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feat: 重构了智能思维导图和摘要插件的事件发射逻辑，并新增了插件开发指南。

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fujie
2025-12-20 17:03:40 +08:00
parent afaa25d441
commit 3ddddb69d7
9 changed files with 587 additions and 473 deletions
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13
plugins/actions/ACTION_PLUGIN_TEMPLATE.py
View File

@@ -101,11 +101,11 @@ HTML_WRAPPER_TEMPLATE = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True,
description="Whether to show operation status updates in the chat interface.",
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="Built-in LLM Model ID used for processing. If empty, uses the current conversation's model.",
)
@@ -231,7 +231,7 @@ class Action:
done: bool = False,
):
"""Emits a status update event."""
if self.valves.show_status and emitter:
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
@@ -301,7 +301,7 @@ class Action:
)
# 5. Determine Model
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
# Note: No hardcoded fallback here, relies on system/user context
@@ -362,4 +362,9 @@ class Action:
# Append error to chat (optional)
body["messages"][-1]["content"] += f"\n\n❌ **Error**: {error_msg}"
await self._emit_status(__event_emitter__, "Processing failed.", done=True)
await self._emit_notification(
__event_emitter__, "Action failed, please check logs.", "error"
)
return body

8
plugins/actions/ACTION_PLUGIN_TEMPLATE_CN.py
View File

@@ -101,11 +101,11 @@ HTML_WRAPPER_TEMPLATE = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True,
description="是否在聊天界面显示操作状态更新。",
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="用于处理的内置 LLM 模型 ID。如果为空，则使用当前对话的模型。",
)
@@ -242,7 +242,7 @@ class Action:
done: bool = False,
):
"""发送状态更新事件。"""
if self.valves.show_status and emitter:
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
@@ -312,7 +312,7 @@ class Action:
)
# 5. 确定模型
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
# 注意: 这里没有硬编码的回退，依赖于系统/用户上下文

292
plugins/actions/PLUGIN_DEVELOPMENT_GUIDE.md Normal file
View File

@@ -0,0 +1,292 @@
# OpenWebUI HTML Action 插件开发指南
> 本文档定义了开发 OpenWebUI Action 插件的标准规范和最佳实践。
## 📐 核心技术规范
### 1. Valves 配置规范 (Pydantic BaseModel)
**命名规则**: 所有字段必须使用 **大写+下划线** (UPPER_CASE)。
```python
class Valves(BaseModel):
SHOW_STATUS: bool = Field(default=True, description="是否显示状态更新")
MODEL_ID: str = Field(default="", description="指定模型 ID，留空则使用当前对话模型")
MIN_TEXT_LENGTH: int = Field(default=50, description="最小字符限制")
CLEAR_PREVIOUS_HTML: bool = Field(default=False, description="是否清除旧结果")
# 根据需要添加更多配置...
```
**常用字段参考**:
| 字段名 | 类型 | 说明 |
|--------|------|------|
| `SHOW_STATUS` | `bool` | 控制是否显示状态更新 |
| `MODEL_ID` | `str` | 允许用户指定 LLM 模型 |
| `MIN_TEXT_LENGTH` | `int` | 设置触发分析的最小字符数 |
| `MAX_TEXT_LENGTH` | `int` | 设置推荐的最大字符数 |
| `CLEAR_PREVIOUS_HTML` | `bool` | 控制是覆盖还是合并旧的插件输出 |
| `LANGUAGE` | `str` | 目标语言 (如 'zh', 'en') |
---
### 2. 事件发送规范 (Event Emission)
**禁止直接调用** `await __event_emitter__`。必须实现并使用以下辅助方法：
```python
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}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""发送通知事件 (info/success/warning/error)。"""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
```
**使用示例**:
```python
# 开始处理
await self._emit_status(__event_emitter__, "正在分析文本...", done=False)
# 处理完成
await self._emit_status(__event_emitter__, "分析完成！", done=True)
await self._emit_notification(__event_emitter__, "报告已生成", "success")
# 发生错误
await self._emit_status(__event_emitter__, "处理失败。", done=True)
await self._emit_notification(__event_emitter__, f"错误: {str(e)}", "error")
```
---
### 3. 日志与调试
- **严禁使用** `print()`
- 必须使用 Python 标准库 `logging`
```python
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# 记录关键路径
logger.info(f"Action: {__name__} started")
# 记录异常 (包含堆栈信息)
logger.error(f"处理失败: {str(e)}", exc_info=True)
```
---
### 4. HTML 注入逻辑
#### 4.1 HTML 包装器
使用统一的注释标记插件内容：
```html
<!-- OPENWEBUI_PLUGIN_OUTPUT -->
<div class="plugin-container">
<!-- STYLE_INSERTION_POINT -->
<!-- CONTENT_INSERTION_POINT -->
<!-- SCRIPT_INSERTION_POINT -->
</div>
```
#### 4.2 合并机制
必须实现以下方法，支持在同一条消息中多次运行插件：
```python
def _remove_existing_html(self, content: str) -> str:
"""移除已存在的插件 HTML 块。"""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
return re.sub(pattern, "", content).strip()
def _merge_html(
self,
existing_html: str,
new_content: str,
new_styles: str = "",
new_scripts: str = "",
user_language: str = "en-US",
) -> str:
"""合并新内容到已有 HTML 容器，或创建新容器。"""
# 实现逻辑...
```
#### 4.3 注入流程
```python
# 1. 提取已有 HTML
existing_html_block = ""
match = re.search(
r"```html\s*(<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?)```",
original_content,
)
if match:
existing_html_block = match.group(1)
# 2. 根据配置决定是否清除旧内容
if self.valves.CLEAR_PREVIOUS_HTML:
original_content = self._remove_existing_html(original_content)
final_html = self._merge_html("", new_content, new_styles, "", user_language)
else:
# 合并到已有 HTML
final_html = self._merge_html(existing_html_block, new_content, new_styles, "", user_language)
# 3. 注入到消息
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
```
---
## 📝 完整代码结构模板
```python
"""
title: 插件名称
author: 作者名
version: 0.1.0
description: 插件描述
"""
import logging
import re
import json
import time
from typing import Optional, Dict, Any, Callable, Awaitable
from pydantic import BaseModel, Field
from starlette.requests import Request
from open_webui.apps.webui.models.users import Users
from open_webui.main import generate_chat_completion
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# ============ 提示词模板 ============
SYSTEM_PROMPT = """你是一个专业的..."""
USER_PROMPT_TEMPLATE = """请分析以下内容: {content}"""
# ============ HTML 模板 ============
HTML_WRAPPER_TEMPLATE = """<!-- OPENWEBUI_PLUGIN_OUTPUT -->
<div class="plugin-container" lang="{user_language}">
<style>/* 样式 */</style>
<!-- CONTENT_INSERTION_POINT -->
</div>
"""
class Action:
class Valves(BaseModel):
SHOW_STATUS: bool = Field(default=True, description="是否显示状态更新")
MODEL_ID: str = Field(default="", description="指定模型 ID")
MIN_TEXT_LENGTH: int = Field(default=50, description="最小字符限制")
CLEAR_PREVIOUS_HTML: bool = Field(default=False, description="是否清除旧结果")
def __init__(self):
self.valves = self.Valves()
# ========== 事件发送辅助方法 ==========
async def _emit_status(self, emitter, description: str, done: bool = False):
if self.valves.SHOW_STATUS and emitter:
await emitter({"type": "status", "data": {"description": description, "done": done}})
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
if emitter:
await emitter({"type": "notification", "data": {"type": ntype, "content": content}})
# ========== HTML 处理辅助方法 ==========
def _remove_existing_html(self, content: str) -> str:
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
return re.sub(pattern, "", content).strip()
def _merge_html(self, existing_html: str, new_content: str, new_styles: str = "", new_scripts: str = "", user_language: str = "en-US") -> str:
# 实现合并逻辑...
pass
# ========== 主入口 ==========
async def action(
self,
body: dict,
__user__: Optional[Dict[str, Any]] = None,
__event_emitter__: Optional[Callable[[Any], Awaitable[None]]] = None,
__request__: Optional[Request] = None,
) -> Optional[dict]:
logger.info(f"Action: {__name__} started")
# 1. 输入校验
messages = body.get("messages", [])
if not messages or not messages[-1].get("content"):
return body
original_content = messages[-1]["content"]
if len(original_content) < self.valves.MIN_TEXT_LENGTH:
await self._emit_notification(__event_emitter__, "文本过短", "warning")
return body
# 2. 发送开始状态
await self._emit_status(__event_emitter__, "正在处理...", done=False)
try:
# 3. 调用 LLM
user_id = __user__.get("id") if __user__ else "default"
user_obj = Users.get_user_by_id(user_id)
target_model = self.valves.MODEL_ID or body.get("model")
payload = {
"model": target_model,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_PROMPT_TEMPLATE.format(content=original_content)},
],
"stream": False,
}
llm_response = await generate_chat_completion(__request__, payload, user_obj)
result = llm_response["choices"][0]["message"]["content"]
# 4. 生成 HTML
# ...
# 5. 注入到消息
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
# 6. 发送成功通知
await self._emit_status(__event_emitter__, "处理完成！", done=True)
await self._emit_notification(__event_emitter__, "操作成功", "success")
except Exception as e:
logger.error(f"处理失败: {e}", exc_info=True)
await self._emit_status(__event_emitter__, "处理失败", done=True)
await self._emit_notification(__event_emitter__, f"错误: {str(e)}", "error")
return body
```
---
## 🎨 UI 设计原则
1. **响应式**: 确保 HTML 在移动端和桌面端都能完美显示
2. **交互性**: 适当添加 JavaScript 交互（如点击展开、切换视图、复制内容）
3. **本地化**: 根据 `__user__.get("language")` 自动适配中英文界面
4. **美学设计**: 优先使用现代 UI 设计
- 毛玻璃效果 (backdrop-filter)
- 渐变色 (linear-gradient)
- 圆角卡片 (border-radius)
- Google Fonts 字体
---
## 📚 参考模板
- [英文模板](./ACTION_PLUGIN_TEMPLATE.py)
- [中文模板](./ACTION_PLUGIN_TEMPLATE_CN.py)

132
plugins/actions/knowledge-card/knowledge_card.py
View File

@@ -71,27 +71,27 @@ HTML_WRAPPER_TEMPLATE = """
class Action:
class Valves(BaseModel):
model_id: str = Field(
MODEL_ID: str = Field(
default="",
description="Model ID used for generating card content. If empty, uses the current model.",
)
min_text_length: int = Field(
MIN_TEXT_LENGTH: int = Field(
default=50,
description="Minimum text length required to generate a flashcard (characters).",
)
max_text_length: int = Field(
MAX_TEXT_LENGTH: int = Field(
default=2000,
description="Recommended maximum text length. For longer texts, deep analysis tools are recommended.",
)
language: str = Field(
LANGUAGE: str = Field(
default="en",
description="Target language for card content (e.g., 'en', 'zh').",
)
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True,
description="Whether to show status updates in the chat interface.",
)
clear_previous_html: bool = Field(
CLEAR_PREVIOUS_HTML: bool = Field(
default=False,
description="Whether to force clear previous plugin results (if True, overwrites instead of merging).",
)
@@ -106,7 +106,7 @@ class Action:
__event_emitter__: Optional[Any] = None,
__request__: Optional[Any] = None,
) -> Optional[dict]:
print(f"action:{__name__} triggered")
logger.info(f"Action: {__name__} triggered")
if not __event_emitter__:
return body
@@ -121,49 +121,34 @@ class Action:
# Check text length
text_length = len(target_message)
if text_length < self.valves.min_text_length:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "warning",
"content": f"Text too short ({text_length} chars), recommended at least {self.valves.min_text_length} chars.",
},
}
)
if text_length < self.valves.MIN_TEXT_LENGTH:
await self._emit_notification(
__event_emitter__,
f"Text too short ({text_length} chars), recommended at least {self.valves.MIN_TEXT_LENGTH} chars.",
"warning",
)
return body
if text_length > self.valves.max_text_length:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": f"Text quite long ({text_length} chars), consider using 'Deep Reading' for deep analysis.",
},
}
)
if text_length > self.valves.MAX_TEXT_LENGTH:
await self._emit_notification(
__event_emitter__,
f"Text quite long ({text_length} chars), consider using 'Deep Reading' for deep analysis.",
"info",
)
# Notify user that we are generating the card
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "⚡ Generating Flash Card...",
},
}
)
await self._emit_notification(
__event_emitter__, "⚡ Generating Flash Card...", "info"
)
try:
# 1. Extract information using LLM
user_id = __user__.get("id") if __user__ else "default"
user_obj = Users.get_user_by_id(user_id)
model = self.valves.model_id if self.valves.model_id else body.get("model")
target_model = (
self.valves.MODEL_ID if self.valves.MODEL_ID else body.get("model")
)
system_prompt = f"""
You are a Flash Card Generation Expert, specializing in creating knowledge cards suitable for learning and memorization. Your task is to distill text into concise, easy-to-remember flashcards.
@@ -178,7 +163,7 @@ Please extract the following fields and return them in JSON format:
4. "tags": List 2-4 classification tags (1-3 words each).
5. "category": Choose a main category (e.g., Concept, Skill, Fact, Method, etc.).
Target Language: {self.valves.language}
Target Language: {self.valves.LANGUAGE}
Important Principles:
- **Minimalism**: Refine each point to the extreme.
@@ -191,7 +176,7 @@ Important Principles:
prompt = f"Please refine the following text into a learning flashcard:\n\n{target_message}"
payload = {
"model": model,
"model": target_model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
@@ -213,16 +198,11 @@ Important Principles:
card_data = json.loads(content)
except Exception as e:
logger.error(f"Failed to parse JSON: {e}, content: {content}")
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": "Failed to generate card data, please try again.",
},
}
)
await self._emit_notification(
__event_emitter__,
"Failed to generate card data, please try again.",
"error",
)
return body
# 2. Generate HTML components
@@ -238,12 +218,12 @@ Important Principles:
if match:
existing_html_block = match.group(1)
if self.valves.clear_previous_html:
if self.valves.CLEAR_PREVIOUS_HTML:
body["messages"][-1]["content"] = self._remove_existing_html(
body["messages"][-1]["content"]
)
final_html = self._merge_html(
"", card_content, card_style, "", self.valves.language
"", card_content, card_style, "", self.valves.LANGUAGE
)
else:
if existing_html_block:
@@ -255,43 +235,43 @@ Important Principles:
card_content,
card_style,
"",
self.valves.language,
self.valves.LANGUAGE,
)
else:
final_html = self._merge_html(
"", card_content, card_style, "", self.valves.language
"", card_content, card_style, "", self.valves.LANGUAGE
)
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] += f"\n\n{html_embed_tag}"
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": "⚡ Flash Card generated successfully!",
},
}
)
await self._emit_notification(
__event_emitter__, "⚡ Flash Card generated successfully!", "success"
)
return body
except Exception as e:
logger.error(f"Error generating knowledge card: {e}")
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"Error generating knowledge card: {str(e)}",
},
}
)
await self._emit_notification(
__event_emitter__, f"Error generating knowledge card: {str(e)}", "error"
)
return body
async def _emit_status(self, emitter, description: str, done: bool = False):
"""Emits a status update event."""
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""Emits a notification event (info/success/warning/error)."""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""Removes existing plugin-generated HTML code blocks from the content."""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"

128
plugins/actions/knowledge-card/闪记卡.py
View File

@@ -71,24 +71,24 @@ HTML_WRAPPER_TEMPLATE = """
class Action:
class Valves(BaseModel):
model_id: str = Field(
MODEL_ID: str = Field(
default="",
description="用于生成卡片内容的模型 ID。如果为空，则使用当前模型。",
)
min_text_length: int = Field(
MIN_TEXT_LENGTH: int = Field(
default=50, description="生成闪记卡所需的最小文本长度（字符数）。"
)
max_text_length: int = Field(
MAX_TEXT_LENGTH: int = Field(
default=2000,
description="建议的最大文本长度。超过此长度建议使用深度分析工具。",
)
language: str = Field(
LANGUAGE: str = Field(
default="zh", description="卡片内容的目标语言 (例如 'zh', 'en')。"
)
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True, description="是否在聊天界面显示状态更新。"
)
clear_previous_html: bool = Field(
CLEAR_PREVIOUS_HTML: bool = Field(
default=False,
description="是否强制清除旧的插件结果（如果为 True，则不合并，直接覆盖）。",
)
@@ -103,7 +103,7 @@ class Action:
__event_emitter__: Optional[Any] = None,
__request__: Optional[Any] = None,
) -> Optional[dict]:
print(f"action:{__name__} triggered")
logger.info(f"Action: {__name__} 触发")
if not __event_emitter__:
return body
@@ -118,49 +118,32 @@ class Action:
# Check text length
text_length = len(target_message)
if text_length < self.valves.min_text_length:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "warning",
"content": f"文本过短（{text_length}字符），建议至少{self.valves.min_text_length}字符。",
},
}
)
if text_length < self.valves.MIN_TEXT_LENGTH:
await self._emit_notification(
__event_emitter__,
f"文本内容过短 ({text_length} 字符)，建议至少 {self.valves.MIN_TEXT_LENGTH} 字符。",
"warning",
)
return body
if text_length > self.valves.max_text_length:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": f"文本较长（{text_length}字符），建议使用'墨海拾贝'进行深度分析。",
},
}
)
if text_length > self.valves.MAX_TEXT_LENGTH:
await self._emit_notification(
__event_emitter__,
f"文本较长（{text_length}字符），建议使用'墨海拾贝'进行深度分析。",
"info",
)
# Notify user that we are generating the card
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "⚡ 正在生成闪记卡...",
},
}
)
await self._emit_notification(__event_emitter__, "⚡ 正在生成闪记卡...", "info")
try:
# 1. Extract information using LLM
user_id = __user__.get("id") if __user__ else "default"
user_obj = Users.get_user_by_id(user_id)
model = self.valves.model_id if self.valves.model_id else body.get("model")
target_model = (
self.valves.MODEL_ID if self.valves.MODEL_ID else body.get("model")
)
system_prompt = f"""
你是一个闪记卡生成专家，专注于创建适合学习和记忆的知识卡片。你的任务是将文本提炼成简洁、易记的学习卡片。
@@ -175,7 +158,7 @@ class Action:
4. "tags": 列出 2-4 个分类标签（每个 2-5 字）
5. "category": 选择一个主分类（如：概念、技能、事实、方法等）
目标语言: {self.valves.language}
目标语言: {self.valves.LANGUAGE}
重要原则：
- **极简主义**: 每个要点都要精炼到极致
@@ -188,7 +171,7 @@ class Action:
prompt = f"请将以下文本提炼成一张学习记忆卡片：\n\n{target_message}"
payload = {
"model": model,
"model": target_model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
@@ -210,16 +193,9 @@ class Action:
card_data = json.loads(content)
except Exception as e:
logger.error(f"Failed to parse JSON: {e}, content: {content}")
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": "生成卡片数据失败，请重试。",
},
}
)
await self._emit_notification(
__event_emitter__, "生成卡片数据失败，请重试。", "error"
)
return body
# 2. Generate HTML components
@@ -235,12 +211,12 @@ class Action:
if match:
existing_html_block = match.group(1)
if self.valves.clear_previous_html:
if self.valves.CLEAR_PREVIOUS_HTML:
body["messages"][-1]["content"] = self._remove_existing_html(
body["messages"][-1]["content"]
)
final_html = self._merge_html(
"", card_content, card_style, "", self.valves.language
"", card_content, card_style, "", self.valves.LANGUAGE
)
else:
if existing_html_block:
@@ -252,43 +228,43 @@ class Action:
card_content,
card_style,
"",
self.valves.language,
self.valves.LANGUAGE,
)
else:
final_html = self._merge_html(
"", card_content, card_style, "", self.valves.language
"", card_content, card_style, "", self.valves.LANGUAGE
)
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] += f"\n\n{html_embed_tag}"
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": "⚡ 闪记卡生成成功！",
},
}
)
await self._emit_notification(
__event_emitter__, "⚡ 闪记卡生成成功！", "success"
)
return body
except Exception as e:
logger.error(f"Error generating knowledge card: {e}")
if self.valves.show_status:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"生成知识卡片时出错: {str(e)}",
},
}
)
await self._emit_notification(
__event_emitter__, f"生成知识卡片时出错: {str(e)}", "error"
)
return body
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}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""发送通知事件 (info/success/warning/error)。"""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"

126
plugins/actions/smart-mind-map/smart_mind_map.py
View File

@@ -394,11 +394,11 @@ SCRIPT_TEMPLATE_MINDMAP = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True,
description="Whether to show action status updates in the chat interface.",
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="Built-in LLM model ID for text analysis. If empty, uses the current conversation's model.",
)
@@ -434,6 +434,20 @@ class Action:
extracted_content = llm_output.strip()
return extracted_content.replace("</script>", "<\\/script>")
async def _emit_status(self, emitter, description: str, done: bool = False):
"""Emits a status update event."""
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""Emits a notification event (info/success/warning/error)."""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""Removes existing plugin-generated HTML code blocks from the content."""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -523,16 +537,11 @@ class Action:
current_year = now.strftime("%Y")
current_timezone_str = "Unknown"
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "Smart Mind Map is starting, generating mind map for you...",
},
}
)
await self._emit_notification(
__event_emitter__,
"Smart Mind Map is starting, generating mind map for you...",
"info",
)
messages = body.get("messages")
if (
@@ -541,13 +550,7 @@ class Action:
or not messages[-1].get("content")
):
error_message = "Unable to retrieve valid user message content."
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "error", "content": error_message},
}
)
await self._emit_notification(__event_emitter__, error_message, "error")
return {
"messages": [{"role": "assistant", "content": f"❌ {error_message}"}]
}
@@ -565,30 +568,20 @@ class Action:
if len(long_text_content) < self.valves.MIN_TEXT_LENGTH:
short_text_message = f"Text content is too short ({len(long_text_content)} characters), unable to perform effective analysis. Please provide at least {self.valves.MIN_TEXT_LENGTH} characters of text."
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "warning", "content": short_text_message},
}
)
await self._emit_notification(
__event_emitter__, short_text_message, "warning"
)
return {
"messages": [
{"role": "assistant", "content": f"⚠️ {short_text_message}"}
]
}
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Smart Mind Map: Analyzing text structure in depth...",
"done": False,
"hidden": False,
},
}
)
await self._emit_status(
__event_emitter__,
"Smart Mind Map: Analyzing text structure in depth...",
False,
)
try:
unique_id = f"id_{int(time.time() * 1000)}"
@@ -603,7 +596,7 @@ class Action:
)
# Determine model to use
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
@@ -684,26 +677,14 @@ class Action:
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{long_text_content}\n\n{html_embed_tag}"
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Smart Mind Map: Drawing completed!",
"done": True,
"hidden": False,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": f"Mind map has been generated, {user_name}!",
},
}
)
await self._emit_status(
__event_emitter__, "Smart Mind Map: Drawing completed!", True
)
await self._emit_notification(
__event_emitter__,
f"Mind map has been generated, {user_name}!",
"success",
)
logger.info("Action: Smart Mind Map (v0.7.2) completed successfully")
except Exception as e:
@@ -714,26 +695,13 @@ class Action:
"content"
] = f"{long_text_content}\n\n❌ **Error:** {user_facing_error}"
if __event_emitter__:
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Smart Mind Map: Processing failed.",
"done": True,
"hidden": False,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"Smart Mind Map generation failed, {user_name}!",
},
}
)
await self._emit_status(
__event_emitter__, "Smart Mind Map: Processing failed.", True
)
await self._emit_notification(
__event_emitter__,
f"Smart Mind Map generation failed, {user_name}!",
"error",
)
return body

114
plugins/actions/smart-mind-map/思维导图.py
View File

@@ -394,10 +394,10 @@ SCRIPT_TEMPLATE_MINDMAP = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True, description="是否在聊天界面显示操作状态更新。"
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="用于文本分析的内置LLM模型ID。如果为空，则使用当前对话的模型。",
)
@@ -433,6 +433,20 @@ class Action:
extracted_content = llm_output.strip()
return extracted_content.replace("</script>", "<\\/script>")
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}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""发送通知事件 (info/success/warning/error)。"""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -522,16 +536,9 @@ class Action:
current_year = now.strftime("%Y")
current_timezone_str = "未知时区"
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "智绘心图已启动，正在为您生成思维导图...",
},
}
)
await self._emit_notification(
__event_emitter__, "智绘心图已启动，正在为您生成思维导图...", "info"
)
messages = body.get("messages")
if (
@@ -540,13 +547,7 @@ class Action:
or not messages[-1].get("content")
):
error_message = "无法获取有效的用户消息内容。"
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "error", "content": error_message},
}
)
await self._emit_notification(__event_emitter__, error_message, "error")
return {
"messages": [{"role": "assistant", "content": f"❌ {error_message}"}]
}
@@ -564,30 +565,18 @@ class Action:
if len(long_text_content) < self.valves.MIN_TEXT_LENGTH:
short_text_message = f"文本内容过短({len(long_text_content)}字符)，无法进行有效分析。请提供至少{self.valves.MIN_TEXT_LENGTH}字符的文本。"
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "warning", "content": short_text_message},
}
)
await self._emit_notification(
__event_emitter__, short_text_message, "warning"
)
return {
"messages": [
{"role": "assistant", "content": f"⚠️ {short_text_message}"}
]
}
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "智绘心图: 深入分析文本结构...",
"done": False,
"hidden": False,
},
}
)
await self._emit_status(
__event_emitter__, "智绘心图: 深入分析文本结构...", False
)
try:
unique_id = f"id_{int(time.time() * 1000)}"
@@ -602,7 +591,7 @@ class Action:
)
# 确定使用的模型
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
@@ -682,26 +671,10 @@ class Action:
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{long_text_content}\n\n{html_embed_tag}"
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "智绘心图: 绘制完成！",
"done": True,
"hidden": False,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": f"思维导图已生成，{user_name}！",
},
}
)
await self._emit_status(__event_emitter__, "智绘心图: 绘制完成！", True)
await self._emit_notification(
__event_emitter__, f"思维导图已生成，{user_name}！", "success"
)
logger.info("Action: 智绘心图 (v12) completed successfully")
except Exception as e:
@@ -712,26 +685,9 @@ class Action:
"content"
] = f"{long_text_content}\n\n❌ **错误:** {user_facing_error}"
if __event_emitter__:
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "智绘心图: 处理失败。",
"done": True,
"hidden": False,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"智绘心图生成失败, {user_name}！",
},
}
)
await self._emit_status(__event_emitter__, "智绘心图: 处理失败。", True)
await self._emit_notification(
__event_emitter__, f"智绘心图生成失败, {user_name}！", "error"
)
return body

130
plugins/actions/summary/summary.py
View File

@@ -305,11 +305,11 @@ CONTENT_TEMPLATE_SUMMARY = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True,
description="Whether to show operation status updates in the chat interface.",
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="Built-in LLM Model ID used for text analysis. If empty, uses the current conversation's model.",
)
@@ -381,6 +381,20 @@ class Action:
"actions_html": actions_html,
}
async def _emit_status(self, emitter, description: str, done: bool = False):
"""Emits a status update event."""
if self.valves.SHOW_STATUS and emitter:
await emitter(
{"type": "status", "data": {"description": description, "done": done}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""Emits a notification event (info/success/warning/error)."""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""Removes existing plugin-generated HTML code blocks from the content."""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -485,13 +499,9 @@ class Action:
if len(original_content) < self.valves.MIN_TEXT_LENGTH:
short_text_message = f"Text content too short ({len(original_content)} chars), recommended at least {self.valves.MIN_TEXT_LENGTH} chars for effective deep analysis.\n\n💡 Tip: For short texts, consider using '⚡ Flash Card' for quick refinement."
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "warning", "content": short_text_message},
}
)
await self._emit_notification(
__event_emitter__, short_text_message, "warning"
)
return {
"messages": [
{"role": "assistant", "content": f"⚠️ {short_text_message}"}
@@ -500,37 +510,22 @@ class Action:
# Recommend for longer texts
if len(original_content) < self.valves.RECOMMENDED_MIN_LENGTH:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": f"Text length is {len(original_content)} chars. Recommended {self.valves.RECOMMENDED_MIN_LENGTH}+ chars for best analysis results.",
},
}
)
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "📖 Deep Reading started, analyzing deeply...",
},
}
await self._emit_notification(
__event_emitter__,
f"Text length is {len(original_content)} chars. Recommended {self.valves.RECOMMENDED_MIN_LENGTH}+ chars for best analysis results.",
"info",
)
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "📖 Deep Reading: Analyzing text, extracting essence...",
"done": False,
},
}
)
await self._emit_notification(
__event_emitter__,
"📖 Deep Reading started, analyzing deeply...",
"info",
)
await self._emit_status(
__event_emitter__,
"📖 Deep Reading: Analyzing text, extracting essence...",
False,
)
formatted_user_prompt = USER_PROMPT_GENERATE_SUMMARY.format(
user_name=user_name,
@@ -542,7 +537,7 @@ class Action:
)
# Determine model to use
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
@@ -611,25 +606,14 @@ class Action:
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "📖 Deep Reading: Analysis complete!",
"done": True,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": f"📖 Deep Reading complete, {user_name}! Deep analysis report generated.",
},
}
)
await self._emit_status(
__event_emitter__, "📖 Deep Reading: Analysis complete!", True
)
await self._emit_notification(
__event_emitter__,
f"📖 Deep Reading complete, {user_name}! Deep analysis report generated.",
"success",
)
except Exception as e:
error_message = f"Deep Reading processing failed: {str(e)}"
@@ -639,25 +623,13 @@ class Action:
"content"
] = f"{original_content}\n\n❌ **Error:** {user_facing_error}"
if __event_emitter__:
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Deep Reading: Processing failed.",
"done": True,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"Deep Reading processing failed, {user_name}!",
},
}
)
await self._emit_status(
__event_emitter__, "Deep Reading: Processing failed.", True
)
await self._emit_notification(
__event_emitter__,
f"Deep Reading processing failed, {user_name}!",
"error",
)
return body

117
plugins/actions/summary/精读.py
View File

@@ -302,10 +302,10 @@ CONTENT_TEMPLATE_SUMMARY = """
class Action:
class Valves(BaseModel):
show_status: bool = Field(
SHOW_STATUS: bool = Field(
default=True, description="是否在聊天界面显示操作状态更新。"
)
LLM_MODEL_ID: str = Field(
MODEL_ID: str = Field(
default="",
description="用于文本分析的内置LLM模型ID。如果为空，则使用当前对话的模型。",
)
@@ -379,6 +379,20 @@ class Action:
"actions_html": actions_html,
}
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}}
)
async def _emit_notification(self, emitter, content: str, ntype: str = "info"):
"""发送通知事件 (info/success/warning/error)。"""
if emitter:
await emitter(
{"type": "notification", "data": {"type": ntype, "content": content}}
)
def _remove_existing_html(self, content: str) -> str:
"""移除内容中已有的插件生成 HTML 代码块 (通过标记识别)。"""
pattern = r"```html\s*<!-- OPENWEBUI_PLUGIN_OUTPUT -->[\s\S]*?```"
@@ -484,13 +498,9 @@ class Action:
if len(original_content) < self.valves.MIN_TEXT_LENGTH:
short_text_message = f"文本内容过短({len(original_content)}字符)，建议至少{self.valves.MIN_TEXT_LENGTH}字符以获得有效的深度分析。\n\n💡 提示：对于短文本，建议使用'⚡ 闪记卡'进行快速提炼。"
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {"type": "warning", "content": short_text_message},
}
)
await self._emit_notification(
__event_emitter__, short_text_message, "warning"
)
return {
"messages": [
{"role": "assistant", "content": f"⚠️ {short_text_message}"}
@@ -499,37 +509,18 @@ class Action:
# Recommend for longer texts
if len(original_content) < self.valves.RECOMMENDED_MIN_LENGTH:
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": f"文本长度为{len(original_content)}字符。建议{self.valves.RECOMMENDED_MIN_LENGTH}字符以上可获得更好的分析效果。",
},
}
)
if __event_emitter__:
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "info",
"content": "📖 精读已启动，正在进行深度分析...",
},
}
await self._emit_notification(
__event_emitter__,
f"文本长度为{len(original_content)}字符。建议{self.valves.RECOMMENDED_MIN_LENGTH}字符以上可获得更好的分析效果。",
"info",
)
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "📖 精读: 深入分析文本，提炼精华...",
"done": False,
},
}
)
await self._emit_notification(
__event_emitter__, "📖 精读已启动，正在进行深度分析...", "info"
)
await self._emit_status(
__event_emitter__, "📖 精读: 深入分析文本，提炼精华...", False
)
formatted_user_prompt = USER_PROMPT_GENERATE_SUMMARY.format(
user_name=user_name,
@@ -541,7 +532,7 @@ class Action:
)
# 确定使用的模型
target_model = self.valves.LLM_MODEL_ID
target_model = self.valves.MODEL_ID
if not target_model:
target_model = body.get("model")
@@ -610,22 +601,12 @@ class Action:
html_embed_tag = f"```html\n{final_html}\n```"
body["messages"][-1]["content"] = f"{original_content}\n\n{html_embed_tag}"
if self.valves.show_status and __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": "📖 精读: 分析完成!", "done": True},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "success",
"content": f"📖 精读完成，{user_name}！深度分析报告已生成。",
},
}
)
await self._emit_status(__event_emitter__, "📖 精读: 分析完成!", True)
await self._emit_notification(
__event_emitter__,
f"📖 精读完成，{user_name}！深度分析报告已生成。",
"success",
)
except Exception as e:
error_message = f"精读处理失败: {str(e)}"
@@ -635,25 +616,9 @@ class Action:
"content"
] = f"{original_content}\n\n❌ **错误:** {user_facing_error}"
if __event_emitter__:
if self.valves.show_status:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "精读: 处理失败。",
"done": True,
},
}
)
await __event_emitter__(
{
"type": "notification",
"data": {
"type": "error",
"content": f"精读处理失败, {user_name}!",
},
}
)
await self._emit_status(__event_emitter__, "精读: 处理失败。", True)
await self._emit_notification(
__event_emitter__, f"精读处理失败, {user_name}!", "error"
)
return body