.length - simplePrefix.length).trim();
}}
// 修复关键字后的冒号
syntaxContent = syntaxContent.replace(/^(data|items|children|theme|config):/gm, '$1');
syntaxContent = syntaxContent.replace(/(\\s)(children|items):/g, '$1$2');
// 确保有 infographic 前缀
if (!syntaxContent.trim().toLowerCase().startsWith('infographic')) {{
syntaxContent = 'infographic list-grid\\n' + syntaxContent;
}}
// 应用模板映射
{template_mapping_js}
for (const [key, value] of Object.entries(TEMPLATE_MAPPING)) {{
const regex = new RegExp(`infographic\\\\s+${{key}}(?=\\\\s|$)`, 'i');
if (regex.test(syntaxContent)) {{
console.log(`[信息图 Markdown] 自动映射: ${{key}} -> ${{value}}`);
syntaxContent = syntaxContent.replace(regex, `infographic ${{value}}`);
break;
}}
}}
console.log("[信息图 Markdown] 清理后语法:", syntaxContent.substring(0, 200) + "...");
// 创建离屏容器
const container = document.createElement('div');
container.id = 'infographic-offscreen-' + uniqueId;
container.style.cssText = 'position:absolute;left:-9999px;top:-9999px;width:' + svgWidth + 'px;';
document.body.appendChild(container);
// 创建并渲染信息图
const instance = new Infographic({{
container: '#' + container.id,
width: svgWidth,
padding: 24,
}});
console.log("[信息图 Markdown] 正在渲染信息图...");
instance.render(syntaxContent);
// 等待渲染完成并导出
await new Promise(resolve => setTimeout(resolve, 1000));
let dataUrl;
if (exportFormat === 'png') {{
dataUrl = await instance.toDataURL({{ type: 'png', dpr: 2 }});
}} else {{
dataUrl = await instance.toDataURL({{ type: 'svg', embedResources: true }});
}}
console.log("[信息图 Markdown] Data URL 已生成,长度:", dataUrl.length);
// 清理
instance.destroy();
document.body.removeChild(container);
// 生成 Markdown 图片
const markdownImage = `![📊 AI 生成的信息图](${{dataUrl}})`;
// 通过 API 更新消息
if (chatId && messageId) {{
const token = localStorage.getItem("token");
// 获取当前消息内容
const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
method: "GET",
headers: {{ "Authorization": `Bearer ${{token}}` }}
}});
if (!getResponse.ok) {{
throw new Error("获取对话数据失败: " + getResponse.status);
}}
const chatData = await getResponse.json();
let originalContent = "";
if (chatData.chat && chatData.chat.messages) {{
const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
if (targetMsg && targetMsg.content) {{
originalContent = targetMsg.content;
}}
}}
// 移除已有的信息图图片
const infographicPattern = /\\n*!\\[📊[^\\]]*\\]\\(data:image\\/[^)]+\\)/g;
let cleanedContent = originalContent.replace(infographicPattern, "");
cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
// 追加新图片
const newContent = cleanedContent + "\\n\\n" + markdownImage;
// 更新消息
const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
method: "POST",
headers: {{
"Content-Type": "application/json",
"Authorization": `Bearer ${{token}}`
}},
body: JSON.stringify({{
type: "chat:message",
data: {{ content: newContent }}
}})
}});
if (updateResponse.ok) {{
console.log("[信息图 Markdown] ✅ 消息更新成功!");
}} else {{
console.error("[信息图 Markdown] API 错误:", updateResponse.status);
}}
}} else {{
console.warn("[信息图 Markdown] ⚠️ 缺少 chatId 或 messageId");
}}
}} catch (error) {{
console.error("[信息图 Markdown] 错误:", error);
}}
}})();
"""
async def action(
self,
body: dict,
__user__: dict = None,
__event_emitter__=None,
__event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
__metadata__: Optional[dict] = None,
__request__: Request = None,
) -> dict:
"""
使用 AntV 生成信息图并作为 Markdown 图片嵌入。
"""
logger.info("动作:信息图转 Markdown 开始")
# 获取用户信息
if isinstance(__user__, (list, tuple)):
user_language = __user__[0].get("language", "zh") if __user__ else "zh"
user_name = __user__[0].get("name", "用户") if __user__[0] else "用户"
user_id = __user__[0].get("id", "unknown_user") if __user__ else "unknown_user"
elif isinstance(__user__, dict):
user_language = __user__.get("language", "zh")
user_name = __user__.get("name", "用户")
user_id = __user__.get("id", "unknown_user")
else:
user_language = "zh"
user_name = "用户"
user_id = "unknown_user"
# 获取当前时间
now = datetime.now()
current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
try:
messages = body.get("messages", [])
if not messages:
raise ValueError("没有可用的消息。")
# 获取最近的消息
message_count = min(self.valves.MESSAGE_COUNT, len(messages))
recent_messages = messages[-message_count:]
# 聚合内容
aggregated_parts = []
for msg in recent_messages:
text_content = self._extract_text_content(msg.get("content"))
if text_content:
aggregated_parts.append(text_content)
if not aggregated_parts:
raise ValueError("消息中未找到文本内容。")
long_text_content = "\n\n---\n\n".join(aggregated_parts)
# 移除已有的 HTML 块
parts = re.split(r"```html.*?```", long_text_content, flags=re.DOTALL)
clean_content = ""
for part in reversed(parts):
if part.strip():
clean_content = part.strip()
break
if not clean_content:
clean_content = long_text_content.strip()
# 检查最小长度
if len(clean_content) < self.valves.MIN_TEXT_LENGTH:
await self._emit_status(
__event_emitter__,
f"⚠️ 内容太短({len(clean_content)} 字符),至少需要 {self.valves.MIN_TEXT_LENGTH} 字符",
True,
)
return body
await self._emit_status(__event_emitter__, "📊 正在分析内容...", False)
# 通过 LLM 生成信息图语法
formatted_user_prompt = USER_PROMPT_GENERATE.format(
user_name=user_name,
current_date_time_str=current_date_time_str,
user_language=user_language,
long_text_content=clean_content,
)
target_model = self.valves.MODEL_ID or body.get("model")
llm_payload = {
"model": target_model,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT_INFOGRAPHIC},
{"role": "user", "content": formatted_user_prompt},
],
"stream": False,
}
user_obj = Users.get_user_by_id(user_id)
if not user_obj:
raise ValueError(f"无法获取用户对象:{user_id}")
await self._emit_status(__event_emitter__, "📊 AI 正在生成信息图语法...", False)
llm_response = await generate_chat_completion(__request__, llm_payload, user_obj)
if not llm_response or "choices" not in llm_response or not llm_response["choices"]:
raise ValueError("无效的 LLM 响应。")
assistant_content = llm_response["choices"][0]["message"]["content"]
infographic_syntax = self._extract_infographic_syntax(assistant_content)
logger.info(f"生成的语法:{infographic_syntax[:200]}...")
# 提取 API 回调所需的 ID
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
unique_id = f"ig_{int(time.time() * 1000)}"
await self._emit_status(__event_emitter__, "📊 正在渲染 SVG...", False)
# 执行 JS 进行渲染和嵌入
if __event_call__:
js_code = self._generate_js_code(
unique_id=unique_id,
chat_id=chat_id,
message_id=message_id,
infographic_syntax=infographic_syntax,
svg_width=self.valves.SVG_WIDTH,
export_format=self.valves.EXPORT_FORMAT,
)
await __event_call__(
{
"type": "execute",
"data": {"code": js_code},
}
)
await self._emit_status(__event_emitter__, "✅ 信息图生成完成!", True)
logger.info("信息图转 Markdown 完成")
except Exception as e:
error_message = f"信息图生成失败:{str(e)}"
logger.error(error_message, exc_info=True)
await self._emit_status(__event_emitter__, f"{error_message}", True)
return body

View File

@@ -0,0 +1,257 @@
"""
title: JS Render PoC
author: Fu-Jie
version: 0.6.0
description: Proof of concept for JS rendering + API write-back pattern. JS renders SVG and updates message via API.
"""
import time
import json
import logging
from typing import Optional, Callable, Awaitable, Any
from pydantic import BaseModel, Field
from fastapi import Request
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Action:
class Valves(BaseModel):
pass
def __init__(self):
self.valves = self.Valves()
def _extract_chat_id(self, body: dict, metadata: Optional[dict]) -> str:
"""Extract chat_id from body or metadata"""
if isinstance(body, dict):
# body["chat_id"] 是 chat_id
chat_id = body.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
chat_id = body_metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
if isinstance(metadata, dict):
chat_id = metadata.get("chat_id")
if isinstance(chat_id, str) and chat_id.strip():
return chat_id.strip()
return ""
def _extract_message_id(self, body: dict, metadata: Optional[dict]) -> str:
"""Extract message_id from body or metadata"""
if isinstance(body, dict):
# body["id"] 是 message_id
message_id = body.get("id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
body_metadata = body.get("metadata", {})
if isinstance(body_metadata, dict):
message_id = body_metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
if isinstance(metadata, dict):
message_id = metadata.get("message_id")
if isinstance(message_id, str) and message_id.strip():
return message_id.strip()
return ""
async def action(
self,
body: dict,
__user__: dict = None,
__event_emitter__=None,
__event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
__metadata__: Optional[dict] = None,
__request__: Request = None,
) -> dict:
"""
PoC: Use __event_call__ to execute JS that renders SVG and updates message via API.
"""
# 准备调试数据
body_for_log = {}
for k, v in body.items():
if k == "messages":
body_for_log[k] = f"[{len(v)} messages]"
else:
body_for_log[k] = v
body_json = json.dumps(body_for_log, ensure_ascii=False, default=str)
metadata_json = (
json.dumps(__metadata__, ensure_ascii=False, default=str)
if __metadata__
else "null"
)
# 转义 JSON 中的特殊字符以便嵌入 JS
body_json_escaped = (
body_json.replace("\\", "\\\\").replace("`", "\\`").replace("${", "\\${")
)
metadata_json_escaped = (
metadata_json.replace("\\", "\\\\")
.replace("`", "\\`")
.replace("${", "\\${")
)
chat_id = self._extract_chat_id(body, __metadata__)
message_id = self._extract_message_id(body, __metadata__)
unique_id = f"poc_{int(time.time() * 1000)}"
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": "🔄 正在渲染...", "done": False},
}
)
if __event_call__:
await __event_call__(
{
"type": "execute",
"data": {
"code": f"""
(async function() {{
const uniqueId = "{unique_id}";
const chatId = "{chat_id}";
const messageId = "{message_id}";
// ===== DEBUG: 输出 Python 端的数据 =====
console.log("[JS Render PoC] ===== DEBUG INFO (from Python) =====");
console.log("[JS Render PoC] body:", `{body_json_escaped}`);
console.log("[JS Render PoC] __metadata__:", `{metadata_json_escaped}`);
console.log("[JS Render PoC] Extracted: chatId=", chatId, "messageId=", messageId);
console.log("[JS Render PoC] =========================================");
try {{
console.log("[JS Render PoC] Starting SVG render...");
// Create SVG
const svg = document.createElementNS("http://www.w3.org/2000/svg", "svg");
svg.setAttribute("width", "200");
svg.setAttribute("height", "200");
svg.setAttribute("viewBox", "0 0 200 200");
svg.setAttribute("xmlns", "http://www.w3.org/2000/svg");
const defs = document.createElementNS("http://www.w3.org/2000/svg", "defs");
const gradient = document.createElementNS("http://www.w3.org/2000/svg", "linearGradient");
gradient.setAttribute("id", "grad-" + uniqueId);
gradient.innerHTML = `
<stop offset="0%" style="stop-color:#1e88e5;stop-opacity:1" />
<stop offset="100%" style="stop-color:#43a047;stop-opacity:1" />
`;
defs.appendChild(gradient);
svg.appendChild(defs);
const circle = document.createElementNS("http://www.w3.org/2000/svg", "circle");
circle.setAttribute("cx", "100");
circle.setAttribute("cy", "100");
circle.setAttribute("r", "80");
circle.setAttribute("fill", `url(#grad-${{uniqueId}})`);
svg.appendChild(circle);
const text = document.createElementNS("http://www.w3.org/2000/svg", "text");
text.setAttribute("x", "100");
text.setAttribute("y", "105");
text.setAttribute("text-anchor", "middle");
text.setAttribute("fill", "white");
text.setAttribute("font-size", "16");
text.setAttribute("font-weight", "bold");
text.textContent = "PoC Success!";
svg.appendChild(text);
// Convert to Base64 Data URI
const svgData = new XMLSerializer().serializeToString(svg);
const base64 = btoa(unescape(encodeURIComponent(svgData)));
const dataUri = "data:image/svg+xml;base64," + base64;
console.log("[JS Render PoC] SVG rendered, data URI length:", dataUri.length);
// Call API - 完全替换方案(更稳定)
if (chatId && messageId) {{
const token = localStorage.getItem("token");
// 1. 获取当前消息内容
const getResponse = await fetch(`/api/v1/chats/${{chatId}}`, {{
method: "GET",
headers: {{ "Authorization": `Bearer ${{token}}` }}
}});
if (!getResponse.ok) {{
throw new Error("Failed to get chat data: " + getResponse.status);
}}
const chatData = await getResponse.json();
console.log("[JS Render PoC] Got chat data");
let originalContent = "";
if (chatData.chat && chatData.chat.messages) {{
const targetMsg = chatData.chat.messages.find(m => m.id === messageId);
if (targetMsg && targetMsg.content) {{
originalContent = targetMsg.content;
console.log("[JS Render PoC] Found original content, length:", originalContent.length);
}}
}}
// 2. 移除已存在的 PoC 图片(如果有的话)
// 匹配 ![JS Render PoC 生成的 SVG](data:...) 格式
const pocImagePattern = /\\n*!\\[JS Render PoC[^\\]]*\\]\\(data:image\\/svg\\+xml;base64,[^)]+\\)/g;
let cleanedContent = originalContent.replace(pocImagePattern, "");
// 移除可能残留的多余空行
cleanedContent = cleanedContent.replace(/\\n{{3,}}/g, "\\n\\n").trim();
if (cleanedContent !== originalContent) {{
console.log("[JS Render PoC] Removed existing PoC image(s)");
}}
// 3. 添加新的 Markdown 图片
const markdownImage = `![JS Render PoC 生成的 SVG](${{dataUri}})`;
const newContent = cleanedContent + "\\n\\n" + markdownImage;
// 3. 使用 chat:message 完全替换
const updateResponse = await fetch(`/api/v1/chats/${{chatId}}/messages/${{messageId}}/event`, {{
method: "POST",
headers: {{
"Content-Type": "application/json",
"Authorization": `Bearer ${{token}}`
}},
body: JSON.stringify({{
type: "chat:message",
data: {{ content: newContent }}
}})
}});
if (updateResponse.ok) {{
console.log("[JS Render PoC] ✅ Message updated successfully!");
}} else {{
console.error("[JS Render PoC] API error:", updateResponse.status, await updateResponse.text());
}}
}} else {{
console.warn("[JS Render PoC] ⚠️ Missing chatId or messageId, cannot persist.");
}}
}} catch (error) {{
console.error("[JS Render PoC] Error:", error);
}}
}})();
"""
},
}
)
if __event_emitter__:
await __event_emitter__(
{"type": "status", "data": {"description": "✅ 渲染完成", "done": True}}
)
return body