528 lines
20 KiB
Python
528 lines
20 KiB
Python
"""
|
||
title: Deep Reading & Summary
|
||
author: Antigravity
|
||
author_url: https://github.com/open-webui
|
||
funding_url: https://github.com/open-webui
|
||
version: 0.1.0
|
||
icon_url: data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0yIDNIMGEyIDIgMCAwIDAgMiAyIi8+PHBhdGggZD0iTTIyIDNIMjBhMiAyIDAgMCAwLTIgMiIvPjxwYXRoIGQ9Ik0yIDdoMjB2MTRhMiAyIDAgMCAxLTIgMmgtMTZhMiAyIDAgMCAxLTItMnYtMTQiLz48cGF0aCBkPSJNMTEgMTJ2NiIvPjxwYXRoIGQ9Ik0xNiAxMnY2Ii8+PHBhdGggZD0iTTYgMTJ2NiIvPjwvc3ZnPg==
|
||
description: Provides deep reading analysis and summarization for long texts.
|
||
requirements: jinja2, markdown
|
||
"""
|
||
|
||
from pydantic import BaseModel, Field
|
||
from typing import Optional, Dict, Any
|
||
import logging
|
||
import re
|
||
from fastapi import Request
|
||
from datetime import datetime
|
||
import pytz
|
||
import markdown
|
||
from jinja2 import Template
|
||
|
||
from open_webui.utils.chat import generate_chat_completion
|
||
from open_webui.models.users import Users
|
||
|
||
logging.basicConfig(
|
||
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
||
)
|
||
logger = logging.getLogger(__name__)
|
||
|
||
# =================================================================
|
||
# 内部 LLM 提示词设计
|
||
# =================================================================
|
||
|
||
SYSTEM_PROMPT_READING_ASSISTANT = """
|
||
你是一个专业的深度文本分析专家，擅长精读长篇文本并提炼精华。你的任务是进行全面、深入的分析。
|
||
|
||
请提供以下内容：
|
||
1. **详细摘要**：用 2-3 段话全面总结文本的核心内容，确保准确性和完整性。不要过于简略，要让读者充分理解文本主旨。
|
||
2. **关键信息点**：列出 5-8 个最重要的事实、观点或论据。每个信息点应该：
|
||
- 具体且有深度
|
||
- 包含必要的细节和背景
|
||
- 使用 Markdown 列表格式
|
||
3. **行动建议**：从文本中识别并提炼出具体的、可执行的行动项。每个建议应该：
|
||
- 明确且可操作
|
||
- 包含执行的优先级或时间建议
|
||
- 如果没有明确的行动项，可以提供学习建议或思考方向
|
||
|
||
请严格遵循以下指导原则：
|
||
- **语言**：所有输出必须使用用户指定的语言。
|
||
- **格式**：请严格按照以下 Markdown 格式输出，确保每个部分都有明确的标题：
|
||
## 摘要
|
||
[这里是详细的摘要内容，2-3段话，可以使用 Markdown 进行**加粗**或*斜体*强调重点]
|
||
|
||
## 关键信息点
|
||
- [关键点1：包含具体细节和背景]
|
||
- [关键点2：包含具体细节和背景]
|
||
- [关键点3：包含具体细节和背景]
|
||
- [至少5个，最多8个关键点]
|
||
|
||
## 行动建议
|
||
- [行动项1：具体、可执行，包含优先级]
|
||
- [行动项2：具体、可执行，包含优先级]
|
||
- [如果没有明确行动项，提供学习建议或思考方向]
|
||
- **深度优先**：分析要深入、全面，不要浮于表面。
|
||
- **行动导向**：重点关注可执行的建议和下一步行动。
|
||
- **只输出分析结果**：不要包含任何额外的寒暄、解释或引导性文字。
|
||
"""
|
||
|
||
USER_PROMPT_GENERATE_SUMMARY = """
|
||
请对以下长篇文本进行深度分析，提供：
|
||
1. 详细的摘要（2-3段话，全面概括文本内容）
|
||
2. 关键信息点列表（5-8个，包含具体细节）
|
||
3. 可执行的行动建议（具体、明确，包含优先级）
|
||
|
||
---
|
||
**用户上下文信息:**
|
||
用户姓名: {user_name}
|
||
当前日期时间: {current_date_time_str}
|
||
当前星期: {current_weekday}
|
||
当前时区: {current_timezone_str}
|
||
用户语言: {user_language}
|
||
---
|
||
|
||
**长篇文本内容:**
|
||
```
|
||
{long_text_content}
|
||
```
|
||
|
||
请进行深入、全面的分析，重点关注可执行的行动建议。
|
||
"""
|
||
|
||
# =================================================================
|
||
# 前端 HTML 模板 (Jinja2 语法)
|
||
# =================================================================
|
||
|
||
HTML_TEMPLATE = """
|
||
<!DOCTYPE html>
|
||
<html lang="{{ user_language }}">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||
<title>精读:深度分析报告</title>
|
||
<style>
|
||
:root {
|
||
--primary-color: #4285f4;
|
||
--secondary-color: #1e88e5;
|
||
--action-color: #34a853;
|
||
--background-color: #f8f9fa;
|
||
--card-bg-color: #ffffff;
|
||
--text-color: #202124;
|
||
--muted-text-color: #5f6368;
|
||
--border-color: #dadce0;
|
||
--header-gradient: linear-gradient(135deg, #4285f4, #1e88e5);
|
||
--shadow: 0 1px 3px rgba(60,64,67,.3);
|
||
--border-radius: 8px;
|
||
--font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
|
||
}
|
||
body {
|
||
font-family: var(--font-family);
|
||
line-height: 1.8;
|
||
color: var(--text-color);
|
||
margin: 0;
|
||
padding: 24px;
|
||
background-color: var(--background-color);
|
||
-webkit-font-smoothing: antialiased;
|
||
-moz-osx-font-smoothing: grayscale;
|
||
}
|
||
.container {
|
||
max-width: 900px;
|
||
margin: 20px auto;
|
||
background: var(--card-bg-color);
|
||
border-radius: var(--border-radius);
|
||
box-shadow: var(--shadow);
|
||
overflow: hidden;
|
||
border: 1px solid var(--border-color);
|
||
}
|
||
.header {
|
||
background: var(--header-gradient);
|
||
color: white;
|
||
padding: 40px;
|
||
text-align: center;
|
||
}
|
||
.header h1 {
|
||
margin: 0;
|
||
font-size: 2.2em;
|
||
font-weight: 500;
|
||
letter-spacing: -0.5px;
|
||
}
|
||
.user-context {
|
||
font-size: 0.9em;
|
||
color: var(--muted-text-color);
|
||
background-color: #f1f3f4;
|
||
padding: 16px 40px;
|
||
display: flex;
|
||
justify-content: space-around;
|
||
flex-wrap: wrap;
|
||
border-bottom: 1px solid var(--border-color);
|
||
}
|
||
.user-context span { margin: 4px 12px; }
|
||
.content { padding: 40px; }
|
||
.section {
|
||
margin-bottom: 32px;
|
||
padding-bottom: 32px;
|
||
border-bottom: 1px solid #e8eaed;
|
||
}
|
||
.section:last-child {
|
||
border-bottom: none;
|
||
margin-bottom: 0;
|
||
padding-bottom: 0;
|
||
}
|
||
.section h2 {
|
||
margin-top: 0;
|
||
margin-bottom: 20px;
|
||
font-size: 1.5em;
|
||
font-weight: 500;
|
||
color: var(--text-color);
|
||
display: flex;
|
||
align-items: center;
|
||
padding-bottom: 12px;
|
||
border-bottom: 2px solid var(--primary-color);
|
||
}
|
||
.section h2 .icon {
|
||
margin-right: 12px;
|
||
font-size: 1.3em;
|
||
line-height: 1;
|
||
}
|
||
.summary-section h2 { border-bottom-color: var(--primary-color); }
|
||
.keypoints-section h2 { border-bottom-color: var(--secondary-color); }
|
||
.actions-section h2 { border-bottom-color: var(--action-color); }
|
||
|
||
.html-content {
|
||
font-size: 1.05em;
|
||
line-height: 1.8;
|
||
}
|
||
.html-content p:first-child { margin-top: 0; }
|
||
.html-content p:last-child { margin-bottom: 0; }
|
||
.html-content ul {
|
||
list-style: none;
|
||
padding-left: 0;
|
||
margin: 16px 0;
|
||
}
|
||
.html-content li {
|
||
padding: 12px 0 12px 32px;
|
||
position: relative;
|
||
margin-bottom: 8px;
|
||
line-height: 1.7;
|
||
}
|
||
.html-content li::before {
|
||
position: absolute;
|
||
left: 0;
|
||
top: 12px;
|
||
font-family: 'Arial';
|
||
font-weight: bold;
|
||
font-size: 1.1em;
|
||
}
|
||
.keypoints-section .html-content li::before {
|
||
content: '•';
|
||
color: var(--secondary-color);
|
||
font-size: 1.5em;
|
||
top: 8px;
|
||
}
|
||
.actions-section .html-content li::before {
|
||
content: '▸';
|
||
color: var(--action-color);
|
||
}
|
||
|
||
.no-content {
|
||
color: var(--muted-text-color);
|
||
font-style: italic;
|
||
padding: 20px;
|
||
background: #f8f9fa;
|
||
border-radius: 4px;
|
||
}
|
||
|
||
.footer {
|
||
text-align: center;
|
||
padding: 24px;
|
||
font-size: 0.85em;
|
||
color: #5f6368;
|
||
background-color: #f8f9fa;
|
||
border-top: 1px solid var(--border-color);
|
||
}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<div class="container">
|
||
<div class="header">
|
||
<h1>📖 精读：深度分析报告</h1>
|
||
</div>
|
||
<div class="user-context">
|
||
<span><strong>用户:</strong> {{ user_name }}</span>
|
||
<span><strong>分析时间:</strong> {{ current_date_time_str }}</span>
|
||
<span><strong>星期:</strong> {{ current_weekday }}</span>
|
||
</div>
|
||
<div class="content">
|
||
<div class="section summary-section">
|
||
<h2><span class="icon">📝</span>详细摘要</h2>
|
||
<div class="html-content">{{ summary_html | safe }}</div>
|
||
</div>
|
||
<div class="section keypoints-section">
|
||
<h2><span class="icon">💡</span>关键信息点</h2>
|
||
<div class="html-content">{{ keypoints_html | safe }}</div>
|
||
</div>
|
||
<div class="section actions-section">
|
||
<h2><span class="icon">🎯</span>行动建议</h2>
|
||
<div class="html-content">{{ actions_html | safe }}</div>
|
||
</div>
|
||
</div>
|
||
<div class="footer">
|
||
<p>© {{ current_year }} 精读 - 深度文本分析服务</p>
|
||
</div>
|
||
</div>
|
||
</body>
|
||
</html>"""
|
||
|
||
|
||
class Action:
|
||
class Valves(BaseModel):
|
||
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(
|
||
default="gemini-2.5-flash",
|
||
description="用于文本分析的内置LLM模型ID。",
|
||
)
|
||
MIN_TEXT_LENGTH: int = Field(
|
||
default=200,
|
||
description="进行深度分析所需的最小文本长度(字符数)。建议200字符以上。",
|
||
)
|
||
RECOMMENDED_MIN_LENGTH: int = Field(
|
||
default=500, description="建议的最小文本长度，以获得最佳分析效果。"
|
||
)
|
||
|
||
def __init__(self):
|
||
self.valves = self.Valves()
|
||
|
||
def _process_llm_output(self, llm_output: str) -> Dict[str, str]:
|
||
"""
|
||
解析LLM的Markdown输出,将其转换为HTML片段。
|
||
"""
|
||
summary_match = re.search(
|
||
r"##\s*摘要\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
|
||
)
|
||
keypoints_match = re.search(
|
||
r"##\s*关键信息点\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
|
||
)
|
||
actions_match = re.search(
|
||
r"##\s*行动建议\s*\n(.*?)(?=\n##|$)", llm_output, re.DOTALL
|
||
)
|
||
|
||
summary_md = summary_match.group(1).strip() if summary_match else ""
|
||
keypoints_md = keypoints_match.group(1).strip() if keypoints_match else ""
|
||
actions_md = actions_match.group(1).strip() if actions_match else ""
|
||
|
||
if not any([summary_md, keypoints_md, actions_md]):
|
||
summary_md = llm_output.strip()
|
||
logger.warning("LLM输出未遵循预期的Markdown格式。将整个输出视为摘要。")
|
||
|
||
# 使用 'nl2br' 扩展将换行符 \n 转换为 <br>
|
||
md_extensions = ["nl2br"]
|
||
summary_html = (
|
||
markdown.markdown(summary_md, extensions=md_extensions)
|
||
if summary_md
|
||
else '<p class="no-content">未能提取摘要信息。</p>'
|
||
)
|
||
keypoints_html = (
|
||
markdown.markdown(keypoints_md, extensions=md_extensions)
|
||
if keypoints_md
|
||
else '<p class="no-content">未能提取关键信息点。</p>'
|
||
)
|
||
actions_html = (
|
||
markdown.markdown(actions_md, extensions=md_extensions)
|
||
if actions_md
|
||
else '<p class="no-content">暂无明确的行动建议。</p>'
|
||
)
|
||
|
||
return {
|
||
"summary_html": summary_html,
|
||
"keypoints_html": keypoints_html,
|
||
"actions_html": actions_html,
|
||
}
|
||
|
||
def _build_html(self, context: dict) -> str:
|
||
"""
|
||
使用 Jinja2 模板和上下文数据构建最终的HTML内容。
|
||
"""
|
||
template = Template(HTML_TEMPLATE)
|
||
return template.render(context)
|
||
|
||
async def action(
|
||
self,
|
||
body: dict,
|
||
__user__: Optional[Dict[str, Any]] = None,
|
||
__event_emitter__: Optional[Any] = None,
|
||
__request__: Optional[Request] = None,
|
||
) -> Optional[dict]:
|
||
logger.info("Action: 精读启动 (v2.0.0 - Deep Reading)")
|
||
|
||
if isinstance(__user__, (list, tuple)):
|
||
user_language = (
|
||
__user__[0].get("language", "zh-CN") if __user__ else "zh-CN"
|
||
)
|
||
user_name = __user__[0].get("name", "用户") if __user__[0] else "用户"
|
||
user_id = (
|
||
__user__[0]["id"]
|
||
if __user__ and "id" in __user__[0]
|
||
else "unknown_user"
|
||
)
|
||
elif isinstance(__user__, dict):
|
||
user_language = __user__.get("language", "zh-CN")
|
||
user_name = __user__.get("name", "用户")
|
||
user_id = __user__.get("id", "unknown_user")
|
||
|
||
now = datetime.now()
|
||
current_date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
|
||
current_weekday = now.strftime("%A")
|
||
current_year = now.strftime("%Y")
|
||
current_timezone_str = "未知时区"
|
||
|
||
original_content = ""
|
||
try:
|
||
messages = body.get("messages", [])
|
||
if not messages or not messages[-1].get("content"):
|
||
raise ValueError("无法获取有效的用户消息内容。")
|
||
|
||
original_content = messages[-1]["content"]
|
||
|
||
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},
|
||
}
|
||
)
|
||
return {
|
||
"messages": [
|
||
{"role": "assistant", "content": f"⚠️ {short_text_message}"}
|
||
]
|
||
}
|
||
|
||
# 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...",
|
||
},
|
||
}
|
||
)
|
||
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,
|
||
},
|
||
}
|
||
)
|
||
|
||
formatted_user_prompt = USER_PROMPT_GENERATE_SUMMARY.format(
|
||
user_name=user_name,
|
||
current_date_time_str=current_date_time_str,
|
||
current_weekday=current_weekday,
|
||
current_timezone_str=current_timezone_str,
|
||
user_language=user_language,
|
||
long_text_content=original_content,
|
||
)
|
||
|
||
llm_payload = {
|
||
"model": self.valves.LLM_MODEL_ID,
|
||
"messages": [
|
||
{"role": "system", "content": SYSTEM_PROMPT_READING_ASSISTANT},
|
||
{"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, 用户ID: {user_id}")
|
||
|
||
llm_response = await generate_chat_completion(
|
||
__request__, llm_payload, user_obj
|
||
)
|
||
assistant_response_content = llm_response["choices"][0]["message"][
|
||
"content"
|
||
]
|
||
|
||
processed_content = self._process_llm_output(assistant_response_content)
|
||
|
||
context = {
|
||
"user_language": user_language,
|
||
"user_name": user_name,
|
||
"current_date_time_str": current_date_time_str,
|
||
"current_weekday": current_weekday,
|
||
"current_year": current_year,
|
||
**processed_content,
|
||
}
|
||
|
||
final_html_content = self._build_html(context)
|
||
html_embed_tag = f"```html\n{final_html_content}\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。",
|
||
},
|
||
}
|
||
)
|
||
|
||
except Exception as e:
|
||
error_message = f"精读处理失败: {str(e)}"
|
||
logger.error(f"精读错误: {error_message}", exc_info=True)
|
||
user_facing_error = f"抱歉, 精读在处理时遇到错误: {str(e)}。\n请检查Open WebUI后端日志获取更多详情。"
|
||
body["messages"][-1][
|
||
"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}!",
|
||
},
|
||
}
|
||
)
|
||
|
||
return body
|