open-webui/Fu-Jie_openwebui-extensions
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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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This commit is contained in:
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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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